A scalable solution for tumor mutational burden from formalin-fixed, paraffin-embedded samples using the Oncomine Tumor Mutation Load Assay

Molecular pathology as basis for timely cancer diagnosis and therapy

Precision and personalized therapeutics have witnessed significant advancements in technology, revolutionizing the capabilities of laboratories to generate vast amounts of genetic data. Coupled with computational resources for analysis and interpretation, and integrated with various other types of data, including genomic data, electronic medical health (EMH) data, and clinical knowledge, these advancements support optimized health decisions. Among these technologies, next-generation sequencing (NGS) stands out as a transformative tool in the field of cancer treatment, playing a crucial role in precision oncology. NGS-based workflows are employed across a range of applications, including gene panels, exome sequencing, and whole-genome sequencing, supporting comprehensive analysis of the entire cancer genome, including mutations, copy number variations, gene expression profiles, and epigenetic modifications. By utilizing the power of NGS, these workflows contribute to enhancing our understanding of disease mechanisms, diagnosis confirmation, identifying therapeutic targets, and guiding personalized treatment decisions. This manuscript explores the diverse applications of NGS in cancer treatment, highlighting its significance in guiding diagnosis and treatment decisions, identifying therapeutic targets, monitoring disease progression, and improving patient outcomes.

Clinical and technical insights of tumour mutational burden in non-small cell lung cancer

Despite the durable responses provided by the introduction of checkpoint inhibitors in advanced Non-Small Cell Lung Cancer (NSCLC) without actionable targets in a subset of patients, a large proportion of them will progress after immunotherapy. Programmed Death Ligand 1 (PD-L1) was the first biomarker approved for immunotherapy, although it has multiple limitations, thus the development of novel biomarkers is an urgent need. Tumour Mutational Burden (TMB) is an emerging biomarker defined as the total number of mutations per coding area of tumour genome. Targeted gene panels have emerged as a cost-effective approach to estimate TMB. However, there is still an unmet need to fully standardize sample requirements, panel size, and bioinformatic pipelines to ensure that TMB is calculated appropriately. In addition, researchers are also evaluating TMB calculation in liquid biopsy. In this work, we summarize the relevant advances and the clinical utility of TMB in NSCLC.

Mismatch repair proteins expression and tumor-infiltrating T-cells in colorectal cancer

Microsatellite instability (MSI) and tumor mutational burden (TMB) are indicators of the tumor mutational load, which can lead to immune cell recruitment. By contrast, the number of tumor-infiltrating T cells (TITs) is indicative of the host immune response to tumor cells. The present study evaluated if the expression of mismatch repair (MMR) proteins can be used as a precise tool to assess immunogenicity in the tumor microenvironment. A total of 73 colorectal cancer cases were enrolled in the present study. MMR protein expression was assessed using four-antibodies immunohistochemistry (IHC) targeting MLH1, MSH2, MSH6 and PMS2. TIT was assessed through IHC by counting CD3+ and CD8+ cells in tumor. The enrolled cases were classified into four groups according to MMR and TIT status i) Mismatch repair-proficient (pMMR) and a high number of TITs (pMMR/TIT-H); ii) pMMR and a low number of TITs (pMMR/TIT-L); iii) mismatch repair-deficient (dMMR) and TIT-H (dMMR/TIT-H); and iv) dMMR/TIT-L]. The present study evaluated the clinicopathological characteristics of the four groups, in addition to the difference of TMB. TMB analysis was counted the number of the somatic mutations through multi-genes panel using next-generation sequencing. Clinicopathological characteristics, including age, sex, pathological depth of invasion and lymph node metastasis, were not found to be statistically different between dMMR/TIT-H and dMMR/TIT-L groups. Tumors among pMMR/TIT-H group were associated with poorly differentiation compared with those in pMMR/TIT-L group (P=0.025). The median TMB among the dMMR/TIT-H group was the highest in four groups but the median TMB was <10 muts/Mb in dMMR/TIT-L, pMMR/TIT-H and pMMR/TIT-L groups, respectively. However, one tumor in the pMMR/TIT-H group showed high TMB. The present findings suggest that assessing MMR status alone may not be sufficient to precisely evaluate the antitumor immune response in the tumor microenvironment.

High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy

This study aimed to determine the association between TMB and treatment outcomes in patients with epidermal growth factor receptor (EGFR)-mutated lung cancer that were treated with tyrosine kinase inhibitors (TKIs). The TMB was assessed using a 409-gene targeted next-generation sequencing panel. We compared the response rate (RR), progression-free survival (PFS), overall survival (OS), and frequency of secondary T790M mutations among the different TMB groups. The median TMB of the study population (n = 88) was 3.36/megabases. We divided 52 (59%) and 36 (41%) patients into the low and high TMB groups, respectively. A high TMB level was significantly associated with liver metastasis and more advanced stage (all p < 0.05). RR was significantly lower in the high TMB group than that of the low TMB group (50.0% vs. 80.7%, all p = 0.0384). In multivariate analysis, high TMB was independently associated with a shorter PFS (hazard ratio [HR] = 1.80, p = 0.0427) and shorter OS (HR = 2.05, p = 0.0397) than that of the low TMB group. Further, high TMB was independently associated with decreased T790M mutation development. These results suggest that high TMB may be a predictive biomarker for adverse treatment outcomes and represent a patients’ subgroup warranting tailored therapeutic approaches.

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.

Genomic landscape of treatment refractory metastatic colorectal cancer

BACKGROUND

Metastatic colorectal cancer (mCRC) is a complex and heterogeneous disease with few standard and targeted treatment options. Next-generation sequencing of tumor tissue was performed to identify cancer driver mutations to discover possible personalized treatment options, as targeted treatment possibilities are limited for this patient population. Results of genomic sequencing in patients with treatment-refractory mCRC are described in this retrospective analysis.

MATERIAL AND METHODS

Clinico-pathological characteristics and genomic sequence results of consecutive patients with refractory mCRC, referred to the Experimental Cancer Therapy Unit (ECTU) at Department of Oncology, Herlev & Gentofte Hospital in the period from 1 October 2015 to 14 December 2018 were reviewed in this retrospective analysis. Tumor tissue from the patients was analyzed by next-generation sequencing using the Oncomine Comprehensive primer panel to detect actionable variants of cancer driver mutations and microsatellite instability status. From August 2018 tumor mutational burden was also analyzed.

RESULTS

A total of 80 patients with treatment-refractory mCRC and in a fairly good performance were referred to the ECTU during this period. Genomic sequencing of tumor tissue was performed for all 80 patients and a cancer driver mutation was identified in 90% (n = 72) of the patients. A total of 31.3% (n = 25) of the patients received therapy either as targetable therapy outside an available trial (n = 2), FDA approved therapy (n = 2), or treatment in phase 1 or 2 trials, independent of the genomic signature 26.3% (n = 21).

CONCLUSION

Most mCRC patients refractory to standard anti-neoplastic therapies, presented with a cancer driver mutation, however, only a few of these mutations gave rise to matched therapies as only 2.5% of the patients from this period received targeted therapy.

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.

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.

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

Development and Validation of StrataNGS, a Multiplex PCR, Semiconductor Sequencing-Based Comprehensive Genomic Profiling Test

Despite widespread use in targeted tumor testing, multiplex PCR/semiconductor (Ion Torrent) sequencing-based assessment of all comprehensive genomic profiling (CGP) variant classes has been limited. Herein, we describe the development and validation of StrataNGS, a 429-gene, multiplex PCR/semiconductor sequencing-based CGP laboratory-developed test performed on co-isolated DNA and RNA from formalin-fixed, paraffin-embedded tumor specimens with ≥2 mm² tumor surface area. Validation was performed in accordance with MolDX CGP validation guidelines using 1986 clinical formalin-fixed, paraffin-embedded samples and an in-house developed optimized bioinformatics pipeline. Across CGP variant classes, accuracy ranged from 0.945 for tumor mutational burden (TMB) status to >0.999 for mutations and gene fusions, positive predictive value ranged from 0.915 for TMB status to 1.00 for gene fusions, and reproducibility ranged from 0.998 for copy number alterations to 1.00 for splice variants and insertions/deletions. StrataNGS TMB estimates were highly correlated to those from whole exome- or FoundationOne CDx-determined TMB (Pearson r = 0.998 and 0.960, respectively); TMB reproducibility was 0.996 (concordance correlation coefficient). Limit of detection for all variant classes was <20% tumor content. Together, we demonstrate that multiplex PCR/semiconductor sequencing-based tumor tissue CGP is feasible using optimized bioinformatic approaches described herein.

Poor performance status patient with long-lasting major response to pembrolizumab in advanced non-small-cell lung cancer with coexisting POLE mutation and deficient mismatch repair pathway

Immunotherapy with immune checkpoint inhibitors (ICIs) represents a major breakthrough in lung cancer treatment. For patients with advanced non-small-cell lung cancer (NSCLC) and poor performance status (PS), the availability of sensitivity markers to immune-checkpoint inhibitors (ICI) would be useful for attending physicians and assist them in their decision-making process. Deficient mismatch repair (dMMR) can lead to high microsatellite instability (MSI-H) and coexist with mutations in polymerase proofreading (DNA polymerase Epsilon POLE and delta 1 POLD1) with a specific mutational signature. This would result in high tumor mutational burden and programmed cell death protein ligand 1 (PD-L1) overexpression. We report herein on a NSCLC case with MSI-H and POLE mutation in a patient with inaugural poor general condition, who exhibited prolonged response to anti-programmed cell death protein (PD-1) therapy. Additionally, there was a marked improvement of the patient's performance status, from PS 3 before ICI administration to PS 1 upon ICI therapy.

Integrated analysis of long non-coding RNAs and mRNAs associated with malignant transformation of gastrointestinal stromal tumors

Malignant transformation of gastrointestinal stromal tumors (GISTs) is correlated with poor prognosis; however, the underlying biological mechanism is not well understood. In the present study, low-risk (LR) GISTs, GISTs categorized as high-risk based on tumor size (HBS), and on mitotic rate (HBM) were collected for RNA sequencing. Candidate hub lncRNAs were selected by Oncomine analysis. Expression of a selected hub lncRNA, DNM3OS, and its correlation with patients’ prognosis were analyzed using FISH staining, followed with the determination of function and underlying mechanism. Our results revealed a series of key pathways and hub lncRNAs involved in the malignant transformation of GISTs. Oncomine analysis revealed a tight association between clinical signatures and DNM3OS and suggested that DNM3OS is a hub lncRNA that is involved in the Hippo signaling pathway. In addition, DNM3OS was upregulated in HBS, HBM, and HBS/M GIST and correlated with worse prognosis in patients with GISTs. In addition, DNM3OS promoted GIST cell proliferation and mitosis by regulating the expression of GLUT4 and CD36. Collectively, these results improve our understanding of the malignant transformation of GISTs and unveil a series of hub lncRNAs in GISTs.

Paired analysis of tumor mutation burden for lung adenocarcinoma and associated idiopathic pulmonary fibrosis

Genetic alterations underlying the development of lung cancer in individuals with idiopathic pulmonary fibrosis (IPF) have remained unclear. To explore whether genetic alterations in IPF tissue contribute to the development of IPF-associated lung cancer, we here evaluated tumor mutation burden (TMB) and somatic variants in 14 paired IPF and tumor samples from patients with IPF-associated lung adenocarcinoma. We also determined TMB for 22 samples of lung adenocarcinoma from patients without IPF. TMB for IPF-associated lung adenocarcinoma was significantly higher than that for matched IPF tissue (median of 2.94 vs. 1.26 mutations/Mb, P = 0.002). Three and 102 somatic variants were detected in IPF and matched lung adenocarcinoma samples, respectively, with only one pair of specimens sharing one somatic variant. TMB for IPF-associated lung adenocarcinoma was similar to that for lung adenocarcinoma samples with driver mutations (median of 2.94 vs. 2.51 mutations/Mb) and lower than that for lung adenocarcinoma samples without known driver mutations (median of 2.94 vs. 5.03 mutations/Mb, P = 0.130) from patients without IPF. Our findings suggest that not only the accumulation of somatic mutations but other factors such as inflammation and oxidative stress might contribute to the development and progression of lung cancer in patients with IPF.

Challenges in bioinformatics approaches to tumor mutation burden analysis

Several immune checkpoint inhibitors (ICIs) have already been introduced into clinical practice or are in advanced phases of clinical experimentation. Extensive efforts are being made to identify robust biomarkers to select patients who may benefit from treatment with ICIs. Tumor mutation burden (TMB) may be a relevant biomarker of response to ICIs in different tumor types; however, its clinical use is challenged by the analytical methods required for its evaluation. The possibility of using targeted next-generation sequencing panels has been investigated as an alternative to the standard whole exome sequencing approach. However, no standardization exists in terms of genes covered, types of mutations included in the estimation of TMB, bioinformatics pipelines for data analysis, and cut-offs used to discriminate samples with high, intermediate or low TMB. Bioinformatics serve a relevant role in the analysis of targeted sequencing data and its standardization is essential to deliver a reliable test in clinical practice. In the present study, cultured and formalin-fixed, paraffin-embedded cell lines were analyzed using a commercial panel for TMB testing; the results were compared with data from the literature and public databases, demonstrating a good correlation. Additionally, the correlation between high tumor mutation burden and microsatellite instability was confirmed. The bioinformatics analyses were conducted using two different pipelines to highlight the challenges associated with the development of an appropriate analytical workflow.

Validation of a Targeted Next-Generation Sequencing Panel for Tumor Mutation Burden Analysis: Results from the Onconetwork Immuno-Oncology Consortium

Tumor mutation burden (TMB) is evaluated as a biomarker of response to immunotherapy. We present the efforts of the Onconetwork Immuno-Oncology Consortium to validate a commercial targeted sequencing test for TMB calculation. A three-phase study was designed to validate the Oncomine Tumor Mutational Load (OTML) assay at nine European laboratories. Phase 1 evaluated reproducibility and accuracy on seven control samples. In phase 2, six formalin-fixed, paraffin-embedded samples tested with FoundationOne were reanalyzed with the OTML panel to evaluate concordance and reproducibility. Phase 3 involved analysis of 90 colorectal cancer samples with known microsatellite instability (MSI) status to evaluate TMB and MSI association. High reproducibility of TMB was demonstrated among the sites in the first and second phases. Strong correlation was also detected between mean and expected TMB in phase 1 (r² = 0.998) and phase 2 (r² = 0.96). Detection of actionable mutations was also confirmed. In colorectal cancer samples, the expected pattern of MSI-high/high-TMB and microsatellite stability/low-TMB was present, and gene signatures produced by the panel suggested the presence of a POLE mutation in two samples. The OTML panel demonstrated robustness and reproducibility for TMB evaluation. Results also suggest the possibility of using the panel for mutational signatures and variant detection. Collaborative efforts between academia and companies are crucial to accelerate the translation of new biomarkers into clinical research.

Comprehensive tumor molecular profile analysis in clinical practice

BACKGROUND

Tumor molecular profile analysis by Next Generation Sequencing technology is currently widely applied in clinical practice and has enabled the detection of predictive biomarkers of response to targeted treatment. In parallel with targeted therapies, immunotherapies are also evolving, revolutionizing cancer therapy, with Programmed Death-ligand 1 (PD-L1), Microsatellite instability (MSI), and Tumor Mutational Burden (TMB) analysis being the biomarkers employed most commonly.

METHODS

In the present study, tumor molecular profile analysis was performed using a 161 gene NGS panel, containing the majority of clinically significant genes for cancer treatment selection. A variety of tumor types have been analyzed, including aggressive and hard to treat cancers such as pancreatic cancer. Besides, the clinical utility of immunotherapy biomarkers (TMB, MSI, PD-L1), was also studied.

RESULTS

Molecular profile analysis was conducted in 610 cancer patients, while in 393 of them a at least one biomarker for immunotherapy response was requested. An actionable alteration was detected in 77.87% of the patients. 54.75% of them received information related to on-label or off-label treatment (Tiers 1A.1, 1A.2, 2B, and 2C.1) and 21.31% received a variant that could be used for clinical trial inclusion. The addition to immunotherapy biomarker to targeted biomarkers' analysis in 191 cases increased the number of patients with an on-label treatment recommendation by 22.92%, while an option for on-label or off-label treatment was provided in 71.35% of the cases.

CONCLUSIONS

Tumor molecular profile analysis using NGS is a first-tier method for a variety of tumor types and provides important information for decision making in the treatment of cancer patients. Importantly, simultaneous analysis for targeted therapy and immunotherapy biomarkers could lead to better tumor characterization and offer actionable information in the majority of patients. Furthermore, our data suggest that one in two patients may be eligible for on-label ICI treatment based on biomarker analysis. However, appropriate interpretation of results from such analysis is essential for implementation in clinical practice and accurate refinement of treatment strategy.

Tumor mutation burden as a biomarker for lung cancer patients treated with pemetrexed and cisplatin (the JIPANG-TR)

The JIPANG study is a randomized phase III study of pemetrexed/cisplatin (Pem/Cis) versus vinorelbine/cisplatin (Vnr/Cis) for completely resected stage II-IIIA non-squamous non-small cell lung cancer (Ns-NSCLC). This study did not meet the primary endpoint (recurrence-free survival, RFS) but Pem/Cis had a similar efficacy to Vnr/Cis with a better tolerability. Tumor mutation burden (TMB) is thought to have a predictive value of immune checkpoint inhibitors. However, the relevance of TMB to cytotoxic chemotherapy remains unknown. This exploratory study investigates the relationship between tumor mutation profiles and clinical outcome of Pem/Cis. Formalin-fixed, paraffin-embedded tumor tissues (n = 389) were obtained from the patients. Mutation status of tissue DNA was analyzed by targeted deep sequencing. Epidermal growth factor receptor (EGFR) mutations were detected frequently in Ns-NSCLC (139/374). Patients without any EGFR mutations experienced longer RFS in the Pem/Cis arm versus Vnr/Cis arms. Pem/Cis in patients with high TMB (≥12-16 mut/Mb) tended to have improved survival. In patients with wild-type EGFR, TMB ≥ 12 mut/Mb was significantly associated with improved RFS with Pem/Cis versus Vnr/Cis (not reached vs 52.5 months; hazard ratio (HR) 0.477). It could be proposed that TMB was predictive of RFS benefit with Pem/Cis versus Vnr/Cis in Ns-NSCLC. Further investigation is required to determine whether TMB combined with EGFR mutation status could be used as a predictive biomarker.

Robust assessment of tumor mutational burden in cytological specimens from lung cancer patients

OBJECTIVES

Tumor mutational burden (TMB) has emerged as a promising predictive biomarker for immune checkpoint inhibitor therapy. While the feasibility of TMB analysis on formalin-fixed paraffin-embedded (FFPE) samples has been thoroughly evaluated, only limited analyses have been performed on cytological samples, and no dedicated study has investigated concordance of TMB between different sample types. Here, we assessed TMB on matched histological and cytological samples from lung cancer patients and evaluated the accuracy of TMB estimation in these sample types.

MATERIALS AND METHODS

We analyzed mutations and resulting TMB in FFPE samples and matched ethanol-fixed cytological smears (n = 12 matched pairs) by using a targeted next-generation sequencing assay (Oncomine™ Tumor Mutational Load). Two different variant allele frequency (VAF) thresholds were used to estimate TMB (VAF = 5% or 10%).

RESULTS

At 5% VAF threshold, 73% (107/147) of mutations were concordantly detected in matched histological and cytological samples. Discordant variants were mainly unique to FFPE samples (34/40 discordant variants) and mostly C:G > T:A transitions with low allelic frequency, likely indicating formalin fixation artifacts. Increasing the VAF threshold to 10% clearly increased the number of concordantly detected mutations in matched histological and cytological samples to 96% (100/106 mutations), and drastically reduced the number of FFPE-only mutations (from 34 to 4 mutations). In contrast, cytological samples showed consistent mutation count and TMB values at both VAF thresholds. Using FFPE samples, 2 out of 12 patients were classified as TMB-high at VAF cutoff of 5% but TMB-low at 10%, whereas cytological specimens allowed consistent patient classification independently from VAF cutoff.

CONCLUSION

Our results show that cytological smears provide more consistent TMB values due to high DNA quality and lack of formalin-fixation induced artifacts. Therefore, cytological samples should be the preferred sample type for robust TMB estimation.

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.

The Promises and Challenges of Tumor Mutation Burden as an Immunotherapy Biomarker: A Perspective from the International Association for the Study of Lung Cancer Pathology Committee

Immune checkpoint inhibitor (ICI) therapies have revolutionized the management of patients with NSCLC and have led to unprecedented improvements in response rates and survival in a subset of patients with this fatal disease. However, the available therapies work only for a minority of patients, are associated with substantial societal cost, and may lead to considerable immune-related adverse events. Therefore, patient selection must be optimized through the use of relevant biomarkers. Programmed death-ligand 1 protein expression by immunohistochemistry is widely used today for the selection of programmed cell death protein 1 inhibitor therapy in patients with NSCLC; however, this approach lacks robust sensitivity and specificity for predicting response. Tumor mutation burden (TMB), or the number of somatic mutations derived from next-generation sequencing techniques, has been widely explored as an alternative or complementary biomarker for response to ICIs. In theory, a higher TMB increases the probability of tumor neoantigen production and therefore, the likelihood of immune recognition and tumor cell killing. Although TMB alone is a simplistic surrogate of this complex interplay, it is a quantitative variable that can be relatively readily measured using currently available sequencing techniques. A large number of clinical trials and retrospective analyses, employing both tumor and blood-based sequencing tools, have evaluated the performance of TMB as a predictive biomarker, and in many cases reveal a correlation between high TMB and ICI response rates and progression-free survival. Many challenges remain before the implementation of TMB as a biomarker in clinical practice. These include the following: (1) identification of therapies whose response is best informed by TMB status; (2) robust definition of a predictive TMB cut point; (3) acceptable sequencing panel size and design; and (4) the need for robust technical and informatic rigor to generate precise and accurate TMB measurements across different laboratories. Finally, effective prediction of response to ICI therapy will likely require integration of TMB with a host of other potential biomarkers, including tumor genomic driver alterations, tumor-immune milieu, and other features of the host immune system. This perspective piece will review the current clinical evidence for TMB as a biomarker and address the technical sequencing considerations and ongoing challenges in the use of TMB in routine practice.

Regorafenib Plus Nivolumab in Patients With Advanced Gastric or Colorectal Cancer: An Open-Label, Dose-Escalation, and Dose-Expansion Phase Ib Trial (REGONIVO, EPOC1603)

PURPOSE

This is a phase Ib trial of regorafenib plus nivolumab for gastric and colorectal cancer.

PATIENTS AND METHODS

Enrolled patients received regorafenib plus nivolumab in a dose-finding part to estimate the maximum tolerated dose. Additional patients were enrolled in a dose-expansion part. Regorafenib of 80-160 mg was administered once daily for 21 days on/7 days off with nivolumab 3 mg/kg every 2 weeks. The primary end point was dose-limiting toxicity (DLT) during the first 4 weeks to estimate the recommended dose.

RESULTS

Fifty patients (25 each with gastric and colorectal cancer) were enrolled. All patients had received ≥ 2 previous lines of chemotherapy, including anti-angiogenetic inhibitors in 96% of patients. Seven patients with gastric cancer had previously been treated with immune checkpoint inhibitors. One patient had microsatellite instability-high colorectal cancer, whereas the remaining patients had microsatellite stable or mismatch repair-proficient tumors. Three DLTs (grade 3 colonic perforation, maculopapular rash, and proteinuria) were observed with regorafenib 160 mg; none were observed with 80 or 120 mg. During the dose-expansion part, regorafenib dose was reduced from 120 to 80 mg because of frequent maculopapular rash. The common grade ≥ 3 treatment-related adverse events were rash (12%), proteinuria (12%), and palmar-plantar erythrodysesthesia (10%). Objective tumor response was observed in 20 patients (40%), including 11 with gastric cancer (44%) and 9 with colorectal cancer (36%). Median progression-free survival was 5.6 and 7.9 months in patients with gastric and colorectal cancer, respectively.

CONCLUSION

The combination of regorafenib 80 mg plus nivolumab had a manageable safety profile and encouraging antitumor activity in patients with gastric and colorectal cancer, which warrants additional investigations in larger cohorts.

Mucinous Histology, BRCA1/2 Mutations, and Elevated Tumor Mutational Burden in Colorectal Cancer

Mucinous colorectal carcinomas (MC) constitute 10% of colorectal malignancies. Recently, an increased risk of colorectal cancer has been demonstrated in germline BRCA1/2 mutation carriers. Furthermore, BRCA1/2 germline mutation carriers have exhibited a higher-than-expected frequency of MC tumors. Here, we investigate the relationship between BRCA mutations and mucinous histology in colorectal carcinoma patients, using both an existing cohort of sequenced colorectal tumors and a prospective case-control study comparing MC and conventional adenocarcinoma (AC) patients tested for BRCA mutations. We discovered that MC tumors exhibit a statistically significantly higher incidence of BRCA mutations in addition to a higher average mutation count when compared to AC tumors in the existing cohort. The strongest predictor of the mutation count was mucinous histology, independently of other variables including microsatellite instability. Contrary to our hypothesis, the first association did not recur in the prospective case-control study, likely due to our pathological definition of MC tumors and small sample size. Finally, we observed a higher tumor mutational burden (TMB) in MC tumors compared with AC tumors. We suggest that the association between MC histology, BRCA mutations, and increased TMB may open the door to the utilization of simple tests (such as histopathologic characterization) to detect patients who may benefit from immunotherapy in colorectal cancer.

Tumor Mutational Burden Determined by Panel Sequencing Predicts Survival After Immunotherapy in Patients With Advanced Gastric Cancer

Objective: Panel-based sequencing is widely used to measure tumor mutational burden (TMB) in clinical trials and is ready to enter routine diagnostics. However, cut-off points to distinguish “TMB-high” from “TMB-low” tumors are not consistent and the clinical implications of TMB in predicting responses to immune checkpoint blockade (ICB) in gastric cancer are not clearly defined. We aimed to assess whether TMB is associated with the response to immunotherapy and to examine its relation with other biomarkers of immunotherapy response in advanced gastric cancer.

Design: In total, 63 patients with advanced gastric cancer treated with ICB were included in the study. Panel-based TMB in gastric tumor samples, treatment responses to ICB, clinicopathological data, and time to progression were retrospectively analyzed. Microsatellite instability (MSI) status, Epstein–Barr virus (EBV) positivity, and programmed death-ligand 1 (PD-L1) combined positive score (CPS) were also analyzed.

Results: TMB ranged from 0 to 446 mutations/megabase (mt/mb) and was significantly associated with MSI (P < 0.001), PD-L1 CPS (P = 0.022), response to ICB (P = 0.04), chemotherapy (P = 0.02) and older patient age (≥65 years; P = 0.0014). The cut-off point of 14.31 mt/mb determined by log-rank statistics for progression-free survival divided the tumors into eight (12.7%) TMB-high and 55 (87.3%) TMB-low tumors. The median TMB of the chemo-refractory group was significantly higher (8.43 mt/mb) compared to that of chemo-naïve group (3.42 mt/mb) (P = 0.02). Patients with TMB-high tumors showed prolonged progression-free survival in univariate [HR, 0.32; 95% confidence interval (CI), 0.12–0.90] and multivariate (HR, 0.21; 95% CI, 0.07–0.69) analyses. In area under the receiver operating curve (AUC) analysis of TMB, PD-L1, EBV, MSI, and their combination, the AUC value was the highest for EBV (0.97), followed by MSI (0.96), PD-L1 (0.81), the combination (0.78), and TMB (0.56).

Conclusion: In addition to EBV, MSI, and PD-L1 CPS, TMB could be used as a predictive biomarker in patients with advanced gastric cancer treated with ICB and may aid clinical decision making.

Biochemical Characterization of a New Oligoalginate Lyase and Its Biotechnological Application in Laminaria japonica Degradation

Oligoalginate lyases catalyze the degradation of alginate polymers and oligomers into monomers, a prerequisite for biotechnological utilizing alginate. In this study, we report the cloning, expression and biochemical characterization of a new polysaccharide lyase (PL) family 17 oligoalginate lyase, OalV17, from the marine bacterium Vibrio sp. SY01. The recombinant OalV17 showed metal ion independent and detergent resistant properties. Furthermore, OalV17 is an exo-type enzyme that yields alginate monomers as the main product and recognizes alginate disaccharides as the minimal substrate. Site-directed mutagenesis followed by kinetic analysis indicates that the residue Arg²³¹ plays a key role in substrate specificity. Furthermore, a rapid and efficient alginate monomer-producing method was developed directly from Laminaria japonica. These results suggest that OalV17 is a potential candidate for saccharification of alginate.

Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project

BACKGROUND

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.

METHODS

Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.

RESULTS

Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.

CONCLUSIONS

Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

Tumor mutational load, CD8+ T cells, expression of PD-L1 and HLA class I to guide immunotherapy decisions in NSCLC patients

Objectives

A minority of NSCLC patients benefit from anti-PD1 immune checkpoint inhibitors. A rational combination of biomarkers is needed. The objective was to determine the predictive value of tumor mutational load (TML), CD8⁺ T cell infiltration, HLA class-I and PD-L1 expression in the tumor.

Materials and methods

Metastatic NSCLC patients were prospectively included in an immune-monitoring trial (NTR7015) between April 2016-August 2017, retrospectively analyzed in FFPE tissue for TML (NGS: 409 cancer-related-genes) and by IHC staining to score PD-L1, CD8⁺ T cell infiltration, HLA class-I. PFS (RECISTv1.1) and OS were analyzed by Kaplan–Meier methodology.

Results

30 patients with adenocarcinoma (67%) or squamous cell carcinoma (33%) were included. High TML was associated with better PFS (p = 0.004) and OS (p = 0.025). Interaction analyses revealed that patients with both high TML and high total CD8⁺ T cell infiltrate (p = 0.023) or no loss of HLA class-I (p = 0.026), patients with high total CD8⁺ T cell infiltrate and no loss of HLA class-I (p = 0.041) or patients with both high PD-L1 and high TML (p = 0.003) or no loss of HLA class-I (p = 0.032) were significantly associated with better PFS. Unsupervised cluster analysis based on these markers revealed three sub-clusters, of which cluster-1A was overrepresented by patients with progressive disease (15 out of 16), with significant effect on PFS (p = 0.007).

Conclusion

This proof-of-concept study suggests that a combination of PD-L1 expression, TML, CD8⁺ T cell infiltration and HLA class-I functions as a better predictive biomarker for response to anti-PD-1 immunotherapy. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Electronic supplementary material

The online version of this article (10.1007/s00262-020-02506-x) contains supplementary material, which is available to authorized users.

TP53, ATRX alterations, and low tumor mutation load feature IDH-wildtype giant cell glioblastoma despite exceptional ultra-mutated tumors

Background

Giant cell glioblastoma (gcGBM) is a rare morphological variant of IDH-wildtype (IDHwt) GBM that occurs in young adults and have a slightly better prognosis than "classic" IDHwt GBM.

Methods

We studied 36 GBMs, 14 with a histopathological diagnosis of gcGBM and 22 with a giant cell component. We analyzed the genetic profile of the most frequently mutated genes in gliomas and assessed the tumor mutation load (TML) by gene-targeted next-generation sequencing. We validated our findings using The Cancer Genome Atlas (TCGA) data.

Results

p53 was altered by gene mutation or protein overexpression in all cases, while driver IDH1, IDH2, BRAF, or H3F3A mutations were infrequent or absent. Compared to IDHwt GBMs, gcGBMs had a significant higher frequency of TP53, ATRX, RB1, and NF1 mutations, while lower frequency of EGFR amplification, CDKN2A deletion, and TERT promoter mutation. Almost all tumors had low TML values. The high TML observed in only 2 tumors was consistent with POLE and MSH2 mutations. In the histopathological review of TCGA IDHwt, TP53-mutant tumors identified giant cells in 37% of the cases. Considering our series and that of the TCGA, patients with TP53-mutant gcGBMs had better overall survival than those with TP53wt GBMs (log-rank test, P < .002).

Conclusions

gcGBMs have molecular features that contrast to "classic" IDHwt GBMs: unusually frequent ATRX mutations and few EGFR amplifications and CDKN2A deletions, especially in tumors with a high number of giant cells. TML is frequently low, although exceptional high TML suggests a potential for immune checkpoint therapy in some cases, which may be relevant for personalized medicine.

Tumor mutational burden assessed by targeted NGS predicts clinical benefit from immune checkpoint inhibitors in non-small cell lung cancer

In non‐small cell lung cancer (NSCLC), immune checkpoint inhibitors (ICIs) significantly improve overall survival (OS). Tumor mutational burden (TMB) has emerged as a predictive biomarker for patients treated with ICIs. Here, we evaluated the predictive power of TMB measured by the Oncomine™ Tumor Mutational Load targeted sequencing assay in 76 NSCLC patients treated with ICIs. TMB was assessed retrospectively in 76 NSCLC patients receiving ICI therapy. Clinical data (RECIST 1.1) were collected and patients were classified as having either durable clinical benefit (DCB) or no durable benefit (NDB). Additionally, genetic alterations and PD‐L1 expression were assessed and compared with TMB and response rate. TMB was significantly higher in patients with DCB than in patients with NDB (median TMB = 8.5 versus 6.0 mutations/Mb, Mann–Whitney p = 0.0244). 64% of patients with high TMB (cut‐off = third tertile, TMB ≥ 9) were responders (DCB) compared to 33% and 29% of patients with intermediate and low TMB, respectively (cut‐off = second and first tertile, TMB = 5–9 and TMB ≤ 4, respectively). TMB‐high patients showed significantly longer progression‐free survival (PFS) and OS (log‐rank test p = 0.0014 for PFS and 0.0197 for OS). While identifying different subgroups of patients, combining PD‐L1 expression and TMB increased the predictive power (from AUC 0.63 to AUC 0.65). Our results show that the TML panel is an effective tool to stratify patients for ICI treatment. A combination of biomarkers might maximize the predictive precision for patient stratification. Our study supports TMB evaluation through targeted NGS in NSCLC patient samples as a tool to predict response to ICI therapy. We offer recommendations for a reliable and cost‐effective assessment of TMB in a routine diagnostic setting. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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.

Tumor mutational burden quantification from targeted gene panels: major advancements and challenges

Tumor mutational burden (TMB), the total number of somatic coding mutations in a tumor, is emerging as a promising biomarker for immunotherapy response in cancer patients. TMB can be quantitated by a number of NGS-based sequencing technologies. Whole Exome Sequencing (WES) allows comprehensive measurement of TMB and is considered the gold standard. However, to date WES remains confined to research settings, due to high cost of the large genomic space sequenced. In the clinical setting, instead, targeted enrichment panels (gene panels) of various genomic sizes are emerging as the routine technology for TMB assessment. This stimulated the development of various methods for panel-based TMB quantification, and prompted the multiplication of studies assessing whether TMB can be confidently estimated from the smaller genomic space sampled by gene panels. In this review, we inventory the collection of available gene panels tested for this purpose, illustrating their technical specifications and describing their accuracy and clinical value in TMB assessment. Moreover, we highlight how various experimental, platform-related or methodological variables, as well as bioinformatic pipelines, influence panel-based TMB quantification. The lack of harmonization in panel-based TMB quantification, of adequate methods to convert TMB estimates across different panels and of robust predictive cutoffs, currently represents one of the main limitations to adopt TMB as a biomarker in clinical practice. This overview on the heterogeneous landscape of panel-based TMB quantification aims at providing a context to discuss common standards and illustrates the strong need of further validation and consolidation studies for the clinical interpretation of panel-based TMB values.

Design and Validation of a Gene-Targeted, Next-Generation Sequencing Panel for Routine Diagnosis in Gliomas

The updated 2016 World Health Organization (WHO) classification system for gliomas integrates molecular alterations and histology to provide a greater diagnostic and prognostic utility than the previous, histology-based classification. The increasing number of markers that are tested in a correct diagnostic procedure makes gene-targeted, next-generation sequencing (NGS) a powerful tool in routine pathology practice. We designed a 14-gene NGS panel specifically aimed at the diagnosis of glioma, which allows simultaneous detection of mutations and copy number variations, including the 1p/19q-codeletion and Epidermal Growth Factor Receptor (EGFR) amplification. To validate this panel, we used reference mutated DNAs, nontumor and non-glioma samples, and 52 glioma samples that were previously characterized. The panel was then prospectively applied to 91 brain lesions. A specificity of 100% and sensitivity of 99.4% was achieved for mutation detection. Orthogonal methods, such as in situ hybridization and immunohistochemical techniques, were used for validation, which showed high concordance. The molecular alterations that were identified allowed diagnosis according to the updated WHO criteria, and helped in the differential diagnosis of difficult cases. This NGS panel is an accurate and sensitive method, which could replace multiple tests for the same sample. Moreover, it is a rapid and cost-effective approach that can be easily implemented in the routine diagnosis of gliomas.

Nivolumab plus ipilimumab combination therapy for the first-line treatment NSCLC: evidence to date

Immune checkpoint inhibitors (ICI) as monotherapy in selected patients as well as in combination with chemotherapy have become the standard of care in the first-line treatment strategy of advanced non-small cell lung cancer (NSCLC) patients. Combination treatment with ICI, such as nivolumab and ipilimumab or durvaluamb and ipilimumab, has also been proposed as potential strategies in this setting in selected advanced NSCLC patients. Characterizing predictive markers of long-term clinical benefit with ICI is a critical objective. Tumor mutational burden has been proposed as a potential predictive biomarker. In this review, we discuss the efficacy of nivolumab and ipilimumab in advanced NSCLC patients as well as the clinical utility of tumor mutational burden in the efficacy of this combination. Ongoing clinical trials with nivolumab and ipilimumab, and the efficacy of this combination in subgroups of NSCLC patients, such as elderly patients and patients with brain metastases, are also discussed.

[Thoracic oncology and tumor mutational burden: Towards new challenges for the pathologist?]

Among the different promising predictive biomarkers in immuno-oncology, the tumor mutational burden (TMB) may soon impose itself in clinical routine practice, in association with PD-L1 immunohistochemistry testing. However, the TMB is used currently in clinical trials only, in particular in the thoracic oncology field. If this biomarker becomes mandatory in the near future, the pathologist will have to respond to new challenges in tight collaboration with the activity of molecular pathology platforms. Given the high incidence of lung cancer in France, this new development could have a strong impact on the daily life of the laboratories. This review addresses the different challenges which could be soon proposed to the laboratories and the pathologists due to the use of TMB assays on a daily practice.