Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice

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.

Precision Oncology in Melanoma and Skin Cancer Surgery

There has been perhaps no greater advance in the prognosis of solid tumors in the last decade than for patients with metastatic melanoma. This is due to significant improvements in treatment based on two key components of melanoma tumor biology (1) the identification of driver mutations with therapeutic potential and (2) the mechanistic understanding of a tumor-specific immune response. With breakthrough findings in such a relatively short period of time, the treatment of patients with metastatic melanoma has become intensely personalized.

Tumor mutational burden predictability in head and neck squamous cell carcinoma patients treated with immunotherapy: systematic review and meta-analysis

BACKGROUND

Tumor mutational burden (TMB) has been demonstrated to predict the response to immune checkpoint inhibitors (ICIs) in various cancers. However, the role of TMB in head and neck squamous cell carcinoma (HNSCC) has not yet been specifically addressed. Since HNSCC patients exhibit a rather limited response to ICIs, there is an unmet need to develop predictive biomarkers to improve patient selection criteria and the clinical benefit of ICI treatment.

METHODS

We conducted a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. HNSCC cohort studies were selected when TMB prior to ICI treatment was evaluated, TMB cutoff value was available, and the prognostic value of TMB was evaluated by time-to-event survival analysis. A total of 11 out of 1960 articles were analyzed, including 1200 HNSCC patients.

RESULTS

The results showed that those patients harboring high TMB exhibited a significantly superior overall response rate (OR = 2.62; 95% CI 1.74-3.94; p < 0.0001) and a survival advantage (HR = 0.53; 95% CI 0.39-0.71; p < 0.0001) after ICI treatment.

CONCLUSION

This is the first meta-analysis to demonstrate a higher response and clinical benefit from ICI therapy in HNSCC patients with high TMB.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside

Background

Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients.

Methods

Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab.

Results

Models incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank).

Discussion

This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.

Tumor Mutational Burden in Breast Cancer: Current Evidence, Challenges, and Opportunities

Tumor mutational burden (TMB) correlates with tumor neoantigen burden, T cell infiltration, and response to immune checkpoint inhibitors in many solid tumor types. Based on data from the phase II KEYNOTE-158 study, the anti-PD-1 antibody pembrolizumab was granted approval for treating patients with advanced solid tumors and TMB ≥ 10 mutations per megabase. However, this trial did not include any patients with metastatic breast cancer; thus, several questions remain unanswered about the true role of TMB as a predictive biomarker of benefit to immune checkpoint inhibitor therapy in breast cancer. In this review, we will discuss the challenges and opportunities in establishing TMB as a predictive biomarker of benefit to immunotherapy in metastatic breast cancer.

VSSP-activated macrophages mediate senescence and tumor inhibition in a preclinical model of advanced prostate cancer

Androgen deprivation therapy (ADT) is a standard therapy for prostate cancer (PCa). Though disseminated disease is initially sensitive to ADT, an important fraction of the patients progresses to castration-resistant prostate cancer (CRPC). For this reason, the identification of novel effective therapies for treating CRPC is needed. Immunotherapeutic strategies focused on macrophages as antitumor effectors, directly enhancing their tumoricidal potential at the tumor microenvironment or their adoptive transfer after ex vivo activation, have arisen as promising therapies in several cancer types. Despite several approaches centered on the activation of tumor-associated macrophages (TAMs) in PCa are under investigation, to date there is no evidence of clinical benefit in patients. In addition, the evidence of the effectiveness of macrophage adoptive transfer on PCa is poor. Here we find that VSSP, an immunomodulator of the myeloid system, decreases TAMs and inhibits prostatic tumor growth when administered to castrated Pten-deficient prostate tumor-bearing mice. In mice bearing castration-resistant Pten^pc-/-; Trp53^pc-/- tumors, VSSP administration showed no effect. Nevertheless, adoptive transfer of macrophages activated ex vivo with VSSP inhibited Pten^pc-/-; Trp53^pc-/- tumor growth through reduction of angiogenesis and tumor cell proliferation and induction of senescence. Taken together, our results highlight the rationale of exploiting macrophage functional programming as a promising strategy for CRPC therapy, with particular emphasis on ex vivo-activated proinflammatory macrophage adoptive transfer. Video abstract.

Role of circulating-tumor DNA in the early-stage non-small cell lung carcinoma as a predictive biomarker

Lung cancer is one of the most common solid malignancies. Tissue biopsy is the standard method for accurately diagnosing lung and many other malignancies over decades. However, molecular profiling of tumors leads to establishing a new horizon in the field of precision medicine, which has now entered the mainstream in clinical practice. In this context, a minimally invasive complementary method has been proposed as a liquid biopsy (LB) which is a blood-based test that is gaining popularity as it provides the opportunity to test genotypes in a unique, less invasive manner. Circulating tumor cells (CTC) captivating the Circulating-tumor DNA (Ct-DNA) are often present in the blood of lung cancer patients and are the fundamental concept behind LB. There are multiple clinical uses of Ct-DNA, including its role in prognostic and therapeutic purposes. The treatment of lung cancer has drastically evolved over time. Therefore, this review article mainly focuses on the current literature on circulating tumor DNA and its clinical implications and future goals in non-small cell lung cancer.

APC mutation correlated with poor response of immunotherapy in colon cancer

OBJECTIVE

APC (adenomatous polyposis coli) gene mutation is a central initialization in colon cancer tumorigenesis. However, the connection between APC gene mutation and immunotherapy efficacy for colon cancer remains unknown. This study aimed to explore the impact of APC mutation on immunotherapy efficacy for colon cancer.

METHODS

Colon cancer data from The Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering Cancer Center (MSKCC) were used for the combined analysis. Survival analysis was performed to evaluate the association between APC mutation and immunotherapy efficacy in colon cancer patients. The expressions of immune check point molecules, tumor mutation burden (TMB), CpG methylation level, tumor purity (TP), microsatellite instability (MSI) status and tumor-infiltrating lymphocyte (TIL) in the two APC status were compared to evaluate the associations between APC mutation and immunotherapy efficacy indicators. Gene set enrichment analysis (GSEA) was performed to identify signaling pathways related to APC mutation.

RESULTS

APC was the most frequently mutated gene in colon cancer. The survival analysis demonstrated that APC mutation was correlated with a worse immunotherapy outcome. APC mutation was associated with lower TMB, lower expression of immune check point molecules (PD-1/PD-L1/PD-L2), higher TP, lower MSI-High proportion and less CD8 + T cells and follicular helper T cells infiltration. GSEA indicated that APC mutation up-regulated mismatch repair pathway, which may play a negative role in evoking an antitumor immune response.

CONCLUSION

APC mutation is associated with worse immunotherapy outcome and inhibition of antitumor immunity. It can be used as a negative biomarker to predict immunotherapy response.

Immunotherapy for Metastatic Non-Small Cell Lung Cancer: Therapeutic Advances and Biomarkers

Immunotherapy has revolutionized the treatment paradigm of non-small cell lung cancer and improved patients' prognosis. Immune checkpoint inhibitors have quickly become standard frontline treatment for metastatic non-oncogene addicted disease, either as a single agent or in combination strategies. However, only a few patients have long-term benefits, and most of them do not respond or develop progressive disease during treatment. Thus, the identification of reliable predictive and prognostic biomarkers remains crucial for patient selection and guiding therapeutic choices. In this review, we provide an overview of the current strategies, highlighting the main clinical challenges and novel potential biomarkers.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

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.

Immunotherapy with immune checkpoint inhibitors and predictive biomarkers in malignant mesothelioma: Work still in progress

Malignant mesothelioma (MM) is a rare and aggressive neoplasm, usually associated with a poor prognosis (5 years survival rate <10%). For unresectable disease, platinum and pemetrexed chemotherapy has been the only standard of care in first line for more than two decades, while no standard treatments have been approved in subsequent lines. Recently, immunotherapy has revolutionized the therapeutic landscape of MM. In fact, the combination of ipilimumab plus nivolumab has been approved in first line setting. Moreover, immune checkpoint inhibitors (ICIs) showed promising results also in second-third line setting after platinum-based chemotherapy. Unfortunately, approximately 20% of patients are primary refractory to ICIs and there is an urgent need for reliable biomarkers to improve patient's selection. Several biological and molecular features have been studied for this goal. In particular, histological subtype (recognized as prognostic factor for MM and predictive factor for chemotherapy response), programmed death ligand 1 (PD-L1) expression, and tumor mutational burden (widely hypothesized as predictive biomarkers for ICIs in several solid tumors) have been evaluated, but with unconclusive results. On the other hand, the deep analysis of tumor infiltrating microenvironment and the improvement in genomic profiling techniques has led to a better knowledge of several mechanisms underlying the MM biology and a greater or poorer immune activation. Consequentially, several potential biomarkers predictive of response to immunotherapy in patients with MM have been identified, also if all these elements need to be further investigated and prospectively validated. In this paper, the main evidences about clinical efficacy of ICIs in MM and the literature data about the most promising predictive biomarkers to immunotherapy are reviewed.

Comparison of efficacy and tolerability of adjuvant therapy for resected high-risk stage III-IV cutaneous melanoma: a systemic review and Bayesian network meta-analysis

Background

Although immune checkpoint inhibitors (ICIs) and targeted therapies have been widely used as adjuvant treatment for resected melanoma, the optimal therapy remains controversial. Therefore, we conducted this updated network meta-analysis (NMA) to assess the efficacy and tolerability of adjuvant therapies for cutaneous melanoma.

Methods

PubMed, Embase, Cochrane library, and Web of Science were systematically searched for relevant literatures published in the last 30 years. Disease-free survival (DFS), overall survival (OS), and serious adverse events were considered as the efficacy and tolerability outcomes.

Results

In all, 27 randomized controlled trials (RCTs) including 16,709 stage III-IV melanoma patients were enrolled in this NMA. For BRAF wild-type melanoma, our analysis showed that both nivolumab and pembrolizumab demonstrated significantly better DFS and tolerability than ipilimumab (10 mg/kg). Nivolumab, pembrolizumab, ipilimumab (3 mg/kg), and ipilimumab (10 mg/kg) all appeared to be effective in prolonging OS, but no therapy demonstrated significantly better OS than ipilimumab (10 mg/kg). Nivolumab + ipilimumab showed the best DFS, but did not appear to be effective in improving OS and ranked only seventh in tolerability. Vaccines and granulocyte-macrophage colony-stimulating factor therapies were well tolerated, but all failed to improve the DFS or OS in stage III melanoma patients. In terms of BRAF mutation-positive melanoma, ICIs (nivolumab + ipilimumab, nivolumab, pembrolizumab, ipilimumab; 10 mg/kg) exhibited comparable efficacy to dabrafenib + trametinib, and all these therapies showed significantly better DFS than placebo.

Conclusion

Considering efficacy and tolerability, nivolumab and pembrolizumab seem to be preferable adjuvant therapies for patients with stage III-IV melanoma. For BRAF mutation-positive patients, more RCTs are still required to determine which is better between ICIs and targeted therapy.

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs

Background

Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD.

Methods

The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy.

Results

Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group.

Conclusions

Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

The prognostic role of tumor mutation burden on survival of breast cancer: a systematic review and meta-analysis

Background

As a potential genetic biomarker, tumor mutation burden (TMB) has made progress in numerous tumors. There are limited data regarding TMB and its prognostic role is controversial in breast cancer. This systematic review and meta-analysis were conducted to assess the prognostic value of TMB on survival of breast cancer.

Methods

The databases PubMed, Embase, Web of Science, and Cochrane Library were searched for articles published through May 31, 2022. Moreover, effective data were extracted from included studies and calculated pooled effects of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS) by STATA 16.0. Heterogeneity was conducted by the I² statistic and p-value. Using publication bias evaluation, sensitivity analysis, and subgroup analysis, the origin of heterogeneity was further investigated.

Results

They were up to 1,722 patients collected from sixteen cohorts for this analysis. The pooled effects of HR for both OS (HR: 1.14, 95% CI: 0.83,1.58, p > 0.01) and PFS (HR: 0.96, 95% CI: 0.53,1.71, p > 0.01) indicated no statistically significant difference in the high TMB and low TMB group. In immune checkpoint inhibitors (ICIs) subgroup, high TMB patients demonstrated benefit of OS (HR: 0.72, 95% CI: 0.59,0.87, p = 0.001) and PFS (HR: 0.52, 95% CI: 0.35,0.77, p < 0.001), whereas difference was not statistically significant in the non-ICIs subgroup (OS, HR:1.76, 95% CI: 0.97,3.20, p = 0.062; PFS, HR:2.31, 95% CI: 0.89,5.97, p = 0.086). In addition, sensitivity analysis revealed that the pooled effects were stable. The funnel plot and Begg's test suggested the absence of publication bias.

Conclusion

Meta-analysis revealed that the prognostic relevance of TMB in breast cancer is limited in scope. High TMB may be associated with longer survival only in ICIs-based treatment, but the association is not evident in non-ICIs-based treatment.

Trial registration

[https://www.crd.york.ac.uk/PROSPERO], Prospective Register of Systematic Reviews (PROSPERO), identifier: CRD42022342488.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10284-1.

Genomic landscape, immune characteristics and prognostic mutation signature of cervical cancer in China

Purpose

This study aimed to analyse the genomic alteration profiles and immune characteristics of a cohort of Chinese cervical cancer patients to understand why certain patients benefited from molecular targeted therapies and immunotherapy as well as their prognostic significance.

Methods

PD-L1 expression and clinicopathological information were obtained from 98 cervical cancer patients. Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analysed by the chi-square test or Fisher's exact test. Differences in gene mutations between our cohort and The Cancer Genome Atlas (TCGA) cohort were tested by Fisher's exact test. Logistic regression was used to analyse factors influencing TMB-high.

Results

Positive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs. 39%, p < 0.001). Frequently mutated genes in cervical cancer included the PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). The TERT gene mutation rate was significantly higher in our cohort than in the TCGA cohort (12% vs. 1%, p < 0.001). The independent predictors of high TMB were KMT2C and LRP1B gene mutations (p < 0.05). We also found that PTEN mutations were associated with worse survival (median PFS, 12.16 vs. 21.75 months, p = 0.0024).

Conclusion

Cervical SCC and AC have different molecular profiles and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We also proposed the prognostic value of PTEN mutations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01376-9.

Methylome Profiling of PD-L1-Expressing Glioblastomas Shows Enrichment of Post-Transcriptional and RNA-Associated Gene Regulation

Glioblastomas are the most frequent primary brain tumors in adults. They show highly malignant behavior and devastating outcomes. Since there are still no targeted therapies available, median survival remains in the range of 12 to 15 months for glioblastoma patients. Programmed Cell Death Ligand 1 (PD-L1) is a promising novel candidate in precision medicine. Here, we performed integrated epigenome-wide methylation profiling of 866,895 methylation-specific sites in 20 glioblastoma samples comparing PD-L1 high- (i.e., TPS (tumor proportion score) > 30%) and PD-L1 low-expressing glioblastomas (i.e., TPS < 10%). We found 12,597 significantly differentially methylated CpGs (DMCG) (Δβ ≥ 0.1 and p-value < 0.05) in PD-L1 high- compared with PD-L1 low-expressing glioblastomas. These DMCGs were annotated to 2546 tiling regions, 139 promoters, 107 genes, and 107 CpG islands. PD-L1 high-expressing glioblastomas showed hypomethylation in 68% of all DMCGs. Interestingly, the list of the top 100 significantly differentially methylated genes showed the enrichment of regulatory RNAs with 19 DMCGs in miRNA, snoRNAs, lincRNAs, and asRNAs. Gene Ontology analysis showed the enrichment of post-transcriptional and RNA-associated pathways in the hypermethylated gene regions. In summary, dissecting the methylomes depending on PD-L1 status revealed significant alterations in RNA regulation and novel molecular targets in glioblastomas.

A Joint Model Considering Measurement Errors for Optimally Identifying Tumor Mutation Burden Threshold

Tumor mutation burden (TMB) is a recognized stratification biomarker for immunotherapy. Nevertheless, the general TMB-high threshold is unstandardized due to severe clinical controversies, with the underlying cause being inconsistency between multiple assessment criteria and imprecision of the TMB value. The existing methods for determining TMB thresholds all consider only a single dimension of clinical benefit and ignore the interference of the TMB error. Our research aims to determine the TMB threshold optimally based on multifaceted clinical efficacies accounting for measurement errors. We report a multi-endpoint joint model as a generalized method for inferring the TMB thresholds, facilitating consistent statistical inference using an iterative numerical estimation procedure considering mis-specified covariates. The model optimizes the division by combining objective response rate and time-to-event outcomes, which may be interrelated due to some shared traits. We augment previous works by enabling subject-specific random effects to govern the communication among distinct endpoints. Our simulations show that the proposed model has advantages over the standard model in terms of precision and stability in parameter estimation and threshold determination. To validate the feasibility of the proposed thresholds, we pool a cohort of 73 patients with non-small-cell lung cancer and 64 patients with nasopharyngeal carcinoma who underwent anti-PD-(L)1 treatment, as well as validation cohorts of 943 patients. Analyses revealed that our approach could grant clinicians a holistic efficacy assessment, culminating in a robust determination of the TMB screening threshold for superior patients. Our methodology has the potential to yield innovative insights into therapeutic selection and support precision immuno-oncology.

Bias and inconsistency in the estimation of tumour mutation burden

BACKGROUND

Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB.

METHODS

We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth.

RESULTS

We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity.

CONCLUSION

Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Association of Tumor Mutational Burden with Efficacy of Pembrolizumab{plus minus}Chemotherapy as First-Line Therapy for Gastric Cancer in the Phase III KEYNOTE-062 Study

PURPOSE

This prespecified exploratory analysis evaluated the association between tumor mutational burden (TMB) status and outcomes of first-line pembrolizumab{plus minus}chemotherapy versus chemotherapy in KEYNOTE-062.

EXPERIMENTAL DESIGN

In patients with advanced gastric cancer and evaluable TMB data, we evaluated the association between TMB (continuous variable; square root scale) assessed with FoundationOne® CDx and clinical outcomes [ORR, PFS, and OS] using logistic (ORR) and Cox proportional hazards (PFS, OS) regression models. Clinical utility of TMB was assessed using the prespecified cutoff of 10mut/Mb.

RESULTS

TMB data were available for 306 of 763 patients (40.1%; pembrolizumab, 107; pembrolizumab+chemotherapy, 100; chemotherapy, 99). TMB was significantly associated with clinical outcomes in patients treated with pembrolizumab and pembrolizumab+chemotherapy (ORR, PFS, and OS; all P<0.05) but not with chemotherapy (all P>0.05). The overall prevalence of TMB {greater than or equal to}10mut/Mb was 16% across treatment groups; 44% of patients who had TMB {greater than or equal to}10mut/Mb had high microsatellite instability (MSI-H) tumors. Improved clinical outcomes (ORR, PFS, and OS) were observed in pembrolizumab-treated patients (pembrolizumab monotherapy and pembrolizumab+chemotherapy) with TMB {greater than or equal to}10mut/Mb. When the analysis was limited to the non-MSI-H subgroup, both the positive association between clinical outcomes with pembrolizumab or pembrolizumab+chemotherapy and TMB as a continuous variable and the clinical utility of pembrolizumab (with or without chemotherapy) versus chemotherapy by TMB cutoff were attenuated.

CONCLUSIONS

This exploratory analysis of KEYNOTE-062 suggests an association between TMB and clinical efficacy with first-line pembrolizumab-based therapy in patients with advanced gastric/gastroesophageal junction adenocarcinoma. However, after the exclusion of patients with MSI-H tumors, the clinical utility of TMB was attenuated.

Pathological Response and Immune Biomarker Assessment in Non-Small-Cell Lung Carcinoma Receiving Neoadjuvant Immune Checkpoint Inhibitors

Simple Summary

Recently, the U.S. Food and Drug Administration (FDA) approved neoadjuvant immunotherapy plus chemotherapy for the treatment of resectable non-small-cell lung carcinoma (NSCLC) due to the clinical benefits reported in several clinical trials. In these settings, the pathological assessment of the tumor bed to quantify a pathological response has been used as a surrogate method of clinical benefit to neoadjuvant therapy. In addition, several clinical trials are including the assessment of tissue-, blood-, or host-based biomarkers to predict therapy response and to monitor the response to neoadjuvant treatment. In this manuscript, we provide an overview of current recommendations for the evaluation of pathological response and describe potential biomarkers used in clinical trials of neoadjuvant immunotherapy in resectable NSCLC.

Abstract

Lung cancer is the leading cause of cancer incidence and mortality worldwide. Adjuvant and neoadjuvant chemotherapy have been used in the perioperative setting of non-small-cell carcinoma (NSCLC); however, the five-year survival rate only improves by about 5%. Neoadjuvant treatment with immune checkpoint inhibitors (ICIs) has become significant due to improved survival in advanced NSCLC patients treated with immunotherapy agents. The assessment of pathology response has been proposed as a surrogate indicator of the benefits of neaodjuvant therapy. An outline of recommendations has been published by the International Association for the Study of Lung Cancer (IASLC) for the evaluation of pathologic response (PR). However, recent studies indicate that evaluations of immune-related changes are distinct in surgical resected samples from patients treated with immunotherapy. Several clinical trials of neoadjuvant immunotherapy in resectable NSCLC have included the study of biomarkers that can predict the response of therapy and monitor the response to treatment. In this review, we provide relevant information on the current recommendations of the assessment of pathological responses in surgical resected NSCLC tumors treated with neoadjuvant immunotherapy, and we describe current and potential biomarkers to predict the benefits of neoadjuvant immunotherapy in patients with resectable NSCLC.

Relationship between tumor mutational burden, gene mutation status, and clinical characteristics in 340 cases of lung adenocarcinoma

Tumor mutational burden (TMB) is an emerging predictive marker of response to immune checkpoint inhibitor therapies. We evaluated the correlation between clinical indicators and high-throughput sequencing results and TMB in lung adenocarcinoma patients, with the aim of finding simpler and more economical factors as surrogate markers for TMB. The medical records, next-generation sequencing data, and immunohistochemistry results of 340 lung adenocarcinoma patients who were admitted to the First Affiliated Hospital of Zhengzhou University between 2019 and 2020 were collected. The mutated genes were screened for, and the obtained mutated genes were subjected to functional enrichment analysis using R software. A protein-protein interaction (PPI) network was also constructed, and significant modules in the network were identified. Gene Ontology (GO) analyses were performed for the core genes. Univariate and multivariate correlation analyses were performed to judge the correlation between gene mutations and TMB. Genes with a junction mutation rate >1 were selected to construct PPI network and 13 high-connection core genes were screened. The results of GO enrichment analysis showed that the biological processes related to mutant core genes mainly included mitotic cell cycle and cell aging. Subsequently, ATM (p = 0.006) and PIK3CA (p = 0.008) mutation positivity were identified by univariate and multivariate correlation analysis, while TP53 (p = 0.003) and EGFR (p = 0.008) mutation negativity were significantly associated with elevated TMB. The results of this study demonstrate that ATM- and PIK3CA-positive and EGFR-negative mutation status are strongly associated with high levels of TMB and have the potential to be predictive biomarkers of response to immune checkpoint inhibitors in lung adenocarcinoma patients.

Tumor mutational burden and somatic mutation status to predict disease recurrence in advanced melanoma

Tumor mutational burden (TMB) has recently been identified as a biomarker of response to immune checkpoint inhibitors in many cancers, including melanoma. Co-assessment of TMB with inflammatory markers and genetic mutations may better predict disease outcomes. The goal of this study was to evaluate the potential for TMB and somatic mutations in combination to predict the recurrence of disease in advanced melanoma. A retrospective review of 85 patients with stage III or IV melanoma whose tumors were analyzed by next-generation sequencing was conducted. Fisher's exact test was used to assess differences in TMB category by somatic mutation status as well as recurrence locations. Kaplan-Meier estimates and Cox-proportional regression model were used for survival analyses. The most frequently detected mutations were TERT (32.9%), CDKN2A (28.2%), KMT2 (25.9%), BRAF V600E (24.7%), and NRAS (24.7%). Patients with TMB-L + BRAFWT status were more likely to have a recurrence [hazard ratio (HR), 3.43; confidence interval (CI), 1.29-9.15; P = 0.01] compared to TMB-H + BRAF WT. Patients with TMB-L + NRASmut were more likely to have a recurrence (HR, 5.29; 95% CI, 1.44-19.45; P = 0.01) compared to TMB-H + NRAS WT. TMB-L tumors were associated with local (P = 0.029) and in-transit (P = 0.004) recurrences. Analysis of TMB alone may be insufficient in understanding the relationship between melanoma's molecular profile and the body's immune system. Classification into BRAFmut, NRASmut, and tumor mutational load groups may aid in identifying patients who are more likely to have disease recurrence in advanced melanoma.

Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer

Immune checkpoint inhibitors (ICIs) have made great progress in the field of tumors and have become a promising direction of tumor treatment. With advancements in genomics and bioinformatics technology, it is possible to individually analyze the neoantigens produced by somatic mutations of each patient. Neoantigen load (NAL), a promising biomarker for predicting the efficacy of ICIs, has been extensively studied. This article reviews the research progress on NAL as a biomarker for predicting the anti-tumor effects of ICI. First, we provide a definition of NAL, and summarize the detection methods, and their relationship with tumor mutation burden. In addition, we describe the common genomic sources of NAL. Finally, we review the predictive value of NAL as a tumor prediction marker based on various clinical studies. This review focuses on the predictive ability of NAL’s ICI efficacy against tumors. In melanoma, lung cancer, and gynecological tumors, NAL can be considered a predictor of treatment efficacy. In contrast, the use of NAL for urinary system and liver tumors requires further research. When NAL alone is insufficient to predict efficacy, its combination with other indicators can improve prediction efficiency. Evaluating the response of predictive biomarkers before the treatment initiation is essential for guiding the clinical treatment of cancer. The predictive power of NAL has great potential; however, it needs to be based on more accurate sequencing platforms and technologies.

Pancreatic Neuroendocrine Neoplasms: Updates on Genomic Changes in Inherited Tumour Syndromes and Sporadic Tumours Based on WHO Classification

Pancreatic neuroendocrine neoplasms (PanNENs) are the neuroendocrine neoplasms with greatest rate of increase in incidence. Approximately 10% of PanNENs arise as inherited tumour syndromes which include multiple endocrine neoplasia type 1, multiple endocrine neoplasia type 4, von Hippel-Lindau syndrome, neurofibromatosis type1, tuberous sclerosis complex 1/2, Cowden syndrome, and Glucagon cell hyperplasia and neoplasia as well as familial insulinomatosis. In sporadic PanNENs, driver mutations in MEN1, DAXX/ATRX and mTOR pathway genes are associated with development and progression in pancreatic neuroendocrine tumours. The other changes are in VEGF pathway, Notch pathway, germline mutations in MUTYH, CHEK2, BRCA2, PHLDA3 as well as other genetic alterations. On the other hand, pancreatic neuroendocrine carcinomas share similar genetic alterations with ductal adenocarcinomas, e.g., TP53, RB1 or KRAS. In addition, microRNA and changes in immune microenvironment were noted in PanNENs. Updates on these genetic knowledges contribute to the development of management strategies for patients with PanNENs.

Pan-cancer analysis reveals sex-specific signatures in the tumor microenvironment

The processes of cancer initiation, progression, and response to therapy are affected by the sex of cancer patients. Immunotherapy responses largely depend on the tumor microenvironment (TME), but how sex may shape some TME features, remains unknown. Here, we analyzed immune infiltration signatures across 19 cancer types from 1771 male and 1137 female patients in The Cancer Genome Atlas to evaluate how sex may affect the tumor mutational burden (TMB), immune scores, stromal scores, tumor purity, immune cells, immune checkpoint genes, and functional pathways in the TME. Pan‐cancer analyses showed higher TMB and tumor purity scores, as well as lower immune and stromal scores in male patients as compared to female patients. Lung adenocarcinoma, lung squamous carcinoma, kidney papillary carcinoma, and head and neck squamous carcinoma showed the most significant sex biases in terms of infiltrating immune cells, immune checkpoint gene expression, and functional pathways. We further focused on lung adenocarcinoma samples in order to identify and validate sex‐specific immune cell biomarkers with prognostic potential. Overall, sex may affect the tumor microenvironment, and sex‐specific TME biomarkers may help tailor cancer immunotherapy in certain cancer types.

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson’s correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8⁺ T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival.

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.

Relationship between Tumor Mutational Burden, PD-L1, Patient Characteristics, and Response to Immune Checkpoint Inhibitors in Head and Neck Squamous Cell Carcinoma

Simple Summary

Immunotherapy has prompted a dramatic change in the management of head and neck squamous cell carcinoma (HNSCC), but the percentage of patients benefiting from treatment is limited to 20% or less. The application of precision oncology to HNSCC introduces the potential for the emergence of biomarkers that may predict a response to immunotherapy and assist with the selection of patients that may benefit from treatment with an immune checkpoint inhibitors. In this retrospective study, the results of tumor mutational burden and programmed death ligand-1 measurements from HNSCC tumors were evaluated independently for their associations with demographics, risk factors, disease characteristics, survival, and response to ICI. Results of this study are expected to assist in laying the groundwork for creating a framework in which PD-L1 and TMB coexist with other variables to predict response to ICI on an individual level.

Abstract

Failure to predict response to immunotherapy (IO) limited its benefit in the treatment of head and neck squamous cell cancer (HNSCC) to 20% of patients or less. Biomarkers including tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) were evaluated as predictors of response to IO, but the results are inconsistent and with a lack of standardization of their methods. In this retrospective study, TMB and PD-L1 were measured by commercially available methodologies and were correlated to demographics, outcome, and response to PD-1 inhibitors. No correlation was found between TMB and PD-L1 levels. High TMB was associated with smoking and laryngeal primaries. PD-L1 was significantly higher in African Americans, patients with earlier stage tumors, nonsmokers, and nonethanol drinkers. Patients with high TMB fared better in univariate and multivariate survival analysis. No correlation was found between PD-L1 expression and prognosis. There was a statistically significant association between PFS and response to IO and TMB. There was no association between response to ICI and PD-L1 in this study, possibly affected by variations in the reporting method. Further studies are needed to characterize the biomarkers for IO in HNSCC, and this study supports further research into the advancement of TMB in prospective studies.

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.

Digital immunoassay for biomarker concentration quantification using solid-state nanopores

Single-molecule counting is the most accurate and precise method for determining the concentration of a biomarker in solution and is leading to the emergence of digital diagnostic platforms enabling precision medicine. In principle, solid-state nanopores—fully electronic sensors with single-molecule sensitivity—are well suited to the task. Here we present a digital immunoassay scheme capable of reliably quantifying the concentration of a target protein in complex biofluids that overcomes specificity, sensitivity, and consistency challenges associated with the use of solid-state nanopores for protein sensing. This is achieved by employing easily-identifiable DNA nanostructures as proxies for the presence (“1”) or absence (“0”) of the target protein captured via a magnetic bead-based sandwich immunoassay. As a proof-of-concept, we demonstrate quantification of the concentration of thyroid-stimulating hormone from human serum samples down to the high femtomolar range. Further optimization to the method will push sensitivity and dynamic range, allowing for development of precision diagnostic tools compatible with point-of-care format.

The concentration of a biomarker in solution can be determined by counting single molecules. Here the authors report a digital immunoassay scheme with solid-state nanopore readout to quantify a target protein and use this to measure thyroid-stimulating hormone from human serum.

Immunotherapy in Breast Cancer Patients: A Focus on the Use of the Currently Available Biomarkers in Oncology

Immune checkpoint inhibitors (ICIs) have remarkably modified the way solid tumors are managed, including breast cancer. Unfortunately, only a relatively small number of breast cancer patients significantly respond to these treatments. To maximize the immunotherapy benefit in breast cancer, several efforts are currently being put forward for the identification of i) the best therapeutic strategy (i.e. ICI monotherapy or in association with chemotherapy, radiotherapy, or other drugs); ii) the optimal timing for administration (e.g. early/advanced stage of disease; adjuvant/neoadjuvant setting); iii) the most effective and reliable predictive biomarkers of response (e.g. tumor-infiltrating lymphocytes, programmed death-ligand 1, microsatellite instability associated with mismatch repair deficiency, and tumor mutational burden). This article reviews the impacts and gaps in the characterization of immune-related biomarkers raised by clinical and translational research studies with immunotherapy treatments. Particular emphasis has been put on the documented evidence of significant clinical benefits of ICI in different randomized clinical trials, along with preanalytical and analytical issues in predictive biomarkers pathological assessment.

Harnessing biomarkers of response to improve therapy selection in esophago-gastric adenocarcinoma

Advanced esophago-gastric (OG) adenocarcinomas have a high mortality rate and new therapeutic options are urgently required. Despite recent advances in understanding the molecular characteristics of OG cancers, tumor heterogeneity poses a challenge in developing new therapeutics capable of improving patient outcomes. Consequently, chemotherapy remains the mainstay of systemic treatment, with the HER2 being the only predictive biomarker routinely targeted in clinical practice. Recent data indicate that immunotherapy will be incorporated into first-line chemotherapy, but further research is required to refine patient selection. This review will summarize the clinical strategies being evaluated to utilize our knowledge of predictive biomarkers with reference to novel therapeutics, and discuss the barriers to implementing precision oncology in OG adenocarcinoma.

TMB in NSCLC: A Broken Dream?

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

TMB or not TMB as a biomarker: That is the question

Immune checkpoint inhibitors (ICIs) have revolutionized the landscape of therapeutic options for many cancers. These treatments have demonstrated improved efficacy and often a more favourable toxicity profile compared to standard cytotoxic chemotherapy. There are considerable differences among responders, with some patients experiencing durable long-term disease control and even remission. Given this variability, determining a proper biomarker to select patients for ICI therapy has become increasingly important. The only biomarker proven to be predictive of overall survival benefit with ICI therapy is PD-L1 expression level measured by immunohistochemistry. Several attempts have been made to identify different predictive biomarkers. One of the most intriguing and divisive is tumor mutational burden (TMB). TMB represents the number of mutations per megabase (Mut/Mb) of DNA that were sequenced in a specific cancer. With a higher number of mutations detected, and consequentially an increase in the number neo-epitopes, then it is more likely that one or more of those neo-antigens could be immunogenic and trigger a T cell response. Initially, TMB was identified as a biomarker for ICIs in melanoma and subsequent studies suggested a possible clinical role for TMB in non-small cell lung cancer. The initial data were not confirmed in a prospective study assessing OS as the primary endpoint. Recently, the FDA has approved pembrolizumab in all cancers with a TMB > 10Mut/Mb[12] based on findings from the phase 2 KEYNOTE-158. Much criticism has emerged about this pan-cancer approval, in particular about the use of TMB as biomarker to select patients. Here we review the data about the importance and role of TMB as possible pan-cancer one-size-fits-all biomarker. We highlight the strengths and intrinsic limitations of such a complex biomarker and its adoption in the daily practice.

Molecular determinants of clinical outcomes with pembrolizumab versus paclitaxel in a randomized, open-label, phase III trial in patients with gastroesophageal adenocarcinoma

BACKGROUND

In the phase III KEYNOTE-061 trial (NCT02370498), pembrolizumab did not significantly improve overall survival versus paclitaxel as second-line therapy for gastric/gastroesophageal junction (GEJ) adenocarcinoma with programmed death-ligand 1 (PD-L1) combined positive score (CPS) ≥1 tumors. The association of tissue tumor mutational burden (tTMB) status and clinical outcomes was determined, including the relationship with CPS and microsatellite instability-high (MSI-H) status.

PATIENTS AND METHODS

In patients with whole exome sequencing (WES) data [420/592 (71%); pembrolizumab, 218; paclitaxel, 202], the association of tTMB with objective response rate (ORR; logistic regression), progression-free survival (PFS; Cox proportional hazards regression), and overall survival (OS; Cox proportional hazards regression) were measured using one-sided (pembrolizumab) and two-sided [paclitaxel] P values. tTMB was also evaluated using FoundationOne®CDx [205/592 (35%)]. Prespecified equivalent cut-offs of 175 mut/exome for WES and 10 mut/Mb for FoundationOne®CDx were used.

RESULTS

WES-tTMB was significantly associated with ORR, PFS, and OS in pembrolizumab-treated (all P < 0.001) but not paclitaxel-treated patients (all P > 0.6) in univariate analysis. The area under the receiver operating characteristics curve for WES-tTMB and response was 0.68 [95% confidence interval (CI) 0.56-0.81] for pembrolizumab and 0.51 (95% CI 0.39-0.63) for paclitaxel in univariate analysis. There was low correlation between WES-tTMB and CPS in both treatment groups (r ≤ 0.16). WES-tTMB remained significantly associated with all clinical endpoints with pembrolizumab after adjusting for CPS and with PFS and OS after excluding known MSI-H tumors (n = 26). FoundationOne®CDx-tTMB demonstrated a positive association with ORR, PFS, and OS in pembrolizumab-treated patients (all P ≤ 0.003) but not PFS or OS in paclitaxel-treated patients (P > 0.1).

CONCLUSION

This exploratory analysis from KEYNOTE-061 is the first to demonstrate a strong association between tTMB and efficacy with pembrolizumab but not paclitaxel in patients with gastric/GEJ adenocarcinoma in a randomized setting. Data further suggest tTMB is a significant and independent predictor beyond PD-L1 status.

MiRNA-based model for predicting the TMB level in colon adenocarcinoma based on a LASSO logistic regression method

Some patients with advanced colon adenocarcinoma (COAD) are not sensitive to radiotherapy and chemotherapy, and as such, immunotherapy has become the most popular option for these patients. However, different patients respond differently to immunotherapy. Tumor mutational burden (TMB) has been used as a predictor of the response of advanced COAD patients to immunotherapy. A high TMB typically indicates that the patient's immune system will respond well to immunotherapy. In addition, while microRNAs (miRNA) have been shown to play an important role in treatment responses associated with the immune system, the relationship between miRNA expression levels and TMB has not been clarified in COAD.We downloaded miRNA data and mutational files of COAD from the Cancer Genome Atlas database. Differentially expressed miRNAs were screened in the training group, and miRNAs used to construct the model were further identified using the LASSO logistic regression method. After building the miRNA-based model, we explored the correlation between the model and TMB. The model was verified by a receiver operating characteristic curve, and the correlation between it and 3 widely used immune checkpoints (programmed death receptor-1, programmed death-ligand 1, and cytotoxic T-lymphocyte associated protein-4) was explored. Functional enrichment analysis of the selected miRNAs was performed, and these respective miRNA target genes were predicted using online tools.Our results showed that a total of 32 differentially expressed miRNAs were used in the construction of the model. The accuracies of the models of the 2 datasets (training and test sets) were 0.987 and 0.934, respectively. Correlation analysis showed that the correlation of the model with programmed death-ligand 1 and cytotoxic T-lymphocyte associated protein-4, as well as TMB, was high, but there was no correlation with programmed death receptor-1. The results of functional enrichment analysis indicated that these 32 miRNAs were involved in many immune-related biological processes and tumor-related pathways.Therefore, this study demonstrated that differentially expressed miRNAs can be used to predict the TMB level, which can help identify advanced COAD patients who will respond well to immunotherapy. The miRNA-based model may be used as a tool to predict the TMB level in patients with advanced COAD.

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.

Integrated analysis of programmed cell death ligand 1 expression reveals increased levels in high-grade glioma

Purpose

Gliomas are the most frequent primary brain tumors of adults. Despite intensive research, there are still no targeted therapies available. Here, we performed an integrated analysis of glioma and programmed cell death ligand 1 (PD-L1) in 90 samples including 58 glioma and 32 control brain tissues.

Methods

To identify PD-L1 expression in glioma, we performed immunohistochemical analysis of PD-L1 tumor proportion score (TPS) using the clinically valid PD-L1 22C3 antibody on 90 samples including controls and WHO grade I–IV gliomas.

Results

We found that PD-L1 is highly expressed in a subfraction of glioma cells. Analysis of PD-L1 levels in different glioma subtypes revealed a strong intertumoral variation of PD-L1 protein. Furthermore, we correlated PD-L1 expression with molecular glioma hallmarks such as MGMT-promoter methylation, IDH1/2 mutations, TERT promoter mutations and LOH1p/19q.

Conclusion

In summary, we found that PD-L1 is highly expressed in a subfraction of glioma, indicating PD-L1 as a potential new marker in glioma assessment opening up novel therapeutic approaches.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00952-9.

Find the Flame: Predictive Biomarkers for Immunotherapy in Melanoma

Immunotherapy has revolutionized the therapeutic landscape of melanoma. In particular, checkpoint inhibition has shown to increase long-term outcome, and, in some cases, it can be virtually curative. However, the absence of clinically validated predictive biomarkers is one of the major causes of unpredictable efficacy of immunotherapy. Indeed, the availability of predictive biomarkers could allow a better stratification of patients, suggesting which type of drugs should be used in a certain clinical context and guiding clinicians in escalating or de-escalating therapy. However, the difficulty in obtaining clinically useful predictive biomarkers reflects the deep complexity of tumor biology. Biomarkers can be classified as tumor-intrinsic biomarkers, microenvironment biomarkers, and systemic biomarkers. Herein we review the available literature to classify and describe predictive biomarkers for checkpoint inhibition in melanoma with the aim of helping clinicians in the decision-making process. We also performed a meta-analysis on the predictive value of PDL-1.

Laboratory and Clinical Implications of Incidental and Secondary Germline Findings During Tumor Testing

CONTEXT.—

Next-generation sequencing is a powerful clinical tool for cancer management but can produce incidental/secondary findings that require special consideration.

OBJECTIVE.—

To discuss clinical and laboratory issues related to incidental or secondary germline findings in the clinical setting of tumor testing and inform future guidelines in this area.

DESIGN.—

A College of American Pathologists workgroup including representation from the American Society of Clinical Oncology, the Association for Molecular Pathology, and the American College of Medical Genetics and Genomics created a review of items that should be considered when developing guidelines for incidental or secondary findings when performing clinical tumor testing.

RESULTS.—

Testing recommendations should be cognizant of the differences among anticipated incidental, unanticipated incidental, and secondary findings, and whether normal tissue is also tested. In addition to defining which variants will be reported, robust recommendations must also take into account test design and validation, reimbursement, cost, infrastructure, impact on reflex testing, and maintenance of proficiency. Care providers need to consider the potential of a test to uncover incidental or secondary findings, the recommendation of upfront counseling, the need for consent, the timing of testing and counseling, and that the exact significance of a finding may not be clear.

CONCLUSIONS.—

As clinical oncology testing panels have become a mainstay of clinical cancer care, guidelines addressing the unique aspects of incidental and secondary findings in oncology testing are needed. This paper highlights clinical and laboratory considerations with regard to incidental/secondary findings and is a clarion call to create recommendations.

Pan-cancer Analysis of Tumor Mutational Burden and Homologous Recombination DNA Damage Repair Using Targeted Next-Generation Sequencing

Purpose

Current variability in methods for tumor mutational burden (TMB) estimation and reporting demonstrates the urgent need for a homogeneous TMB assessment approach. Here, we compared TMB distributions in different cancer types using two customized targeted panels commonly used in clinical practice.

Materials and Methods

TMB spectra of 295- and 1021-gene panels in multiple cancer types were compared using targeted next-generation sequencing (NGS). The TMB distributions across a diverse cohort of 2,332 cancer cases were then investigated for their associations with clinical features. Treatment response data were collected for 222 patients who received immune-checkpoint inhibitors (ICIs) and their homologous recombination DNA damage repair (HR-DDR) and programmed death-ligand 1 (PD-L1) expression were additionally assessed and compared with the TMB and response rate.

Results

The median TMB between gene panels was similar despite a wide range in TMB values. The highest TMB was eight and 10 in patients with squamous cell carcinoma and esophageal carcinoma according to the classification of histopathology and cancer types, respectively. Twenty-three out of 103 patients (22.3%) were HR-DDR–positive and could benefit from ICI therapy; out of those 23 patients, seven patients had high TMB (p=0.004). Additionally, PD-L1 expression was not associated with TMB or treatment response among patients receiving ICIs.

Conclusion

Targeted NGS assays demonstrated the ability to evaluate TMB in pan-cancer samples as a tool to predict response to ICIs. In addition, TMB integrated with HR-DDR–positive status could be a significant biomarker for predicting ICI response in patients.

The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker

Tumor mutational burden (TMB) reflects cancer mutation quantity. Mutations are processed to neo-antigens and presented by major histocompatibility complex (MHC) proteins to T cells. To evade immune eradication, cancers exploit checkpoints that dampen T cell reactivity. Immune checkpoint inhibitors (ICIs) have transformed cancer treatment by enabling T cell reactivation; however, response biomarkers are required, as most patients do not benefit. Higher TMB results in more neo-antigens, increasing chances for T cell recognition, and clinically correlates with better ICI outcomes. Nevertheless, TMB is an imperfect response biomarker. A composite predictor that also includes critical variables, such as MHC and T cell receptor repertoire, is needed.

[A CASE OF CASTRATION-RESISTANT PROSTATE CANCER THAT COMPLETELY AMELIORATED AFTER TRANSITION TO BEST SUPPORTIVE CARE]

The patient was a 78-year-old man who, at 67 years of age, had been diagnosed with prostate cancer cT3bN1M0 (Gleason score 5+5) and started on androgen ablation therapy. Thereafter, the cancer had developed into castration-resistant prostate cancer; thus, after surgical castration, the patient was treated with 27 courses of docetaxel, as well as enzalutamide and abiraterone. However, new metastases appeared in the paraaortic lymph nodes, post which, the patient was treated with 25 courses of cabazitaxel. However, the paraaortic and intrapelvic lymph nodes became enlarged, the patient developed rectal occlusion and urinary retention due to growth of the primary tumor, and his general condition deteriorated. Hence, the patient decided to abandon treatment approximately 10 years after initial diagnosis, underwent cystostomy, and transitioned to best supportive care. After stopping treatment, his general condition started to improve, and approximately 6 months later, his PSA levels had fallen from 55.5 ng/mL to 19.3 ng/mL and the lymph nodes had also reduced in size. When the cancer was treated with local radiation, the enlarged tumor disappeared, and the patient was able to urinate again. It has now been 2.5 years since treatment was stopped, and both, the paraaortic and intrapelvic lymph nodes have reduced in size to < 1 cm, and the PSA value continues to remain less than 0.008 ng/ml.