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Purpose: Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance. Methods: We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method. Results: HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity. Conclusion: The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint inhibitors (ICIs) have emerged as a promising treatment option for esophageal cancer (EC). Although ICIs enable long-term survival in some patients, the efficacy of ICIs varies widely among patients. Therefore, predictive biomarkers are necessary for identifying patients who are most likely to benefit from ICIs to improve the efficacy of the treatment. We retrospectively analyzed the outcomes of combination therapy, including nivolumab plus ipilimumab or chemotherapy plus anti-programmed cell death 1 (PD-1) antibodies in our institute to identify biomarkers. Methods: Twenty-seven patients received nivolumab plus ipilimumab, and thirty-six patients received chemotherapy plus anti-PD-1 antibodies were included in this study. We analyzed patient characteristics, efficacy, and safety. Multivariable analysis of biomarkers evaluated the correlation among overall survival (OS), progression-free survival (PFS), and the following variables: body mass index, performance status, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein level, and albumin level before treatment. Results: In multivariable analysis, albumin level was significantly correlated with PFS in the cisplatin plus 5-fluorouracil (CF) plus pembrolizumab group. NLR and albumin level were significantly correlated with OS in the nivolumab plus ipilimumab group. Other variables, including PS, BMI, and CRP did not correlate with any of the outcomes. Conclusions: High NLR in EC patients prior to treatment was significantly less effective for ICIs. In chemotherapy combined with ICIs, NLR before the treatment was not associated with treatment efficacy, suggesting combination chemotherapy may be beneficial for EC patients with high NLR. NLR may be an indicator of immunocompetence in anti-tumor immunity and a convenient predictive biomarker for selecting appropriate treatments including ICIs. [ABSTRACT FROM AUTHOR]

Recent strides in understanding the molecular underpinnings of head and neck cancers have sparked considerable interest in identifying precise biomarkers that can enhance prognostication and enable personalized treatment strategies. Immunotherapy has particularly revolutionized the therapeutic landscape for head and neck squamous cell carcinoma, offering new avenues for treatment. This review comprehensively examines the application and limitations of the established and emerging/novel biomarkers for head and neck squamous cell carcinoma. Established biomarkers, including well-characterized genetic mutations, protein expressions, and clinical factors, have been extensively studied and validated in clinical practice. Novel biomarkers identified through molecular analyses, including novel genetic alterations, immune-related markers, and molecular signatures, are currently being investigated and validated in preclinical and clinical settings. Biomarkers hold the potential to deepen our understanding of head and neck squamous cell carcinoma biology and guide therapeutic strategies. The evolving paradigm of predictive biomarkers facilitates the study of individual responses to specific treatments, including targeted therapy and immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Tumor-induced coagulation is widely observed in cancer patients. Moreover, it is associated with tumorigenesis, tumor progression and metastasis, by creating a proliferative and proangiogenic microenvironment. Therefore, D-dimer, a fibrin degradation product, correlates with tumor prognosis in several cancer types. Objectives: This study aims to investigate whether D-dimer levels can be a predictive and monitoring indicator for chemotherapy response in metastatic breast cancer (MBC) patients. Design: This was a prospective study. Methods: This study included two groups, 76 patients diagnosed with metastatic breast carcinoma and 25 patients with primary breast carcinoma. Plasma D-dimer levels were measured prospectively before chemotherapy initiation, and after the fourth treatment cycle in MBC patients. D-dimer levels before chemotherapy (D0) were analyzed using Receiver Operating Characteristic (ROC) curves to determine the optimal cut-off baseline values of D0, and to evaluate their discriminatory abilities in predicting response to chemotherapy. Results: In the preliminary response evaluation, the mean level of D-dimer significantly decreased by 0.65 μg/ml in patients with partial response patterns, and by 0.5 μg/ml in patients with stable disease. In the disease progression group, a marked increase was seen in D-dimer levels by 1.2 μg/ml. Analysis of ROC curves showed that D-dimer levels at D0 could discriminate the response to chemotherapy, whereas progressive disease rate correlated with higher levels of D-dimer. Conclusion: D-dimer level in plasma is a useful predictive and monitoring marker of response to chemotherapy in metastatic breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance. Methods: We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method. Results: HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity. Conclusion: The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB. [ABSTRACT FROM AUTHOR]

Introduction: The methylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) promoter is a valid biomarker for predicting response to therapy with alkylating agents and, independently, prognosis in IDH-wildtype(IDH-w) glioblastoma. We aim to study the impact of its methylation in overall survival of the unresectable IDH-w glioblastoma undergoing biopsy and systemic treatment. Methods: We collected six-year retrospective (2017–2023) data at a quaternary neurosurgery center for patients undergoing biopsy as the only surgical procedure for an unresectable IDH wildtype glioblastoma. Data was collected from patient records including neuro-oncology multidisciplinary team meeting (MDT) documentation. Patients were grouped into categories according to different types of treatment received after biopsy (no treatment, chemotherapy (CT), radiotherapy (RT), chemoradiotherapy (CRT), chemoradiotherapy with adjuvant temozolomide (CRT with adjuvant TMZ), EORTC-NCIC protocol followed by second line treatment) and according to methylation status (unmethylated (< 5%), borderline methylated (5–15%) and strongly methylated (> 15%)). Survival analysis was performed. Results: 166 glioblastoma IDH wildtype patients were included in the study with mean age of 62.5 years (M: F = 1.5: 1). 70 (49.3%) patients had unmethylated MGMT status (< 5%), 29 (20.4%) patients had borderline methylated MGMT status (5–15%) and 43 (30.2%) patients had methylated MGMT status (> 15%). 36 (25.3%) patients did not receive any treatment post biopsy, 13 (9.1%) received CT only, 27 (19%) RT only, 12 (8.4%) CRT, 33 (23.2%) CRT with adjuvant TMZ, whereas 21 (14.7%) received EORTC-NCIC protocol along with second line treatment. In biopsy only group, there was no notable difference in survival outcomes among the different methylation statuses. For biopsy and any-other-form-of-treatment methylated groups showed a distinct trend of better survival compared to the borderline or unmethylated groups. Overall, methylated patients had better survival as compared to unmethylated or borderline groups. Conclusion: Methylated MGMT status are predictors for better overall survival in unresectable IDH wildtype glioblastoma patients undergoing biopsy and treatment regardless of the treatment modality. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent research has helped us understand more about the role of microbes in the health and disease of the lungs. The detection of microbes and microbial products in sputum may improve early lung cancer diagnosis. The monitoring of the microbiome of the lungs over time may help predict the response to and side effects of treatment. However, studies have not yet examined how diet and air pollution affect the lung microbiome and how it might be linked to the development and progression of lung cancer. By examining the lung microbiome, dietary patterns, and air pollutants, we hope to prevent and manage lung cancer in the future. The past several years have provided a more profound understanding of the role of microbial species in the lung. The respiratory tract is a delicate ecosystem of bacteria, fungi, parasites, and viruses. Detecting microbial DNA, pathogen-associated molecular patterns (PAMPs), and metabolites in sputum is poised to revolutionize the early diagnosis of lung cancer. The longitudinal monitoring of the lung microbiome holds the potential to predict treatment response and side effects, enabling more personalized and effective treatment options. However, most studies into the lung microbiota have been observational and have not adequately considered the impact of dietary intake and air pollutants. This gap makes it challenging to establish a direct causal relationship between environmental exposure, changes in the composition of the microbiota, lung carcinogenesis, and tumor progression. A holistic understanding of the lung microbiota that considers both diet and air pollutants may pave the way to improved prevention and management strategies for lung cancer. [ABSTRACT FROM AUTHOR]

B‐cell acute lymphoblastic leukaemia (B‐ALL) is the most prevalent hematologic malignancy in children and a leading cause of mortality. Managing B‐ALL remains challenging due to its heterogeneity and relapse risk. This study aimed to delineate the molecular features of paediatric B‐ALL and explore the clinical utility of circulating tumour DNA (ctDNA). We analysed 146 patients with paediatric B‐ALL who received systemic chemotherapy. The mutational landscape was profiled in bone marrow (BM) and plasma samples using next‐generation sequencing. Minimal residual disease (MRD) testing on day 19 of induction therapy evaluated treatment efficacy. RNA sequencing identified gene fusions in 61% of patients, including 37 novel fusions. Specifically, the KMT2A‐TRIM29 novel fusion was validated in a boy who responded well to initial therapy but relapsed after 1 year. Elevated mutation counts and maximum variant allele frequency in baseline BM were associated with significantly poorer chemotherapy response (p = 0.0012 and 0.028, respectively). MRD‐negative patients exhibited upregulation of immune‐related pathways (p < 0.01) and increased CD8+ T cell infiltration (p = 0.047). Baseline plasma ctDNA exhibited high mutational concordance with the paired BM samples and was significantly associated with chemotherapy efficacy. These findings suggest that ctDNA and BM profiling offer promising prognostic insights for paediatric B‐ALL management. [ABSTRACT FROM AUTHOR]

Background: It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models. Methods: This was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11–14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics. Results: FPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power. Conclusion: The combination of FPG and IGFBP-2 at early gestation (11–14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts. Novelty statements: It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus. The present study demonstrates that the combination of fasting plasma glucose and insulin-like growth factor binding protein 2 at early gestation can predict later development of gestational diabetes with moderate discriminant power. [ABSTRACT FROM AUTHOR]

The nature of microRNA (miRNA) dysfunction in carcinogenesis remains controversial because of the complex connection between miRNA structural diversity and biological processes. Here, we found that oncofetal IGF2BP3 regulates the selective production of a subset of 3'-isoforms (3'-isomiRs), including miR-21-5p and Let-7 family, which induces significant changes in their cellular seed occupancy and structural components, establishing a cancer-specific gene expression profile. The D-score, reflecting dominant production of a representative miR-21-5p+C (a 3'-isomiR), discriminated between clinical early-stage lung adenocarcinoma (LUAD) cases with low and high recurrence risks, and was associated with molecular features of cell cycle progression, epithelial-mesenchymal transition pressure, and immune evasion. We found that IGF2BP3 controls the production of miR-21-5p+C by directing the nuclear Drosha complex to select the cleavage site. IGF2BP3 was also involved in the production of 3'-isomiRs of miR-425-5p and miR-454-3p. IGF2BP3-regulated these three miRNAs are suggested to be associated with the regulation of p53, TGF-β, and TNF pathways in LUAD. Knockdown of IGF2BP3 also induced a selective upregulation of Let-7 3'-isomiRs, leading to increased cellular Let-7 seed occupancy and broad repression of its target genes encoding cell cycle regulators. The D-score is an index that reflects this cellular situation. Our results suggest that the aberrant regulation of miRNA structural diversity is a critical component for controlling cellular networks, thus supporting the establishment of a malignant gene expression profile in early stage LUAD. [ABSTRACT FROM AUTHOR]

Abstract Background It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models. Methods This was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11–14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics. Results FPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power. Conclusion The combination of FPG and IGFBP-2 at early gestation (11–14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Abstract Background Gestational diabetes mellitus (GDM) significantly impacts maternal and infant health both immediately and over the long term, yet effective early diagnostic biomarkers are currently lacking. Thus, it is essential to identify early diagnostic biomarkers for GDM risk screening. Extrachromosomal circular DNA (eccDNA), being more stable than linear DNA and involved in disease pathologies, is a viable biomarker candidate for diverse conditions. In this study, eccDNA biomarkers identified for early diagnosis and assessment of GDM risk were explored. Methods Using Circle-seq, we identified plasma eccDNA profiles in five pregnant women who later developed GDM and five matched healthy controls at 11–13 weeks of gestation. These profiles were subsequently analyzed through bioinformatics and validated through outward PCR combined with Sanger sequencing. Furthermore, candidate eccDNA was validated by quantitative PCR (qPCR) in a larger cohort of 70 women who developed GDM and 70 normal glucose-tolerant (NGT) subjects. A ROC curve assessed the eccDNA’s diagnostic potential for GDM. Results 2217 eccDNAs were differentially detected between future GDM patients and controls, with 1289 increased and 928 decreased in abundance. KEGG analysis linked eccDNA genes mainly to GDM-related pathways such as Rap1, MAPK, and PI3K-Akt, and Insulin resistance, among others. Validation confirmed a significant decrease in eccDNA PRDM16circle in the plasma of 70 women who developed GDM compared to 70 NGT women, consistent with the eccDNA-seq results. PRDM16circle showed significant diagnostic value in 11–13 weeks of gestation (AUC = 0.941, p

Abstract Background Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. Methods Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. Results The gene expression of hundreds of DEGs (FDR p 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p

Abstract Background Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. Methods We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. Results Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. Conclusion Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

AbstractBiomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this article, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics for survival outcomes to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: The development of immune checkpoint inhibitors has revolutionized the treatment of lung cancer by becoming the standard therapy for advanced non-small cell lung cancer that lacks specific genetic mutations. However, not all patients respond equally, underscoring the need for biomarkers to predict treatment response. To address this, a study was conducted with 55 lung cancer patients treated with immune checkpoint inhibitors to investigate whether biomarkers like TCRβ diversity and certain cytokines linked to T cell activity could predict the response to immunotherapy. While higher TCRβ clonality and specific cytokine levels appeared to be associated with improved survival rates, the findings were not statistically significant. Specifically, higher levels of IL-2 and IL-15 were linked to shorter overall survival, with high IL-15 levels increasing the risk of death threefold in multivariable analysis. Although further research with larger sample sizes is needed for confirmation, these results offer promising insights into potential markers for predicting responses to immune checkpoint inhibitors. The development of immune checkpoint inhibitors (ICIs) has changed the therapeutic paradigm of lung cancer (LC), becoming the standard of treatment for previously untreated advanced non-small cell lung cancer (NSCLC) without actionable mutations. It has allowed the achievement of durable responses and resulted in significant survival benefits. However, not all patients respond; hence, molecular biomarkers are needed to help us predict which patients will respond. With this objective, a prospective observational study was designed, including a cohort of 55 patients with NSCLC who received ICIs. We studied whether biomarkers such as TCRβ and specific cytokines involved in the regulation of T cell activity were related to the immunotherapy response. In the survival analysis, it was found that patients with higher TCRβ clonality, lower TCRβ evenness, higher TCRβ Shannon diversity and lower TCRβ convergence had higher overall survival (OS) and progression-free survival (PFS). However, no statistically significant association was observed. Regarding cytokines, those patients with higher levels of IL-2 and IL-15 presented statistically significantly shorter OS and PFS, respectively. In fact, in the multivariable analysis, the high IL-15 level increased the risk of death by three times. Although the sample size was small and more studies are needed to confirm our results, our study reveals promising markers of responses to ICIs. [ABSTRACT FROM AUTHOR]

Neoadjuvant therapy (NAT) for early-stage pancreatic ductal adenocarcinoma (PDA) has recently gained prominence. We investigated the clinical significance of mucin 5 AC (MUC5AC), which exists in two major glycoforms, a less-glycosylated immature isoform (IM) and a heavily glycosylated mature isoform (MM), as a biomarker in resected PDA. Immunohistochemistry was performed on 100 resected PDAs to evaluate the expression of the IM and MM of MUC5AC using their respective monoclonal antibodies, CLH2 (NBP2-44455) and 45M1 (ab3649). MUC5AC localization (cytoplasmic, apical, and extra-cellular (EC)) was determined, and the H-scores were calculated. Univariate and multivariate (MVA) Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). Of 100 resected PDA patients, 43 received NAT, and 57 were treatment-naïve with upfront surgery (UpS). In the study population (n = 100), IM expression (H-scores for objective response vs. no response vs. UpS = 104 vs. 152 vs. 163, p = 0.01) and MM-MUC5AC detection rates (56% vs. 63% vs. 82%, p = 0.02) were significantly different. In the NAT group, MM-MUC5AC-negative patients had significantly better PFS according to the MVA (Hazard Ratio: 0.2, 95% CI: 0.059–0.766, p = 0.01). Similar results were noted in a FOLFIRINOX sub-group (n = 36). We established an association of MUC5AC expression with treatment response and outcomes. [ABSTRACT FROM AUTHOR]

Purpose: To report the prospective long-term outcome data of patients whose chemotherapy decision was guided by the EndoPredict test. Methods: Patients with hormone receptor-positive HER2-negative early breast cancer with 0–3 positive lymph nodes were enrolled. The EndoPredict test was carried out on all tumor samples. Treatment compliance, local recurrence, distant metastases, and survival were evaluated. Associations of EPclin risk stratification with 5-year disease-free survival and distant metastasis-free survival were evaluated by time-to-event analysis. Results: 368 consecutive patients were included in the analysis. Median follow-up was 8.2 years. EndoPredict allocated 238 (65%) in the low-risk and 130 (35%) patients in the high-risk group. Risk for disease recurrence or death in EPclin high-risk patients was twofold higher than in EPclin low-risk patients (hazard ratio [HR] 2.08; 95% CI 1.26–3.44; p = 0.004). EPclin low-risk patients had a 5-year disease-free survival of 95.3% (95% CI 92.6–98.0%). EPclin high-risk patients were at higher risk of developing distant metastases or death (HR 2.21; 95% CI 1.27–3.88; p = 0.005). EPclin high-risk patients who underwent chemotherapy had a 5-year DFS of 89.1% (95% CI 82.7–96.1%) in contrast to high-risk patients without chemotherapy (68.9%; 95% CI 56.2–84.5%; HR 0.46; 95% CI 0.23–0.95; p = 0.036). EPclin high-risk patients were at higher risk of experiencing distant metastases or death than EPclin low-risk patients regardless of menopausal status (premenopausal: HR 3.55; 95% CI 1.17–12.32; p = 0.025; postmenopausal: HR 1.92; 95% CI 0.99–3.7; p = 0.054). Conclusion: EndoPredict can guide decisions on adjuvant chemotherapy in early luminal breast cancer. EndoPredict risk stratification is also applicable in premenopausal women. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) significantly impacts maternal and infant health both immediately and over the long term, yet effective early diagnostic biomarkers are currently lacking. Thus, it is essential to identify early diagnostic biomarkers for GDM risk screening. Extrachromosomal circular DNA (eccDNA), being more stable than linear DNA and involved in disease pathologies, is a viable biomarker candidate for diverse conditions. In this study, eccDNA biomarkers identified for early diagnosis and assessment of GDM risk were explored. Methods: Using Circle-seq, we identified plasma eccDNA profiles in five pregnant women who later developed GDM and five matched healthy controls at 11–13 weeks of gestation. These profiles were subsequently analyzed through bioinformatics and validated through outward PCR combined with Sanger sequencing. Furthermore, candidate eccDNA was validated by quantitative PCR (qPCR) in a larger cohort of 70 women who developed GDM and 70 normal glucose-tolerant (NGT) subjects. A ROC curve assessed the eccDNA's diagnostic potential for GDM. Results: 2217 eccDNAs were differentially detected between future GDM patients and controls, with 1289 increased and 928 decreased in abundance. KEGG analysis linked eccDNA genes mainly to GDM-related pathways such as Rap1, MAPK, and PI3K-Akt, and Insulin resistance, among others. Validation confirmed a significant decrease in eccDNA PRDM16circle in the plasma of 70 women who developed GDM compared to 70 NGT women, consistent with the eccDNA-seq results. PRDM16circle showed significant diagnostic value in 11–13 weeks of gestation (AUC = 0.941, p < 0.001). Conclusions: Our study first demonstrats that eccDNAs are aberrantly produced in women who develop GDM, including PRDM16circle, which can predict GDM at an early stage of pregnancy, indicating its potential as a biomarker. Trial registration: ChiCTR2300075971, http://www.chictr.org.cn. Registered 20 September 2023. [ABSTRACT FROM AUTHOR]

Background & aims: HBV infection initiates autoimmune responses, leading to autoantibody generation. This research explores the role of autoantibodies in HBV-related Acute-on-Chronic Liver Failure (ACLF), offering novel perspectives for clinical management. Method: We applied immunoprecipitation and iTRAQ techniques to screen for autoantibodies in serum from HBV-related cirrhosis patients and conducted detection with conformation-stabilizing ELISA in a cohort of 238 HBV-infected individuals and 49 health controls. Our results were validated in a retrospective cohort comprising 106 ACLF patients and further assessed through immunohistochemical analysis in liver tissues from an additional 10 ACLF cases. Results: Utilizing iTRAQ, we identified Argonaute1-3 autoantibodies (AGO-Abs) in this research. AGO2-Abs notably increased in cirrhosis, decompensation, and further in ACLF, unlike AGO1-Abs and AGO3-Abs. This reflects disease severity correlation. Logistic regression and COX models confirmed AGO2-Abs as independent prognostic indicators for decompensated liver cirrhosis (DLC) and ACLF. In the ROC analysis, AGO2-Abs showed significant diagnostic value for predicting 28- and 90-day mortality (AUROC = 0.853 and 0.854, respectively). Furthermore, combining AGO2-Abs with the Child-Pugh, MELD, and AARC scores significantly improved their predictive accuracy (P < 0.05). Kaplan-Meier analysis showed poorer survival for AGO2-Abs levels above 99.14mg/ml. These findings were supported by a retrospective validation cohort. Additionally, immunohistochemistry revealed band-like AGO2 expression in periportal liver areas, with AGO2-Abs levels correlating with total bilirubin, indicating a potential role in exacerbating liver damage through periportal functions. Conclusions: AGO2-Abs is a robust biomarker for predicting the mortality of patients with HBV-related ACLF. [ABSTRACT FROM AUTHOR]

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests. [ABSTRACT FROM AUTHOR]

Introduction: This study was undertaken to investigate whether sustained rather than a single measure of corneal nerve loss was associated with the onset of diabetic peripheral neuropathy (DPN) and the progression of neuropathic symptoms and deficits in individuals with type 2 diabetes (T2D). Methods: Participants underwent clinical, metabolic testing and assessment of neuropathic symptoms, vibration perception threshold (VPT), sudomotor function, and corneal confocal microscopy (CCM) at baseline, 1, 2, and 4-7 years. Sustained corneal nerve loss was defined as abnormal corneal nerve fiber density (CNFD, <24 fibers/mm²), corneal nerve branch density (CNBD, <21 branches/mm²), and corneal nerve fiber length (CNFL, <16 mm/mm²) persisting for =50% of the study duration. Results: A total of 107 participants with a mean duration of T2D of 13.3 ± 7.3 years, aged 54.8 ± 8.5 years, underwent baseline and follow-up assessments over a median duration of 4 years, ranging from 1 to 7 years. The DPN prevalence at baseline was 18/107 (16.8%), and of the 89 participants without DPN at baseline, 13 (14.6%) developed DPN during follow-up. Approximately half of the cohort had sustained corneal nerve damage, and corneal nerve measures were significantly lower in this group than those without sustained damage (p < 0.0001). Sustained corneal nerve damage was associated with the development of DPN (p < 0.0001), a progressive loss of vibration perception (p = 0.05), an increased incidence of burning pain, numbness, or a combination of both (p = 0.01-0.001), and a borderline association with progressive sudomotor dysfunction (p = 0.07). Sustained abnormal CNFL effectively distinguished between participants who developed DPN and those who did not (AUC: 76.3, 95% CI: 65.9-86.8%, p < 0.0001), while baseline and other sustained measures did not predict DPN onset. Conclusion: Sustained abnormal CCM is associated with more severe corneal nerve damage, DPN development, and the progression of neuropathic symptoms and deficits. Regular CCM monitoring may enable the identification of those at greater risk of developing and worsening DPN who may benefit from more aggressive risk factor reduction. [ABSTRACT FROM AUTHOR]

Background: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. Methods: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. Results: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. Conclusion: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients. [ABSTRACT FROM AUTHOR]

Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple‐outcome setting with continuous, binary, and censored time‐to‐event outcomes. Our definition provides a trade‐off between the subgroup size and the conditional average treatment effects (CATE) in the subgroup with respect to each of the outcomes while taking the relative contribution of the outcomes into account. We conduct simulations to illustrate the proposed definition. By examining the outcomes of urinary tract infection and renal scarring in the RIVUR clinical trial, we identify a subgroup of children that would benefit the most from long‐term antimicrobial prophylaxis. [ABSTRACT FROM AUTHOR]

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) have transformed the treatment of ovarian cancer, particularly benefiting patients whose tumors harbor genomic events that result in impaired homologous recombination (HR) repair. The use of PARPi over recent years has expanded to include subpopulations of patients with breast, pancreatic, and prostate cancers. Their potential to benefit patients with non-ovarian gynecologic cancers is being recognized. This review examines the underlying biological rationale for exploring PARPi in non-ovarian gynecologic cancers. We consider the clinical data and place this in the context of the current treatment landscape. We review the development of PARPi strategies for treating patients with endometrial, cervical, uterine leiomyosarcoma, and vulvar cancers. Furthermore, we discuss future directions and the importance of understanding HR deficiency in the context of each cancer type. Plain language summary: PARP inhibitors in non-ovarian gynecologic cancers Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) have transformed the way ovarian cancer is treated, especially for patients whose tumors have specific genetic issues affecting their ability to repair DNA. Over time, PARPi are being used for certain groups of patients with breast, pancreatic, and prostate cancers. More recently, their potential to help people with other types of gynecologic cancers than ovarian have been studied. In this review, we explore the reasons behind looking into PARPi for these non-ovarian gynecologic cancers. We analyze the clinical data and compare it to the current treatment options available, focusing on endometrial, cervical, uterine leiomyosarcoma, and vulvar cancers. Additionally, we discuss about future directions and stress the importance of understanding the specific DNA repair context for each type of cancer. Especially, we discuss the tests that aims to define who may benefit from the drug, with focus on the homologous recombination deficiency. [ABSTRACT FROM AUTHOR]

Background: Patients with peritoneal metastasis (PM) from gastric cancer (GC) exhibit poor prognosis. Chemoimmunotherapy offers promising clinical benefits; however, its efficacy and predictive biomarkers in a conversion therapy setting remain unclear. The authors aimed to retrospectively evaluate chemoimmunotherapy efficacy in a conversion therapy setting for GC patients with PM and establish a prediction model for assessing clinical benefits. Materials and methods: A retrospective evaluation of clinical outcomes encompassed 55 GC patients with PM who underwent chemoimmunotherapy in a conversion therapy setting. Baseline PM specimens were collected for genomic and transcriptomic profiling. Clinicopathological factors, gene signatures, and tumor immune microenvironment were evaluated to identify predictive markers and develop a prediction model. Results: Chemoimmunotherapy achieved a 41.8% objective response rate and 72.4% R0 resection rate in GC patients with PM. Patients with conversion surgery showed better overall survival (OS) than those without the surgery (median OS: not reached vs 7.82 m, P<0.0001). Responders to chemoimmunotherapy showed higher ERBB2 and ERBB3 mutation frequencies, CTLA4 and HLA-DQB1 expression, and CD8+ T cell infiltration, but lower CDH1 mutation and naïve CD4+ T cell infiltration, compared to nonresponders. A prediction model was established integrating CDH1 and ERBB3 mutations, HLA-DQB1 expression, and naïve CD4+ T cell infiltration (AUC= 0.918), which were further tested using an independent external cohort (AUC= 0.785). Conclusion: This exploratory study comprehensively evaluated clinicopathological, genomic, and immune features and developed a novel prediction model, providing a rational basis for the selection of GC patients with PM for chemoimmunotherapy-involved conversion therapy. [ABSTRACT FROM AUTHOR]

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Recent strides in understanding the molecular underpinnings of head and neck cancers have sparked considerable interest in identifying precise biomarkers that can enhance prognostication and enable personalized treatment strategies. Immunotherapy has particularly revolutionized the therapeutic landscape for head and neck squamous cell carcinoma, offering new avenues for treatment. This review comprehensively examines the application and limitations of the established and emerging/novel biomarkers for head and neck squamous cell carcinoma. Established biomarkers, including well-characterized genetic mutations, protein expressions, and clinical factors, have been extensively studied and validated in clinical practice. Novel biomarkers identified through molecular analyses, including novel genetic alterations, immune-related markers, and molecular signatures, are currently being investigated and validated in preclinical and clinical settings. Biomarkers hold the potential to deepen our understanding of head and neck squamous cell carcinoma biology and guide therapeutic strategies. The evolving paradigm of predictive biomarkers facilitates the study of individual responses to specific treatments, including targeted therapy and immunotherapy.

Summary: Background: This phase II prospective trial aimed to investigate the efficacy and safety of adebrelimab (PD-L1 antibody) plus first-line chemotherapy followed by sequential thoracic radiotherapy (TRT) combined with adebrelimab in extensive-stage small–cell lung cancer (ES-SCLC). Biomarkers associated with potential therapeutic effects were also explored. Methods: Patients with previously untreated ES-SCLC were enrolled at Shandong Cancer Hospital and Institute (Jinan, China). Patients received 4–6 cycles of adebrelimab (20 mg/kg, D1, Q3W) combined with EP/EC (etoposide, 100 mg/m2, D1-3, Q3W and cisplatin, 75 mg/m2, D1, Q3W or carboplatin, AUC = 5, D1, Q3W). Then patients with response sequentially underwent consolidative TRT (≥30 Gy in 10 fractions or ≥50 Gy in 25 fractions, involved-field irradiation), and maintenance adebrelimab until disease progression or intolerable adverse events (AEs). The primary endpoint was overall survival (OS). Genomic and circulating tumour DNA (ctDNA) profiling were also analyzed with tumour tissues and peripheral blood. This trial was registered with ClinicalTrials.gov, NCT04562337. Findings: From October 2020 to April 2023, 67 patients diagnosed with ES-SCLC were enrolled and received at least one dose of study treatment. All patients were included in the efficacy and safety analyses. 45 patients received sequential TRT as planned. The median OS and progression-free survival (PFS) was 21.4 months (95% CI: 17.2–not reached months) and 10.1 months (95% CI: 6.9–15.5 months), respectively. The confirmed objective response rate was 71.6% (48/67, 95% CI: 59.3–82.0%) and disease control rate was 89.6% (60/67, 95% CI: 79.7–95.7%). There were no treatment-related deaths. The most common grade 3 or higher treatment-related adverse events (TRAEs) were hematological toxicities. The incidence of any grade and G3+ pneumonitis was 25% (17/67) and 6% (4/67), respectively. No unexpected adverse events were observed. Patients without co-mutations of TP53/RB1 in both tissue and peripheral blood displayed longer PFS (tissue, P = 0.071; ctDNA, P = 0.060) and OS (tissue, P = 0.032; ctDNA, P = 0.031). Interpretation: Adebrelimab plus chemotherapy and sequential TRT as first-line therapy for ES-SCLC showed promising efficacy and acceptable safety. Funding: This study was funded by the National Natural Science Foundation of China (82172865), Jiangsu Hengrui Pharmaceuticals Co., Ltd. and Amoy Diagnostics Co., Ltd.

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

Abstract Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPβ and NFIC. As a result, silencing of C/EBPβ and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPβ and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Abstract Background Neuroblastoma (NB), a type of solid tumor in children, has a poor prognosis. Few blood biomarkers can accurately predict the prognosis, including recurrence and survival, in children with NB. In this study, we found that the serum total cholesterol (Tchol) level was associated with the prognosis of patients through a retrospective study. Methods Multivariate Cox regression model was used to identify the independent risk factors in the children with NB. Kaplan–Meier method was used to analyze the correlation between the common biomarkers, including the serum Tchol level, and the prognosis of the patients. ROC curves were used to predict the accuracy of the International Neuroblastoma Staging System (INSS) stage and Children’s Oncology Group (COG) risk stratification after adding the serum Tchol level. Results Compared with the other patients, serum Tchol level was significantly increased in the relapsed and died patients (P

Simple Summary: Pleural mesothelioma (PM) is characterized by its poor prognosis due to late-stage diagnosis, limited effective treatment options and the lack of biomarkers for treatment decision. The role of macroscopic radical surgery has therefore been questioned. Thus, there is an urgent need for predictive biomarkers aiding clinicians in tailoring more personalized and effective therapeutic strategies. Besides validating the prognostic role of Ki67 expression in tumor samples in 70 PM patients from two different centers with detailed clinical characteristics, here we suggest for the first time Ki67 as a biomarker to predict the patient's benefit from lung-sparing surgery within multimodality therapy in epithelioid mesothelioma. Pleural mesothelioma (PM), linked to asbestos-induced inflammation, carries a poor prognosis. Therapy ranges from therapy limitation to aggressive multimodality treatment. Given the uncertainty about treatment benefits for patients, this study aimed to assess the role of Ki67 as a prognostic and predictive parameter in PM. Ki67 was measured in the specimens of 70 PM patients (17 female, 53 male) from two centers and correlated to overall survival (OS) and therapy outcome. The median OS was 16.1 months. The level of Ki67 expression was divided into low (≤15%) and high (>15%). A low value of Ki67 expression was associated with a longer OS (Ki67 ≤ 15%: 31.2 (95% CI 6.5–55.8) months vs. Ki67 > 15%: 11.1 (95% CI 7.7–14.6) months, p = 0.012). The 5-year survival represents 22% in the low Ki67 expression group, in contrast to 5% in the high Ki67 expression group. We found a significant interaction term of Ki67 with multimodality treatment (p = 0.031) translating to an OS of 48.1 months in the low expression Ki67 group compared to 24.3 months in the high Ki67 expression group when receiving surgery within multimodality therapy. Therefore, Ki67 stands out as a validated prognostic and, most importantly, novel predictive biomarker for treatment benefits, particularly regarding surgery within multimodality therapy. [ABSTRACT FROM AUTHOR]

UICC stage IV small-cell lung cancer (SCLC) is a highly aggressive malignancy without curative treatment options. Several randomized trials have demonstrated improved survival rates through the addition of checkpoint inhibitors to first-line platin-based chemotherapy. Consequently, a combination of chemo- and immunotherapy has become standard palliative treatment. However, no reliable predictive biomarkers for treatment response exist. Neither PD-L1 expression nor tumor mutational burden have proven to be effective predictive biomarkers. In this study, we compared the cellular immune statuses of SCLC patients to a healthy control cohort and investigated changes in peripheral blood B, T, and NK lymphocytes, as well as several of their respective subsets, during treatment with immunochemotherapy (ICT) using flow cytometry. Our findings revealed a significant decrease in B cells, while T cells showed a trend to increase throughout ICT. Notably, high levels of exhausted CD4+ and CD8+ cells, alongside NK subsets, increased significantly during treatment. Furthermore, we correlated decreases/increases in subsets after two cycles of ICT with survival. Specifically, a decrease in Th17 cells indicated a better overall survival. Based on these findings, we suggest conducting further investigation into Th17 cells as a potential early predictive biomarkers for response in patients receiving palliative ICT for stage IV SCLC. [ABSTRACT FROM AUTHOR]

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two‐stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log‐rank statistics for true biomarker status strata. These adjusted log‐rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component‐wise hypothesis. We discuss the power analysis with the control of the type‐I error rate by using the correlations between the adjusted log‐rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall‐cell lung cancer. [ABSTRACT FROM AUTHOR]

The realm of cancer therapy has been profoundly altered with the emergence of immune checkpoint blockade (ICB) therapy, providing improved survival prospects for many patients with some cancers. However, the challenge of achieving efficient or sustained therapeutic benefits underscores the critical imperative to optimize ICB strategies. This review elucidates the pivotal role of predictive biomarkers in optimizing precision ICB therapy, deciphering the intricate dynamics associated with the response heterogeneity. Furthermore, it critically examines the application of nanotechnology-driven drug delivery as a promising avenue to amplify ICB efficacy, facilitating controlled and targeted drug release. Recognizing the comprehensive and dynamic interplay among tumor cells, immune cells, and stromal cells has catalyzed the transformative advances in reverse translational research. This approach enables researchers to gain insights into the underlying mechanisms of ICB therapy, therapeutic responses, and resistance mechanisms. The convergence of predictive biomarkers, revolutionary nanotechnology, and reverse translational research emerges as an indispensable focal point, propelling the frontiers of precision oncology within the complex landscape of ICB therapy. [ABSTRACT FROM AUTHOR]

Purpose: HER2 overexpressing circulating tumor cells (CTCs) are observed in up to 25% of HER2-negative metastatic breast cancer patients. Since targeted anti-HER2 therapy has drastically improved clinical outcomes of patients with HER2-positive breast cancer, we hypothesized that patients with HER2 overexpressing CTCs might benefit from the addition of trastuzumab to chemotherapy. Methods: In this single-arm, phase II trial, patients with HER2-positive CTCs received trastuzumab as addition to first-line treatment with taxane chemotherapy. Patients with detectable CTCs but without HER2 overexpression that received taxane chemotherapy only, were used as control group. The primary outcome measure was progression-free rate at 6 months (PFR6), with a target of 80%. In November 2022, the study was terminated early due to slow patient accrual. Results: 63 patients were screened, of which eight patients had HER2-positive CTCs and were treated with trastuzumab. The median number of CTCs was 15 per 7.5 ml of blood (range 1–131) in patients with HER2-positive CTCs, compared to median 5 (range 1–1047) in the control group. PFR6 was 50% in the trastuzumab group and 54% in the taxane monotherapy group, with no significant difference in median PFS (8 versus 9 months, p = 0.51). Conclusion: No clinical benefit of trastuzumab was observed, although this study was performed in a limited number of patients. Additionally, we observed a strong correlation between the number of evaluable CTCs and the presence of HER2-positive CTCs. We argue that randomized studies investigating agents that are proven to be solely effective in the HER2-positive patient group in patients with HER2-positive CTCs and HER2-negative tissue are currently infeasible. Several factors contribute to this impracticality, including the need for more stringent thresholds, and the rapidly evolving landscape of cancer treatments. [ABSTRACT FROM AUTHOR]

Objectives: To explore the predictive value of inflammatory‐nutritional score (INS) and a nomogram for survivals in head and neck soft tissue sarcoma (HNSTS) patients with negative resection margins (R0). Methods: Clinical characteristics and hematological features of 315 HNSTS patients underwent R0 surgery were analyzed. Results: The 5‐year overall survival (OS) rate, 3‐year recurrence‐free survival rate and disease‐free survival (DFS) rate were 77.3%, 61.0% and 55.4%, respectively. High INS was associated with a deep tumor location (p < 0.001), high tumor grade (p < 0.001), and advanced AJCC stage (p < 0.001). The low‐risk group (INS 0) exhibited a higher 5‐year OS rate and 3‐year DFS rate than others (87.6% vs. 81.3% vs. 53.3%, p < 0.001; 62.2% vs. 56.9% vs. 37.9%, p = 0.007). The INS (p = 0.023), tumor depth (p < 0.001), pT classification (p = 0.022), pN classification (p < 0.001) and tumor grade (p < 0.001) were independent survival predictors. Moreover, a novel nomogram for predicting OS was generated and assessed by the concordance index, exhibiting a better performance than the p7TNMG classification alone (p < 0.001). Conclusions: For R0 resected HNSTS patients, the oncological outcomes can be predicted using the INS system and a specific nomogram. [ABSTRACT FROM AUTHOR]

Purposes: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma‐CpGs island methylator phenotype (G‐CIMP) but have an unmethylated promoter of O‐6‐methylguanine‐DNA methyltransferase (unMGMT). Methods: The discovery‐validation approach was planned incorporating a series of G‐CIMP−/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi‐CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration. Results: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy‐related CpGs, from which a 10‐CpG risk signature was further constructed. Both the 64‐CpG panel and the 10‐CpG risk signature were validated showing significant correlations with overall survival of G‐CIMP−/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10‐CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression. Conclusions: The 64 TMZ efficacy‐related CpGs and in particular the 10‐CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G‐CIMP−/unMGMT GBMs. [ABSTRACT FROM AUTHOR]

In early oncology clinical trials there is often limited data for biomarkers and their association with response to treatment. Thus, it is challenging to decide whether a biomarker should be used for patient selection and enrollment. Most evidence about any potential predictive biomarker comes from preclinical research and, sometimes, clinical observations. How to translate the preclinical predictive biomarker data to clinical study remains an active field of research. Here, we propose a method to incorporate existing knowledge about a predictive biomarker – its prevalence, association with response and the performance of the assay used to measure the biomarker – to estimate the response rate in a clinical study designed with or without using the predictive biomarker. Importantly, we quantify the uncertainty associated with the biomarker and its predictability in a probabilistic model. This model estimates the distribution of the clinical response when a predictive biomarker is used to select patients and compares it to unselected cohort. We applied this method to two real world cases of approved biomarker-guided therapies to demonstrate its utility and potential value. This approach helps to make a data-driven decision whether to select patients with a predictive biomarker in early oncology clinical development. [ABSTRACT FROM AUTHOR]

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPβ and NFIC. As a result, silencing of C/EBPβ and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPβ and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients. [ABSTRACT FROM AUTHOR]

Background: Neuroblastoma (NB), a type of solid tumor in children, has a poor prognosis. Few blood biomarkers can accurately predict the prognosis, including recurrence and survival, in children with NB. In this study, we found that the serum total cholesterol (Tchol) level was associated with the prognosis of patients through a retrospective study. Methods: Multivariate Cox regression model was used to identify the independent risk factors in the children with NB. Kaplan–Meier method was used to analyze the correlation between the common biomarkers, including the serum Tchol level, and the prognosis of the patients. ROC curves were used to predict the accuracy of the International Neuroblastoma Staging System (INSS) stage and Children's Oncology Group (COG) risk stratification after adding the serum Tchol level. Results: Compared with the other patients, serum Tchol level was significantly increased in the relapsed and died patients (P < 0.05). Subsequently, serum Tchol level was found as an independent risk factor to affect the outcome of patients (P < 0.05). Finally, we added serum Tchol level into traditional stage and risk classification system to form the new INSS stage and COG risk classification system. It was found that the areas under the ROC curve (AUC) of recurrence-free survival in the new INSS stage and COG risk classification system were increased to 0.691 (95%CI: 0.535–0.847) and 0.748 (95%CI: 0.622–0.874), respectively. Moreover, the AUC areas of overall survival in the new INSS stage and COG risk classification system were increased to 0.722 (95%CI: 0.561–0.883) and 0.668 (95%CI: 0.496–0.819), respectively. Conclusion: We found that serum Tchol level, a clinical biomarker, is a risk factor for recurrence and death among the children with NB. The serum Tchol level could significantly increase the accuracy of the prediction for NB prognosis. [ABSTRACT FROM AUTHOR]

Locally advanced or metastatic head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis. The introduction of PD-1 inhibitors has led to a significant improvement in survival, but only a subpopulation of patients responds to therapy. Current biomarkers cannot reliably identify these patients. The identification of biomarkers for the prediction and monitoring of immunotherapy is therefore of great importance. In this study, we characterized lymphocyte subsets in the peripheral blood of HNSCC patients under PD-1 inhibition. Patients with primary response (n=11) to PD-1 inhibition showed an increase of the CD3+ effector memory (CD3/EM) population and an elevated expression of the activation marker CD69 in CD3+ T cells, particularly in the CD3/EM subpopulation at 3 months when treatment response was assessed. In contrast, patients with primary treatment failure and progressive disease (n=9) despite PD-1 inhibition had lower absolute lymphocyte counts and an increased expression of CTLA-4 in CD3+ T cells at the time of treatment failure compared with baseline, particularly in CD4+ and CD8+ effector memory populations. Our results demonstrate that HNSCC patients' response to immune checkpoint inhibition shows a distinct immune signature in peripheral blood, which could help identify refractory patients earlier. Furthermore, strategies to overcome primary therapy failure by inducing a beneficial T cell phenotype or adding alternative immune checkpoint inhibitors could improve response rates and survival of HNSCC patients. [ABSTRACT FROM AUTHOR]

In precision medicine, there is much interest in estimating the expected-to-benefit (EB) subset, i.e. the subset of patients who are expected to benefit from a new treatment based on a collection of baseline characteristics. There are many statistical methods for estimating the EB subset, most of which produce a 'point estimate' without a confidence statement to address uncertainty. Confidence intervals for the EB subset have been defined only recently, and their construction is a new area for methodological research. This article proposes a pseudo-response approach to EB subset estimation and confidence interval construction. Compared to existing methods, the pseudo-response approach allows us to focus on modelling a conditional treatment effect function (as opposed to the conditional mean outcome given treatment and baseline covariates) and is able to incorporate information from baseline covariates that are not involved in defining the EB subset. Simulation results show that incorporating such covariates can improve estimation efficiency and reduce the size of the confidence interval for the EB subset. The methodology is applied to a randomized clinical trial comparing two drugs for treating HIV infection. [ABSTRACT FROM AUTHOR]

Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Thyroid cancer is a prevalent endocrine malignancy whose global incidence has risen over the past several decades. Ferroptosis, a regulated form of cell death distinguished by the excessive buildup of iron-dependent lipid peroxidates, stands out from other programmed cell death pathways in terms of morphological and molecular characteristics. Increasing evidence suggests a close association between thyroid cancer and ferroptosis, that is, inducing ferroptosis effectively suppresses the proliferation of thyroid cancer cells and impede tumor advancement. Therefore, ferroptosis represents a promising therapeutic target for the clinical management of thyroid cancer in clinical settings. Alterations in ferroptosis-related genes hold potential for prognostic prediction in thyroid cancer. This review summarizes current studies on the role of ferroptosis in thyroid cancer, elucidating its mechanisms, therapeutic targets, and predictive biomarkers. The findings underscore the significance of ferroptosis in thyroid cancer and offer valuable insights into the development of innovative treatment strategies and accurate predictors for the thyroid cancer.

Background & aimsHBV infection initiates autoimmune responses, leading to autoantibody generation. This research explores the role of autoantibodies in HBV-related Acute-on-Chronic Liver Failure (ACLF), offering novel perspectives for clinical management.MethodWe applied immunoprecipitation and iTRAQ techniques to screen for autoantibodies in serum from HBV-related cirrhosis patients and conducted detection with conformation- stabilizing ELISA in a cohort of 238 HBV-infected individuals and 49 health controls. Our results were validated in a retrospective cohort comprising 106 ACLF patients and further assessed through immunohistochemical analysis in liver tissues from an additional 10 ACLF cases.ResultsUtilizing iTRAQ, we identified Argonaute1-3 autoantibodies (AGO-Abs) in this research. AGO2-Abs notably increased in cirrhosis, decompensation, and further in ACLF, unlike AGO1-Abs and AGO3-Abs. This reflects disease severity correlation. Logistic regression and COX models confirmed AGO2-Abs as independent prognostic indicators for decompensated liver cirrhosis (DLC) and ACLF. In the ROC analysis, AGO2-Abs showed significant diagnostic value for predicting 28- and 90-day mortality (AUROC = 0.853 and 0.854, respectively). Furthermore, combining AGO2-Abs with the Child-Pugh, MELD, and AARC scores significantly improved their predictive accuracy (P < 0.05). Kaplan-Meier analysis showed poorer survival for AGO2-Abs levels above 99.14μg/ml. These findings were supported by a retrospective validation cohort. Additionally, immunohistochemistry revealed band-like AGO2 expression in periportal liver areas, with AGO2-Abs levels correlating with total bilirubin, indicating a potential role in exacerbating liver damage through periportal functions.ConclusionsAGO2-Abs is a robust biomarker for predicting the mortality of patients with HBV-related ACLF.

IntroductionThis study was undertaken to investigate whether sustained rather than a single measure of corneal nerve loss was associated with the onset of diabetic peripheral neuropathy (DPN) and the progression of neuropathic symptoms and deficits in individuals with type 2 diabetes (T2D).MethodsParticipants underwent clinical, metabolic testing and assessment of neuropathic symptoms, vibration perception threshold (VPT), sudomotor function, and corneal confocal microscopy (CCM) at baseline, 1, 2, and 4–7 years. Sustained corneal nerve loss was defined as abnormal corneal nerve fiber density (CNFD,

Abstract Background Predicting tumor responses to neoadjuvant chemotherapy (NAC) is critical for evaluating prognosis and designing treatment strategies for patients with breast cancer; however, there are no reliable biomarkers that can effectively assess tumor responses. Therefore, we aimed to evaluate the clinical feasibility of using extracellular vesicles (EVs) to predict tumor response after NAC. Methods Drug-resistant triple-negative breast cancer (TNBC) cell lines were successfully established, which developed specific morphologies and rapidly growing features. To detect resistance to chemotherapeutic drugs, EVs were isolated from cultured cells and plasma samples collected post-NAC from 36 patients with breast cancer. Results Among the differentially expressed gene profiles between parental and drug-resistant cell lines, drug efflux transporters such as MDR1, MRP1, and BCRP were highly expressed in resistant cell lines. Drug efflux transporters have been identified not only in cell lines but also in EVs released from parental cells using immunoaffinity-based EV isolation. The expression of drug resistance markers in EVs was relatively high in patients with residual disease compared to those with a pathological complete response. Conclusions The optimal combination of drug-resistant EV markers was significantly efficient in predicting resistance to NAC with 81.82% sensitivity and 92.86% specificity.