Designing gene panels for tumor mutational burden estimation: the need to shift from 'correlation' to 'accuracy'

Whole transcriptome sequencing identifies a competitive endogenous RNA network that regulates the immunity of bladder cancer

Several types of non-coding RNAs such as circRNAs, lncRNAs, and miRNAs have been identified to regulate mRNAs through the mechanism known as the competitive endogenous RNA (ceRNA) network. To explore the role of the ceRNA regulatory network in the immune microenvironment of bladder cancer, whole-transcriptome sequencing of bladder tumor and its peritumoral tissues from 38 bladder cancer patients, with a total of 63 samples, was performed to screen differentially expressed circ-, lnc-, mi-, and mRNAs to construct a circ/lnc-mi-mRNA regulatory network with pruning algorithms. We excavated a key immune-related gene BDNF to build the final ceRNA network as hsa-miR-107 sponged by hsa-circ-000211, AC108488.1, and LINC00163. Finally, a meta-analysis of 7 public datasets demonstrated that low expression of BDNF and high expression of hsa-miR-107 were associated with longer survival. Our study identified a ceRNA regulatory network as a potentially new prognostic marker and molecular therapeutic target of bladder cancer.

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

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.

Restriction site associated DNA sequencing for tumour mutation burden estimation and mutation signature analysis

BACKGROUND

Genome-wide measures of genetic disruption such as tumour mutation burden (TMB) and mutation signatures are emerging as useful biomarkers to stratify patients for treatment. Clinicians commonly use cancer gene panels for tumour mutation burden estimation, and whole genome sequencing is the gold standard for mutation signature analysis. However, the accuracy and cost associated with these assays limits their utility at scale.

METHODS

WGS data from 560 breast cancer patients was used for in silico library simulations to evaluate the accuracy of an FDA approved cancer gene panel as well as restriction enzyme associated DNA sequencing (RADseq) libraries for TMB estimation and mutation signature analysis. We also transfected a mouse mammary cell line with APOBEC enzymes and sequenced resulting clones to evaluate the efficacy of RADseq in an experimental setting.

RESULTS

RADseq had improved accuracy of TMB estimation and derivation of mutation profiles when compared to the FDA approved cancer panel. Using simulated immune checkpoint blockade (ICB) trials, we show that inaccurate TMB estimation leads to a reduction in power for deriving an optimal TMB cutoff to stratify patients for immune checkpoint blockade treatment. Additionally, prioritisation of APOBEC hypermutated tumours in these trials optimises TMB cutoff determination for breast cancer. The utility of RADseq in an experimental setting was also demonstrated, based on characterisation of an APOBEC mutation signature in an APOBEC3A transfected mouse cell line.

CONCLUSION

In conclusion, our work demonstrates that RADseq has the potential to be used as a cost-effective, accurate solution for TMB estimation and mutation signature analysis by both clinicians and basic researchers.

Utility of Tumor Mutational Burden as a Biomarker for Response to Immune Checkpoint Inhibition in the VA Population

PURPOSE

Immune checkpoint inhibitors (ICIs) are used for an increasing number of indications across various tumor types, as well as several tumor-agnostic indications in patients with advanced cancer. Although many patients benefit from ICI therapy, others do not, highlighting a need for better predictive biomarkers. Tumor mutational burden (TMB) reflects the global number of mutations within a tumor and has been widely explored as a predictive biomarker of ICI response. The current tumor type-agnostic US Food and Drug Administration approval of pembrolizumab for metastatic solid tumors defines high TMB (TMB-H) as ≥10 mut/Mb as measured by FoundationOne CDx. This fixed cutoff may not be the ideal value across all solid tumors.

METHODS

We performed a retrospective analysis of the association of survival outcomes with TMB in patients treated with ICI for five major cancer types, using real-world data from the VA. Survival was measured from initiation of ICI, and Kaplan-Meier survival curves were compared by log-rank test.

RESULTS

Overall survival (OS) was significantly longer for patients with TMB-H versus TMB low tumors in non-small-cell lung cancer (NSCLC; n = 1,593), head and neck (H&N) cancer (n = 222), and urothelial cancer (n = 332). OS was not significantly different based on TMB status in melanoma (n = 207) or esophageal/gastric cancer (n = 248).

CONCLUSION

Consistent with previous studies, a predictive value of TMB ≥10 mut/Mb for ICI response was found in NSCLC and H&N, but not in esophageal/gastric cancer. Although inconclusive in the literature, significant association was found in urothelial cancer. The predictive value of TMB in melanoma was inconclusive. Our analysis does not support the use of a fixed threshold for TMB as a standalone predictive biomarker for ICI across all solid tumors.

Monitoring the Outcomes of Systemic Chemotherapy Including Immune Checkpoint Inhibitor for HER2-Positive Metastatic Gastric Cancer by Liquid Biopsy

PURPOSE

For precision medicine, exploration and monitoring of molecular biomarkers are essential. However, in advanced gastric cancer (GC) with visceral lesions, an invasive procedure cannot be performed repeatedly for the follow-up of molecular biomarkers.

MATERIALS AND METHODS

To verify the clinical implication of serial liquid biopsies targeting circulating tumor DNA (ctDNA) on treatment response, we conducted targeted deep sequencing for serially collected ctDNA of 15 HER2-positive metastatic GC patients treated with anti-PD-1 inhibitor in combination with standard systemic treatment.

RESULTS

In the baseline ctDNAs, 14 patients (93%) harbored more than one genetic alteration. A number of mutations in well-known cancer-related genes, such as KRAS and PIK3CA, were identified. Copy number alterations were identified in eight GCs (53.3%), and amplification of the ERBB2 gene (6/15, 40.0%) was the most recurrent. When we calculated the mean variant allele frequency (VAF) of mutations in each ctDNA as the molecular tumor burden index (mTBI), the mTBI trend was largely consistent with the VAF profiles in both responder and non-responder groups. Notably, in the longitudinal analysis of ctDNA, mTBI provided 2-42 weeks (mean 13.4 weeks) lead time in the detection of disease progression compared to conventional follow-up with CT imaging.

CONCLUSION

Our data indicate that the serial genetic alteration profiling of ctDNA is feasible to predict treatment response in HER2-positive GC patients in a minimally invasive manner. Practically, ctDNA profiles are useful not only for the molecular diagnosis of GC but also for the selection of GC patients with poor prognosis for systemic treatment (ClinicalTrials.gov identifier: NCT02901301).

Comparing Genomic Landscapes of Oral and Cutaneous Squamous Cell Carcinoma of the Head and Neck: Quest for Novel Diagnostic Markers

Squamous cell carcinoma is the most common head and neck malignancy arising from the oral mucosa and the skin. The histologic and immunohistochemical features of oral squamous cell carcinoma (OSCC) and head and neck cutaneous squamous cell carcinoma (HNcSCC) are similar, making it difficult to identify the primary site in cases of metastases. With the advent of immunotherapy, reliable distinction of OSCC and HNcSCC at metastatic sites has important treatment and prognostic implications. Here, we investigate and compare the genomic landscape of OSCC and HNcSCC to identify diagnostically useful biomarkers. Whole-genome sequencing data from 57 OSCC and 41 HNcSCC patients were obtained for tumor and matched normal samples. Tumor mutation burden (TMB), Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, frequent chromosomal alterations, somatic single nucleotide, and copy number variations were analyzed. The median TMB of 3.75 in primary OSCC was significantly lower (P < .001) than that of 147.51 mutations/Mb in primary HNcSCC. The COSMIC mutation signatures were significantly different (P < .001) between OSCC and HNcSCC. OSCC showed COSMIC single-base substitution (SBS) mutation signature 1 and AID/APOBEC activity-associated signature 2 and/or 13. All except 1 HNcSCC from hair-bearing scalp showed UV damage-associated COSMIC SBS mutation signature 7. Both OSCC and HNcSCC demonstrated a predominance of tumor suppressor gene mutations, predominantly TP53. The most frequently mutated oncogenes were PIK3CA and MUC4 in OSCC and HNcSCC, respectively. The metastases of OSCC and HNcSCC demonstrated TMB and COSMIC SBS mutation signatures similar to their primary counterparts. The combination of high TMB and UV signature in a metastatic keratinizing squamous cell carcinoma suggests HNcSCC as the primary site and may also facilitate decisions regarding immunotherapy. HNcSCC and OSCC show distinct genomic profiles despite histologic and immunohistochemical similarities. Their genomic characteristics may underlie differences in behavior and guide treatment decisions in recurrent and metastatic settings.

Mutation Burden Independently Predicts Survival in the Pan-Cancer Atlas

PURPOSE

Long-standing clinical predictors of cancer survival have included histopathologic type, stage, and grade. We hypothesized that the principal categories of tumor somatic mutations might also portend survival. We investigated this hypothesis using the Pan-Cancer Atlas, encompassing clinical, genomic, and outcome data of 10,652 patients and 32 cancer types.

METHODS

We evaluated the prognostic capability of cancer type, stage, grade and the burden of each major mutation category on overall and disease-specific survival. Mutation categories included short substitution and insertion-deletion mutations (SMs), copy number alterations (CNAs), and gene fusions.

RESULTS

SM count and CNA fraction proved to be strong independent predictors of survival (joint P = 5.3e-95) that remained highly significant when adjusted for the traditional factors. Importantly, the relationship between mutation burden and survival proved to be nonlinear (P = 9.5e-56); survival improved at both low- and high-burden extremes. In clinically predictive modeling, SM count together with CNA fraction meaningfully distinguished survival even among patients sharing a given cancer type, stage, or grade.

CONCLUSION

Burden of somatic mutation is a key index of survival of analogous clinical utility to these traditional factors.

Universal cutoff for tumor mutational burden in predicting the efficacy of anti-PD-(L)1 therapy for advanced cancers

Background: The US Food and Drug Administration (FDA)'s tumor-agnostic approval of pembrolizumab in high tumor mutational burden (TMB-high, i.e., TMB≥10 mut/Mb) cases, based on the data from KEYNOTE-158, has raised considerable concerns among the immuno-oncology community. This study aims to statistically infer the optimal universal cutoff in defining TMB-high that is predictive of the efficacy of anti-PD-(L) 1 therapy in advanced solid tumors. Methods: We integrated MSK-IMPACT TMB data from a public cohort and the objective response rate (ORR) for anti-PD-(L) 1 monotherapy across diverse cancer types in published trials. The optimal TMB cutoff was determined by varying the universal cutoff to define TMB-high across cancer types and examining the cancer-level correlation between objective response rate and the proportion of TMB-high cases. The utility of this cutoff in predicting overall survival (OS) benefits from anti-PD-(L) 1 therapy was then evaluated in a validation cohort of advanced cancers with coupled MSK-IMPACT TMB and OS data. In silico analysis of whole-exome sequencing data from The Cancer Genome Atlas was further employed to assess the generalizability of the identified cutoff among panels comprising several hundred genes. Results: The cancer type-level analysis identified 10 mut/Mb as the optimal cutoff for MSK-IMPACT in defining TMB-high, with the corresponding TMB-high (TMB≥10 mut/Mb) percentage strongly correlated with ORR for PD-(L) 1 blockade across cancer types [correlation coefficient, 0.72 (95% CI, 0.45-0.88)]. This cutoff was also the optimum in defining TMB-high (via MSK-IMPACT) when predicting OS benefits from anti-PD-(L) 1 therapy in the validation cohort. In this cohort, TMB≥10 mut/Mb was associated with significantly improved OS (hazard ratio, 0.58 [95% CI, 0.48-0.71]; p < 0.001). Moreover, in silico analyses revealed excellent agreement of TMB≥10 mut/Mb cases between MSK-IMPACT and the FDA-approved panels and between MSK-IMPACT and various randomly sampled panels. Conclusion: Our study demonstrates that 10 mut/Mb is the optimal, universal cutoff for TMB-high that guides the clinical application of anti-PD-(L) 1 therapy for advanced solid tumors. It also provides rigorous evidence beyond KEYNOTE-158 for the utility of TMB≥10 mut/Mb in predicting the efficacy of PD-(L) 1 blockade in broader settings, which could help to mitigate the challenges in embracing the tumor-agnostic approval of pembrolizumab in TMB-high cases.

Liquid biopsy on the horizon in immunotherapy of non-small cell lung cancer: current status, challenges, and perspectives

Non-small cell lung cancer (NSCLC) is one of the most threatening malignancies to human health and life. In most cases, patients with NSCLC are already at an advanced stage when they are diagnosed. In recent years, lung cancer has made great progress in precision therapy, but the efficacy of immunotherapy is unstable, and its response rate varies from patient to patient. Several biomarkers have been proposed to predict the outcomes of immunotherapy, such as programmed cell death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB). Nevertheless, the detection assays are invasive and demanding on tumor tissue. To effectively predict the outcomes of immunotherapy, novel biomarkers are needed to improve the performance of conventional biomarkers. Liquid biopsy is to capture and detect circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes in body fluids, such as blood, saliva, urine, pleural fluid and cerebrospinal fluid as samples from patients, so as to make analysis and diagnosis of cancer and other diseases. The application of liquid biopsy provides a new possible solution, as it has several advantages such as non-invasive, real-time dynamic monitoring, and overcoming tumor heterogeneity. Liquid biopsy has shown predictive value in immunotherapy, significantly improving the precision treatment of lung cancer patients. Herein, we review the application of liquid biopsy in predicting the outcomes of immunotherapy in NSCLC patients, and discuss the challenges and future directions in this field.

S. B, Chatterjee M, Prabhash K, Sreekanthreddy P, Rishi KD, Goswami HM, Veldore VH. An absolute approach to using whole exome DNA and RNA workflow for cancer biomarker testing

Introduction

The concept of personalized medicine in cancer has emerged rapidly with the advancement of genome sequencing and the identification of clinically relevant variants that contribute to disease prognosis and facilitates targeted therapy options. In this study, we propose to validate a whole exome-based tumor molecular profiling for DNA and RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissue.

Methods

The study included 166 patients across 17 different cancer types. The scope of this study includes the identification of single-nucleotide variants (SNVs), insertions/deletions (INDELS), copy number alterations (CNAs), gene fusions, tumor mutational burden (TMB), and microsatellite instability (MSI). The assay yielded a mean read depth of 200×, with &gt;80% of on-target reads and a mean uniformity of &gt;90%. Clinical maturation of whole exome sequencing (WES) (DNA and RNA)- based assay was achieved by analytical and clinical validations for all the types of genomic alterations in multiple cancers. We here demonstrate a limit of detection (LOD) of 5% for SNVs and 10% for INDELS with 97.5% specificity, 100% sensitivity, and 100% reproducibility.

Results

The results were &gt;98% concordant with other orthogonal techniques and appeared to be more robust and comprehensive in detecting all the clinically relevant alterations. Our study demonstrates the clinical utility of the exome-based approach of comprehensive genomic profiling (CGP) for cancer patients at diagnosis and disease progression.

Discussion

The assay provides a consolidated picture of tumor heterogeneity and prognostic and predictive biomarkers, thus helping in precision oncology practice. The primary intended use of WES (DNA+RNA) assay would be for patients with rare cancers as well as for patients with unknown primary tumors, and this category constitutes nearly 20–30% of all cancers. The WES approach may also help us understand the clonal evolution during disease progression to precisely plan the treatment in advanced stage disease.

Tumor Mutational Burden by Whole-Genome Sequencing in Resected NSCLC of Never Smokers

BACKGROUND

Data are scarce about tumor mutational burden (TMB) as a biomarker in never smokers with non-small cell lung cancer (NSCLC).

METHODS

TMB was assessed by whole-genome sequencing (WGS) and compared with in silico reduced whole-exome sequencing (WES) and targeted commercial next-generation sequencing (NGS) gene panels in 92 paired tumor-normal samples from never smokers who underwent NSCLC resection with curative intent. Analyses were performed to test for association with survival after surgery and to identify the optimal prognostic TMB cutoff.

RESULTS

Tumors of never smokers with NSCLC had low TMB scores (median 1.57 mutations/Mb; range, 0.13-17.94). A TMB cutoff of 1.70 mutations/Mb was associated with a 5-year overall survival of 58% in the high-TMB (42% of cases) compared with 86% in low-TMB patients (Wald P = 0.0029). TMB scores from WGS and WES were highly correlated (Spearman ρ = 0.93, P < 2.2e-16). TMB scores from NGS panels demonstrated high intraindividual fluctuations and identified high-TMB patients with 65% concordance in average compared with WGS.

CONCLUSIONS

In resected NSCLC of never smokers, high TMB was associated with worse prognosis. WES provided a good estimate of TMB while targeted NGS panels seem to lack adequate depth and resolution in the setting of low mutation burden.

IMPACT

TMB is a prognostic indicator of survival in resected NSCLC from individuals who never smoked. In this setting of low mutation counts, TMB can be accurately measured by WGS or WES, but not NGS panels.

Systematic Pan-Cancer Analysis Identifies CDK1 as an Immunological and Prognostic Biomarker

Cyclin-dependent kinase 1 (CDK1) plays an important role in cancer development, progression, and the overall process of tumorigenesis. However, no pan-cancer analysis has been reported for CDK1, and the predictive role of CDK1 in immune checkpoint inhibitors (ICIs) therapy response remains unexplored. Thus, in this study, we first investigated the potential oncogenic role of CDK1 in 33 tumors by multidimensional bioinformatics analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Bioinformatic analysis and immunohistochemical experiments confirmed that CDK1 is significantly upregulated in most common cancers and is strongly associated with prognosis. Further analysis indicated that CDK1 may influence tumor immunity mainly by mediating the degree of tumor infiltration of immune-associated cells, and the effect of CDK1 on immunity is diverse across tumor types in tumor microenvironment. CDK1 was also positively correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) in certain cancer types, linking its expression to the assessment of possible treatment response. The results of the pan-cancer analysis study showed that the CDK1 gene was positively associated with the expression of three classes of RNA methylation regulatory proteins, and affects RNA function through multiple mechanisms of action and plays an important role in the posttranscriptional regulation of the tumor microenvironment. These findings shed light on the role of the CDK1 gene in cancer progression and provide information to further study the CDK1 gene as a potential target for pan-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.

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Tumour mutation burden and other exome-wide biomarkers are used to determine which patients will benefit from immunotherapy. However, the cost of whole exome sequencing limits the widespread use of such biomarkers. Here, we introduce a data-driven framework for the design of targeted gene panels for estimating a broad class of biomarkers including tumour mutation burden and tumour indel burden. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a procedure for constructing biomarker estimators. Our approach allows the practitioner to select a targeted gene panel of prespecified size and construct an estimator that only depends on the selected genes. Alternatively, our method may be applied to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using data from three non small-cell lung cancer studies, as well as data from six other cancer types.

Prevalence and Spectrum of Predisposition Genes With Germline Mutations Among Chinese Patients With Bowel Cancer

Background: Bowel cancer is the third-most common cancer and the second leading cause of cancer-related death worldwide. Bowel cancer has a substantial hereditary component; however, additional hereditary risk factors involved in bowel cancer pathogenesis have not been systematically defined.

Materials and Methods: A total of 573 patients with bowel cancer were enrolled in the present study, of whom 93.72% had colorectal cancer (CRC). Germline mutations were integrated with somatic mutation information via utilizing target next-generation sequencing.

Results: Pathogenic/Likely Pathogenic (P/LP) germline alterations were identified in 47 (8.2%) patients with bowel cancer and the ratio of the number of these patients with family history was significantly higher in the P/LP group than that noted in the non-pathogenic (Non-P) group. Certain rare germline alterations were noted, such as those noted in the following genes: FANCD2, CDH1, and FLCN. A total of 32 patients (68.1%) had germline alterations in the DNA-damage repair (DDR) genes and homologous recombination (HR) accounted for the highest proportion of this subgroup. By comparing 573 patients with bowel cancer with reference controls (China_MAPs database), significant associations (p < 0.01) were observed between the incidence of bowel cancer and the presence of mutations in APC, ATM, MLH1, FANCD2, MSH3, MSH6, PMS1, and RAD51D. Somatic gene differential analysis revealed a marked difference in 18 genes and a significant difference was also noted in tumor mutation burden (TMB) between germline mutation carriers and non-germline mutation subjects (p < 0.001). In addition, TMB in DDR mutation groups indicated a dramatic difference compared with the non-DDR mutation group (p < 0.01). However, no statistically significant differences in TMB were noted among detailed DDR pathways for patients with bowel cancer, irrespective of the presence of germline mutations. Moreover, a significantly higher level (p < 0.0001) of mutation count was observed in the DDR group from The Cancer Genome Atlas (TCGA) database and the DDR and non-DDR alteration groups displayed various immune profiles.

Conclusion: Chinese patients with bowel cancer exhibited a distinct spectrum of germline variants, with distinct molecular characteristics such as TMB and DDR. Furthermore, the information on somatic mutations obtained from TCGA database indicated that a deeper understanding of the interactions among DDR and immune cells would be useful to further investigate the role of DDR in bowel cancer.

Prediction performance of twelve tumor mutation burden panels in melanoma and non-small cell lung cancer

As a potential biomarker to predict the response to immunotherapy, tumor mutation burden (TMB) which can be estimated by the cancer gene panel (CGP) has received considerable attention. However, it is not clear which CGP is better in predicting the efficacy of immunotherapy. To evaluate the twelve CGPs, we compared them on 13 datasets of melanoma and non-small cell lung cancer (NSCLC) from the perspective of gene composition, reliability of measuring TMB and prediction performance of patient treatment benefits. The larger CGPs generally performed better, but their proportions of driver genes and function densities were smaller. The CGPs performed differently on melanoma and NSCLC patients treated with two blockades. Moreover, their ability to classify and predict patients with or without long-term clinical benefits was similar but not good enough, so it is necessary to explore a higher-performance biomarker.

Estimating tumor mutational burden across multiple cancer types using whole-exome sequencing

Background

Tumor mutational burden (TMB) is emerging as a promising biomarker in immune checkpoint inhibitor (ICI) therapy. Despite whole-exome sequencing (WES) being the gold standard for quantifying TMB, TMB is determined by selected targeted panels in most cases, and WES-derived TMB data are lacking due to the greater cost and complexity. Determining TMB thresholds is another issue that needs attention.

Methods

A total of 309 patients who had received ICI therapy, representing five cancers (listed in “Results”), were recruited. Among them, 269 patients were evaluable for survival analysis. Tumor and matched blood samples from the patients were analyzed using WES and somatic mutations were determined. TMB is defined as the total number of somatic nonsynonymous mutations in the tumor exome in our study. The patients were divided into different TMB subgroups according to a common fixed number (10 mutations/Mb) or the top tertile within each tumor type.

Results

The distribution of WES-derived median TMBs was highly variable across different tumor types, ranging from 2.71 (cholangiocarcinoma) to 2.97 (nervous system tumor), 3.69 (gastric cancer), 4.31 (hepatocellular carcinoma), and 4.64 [colorectal cancer (CRC)] mutations/Mb. In CRC, the survival benefit of TMB-high patients was significant using both the top tertile and the 10 mutations/Mb threshold. In hepatocellular carcinoma, the 10 mutations/Mb threshold showed an advantage over the top tertile threshold. Among patients with nervous system tumors, cholangiocarcinoma, and gastric cancer, no obvious survival differences were observed between the TMB-high and TMB-low groups with either TMB stratification approach.

Conclusions

The TMB threshold criterion may vary for different cancers. Our data suggest that TMB is unable to predict ICI benefit across all cancer types in Chinese patients. However, it may be an effective biomarker for predicting the clinical benefit of ICI therapy for patients with CRC.

Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.

Tumor Mutational Burden Predicting the Efficacy of Immune Checkpoint Inhibitors in Colorectal Cancer: A Systematic Review and Meta-Analysis

Objectives

For colorectal cancer patients, traditional biomarker deficient mismatch repair/microsatellite instability (dMMR/MSI) is an accurate predictor of immune checkpoint inhibitors (ICIs). Recent years, researchers considered tumor mutation burden (TMB) as another predictive biomarker which means the number of nonsynonymous mutations in cancer cells. Several studies have proven that TMB can evaluate the efficacy of ICI therapy in diverse types of cancer, especially in non-small cell lung cancer and melanoma. However, studies on the association between TMB and the response to ICI therapy in colorectal cancer alone are still lacking. In this study, we aim to verify the effect of TMB as a biomarker in predicting the efficacy of ICIs in colorectal cancer.

Methods

We searched the PubMed and Ovid MEDLINE databases up to May 1, 2021 and screened studies for eligibility. Thirteen studies published from 2015 to 2021 with 5062 patients were included finally. We extracted and calculated hazard ratios (HRs) and odds ratios (ORs) of overall survival (OS) and objective response rates (ORRs) and their 95% confidence intervals (95% CIs). Pooled HR and OR were evaluated to compare OS and ORR between TMB-high and TMB-low groups in colorectal cancer patients. Meanwhile, we assessed heterogeneity with the I² statistic and p-values and performed publication bias assessments, sensitivity analyses, and subgroup analyses to search the cause of heterogeneity.

Results

The TMB-high patient group had a longer OS than the TMB-low patient group (HR = 0.68, 95% CI: 0.51, 0.92, p = 0.013) among colorectal cancer patients receiving ICIs. In addition, the TMB-high patient group was superior in terms of ORR (OR = 19.25, 95% CI: 10.06, 36.82, p < 0.001) compared to the TMB-low patient group.

Conclusions

In conclusion, this meta-analysis revealed that TMB can be used as a potential predictive biomarker of colorectal cancer patients receiving ICI therapy. Nevertheless, this finding is not stable enough. Therefore, many more randomized controlled trials are needed to prove that TMB is reliable enough to be used clinically to predict the efficacy of immunotherapy in colorectal cancer. And the most relevant biomarker remains to be determined when TMB high overlaps with other biomarkers like MSI and TILs.

Liquid Biopsy Biomarkers for Immunotherapy in Non-Small Cell Lung Carcinoma: Lessons Learned and the Road Ahead

Over the recent years, advances in the development of anti-cancer treatments, particularly the implementation of ICIs (immune checkpoint inhibitors), have resulted in increased survival rates in NSCLC (non-small cell lung cancer) patients. However, a significant proportion of patients does not seem respond to immunotherapy, and some individuals even develop secondary resistance to treatment. Therefore, it is imperative to correctly identify the patients that will benefit from ICI therapy in order to tailor therapeutic options in an individualised setting, ultimately benefitting both the patient and the health system. Many different biomarkers have been explored to correctly stratify patients and predict response to immunotherapy, but liquid biopsy approaches have recently arisen as an interesting opportunity to predict and monitor treatment response due to their logistic accessibility. This review summarises the current data and efforts in the field of ICI response biomarkers in NSCLC patients and highlights advantages and limitations as we discuss the road to clinical implementation.

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

Simple Summary

Immunotherapy has been a promising therapeutic approach for cancer treatment in recent years. Although cancer immunotherapy has achieved remarkable success, treatment response is only observed in a small number of patients. As nonresponders need to endure high treatment costs and toxicities with little benefit from treatment, identifying potential predictive biomarkers is critical to optimize the benefits of immunotherapy in patients. The total number of mutations in the tumor genome is a useful biomarker. Patients with a large number of mutations tend to respond better to cancer immunotherapy. However, assessment of the total number of mutations may not be easy. In this study, we identified gene sets with only a small number of genes whose mutations serve as an indicator of the total number of mutations. These cancer-specific gene sets can be used as a cost-effective approach to stratify patients with a large number of mutations in clinical practice.

Abstract

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.

Blood tumor mutation burden can predict the clinical response to immune checkpoint inhibitors in advanced non-small cell lung cancer patients

BACKGROUND

Tissue tumor mutation burden (tTMB) assessed by whole-exome sequencing (WES), which has been regarded as the gold standard method of tTMB measurement, can predict the clinical benefits of immune checkpoint inhibitors (ICIs). Multiple studies have investigated the feasibility of utilizing large panels to evaluate TMB but have obtained conflicting results. Furthermore, whether blood TMB (bTMB) can also be a predictive biomarker in NSCLC has not been determined.

METHODS

Fifty-six advanced NSCLC patients treated with ICIs were enrolled, including an exploratory cohort (n = 42) and a small independent validation cohort (n = 14). Next-generation sequencing was performed on tumor and plasma samples collected prior to ICI treatment using a panel consisting of 520 cancer-related genes (OncoScreen) to evaluate tTMB/bTMB. WES was also performed on tumor samples to serve as references.

RESULTS

A positive correlation between tTMB derived from WES and OncoScreen was observed. OncoScreen-derived tTMB showed a positive correlation with OncoScreen-derived bTMB. Patients with OncoScreen-derived tTMB [Formula: see text] 7 mutations/Mb (p = 0.003) or bTMB [Formula: see text] 11 mutations/Mb (p = 0.0029) had superior progression-free survival (PFS). In the small validation cohort, patients with OncoScreen-derived bTMB [Formula: see text] 11 mutations/Mb exhibited longer PFS (p = 0.192) with a nonsignificant difference. In all 42 patients who had available bTMB and PFS, patients with bTMB [Formula: see text] 11 mutations/Mb had significantly longer PFS (p = 0.011) than those with bTMB [Formula: see text] 11 mutations/Mb.

CONCLUSION

Our study confirmed the feasibility of using large panels to estimate TMB. We also demonstrated that bTMB can serve as a potential biomarker for predicting the efficacy of ICIs in NSCLC.

Clinical benefit for clinical sequencing using cancer panel testing

BACKGROUND

Clinical sequencing using a panel of genes has recently been applied worldwide for patients with refractory solid tumors, but the significance of clinical sequencing using gene panel testing remains uncertain. Here we sought to clarify the feasibility and utility of clinical sequencing in the treatment of refractory tumors at our hospital.

METHODS

A total of 39 patients with advanced solid tumors treated at our hospital between 2018 and 2020 were enrolled in the clinical sequencing. Among them, we identified 36 patients whose tissue samples were of suitable quality for clinical sequencing, and we analyzed the genomic profiles of these tumors.

RESULTS

Pathogenic alterations were detected in 28 (78%) of the 36 patients. The most common mutation was TP53 (55%), followed by KRAS (22%), and the highest frequency of gene amplification was ERBB2 (17%). Nine of the 36 patients were identified as candidates for novel molecular-targeted therapy based on their actionable gene alterations, but only one case ended up receiving novel targeted therapy following the genetic tests.

CONCLUSIONS

Our current results suggested that clinical sequencing might be useful for the detection of pathogenic alterations and the management of additional cancer treatment. However, molecular target based on actionable genomic alteration does not always bridge to subsequent therapy due to clinical deterioration, refusal for unapproved drug, and complexity of clinical trial access. Both improved optimal timing of clinical sequencing and a consensus about its off-label use might help patients receive greater benefit from clinical sequencing.

Biomarkers for precision immunotherapy in the metastatic setting: hope or reality?

Precision immunotherapy is a crucial approach to improve the efficacy of anti-cancer treatments, particularly in the metastatic setting. In this respect, accurate patient selection takes advantage of the multidimensional integration of patients' clinical information and tumour-specific biomarkers status. Among these biomarkers, programmed death-ligand 1, tumour-infiltrating lymphocytes, microsatellite instability, mismatch repair and tumour mutational burden have been widely investigated. However, novel tumour-specific biomarkers and testing methods will further improve patients' outcomes. Here, we discuss the currently available strategies for the implementation of a precision immunotherapy approach in the clinical management of metastatic solid tumours and highlight future perspectives.

Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

Patients with late stage resected cutaneous melanoma have poor overall survival (OS) and experience irreversible adverse events from systemic therapy. There is a clinical need to identify biomarkers to predict outcome. Performing germline/tumour whole-exome sequencing of 44 stage III/IV melanoma patients we identified pathogenic germline mutations in CDKN2A, CDK4, ATM, POLH, MRE11A, RECQL4 and XPC, affecting 7/44 patients. These mutations were associated with poor OS (p = 0.0082). We confirmed our findings in The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma cohort where we identified pathogenic variants in 40/455 patients (p = 0.0203). Combining these cohorts (n = 499) further strengthened these findings showing germline carriers had worse OS (p = 0.0009). Additionally, we determined whether tumour mutation burden (TMB) or BRAF status were prognostic markers of survival. Low TMB rate (< 20 Mut/Mb; p = 0.0034) and BRAF p.V600 mutation (p = 0.0355) were associated with worse progression-free survival. Combining these biomarkers indicated that V600 mutant patients had significantly lower TMB (p = 0.0155). This was confirmed in the TCGA (n = 443, p = 0.0007). Integrative analysis showed germline mutation status conferred the highest risk (HR 5.2, 95% CI 1.72–15.7). Stage IV (HR 2.5, 0.74–8.6) and low TMB (HR 2.3, 0.57–9.4) were similar, whereas BRAF V600 status was the weakest prognostic biomarker (HR 1.5, 95% CI 0.44–5.2).

Choosing tumor mutational burden wisely for immunotherapy: A hard road to explore

Immunotherapy has revolutionized the treatment of cancer due to its remarkable efficacy and extensive survival benefit in multiple tumor types. However, predictive biomarkers are required to identify patients who are likely to respond to immunotherapy. Recently, tumor mutational burden (TMB) has been shown to be associated with clinical outcomes in diverse cancers, such as melanoma, non-small-cell lung cancer and colorectal cancer. Several studies have demonstrated that high TMB can effectively predict the objective response rate and progression-free survival, but the ability of TMB to predict overall survival is limited. Thus, the clinical utility of TMB as a predictive and prognostic biomarker in immunotherapy is currently controversial. Importantly, multiple factors can affect the accurate assessment of TMB and further interfere with its prediction of clinical outcomes. These factors include preanalytical factors such as sample status, analytical factors such as differences in platforms and methods for determining TMB and variability of cutoff values, and postanalytical factors such as inconsistent interpretation and reporting of results. In addition, the optimal definition and quantification of TMB are unclear and require harmonization and standardization for reliable clinical application. This review elaborates on the factors affecting TMB status in primary tumors, summarizes the clinical utility of TMB as a biomarker in immunotherapy, and evaluates the impact of each analysis stage on the accurate estimation of TMB, especially its quantification, aiming to facilitate TMB assessment in routine clinical settings.

Impact of high tumor mutational burden in solid tumors and challenges for biomarker application

Accurate identification of patients with solid tumors likely to respond to immunotherapy is crucial. Tumor mutational burden (TMB) measures the number of somatic mutations in a tumor and is an emerging prognostic and predictive biomarker for anti-programmed cell death (PD) 1/anti-PD-ligand 1 therapy and other immunotherapeutic agents. Tumor mutational burden is assessed optimally by whole exome sequencing, but next generation sequencing provides TMB estimates in a more timely and cost-effective manner. Blood-based measurement of TMB in plasma offers an alternative to the need for adequate tumor tissue for molecular testing, and has demonstrated the ability to identify patients who derive benefit from immunotherapy. Tumor mutational burden has diverse prognostic impact in different solid tumor types and also has a demonstrated role in predicting improved survival in patients receiving immunotherapy. There are challenges to TMB adoption into standard clinical practice, including variations in its definition, with the mutational number defining TMB-high appearing to vary across cancer types. The magnitude of TMB also varies across different tumor types, with the highest levels reported in melanoma and other skin cancers (where ultraviolet light is the dominant mutational process), followed by non-small cell lung cancer and other squamous carcinomas. Concerns regarding inter-laboratory and inter-platform variations in analysis methods have been raised, highlighting the need for standardization. Integration of other genomic or pathological biomarkers with TMB may increase its prognostic and predictive capabilities and validation of individual or combination models in prospective trials is warranted.

Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability

INTRODUCTION

Tumor mutational burden (TMB) has been proposed as a novel predictive biomarker for the stratification of patients with NSCLC undergoing immune checkpoint inhibitor (ICI) treatment. The assessment of TMB has recently been established using large targeted sequencing panels, and numerous studies are ongoing to harmonize TMB assessment. "Correlation" or the coefficient of determination has generally been used to evaluate the association between different panels. We hypothesized that these metrics might overestimate the comparability, especially for lower TMB values.

METHODS

A total of 30 samples from patients with NSCLC undergoing ICI treatment were consecutively sequenced using the following three large, targeted sequencing panels: FoundationOne, Oncomine TML, and QiaSeq TMB. The TMB values were compared in the whole patient population and in a subset of patients in which the TMB assessed by FoundationOne was between 5 and 25 mutations/Mb. Prediction of durable clinical benefit (>6 mo with no progression) was assessed using receiver operator characteristics, and optimal cutoff values were calculated using the Youden J statistic.

RESULTS

Correlation between the three targeted sequencing panels was strong in the whole patient population (R² > 0.79) but was dramatically reduced in the subset of patients with TMB of 5 to 25 mutations/Mb. The agreement assessed using the Bland-Altman method was also very low. All panels were able to predict durable clinical benefit in the TMB-high population.

CONCLUSIONS

Assessment of TMB using the three targeted sequencing panels was possible and predictive of response to ICI treatment, but correlation was an inappropriate measurement to assess the association between the respective panels.

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?

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.

Tumor mutational and indel burden: a systematic pan-cancer evaluation as prognostic biomarkers

Background

Tumor mutational burden (TMB) has been widely studied as a predictive biomarker of response to immune checkpoint inhibitors (ICIs). Besides, evidence suggests frameshift indels are a highly immunogenic mutational class and thus a potentially superior biomarker. However, the general prognostic impact of TMB and indel burden in patients with solid tumors has not been systematically investigated.

Methods

We analyzed 20 primary solid cancer types from The Cancer Genome Atlas (TCGA) database. Clinicopathologic factors, TMB and indel burden were collected or calculated. For each cancer type, the impact of TMB or indel burden on overall survival (OS) was evaluated using the Kaplan-Meier method and Cox regression with the method of inverse probability of treatment weighting.

Results

Twenty cancer types from 6,035 patients were analyzed. Survival analysis showed that TMB had a significant impact on OS in 14 out of these 20 cancer types. According to the general survival impact of TMB, they could be classified into three groups, namely the TMB-Worse (eight cancer types), TMB-Better (six cancer types) and TMB-Similar (six cancer types) group, in which higher TMB was associated with inferior, superior, or similar OS, respectively. The survival impacts of TMB in the TMB-Worse and TMB-Better groups were generally consistent when limited to genes from two FDA-approved panels. Notably, in two out of the six cancer types in the TMB-Similar group, the indel burden significantly affected OS.

Conclusions

TMB, as well as indel burden, has divergent prognostic impact in different cancer types, thus could be incorporated in prognostication and risk stratification. More importantly, the general prognostic impact should be taken into account when establishing the predictive function of TMB to ICI treatment.

Comprehensive analysis of POLE and POLD1 Gene Variations identifies cancer patients potentially benefit from immunotherapy in Chinese population

POLE/POLD1 gene variants have been suggested as potential markers for immunotherapy due to their significant association with the tumor mutational burden (TMB), an effective indicator for response prediction in immunotherapy. However, the correlation of POLE/POLD1 variants with MSI, MMR, TMB, MMR-related and key driver gene mutations needs to be defined to support patient recruitment and therapeutic effect assessment in immunotherapy. 1,392 Chinese cancer patients were recruited, and the correlation of POLE/POLD1 variants with existing immunotherapeutic markers and cancer pathways was investigated. A next-generation sequencing panel including 605 cancer-related genes was used for variant sequencing. It was found that the frequency of POLE variants was not statistically different from that in COSMIC database, while the frequency of POLD1 variants was significantly higher in lung cancer. c.857 C > G and c.2091dupC were potential high frequency variants in Chinese cancer patients. Patients carrying POLE damaging variants were significantly younger than POLE/POLD1 WT patients. Patients carrying POLE/POLD1 damaging variants exhibited significantly higher TMB and frequency of MMR gene variants than POLE/POLD1 WT patients. Patients with POLE damaging variants also exhibited significantly higher frequency of driver gene variants than POLE/POLD1 WT patients. Further analysis showed that POLE damaging variants may affect the cancer development through MMR, TGFβ and RTK/RAS/RAF signaling pathways, and POLD1 through MMR pathways. In conclusion, this study identified key characteristics and regions of POLE/POLD1 genes that correlates with TMB, MMR gene mutations and key driver gene mutations, and provided theoretical and practical basis for patient selection based on POLE/POLD1 gene status in immunotherapy.

In-house Implementation of Tumor Mutational Burden Testing to Predict Durable Clinical Benefit in Non-small Cell Lung Cancer and Melanoma Patients

Tumor mutational burden (TMB) has emerged as an important potential biomarker for prediction of response to immune-checkpoint inhibitors (ICIs), notably in non-small cell lung cancer (NSCLC). However, its in-house assessment in routine clinical practice is currently challenging and validation is urgently needed. We have analyzed sixty NSCLC and thirty-six melanoma patients with ICI treatment, using the FoundationOne test (FO) in addition to in-house testing using the Oncomine TML (OTML) panel and evaluated the durable clinical benefit (DCB), defined by >6 months without progressive disease. Comparison of TMB values obtained by both tests demonstrated a high correlation in NSCLC (R² = 0.73) and melanoma (R² = 0.94). The association of TMB with DCB was comparable between OTML (area-under the curve (AUC) = 0.67) and FO (AUC = 0.71) in NSCLC. Median TMB was higher in the DCB cohort and progression-free survival (PFS) was prolonged in patients with high TMB (OTML HR = 0.35; FO HR = 0.45). In contrast, we detected no differences in PFS and median TMB in our melanoma cohort. Combining TMB with PD-L1 and CD8-expression by immunohistochemistry improved the predictive value. We conclude that in our cohort both approaches are equally able to assess TMB and to predict DCB in NSCLC.