Search

Simple Summary: This study comprehensively investigated the genomic landscape of >3000 gynecological malignancies (endometrial, cervical, and ovarian cancers) in Japan. The Center for Cancer Genomics and Advanced Therapeutics database used in this study is a useful tool containing real-world data of patients with poor prognoses, as comprehensive genomic profiling tests are limited to patients with cancer who have completed standardized treatments in Japan. Genomic profiling based on histological subtypes, tumor mutational burden, and microsatellite instability highlights actionable mutations for future drug development for each gynecological cancer. This study aimed to comprehensively clarify the genomic landscape and its association with tumor mutational burden-high (TMB-H, ≥10 mut/Mb) and microsatellite instability-high (MSI-H) in endometrial, cervical, and ovarian cancers. We obtained genomic datasets of a comprehensive genomic profiling test, FoundationOne® CDx, with clinical information using the "Center for Cancer Genomics and Advanced Therapeutics" (C-CAT) database in Japan. Patients can undergo the tests only after standardized treatments under universal health insurance coverage. Endometrial cancers were characterized by a high frequency of TMB-H and MSI-H, especially in endometrioid carcinomas. The lower ratio of POLE exonuclease mutations and the higher ratio of TP53 mutations compared to previous reports suggested the prognostic effects of the molecular subtypes. Among the 839 cervical cancer samples, frequent mutations of KRAS, TP53, PIK3CA, STK11, CDKN2A, and ERBB2 were observed in adenocarcinomas, whereas the ratio of TMB-H was significantly higher in squamous cell carcinomas. Among the 1606 ovarian cancer samples, genomic profiling of serous, clear cell, endometrioid, and mucinous carcinomas was characterized. Pathogenic mutations in the POLE exonuclease domain were associated with high TMB, and the mutation ratio was low in both cervical and ovarian cancers. The C-CAT database is useful for determining the mutational landscape of each cancer type and histological subtype. As the dataset is exclusively collected from patients after the standardized treatments, the information on "druggable" alterations highlights the unmet needs for drug development in major gynecological cancers. [ABSTRACT FROM AUTHOR]

Objectives: To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS). Methods: We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer–Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA). Results: Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82–10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789–0.991) and an accuracy of 0.889 (95% CI 0.708–0.977). Conclusion: The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: When employing a high tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint inhibitor (ICI) response, one faces various challenges. It is important to understand TMB in the context of the tumor microenvironment and genomic features. This review provides an update on these challenges and offers insights into improvement strategies for the future, aiming to enhance the predictive accuracy of this biomarker for clinical use. A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings. [ABSTRACT FROM AUTHOR]

Simple Summary: A multigene panel test, known as comprehensive genomic profiling (CGP), is now available in Japan for advanced cancer patients to identify suitable genotype-matched therapies. However, well-established biomarkers to guide these genotype-matched therapies are limited. Test failures can arise due to clinical factors affecting DNA yield and quality. Consequently, only 8% of patients undergoing CGP can access matched therapies. To address this issue, a retrospective analysis of clinical data from patients who underwent the FoundationOne® CDx at Kyushu University Hospital was conducted. We found that alterations in the homologous recombination repair (HRR) genes were associated with a high mutational burden. Data from public sources suggested that patients with HRR gene alterations had higher TMB and showed significantly longer survival of immunotherapy. Although immunotherapy has a key role in cancer treatment, predicting its efficacy through biomarkers remains challenging. Investigating HRR gene alterations could help select patients who are more likely to benefit from immunotherapy. Background: Comprehensive genomic profiling (CGP) has become generally accepted practice in cancer care since CGP has become reimbursed by national healthcare insurance in Japan in 2019. However, its usefulness for cancer patients is insufficient for several reasons. Methods: In an observational clinical study of FoundationOne® CDx, potential biomarkers were explored and the cause of testing failure was investigated. A total of 220 cancer patients were enrolled in the study during the period from 2018 to 2019 at Kyushu University Hospital. Results: The primary tumor sites of the 220 cases were breast (115), colon (29), stomach (19), and pancreas (20). The present dataset suggested that homologous recombination repair (HRR) gene alterations were positively associated with tumor mutational burden-high (TMB-high) (p = 0.0099). A public dataset confirmed that patients with HRR gene alterations had a higher TMB and showed significantly longer survival of immunotherapy. In the present study, 18 cases failed sequencing. A lower percentage of tumor cell nuclei was the most common reason for testing failures (p = 0.037). Cases that received neoadjuvant chemotherapy before sampling tended to fail testing. Conclusions: HRR gene alterations can be a potential biomarker predicting TMB-high and a good response to immunotherapy. For successful sequencing, samples with lower percentages of tumor cell nuclei and previous neoadjuvant chemotherapy should be avoided. [ABSTRACT FROM AUTHOR]

Head and neck cancer (HNC) is a tumor occurring in various primary sites with limited chemotherapy options for its treatment. Recently, comprehensive genomic profiling (CGP) testing has become clinically widespread. In this study, we examined the utility of CGP in diagnosing and treating HNC. This study included 29 patients with HNC who underwent CGP testing at the Gifu University Hospital between December 2019 and April 2022. We analyzed the types of gene mutations and tumor mutational burden (TMB) based on the CGP results. Squamous cell carcinoma accounted for 55.2%, and other cancers accounted for 44.8%. And we investigated the correlation of prognosis with gene mutations and TMB. Gene mutations were detected in TP53(48.3%), CDKN2A (27.6%), CDKN2B (17.2%), NOTCH1 (17.2%), PIK3CA (17.2%), ARID1A (13.8%), and NF1 (13.8%). TP53, CDKN2A and CDKN2B mutations significantly decreased survival rate in HNC. Five cases (17.2%) were TMB-high and 82.8% were TMB-low. In SCC cases treated with immune checkpoint inhibitors, TMB-high had better Overall survival than TMB-low. And all patients with TMB-high were oropharyngeal cancer. Although there were no cases in which effective treatment was actually performed based on the results of CGP, many gene mutations have been detected and several gene mutations correlated with prognosis. Furthermore, TMB can be used as a biomarker to predict the therapeutic effects of immune checkpoint inhibitors in cases of SCC. [ABSTRACT FROM AUTHOR]

Background: Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL). Methods: We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high-and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples. Results: Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels. Conclusions: Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker. [ABSTRACT FROM AUTHOR]

Background: Cyclin B2 (CCNB2) is associated with cell cycle progression, acting as a cell cycle checkpoint in progression of G2/M transition. In many cancer patients, it has been observed that overexpression of CCNB2 enhances tumor invasiveness and leads to adverse prognosis. However, the association of CCNB2 with the tumor microenvironment remains unclear. Therefore, it is necessary to clarify the associations of CCNB2 with the immune status and prognosis of breast carcinoma (BRCA). Methods: Gene expression and clinical data for BRCA were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, followed by association analyses of CCNB2 expression with prognosis, immune cell infiltration, and immune checkpoints. This study further performed drug sensitivity analysis and constructed a prognostic nomogram for CCNB2. Results: 3619 differentially expressed genes were identified in BRCA, including CCNB2 that emerged as a key gene in the network. High CCNB2 expression correlated with poor prognosis. Functional analysis demonstrated enrichment of CCNB2 co-expressed genes with the cell cycle, cancer progression, cell energy, and immune pathways. Microsatellite instability and tumor mutation burden analyses indicated CCNB2 as a candidate immunotherapy target. Tumor-infiltrating myeloid-derived suppressor cells, regulatory T cells, and T helper 2 cells were associated with CCNB2-related tumor progression and metastasis. CCNB2 expression positively correlated with immune checkpoints, indicating that high CCNB2 expression might facilitate tumor immune escape. Tumors with high CCNB2 expression showed sensitivity to phosphoinositide 3-kinase-protein kinase B-mammalian target of rapamycin and cyclin-dependent kinase (CDK) 4/6 inhibitors, and the nomogram had good prognostic predictive ability for patients with BRCA. Conclusions: CCNB2 may play a crucial role in tumorigenesis and serve as an independent prognostic biomarker associated with tumor microenvironment, tumor immune infiltration and immunotherapy in BRCA.

BackgroundComprehensive molecular profiling of tissue samples that can help guide therapy management is not widely available across the globe.MethodsComprehensive molecular profiling through Caris Molecular Intelligence involves the analysis of DNA through next-generation sequencing, chromogenic or fluorescent in situ hybridization, pyrosequencing, and copy number alterations; RNA through whole-transcriptome sequencing and multiplex PCR of RNA; and protein through immunohistochemistry.ResultsHere we describe the experience of molecular profiling of tumor tissue samples from patients diagnosed with advanced solid tumors and treated in two countries, the United Arab Emirates and Thailand. Tumor cancer cases submitted to Caris Life Sciences (Phoenix, Arizona, USA) for molecular profiling from the UAE and Thailand were retrospectively analyzed (data accessed between 2019 and 2020) for their molecular alterations and clinical biomarkers, without regard to ethnicity. A total of 451 samples from 35 distinct types of advanced cancers were examined for mutations, amplifications, overexpression, exon copy number alterations, microsatellite instability, deficient mismatch repair, tumor mutational burden, and fusions. Interrogating each step of the biological pathway, from DNA to RNA to distinct protein, identified an alteration with an associated therapy for 75% of these tumor samples. The most common alterations identified included elevated PDL-1 that can be targeted with an immune checkpoint inhibitors and amplification of HER2 for which a variety of anti HER2 therapies are available.ConclusionComprehensive molecular profiling in patients with advanced malignancies can help optimize therapeutic management allowing for improved prognostic outcome.

Background:In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.Methods:This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.Results:Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).Conclusion:We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Abstract Background Insufficient understanding of the pathogenesis and tumor immunology of triple‐negative breast cancer (TNBC) has limited the development of immunotherapy. The importance of tumor microenvironment (TME) in immunotyping, prognostic assessment and immunotherapy efficacy of cancer has been emphasized, however, potential immunogenic cell death (ICD) related genes function in TME of TNBC has been rarely investigated. Aims To initially explore the role and related mechanisms of ICD in TNBC, especially the role played in the TME of TNBC, and to identify different relevant subtypes based on ICD, and then develop an ICD‐related risk score to predict each TNBC patient TME status, prognosis and immunotherapy response. Methods and results In this study, we identified distinct ICD‐related modification patterns based on 158 TNBC cases in the TCGA‐TNBC cohort. We then investigated the possible correlation between ICD‐related modification patterns and TME cell infiltration characteristics in TNBC. By using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis, we created a risk scoring system (ICD score) to quantifiably evaluate the impact of ICD‐related modification patterns in individual TNBC patient. Two different ICD‐related modification patterns were found with significant differences in immune infiltration. Lower ICD score was correlated with higher immune infiltration, tumor mutational burden and significantly enriched in immune‐related pathways, indicating a strong ability to activate immune response, which might account for relatively favorable prognosis of TNBC patients and could serve as a predictor to select suitable candidates for immunotherapy. We used two independent cohorts, GSE58812 cohort and Metabric cohort to validate prognosis and immunohistochemistry for preliminary in vitro validation. Conclusion This study evidenced that the ICD‐related modification patterns might exert pivotal roles in the immune infiltration landscape of TNBC and ICD score might act as potential predictors of prognostic assessment and immunotherapy response. This research provides unique insights for individualize immune treatment strategies and promising immunotherapy candidates screening.

Abstract Background The efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) patients harboring neurotrophin receptor kinase (NTRK) family mutations remains obscure. Methods The Zehir cohort from cBioPortal was used to analyze the mutations (MT) frequency of NTRK family in patients with NSCLC, and their correlation with clinical characteristics and patient survival. The influence of NTRK MT on ICIs efficacy was evaluated in ICIs-treated patients from Samstein cohort and further validated by use of data from OAK/POPLAR cohort. Results In the Zehir cohort, a significant difference was observed in median overall survival (mOS) between patients with NTRK MT and wild-type (WT) (mOS: 18.97 vs. 21.27 months, HR = 1.34, 95%CI 1.00-1.78; log-rank P = 0.047). In Samstein cohort, the mOS of NTRK mutant patients receiving ICIs has improved compared to WT patients (mOS: 21.00 vs. 11.00 months, log-rank P = 0.103). Notably, in subgroup analysis, ICIs significantly prolonged mOS in patients with NTRK3 MT than in WT patients (mOS: not available vs. 11.00 months, HR = 0.36, 95%CI 0.16–0.81; log-rank P = 0.009). Identical mOS between NTRK MT and WT patients receiving ICIs treatment (mOS: 13.24 vs. 13.50 months, log-rank P = 0.775) was observed in OAK/POPLAR cohort. Moreover, a similar programmed death ligand 1 (PD-L1) expression, but higher tumor mutational burden (TMB), blood TMB (bTMB) and enriched anti-tumor immunity were observed in NTRK MT compared to WT (P

Abstract Purpose To investigate the correlations between metabolic parameters (MPs) of 18F‐fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT), serum tumor markers (STMs), and tumor mutational burden (TMB) in patients with non‐small cell lung cancer (NSCLC). Materials and Methods In this retrospective study, we enrolled 129 patients with NSCLC (males, 78; females, 51) who underwent baseline TMB and STM tests and 18F‐FDG PET/CT scans before treatment between March 2018 and September 2022. Patients were categorized into TMB‐high (TMB ≥10 mutations/Mb; n = 27 [20.9%]) and non‐TMB‐high (TMB

Abstract Background Therapeutic approaches for extrahepatic cholangiocarcinoma (EHCC) are limited, due to insufficient understanding to biomarkers related to prognosis and drug response. Here, we comprehensively assess the molecular characterization of EHCC with clinical implications. Methods Whole‐exome sequencing (WES) on 37 tissue samples of EHCC were performed to evaluate genomic alterations, tumor mutational burden (TMB) and microsatellite instability (MSI). Results Mutation of KRAS (16%) was significantly correlated to poor OS. ERBB2 mutation was associated with improved OS. ERBB2, KRAS, and ARID1A were three potentially actionable targets. TMB ≥10 mutations per megabase was detected in 13 (35.1%) cases. Six patients (16.2%) with MSIsensor scores ≥10 were found. In multivariate Cox analysis, patients with MSIsensor sore exceed a certain threshold (MSIsensor score ≥0.36, value approximately above the 20th percentile as thresholds) showed a significant association with the improved OS (HR = 0.16; 95% CI: 0.056–0.46, p

Immune checkpoint inhibitors (ICIs) targeting the cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and programed cell death protein 1 (PD-1) or its ligand PD-L1 have increased the survival and cure rates for patients with many cancer types in various disease settings. However, only 10-40% of cancer patients benefited from these ICIs, of whom ~ 20% have treatment interruption or discontinuation due to immune-related adverse events that can be severe and even fatal. Current efforts in precision immunotherapy are focused on improving biomarker-based patient selection for currently available ICIs and exploring rationale combination and novel strategies to expand the benefit of immunotherapy to more cancer patients. Neoantigens arise from ~ 10% of the non-synonymous somatic mutations in cancer cells, are important targets of T cell-mediated anti-tumor immunity for individual patients. Advances in next generation sequencing technology and computational bioinformatics have enable the identification of genomic alterations, putative neoantigens, and gene expression profiling in individual tumors for personal oncology in a rapid and cost-effective way. Among the genomic biomarkers, defective mismatch DNA repair (dMMR), microsatellite instability high (MSI-H) and high tumor mutational burden (H-TMB) have received FDA approvals for selecting patients for ICI treatment. All these biomarkers measure high neoantigen load and tumor antigenicity, supporting the current development of neoantigen-based personalized cancer vaccines for patients with high TMB tumor. Several studies have shown neoantigen vaccines are feasible, safe and have promising clinical activity in patients with high TMB tumors in both metastatic and adjuvant settings. This review summarizes the emerging data and technologies for neoantigen-based personalized immunotherapy.

IntroductionTumor mutational burden (TMB) and APOBEC mutational signatures are potential prognostic markers in patients with advanced urothelial carcinoma (aUC). Their utility in predicting outcomes to specific therapies in aUC warrants additional study.MethodsWe retrospectively reviewed consecutive UC cases assessed with UCSF500, an institutional assay that uses hybrid capture enrichment of target DNA to interrogate 479 common cancer genes. Hypermutated tumors (HM), defined as having TMB ≥10 mutations/Mb, were also assessed for APOBEC mutational signatures, while non-HM (NHM) tumors were not assessed due to low TMB. The logrank test was used to determine if there were differences in overall survival (OS) and progression-free survival (PFS) among patient groups of interest.ResultsAmong 75 aUC patients who had UCSF500 testing, 46 patients were evaluable for TMB, of which 19 patients (41%) had HM tumors and the rest had NHM tumors (27 patients). An additional 29 patients had unknown TMB status. Among 19 HM patients, all 16 patients who were evaluable for analysis had APOBEC signatures. HM patients (N=19) were compared with NHM patients (N=27) and had improved OS from diagnosis (125.3 months vs 35.7 months, p=0.06) but inferior OS for patients treated with chemotherapy (7.0 months vs 13.1 months, p=0.04). Patients with APOBEC (N=16) were compared with remaining 56 patients, comprised of 27 NHM patients and 29 patients with unknown TMB, showing APOBEC patients to have improved OS from diagnosis (125.3 months vs 44.5 months, p=0.05) but inferior OS for patients treated with chemotherapy (7.0 months vs 13.1 months, p=0.05). Neither APOBEC nor HM status were associated with response to immunotherapy.ConclusionsIn a large, single-institution aUC cohort assessed with UCSF500, an institutional NGS panel, HM tumors were common and all such tumors that were evaluated for mutational signature analysis had APOBEC signatures. APOBEC signatures and high TMB were prognostic of improved OS from diagnosis and both analyses also predicted inferior outcomes with chemotherapy treatment.

BackgroundTumor mutational burden (TMB) may be a predictive biomarker of immune checkpoint inhibitor (ICI) responsiveness. Genomic landscape heterogeneity is a well-established cancer feature. Molecular characteristics may differ even within the same tumor specimen and undoubtedly evolve with time. However, the degree to which TMB differs between tumor biopsies within the same patient has not been established.MethodsWe curated data on 202 patients enrolled in the PREDICT study (NCT02478931), seen at the University of California San Diego (UCSD), who had 404 tissue biopsies for TMB (two per patient, mean of 722 days between biopsies) to assess difference in TMB before and after treatment in a pan-cancer cohort. We also performed an orthogonal analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database to examine difference in TMB between first and last biopsies.ResultsThe mean (95% CI) TMB difference between samples was 0.583 [- 0.900-2.064] (p = 0.15). Pearson correlation showed a flat line for time elapsed between biopsies versus TMB change indicating no correlation (R2 = 0.0001; p = 0.8778). However, in 55 patients who received ICIs, there was an increase in TMB (before versus after mean mutations/megabase [range] 12.50 [range, 0.00-98.31] versus 14.14 [range, 0.00-100.0], p = 0.025). Analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database also indicated largely stable TMB patterns; TMB increases were only observed in specific tumors (e.g., breast, colorectal, glioma) within certain time intervals.ConclusionsOverall, our results suggest that tissue TMB remains stable with time, though specific therapies such as immunotherapy may correlate with an increase in TMB.Trial registrationNCT02478931 , registered June 23, 2015.

BackgroundChemotherapy improves overall survival after surgery and radiotherapy for newly diagnosed high-risk IDH-mutant low-grade gliomas (LGGs), but a proportion of patients treated with temozolomide (TMZ) will develop recurrent tumors with TMZ-induced hypermutation. We aimed to determine the prevalence of TMZ-induced hypermutation at recurrence and prognostic implications.MethodsWe sequenced recurrent tumors from 82 patients with initially low-grade IDH-mutant gliomas who underwent reoperation and correlated hypermutation status with grade at recurrence and subsequent clinical outcomes.ResultsHypermutation was associated with high-grade disease at the time of reoperation (OR 12.0 95% CI 2.5-115.5, P = .002) and was identified at transformation in 57% of recurrent LGGs previously exposed to TMZ. After anaplastic (grade III) transformation, hypermutation was associated with shorter survival on univariate and multivariate analysis (HR 3.4, 95% CI 1.2-9.9, P = .024), controlling for tumor grade, subtype, age, and prior radiotherapy. The effect of hypermutation on survival after transformation was validated in an independent, published dataset. Hypermutated (HM) tumors were more likely to develop discontiguous foci of disease in the brain and spine (P = .003). To estimate the overall incidence of high-grade transformation among low-grade IDH-mutant tumors, data from a phase II trial of TMZ for LGG were analyzed. Eight-year transformation-free survival was 53.8% (95% CI 42.8-69.2), and 61% of analyzed transformed cases were HM.ConclusionsTMZ-induced hypermutation is a common event in transformed LGG previously treated with TMZ and is associated with worse prognosis and development of discontiguous disease after recurrence. These findings impact tumor classification at recurrence, prognostication, and clinical trial design.

BACKGROUND: Nuclear protein transport is essential in guiding the traffic of important proteins and RNAs between the nucleus and cytoplasm. Export of proteins from the nucleus is mostly regulated by Exportin 1 (XPO1). In cancer, XPO1 is almost universally hyperactive and can promote the export of important tumor suppressors to the cytoplasm. Currently, there are no studies evaluating XPO1 amplifications and mutations in NSCLC and the impact on outcomes. METHODS: Tumor samples were analyzed using next-generation sequencing (NGS) (NextSeq, 592 Genes), immunohistochemistry (IHC), and whole transcriptome sequencing (WTS, NovaSeq) (Caris Life Sciences, Phoenix, AZ). Survival was extracted from insurance claims data and calculated from time of tissue collection to last contact using Kaplan-Meier estimate. RESULTS: Among 18,218 NSCLC tumors sequenced, 26 harbored XPO1 mutations and 24 had amplifications. XPO1 mutant tumors were more likely to have high TMB (79% vs. 52%, p = 0.007) and less likely to have high PD-L1 (32% vs. 68%, p = 0.03). KRAS co-mutations were seen in 19% (n = 5) and EGFR co-mutations were rare (n = 2). Among the 17,449 NSCLC tumors with clinical data, there were 24 XPO1 mutant. Comparison of survival between XPO1 mutant and WT showed a negative association with a hazard ratio (HR) of 1.932 (95% CI: 1.144-3.264 p = 0.012). XPO1 amplification was not associated with survival. CONCLUSIONS: XPO1 pathogenic mutations were associated with a poor survival in NSCLC. Although XPO1 mutations are rare in NSCLC, further studies to assess its associations with treatment responses are warranted.

Neoadjuvant PD-1 blockade may be efficacious in some individuals with high-risk, resectable oral cavity head and neck cancer. To explore correlates of response patterns to neoadjuvant nivolumab treatment and post-surgical recurrences, we analyzed longitudinal tumor and blood samples in a cohort of 12 individuals displaying 33% responsiveness. Pretreatment tumor-based detection of FLT4 mutations and PTEN signature enrichment favors response, and high tumor mutational burden improves recurrence-free survival. In contrast, preexisting and/or acquired mutations (in CDKN2A, YAP1, or JAK2) correlate with innate resistance and/or tumor recurrence. Immunologically, tumor response after therapy entails T cell receptor repertoire diversification in peripheral blood and intratumoral expansion of preexisting T cell clones. A high ratio of regulatory T to T helper 17 cells in pretreatment blood predicts low T cell receptor repertoire diversity in pretreatment blood, a low cytolytic T cell signature in pretreatment tumors, and innate resistance. Our study provides a molecular framework to advance neoadjuvant anti-PD-1 therapy for individuals with resectable head and neck cancer.

Abstract Background Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. Methods By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. Results By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. Conclusions The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling.

Abstract Background There is an unmet need to identify novel predictive biomarkers that enable more accurate identification of individuals who can benefit from immune checkpoint inhibitor (ICI) therapy. The US FDA recently approved tumor mutational burden (TMB) score of ≥ 10 mut/Mb as a threshold for pembrolizumab treatment of solid tumors. Our study aimed to test the hypothesis that specific gene mutation signature may predict the efficacy of ICI therapy more precisely than high TMB (≥ 10). Methods We selected 20 candidate genes that may predict for the efficacy of ICI therapy by the analysis of data from a published cohort of 350 advanced non-small cell lung cancer (NSCLC) patients. Then, we compared the influences of various gene mutation signatures on the efficacy of ICI treatment. They were also compared with PD-L1 and TMB. The Kaplan-Meier method was utilized to evaluate the prognosis univariates, while selected univariates were adopted to develop a systematic nomogram. Results A high mutation signature, where three or more of the 20 selected genes were mutated, was associated with the significant benefits of ICI therapy. Specifically, patients with high mutation signature were confirmed to have better prognosis for ICI treatment, compared with those with wild type (the median PFS: 7.17 vs. 2.90 months, p = 0.0004, HR = 0.47 (95% [CI]:0.32–0.68); the median OS: unreached vs. 9 months, p = 1.8E-8, HR = 0.17 (95% [CI]:0.11–0.25)). Moreover, those patients with the high mutation signature achieved significant ICI treatment benefits, while there was no difference of OS and PFS between patients without the signature but TMB-H (≥ 10) and those without the signature and low TMB(

The detection of driver oncogenic variants and the recent identification of tumor-agnostic genomic biomarkers has driven the use of comprehensive genomic profiling (CGP) for disease diagnosis, prognosis, and treatment selection. The Oncomine™ Comprehensive Assay Plus (OCA+) panel uses DNA and RNA to detect single nucleotide variants (SNVs), small insertions/deletions (Indels), and structural variants (SVs) across 501 genes. Moreover, microsatellite instability (MSI), tumor mutational burden (TMB), and homologous recombination deficiency (HRD) status are assessed in a single workflow. Herein, we present the analytical validation and clinical utilization of OCA+. By using commercial reference materials, we found good analytical sensitivity, specificity, and precision for all biomarkers analyzed. The limit of detection (LoD) was validated for SNVs and Indels at 4%, except for Indels located in homopolymeric regions, where the LoD was 10%. An additional set of 81 tumor samples, including cytology smears, were sequenced to assess the clinical utility of the OCA+ across different tumor types. Among the clinical cohort, OCA+ demonstrated 100% accuracy, sensitivity, and specificity for all biomarkers analyzed, except for MSI assessment of endometrial cancer cases, where 83% accuracy and 67% sensitivity were achieved, compared to PCR and IHC. The validation of accuracy and robustness of this assay supports the OCA+’s utility for solid tumor CGP.

Background: Biomarker testing has gradually become standard of care in precision oncology to help physicians select optimal treatment for patients. Compared to single-gene or small gene panel testing, comprehensive genomic profiling (CGP) has emerged as a more time- and tissue-efficient method. This study demonstrated in-depth analytical validation of K-4CARE, a CGP assay that integrates circulating tumor DNA (ctDNA) tracking for residual cancer surveillance.Methods: The assay utilized a panel of 473 cancer-relevant genes with a total length of 1.7 Mb. Reference standards were used to evaluate limit of detection (LOD), concordance, sensitivity, specificity and precision of the assay to detect single nucleotide variants (SNVs), small insertion/deletions (Indels), gene amplification and fusion, microsatellite instability (MSI) and tumor mutational burden (TMB). The assay was then benchmarked against orthogonal methods using 155 clinical samples from 10 cancer types. In selected cancers, top tumor-derived somatic mutations, as ranked by our proprietary algorithm, were used to detect ctDNA in the plasma.Results: For detection of somatic SNVs and Indels, gene fusion and amplification, the assay had sensitivity of >99%, 94% and >99% respectively, and specificity of >99%. Detection of germline variants also achieved sensitivity and specificity of >99%. For TMB measurement, the correlation coefficient between whole-exome sequencing and our targeted panel was 97%. MSI analysis when benchmarked against polymerase chain reaction method showed sensitivity of 94% and specificity of >99%. The concordance between our assay and the TruSight Oncology 500 assay for detection of somatic variants, TMB and MSI measurement was 100%, 89%, and 98% respectively. When CGP-informed mutations were used to personalize ctDNA tracking, the detection rate of ctDNA in liquid biopsy was 79%, and clinical utility in cancer surveillance was demonstrated in 2 case studies.Conclusion: K-4CARE™ assay provides comprehensive and reliable genomic information that fulfills all guideline-based biomarker testing for both targeted therapy and immunotherapy. Integration of ctDNA tracking helps clinicians to further monitor treatment response and ultimately provide well-rounded care to cancer patients.

Abstract Objectives Primary pulmonary lymphoepithelioma‐like carcinoma (PLELC) is a subtype of lung carcinoma associated with the Epstein–Barr virus (EBV). The clinical predictive biomarkers of immune checkpoint blockade (ICB) in PLELC require further investigation. Methods We prospectively analysed EBV levels in the blood and immune tumor biomarkers of 31 patients with ICB‐treated PLELC. Viral EBNA‐1 and BamHI‐W DNA fragments in the plasma were quantified in parallel using quantitative polymerase chain reaction. Results Progression‐free survival (PFS) was significantly longer in EBNA‐1 high or BamHI‐W high groups. A longer PFS was also observed in patients with both high plasma EBNA‐1 or BamHI‐W and PD‐L1 ≥ 1%. Intriguingly, the tumor mutational burden was inversely correlated with EBNA‐1 and BamHI‐W. Plasma EBV load was negatively associated with intratumoral CD8+ immune cell infiltration. Dynamic changes in plasma EBV DNA level were in accordance with the changes in tumor volume. An increase in EBV DNA levels during treatment indicated molecular progression that preceded the imaging progression by several months. Conclusions Plasma EBV DNA could be a useful and easy‐to‐use biomarker for predicting the clinical activity of ICB in PLELC and could serve to monitor disease progression earlier than computed tomography imaging.

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Background: The efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) patients harboring neurotrophin receptor kinase (NTRK) family mutations remains obscure. Methods: The Zehir cohort from cBioPortal was used to analyze the mutations (MT) frequency of NTRK family in patients with NSCLC, and their correlation with clinical characteristics and patient survival. The influence of NTRK MT on ICIs efficacy was evaluated in ICIs-treated patients from Samstein cohort and further validated by use of data from OAK/POPLAR cohort. Results: In the Zehir cohort, a significant difference was observed in median overall survival (mOS) between patients with NTRK MT and wild-type (WT) (mOS: 18.97 vs. 21.27 months, HR = 1.34, 95%CI 1.00-1.78; log-rank P = 0.047). In Samstein cohort, the mOS of NTRK mutant patients receiving ICIs has improved compared to WT patients (mOS: 21.00 vs. 11.00 months, log-rank P = 0.103). Notably, in subgroup analysis, ICIs significantly prolonged mOS in patients with NTRK3 MT than in WT patients (mOS: not available vs. 11.00 months, HR = 0.36, 95%CI 0.16–0.81; log-rank P = 0.009). Identical mOS between NTRK MT and WT patients receiving ICIs treatment (mOS: 13.24 vs. 13.50 months, log-rank P = 0.775) was observed in OAK/POPLAR cohort. Moreover, a similar programmed death ligand 1 (PD-L1) expression, but higher tumor mutational burden (TMB), blood TMB (bTMB) and enriched anti-tumor immunity were observed in NTRK MT compared to WT (P < 0.05). Conclusion: Taking high TMB or bTMB into consideration, patients with NTRK mutant NSCLC could benefit from ICIs treatment. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the correlations between metabolic parameters (MPs) of 18F‐fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT), serum tumor markers (STMs), and tumor mutational burden (TMB) in patients with non‐small cell lung cancer (NSCLC). Materials and Methods: In this retrospective study, we enrolled 129 patients with NSCLC (males, 78; females, 51) who underwent baseline TMB and STM tests and 18F‐FDG PET/CT scans before treatment between March 2018 and September 2022. Patients were categorized into TMB‐high (TMB ≥10 mutations/Mb; n = 27 [20.9%]) and non‐TMB‐high (TMB <10 mutations/Mb; n = 102 [79.1%]) groups. Binary logistic regression analyses were performed to determine independent predictors of TMB‐high. Univariate and multivariate linear regression analyses were performed to determine independent predictors of TMB level on a log scale. Subgroup analyses for adenocarcinoma (ADC), ADC with EGFR+, ADC with EGFR−, and squamous cell carcinoma (SCC) were performed. Results: For ADC, all MPs (SULpeak, SULmax, SULmean, MTV, and TLG) were significantly higher in the TMB‐high group than the non‐TMB‐high group; smoker (odds ratio [OR] = 27.08, p = 0.018), EGFR+ (OR = 0.03, p = 0.033), KRAS+ (OR = 7.98, p = 0.083), high CEA (OR = 33.56, p = 0.029), and high CA125 (OR = 13.68, p = 0.030) were independent predictors of TMB‐high; and all MPs showed significant positive linear correlations with TMB on a log scale, with SULpeak as an independent predictor. However, no significant correlation was observed for SCC. Conclusion: MPs and STMs can predict the TMB level for patients with ADC, and may serve as potential substitutes for TMB with increased value and easy implementation in guiding immunotherapy through noninvasive methods. [ABSTRACT FROM AUTHOR]

This prespecified exploratory analysis evaluated the association of gene expression signatures, tumor mutational burden (TMB), and multiplex immunohistochemistry (mIHC) tumor microenvironment–associated cell phenotypes with clinical outcomes of pembrolizumab in advanced recurrent ovarian cancer (ROC) from the phase II KEYNOTE-100 study. Pembrolizumab-treated patients with evaluable RNA-sequencing (n = 317), whole exome sequencing (n = 293), or select mIHC (n = 125) data were evaluated. The association between outcomes (objective response rate [ORR], progression-free survival [PFS], and overall survival [OS]) and gene expression signatures (T-cell–inflamed gene expression profile [Tcell inf GEP] and 10 non-Tcell inf GEP signatures), TMB, and prespecified mIHC cell phenotype densities as continuous variables was evaluated using logistic (ORR) and Cox proportional hazards regression (PFS; OS). One-sided p -values were calculated at prespecified α = 0.05 for Tcell inf GEP, TMB, and mIHC cell phenotypes and at α = 0.10 for non-Tcell inf GEP signatures; all but Tcell inf GEP and TMB were adjusted for multiplicity. No evidence of associations between ORR and key axes of gene expression was observed. Negative associations were observed between outcomes and Tcell inf GEP-adjusted glycolysis (PFS, adjusted- p = 0.019; OS, adjusted- p = 0.085) and hypoxia (PFS, adjusted- p = 0.064) signatures. TMB as a continuous variable was not associated with outcomes (p > 0.05). Positive associations were observed between densities of myeloid cell phenotypes CD11c+ and CD11c+/MHCII−/CD163−/CD68− in the tumor compartment and ORR (adjusted- p = 0.025 and 0.013, respectively). This exploratory analysis in advanced ROC did not find evidence for associations between gene expression signatures and outcomes of pembrolizumab. mIHC analysis suggests CD11c+ and CD11c+/MHCII−/CD163−/CD68− phenotypes representing myeloid cell populations may be associated with improved outcomes with pembrolizumab in advanced ROC. Clinical trial registration: ClinicalTrials.gov , NCT02674061. [Display omitted] • Association of molecular determinants with outcomes of pembrolizumab in advanced recurrent ovarian cancer was evaluated. • No evidence of associations between ORR and key axes of gene expression was observed with pembrolizumab monotherapy. • T-cell–inflamed GEP-adjusted glycolysis and hypoxia signatures were negatively associated with clinical outcomes. • Continuous TMB was not associated with efficacy, but TMB ≥175 mut/exome (≈ ≥10 mut/Mb) trended toward improved outcomes. • Higher densities of myeloid cell phenotypes CD11c + and CD11c+/MHCII−/CD163−/CD68− trended toward improved efficacy. [ABSTRACT FROM AUTHOR]

BACKGROUND: Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored. OBJECTIVE: This study was formulated to investigate the prognostic value of CAF-related genes in LUAD. METHODS: Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups. RESULTS: Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden. CONCLUSION: An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD. [ABSTRACT FROM AUTHOR]

Background: Therapeutic approaches for extrahepatic cholangiocarcinoma (EHCC) are limited, due to insufficient understanding to biomarkers related to prognosis and drug response. Here, we comprehensively assess the molecular characterization of EHCC with clinical implications. Methods: Whole‐exome sequencing (WES) on 37 tissue samples of EHCC were performed to evaluate genomic alterations, tumor mutational burden (TMB) and microsatellite instability (MSI). Results: Mutation of KRAS (16%) was significantly correlated to poor OS. ERBB2 mutation was associated with improved OS. ERBB2, KRAS, and ARID1A were three potentially actionable targets. TMB ≥10 mutations per megabase was detected in 13 (35.1%) cases. Six patients (16.2%) with MSIsensor scores ≥10 were found. In multivariate Cox analysis, patients with MSIsensor sore exceed a certain threshold (MSIsensor score ≥0.36, value approximately above the 20th percentile as thresholds) showed a significant association with the improved OS (HR = 0.16; 95% CI: 0.056–0.46, p < 0.001), as well as patients with both TMB ≥3.47 mutations per megabase (value approximately above the 20th percentile) and MSIsensor score ≥0.36. Conclusions: TMB and MSI are potential biomarkers associated with better prognosis for EHCC patients. Furthermore, our study highlights important genetic alteration and potential therapeutic targets in EHCC. [ABSTRACT FROM AUTHOR]

Metaplastic breast cancer (MBC) is a rare disease, and there was rarely reported the treatment after recurrence and metastasis. Here, we report the treatment of an adult patient who suffered from MBC with lung, lymph nodes and left pleura metastasis after radical surgery. The next-generation sequencing result demonstrated that it had tumor mutational burden (TMB) of 12.0 Muts/Mb and microsatellite stability. The patient received sintilimab, an immune checkpoint inhibitor, plus chemotherapy and achieved partial response (PR). This is a report of a good outcome of metastatic MBC achieving 24 months of progression-free survival (PFS) and 39 months of overall survival (OS) with a combination therapy of immune checkpoint inhibitor and chemotherapy. Immuno-chemotherapy may have antitumor activity for relapse MBC. TMB may serve as a potential predictor associated with PD-1 inhibitors in MBC and help clinicians make an optimum treatment strategy. [ABSTRACT FROM AUTHOR]

Background: Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis and limited treatment options for metastases. However, new effective regimens are emerging for specific conditions in metastatic ACC. Case presentation: We report a case of a 36-year-old man diagnosed with metastatic ACC who had a large left adrenal mass (158 mm x 112 mm) and multiple metastases in the liver and lungs. Genetic testing revealed a microsatellite instability-high (MSI-H) tumor, a splice mutation in MLH1, and a high tumor mutational burden (TMB). After the left adrenalectomy, he received sequential treatment with a combination of mitotane, etoposide, paraplatin (EPM), and sintilimab. His condition has been assessed as a stable disease since the sixth cycle of the combined regimen. Conclusion: This case highlights the remarkable response of our patient's ACC with MSI-H tumor, MLH1 spice mutation, and high TMB to treatment with a novel combination of EP-M and sintilimab. Our findings suggest a promising therapeutic option for patients with similar molecular profiles. [ABSTRACT FROM AUTHOR]

Background: Programmed death‐ligand 1 (PD‐L1) is an important screening biomarker to select patients with gastric cancer (GC) for optimized treatment, including immune checkpoint inhibitors (ICI). Methods: In this single‐institution retrospective cohort study, patients with metastatic GC with available PD‐L1 results between October 2019 and September 2021 were identified by reviewing their electronic medical records. Genomic data were obtained from the Samsung Medical Center Clinical Sequencing Platform. Results: Among the 399 patients, 276 (69%) had a PD‐L1 combined positive score (CPS) ≥1, 155 (39%) had a CPS between 1 and 5, and 121 (30%) had a CPS ≥5. Of the 121 patients with CPS ≥5, 28 (23%) had a known etiology for "inflamed tumor," with Epstein–Barr virus (EBV) positivity (N = 11) or high tumor mutational burden (TMB) (N = 17), which included microsatellite instability (MSI) (N = 9). PD‐L1 CPS ≥5 was observed in 11/11 (100%) patients with EBV positivity, 9/12 (75%) patients with MSI, and 17/33 (52%) patients with high TMB. For the 108 patients who received ICI therapy, CPS ≥5 was the only predictor significantly associated with survival in multivariable analyses, including TMB, MSI, or EBV. Objective response rate (ORR) was 49% in patients with CPS ≥5, 30% in patients with 1 ≤ CPS <5, and 19% in patients with CPS <1. Among the 31 responders to ICI therapy, 27 (87%) had a CPS of ≥1. Mutations in TET2, IRS2, DOT1L, PTPRT, and LRP1B were associated with a higher ORR (63%–100%), whereas MDC1 mutations were associated with a low ORR (22%). Conclusions: PD‐L1 expression is an independent and sensitive biomarker for ICI therapy. Considering its significant association with several gene alterations, including PIK3CA mutations and MET amplification, combining ICI therapy with other targeted agents may be a promising therapeutic strategy for GC. [ABSTRACT FROM AUTHOR]

Summary: Immune checkpoint inhibitors have transformed cancer therapy, but their optimal use is still constrained by lack of response and toxicity. Biomarkers of response may facilitate drug development by allowing appropriate therapy selection and focusing clinical trial enrollment. However, aside from PD‐L1 staining in a subset of tumors and rarely mismatch repair deficiency, no biomarkers are routinely used in the clinic. In addition, severe toxicities may cause severe morbidity, therapy discontinuation, and even death. Here, we review the state of the field with a focus on our research in therapeutic biomarkers and toxicities from immune checkpoint inhibitors. [ABSTRACT FROM AUTHOR]

Purpose: Triple-negative breast cancer (TNBC) is a molecularly complex and heterogeneous breast cancer subtype with distinct biological features and clinical behavior. Although TNBC is associated with an increased risk of metastasis and recurrence, the molecular mechanisms underlying TNBC metastasis remain unclear. We performed whole-exome sequencing (WES) analysis of primary TNBC and paired recurrent tumors to investigate the genetic profile of TNBC. Methods: Genomic DNA extracted from 35 formalin-fixed paraffin-embedded tissue samples from 26 TNBC patients was subjected to WES. Of these, 15 were primary tumors that did not have recurrence, and 11 were primary tumors that had recurrence (nine paired primary and recurrent tumors). Tumors were analyzed for single-nucleotide variants and insertions/deletions. Results: The tumor mutational burden (TMB) was 7.6 variants/megabase in primary tumors that recurred (n = 9); 8.2 variants/megabase in corresponding recurrent tumors (n = 9); and 7.3 variants/megabase in primary tumors that did not recur (n = 15). MUC3A was the most frequently mutated gene in all groups. Mutations in MAP3K1 and MUC16 were more common in our dataset. No alterations in PI3KCA were detected in our dataset. Conclusions: We found similar mutational profiles between primary and paired recurrent tumors, suggesting that genomic features may be retained during local recurrence. [ABSTRACT FROM AUTHOR]

Conjunctival melanoma (CM) is a rare but aggressive cancer. Over the past decade, molecular studies using rapidly advancing technologies have increasingly improved our understanding of CM genetics. CMs are mainly characterized by dysregulated MAPK and PI3K/AKT/mTOR pathways, driven by commonly mutated (BRAF, NRAS, NF1) or less commonly mutated (KIT, PTEN) genes. Another group of genes frequently mutated in CMs include TERT and ATRX, with known roles in telomere maintenance and chromatin remodeling/epigenetic regulation. Uveal melanoma-related genes (BAP1, SF3B1, GNAQ/11) can also be mutated in CMs, albeit infrequently. Additional CM-related mutated genes have increasingly been identified using more comprehensive genetic analyses, awaiting further confirmation in additional/larger studies. As a tumor arising in a partly sun-exposed mucosal tissue, CM exhibits a distinct genomic profile, including the frequent presence of an ultraviolet (UV) signature (and high mutational load) and also the common occurrence of large structural variations (distributed across the genome) in addition to specific gene mutations. The knowledge gained from CM genetic studies to date has led to new therapeutic avenues, including the use of targeted and/or immuno-therapies with promising outcomes in several cases. Accordingly, the implementation of tumor genetic testing into the routine clinical care of CM patients holds promise to further improve and personalize their treatments. Likewise, a growing knowledge of poor prognosis-associated genetic changes in CMs (NRAS, TERT, and uveal melanoma signature mutations and chromosome 10q deletions) may ultimately guide future strategies for prognostic testing to further improve clinical outcomes (by tailoring surveillance and considering prophylactic treatments in patients with high-risk primary tumors). [ABSTRACT FROM AUTHOR]

The JADE family comprises three members encoded by individual genes and roles for these proteins have been identified in chromatin remodeling, cell cycle progression, cell regeneration and the DNA damage response. JADE family members, and in particular JADE2 have not been studied in any great detail in cancer. Using a series of standard biological and bioinformatics approaches we investigated JADE2 expression in surgically resected non-small cell lung cancer (NSCLC) for both mRNA and protein to examine for correlations between JADE2 expression and overall survival. Additional correlations were identified using bioinformatic analyses on multiple online datasets. Our analysis demonstrates that JADE2 expression is significantly altered in NSCLC. High expression of JADE2 is associated with a better 5-year overall survival. Links between JADE2 mRNA expression and a number of mutated genes were identified, and associations between JADE2 expression and tumor mutational burden and immune cell infiltration were explored. Potential new drugs that can target JADE2 were identified. The results of this biomarker-driven study suggest that JADE2 may have potential clinical utility in the diagnosis, prognosis and stratification of patients into various therapeutically targetable options. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) targeting the cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and programed cell death protein 1 (PD-1) or its ligand PD-L1 have increased the survival and cure rates for patients with many cancer types in various disease settings. However, only 10-40% of cancer patients benefited from these ICIs, of whom ~ 20% have treatment interruption or discontinuation due to immune-related adverse events that can be severe and even fatal. Current efforts in precision immunotherapy are focused on improving biomarker-based patient selection for currently available ICIs and exploring rationale combination and novel strategies to expand the benefit of immunotherapy to more cancer patients. Neoantigens arise from ~ 10% of the non-synonymous somatic mutations in cancer cells, are important targets of T cell-mediated anti-tumor immunity for individual patients. Advances in next generation sequencing technology and computational bioinformatics have enable the identification of genomic alterations, putative neoantigens, and gene expression profiling in individual tumors for personal oncology in a rapid and cost-effective way. Among the genomic biomarkers, defective mismatch DNA repair (dMMR), microsatellite instability high (MSI-H) and high tumor mutational burden (H-TMB) have received FDA approvals for selecting patients for ICI treatment. All these biomarkers measure high neoantigen load and tumor antigenicity, supporting the current development of neoantigen-based personalized cancer vaccines for patients with high TMB tumor. Several studies have shown neoantigen vaccines are feasible, safe and have promising clinical activity in patients with high TMB tumors in both metastatic and adjuvant settings. This review summarizes the emerging data and technologies for neoantigen-based personalized immunotherapy.

Abstract Background We performed an integrated biomarker evaluation in pembrolizumab‐treated patients with R/M HNSCC enrolled in KEYNOTE‐012 or KEYNOTE‐055. The relationship between biomarkers and HPV status was explored. Methods We evaluated PD‐L1 (combined positive score [CPS]), TMB, T‐cell‐inflamed gene expression profile (TcellinfGEP), and HPV status. Associations between biomarkers were evaluated by logistic regression (ORR) and Cox regression (PFS, OS). Results Two hundred and fifty‐seven patients (KEYNOTE‐012, n = 106; KEYNOTE‐055, n = 151) had TMB data available; of these, 254 had PD‐L1 and 236 had TcellinfGEP. TMB, PD‐L1, and TcellinfGEP were each significantly associated with ORR (p

Background: Data supporting high tumor mutational burden (TMB-H) as a lone biomarker for an immune-responsive tumor microenvironment (TME) in metastatic breast cancer (MBC) are weak, yet tumor agnostic approval in TMBH advanced tumors provides immune checkpoint inhibition (ICI) as a clinical option. We evaluated concurrent predictors of immune-responsive and nonresponsive TME within MBC. Methods: Tumor samples from patients with MBC (N=5621) were analyzed by next-generation sequencing of DNA (592-gene panel or whole exome) and RNA (whole transcriptome) at Caris Life Sciences (Phoenix, AZ). TMB-H threshold was set to = 10 muts/Mb. PDL-1 was evaluated using SP142 antibody. Gene expression profiling and RNA deconvolution were used to estimate immune and stromal cell population abundance in the TME, and transcriptomic signature of immunotherapy response (T cell-inflamed score). Results: 461 (8.2%) TMB-H MBC samples were identified. Consistent with prior studies, TMB-H tumors exhibited significant dMMR/MSI-H enrichment (7 vs. 0%, p<0.0001) and PD-L1+ expression (36 vs. 28%, p<0.05) compared to TMB-L. Across all samples, T cell-inflamed scores were weakly correlated with TMB. TMB-H was not associated with significantly increased immune responsive cell types (CD8+ T-cells, NK cells, or B cells) or immune response gene signatures (e.g. antigen presentation), yet positive trends were observed, while immunosuppressive fibroblasts were significantly decreased in TMB-H tumors (0.84-fold change compared to TMB-L, P<0.05). HR+/HER2-breast cancer was the only subtype in which TMB-H tumors exhibited increased T cell-inflamed scores vs. TMB-L. Concurrent PD-L1+ or dMMR/MSI-H with TMB-H was associated with high T cell-inflamed scores in both HR+/HER2- and TNBC. Among several associated biomarkers, B2M mutations and CD274 amplifications were positively associated with T-cell inflamed scores in TMB-H tumors; CDH1 and ERBB2 mutations were negatively associated. Conclusion: High TMB alone does not strongly correlate with immune infiltrate or immune-related gene signatures in MBC. TMB-H predicts T-cell inflamed signature compared to TMB-L in HR+/HER2- tumors only. Along with MSI-H and PD-L1+, several biomarkers, including B2M mutation and CD274 amplification, may help predict ICI benefit amongst TMB-H tumors. Co-occurring biomarkers within TMB-H breast cancer warrant evaluation in larger cohorts for response or resistance to ICI to develop composite predictive biomarkers in MBC. [ABSTRACT FROM AUTHOR]

Neuropilin 1 (NRP1) is a transmembrane glycoprotein, nontyrosine kinase receptor that plays an important role in axonal growth and angiogenesis in the nervous system. Although currently more and more studies have shown that NRP1 plays an important role in some cancers, no systematic pan‐cancer analysis of NRP‐1 has been performed to date. Therefore, we aimed to investigate the associated immune function and prognostic value of NRP1 in 33 tumors of various cancer types. In this study, based on The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Genotype Tissue Expression, cBioportal for cancer genomics, and Human Protein Atlas (HPA databases), various bioinformatics analysis methods were used to investigate the potential carcinogenic effects of NRP1 activation, pan‐cancer analysis of NRP1 expression, and the relationship between NRP1 expression and prognosis indicators including overall survival, disease‐specific survival, disease‐free interval, and progression‐free interval, tumor mutational burden (TMB), and microsatellite instability (MSI). The results showed that NRP1 was highly expressed in most tumors. In addition, NRP1 was found to be positively or negatively correlated with the prognosis of different tumors. Also, the expression of NRP1 was associated with TMB and MSI in in 27 and 21 different types of tumors, respectively, and with DNA methylation in almost all the various types of tumors. The expression of the NRP1 gene was negatively correlated with the infiltration levels of most immune cells. In addition, the correlation between the level of immune cell infiltration and NRP1 expression varied according to immune cell subtype. Our study suggests that NRP1 plays an important role in tumor development and tumor immunity and could potentially be used as a prognostic indicator in a variety of malignancies. Significance statement: Neuropilin 1 (NRP1) as an immunological and prognostic biomarker The increasing demand for targeted tumor treatment can significantly guide clinical practice by comprehensively identifying treatment targets for malignant tumors. In this study, pan‐cancer analysis revealed that NRP1 has an excellent performance as an immunological and prognostic biomarker, so this study will explore these findings in detail. [ABSTRACT FROM AUTHOR]

Simple Summary: The tumor mutational burden (TMB) can be defined as the number of somatic mutations per megabase of the sequenced genome. It correlates with tumor neoantigen burden, T cell infiltration, and response to immune checkpoint inhibitors in many solid tumor types. In addition, TMB has been used as a biomarker of benefit to pembrolizumab in a tissue-agnostic manner; however, in breast cancer limited data exist regarding the true role of this biomarker in clinical practice. This review describes current knowledge about TMB in breast cancer and its potential role as a predictive biomarker of immunotherapy in the advanced setting. We also discuss the need for additional work to better understand how to integrate TMB in clinical practice, including the establishment of the best genomic tool to determine TMB, the ideal TMB cutoff, and the optimal immunotherapy regimen. Tumor mutational burden (TMB) correlates with tumor neoantigen burden, T cell infiltration, and response to immune checkpoint inhibitors in many solid tumor types. Based on data from the phase II KEYNOTE-158 study, the anti-PD-1 antibody pembrolizumab was granted approval for treating patients with advanced solid tumors and TMB ≥ 10 mutations per megabase. However, this trial did not include any patients with metastatic breast cancer; thus, several questions remain unanswered about the true role of TMB as a predictive biomarker of benefit to immune checkpoint inhibitor therapy in breast cancer. In this review, we will discuss the challenges and opportunities in establishing TMB as a predictive biomarker of benefit to immunotherapy in metastatic breast cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we explored the potential of using F-18 fluorodeoxyglucose positron emission tomography (FDG PET), to predict the genetic characteristics and prognosis of patients with stage IV colorectal cancer. We used a machine learning approach to analyze association between image patterns and the tumor mutational burden (TMB), which can provide information for selecting treatment options and predicting prognosis. Our results showed that several radiomic features from the PET images were significantly associated with TMB. Furthermore, we developed a scoring system based on these features, which was found to be a significant predictor of patient survival. This research suggests that FDG PET could be a surrogate marker of TMB in advanced colorectal cancer. It offers a non-invasive method to assess a tumor's genetic characteristics and predict patient outcomes, potentially leading to more personalized and effective treatment strategies. Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC). Methods: Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were selected by logistic regression analysis. The performances of seven ML algorithms to predict high TMB were compared by the area under the receiver's operating characteristic curves (AUCs) and validated by five-fold cross-validation. A PET radiomic score was calculated by averaging the z-score of each radiomic feature. The prognostic power of the PET radiomic score was assessed using Cox proportional hazards regression analysis. Results: Ten significant radiomic features associated with TMB were selected: surface-to-volume ratio, total lesion glycolysis, tumor volume, area, compacity, complexity, entropy, correlation, coarseness, and zone size non-uniformity. The k-nearest neighbors model obtained the good performance for prediction of high TMB (AUC: 0.791, accuracy: 0.814, sensitivity: 0.619, specificity: 0.871). On multivariable Cox regression analysis, the PET radiomic score (Hazard ratio = 4.498, 95% confidential interval = 1.024–19.759; p = 0.046) was a significant independent prognostic factor for OS. Conclusions: This study demonstrates that PET-based radiomics are useful image biomarkers for the prediction of TMB status in stage IV CRC. PET radiomic score, which integrates significant radiomic features, has the potential to predict survival in stage IV CRC patients. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) kills millions of people every year. Recently, FDA and researchers proved the significance of high tumor mutational burden (TMB) in treating solid tumors. But no scholar has constructed a TMBderived computing framework to select sensitive immunotherapy/chemotherapy for the LUAD population with different prognoses. Methods: The datasets were collected from TCGA, GTEx, and GEO. We constructed the TMB-derived immune lncRNA prognostic index (TILPI) computing framework based on TMB-related genes identified by weighted gene co-expression network analysis (WGCNA), oncogenes, and immunerelated genes. Furthermore, we mapped the immune landscape based on eight algorithms. We explored the immunotherapy sensitivity of different prognostic populations based on immunotherapy response, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS) model. Furthermore, the molecular docking models were constructed for sensitive drugs identified by the pRRophetic package, oncopredict package, and connectivity map (CMap). Results: The TILPI computing framework was based on the expression of TMBderived immune lncRNA signature (TILncSig), which consisted of AC091057.1, AC112721.1, AC114763.1, AC129492.1, LINC00592, and TARID. TILPI divided all LUAD patients into two populations with different prognoses. The random grouping verification, survival analysis, 3D PCA, and ROC curve (AUC=0.74) firmly proved the reliability of TILPI. TILPI was associated with clinical characteristics, including smoking and pathological stage. Furthermore, we estimated three types of immune cells threatening the survival of patients based on multiple algorithms. They were macrophage M0, T cell CD4 Th2, and T cell CD4 memory activated. Nevertheless, five immune cells, including B cell, endothelial cell, eosinophil, mast cell, and T cell CD4 memory resting, prolonged the survival. In addition, the immunotherapy response and TIDE model proved the sensitivity of the low-TILPI population to immunotherapy. We also identified seven intersected drugs for the LUAD population with poor prognosis, which included docetaxel, gemcitabine, paclitaxel, palbociclib, pyrimethamine, thapsigargin, and vinorelbine. Their molecular docking models and best binding energy were also constructed and calculated. Conclusions: We divided all LUAD patients into two populations with different prognoses. The good prognosis population was sensitive to immunotherapy, while the people with poor prognosis benefitted from 7 drugs. [ABSTRACT FROM AUTHOR]

Background: Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. Methods: By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. Results: By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. Conclusions: The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling. [ABSTRACT FROM AUTHOR]

Purpose Oligometastatic non-small cell lung cancer (NSCLC) patients have been increasingly regarded as a distinct group that could benefit from local treatment to achieve a better clinical outcome. However, current definitions of oligometastasis are solely numerical, which are imprecise because of ignoring the biological heterogeneity caused by genomic characteristics. Our study aimed to profile the molecular alterations of oligometastatic NSCLC and elucidate its potential difference from polymetastasis. Materials and Methods We performed next-generation sequencing to analyze tumors and paired peripheral blood from 77 oligometastatic and 21 polymetastatic NSCLC patients to reveal their genomic characteristics and assess the genetic heterogeneity. Results We found ERBB2, ALK, MLL4, PIK3CB, and TOP2A were mutated at a significantly lower frequency in oligometastasis compared with polymetastasis. EGFR and KEAP1 alterations were mutually exclusive in oligometastatic group. More importantly, oligometastasis has a unique significant enrichment of apoptosis signaling pathway. In contrast to polymetastasis, a highly enriched COSMIC signature 4 and a special mutational process, COSMIC signature 14, were observed in the oligometastatic cohort. According to OncoKB database, 74.03% of oligometastatic NSCLC patients harbored at least one actionable alteration. The median tumor mutation burden of oligometastasis was 5.00 mutations/Mb, which was significantly associated with smoking, DNA damage repair genes, TP53 mutation, SMARCA4 mutation, LRP1B mutation, ABL1 mutation. Conclusion Our results shall help redefine oligometastasis beyond simple lesion enumeration that will ultimately improve the selection of patients with real oligometastatic state and optimize personalized cancer therapy for oligometastatic NSCLC. [ABSTRACT FROM AUTHOR]

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring. [ABSTRACT FROM AUTHOR]