Analysis of tumor mutational burden: correlation of five large gene panels with whole exome sequencing

Enhancing the quality of panel-based tumor mutation burden assessment: a comprehensive study of real-world and in-silico outcomes

Targeted panel-based tumor mutation burden (TMB) assays are widely employed to guide immunotherapy for patients with solid tumors. However, the accuracy and consistency of this method can be compromised due to the variability in technical details across different laboratories, particularly in terms of panel size, somatic mutation detection and TMB calculation rules. Currently, systematic evaluations of the impact of these technical factors on existing assays and best practice recommendations remain lacking. We assessed the performance of 50 participating panel-based TMB assays involving 38 unique methods using cell line samples. In silico experiments utilizing TCGA MC3 datasets were performed to further dissect the impact of technical factors. Here we show that the panel sizes beyond 1.04 Mb and 389 genes are necessary for the basic discrete accuracy, as determined by over 40,000 synthetic panels. The somatic mutation detection should maintain a reciprocal gap of recall and precision less than 0.179 for reliable psTMB calculation results. The inclusion of synonymous, nonsense and hotspot mutations could enhance the accuracy of panel-based TMB assay. A 5% variant allele frequency cut-off is suitable for TMB assays using tumor samples with at least 20% tumor purity. In conclusion, this multicenter study elucidates the major technical factors as sources of variability in panel-based TMB assays and proposed comprehensive recommendations for the enhancement of accuracy and consistency. These findings will assist clinical laboratories in optimizing the methodological details through bioinformatic experiments to enhance the reliability of panel-based methods.

Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies

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.

Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis

Tumor mutational burden (TMB), when at a high level, is an emerging indicative factor of sensitivity to immune checkpoint inhibitors. Previous studies have shown that the more affordable and accurate targeted panels can be used to measure TMB as a substitute for whole exome sequencing (WES). However, additional processes, such as hotspot mutations exclusion and TMB adjustment, are usually required to deal with the effect of the limited panel sizes. A comprehensive investigation of the effective factors is needed for accurate TMB estimation by targeted panels. In this study, we quantitatively evaluated the variances of TMB values calculated by WES and targeted panels using 10,000 simulated targeted panels with panel sizes ranging from 0.2 to 3.1 million bases. With The Cancer Genome Atlas (TCGA) cancer samples and mutation profiles, we fixed regressions on WES-TMBs and panel-TMBs to assess the performance of a given targeted panel. Panel size was found as one of the major effective factors of TMB estimation. Meanwhile, by investigating the well-performing small panels that reported TMB values similar to those of WES, we demonstrated the evidence of the cancer type-specific impacts of genes on TMB estimation and identified high-impact gene sets for different cancer types based on the TCGA data. This study revealed the quantitative correlations between TMB variance and panel size, and the potential impacts of individual genes on TMB estimation. Our results suggested that for cancer patients diagnosed using targeted panels, it would be highly beneficial to have the capability to directly measure TMB from the targeted sequencing data. This would greatly assist in making decisions regarding the use of immunotherapies.

Understanding the Impact of Population and Cancer Type on Tumor Mutation Burden Scores: A Comprehensive Whole-Exome Study in Cancer Patients From India

PURPOSE

The purpose of this study was to understand the impact of population diversity and geographic variation on tumor mutation burden (TMB) scores across cancers and its implication on stratification of patients for immune checkpoint inhibitor (ICI) therapy.

MATERIALS AND METHODS

This retrospective study used whole-exome sequencing (WES) to profile 1,233 Indian patients with cancer across 30 different cancer types and to estimate their TMB scores. A WES-based pipeline was adopted, along with an indigenously developed strategy for arriving at true somatic mutations. A robust unsupervised machine learning approach was used to understand the distribution of TMB scores across different populations and within the population.

RESULTS

The results of the study showed a biphasic distribution of TMB scores in most cancers, with different threshold scores across cancer types. Patients with cancer in India had higher TMB scores compared with the Caucasian patients. We also observed that the TMB score value at 90th percentile (predicting high efficacy to ICI) was high in four different cancer types (sarcoma, ovary, head and neck, and breast) in the Indian cohort as compared with The Cancer Genome Atlas or public cohort. However, in lung and colorectal cancers, the TMB score distribution was similar between the two population cohorts.

CONCLUSION

The findings of this study indicate that it is crucial to benchmark both cancer-specific and population-specific TMB distributions to establish a TMB threshold for each cancer in various populations. Additional prospective studies on much larger population across different cancers are warranted to validate this observation to become the standard of care.

Reliability of panel-based mutational signatures for immune-checkpoint-inhibition efficacy prediction in non-small cell lung cancer

OBJECTIVES

Mutational signatures (MS) are gaining traction for deriving therapeutic insights for immune checkpoint inhibition (ICI). We asked if MS attributions from comprehensive targeted sequencing assays are reliable enough for predicting ICI efficacy in non-small cell lung cancer (NSCLC).

METHODS

Somatic mutations of m = 126 patients were assayed using panel-based sequencing of 523 cancer-related genes. In silico simulations of MS attributions for various panels were performed on a separate dataset of m = 101 whole genome sequenced patients. Non-synonymous mutations were deconvoluted using COSMIC v3.3 signatures and used to test a previously published machine learning classifier.

RESULTS

The ICI efficacy predictor performed poorly with an accuracy of 0.51-0.09+0.09, average precision of 0.52-0.11+0.11, and an area under the receiver operating characteristic curve of 0.50-0.09+0.10. Theoretical arguments, experimental data, and in silico simulations pointed to false negative rates (FNR) related to panel size. A secondary effect was observed, where deconvolution of small ensembles of point mutations lead to reconstruction errors and misattributions.

CONCLUSION

MS attributions from current targeted panel sequencing are not reliable enough to predict ICI efficacy. We suggest that, for downstream classification tasks in NSCLC, signature attributions be based on whole exome or genome sequencing instead.

Associations of tissue tumor mutational burden and mutational status with clinical outcomes in KEYNOTE-042: pembrolizumab versus chemotherapy for advanced PD-L1-positive NSCLC

BACKGROUND

We evaluated whether tissue tumor mutational burden (tTMB) and STK11, KEAP1, and KRAS mutations have clinical utility as biomarkers for pembrolizumab monotherapy versus platinum-based chemotherapy in patients with programmed death ligand- 1 (PD-L1)-positive (tumor proportion score ≥1%) advanced/metastatic non-small-cell lung cancer (NSCLC) without EGFR/ALK alterations in the phase III KEYNOTE-042 trial.

PATIENTS AND METHODS

This retrospective exploratory analysis assessed prevalence of tTMB and STK11, KEAP1, and KRAS mutations determined by whole-exome sequencing of tumor tissue and matched normal DNA and their associations with outcomes in KEYNOTE-042. Clinical utility of tTMB was assessed using a prespecified cut point of 175 mutations/exome.

RESULTS

Of 793 patients, 345 (43.5%) had tTMB ≥175 mutations/exome and 448 patients (56.5%) had tTMB <175 mutations/exome. No association was observed between PD-L1 expression and tTMB. Continuous tTMB score was associated with improved overall survival (OS) and progression-free survival among patients receiving pembrolizumab (Wald test, one-sided P < 0.001) but not those receiving chemotherapy (Wald test, two-sided P > 0.05). tTMB ≥175 mutations/exome was associated with improved outcomes for pembrolizumab versus chemotherapy, whereas tTMB <175 mutations/exome was not {OS: hazard ratio, 0.62 [95% confidence interval (CI) 0.48-0.80] and 1.09 (95% CI 0.88-1.36); progression-free survival: 0.75 (0.59-0.95) and 1.27 (1.04-1.55), respectively}. Improved OS [hazard ratio (95% CI)] for pembrolizumab versus chemotherapy was observed regardless of STK11 [STK11 mutant (n = 33): 0.37 (0.16-0.86), STK11 wild-type (n = 396): 0.83 (0.65-1.05)]; KEAP1 [KEAP1 mutant (n = 64): 0.75 (0.42-1.35), KEAP1 wild-type (n = 365): 0.78 (0.61-0.99)], or KRAS [KRAS mutant (n = 69): 0.42 (0.22-0.81); KRAS wild-type (n = 232): 0.86 (0.63-1.18)] mutation status.

CONCLUSION

tTMB with a cut point of ≥175 mutations/exome is a potential predictive biomarker for pembrolizumab monotherapy for advanced/metastatic PD-L1 tumor proportion score ≥1% NSCLC. Pembrolizumab is a standard first-line treatment in this setting regardless of STK11, KEAP1, or KRAS mutation status.

Evolution of predictive and prognostic biomarkers in the treatment of advanced gastric cancer

Despite new therapeutic options, advanced gastric cancer remains associated with a poor prognosis compared with other cancers. Recent gains in the treatment of gastric cancer were accompanied by the identification of novel biomarkers associated with various cellular pathways and corresponding diagnostic technologies. It is expected that the standardization of clinical workflow and technological refinements in biomarker assessment will support greater personalization and further improve treatment outcomes. In this article, we review the current state of prognostic and predictive biomarkers in gastric cancer.

The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.

Neoadjuvant Chemo-Immunotherapy for Locally Advanced Non-Small-Cell Lung Cancer: A Review of the Literature

Non-small cell lung cancer accounts for approximately 80–85% of all lung cancers and at present represents the main cause of cancer death among both men and women. To date, surgery represents the cornerstone; nevertheless, around 40% of completely resected patients develop disease recurrence. Therefore, combining neoadjuvant chemo-immunotherapy and surgery might lead to improved survival. Immunotherapy is normally well tolerated, although significant adverse reactions have been reported in certain patients treated with inhibitors of immune checkpoints. In this review, we explore the current literature on the use of neoadjuvant chemo-immunotherapy followed by surgery for treatment of locally advanced non-small-cell lung cancer, with particular attention to the histological aspects, ongoing trials, and the most common surgical approaches. In conclusion, neoadjuvant immunotherapy whether combined or not with chemotherapy reveals a promising survival benefit for patients with advanced non-small-cell lung cancer; nevertheless, more data remain necessary to identify the best candidates for neoadjuvant regimens.

A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape

Background

Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.

Methods

We constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.

Results

This study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.

Conclusions

The mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups’ differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01024-y.

Tumour mutational burden: an overview for pathologists

Cancer immunotherapy holds great promise and has shown durable responses in many patients; however, these responses are not uniform in all patients or all tumour streams. There is an ongoing clinical need for objective diagnostic biomarkers to identify patients that will respond to immunotherapies. Tumour mutational burden (TMB) is a diagnostic biomarker that can stratify cancer patients for response to immune checkpoint inhibitor therapies. It is commonly defined as the average number of somatic mutations per megabase in a tumour exome. Here we describe the TMB biomarker, how it is determined, its underlying molecular basis, the relationship to neoantigens and the issues around its clinical use. This overview is directed toward practising pathologists wishing to be informed of this predictive biomarker.

Xenograft cancer vaccines prepared from immunodeficient mice increase tumor antigen diversity and host T cell efficiency against colorectal cancers

Autologous cancer vaccines (ACVs) are a desirable approach for personalized medicine, but the efficiency of ACVs remains unsatisfactory due to their low immunogenicity. This study developed a platform that can enhance the immunogenicity of ACVs by transplanting the tumors into immunodeficient mice. The CT26 cell line was inoculated into severe combined immunodeficient mice (SCID) for vaccine preparation where escalates tumor development, subsequently diversifying the tumor antigenic topology. CT26/SCID cancer vaccines significantly inhibited tumor growth, increased the amount of tumor infiltrating lymphocytes, and triggered Th-1 predominant immune responses. Tumor antigenic profiles of CT26/SCID cells were further analyzed by liquid chromatography-tandem mass spectrometry. Compared to CT26 parental cells, a total of 428 differentially expressed proteins (DEPs) were detected. These DEPs revealed that CT26/SCID cells overexpressed several novel therapeutic targets, including KNG1, apoA-I and, β2-GPI, which can trigger cytotoxic T cells towards Th-1 predominant immune responses and directly suppress proliferation in tumors. CT26/SCID cancer vaccines can be easily manufactured, while traits of triggering stronger antigen-specific Th-1 immune activity against tumors, are retained. Results of this study provide an effective proof-of-concept of an ACV for personalized cancer immunotherapy.

Mismatch Repair Deficiency and Somatic Mutations in Human Sinonasal Tumors

Simple Summary

Sinonasal carcinomas are rare tumors with an overall poor prognosis. Due to limitations in local therapeutic approaches, systemic neo-adjuvant or adjuvant therapies are becoming increasingly important in order to improve patient outcome. This study aimed to examine potentially therapeutic targetable molecular alterations in different sinonasal tumors, including deficiency in mismatch repair proteins and microsatellite instability as well as driver mutations. According to our results, immunohistochemical (IHC) analysis of mismatch repair (MMR) proteins and sequencing-based panel analysis should be integrated into the diagnostics of clinically aggressive inverted sinonasal papilloma (ISP) and sinonasal squamous cell carcinoma (SNSCC) in order to enable the therapeutic possibility of a targeted therapy.

Abstract

Due to limitations in local therapy approaches for sinonasal tumors, improvement in systemic therapies plays a pivotal role for prolongation of the patient’s survival. The aim of this study was to examine potential biomarkers, including deficiency in mismatch repair proteins (dMMR)/microsatellite instability (MSI-H) in sinonasal cancers and their precancerous lesions. A comprehensive analysis of 10 sinonasal cancer cell lines by whole exome sequencing, screening 174 sinonasal tumors by immunohistochemistry (IHC) for mismatch repair deficiency and next generation sequencing (NGS) of 136 tumor samples revealed a dMMR/MSI-H sinonasal squamous cell carcinoma (SNSCC) cell line based on a somatic missense mutation in MLH1 and an overall frequency of dMMR/MSI-H SNSCC of 3.2% (4/125). Targetable EGFR mutations were found in 89.3% (25/28) of inverted sinonasal papilloma (ISP) and in 60% (6/10) of ISP-associated carcinomas. While PIK3CA and EGFR mutations were not mutually exclusive, KRAS mutated tumors were an EGFR-wildtype. The effect of potential driver mutations in FGFR2, FGFR3, BRAF, HRAS, MAP2K1, PTEN, NOTCH1 and CARD11 need further investigation. Our results suggest that biomarker testing, including MMR-IHC and NGS panel analysis, should be integrated into the diagnostics of clinically aggressive ISPs and SNSCC to assess prognosis and facilitate therapeutic decisions.

Evaluation of tumor mutational burden in small early hepatocellular carcinoma and progressed hepatocellular carcinoma

While researchers know that tumor mutational burden (TMB) is low in hepatocellular carcinoma (HCC), prior studies have not investigated TMB in cirrhosis, small early HCC and progressed HCC. HCC (n = 18) and cirrhosis (n = 6) cases were identified. TMB was determined by a 1.7 megabase, 409-gene next-generation sequencing panel. TMB values were defined as the number of nonsynonymous variants per megabase of sequence. There was no significant difference between cirrhosis versus small early HCC or between cohorts when stratified by size, early versus progressed, differentiation or morphology. There was a significant difference between cirrhosis and small early HCC versus progressed HCC (p = 0.045), suggesting TMB may be related to HCC progression. TMB similarities in small early HCC and background cirrhosis suggest TMB is not a useful tool for diagnosing small early HCC. Additional study is needed to address TMB in histological and molecular subsets of HCC.

PD-L1 Expression Is Significantly Associated with Tumor Mutation Burden and Microsatellite Instability Score

Simple Summary

Biomarkers for predicting the response to immune checkpoint blockade (ICB) includes programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC), microsatellite instability (MSI), and tumor mutation burden (TMB). This study investigated the relationship of these biomarkers using comprehensive cancer panel assay (CCPA) with >500 genes in 588 advanced cancer patients. The work demonstrates that PD-L1 expression is significantly associated with TMB and MSI score, according to primary tumor origin.

Abstract

Programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC), microsatellite instability (MSI), and tumor mutation burden (TMB) have been proposed as a predictive biomarker to predict response to immune checkpoint blockade (ICB). We aimed to find the relationship of PD-L1 IHC to TMB and MSI using a comprehensive cancer panel assay (CCPA) with >500 genes in advanced cancer patients. CCPA results from 588 archived tissue samples were analyzed for TMB and MSI. In seven samples, whole exome sequencing confirmed TMB with Pearson’s correlation coefficient of 0.972 and all MSI-high cases were validated by pentaplex PCR. Association of TMB and MSI with their corresponding PD-L1 IHC was analyzed. The median TMB value of 588 cases was 8.25 mutations (mut)/Mb (range 0–426.8) with different distributions among the tumor types, with high proportions of high-TMB (>10mut/Mb) in tumors from melanoma, colorectal, gastric, and biliary tract. The TMB values significantly correlated with PD-L1 expression, and this correlation was prominent in gastric and biliary tract cancers. Moreover, the MSI score, the proportion of unstable MSI sites to total assessed MSI sites, showed a significant correlation with the TMB values and PD-L1 scores. This study demonstrates that PD-L1 expression is significantly associated with TMB and MSI score and this correlation depends on the location of the primary tumor.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Tremelimumab plus durvalumab retreatment and 4-year outcomes in patients with mesothelioma: a follow-up of the open label, non-randomised, phase 2 NIBIT-MESO-1 study

BACKGROUND

The NIBIT-MESO-1 study demonstrated the efficacy and safety of tremelimumab combined with durvalumab in patient with unresectable mesothelioma followed up for a median of 52 months [IQR 49-53]. Here, we report 4-year survival and outcomes after retreatment, and the role of tumour mutational burden (TMB) in identifying patients who might have a better outcome in response to combined therapy.

METHODS

NIBIT-MESO-1 was an open-label, non-randomised, phase 2 trial of patients with unresectable pleural or peritoneal mesothelioma who received intravenous tremelimumab (1 mg/kg bodyweight) and durvalumab (20 mg/kg bodyweight) every 4 weeks for four doses, followed by maintenance intravenous durvalumab at the same dose and schedule for nine doses. In this follow-up study, patients with disease progression following initial clinical benefit-ie, a partial repsonse or stable disease-were eligible for retreatment and with the same doses and schedules for tremelimumab and durvalumab as used in the NIBIT-MESO-1 trial. The primary endpoint, immune-related objective response rate, was evaluated per immune-related modified Response Evaluation Criteria in Solid Tumors (RECIST) or immune-related RECIST 1.1 criteria for patients with pleural or peritoneal malignant mesothelioma, respectively. Key secondary endpoints were overall survival and safety, and TMB was also evaluated post hoc in patients who had tumour tissue available before treatment. The intention-to-treat population was used for analysis of all efficacy endpoints. This study is registered with ClinicalTrials.gov, number NCT02588131.

FINDINGS

40 patients were enrolled in the NIBIT-MESO-1 study between Oct 30, 2015, and Oct 12, 2016. At data cut-off, April 30, 2020, five (13%) of 40 patients were alive, and 35 (88%) patients had died of progressive disease. At a median follow-up of 52 months (IQR 49-53), median overall survival was 16·5 months (95% CI 13·7-19·2). Survival was 20% (eight of 40 patients) at 36 months and 15% (six of 40 patients) and 48 months. 17 (43%) of 40 patients met the criteria for enrolment in the retreatment study and were retreated with at least one dose of tremelimumab and durvalumab. No immune-related objective responses were observed in the 17 retreated patients. Seven (41%) of 17 patients achieved immune-related stable disease. From the start of retreatment to a median follow-up of 24 months (22·0-25·0), median overall survival was 12·5 months (95% CI 0·0-25·8), and survival at 12 months was 52·9%, at 18 months was 35·3%, and at 24 months was 23·5%. There were no grade 3-4 immune-related adverse events in the retreatment cohort. In a post-hoc analysis of 28 patients for whom tumour tissue before treatment was available, patients with a TMB higher than the median value of 8·3 mutations per Mb had a higher median overall survival compared with patients with TMB below the median value, but this difference was non-significant. Moreover, when patients were additionally stratified for ICI retreatment (n=13), there was a significant difference in survival between those with a TMB higher than the median of 8·3 mutations per Mb and those with TMB lower than the median in the retreated cohort (41·3 months vs 17·4 months; p=0·02).

INTERPRETATION

Tremelimumab combined with durvalumab was associated with long-term survival in patients with mesothelioma. Retreatment was safe and resulted in clinically meaningful outcomes, thus suggesting its potential application in the clinical practice of mesothalioma patients.

FUNDING

NIBIT Foundation, Fondazione AIRC, AstraZeneca.

Paired analysis of tumor mutation burden calculated by targeted deep sequencing panel and whole exome sequencing in non-small cell lung cancer

Owing to rapid advancements in NGS (next generation sequen-cing), genomic alteration is now considered an essential pre-dictive biomarkers that impact the treatment decision in many cases of cancer. Among the various predictive biomarkers, tumor mutation burden (TMB) was identified by NGS and was con-sidered to be useful in predicting a clinical response in cancer cases treated by immunotherapy. In this study, we directly com-pared the lab-developed-test (LDT) results by target sequencing panel, K-MASTER panel v3.0 and whole-exome sequencing (WES) to evaluate the concordance of TMB. As an initial step, the reference materials (n = 3) with known TMB status were used as an exploratory test. To validate and evaluate TMB, we used one hundred samples that were acquired from surgically resected tissues of non-small cell lung cancer (NSCLC) patients. The TMB of each sample was tested by using both LDT and WES methods, which extracted the DNA from samples at the same time. In addition, we evaluated the impact of capture re-gion, which might lead to different values of TMB; the evalu-ation of capture region was based on the size of NGS and target sequencing panels. In this pilot study, TMB was evalu-ated by LDT and WES by using duplicated reference samples; the results of TMB showed high concordance rate (R² = 0.887). This was also reflected in clinical samples (n = 100), which showed R² of 0.71. The difference between the coding sequence ratio (3.49%) and the ratio of mutations (4.8%) indicated that the LDT panel identified a relatively higher number of mutations. It was feasible to calculate TMB with LDT panel, which can be useful in clinical practice. Furthermore, a customized approach must be developed for calculating TMB, which differs according to cancer types and specific clinical settings.

Canine Oncopanel: A capture-based, NGS platform for evaluating the mutational landscape and detecting putative driver mutations in canine cancers

Canine cancer, a significant cause of mortality in domestic dogs, is a powerful comparative model for human cancers. Revealing genetic alterations driving the oncogenesis of canine cancers holds great potential to deepen our understanding of the cancer biology, guide therapeutic development, and improve cancer management in both dogs and people. Next generation sequencing (NGS) based-diagnostic panels have been routinely used in human oncology for the identification of clinically-actionable mutations, enabling tailored treatments based on the individual's unique mutation profiles. Here, we report the development of a comprehensive canine cancer gene panel, the Canine Oncopanel, using a hybridization capture-based targeted NGS method. The Canine Oncopanel allows deep sequencing of 283 cancer genes and the detection of somatic mutations within these genes. Vigorous optimization was performed to achieve robust, high-standard performance using metrics of similar cancer panels in human oncology as benchmarks. Validation of the Canine Oncopanel on reference tumour samples with known mutations demonstrated that it can detect variants previously identified by alternative methods, with high accuracy and sensitivity. Putative drivers were detected in over 90% of clinical samples, showing high sensitivity. The Canine Oncopanel is suitable to map mutation profiles and identify putative driver mutations across common and rare cancer types in dogs. The data generated by the Canine Oncopanel presents a rich resource of putative oncogenic driver mutations and potential clinically relevant markers, paving the way for personalized diagnostics and precision medicine in canine oncology.

Prevalence and Landscape of Actionable Genomic Alterations in Renal Cell Carcinoma

PURPOSE

We report our experience with next-generation sequencing to characterize the landscape of actionable genomic alterations in renal cell carcinoma (RCC).

EXPERIMENTAL DESIGN

A query of our institutional clinical sequencing database (MSK-IMPACT) was performed that included tumor samples from 38,468 individuals across all cancer types. Somatic variations were annotated using a precision knowledge database (OncoKB) and the available clinical data stratified by level of evidence. Alterations associated with response to immune-checkpoint blockade (ICB) were analyzed separately; these included DNA mismatch repair (MMR) gene alterations, tumor mutational burden (TMB), and microsatellite instability (MSI). Data from The Cancer Genome Atlas (TCGA) consortium as well as public data from several clinical trials in metastatic RCC were used for validation purposes. Multiregional sequencing data from the TRAcking Cancer Evolution through Therapy (TRACERx) RENAL cohort were used to assess the clonality of somatic mutations.

RESULTS

Of the 753 individuals with RCC identified in the MSK-IMPACT cohort, 115 showed evidence of targetable alterations, which represented a prevalence of 15.3% [95% confidence interval (CI), 12.7%-17.8%). When stratified by levels of evidence, the alterations identified corresponded to levels 2 (11.3%), 3A (5.2%), and 3B (83.5%). A low prevalence was recapitulated in the TCGA cohort at 9.1% (95% CI, 6.9%-11.2%). Copy-number variations predominated in papillary RCC tumors, largely due to amplifications in the MET gene. Notably, higher rates of actionability were found in individuals with metastatic disease (stage IV) compared with those with localized disease (OR, 2.50; 95% CI, 1.16-6.16; Fisher's P = 0.01). On the other hand, the prevalence of alterations associated with response to ICB therapy was found to be approximately 5% in both the MSK-IMPACT and TCGA cohorts and no associations with disease stage were identified (OR, 1.35; 95% CI, 0.46-5.40; P = 0.8). Finally, multiregional sequencing revealed that the vast majority of actionable mutations occurred later during tumor evolution and were only present subclonally in RCC tumors.

CONCLUSIONS

RCC harbors a low prevalence of clinically actionable alterations compared with other tumors and the evidence supporting their clinical use is limited. These aberrations were found to be more common in advanced disease and seem to occur later during tumor evolution. Our study provides new insights on the role of targeted therapies for RCC and highlights the need for additional research to improve treatment selection using genomic profiling.

Multi-Gene Testing Overview with a Clinical Perspective in Metastatic Triple-Negative Breast Cancer

Next-generation sequencing (NGS) is the technology of choice for the routine screening of tumor samples in clinical practice. In this setting, the targeted sequencing of a restricted number of clinically relevant genes represents the most practical option when looking for genetic variants associated with cancer, as well as for the choice of targeted treatments. In this review, we analyze available NGS platforms and clinical applications of multi-gene testing in breast cancer, with a focus on metastatic triple-negative breast cancer (mTNBC). We make an overview of the clinical utility of multi-gene testing in mTNBC, and then, as immunotherapy is emerging as a possible targeted therapy for mTNBC, we also briefly report on the results of the latest clinical trials involving immune checkpoint inhibitors (ICIs) and TNBC, where NGS could play a role for the potential predictive utility of homologous recombination repair deficiency (HRD) and tumor mutational burden (TMB).

TMB in NSCLC: A Broken Dream?

Although immune checkpoint inhibitors have changed the treatment paradigm of a variety of cancers, including non-small-cell lung cancer, not all patients respond to immunotherapy in the same way. Predictive biomarkers for patient selection are thus needed. Tumor mutation burden (TMB), defined as the total number of somatic/acquired mutations per coding area of a tumor genome (Mut/Mb), has emerged as a potential predictive biomarker of response to immune checkpoint inhibitors. We found that the limited use of TMB in clinical practice is due to the difficulty in its detection and compounded by several different biological, methodological and economic issues. The incorporation of both TMB and PD-L1 expression or other biomarkers into multivariable predictive models could result in greater predictive power.

Influence of low tumor content on tumor mutational burden estimation by whole-exome sequencing and targeted panel sequencing

BACKGROUND

Tumor mutational burden (TMB) is a promising biomarker for stratifying patient subpopulation who would benefit from immune checkpoint blockade (ICB) therapies. Although great efforts have been made for standardizing TMB measurement, mutation calling and TMB quantification can be challenging in samples with low tumor content including liquid biopsies. The effect of varying tumor content on TMB estimation by different assay methods has never been systematically investigated.

METHOD

We established a series of reference standard DNA samples derived from 11 pairs of tumor-normal matched human cell lines across different cancer types. Each tumor cell line was mixed with its matched normal at 0% (control), 1%, 2%, 5%, and 10% mass-to-mass ratio to mimic the clinical samples with low tumor content. TMB of these reference standards was evaluated by both ∼1000× whole-exome sequencing (wesTMB) and targeted panel sequencing (psTMB) at four different vendors. Both regression and classification analyses of TMB were performed for theoretical investigation and clinical practice purposes.

RESULTS

Linear regression model was established that demonstrated in silico psTMB determined by regions of interest (ROI) as a great representative of wesTMB based on TCGA dataset. It was also true in our reference standard samples as the predicted psTMB interval based on the observed wesTMB captured the intended 90% of the in silico psTMB values. Although ∼1000× deep WES was applied, reference standard samples with less than 5% of tumor proportions are below the assay limit of detection (LoD) of wesTMB quantification. However, predicted wesTMB based on observed psTMB accurately classify (>0.97 AUC) for TMB high and low patient stratification even in samples with 2% of tumor content, which is more clinically relevant, as TMB determination should be a qualitative assay for TMB high and low patient classification. One targeted panel sequencing vendor using an optimized blood psTMB pipeline can further classify TMB status accurately (>0.82 AUC) in samples with only 1% of tumor content.

CONCLUSIONS

We developed a linear model to establish the quantitative correlation between wesTMB and psTMB. A set of DNA reference standards was produced in aid to standardize TMB measurements in samples with low tumor content across different targeted sequencing panels. This study is a significant contribution aiming to harmonize TMB estimation and extend its future application in clinical samples with low tumor content including liquid biopsy.

Mutational burden, MHC-I expression and immune infiltration as limiting factors for in situ vaccination by TNFα and IL-12 gene electrotransfer

In situ vaccination is a promising immunotherapeutic approach, where various local ablative therapies are used to induce an immune response against tumor antigens that are released from the therapy-killed tumor cells. We recently proposed using intratumoral gene electrotransfer for concomitant transfection of a cytotoxic cytokine tumor necrosis factor-α (TNFα) to induce in situ vaccination, and an immunostimulatory cytokine interleukin 12 (IL-12) to boost the primed immune response. Here, our aim was to test the local and systemic effectiveness of the approach in tree syngeneic mouse tumor models and associate it with tumor immune profiles, characterized by tumor mutational burden, immune infiltration and expression of PD-L1 and MHC-I on tumor cells. While none of the tested characteristic proved predictive for local effectiveness, high tumor mutational burden, immune infiltration and MHC-I expression were associated with higher abscopal effectiveness. Hence, we have confirmed that both the abundance and presentation of tumor antigens as well as the absence of immunosuppressive mechanisms are important for effective in situ vaccination. These findings provide important indications for future development of in situ vaccination based treatments, and for the selection of tumor types that will most likely benefit from it.

Panels and models for accurate prediction of tumor mutation burden in tumor samples

Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.

Gene expression and immune infiltration in melanoma patients with different mutation burden

BACKGROUND

Immunotherapy is a vital component in cancer treatment. However, due to the complex genetic bases of cancer, a clear prediction index for efficacy has not been established. Tumor mutation burden (TMB) is one of the essential factors that affect immunotherapeutic efficacies, but it has not been determined whether the mutation is associated with the survival of Skin Cutaneous Melanoma (SKCM) patients. This study aimed at evaluating the correlation between TMB and immune infiltration.

METHODS

Somatic mutation profiles (n = 467), transcriptome data (n = 471), and their clinical information (n = 447) of all SKCM samples were downloaded from The Cancer Genome Atlas (TCGA) database. For each sample, TMB was calculated as the number of variants per megabase. Based on K-M survival analysis, they were allocated into the high-TMB and low-TMB groups (the optimal cutoff was determined by the 'surv_cutpoint' algorithm of survival R package). Then, Gene ontology (GO) and Gene Set Enrichment Analyses (GSEA) were performed, with immune-associated biological pathways found to be significantly enriched in the low-TMB group. Therefore, immune genes that were differentially expressed between the two groups were evaluated in Cox regression to determine their prognostic values, and a four-gene TMB immune prognostic model (TMB-IP) was constructed.

RESULTS

Elevated TMB levels were associated with better survival outcomes in SKCM patients. Based on the cutoff value in OS analysis, they were divided into high-TMB and low-TMB groups. GSEA revealed that the low-TMB group was associated with immunity while intersection analysis revealed that there were 38 differentially expressed immune-related genes between the two groups. Four TMB-associated immune genes were used to construct a TMB-IP model. The AUC of the ROC curve of this model reached a maximum of 0.75 (95%CI, 0.66-0.85) for OS outcomes. Validation in each clinical subgroup confirmed the efficacy of the model to distinguish between high and low TMB-IP score patients.

CONCLUSIONS

In SKCM patients, low TMB was associated with worse survival outcomes and enriched immune-associated pathways. The four TMB-associated immune genes model can effectively distinguish between high and low-risk patients.

Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application

Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.

Tumor Mutational Burden as a Predictor of Immunotherapy Response: Is More Always Better?

Immune checkpoint inhibitors, including antibodies that block programmed cell death protein-1 (PD-1) and PD-L1, have transformed the management of many cancers. However, the majority of patients have primary or acquired resistance to these immunotherapies. There is a significant unmet need for predictive biomarkers that can reliably identify patients who derive a clinically meaningful response from PD-1/PD-L1 blockade. High tumor mutational burden (TMB-H) has shown promise as a biomarker in lung cancer, but the broad applicability of TMB-H as a biomarker of response across all solid tumors is unclear. The FDA has approved the PD-1 inhibitor, pembrolizumab, as a therapy for all solid tumors with TMB equal to or greater than 10 mutations/megabase as measured by the FoundationOne CDx assay. This approval was based on an exploratory analysis of the KEYNOTE-158 study, which was a single-arm, phase II multi-cohort study of pembrolizumab for select, previously treated advanced solid tumors. Here, we elucidate the caveats of using TMB as a biomarker with a universal threshold across all solid tumors. While we recognize the importance of this and other FDA pan-cancer approvals, several questions about TMB as a predictive biomarker remain unanswered. In this perspective, we discuss clinical trial evidence in this area. We review the relationship between TMB and the tumor immune microenvironment. We highlight the risks of extrapolating evidence from a limited number of tumor histologies to all solid tumors, and we propose avenues for future research.

RNA-Seq Reveals Differences in Expressed Tumor Mutation Burden in Colorectal and Endometrial Cancers with and without Defective DNA-Mismatch Repair

Tumor mutation burden (TMB) is an emerging biomarker of immunotherapy response. RNA sequencing in FFPE tissue samples was used for determining TMB in microsatellite-stable (MSS) and microsatellite instability-high (MSI-H) tumors in patients with colorectal or endometrial cancer. Tissue from tumors and paired normal tissue from 46 MSI-H and 12 MSS cases were included. Of the MSI-H tumors, 29 had defective DNA mismatch-repair mutations, and 17 had MLH1 promoter hypermethylation. TMB was measured using the expressed somatic nucleotide variants (eTMB). A method of accurate measurement of eTMB was developed that removes FFPE-derived artifacts by leveraging mutation signatures. There was a significant difference in the median eTMB values observed between MSI-H and MSS cases: 27.3 versus 6.7 mutations/megabase (mut/Mb) (P = 3.5 × 10^-9). Among tumors with defective DNA-mismatch repair, those with mismatch-repair mutations had a significantly higher median eTMB than those with hypermethylation: 28.1 versus 17.5 mut/Mb (P = 0.037). Multivariate analysis showed that MSI status, tumor type (endometrial or colorectal), and age were significantly associated with eTMB. Additionally, using whole-exome sequencing in a subset of these patients, it was determined that DNA TMB correlated well with eTMB (Spearman correlation coefficient, 0.83). These results demonstrate that RNA sequencing can be used for measuring eTMB in FFPE tumor specimens.