Abstract P054: Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.MethodsEleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.ResultsStudy results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.ConclusionsIncreasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

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An Exploration of the Tumor Microenvironment Identified a Novel Five-Gene Model for Predicting Outcomes in Bladder Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.642527 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xinjie Li ◽

Jiahao Feng ◽

Yazhou Sun ◽

Xin Li

Keyword(s):

Bladder Cancer ◽

Tumor Microenvironment ◽

Current Knowledge ◽

Gene Signature ◽

Gene Model ◽

Mutational Burden ◽

Common Cancer ◽

Tumor Mutational Burden ◽

Cancer Types ◽

Prognostic Prediction

Bladder cancer (BC) is one of the top ten most common cancer types globally, accounting for approximately 7% of all male malignancies. In the last few decades, cancer research has focused on identifying oncogenes and tumor suppressors. Recent studies have revealed that the interplay between tumor cells and the tumor microenvironment (TME) plays an important role in the initiation and development of cancer. However, the current knowledge regarding its effect on BC is scarce. This study aims to explore how the TME influences the development of BC. We focused on immune and stromal components, which represent the major components of TME. We found that the proportion of immune and stromal components within the TME was associated with the prognosis of BC. Furthermore, based on the scores of immune and stromal components, 811 TME-related differentially expressed genes were identified. Three subclasses with distinct biological features were divided based on these TME-genes. Finally, five prognostic genes were identified and used to develop a prognostic prediction model for BC patients based on TME-related genes. Additionally, we validated the prognostic value of the five-gene model using three independent cohorts. By further analyzing features based on the five-gene signature, higher CD8+ T cells, higher tumor mutational burden, and higher chemosensitivity were found in the low-risk group, which presented a better prognosis. In conclusion, our exploration comprehensively analyzed the TME and identified TME-related prognostic genes for BC, providing new insights into potential therapeutic targets.

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Integrated analysis of the immunological and genetic status in and across cancer types: impact of mutational signatures beyond tumor mutational burden

OncoImmunology ◽

10.1080/2162402x.2018.1526613 ◽

2018 ◽

Vol 7 (12) ◽

pp. e1526613 ◽

Cited By ~ 18

Author(s):

Jan Budczies ◽

Anja Seidel ◽

Petros Christopoulos ◽

Volker Endris ◽

Matthias Kloor ◽

...

Keyword(s):

Integrated Analysis ◽

Mutational Signatures ◽

Mutational Burden ◽

Genetic Status ◽

Tumor Mutational Burden ◽

Cancer Types

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DNA Damage Repair Gene Set as a Potential Biomarker for Stratifying Patients with High Tumor Mutational Burden

Biology ◽

10.3390/biology10060528 ◽

2021 ◽

Vol 10 (6) ◽

pp. 528

Author(s):

To-Yuan Chiu ◽

Ryan Weihsiang Lin ◽

Chien-Jung Huang ◽

Da-Wei Yeh ◽

Yu-Chao Wang

Keyword(s):

Dna Damage ◽

Clinical Practice ◽

Dna Damage Repair ◽

Mutation Status ◽

Gene Set ◽

Mutational Burden ◽

Damage Repair ◽

Potential Biomarker ◽

Tumor Mutational Burden ◽

Cancer Types

Tumor mutational burden (TMB) is a promising predictive biomarker for cancer immunotherapy. Patients with a high TMB have better responses to immune checkpoint inhibitors. Currently, the gold standard for determining TMB is whole-exome sequencing (WES). However, high cost, long turnaround time, infrastructure requirements, and bioinformatics demands have prevented WES from being implemented in routine clinical practice. Panel-sequencing-based estimates of TMB have gradually replaced WES TMB; however, panel design biases could lead to overestimation of TMB. To stratify TMB-high patients better without sequencing all genes and avoid overestimating TMB, we focused on DNA damage repair (DDR) genes, in which dysfunction may increase somatic mutation rates. We extensively explored the association between the mutation status of DDR genes and TMB in different cancer types. By analyzing the mutation data from The Cancer Genome Atlas, which includes information for 33 different cancer types, we observed no single DDR gene/pathway in which mutation status was significantly associated with high TMB across all 33 cancer types. Therefore, a computational algorithm was proposed to identify a cancer-specific gene set as a surrogate for stratifying patients with high TMB in each cancer. We applied our algorithm to skin cutaneous melanoma and lung adenocarcinoma, demonstrating that the mutation status of the identified cancer-specific DDR gene sets, which included only 9 and 14 genes, respectively, was significantly associated with TMB. The cancer-specific DDR gene set can be used as a cost-effective approach to stratify patients with high TMB in clinical practice.

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Multiomics Analysis of Tumor Mutational Burden across Cancer Types

Computational and Structural Biotechnology Journal ◽

10.1016/j.csbj.2021.10.013 ◽

2021 ◽

Author(s):

Lin Li ◽

Long Bai ◽

Huan Lin ◽

Lin Dong ◽

Rumeng Zhang ◽

...

Keyword(s):

Mutational Burden ◽

Tumor Mutational Burden ◽

Cancer Types

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Predictive Biomarkers for Immunotherapy Response Beyond PD-1/PD-L1

ONCOLOGY ◽

10.46883/onc.3408.321 ◽

2020 ◽

Vol 34 (08) ◽

pp. 321-327

Author(s):

Sourat Darabi ◽

David Braxton ◽

Burton Eisenberg ◽

Michael Demeure

Keyword(s):

Predictive Biomarkers ◽

Clinical Use ◽

Human Immune System ◽

Careful Evaluation ◽

Dna Mismatch ◽

Mutational Burden ◽

Tumor Mutational Burden ◽

Cancer Types ◽

Human Immune ◽

Made In

Advances in immuno-oncology over the last several years have led to FDA approvals of novel agents. As our understanding of immune response and its checkpoints has evolved, further advances have been made in treatment for several cancer types. To predict a response to immunotherapy, the initial biomarkers used were expression of the PD-1 receptor and PD-L1, as assessed by immunohistochemistry. More recently, predictive biomarkers have included microsatellite instability, DNA mismatch repair, and tumor mutational burden. Although these markers may be clinically relevant in predicting an immunotherapy response, cancer immunotherapy fails some patients. Improved understanding of the human immune system is necessary, as is a careful evaluation of the methods used to predict and assess response to immunooncology treatments. With the application of therapeutic immune-modulating agents, more comprehensive assays, and associated bioinformatics tools to accurately assess the tumor microenvironment, we may better predict responses to immuno-oncology agents and the ever-increasing complexity of their clinical use.

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Assessment of Tumor Mutational Burden in Pediatric Tumors by Real-Life Whole-Exome Sequencing and In Silico Simulation of Targeted Gene Panels: How the Choice of Method Could Affect the Clinical Decision?

Cancers ◽

10.3390/cancers12010230 ◽

2020 ◽

Vol 12 (1) ◽

pp. 230 ◽

Cited By ~ 1

Author(s):

Hana Noskova ◽

Michal Kyr ◽

Karol Pal ◽

Tomas Merta ◽

Peter Mudry ◽

...

Keyword(s):

Pediatric Oncology ◽

Pediatric Cancer ◽

In Silico ◽

Real Life ◽

Laboratory Techniques ◽

Mutational Burden ◽

Whole Exome ◽

Tumor Mutational Burden ◽

Cancer Types ◽

Gene Panels

Background: Tumor mutational burden (TMB) is an emerging genomic biomarker in cancer that has been associated with improved response to immune checkpoint inhibitors (ICIs) in adult cancers. It was described that variability in TMB assessment is introduced by different laboratory techniques and various settings of bioinformatic pipelines. In pediatric oncology, no study has been published describing this variability so far. Methods: In our study, we performed whole exome sequencing (WES, both germline and somatic) and calculated TMB in 106 patients with high-risk/recurrent pediatric solid tumors of 28 distinct cancer types. Subsequently, we used WES data for TMB calculation using an in silico approach simulating two The Food and Drug Administration (FDA)-approved/authorized comprehensive genomic panels for cancer. Results: We describe a strong correlation between WES-based and panel-based TMBs; however, we show that this high correlation is significantly affected by inclusion of only a few hypermutated cases. In the series of nine cases, we determined TMB in two sequentially collected tumor tissue specimens and observed an increase in TMB along with tumor progression. Furthermore, we evaluated the extent to which potential ICI indication could be affected by variability in techniques and bioinformatic pipelines used for TMB assessment. We confirmed that this technological variability could significantly affect ICI indication in pediatric cancer patients; however, this significance decreases with the increasing cut-off values. Conclusions: For the first time in pediatric oncology, we assessed the reliability of TMB estimation across multiple pediatric cancer types using real-life WES and in silico analysis of two major targeted gene panels and confirmed a significant technological variability to be introduced by different laboratory techniques and various settings of bioinformatic pipelines.

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Analysis of immune checkpoint blockade biomarkers in elderly patients using large-scale cancer genomics data.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.2543 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 2543-2543

Author(s):

Rossin Erbe ◽

Zheyu Wang ◽

Sharon Wu ◽

Joanne Xiu ◽

Neeha Zaidi ◽

...

Keyword(s):

Gene Expression ◽

Elderly Patients ◽

Immune Checkpoint ◽

Large Scale ◽

Life Sciences ◽

Immune Checkpoint Blockade ◽

Mutational Burden ◽

Age Related ◽

Tumor Mutational Burden ◽

Cancer Types

2543 Background: Immune checkpoint blockade (ICB) immunotherapy in some cases elicits striking patient responses, but its efficacy appears to be dependent on several incompletely understood factors. Most studies of ICB therapies in elderly patients have concluded that they received no reduced benefit or even increased benefit compared to the younger patients analyzed, despite the systemic age-related immune changes that might be expected to produce a less effective immune response, such as loss of the capacity to generate new naive T cells. To understand and apply these results, it is necessary to investigate the relationship of age and the immune tumor microenvironment. Methods: We apply bioinformatics methods to genomic, transcriptomic, and clinical data from 9,523 patients across 31 cancer types from TCGA, 15,557 patients with breast, colon, or head and neck cancers from Caris Life Sciences, and 37,961 patients across 8 cancer types collected by GENIE. From these data we apply multivariate linear models across and within individual tumor types to estimate age-related associations to tumor mutational burden (TMB), T cell receptor diversity (miTCR), differential gene expression (edgeR), pathway enrichment (mSigDB and fgsea), and immune cell type infiltration (Quantiseq and MIXTURE). Results: Our analysis of large-scale molecular and clinical databases associates patient age with changes in several major biomarkers of ICB response. Notably, a robust correlation between increased tumor mutational burden and age was found across three different large cohorts (TCGA, Caris Life Sciences, and GENIE) in most ICB-approved cancer types. In the TCGA data, TMB increased with age pan-cancer (p < 1x10-16) and in 7 of 9 ICB-approved cancer types. These associations were validated in the larger cohort of patient samples in GENIE, which demonstrated correlations between increased TMB levels and patient age in all eight ICB-approved cancer types assayed (Table), as well as in the Caris colorectal (q < 0.001) and breast (q < 0.001) cancer cohorts. Significant associations of age to other biomarkers of ICB response (checkpoint gene expression, immune infiltration, and immune related pathway signaling) will be presented. Conclusions: These results provide context for the efficacy of ICB in elderly patients, highlight potential biomarkers for the treatment of elderly patients with immunotherapies, and strongly suggest the value of large-scale prospective study of elderly cancer patients treated with ICB.[Table: see text]

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Pan-cancer analysis of tumor mutational burden and homologous recombination DNA damage repair by targeted next-generation sequencing

10.21203/rs.3.rs-49293/v1 ◽

2020 ◽

Author(s):

Hai-Yun Wang ◽

Ling Deng ◽

Ying-Qing Li ◽

Xiao Zhang ◽

Ya-Kang Long ◽

...

Keyword(s):

Dna Damage ◽

Treatment Response ◽

Targeted Next Generation Sequencing ◽

Mutational Burden ◽

Damage Repair ◽

Tumor Mutational Burden ◽

Cancer Types ◽

Gene Panels ◽

Pan Cancer ◽

Generation Sequencing

Abstract Background: Current variability in methods for tumor mutational burden (TMB) estimation and reporting urges the need for a homogeneous TMB assessment. Here we compared the TMB distributions in different cancer types using two customized targeted panels commonly used in clinical practice. Methods: TMB spectrum of the 295- and 1021-Gene panels in multiple cancer types were compared using targeted next-generation sequencing (NGS). Then the TMB distributions across a diverse cohort of 2,332 cancer cases were investigated for their associations to clinical features. Treatment response data was collected for 222 patients who received immune-checkpoint inhibitors (ICIs) and their homologous recombination DNA damage repair (HR-DDR) and PD-L1 expression were additionally assessed, and compared with TMB and response rate. Results: The median TMB between the gene panels were similar despite wide range in TMB values. Highest TMB was 8 and 10 in patients with squamous cell carcinoma and esophageal carcinoma according to the classification of histopathology and cancer types, respectively. Patients with high TMB and HR-DDR positive status could benefit from ICIs therapies (23 patients versus 7 patients with treatment response, P = 0.004). Additionally, PD-L1 expression was not associated with TMB and treatment response among patients receiving ICIs. Conclusions: Targeted NGS assays demonstrated advantageous ability to evaluate TMB in pan-cancer samples as a tool to predict response to ICIs. Also, TMB integrated with HR-DDR positive status could be a significant biomarker for predicting ICIs response in patients.

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