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Simple Summary: Inflammation-based prognostic markers have been recognized as useful in a range of oncologic conditions. Given the high mortality rate associated with pancreatic ductal adenocarcinoma (PDAC), the demand for accurate prognostic tools is significant. Nonetheless, the literature still shows conflicting results regarding the effectiveness of the various available scoring systems. Our data confirm that NLR and PNI are useful biomarkers regarding overall survival in PDAC. Especially in advanced disease stages, they seem to be of considerable benefit as easily accessible tools for prognosis prediction. Purpose: Prognostic inflammation-based parameters have been reported as useful tools in various oncologic diseases. Pancreatic ductal adenocarcinoma (PDAC) is characterized by a high mortality rate, making reliable prognostic markers highly desirable. However, there is still inconsistency in the literature regarding the efficacy of the different available scores. Methods: A total of 207 patients, who underwent primary resection of PDAC from January 2000 to December 2018 at the University Hospital of Erlangen, were included in this retrospective single-center study. Different biomarkers, including the preoperative neutrophil–lymphocyte ratio (NLR), the platelet–lymphocyte ratio (PLR), the c-reactive protein (CRP)–albumin ratio (CAR), the lymphocyte–CRP ratio (LCR), the prognostic nutritional index (PNI) and the modified Glasgow prognostic score (mGPS) were analyzed for their ability to predict overall survival (OS). Results: In our cohort, the median overall survival was 20.7 months. Among the investigated biomarkers, NLR and PNI were identified as independent prognostic markers (Hazard Ratio (HR) 1.6 (1.0–2.5), p = 0.048 and HR 0.6 (0.4–0.9), p = 0.018), whereas PLR, CAR, LCR and mGPS did not reach significance in the multivariate analysis. Subgroup analysis revealed that the prognostic value of NLR and PNI is particularly evident in locally advanced tumor stages (pT3/4 and pN+). Conclusions: The NLR and PNI could serve as valuable tools for estimating prognosis in patients with PDAC undergoing pancreatic resection in curative intention, especially in locally advanced tumor stages. However, conflicting results in the current literature highlight the need for further prospective studies to validate these findings. [ABSTRACT FROM AUTHOR]

Simple Summary: Esophageal cancer, a highly lethal tumor, contributes to 5% of all cancer deaths, with its primary subtypes being esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most studies focus on ESCC, this study investigates EAC using single-cell RNA sequencing (scRNA-seq) to analyze CD45+ immune cells from tumors and matched non-tumor tissues in therapy-naïve patients. By examining the transcriptional profiles of these immune cells and the entire transcriptome in a cohort of 23 patients, this study identifies distinct transcriptional signatures. These signatures were used to stratify a large cohort of TCGA EAC patients, revealing strong associations with prognosis and clinical outcomes. The findings suggest that these transcriptional profiles can improve prognosis accuracy post-surgery and potentially guide effective therapies, including immunotherapy, for EAC patients. Esophageal cancer is a highly lethal malignancy, representing 5% of all cancer-related deaths. The two main subtypes are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most research has focused on ESCC, few studies have analyzed EAC for transcriptional signatures linked to diagnosis or prognosis. In this study, we utilized single-cell RNA sequencing and bulk RNA sequencing to identify specific immune cell types that contribute to anti-tumor responses, as well as differentially expressed genes (DEGs). We have characterized transcriptional signatures, validated against a wide cohort of TCGA patients, that are capable of predicting clinical outcomes and the prognosis of EAC post-surgery with efficacy comparable to the currently accepted prognostic factors. In conclusion, our findings provide insights into the immune landscape and therapeutic targets of EAC, proposing novel immunological biomarkers for predicting prognosis, aiding in patient stratification for post-surgical outcomes, follow-up, and personalized adjuvant therapy decisions. [ABSTRACT FROM AUTHOR]

Simple Summary: Response to treatment and prognosis after 223Ra treatment varies among patients. Using a combination of clinical, biochemical, and radiomic data from bone scintigraphy and 18F-Fluorocholine PET/CT obtained from our prospective ChoPET-Rad study, we identified predictive and prognostic variables. The goal was to create a nomogram able to predict therapeutic failure and overall survival, aiding in the selection of more suitable candidates for this treatment. Purpose: We aimed to develop a nomogram able to predict treatment failure, skeletal events, and overall survival (OS) in patients with castration-resistant prostate cancer with bone metastases (CRPC-BM) treated with Radium-223 dichloride (223Ra). Patients and Methods: Patients from the Castilla-La Mancha Spanish region were prospectively included in the ChoPET-Rad multicenter study from January 2015 to December 2022. Patients underwent baseline, interim, and end-of-treatment bone scintigraphy (BS) and 18F-Fluorocholine PET/CT (FCH PET/CT) scans, obtaining multiple imaging radiomics as well as clinical and biochemical variables during follow-up and studying their association with the previously defined end-points. Survival analysis was performed using the Kaplan–Meier method and Cox regression. Multivariate logistic and Cox regression models were calculated, and these models were depicted by means of nomograms. Results: Median progression-free survival (PFS) and OS were 4 and 14 months (mo), respectively. The variables that showed independent and significant association with therapeutic failure were baseline alkaline phosphatase (AP) levels (p = 0.022) and the characteristics of BM on the CT portion of PET/CT (p = 0.017). In the case of OS, the significant variables were therapeutic failure (p = 0.038), the number of lines received after 223Ra (p < 0.001), average SUVmax (p = 0.002), bone marrow infiltration in FCH PET/CT (p = 0.006), and interim FCH PET/CT response (p = 0.048). Final nomograms included these variables, showing good discrimination among the 100 patients included in our study. In the study of skeletal events, only OS showed a significant association in the multivariate analysis, resulting in an inconsistent nomogram design. Conclusions: FCH PET/CT appears to be a good tool for evaluating patients eligible for treatment with 223Ra, as well as for their follow-up. Thus, findings derived from it, such as the morphological characteristics of BM in the CT, bone marrow infiltration, or the response to 223Ra in the interim study, have proven to be solid and useful variables in the creation of nomograms for predicting therapeutic failure and OS. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence of metastatic melanoma is rising, making it imperative to identify patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker, using segmentations from 146 baseline and 146 first follow-up CT scans, to predict best overall response, progression-free survival, and overall survival across various immunotherapies. We volumetrically segmented the total tumour load, excluding cerebral metastases. This study also examined whether reducing the number of segmented metastases per patient affects predictive accuracy. The findings suggest that delta radiomics could enhance the prediction of best overall response, progression-free survival, and overall survival in metastatic melanoma patients undergoing first-line immunotherapy. Although volumetric whole tumour load segmentation is complex, it may provide predictive benefits. Background: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. Methods: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. Results: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. Conclusions: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous. [ABSTRACT FROM AUTHOR]

Simple Summary: We investigated whether the baseline plasma cell-free DNA might help to differentiate malignant nodules from benign lesions observed in screening low-dose computed tomography (LDCT) examinations. Plasma cell-free DNA was determined before the first LDCT in 137 participants in the ITALUNG trial, including 29 with screen-detected malignant nodules (17 prevalent and 12 incident) and 108 with benign nodules. In subjects with prevalent lung cancers (LC), the radiological characteristics well differentiated the malignant nodule, and the cell-free DNA was markedly increased. A total of 75% of subjects with incident LC showed a baseline cell-free DNA ≥ 3.15 ng/mL, compared to 34% of subjects with benign nodules (p = 0.006). Moreover, the cell-free DNA correlated (p = 0.001) with the tumor growth measured with nodular volume doubling time. The baseline plasma cell-free DNA is an independent, potentially useful biomarker in the LC screening process and should be further investigated. The role of total plasma cell-free DNA (cfDNA) in lung cancer (LC) screening with low-dose computed tomography (LDCT) is uncertain. We hypothesized that cfDNA could support differentiation between malignant and benign nodules observed in LDCT. The baseline cfDNA was measured in 137 subjects of the ITALUNG trial, including 29 subjects with screen-detected LC (17 prevalent and 12 incident) and 108 subjects with benign nodules. The predictive capability of baseline cfDNA to differentiate malignant and benign nodules was compared to that of Lung-RADS classification and Brock score at initial LDCT (iLDCT). Subjects with prevalent LC showed both well-discriminating radiological characteristics of the malignant nodule (16 of 17 were classified as Lung-RADS 4) and markedly increased cfDNA (mean 18.8 ng/mL). The mean diameters and Brock scores of malignant nodules at iLDCT in subjects who were diagnosed with incident LC were not different from those of benign nodules. However, 75% (9/12) of subjects with incident LC showed a baseline cfDNA ≥ 3.15 ng/mL, compared to 34% (37/108) of subjects with benign nodules (p = 0.006). Moreover, baseline cfDNA was correlated (p = 0.001) with tumor growth, measured with volume doubling time. In conclusion, increased baseline cfDNA may help to differentiate subjects with malignant and benign nodules at LDCT. [ABSTRACT FROM AUTHOR]

Simple Summary: We conducted a study at the Melanoma Unit of the Hospital Clinic of Barcelona to investigate brain metastases in patients with cutaneous melanoma. We collected data from patients diagnosed between January 1998 and September 2023. Patients with melanoma in situ or those with prior lung or breast cancer were excluded. Our aim was to identify factors associated with the development and survival outcomes of brain metastases. We analyzed patient demographics, tumor characteristics, and survival data. The diagnosis of brain metastases was confirmed using imaging techniques, and biopsies were performed when feasible. Our study followed strict guidelines for reporting observational studies. We found that younger age and larger primary tumor thickness increased the risk of developing brain metastases. Additionally, the presence of ulceration and microscopic satellitosis in the primary tumor were associated with a higher risk. Melanomas located on the trunk had a higher risk compared to those on the extremities. Patients with brain metastases had a median survival of around six months. Neurological symptoms and leptomeningeal involvement were associated with poorer survival outcomes. Higher number of brain lesions and elevated levels of lactate dehydrogenase (LDH) also predicted worse survival. Our findings highlight the importance of early detection and monitoring of melanoma patients, especially those at higher risk of brain metastases. Understanding these factors can aid in personalized treatment approaches and improving patient outcomes. Background: Melanoma is the cancer with the highest risk of dissemination to the central nervous system (CNS), one of the leading causes of mortality from this cancer. Objective: To identify patients at higher risk of developing CNS metastases and to evaluate associated prognostic factors. Methods: A cohort study (1998–2023) assessed patients who developed CNS melanoma metastases. Multivariate logistic regression was used to identify predictive factors at melanoma diagnosis for CNS metastasis. Cox regression analysis evaluated the CNS-independent metastasis-related variables impacting survival. Results: Out of 4718 patients, 380 (8.05%) developed CNS metastases. Multivariate logistic regression showed that a higher Breslow index, mitotic rate ≥ 1 mm2, ulceration, and microscopic satellitosis were significant risk factors for CNS metastasis development. Higher patient age and the location of the primary tumor in the upper or lower extremities were protective factors. In survival analysis, post-CNS metastasis, symptomatic disease, prior non-CNS metastases, CNS debut with multiple metastases, elevated LDH levels, and leptomeningeal involvement correlated with poorer survival. Conclusion: Predictive factors in the primary tumor independently associated with brain metastases include microscopic satellitosis, ulceration, higher Breslow index, and trunk location. Prognostic factors for lower survival in CNS disease include symptomatic disease, multiple CNS metastases, and previous metastases from different sites. [ABSTRACT FROM AUTHOR]

Simple Summary: Microsatellite instability (MSI) is a hallmark of colorectal cancer (CRC) that is present in 10–15% of all patients with this condition. MSI results from the inactivation of the mismatch repair (MMR) pathway in tumors due to germline MMR mutations, known as Lynch syndrome or sporadic epigenetic gene silencing, leading to an increased mutation rate and genomic instability. In this study, we assessed the utility of the polymerase chain reaction (PCR)-based molecular method as an alternative to immunohistochemistry for determining MSI status in formalin-fixed paraffin-embedded tissues and investigated the clinicopathological significance and prognostic value of MSI in CRC. We found that patients with MSI-high (i.e., high MSI) had better overall and disease-free survival than those with microsatellite stability, and this result was significant in patients with right-sided CRC. Our results demonstrate that PCR-based MSI detection may serve as a useful prognostic predictor in patients with CRC and may have clinical value in the management of these patients. Given the crucial predictive implications of microsatellite instability (MSI) in colorectal cancer (CRC), MSI screening is commonly performed in those with and at risk for CRC. Here, we compared results from immunohistochemistry (IHC) and the droplet digital PCR (ddPCR) MSI assay on formalin-fixed paraffin-embedded tumor samples from 48 patients who underwent surgery for colon and rectal cancer by calculating Cohen's kappa measurement (k), revealing high agreement between the methods (k = 0.915). We performed Kaplan–Meier survival analyses and univariate and multivariate Cox regression to assess the prognostic significance of ddPCR-based MSI and to identify clinicopathological features associated with CRC outcome. Patients with MSI-high had better overall survival (OS; p = 0.038) and disease-free survival (DFS; p = 0.049) than those with microsatellite stability (MSS). When stratified by primary tumor location, right-sided CRC patients with MSI-high showed improved DFS, relative to those with MSS (p < 0.001), but left-sided CRC patients did not. In multivariate analyses, MSI-high was associated with improved OS (hazard ratio (HR) = 0.221, 95% confidence interval (CI): 0.026–0.870, p = 0.042), whereas the loss of DNA mismatch repair protein MutL homolog 1 (MLH1) expression was associated with worse OS (HR = 0.133, 95% CI: 0.001–1.152, p = 0.049). Our results suggest ddPCR is a promising tool for MSI detection. Given the opposing effects of MSI-high and MLH1 loss on OS, both ddPCR and IHC may be complementary for the prognostic assessment of CRC. [ABSTRACT FROM AUTHOR]

Simple Summary: Predicting which patients will respond to therapy or experience disease relapse can help clinicians select treatments that could slow down or prevent the spread of cancer. Clinicians have routinely used imaging to measure the size of the tumor to assess whether or not it is responding to treatment. A recent development for monitoring tumors involves a test to detect mutated DNA shed by tumors into the blood, called circulating tumor DNA (ctDNA). The authors searched the scientific literature to find studies that have combined imaging and ctDNA to build tools that can predict treatment response or patient survival. The authors noted that only a few studies have been reported, indicating that this field is new and needs further exploration. These early studies, however, showed that combining these two clinical tests (imaging + ctDNA) may improve the prediction of tumors' response to therapy and the return of cancer. While promising, these tools need to be refined to improve the accuracy of the predictions and the results confirmed in more extensive studies. Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings. [ABSTRACT FROM AUTHOR]

Simple Summary: In the context of endometrial cancer, molecular classification is becoming increasingly significant as the molecular class determines appropriate treatments and the prognosis. However, performing clinical testing for molecular classification in a large number of patients entails significant financial and time costs. Therefore, there is a need to develop a substantial molecular profile screening method for endometrial cancers. The objective of this study was to explore whether the molecular classification of endometrial cancer could be predicted from digital images of hematoxylin and eosin (H&E)-stained slides using deep learning as a screening tool. After making adjustments to the training data set and hyperparameters, we confirmed the feasibility of estimating the mismatch repair status from histological digital images of endometrial cancer. Deep learning was found to be effective for predicting one aspect of the molecular classification from H&E-stained histological digital images. The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most lethal form of skin cancer and has seen a rising incidence globally over the last 10 years. A majority of melanomas arise from the skin as a result of exposure to ultraviolet radiation. However, even within this group of "cutaneous melanomas", there is considerable clinical and molecular heterogeneity. In this study, we sought to understand how different genetic "drivers" in a group of 254 patients with cutaneous melanomas influenced overall survival and response to immunotherapy. We found that NRAS mutation correlated with decreased survival and that tumor mutational burden (an estimate of the amount of genetic change within each tumor) correlated with improved responses to immune checkpoint inhibition. We also found that almost all cutaneous melanomas have a driver mutation in the MAPK pathway, underscoring the key role of MAPK-targeted therapies in melanoma drug development. Background: Cutaneous melanoma (CM) can be molecularly classified into four groups: BRAF mutant, NRAS mutant, NF1 mutant and triple wild-type (TWT) tumors lacking any of these three alterations. In the era of immune checkpoint inhibition (ICI) and targeted molecular therapy, the clinical significance of these groups remains unclear. Here, we integrate targeted DNA sequencing with comprehensive clinical follow-up in CM patients. Methods: This was a retrospective cohort study that assessed clinical and molecular features from patients with localized or metastatic CM who underwent targeted next-generation sequencing as part of routine clinical care. A total of 254 patients with CM who had a CLIA-certified targeted sequencing assay performed on their tumor tissue were included. Results: Of the 254 patients with cutaneous melanoma, 77 were BRAF mutant (30.3%), 77 were NRAS mutant (30.3%), 47 were NF1 mutant (18.5%), 33 were TWT (13.0%) and the remaining 20 (7.9%) carried mutations in multiple driver genes (BRAF/NRAS/NF1 co-mutated). The majority of this co-mutation group carried mutations in NF1 (n = 19 or 90%) with co-occurring mutations in BRAF or NRAS, often with a weaker oncogenic variant. Consistently, NF1 mutant tumors harbored numerous significantly co-altered genes compared to BRAF or NRAS mutant tumors. The majority of TWT tumors (n = 29, 87.9%) harbor a pathogenic mutation within a known Ras/MAPK signaling pathway component. Of the 154 cases with available TMB data, the median TMB was 20 (range 0.7–266 mutations/Mb). A total of 14 cases (9.1%) were classified as having a low TMB (≤5 mutations/Mb), 64 of 154 (41.6%) had an intermediate TMB (>5 and ≤20 mutations/Mb), 40 of 154 (26.0%) had a high TMB (>20 and ≤50 mutations/Mb) and 36 of 154 (23.4%) were classified as having a very high TMB (>50 mutations/Mb). NRAS mutant melanoma demonstrated significantly decreased overall survival on multivariable analysis (HR for death 2.95, 95% CI 1.13–7.69, p = 0.027, log-rank test) compared with other TCGA molecular subgroups. Of the 116 patients in our cohort with available treatment data, 36 received a combination of dual ICI with anti-CTLA4 and anti-PD1 inhibition as first-line therapy. Elevated TMB was associated with significantly longer progression-free survival following dual-agent ICI (HR 0.26, 95% CI 0.07–0.90, p = 0.033, log-rank test). Conclusions: NRAS mutation in CMs correlated with significantly worse overall survival. Elevated TMB was associated with increased progression-free survival for patients treated with a combination of dual ICI, supporting the potential utility of TMB as a predictive biomarker for ICI response in melanoma. [ABSTRACT FROM AUTHOR]

Simple Summary: While both the fields of cancer metastasis research and cancer immunology have been heavily investigated, their combination—the immunology of metastasis—is underrepresented given the fact that metastasis is responsible for up to 90% of cancer deaths. Additionally, evading detection by the immune system is a key prerequisite for the spread of tumor cells to distant organs. In this review, we explore the connections between a master regulator of metastasis, MACC1, and both its direct and indirect links with immunological processes. Specifically, we highlight MACC1-induced alterations of signaling pathways and secreted factors and how this translates into changed immunological outcomes including effects on immune cell infiltration, activity, and their regulation through immunological checkpoints. Metastasis remains the most critical factor limiting patient survival and the most challenging part of cancer-targeted therapy. Identifying the causal drivers of metastasis and characterizing their properties in various key aspects of cancer biology is essential for the development of novel metastasis-targeting approaches. Metastasis-associated in colon cancer 1 (MACC1) is a prognostic and predictive biomarker that is now recognized in more than 20 cancer entities. Although MACC1 can already be linked with many hallmarks of cancer, one key process—the facilitation of immune evasion—remains poorly understood. In this review, we explore the direct and indirect links between MACC1 and the mechanisms of immune escape. Therein, we highlight the signaling pathways and secreted factors influenced by MACC1 as well as their effects on the infiltration and anti-tumor function of immune cells. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we evaluated the status and dynamics of peripheral T cell subpopulations in hepatocellular carcinoma (HCC) patients receiving immune checkpoint blockade with atezolizumab plus bevacizumab (Atez/Bev) treatment and explored biomarkers that are predictive of therapeutic response. The results showed that a higher proportion of CD8+ central memory T cells at baseline is associated with tumor inflammation in HCC, as well as with longer progression-free survival in response to Atez/Bev treatment, especially in cases with an increased proportion of CD8+ effector memory T cells after 3 weeks. Our findings suggest that the peripheral T cell subpopulation can serve as a potential noninvasive biomarker predicting benefits from Atez/Bev treatment. The therapeutic benefits of the immunotherapeutic combination of atezolizumab and bevacizumab (Atez/Bev) in hepatocellular carcinoma (HCC) vary. Therapeutic biomarkers might help improve outcomes for HCC patients receiving Atez/Bev therapy. The role of systemic immune profiles in HCC progression also remains unclear. This study aimed to evaluate the status and dynamics of peripheral T cell subpopulations in HCC patients receiving Atez/Bev treatment and to explore biomarkers predictive of a therapeutic response. We enrolled 83 unresectable advanced HCC patients who commenced Atez/Bev treatment at our hospital between October 2020 and June 2022. Peripheral T cell subpopulations in peripheral blood mononuclear cells at baseline and 3 weeks post-treatment were investigated using flow cytometry and compared with those in control samples from 18 healthy individuals. We retrospectively analyzed the association between peripheral T cell subpopulation profiles and clinical outcomes. Baseline peripheral T cell subpopulations could be profiled in 70 patients with sufficient cell counts, among whom 3-week subpopulations could be evaluated in 51 patients. Multivariate analysis showed that a high baseline proportion of CD8+ central memory T (TCM) cells was independently associated with longer progression-free survival (PFS). Further, overall survival (OS) was significantly prolonged in patients with increased CD8+ effector memory T (TEM) cell proportions. In conclusion, TCM proportion at baseline might be a good indicator of the efficacy of Atez/Bev therapy. Furthermore, observation of increasing TEM proportions might be an early predictor of the potential clinical benefits of treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Deep-learning-based radiogenomic (DLR) models show promising performance in assisting with lung cancer care. The primary aim of our study was to develop and validate a DLR model to predict EGFR mutation status in non-small-cell lung cancer (NSCLC) patients. Using 990 patients from two clinical trials, the study employed a machine learning pipeline that analysed CT images with manually selected tumour regions. Two deep convolutional neural networks segmented lung masses and nodules from 3D regions of the CT image. The combined radiomics and DLR model achieved 88% accuracy in predicting EGFR mutations, outperforming individual models. The semantic features extracted from CT images also contributed to accurate predictions. The study suggests that this AI-based model in combination with CT semantic features could serve as a non-invasive biomarker that aids in predicting EGFR mutation status with significant accuracy. Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model's accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation found in an individual's DNA sequences. SNPs can occur in both coding and non-coding regions of the genome and can affect gene expression, protein function, and disease susceptibility. In this systematic review, we evaluate the potential of SNPs as biomarkers in the assessment of oral mucositis (OM) severity in head and neck cancer (HNC) patients treated with concomitant chemoradiation (CRT). This systematic review demonstrates that SNPs in different biological pathways have the potential to be biomarkers and to function as predictors of patients who will develop severe mucositis. The most studied biological pathway was that involving DNA damage repair genes. However, considering the involvement of cytokines and inflammatory pathways in the pathophysiology of mucositis, we believe that this is a promising area in the study of the prediction of this condition. We conclude that SNPs can be used as possible biomarkers for the assessment of OM intensity in HNC patients, and further research is needed to explore the potential of SNPs in personalized medicine for HNC treatment. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation found in an individual's DNA sequences. SNPs can occur in both coding and non-coding regions of the genome and can affect gene expression, protein function, and disease susceptibility. In this systematic review, we evaluate the potential of SNPs as biomarkers in the assessment of oral mucositis (OM) severity in head and neck cancer (HNC) patients treated with concomitant chemoradiation (CRT). The study selection process involved screening 66 articles from different platforms, and after removing duplicates and excluding articles that did not meet the eligibility criteria, 23 articles were included for full-text evaluation. Among them, genes from several pathways were analyzed. The DNA damage repair pathways had the highest number of genes studied. The most frequently analyzed gene was XRCC1. The proinflammatory cytokine pathways evaluated were TNF, with three articles, and NF-κB, with one article. Most included studies showed a potential association between certain SNPs and high-grade mucositis. We conclude that SNPs can be used as possible biomarkers for the assessment of OM intensity in HNC patients, and further research is needed to explore the potential of SNPs in personalized medicine for HNC treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: A tumor tissue is composed of not only cancer cells but also other cell types and microorganisms that communicate among themselves in a three-dimensional (3D) space to support cancer cell growth. Using two different gynecologic tumor tissue samples, we integrated multiple new techniques using a suite of newly developed analytical methods to simultaneously identify expression of genes, metabolites, and proteins in single tissue 'voxels'. These tissue voxels contain cells, genes from those cells and microorganisms, and the stromal context of proteins (collagen), glycans, metabolites, and peptides, all identified in the 3D space within a tumor tissue. We have successfully demonstrated different arrays of analytes expressed by cancer cells and their neighboring cells in different regions of the tumor tissue. Understanding how cancer cells communicate with other cell types in the 3D space of the tumor tissue will allow for the identification of new therapeutic targets for the treatment of these diseases. Most platforms used for the molecular reconstruction of the tumor–immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell–cell or cell–extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: The prognosis for patients with glioblastoma (GBM) remains dismal despite advances in tumor detection and treatment. The aim of our retrospective study was to develop a multiparametric model incorporating baseline MRI imaging, histopathological biomarkers, and demographic data to predict prognosis in patients with GBM. A training model was developed with 116 patients and validated with an external cohort of 40 patients. Three significant variables were identified as independent predictors of survival ≥ 18 months: radiomics, age, and MGMT status. Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored the predictive value of neutrophils and neutrophil-related biomarkers as auxiliary diagnosis biomarkers of NSCLC in an ongoing large cohort. IL-6 and IL-1RA were identified as independent risk factors for NSCLC. These findings can improve the predictive performance beyond epidemiological variables and classic neutrophil-related biomarkers in identifying NSCLC. Background: Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC). Methods: We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement. Results: Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%. Conclusions: IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease. [ABSTRACT FROM AUTHOR]

Simple Summary: The growing use of immune checkpoint inhibitors (ICIs) for the treatment of patients with solid tumors has led to a proportional increase in the incidence of toxic effects in the form of immune-related adverse events (irAEs). In this study, we show that two readily available blood cell parameters, namely, high absolute lymphocyte count before ICI initiation and early decline in absolute neutrophil count after ICI initiation, can predict irAEs during follow-up. Interestingly, however, the predictive ability of both pre-ICI absolute lymphocyte count and post-ICI absolute neutrophil count differ significantly between men and women. In the prediction of irAEs, we also describe an interaction between female gender and a decrease in absolute neutrophil count after the first cycle of ICI therapy. These findings should lead to the development of new predictive models for irAEs that are able to capture sex-related differences in ICI-induced toxicity. Several factors have been associated with the occurrence of immune-related adverse events (irAEs) induced by immune checkpoint inhibitor (ICI) therapy. Despite their availability, the predictive value of circulating blood cell parameters remains underexplored. Our aim was to investigate whether baseline values of and early changes in absolute neutrophil count (ANC), absolute lymphocyte count (ALC), other blood cell counts, and lymphocyte-related ratios can predict irAEs and whether sex may differentially influence this potential predictive ability. Of the 145 patients included, 52 patients (35.8%) experienced at least one irAE, with a 1-year cumulative incidence of 41.6%. Using Fine and Gray competing risk models, we identified female sex (hazard ratio (HR) = 2.17, 95% confidence interval (CI) = 1.20–3.85), high ALC before ICI initiation (HR = 1.63, 95% CI = 1.09–2.45), and low ANC after ICI initiation (HR = 0.81, 95% CI = 0.69–0.96) as predictors of irAEs. However, ALC and ANC may only have an impact on the risk of irAEs in women (stratified for female sex, ALC-related HR = 2.61, 95% CI = 1.40–4.86 and ANC-related HR = 0.57, 95% CI = 0.41–0.81). Priority should be given to developing models to predict ICI-related toxicity and their validation in various settings, and such models should assess the impact of patient sex on the risk of toxicity. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck cancer patients are frequently treated with primary chemoradiation, but response to therapy is hard to predict. In this study, we identified the expression of the SEC62 gene as a significant and independent predictor of patient outcome in a cohort of 127 head and neck cancer patients undergoing primary chemoradiation. Further significant prognostic factors indicating a significantly shortened overall and progression-free survival included response to therapy (RECIST1.1), lymph node metastases, distant metastases, tobacco consumption, recurrence of disease, and advanced clinical stage of disease. Together, SEC62 represents a promising and valid prognostic biomarker in this treatment setting of head and neck cancer. Its role in tumor cell biology and potential therapeutic strategies targeting SEC62 should be further investigated. Primary chemoradiotherapy (CRT) is an established treatment option for locally advanced head and neck squamous cell carcinomas (HNSCC) usually combining intensity modified radiotherapy with concurrent platinum-based chemotherapy. Though the majority of patients can be cured with this regimen, treatment response is highly heterogeneous and can hardly be predicted. SEC62 represents a metastasis stimulating oncogene that is frequently overexpressed in various cancer entities and is associated with poor outcome. Its role in HNSCC patients undergoing CRT has not been investigated so far. A total of 127 HNSCC patients treated with primary CRT were included in this study. The median follow-up was 5.4 years. Pretherapeutic tissue samples of the primary tumors were used for immunohistochemistry targeting SEC62. SEC62 expression, clinical and histopathological parameters, as well as patient outcome, were correlated in univariate and multivariate survival analyses. High SEC62 expression correlated with a significantly shorter overall survival (p = 0.015) and advanced lymph node metastases (p = 0.024). Further significant predictors of poor overall and progression-free survival included response to therapy (RECIST1.1), nodal status, distant metastases, tobacco consumption, recurrence of disease, and UICC stage. In a multivariate Cox hazard proportional regression analysis, only SEC62 expression (p = 0.046) and response to therapy (p < 0.0001) maintained statistical significance as independent predictors of the patients' overall survival. This study identified SEC62 as an independent prognostic biomarker in HNSCC patients treated with primary CRT. The role of SEC62 as a potential therapeutic target and its interaction with radiation-induced molecular alterations in head and neck cancer cells should further be investigated. [ABSTRACT FROM AUTHOR]

Simple Summary: Ovarian cancer is one of leading cause of death among women and early detection is important for timely treatment. For its detection at early stages, machine learning can be significantly important to speed up the screening and provide more accurate results. A stacked model is designed in this study, combining the strengths of mutliple models to obtain better accuracy compared to existing models. With a 96.87% accuracy, the model proves to be robust and accurate. The use of explainable AI elaborates on the importance of various appropriate features to enhance cancer detection accuracy in this regard. The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models. [ABSTRACT FROM AUTHOR]

Simple Summary: A considerable number of patients with colorectal cancer (CRC) do not have elevated serum carcinoembryonic antigen (CEA) levels before undergoing radical surgery for colorectal cancer, but they may still have a poor prognosis. It is imperative to find sensitive and dependable prognostic markers to quickly identify high-risk populations who are susceptible to adverse outcomes in order to offer timely interventions to improve prognosis. In this real-world study of more than 1000 samples, a nomogram based on serum tumor markers and other clinicopathological features was used to accurately forecast the 3-year and 5-year overall survival rates of stage I–III CRC patients after radical resection with normal preoperative CEA, and its predictive power and clinical applicability markedly exceed those of the AJCC 8th TNM stage. In addition, we found the potential prognostic values of carbohydrate antigen 125 (CA125) and carbohydrate antigen 242 (CA242) as supplementary tumor markers in CRC patients who have normal preoperative CEA. We aimed to develop a clinical predictive model for predicting the overall survival (OS) in stage I–III CRC patients after radical resection with normal preoperative CEA. This study included 1082 consecutive patients. They were further divided into a training set (70%) and a validation set (30%). The selection of variables for the model was informed by the Akaike information criterion. After that, the clinical predictive model was constructed, evaluated, and validated. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the models. Age, histologic type, pT stage, pN stage, carbohydrate antigen 242 (CA242), and carbohydrate antigen 125 (CA125) were selected to establish a clinical prediction model for OS. The concordance index (C-index) (0.748 for the training set and 0.702 for the validation set) indicated that the nomogram had good discrimination ability. The decision curve analysis highlighted that the model has superior efficiency in clinical decision-making. NRI and IDI showed that the established nomogram markedly outperformed the TNM stage. The new clinical prediction model was notably superior to the AJCC 8th TNM stage, and it can be used to accurately assess the OS of stage I–III CRC patients undergoing radical resection with normal preoperative CEA. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer (CRC) is one of the most common malignancies worldwide, causing thousands to die each year. Its complex molecular nature leads to significant heterogeneity and variable responses to therapy. ABC proteins, which for many years were regarded as the pillar of the resistance to chemotherapy because they export anticancer drugs from cancer cells, have recently been identified as interesting molecular markers associated with many other physiological functions. We previously reported that during the phenotypic transition, CRC differentially regulates the expression of two transporters, ABCC4 and ABCG2. In cells with a mesenchymal and invasive phenotype, ABCC4 is upregulated, and ABCG2 is downregulated. We have therefore decided to explore this phenomenon by analysing samples from CRC patients with high expression of either ABCC4 or ABCG2 to determine their potential use as markers of therapeutic outcome. Background: Our previous findings proved that ABCC4 and ABCG2 proteins present much more complex roles in colorectal cancer (CRC) than typically cancer-associated functions as drug exporters. Our objective was to evaluate their predictive/diagnostic potential. Methods: CRC patients' transcriptomic data from the Gene Expression Omnibus database (GSE18105, GSE21510 and GSE41568) were discriminated into two subpopulations presenting either high expression levels of ABCC4 (ABCC4 High) or ABCG2 (ABCG2 High). Subpopulations were analysed using various bioinformatical tools and platforms (KEEG, Gene Ontology, FunRich v3.1.3, TIMER2.0 and STRING 12.0). Results: The analysed subpopulations present different gene expression patterns. The protein–protein interaction network of subpopulation-specific genes revealed the top hub proteins in ABCC4 High: RPS27A, SRSF1, DDX3X, BPTF, RBBP7, POLR1B, HNRNPA2B1, PSMD14, NOP58 and EIF2S3 and in ABCG2 High: MAPK3, HIST2H2BE, LMNA, HIST1H2BD, HIST1H2BK, HIST1H2AC, FYN, TLR4, FLNA and HIST1H2AJ. Additionally, our multi-omics analysis proved that the ABCC4 expression correlates with substantially increased tumour-associated macrophage infiltration and sensitivity to FOLFOX treatment. Conclusions: ABCC4 and ABCG2 may be used to distinguish CRC subpopulations that present different molecular and physiological functions. The ABCC4 High subpopulation demonstrates significant EMT reprogramming, RNA metabolism and high response to DNA damage stimuli. The ABCG2 High subpopulation may resist the anti-EGFR therapy, presenting higher proteolytical activity. [ABSTRACT FROM AUTHOR]

Simple Summary: The expression of the cysteine–glutamate cotransporter, system xc−, is increased in cancer cells across many cancer types. Imaging system xc− provides new insights into tumour behaviour. The radiotracer (4S)-4-(3-[18F]Fluoropropyl)-L-glutamic acid (18F-FSPG) is specifically transported by system xc−, allowing for a non-invasive method of measuring this transporter's activity. This review summarises the data available on the use of 18F-FSPG in human cancer patients, exploring its advantages and disadvantages, and suggests possible future uses of 18F-FSPG in the assessment of early treatment response and treatment resistance. System xc− is upregulated in cancer cells and can be imaged using novel radiotracers, most commonly with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid (18F-FSPG). The aim of this review was to summarise the use of 18F-FSPG in humans, explore the benefits and limitations of 18F-FSPG, and assess the potential for further use of 18F-FSPG in cancer patients. To date, ten papers have described the use of 18F-FSPG in human cancers. These studies involved small numbers of patients (range 1–26) and assessed the use of 18F-FSPG as a general oncological diagnostic agent across different cancer types. These clinical trials were contrasting in their findings, limiting the scope of 18F-FSPG PET/CT as a purely diagnostic agent, primarily due to heterogeneity of 18F-FSPG retention both between cancer types and patients. Despite these limitations, a potential further application for 18F-FSPG is in the assessment of early treatment response and prediction of treatment resistance. Animal models of cancer have shown that changes in 18F-FSPG retention following effective therapy precede glycolytic changes, as indicated by 18F-FDG, and changes in tumour volume, as measured by CT. If these results could be replicated in human clinical trials, imaging with 18F-FSPG PET/CT would offer an exciting route towards addressing the currently unmet clinical needs of treatment resistance prediction and early imaging assessment of therapy response. [ABSTRACT FROM AUTHOR]

Simple Summary: The neurosurgery patient population belongs to the moderate- to high-risk venous thromboembolism (VTE) population. There is no specific clinical prediction model for the incidence of postoperative VTE in neurosurgery. This study developed a comprehensive model by combining specific laboratory biomarkers, a large sample size, and various perioperative variables to standardize primary VTE prevention, and the model exhibited strong predictive performance across multiple validation cohorts. Neurosurgeons can utilize this model to make informed decisions regarding appropriate VTE primary prevention strategies during the early postoperative period. Background: Based on the literature and data on its clinical trials, the incidence of venous thromboembolism (VTE) in patients undergoing neurosurgery has been 3.0%~26%. We used advanced machine learning techniques and statistical methods to provide a clinical prediction model for VTE after neurosurgery. Methods: All patients (n = 5867) who underwent neurosurgery from the development and retrospective internal validation cohorts were obtained from May 2017 to April 2022 at the Department of Neurosurgery at the Sanbo Brain Hospital. The clinical and biomarker variables were divided into pre-, intra-, and postoperative. A univariate logistic regression (LR) was applied to explore the 67 candidate predictors with VTE. We used a multivariable logistic regression (MLR) to select all significant MLR variables of MLR to build the clinical risk prediction model. We used a random forest to calculate the importance of significant variables of MLR. In addition, we conducted prospective internal (n = 490) and external validation (n = 2301) for the model. Results: Eight variables were selected for inclusion in the final clinical prediction model: D-dimer before surgery, activated partial thromboplastin time before neurosurgery, age, craniopharyngioma, duration of operation, disturbance of consciousness on the second day after surgery and high dose of mannitol, and highest D-dimer within 72 h after surgery. The area under the curve (AUC) values for the development, retrospective internal validation, and prospective internal validation cohorts were 0.78, 0.77, and 0.79, respectively. The external validation set had the highest AUC value of 0.85. Conclusions: This validated clinical prediction model, including eight clinical factors and biomarkers, predicted the risk of VTE following neurosurgery. Looking forward to further research exploring the standardization of clinical decision-making for primary VTE prevention based on this model. [ABSTRACT FROM AUTHOR]

Simple Summary: In oncology, the identification of early predictors of response/survival is of particular interest. C-reactive protein (CRP) concentrations have been associated with advanced non-small-cell lung cancer and poor prognosis. We characterized the association between anticancer drug exposure, tumor size as a marker of tumor dynamics, and CRP as a marker of inflammation and derived different predictors. CRP at the beginning of treatment cycle 3 (day 42) was identified as the strongest predictor of both progression-free survival and overall survival, and the inflammatory status, monitored by CRP concentration, emerged as a promising prognostic marker. The high significance of longitudinal CRP concentrations compared to baseline concentrations provided a true reflection of the patient status. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies, allowing the identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and offer alternative treatment decisions. In oncology, longitudinal biomarkers reflecting the patient's status and disease evolution can offer reliable predictions of the patient's response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions. [ABSTRACT FROM AUTHOR]

Simple Summary: The human papillomavirus (HPV) is an important prognostic marker in oropharyngeal squamous cell carcinoma (OPSCC) due to its involvement in carcinogenesis. The presence of the virus itself or markers of a past infection are usually determined by tissue specimen. This work applies predictive machine learning models to objectified imaging features extracted from routine imaging of patients newly diagnosed with OPSCC to classify cases of HPV-positive and negative tumors. In addition to results and classification performance being in line with existing literature on imaging-based HPV classification, imaging features of the parotid gland could be shown to yield relevant information. Parotid gland imaging offers potential as a screening tool and may aid clinical decision making in cases of cancer with unknown primary in the future. Background: In treatment of oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus status (HPV) plays a crucial role. The HPV-positive subtype tends to affect younger patients and is associated with a more favorable prognosis. HPV-associated lesions have been described in the parotid gland, which is included in routine imaging for OPSCC. This work aims to explore the ability of an ML system to classify HPV status based on imaging of the parotid gland, which is routinely depicted on staging imaging. Methods: Using a radiomics approach, we investigate the ability of five contemporary machine learning (ML) models to distinguish between HPV-positive and HPV-negative OPSCC based on non-contrast computed tomography (CT) data of tumor volume (TM), locoregional lymph node metastasis (LNM), and the parotid gland (Parotid). After exclusion of cases affected by streak artefacts, 53 patients (training set: 39; evaluation set: 14) were retrospectively evaluated. Classification performances were tested for significance against random optimistic results. Results: The best results are AUC 0.71 by XGBoost (XGB) for TM, AUC 0.82 by multi-layer perceptron (MLP) for LNM, AUC 0.76 by random forest (RF) for Parotid, and AUC 0.86 by XGB for a combination of all three regions of interest (ROIs). Conclusions: The results suggest involvement of the parotid gland in HPV infections of the oropharyngeal region. While the role of HPV in parotid lesions is under active discussion, the migration of the virus from the oral cavity to the parotid gland seems plausible. The imaging of the parotid gland offers the benefit of fewer streak artifacts due to teeth and dental implants and the potential to screen for HPV in cases of an absent or unlocatable tumor. Future investigation can be directed to validation of the results in independent datasets and to the potential of improvement of current classification models by addition of information based on the parotid gland. [ABSTRACT FROM AUTHOR]

Simple Summary: Multiparametric magnetic resonance imaging (mpMRI) is now standard practice for suspected prostate cancer (PCa) patients, significantly enhancing risk assessment and PCa detection. Integrating MRI into clinical staging allows for more precise, personalized treatment planning in cases of extraprostatic cancer extension. Adverse MRI findings, such as a macroscopic extracapsular extension on MRI (mECE+), capsular disruption, extended tumor capsular contact length (TCCL), Grade Group (GG) ≥ 4, positive surgical margins (PSM), and pECE+ on pathology, were associated with higher biochemical recurrence (BCR) risk. Particularly in low/intermediate-risk patients (pECE− and GG < 4), adverse MRI characteristics correlated with elevated BCR risk. This feature highlights the importance of incorporating predictive MRI features pre-surgery to aid clinical decisions and enhance outcomes in prostate cancer. Adverse MRI features assist in identifying low/intermediate-risk patients needing closer monitoring. Objectives: This study aimed to assess the impact of the covariates derived from a predictive model for detecting extracapsular extension on pathology (pECE+) on biochemical recurrence-free survival (BCRFS) within 4 years after robotic-assisted radical prostatectomy (RARP). Methods: Retrospective data analysis was conducted from a single center between 2015 and 2022. Variables under consideration included prostate-specific antigen (PSA) levels, patient age, prostate volume, MRI semantic features, and Grade Group (GG). We also assessed the influence of pECE+ and positive surgical margins on BCRFS. To attain these goals, we used the Kaplan–Meier survival function and the multivariable Cox regression model. Additionally, we analyzed the MRI features on BCR (biochemical recurrence) in low/intermediate risk patients. Results: A total of 177 participants with a follow-up exceeding 6 months post-RARP were included. The 1-year, 2-year, and 4-year risks of BCR after radical prostatectomy were 5%, 13%, and 21%, respectively. The non-parametric approach for the survival analysis showed that adverse MRI features such as macroscopic ECE on MRI (mECE+), capsular disruption, high tumor capsular contact length (TCCL), GG ≥ 4, positive surgical margins (PSM), and pECE+ on pathology were risk factors for BCR. In low/intermediate-risk patients (pECE− and GG < 4), the presence of adverse MRI features has been shown to increase the risk of BCR. Conclusions: The study highlights the importance of incorporating predictive MRI features for detecting extracapsular extension pre-surgery in influencing early outcomes and clinical decision making; mECE+, TCCL, capsular disruption, and GG ≥ 4 based on pre-surgical biopsy were independent prognostic factors for early BCR. The presence of adverse features on MRI can assist in identifying low/intermediate-risk patients who will benefit from closer monitoring. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is considered as the most common malignancy among females, and its treatment takes many forms and types. Neoadjuvant chemotherapy (NACT), which is the treatment precedes the surgical intervention, became the preferred treatment approach for some subtypes of breast tumors. However, some patients exhibit good response to the neoadjuvant treatment, while others do not. Therefore, the proactive prediction of patients' response to NACT is a necessity to reduce the exposure to unnecessary doses of treatment, treatment costs, and side effects. Many researchers proposed prediction models to predict patients' response to NACT either at early stage of treatment or prior to the initiation of the first cycle. They used various radiomics, pathological, and clinical predictors and markers. This review discusses some of the researches conducted the last decade based on statistical, machine learning, or deep learning approaches. Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: This systematic review evaluates the potential of magnetic resonance imaging (MRI) to predict tumor biology in primary squamous cell carcinoma of the head and neck (HNSCC). Fifty-eight articles were analyzed, examining the relationship between MRI parameters and biological features. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower diffusion-weighted metrics. Moreover, lower diffusion values were also with a high Ki-67 proliferation index, indicating high cellularity. Several perfusion parameters describing the vascularity were significantly associated with HIF-1α. Analysis results of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) were inconclusive. Larger datasets are needed to develop and validate radiomic-based prediction models, which already show promising results in capturing diverse tumor biology features. Overall, MRI holds potential for non-invasive and rapid tumor biology characterization, enhancing future clinical outcome predictions and personalized patient management for HNSCC. Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC. [ABSTRACT FROM AUTHOR]

Simple Summary: This comprehensive systematic review provides valuable insights into the landscape of biomarkers in clear cell renal cell carcinoma (ccRCC) and their potential applications in the prediction of treatment response, prognosis, and therapeutic monitoring. One of the major challenges in ccRCC is determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy. This review aims to provide a comprehensive overview of biomarkers in ccRCC and their utility in the prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in ccRCC. Background: Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease. Methods: A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings. Results: The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures. Conclusion: This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC. [ABSTRACT FROM AUTHOR]

Simple Summary: Metastasis poses a significant challenge in the advancement of oral squamous cell carcinoma, and it is closely associated with the epithelial–mesenchymal transition of tumor cells. In the current study, we investigated the expression profiles of connexin 43 and EMMPRIN, along with the known epithelial–mesenchymal transition markers E-cadherin and vimentin, using immunohistochemistry throughout the metastatic process. Additionally, we examined their prognostic impact both as a combined marker system and individually. The findings of this study reveal that a combined biomarker system can reliably predict overall and disease-free survival. Notably, alterations in EMMPRIN expression were found to have the highest prognostic impact, suggesting its potential as a therapeutic target for antimetastatic interventions. Background: Metastatic oral squamous cell carcinoma (OSCC) is associated with poor patient prognosis. Metastasis is a complex process involving various proteins, tumor cell alterations, including changes attributable to the epithelial-to-mesenchymal transition (EMT) process, and interactions with the tumor microenvironment (TME). In this study, we investigate a combined protein marker system consisting of connexin 43 (Cx43), EMMPRIN (CD147), E-cadherin, and vimentin, with a focus on their roles in the invasive metastatic progression of OSCC and their potential utility in predicting prognosis. Methods: We conducted an immunohistochemical analysis to assess the protein expression profiles of Cx43, EMMPRIN, E-cadherin, and vimentin using tissue samples obtained from 24 OSCC patients. The metastatic process was mapped through different regions of interest (ROIs), including adjacent healthy oral mucosa (OM), center of primary OSCC, invasive front (IF), and local cervical lymph node metastases (LNM). The primary clinical endpoints were disease-free survival (DFS) and overall survival (OS). Results: Substantial changes in the expression profiles of the different marker proteins were observed among the different ROIs, with all p-values < 0.05, signifying statistical significance. Multivariable Cox regression analysis results showed a significant effect of increased EMMPRIN expression toward the IF on DFS (p = 0.019) and OS (p = 0.023). Furthermore, the combined predictive analysis showed a significant predictive value of the marker system for DFS (p = 0.0017) and OS (p = 0.00044). Conclusions: The combined marker system exhibited a significant ability to predict patient prognosis. An increase in EMMPRIN expression toward the IF showed the strongest effect and could be an interesting new antimetastatic therapy approach. [ABSTRACT FROM AUTHOR]

Simple Summary: A precise mass spectrometry-based method was utilized to study proteins in the blood samples of over a thousand cancer patients. By accurately identifying and measuring protein levels using mass spectrometry, we focused on multiple myeloma and found potential markers for diagnosing the disease. These markers, including the complement C1 complex, JCHAIN, and CD5L, were combined in a prediction model with high accuracy for identifying multiple myeloma patients. Our findings could significantly impact cancer research by improving diagnostic tools. Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioma tumor aggressiveness is expressed as tumor grading which is crucial in guiding treatment decisions and clinical trial participation. Accurate and standardized grading systems are essential to optimize care and improve outcomes. However, integrating molecular and clinical information in the grading process has the potential to expose molecular markers that have gained importance in understanding tumor biology as a means of identifying druggable targets. In this study, a novel approach called GradWise is introduced with the goal of enhancing feature selection performance while employing various machine learning models of glioma grading. GradWise combines a rank-based weighted hybrid filter (mRMR) and an embedded feature selection method (LASSO) to select the most relevant features from clinical and molecular predictors and was evaluated using two commonly employed public biomedical datasets, TCGA and CGGA, utilizing two feature selection methods and five supervised models. The findings support existing evidence and provide pioneering results for glioma-specific biomarkers, highlighting the effectiveness of the approach and future directions for biological mechanisms of glioma progression to higher grades. Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Given the variable aggressiveness of PCa, patients with indolent PCa do not require intervention, but rather require active surveillance and close lifelong follow-up, while those with invasive PCa require surgery, various types of radiation therapy, androgen-deprivation therapy (ADT), or multimodal treatment. Hence, it is critical to accurately distinguish indolent from invasive PCa for prognosis evaluation and treatment decision-making. The aim of the present study was to investigate the value of MR radiomics feature-based machine learning (ML) models in predicting the Ki67 index and Gleason grade group (GGG) of PCa. Biparametric magnetic resonance imaging (bpMRI) radiomics-based ML models to predict immuno-histochemically-determined Ki67 expression and the GGG demonstrated the ability to identify aggressive PCa. A preliminary exploration was performed in the conjoint analysis, laying the theoretical foundation for models predicting two or more variables; such models are expected to provide more comprehensive pathological information and provide valuable guidance for clinical decision-making in a noninvasive, synchronous, and objective manner. Purpose: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. Methods: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. Results: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). Conclusion: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with hepatocellular carcinoma are often monitored using serum markers such as alfa-fetoprotein. However, serum markers are not always accurate in predicting treatment success or the future course of the disease. In this research, we looked at the potential of DNA methylation as a new way to gauge treatment outcomes in patients with intermediate-stage hepatocellular carcinoma. We found that using these DNA changes might be more precise than just relying on serum markers. Even better, when we combined the information from both DNA methylation and serum markers, our predictions improved further. These discoveries could help doctors better personalize treatment plans, especially for patients who need close monitoring or additional therapy after their initial treatment. Introduction: Alfa-fetoprotein (AFP), as the main serum tumor marker of hepatocellular carcinoma (HCC), is limited in terms of specificity and ability to predict outcomes. This study investigated the clinical utility of DNA methylation biomarkers to predict therapeutic responses and prognosis in intermediate-stage HCC. Methods: This study enrolled 72 patients with intermediate-stage HCC who underwent locoregional therapy (LRT) between 2020 and 2021. The immediate therapeutic response and disease status during a two-year follow-up were recorded. Analysis was performed on 10 selected DNA methylation biomarkers via pyrosequencing analysis of plasma collected before and after LRT. Results: Analysis was performed on 53 patients with complete responses and 19 patients with disease progression after LRT. The mean follow-up duration was 2.4 ± 0.6 years. A methylation prediction model for tumor response (MMTR) and a methylation prediction model for early progression (MMEP) were constructed. The area under the curve (AUC) for sensitivity and specificity of MMTR was 0.79 for complete response and 0.759 for overall survival. The corresponding AUCs for sensitivity and specificity of AFP and protein induced by vitamin K absence-II (PIVKA-II) were 0.717 and 0.708, respectively. Note that the MMTR index was the only significant predictor in multivariate analysis. The AUC for sensitivity and specificity of the MMEP in predicting early progression was 0.79. The corresponding AUCs for sensitivity and specificity of AFP and PIVKA-II were 0.758 and 0.714, respectively. Multivariate analysis revealed that platelet count, beyond up-to-7 criteria, and the MMEP index were strongly correlated with early tumor progression. Combining the indexes and serum markers further improved the predictive accuracy (AUC = 0.922). Multivariate analysis revealed the MMEP index was the only independent risk factor for overall survival. Discussion/Conclusions: This study indicates that these methylation markers could potentially outperform current serum markers in terms of accuracy and reliability in assessing treatment response and predicting outcomes. Combining methylation markers and serum markers further improved predictive accuracy, indicating that a multi-marker approach may be more effective in clinical practice. These findings suggest that DNA methylation biomarkers may be a useful tool for managing intermediate-stage HCC patients and guiding personalized treatment, particularly for those who are at high risk for close surveillance or adjuvant treatment after LRT. [ABSTRACT FROM AUTHOR]

Simple Summary: Atezolizumab plus bevacizumab (Atez/Bev) is the first-line treatment for unresectable advanced hepatocellular carcinoma (HCC). The tumor markers (TMs) for HCC include alpha-fetoprotein (AFP), fucosylated alpha-fetoprotein (AFP-L3), and des-gamma carboxyprothrombin (DCP). A TM score combining these markers has been reported to be useful in predicting HCC prognosis. This retrospective study aimed to evaluate the ability of this previously reported TM score involving AFP, AFP-L3, and DCP as TMs in predicting prognosis and therapeutic efficacy in HCC patients administered Atez/Bev as first-line treatment. The TM score was found to be effective in stratifying overall survival and progression-free survival in 371 patients with unresectable advanced HCC treated with Atez/Bev. The TM score proved to be a simple and useful prognostic marker and therapeutic efficacy indicator for advanced HCC patients administered Atez/Bev as first-line treatment. Aim: This study aimed to evaluate the ability of a previously reported tumor marker (TM) score involving alpha-fetoprotein (AFP), fucosylated AFP (AFP-L3), and des gamma-carboxy prothrombin (DCP) as TMs in predicting the prognosis and therapeutic efficacy in hepatocellular carcinoma (HCC) patients administered atezolizumab plus bevacizumab (Atez/Bev) as first-line treatment. Materials/Methods: The study period covered September 2020 to December 2022 and involved 371 HCC patients treated with Atez/Bev. The values of the TMs AFP, AFP-L3, and DCP were measured upon introducing Atez/Bev. Elevations in the values of AFP (≥100 ng/mL), AFP-L3 (≥10%), and DCP (≥100 mAU/mL) were considered to indicate a positive TM. The number of positive TMs was summed up and used as the TM score, as previously proposed. Hepatic reserve function was assessed using the modified albumin–bilirubin grade (mALBI). Predictive values for prognosis were evaluated retrospectively. Results: A TM score of 0 was shown in 81 HCC patients (21.8%), 1 in 110 (29.6%), 2 in 112 (29.9%), and 3 in 68 (18.3%). The median overall survival (OS) times for TM scores 0, 1, 2, and 3 were not applicable [NA] (95% CI NA-NA), 24.0 months (95% CI 17.8-NA), 16.7 months (95% CI 17.8-NA), and NA (95% CI 8.3-NA), respectively (p < 0.001). The median progression-free survival (PFS) times for TM scores 0, 1, 2, and 3 were 16.5 months (95% CI 8.0-not applicable [NA]), 13.8 months (95% CI 10.6–21.3), 7.7 months (95% CI 5.3–8.9), and 5.8 months (95% CI 3.0–7.6), respectively (p < 0.001). OS was well stratified in mALBI 1/2a and mALBI 2a/2b. PFS was well stratified in mALBI 2a/2b, but not in mALBI 1/2a. Conclusions: The TM score involving AFP, AFP-L3, and DCP as TMs was useful in predicting the prognosis and therapeutic efficacy in terms of OS and PFS in HCC patients administered Atez/Bev as first-line treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Studies aimed at prediction of chemotherapeutic efficacy using patient-derived ex vivo cultures (referred to here as "functional testing") have been increasing. The present review provides information on the various types of ex vivo cultures and endpoint assays that employ a range of surrogate biomarkers of drug response. As ex vivo cultures for functional testing, two-dimensional cultures, spheroids, organoids, explants (including histoculture), microfluid-based culture, and micro-organospheres are introduced. The endpoint assays described include ATP-based bulk assay, dynamic BH3 profiling, optical metabolic imaging, fluorescence lifetime imaging microscopy, fluorescent dye-based assay, mass accumulation rate assay, live cell imaging-based assay, and immunostaining for drug-specific response biomarkers. The advantages and disadvantages of these culture systems and endpoint assays are discussed. Prediction of therapeutic outcomes is important for cancer patients in order to reduce side effects and improve the efficacy of anti-cancer drugs. Currently, the most widely accepted method for predicting the efficacy of anti-cancer drugs is gene panel testing based on next-generation sequencing. However, gene panel testing has several limitations. For example, only 10% of cancer patients are estimated to have druggable mutations, even if whole-exome sequencing is applied. Additionally, even if optimal drugs are selected, a significant proportion of patients derive no benefit from the indicated drug treatment. Furthermore, most of the anti-cancer drugs selected by gene panel testing are molecularly targeted drugs, and the efficacies of cytotoxic drugs remain difficult to predict. Apart from gene panel testing, attempts to predict chemotherapeutic efficacy using ex vivo cultures from cancer patients have been increasing. Several groups have retrospectively demonstrated correlations between ex vivo drug sensitivity and clinical outcome. For ex vivo culture, surgically resected tumor tissue is the most abundant source. However, patients with recurrent or metastatic tumors do not usually undergo surgery, and chemotherapy may be the only option for those with inoperable tumors. Therefore, predictive methods using small amounts of cancer tissue from diagnostic materials such as endoscopic, fine-needle aspirates, needle cores and liquid biopsies are needed. To achieve this, various types of ex vivo culture and endpoint assays using effective surrogate biomarkers of drug sensitivity have recently been developed. Here, we review the variety of ex vivo cultures and endpoint assays currently available. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite being in use for almost 50 years, monoclonal antibodies face limitations in their implementation in clinical practice, particularly in pediatrics and pediatric cancer. Although technological advancements and research into new therapeutic targets have led to the development of sophisticated and effective molecules, translational barriers still exist. Integrating monoclonal antibodies (mAbs) into current treatment protocols and ensuring accessibility for all children with cancer globally remains a challenge. This review examines the biological, clinical, economic, and social limitations hindering the global implementation of mAbs in pediatric cancer, with a particular focus on anti-GD2 mAbs. Monoclonal antibodies (mAbs), as the name implies, are clonal antibodies that bind to the same antigen. mAbs are broadly used as diagnostic or therapeutic tools for neoplasms, autoimmune diseases, allergic conditions, and infections. Although most mAbs are approved for treating adult cancers, few are applicable to childhood malignancies, limited mostly to hematological cancers. As for solid tumors, only anti-disialoganglioside (GD2) mAbs are approved specifically for neuroblastoma. Inequities of drug access have continued, affecting most therapeutic mAbs globally. To understand these challenges, a deeper dive into the complex transition from basic research to the clinic, or between marketing and regulatory agencies, is timely. This review focuses on current mAbs approved or under investigation in pediatric cancer, with special attention on solid tumors and anti-GD2 mAbs, and the hurdles that limit their broad global access. Beyond understanding the mechanisms of drug resistance, the continual discovery of next generation drugs safer for children and easier to administer, the discovery of predictive biomarkers to avoid futility should ease the acceptance by patient, health care professionals and regulatory agencies, in order to expand clinical utility. With a better integration into the multimodal treatment for each disease, protocols that align with the regional clinical practice should also improve acceptance and cost-effectiveness. Communication and collaboration between academic institutions, pharmaceutical companies, and regulatory agencies should help to ensure accessible, affordable, and sustainable health care for all. [ABSTRACT FROM AUTHOR]

Simple Summary: Indeterminate pulmonary nodules detected by computer tomography are a common clinical finding, but the path to determine malignancy can cause harm to patients. We aimed to comprehensively assess other types of noninvasive information that could help clinicians diagnose lung cancer, including semantic imaging features, quantitative radiomic imaging features and proteomic, glycomic, and autoantibody–antigen complex blood-based biomarkers. Utilizing these data, we generated a malignancy risk prediction model called PSR (plasma, semantic, radiomic) comprising nine imaging and molecular biomarkers. The PSR model performed well in two cohorts and against a clinical risk prediction model. Adding known clinical risk factors for lung cancer further improved the PSR model, indicating that our discovered markers held independent clinical utility. Our study revealed novel biomarkers and a risk prediction model to help assess cancer risk in patients with indeterminate pulmonary nodules. The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody–antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules. [ABSTRACT FROM AUTHOR]

Simple Summary: Existing research on predicting biochemical recurrence after prostate surgery has been insufficient. Here, we aimed to predict biochemical recurrence after radical prostatectomy leveraging recent advances in deep learning. We combined clinical variables with multiparametric magnetic resonance imaging using deep learning methods. Our method performed better than existing methods. Our method could direct patients to individualized care using routine medical imaging and could achieve better patient care. Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical–radiomics model (CRM-Multi), and combined clinical–DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics. [ABSTRACT FROM AUTHOR]

Simple Summary: Today's standard of treatment for advanced head and neck squamous cell carcinoma (HNSCC) is invasive surgical resection. Radiation co-therapy has received a great deal of attention for its ability to reduce tumor recurrence, common in these cancers. The aim of our study was to investigate the ability of tumor-targeted radiation therapy to generate tumor antigens capable of eliciting host immune response pathways. Using a murine model of HNSCC with tumor cells injected into immune-competent mice, we determined that antigens generated by the irradiation of tumor cells induced potent immune responses compared to other methods for tumor antigen preparations. This response was also found to display comparable sensitivity, but greater specificity for T cell activation compared to conventional anti-CD3 and CD28 antibodies. This information may be utilized to predict patient responses to radiation and immune therapy. Head and neck squamous cell carcinoma (HNSCC) is common and deadly, and there is a need for improved strategies to predict treatment responses. Ionizing radiation (IR) has been demonstrated to improve HNSCC outcomes, but its effects on immune responses are not well characterized. We determined the impact of IR on T cell immune responses ex vivo. Human and mouse HNSCC cells were exposed to IR ranging from 20 to 200 Gy to determine cell viability and the ability to stimulate T-cell-specific responses. Lymph node cells of LY2 and MOC2 tumor-bearing or non-tumor-bearing mice were re-stimulated with a tumor antigen derived from LY2 or MOC2 cells treated with 200 Gy IR, ultraviolet (UV) exposure, or freeze/thaw cycle treatments. T cell proliferation and cytokine production were compared to T cells restimulated with plate-bound CD3 and CD28 antibodies. Human and mouse HNSCC cells showed reduced viability in response to ionizing radiation in a dose-dependent manner, and induced expression of T cell chemotactic cytokines. Tumor antigens derived from IR-treated LY2 and MOC2 cells induced greater proliferation of lymph node cells from tumor-bearing mice and induced unique T cell cytokine expression profiles. Our results demonstrate that IR induces potent tumoral immune responses, and IR-generated tumor antigens can potentially serve as an indicator of antitumor immune responses to HNSCC in ex vivo T cell restimulation assays. [ABSTRACT FROM AUTHOR]

Simple Summary: Highly aggressive cutaneous squamous-cell carcinomas (cSCCs) are the primary cause of death in patients with the rare skin disease recessive dystrophic epidermolysis bullosa (RDEB). Currently the detection of RDEB-SCCs requires invasive skin biopsies with a high burden for patients and limitations in surveying widespread areas. The aim of our study was to use machine learning as a tool to identify short RNA molecules that are capable of identifying tumors in RDEB patients. As there is only a limited number of RDEB-patients, we included data on patients with a different type of SCCs who, however, show similarity in RNA profiles. This finally facilitated us to nominate sets of short RNAs that can discriminate between tumor and healthy cells. Machine learning has been proven to be a powerful tool in the identification of diagnostic tumor biomarkers but is often impeded in rare cancers due to small patient numbers. In patients suffering from recessive dystrophic epidermolysis bullosa (RDEB), early-in-life development of particularly aggressive cutaneous squamous-cell carcinomas (cSCCs) represents a major threat and timely detection is crucial to facilitate prompt tumor excision. As miRNAs have been shown to hold great potential as liquid biopsy markers, we characterized miRNA signatures derived from cultured primary cells specific for the potential detection of tumors in RDEB patients. To address the limitation in RDEB-sample accessibility, we analyzed the similarity of RDEB miRNA profiles with other tumor entities derived from the Cancer Genome Atlas (TCGA) repository. Due to the similarity in miRNA expression with RDEB-SCC, we used HN-SCC data to train a tumor prediction model. Three models with varying complexity using 33, 10 and 3 miRNAs were derived from the elastic net logistic regression model. The predictive performance of all three models was determined on an independent HN-SCC test dataset (AUC-ROC: 100%, 83% and 96%), as well as on cell-based RDEB miRNA-Seq data (AUC-ROC: 100%, 100% and 91%). In addition, the ability of the models to predict tumor samples based on RDEB exosomes (AUC-ROC: 100%, 93% and 100%) demonstrated the potential feasibility in a clinical setting. Our results support the feasibility of this approach to identify a diagnostic miRNA signature, by exploiting publicly available data and will lay the base for an improvement of early RDEB-SCC detection. [ABSTRACT FROM AUTHOR]

Simple Summary: High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). In this study of 408 patients enrolled in a prospective early-stage TNBC neoadjuvant chemotherapy trial, we aimed to identify clinicopathologic features that could be combined with sTILs to better predict pCR. Applying a training set and a testing set, we found that integrating high Ki-67 (cutoff > 35%) and high sTIL (cutoff ≥ 20%) in a model of computed response scores could predict a pCR rate of 65%. This model may refine the selection of early-stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies. High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associated with pCR could enhance our ability to predict the group of patients on whom treatment de-escalation strategies could be tested. In this prospective early-stage TNBC neoadjuvant chemotherapy study, pretreatment biopsies from 408 patients were evaluated for their clinical and demographic features, as well as biomarkers including sTILs, Ki-67, PD-L1 and androgen receptor. Multivariate logistic regression models were developed to generate a computed response score to predict pCR. The pCR rate for the entire cohort was 41%. Recursive partitioning analysis identified ≥20% as the optimal cutoff for sTILs to denote 35% (143/408) of patients as having high sTILs, with a pCR rate of 59%, and 65% (265/408) of patients as having low sTILs, with a pCR rate of 31%. High Ki-67 (cutoff > 35%) was identified as the only predictor of pCR in addition to sTILs in the training set. This finding was verified in the testing set, where the highest computed response score encompassing both high sTILa and high Ki-67 predicted a pCR rate of 65%. Integrating Ki67 and sTIL may refine the selection of early stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: There is a need to identify new translational prognostic biomarkers in head and neck cancer. Metabolomics, the study of small molecules resulting from cellular metabolism, is an emerging and promising field regarding head and neck cancer. We performed metabolomics on patients' blood prior to treatment and found that it can divide patients into high-risk and low-risk groups based on their cancer progression and survival. We believe our study provides compelling results to consider metabolomics as a translational prognostic biomarker and that it may offer novel information for patient risk stratification. With continued research, we hope to gain a fuller understanding of how metabolomics may aid in the early detection, prognosis, treatment monitoring, and targeted therapies of head and neck cancer. There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials. [ABSTRACT FROM AUTHOR]

Simple Summary: A several immunotherapy-based combinations regimen improved overall survival, and became the new first-line standard of care for metastatic clear cell renal cell carcinoma (mccRCC) patients. However, a subset of patients, approximatively 30 to 60%, are refractory to these therapies or develop a secondary acquired resistance. Currently no validated biomarkers are available in routine practice to adapt treatment strategies. Here, we summarize the potential biological biomarkers for immune checkpoint inhibitors that may have arisen from the studies of immune checkpoints. Renal cell carcinoma (RCC) represents around 2% of cancer-related deaths worldwide per year. RCC is an immunogenic malignancy, and treatment of metastatic RCC (mRCC) has greatly improved since the advent of the new immunotherapy agents, including immune checkpoint inhibitors (ICIs). However, it should be stressed that a large proportion of patients does not respond to these therapies. There is thus an urgent need to identify predictive biomarkers of efficacy or resistance associated with ICIs or ICI/Tyrosine kinase inhibitor (TKI) combinations; this is a major challenge to achieve precision medicine for mRCC in routine practice. To identify potential biomarkers, it is necessary to improve our knowledge on the biology of immune checkpoints. A lot of efforts have been made over the last decade in the field of immuno-oncology. We summarize here the main data obtained in this field when considering mRCC. As for clinical biomarkers, clinician and scientific experts of the domain are facing difficulties in identifying such molecular entities, probably due to the complexity of immuno-oncology and the constant adaptation of tumor cells to their changing environment. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic cancer is one of the deadliest forms of cancer, with low survival rates and limited treatment options. Anoikis resistance, or the ability of cancer cells to survive and grow in the absence of attachment to the extracellular matrix, is thought to play a critical role in pancreatic cancer progression and treatment resistance. In this study, a multi-omics approach was used to identify a set of signature genes associated with anoikis in pancreatic cancer. The approach involved integrating data from large-scale cancer genomics databases such as TCGA and GEO, as well as single cell sequencing databases and in vitro assays. The identified signature genes have the potential to serve as prognostic biomarkers, allowing clinicians to predict patient outcomes and tailor treatment strategies accordingly. Additionally, these genes may also predict the chemo-sensitivity of pancreatic cancer cells, helping to guide the selection of chemotherapy regimens. The use of a comprehensive multi-omics approach provides a complete understanding of the molecular mechanisms underlying anoikis resistance in pancreatic cancer. This study highlights the importance of integrating multiple data sources and experimental approaches in cancer research to facilitate the discovery of new biomarkers and therapeutic targets. Such approaches have the potential to improve patient outcomes and lead to more effective cancer treatments. Anoikis, a form of apoptosis that occurs due to detachment of cells from the extracellular matrix, has been linked to the development of cancer in several studies. However, its role in pancreatic cancer remains incompletely understood. In this study, we utilized univariate Cox regression and LASSO regression analyses to establish a prognostic model for pancreatic adenocarcinoma based on anoikis-related genes in the TCGA database. Additionally, we performed univariate and multifactorial Cox analyses of protein expression results for TCGA pancreatic adenocarcinoma. We further explored the difference in immune infiltration between the high-risk and low-risk groups and verified the expression of the screened genes using quantitative real-time PCR (qRT-PCR). Our findings indicate that numerous anoikis-related genes are linked to pancreatic adenocarcinoma prognosis. We identified seven prognostic genes (MET, DYNLL2, CDK1, TNFSF10, PIP5K1C, MSLN, GKN1) and validated that their related proteins, such as EGFR and MMP2, have a significant impact on the prognosis of pancreatic adenocarcinoma. Based on clustering analyses of the seven prognostic genes, patients could be classified into three distinct categories, for which somatic mutations varied significantly across the groups. High-risk and low-risk groups also exhibited significant differences in immune infiltration. All genes were found to be highly expressed in pancreatic cancer cell lines (ASPC-1, CFPAC-1) as compared to a normal pancreatic cell line (HPDE). Based on the seven anoikis-related genes, we formulated a robust prognostic model with high predictive accuracy. We also identified the significant impact of KRAS, P53, and CDKN2A mutations on the prognosis of this fatal disease. Therefore, our study highlights the crucial role of anoikis in the development of the pancreatic adenocarcinoma tumor microenvironment. [ABSTRACT FROM AUTHOR]

Simple Summary: Endogenous retroviruses (ERVs) are integrated retroviral elements that cover 8% of the human genome, representing thereby four-fold the fraction of the genome encoding for protein-coding genes. Overall, their expression represents up to 0.5% of all non-rRNA transcripts in mesothelioma, and some are specifically re-activated. This part of the genome has been considered until recently "junk DNA" or "DNA dark matter". However, analysis of ERV expression has recently gained attention in several cancers, especially in the context of immunotherapy. In this review, our aim is to raise the curiosity of non-specialists by illustrating the potential of exploring the expression of "DNA dark matter" for prognosis and immunotherapy, potentially as predictive biomarkers in mesothelioma. Recent high-throughput RNA sequencing technologies have confirmed that a large part of the non-coding genome is transcribed. The priority for further investigations is nevertheless generally given in cancer to coding sequences, due to the obvious interest of finding therapeutic targets. In addition, several RNA-sequencing pipelines eliminate repetitive sequences, which are difficult to analyze. In this review, we shall focus on endogenous retroviruses. These sequences are remnants of ancestral germline infections by exogenous retroviruses. These sequences represent 8% of human genome, meaning four-fold the fraction of the genome encoding for proteins. These sequences are generally mostly repressed in normal adult tissues, but pathological conditions lead to their de-repression. Specific mesothelioma-associated endogenous retrovirus expression and their association to clinical outcome is discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: This study was a retrospective investigation of a Japanese cohort of 205 metastatic castration-resistant prostate cancer (mCRPC) patients who received Ra-223 in 14 hospitals between July 2016 and August 2020 and for whom bone scintigraphy before and after the radium-223 treatment was available. Following treatment, alkaline phosphatase (ALP) decline (%ALP < 0%) was noted in 72.2% (148/205), automated bone scan index (aBSI) decline (%aBSI < 0%) in 52.7% (108/205), and PSA decline (%PSA < 0%) in 27.8% (57/205). Furthermore, a reduction in both aBSI and ALP was seen in 87 (42.4%), a reduction in only ALP was seen in 61 (29.8%), a reduction in only aBSI was seen in 21 (10.2%), and in both aBSI and ALP increasing/stable (≥0%) was seen in 36 (17.6%) patients. Multiparametric analysis showed changes in PSA (HR 4.30, 95% CI 2.32–8.77, p < 0.0001), aBSI (HR 2.22, 95%CI 1.43–3.59, p = 0.0003), and ALP (HR 2.06, 95%CI 1.35–3.14, p = 0.0008) as significant prognostic factors for OS. For mCRPC patients treated with Ra-223, aBSI change is useful as an imaging biomarker for treatment response assessment and survival prediction. To evaluate the usefulness of change in the automated bone scan index (aBSI) value derived from bone scintigraphy findings as an imaging biomarker for the assessment of treatment response and survival prediction in metastatic castration-resistant prostate cancer (mCRPC) patients treated with Ra-223. This study was a retrospective investigation of a Japanese cohort of 205 mCRPC patients who received Ra-223 in 14 hospitals between July 2016 and August 2020 and for whom bone scintigraphy before and after radium-223 treatment was available. Correlations of aBSI change, with changes in the serum markers alkaline phosphatase (ALP) and prostate-specific antigen (PSA) were evaluated. Additionally, the association of those changes with overall survival (OS) was assessed using the Cox proportional-hazards model and Kaplan–Meier curve results. Of the 205 patients enrolled, 165 (80.5%) completed six cycles of Ra-223. Following treatment, ALP decline (%ALP < 0%) was noted in 72.2% (148/205), aBSI decline (%aBSI < 0%) in 52.7% (108/205), and PSA decline (%PSA < 0%) in 27.8% (57/205). Furthermore, a reduction in both aBSI and ALP was seen in 87 (42.4%), a reduction in only ALP was seen in 61 (29.8%), a reduction in only aBSI was seen in 21 (10.2%), and in both aBSI and ALP increasing/stable (≥0%) was seen in 36 (17.6%) patients. Multiparametric analysis showed changes in PSA [hazard ratio (HR) 4.30, 95% confidence interval (CI) 2.32–8.77, p < 0.0001], aBSI (HR 2.22, 95%CI 1.43–3.59, p = 0.0003), and ALP (HR 2.06, 95%CI 1.35–3.14, p = 0.0008) as significant prognostic factors for OS. For mCRPC patients treated with Ra-223, aBSI change is useful as an imaging biomarker for treatment response assessment and survival prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: Gastroesophageal adenocarcinoma (GOA) is a cancer that has poor survival. Most cases are diagnosed when a cure is not possible, and treatment often has many side effects. It occurs less often and is associated with better outcomes in females. The reason for this is not known. We sought to use samples from a clinical trial in older adults with GOA to investigate whether this observation could be related to oestrogen and its action through oestrogen receptors. We found no clear link between outcome and oestrogen receptor expression but did note improved survival with older age and female sex. Gastroesophageal adenocarcinoma is a disease of older adults that is associated with a very poor prognosis. It is less common and has better outcomes in females. The reason for this is unknown but may relate to signalling via the main oestrogen receptors (ER) α and β. In this study, we sought to investigate this using the GO2 clinical trial patient cohort. GO2 recruited older and/or frail patients with advanced gastroesophageal cancer. Immunohistochemistry was performed on tumour samples from 194 patients. The median age of the population was 76 years (range 52–90), and 25.3% were female. Only one (0.5%) tumour sample was positive for ERα, compared to 70.6% for ERβ expression. There was no survival impact according to ERβ expression level. Female sex and younger age were associated with lower ERβ expression. Female sex was also associated with improved overall survival. To our knowledge, this is the largest study worldwide of ER expression in a cohort of patients with advanced gastroesophageal adenocarcinoma. It is also unique, given the age of the population. We have demonstrated that female sex is associated with better survival outcomes with palliative chemotherapy but that this does not appear to be related to ER IHC expression. The differing ER expression according to age supports the concept of a different disease biology with age. [ABSTRACT FROM AUTHOR]