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

Role of tumor mutational burden in patients with urothelial carcinoma treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Background

The predictive value of tumor mutation burden (TMB) on the efficacy of immunotherapy has been confirmed in multiple cancer types in previous studies. For urothelial carcinoma (UC) patients treated with immune checkpoint inhibitors (ICIs), whether TMB is a suitable biomarker to predict the benefit of ICIs remains a matter of much debate. We conducted this meta-analysis to evaluate the role of TMB in patients with UC treated with ICIs.

Methods

Two investigators independently searched the literature, screened eligible studies, extracted valid data, and scored quality assessments. Meta-analyses of the effect size hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS), and effect size odds ratio (OR) for objective response rate (ORR) were performed and visualized with forest plots using the STATA14.0 software. The statistical difference in benefit from ICIs for UC patients between the high TMB group and the low TMB group was significant when the p-value <0.05. Sensitivity analysis and publication bias further verified the stability and reliability of statistical results.

Results

A total of 2,499 patients from 14 studies were included in this meta-analysis. The results indicated that UC patients with high TMB showed significantly longer OS and PFS than those with low TMB after ICI treatment (OS: HR 0.69, 95% CI 0.62, 0.76, p < 0.05; PFS: HR 0.67, 95% CI 0.59, 0.76, p < 0.05). The high TMB group exhibited a superior response to ICIs than the low TMB group, with no significant difference (OR 1.64, 95% CI 0.94, 2.86, p = 0.08). The results were stable and reliable, with no publication bias.

Conclusions

This meta-analysis demonstrated that UC patients with high TMB exhibited significantly longer survival than those with low TMB after ICI treatment. TMB may be a favorable predictor for UC immunotherapy in future clinical practice.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42025642602.

Nonhypermutator Cancers Access Driver Mutations Through Reversals in Germline Mutational Bias

Cancer is an evolutionary disease driven by mutations in asexually reproducing somatic cells. In asexual microbes, bias reversals in the mutation spectrum can speed adaptation by increasing access to previously undersampled beneficial mutations. By analyzing tumors from 20 tissues, along with normal tissue and the germline, we demonstrate this effect in cancer. Nonhypermutated tumors reverse the germline mutation bias and have consistent spectra across tissues. These spectra changes carry the signature of hypoxia, and they facilitate positive selection in cancer genes. Hypermutated and nonhypermutated tumors thus acquire driver mutations differently: hypermutated tumors by higher mutation rates and nonhypermutated tumors by changing the mutation spectrum to reverse the germline mutation bias.

Identifying predictors of overall survival among patients with TMB-low metastatic cancer treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly advanced cancer therapy, yet their efficacy in tumors with low tumor mutational burden (TMB) remains suboptimal. In this study, we aimed to elucidate the impact of somatic mutations on overall survival (OS) in TMB-low patients treated with ICIs and to explore the potential for personalized treatment selection through machine learning.

METHODS

We conducted a comprehensive analysis of 1172 TMB-low (TMB < 10 mutations per megabase) patients with cancer receiving ICIs, examining the association between specific gene mutations and OS. Additionally, we developed a decision tree model (DTM) to predict OS based on clinical features and tumor mutational profiles.

RESULTS

Our findings reveal that mutations in DAXX, HLA-A, H3C2, IGF1R, CTNNB1, SMARCA4, KMT2D, and TP53 are significantly associated with poorer survival outcomes in the multivariate analysis. Remarkably, for renal cell carcinoma (RCC) patients, VHL mutations predicted improved OS following ICI even when adjusted for age, sex, and microsatellite instability (MSI) status in both multivariate analysis and the DTM model.

CONCLUSIONS

These results reinforce the prevailing notion that TMB alone does not predict ICI response, highlighting the critical role of individual gene mutations in TMB-low tumors under ICI therapy. Furthermore, our study demonstrates the promise of machine learning models in optimizing ICI treatment decisions, paving the way for more precise and effective therapeutic strategies in this patient population.

Advancing Breast Cancer Treatment: The Role of Immunotherapy and Cancer Vaccines in Overcoming Therapeutic Challenges

Breast cancer (BC) remains a significant global health challenge due to its complex biology, which complicates both diagnosis and treatment. Immunotherapy and cancer vaccines have emerged as promising alternatives, harnessing the body's immune system to precisely target and eliminate cancer cells. However, several key factors influence the selection and effectiveness of these therapies, including BC subtype, tumor mutational burden (TMB), tumor-infiltrating lymphocytes (TILs), PD-L1 expression, HER2 resistance, and the tumor microenvironment (TME). BC subtypes play a critical role in shaping treatment responses. Triple-negative breast cancer (TNBC) exhibits the highest sensitivity to immunotherapy, while HER2-positive and hormone receptor-positive (HR+) subtypes often require combination strategies for optimal outcomes. High TMB enhances immune responses by generating neoantigens, making tumors more susceptible to immune checkpoint inhibitors (ICIs); whereas, low TMB may indicate resistance. Similarly, elevated TIL levels are associated with better immunotherapy efficacy, while PD-L1 expression serves as a key predictor of checkpoint inhibitor success. Meanwhile, HER2 resistance and an immunosuppressive TME contribute to immune evasion, highlighting the need for multi-faceted treatment approaches. Current breast cancer immunotherapies encompass a range of targeted treatments. HER2-directed therapies, such as trastuzumab and pertuzumab, block HER2 dimerization and enhance antibody-dependent cellular cytotoxicity (ADCC), while small-molecule inhibitors, like lapatinib and tucatinib, suppress HER2 signaling to curb tumor growth. Antibody-drug conjugates (ADCs) improve tumor targeting by coupling monoclonal antibodies with cytotoxic agents, minimizing off-target effects. Meanwhile, ICIs, including pembrolizumab, restore T-cell function, and CAR-macrophage (CAR-M) therapy leverages macrophages to reshape the TME and overcome immunotherapy resistance. While immunotherapy, particularly in TNBC, has demonstrated promise by eliciting durable immune responses, its efficacy varies across subtypes. Challenges such as immune-related adverse events, resistance mechanisms, high costs, and delayed responses remain barriers to widespread success. Breast cancer vaccines-including protein-based, whole-cell, mRNA, dendritic cell, and epitope-based vaccines-aim to stimulate tumor-specific immunity. Though clinical success has been limited, ongoing research is refining vaccine formulations, integrating combination therapies, and identifying biomarkers for improved patient stratification. Future advancements in BC treatment will depend on optimizing immunotherapy through biomarker-driven approaches, addressing tumor heterogeneity, and developing innovative combination therapies to overcome resistance. By leveraging these strategies, researchers aim to enhance treatment efficacy and ultimately improve patient outcomes.

Genomic and Immune Landscape of Non-Small Cell Lung Cancer Brain Metastases

PURPOSE

Metastatic spread of non-small cell lung cancer (NSCLC) to the brain is a commonly occurring and challenging clinical problem, often resulting in patient mortality. Systemic therapies including immunotherapy have modest efficacy in treating brain metastases. Moreover, the local immune environment of brain metastases remains poorly described. This study aims to understand the genomic and immune landscape of NSCLC brain metastases.

METHODS

A total of 3,060 patients with NSCLC sequenced with the Strata Select assay on the Strata Oncology Platform were analyzed. Genomic alterations, tumor mutation burden (TMB), PD-L1 expression, and immune gene expression were compared across different tissue sites and histologies and within brain metastases.

RESULTS

A significant increase in TMB was observed in the brain metastasis samples compared with nonbrain metastasis samples. Mutations in TP53, KRAS, and CDKNA2A were more prevalent within the brain metastasis cohort compared with other tissue locations. In addition, PD-L1 expression was significantly decreased within brain metastasis samples compared with other sites. The overall immune landscape within the brain metastasis samples was largely reduced compared with primary lung samples. However, an immune-enriched brain metastasis cohort was identified with higher expressions of PD-L1 and other immune-related genes.

CONCLUSION

The overall TMB is increased within brain metastases compared with primary lung and other metastasis sites and is associated with a markedly diminished overall immune landscape. The identification of an immune-enriched brain metastasis subgroup suggests potential heterogeneity within the brain metastasis patient cohort, which might have implications for the development of targeted therapies.

Quantification of PD-L1 expression and tumor mutational burden in biologically distinct advanced pancreatic cancers responding to pembrolizumab: case reports

Background

The advent of checkpoint therapy is one of the most important recent advancements in cancer therapy. Though checkpoint therapy is a mainstay in some cancers, it has been largely ineffective in treating cancers of the pancreas. Pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors are seldom responsive to checkpoint inhibition.

Case presentations

Here we present two cases of advanced pancreatic cancers that either failed to respond or recurred following conventional treatments. Tissue from each tumor was sequenced and analyzed for PD-L1 expression. Each patient was started on checkpoint blockade after assessing for a predictive biomarker, either the combined positive score or the tumor mutational burden. In each case, checkpoint blockade led to durable radiographic responses.

Conclusions

We therefore propose that it is reasonable to assess combined positive score and tumor mutational burden in refractory or recurrent pancreatic cancers when initiation of ICB is being considered.

PD-1 blockade plus cisplatin-based chemotherapy in patients with small cell/neuroendocrine bladder and prostate cancers

Small cell neuroendocrine cancers share biologic similarities across tissue types, including transient response to platinum-based chemotherapy with rapid progression of disease. We report a phase 1b study of pembrolizumab in combination with platinum-based chemotherapy in 15 patients with stage III-IV small cell bladder (cohort 1) or small cell/neuroendocrine prostate cancers (cohort 2). Overall response rate (ORR) is 43% with two-year overall survival (OS) rate of 86% (95% confidence interval [CI]: 0.63, 1.00) for cohort 1 and 57% (95% CI: 0.30, 1.00) for cohort 2. Treatment is tolerated well with grade 3 or higher adverse events occurring in 40% of patients with no deaths or treatment cessation secondary to toxicity. Single-cell and T cell receptor sequencing of serial peripheral blood samples reveals clonal expansion of diverse T cell repertoire correlating with progression-free survival. Our results demonstrate promising efficacy and safety of this treatment combination and support future investigation of this biomarker. This study was registered at ClinicalTrials.gov (NCT03582475).

ICOSLG Is Associated with Anti-PD-1 and Concomitant Antihistamine Treatment Response in Advanced Melanoma

We previously demonstrated that patients with metastatic unresectable stage IIIb-IV melanoma receiving cetirizine (a second-generation H1 antagonist antihistamine) premedication with immunotherapy had better outcomes than those not receiving cetirizine. In this retrospective study, we searched for a gene signature potentially predictive of the response to the addition of cetirizine to checkpoint inhibition (nivolumab or pembrolizumab with or without previous ipilimumab). Transcriptomic analysis showed that inducible T cell costimulator ligand (ICOSLG) expression directly correlated with the disease control rate (DCR) when detected with a loading value > 0.3. A multivariable logistic regression model showed a positive association between the DCR and ICOSLG expression for progression-free survival and overall survival. ICOSLG expression was associated with CD64, a specific marker of M1 macrophages, at baseline in the patient samples who received cetirizine concomitantly with checkpoint inhibitors, but this association was not present in subjects who had not received cetirizine. In conclusion, our results show that the clinical advantage of concomitant treatment with cetirizine during checkpoint inhibition in patients with malignant melanoma is associated with high ICOSLG expression, which could predict the response to immune checkpoint inhibitor blockade.

Cancer Biomarkers and Precision Oncology: A Review of Recent Trends and Innovations

The discovery of cancer-specific biomarkers has resulted in major advancements in the field of cancer diagnostics and therapeutics, therefore significantly lowering cancer-related morbidity and mortality. Cancer biomarkers can be generally classified as prognostic biomarkers that predict specific disease outcomes and predictive biomarkers that predict disease response to targeted therapeutic interventions. As research in the area of predictive biomarkers continues to grow, precision medicine becomes far more integrated in cancer treatment. This article presents a general overview on the most recent advancements in the area of cancer biomarkers, immunotherapy, artificial intelligence, and pharmacogenomics of the Middle East.

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

OBJECTIVES

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions.

METHODS

A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed.

RESULTS

The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations.

CONCLUSIONS

This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

Sequencing of Checkpoint or BRAF/MEK Inhibitors on Brain Metastases in Melanoma

BACKGROUND

The impact of the order of treatment with checkpoint inhibitors or BRAF/MEK inhibitors on the development of brain metastases in patients with metastatic unresectable BRAFV600-mutant melanoma is unknown. The SECOMBIT trial examined the impact of the order of receipt of these treatments in such patients.

METHODS

In this three-arm trial, we reviewed patients without brain metastases who received the BRAF/MEK inhibitors encorafenib and binimetinib until they had progressive disease followed by the immune checkpoint inhibitors ipilimumab and nivolumab (arm A); or treatment with ipilimumab and nivolumab until they had progressive disease followed by encorafenib and binimetinib (arm B); or treatment with encorafenib and binimetinib for 8 weeks followed by ipilimumab and nivolumab until they had progressive disease followed by retreatment with encorafenib arm binimetinib (arm C).

RESULTS

Brain metastases were discovered during the trial in 23/69 patients in arm A, 11/69 in arm B, and 9/68 in arm C. At a median follow-up of 56 months, the 60-month brain metastases-free survival rates were 56% for arm A, 80% for arm B (hazard ratio [HR] vs. A: 0.40, 95% confidence interval [CI] 0.23 to 0.58), and 85% for arm C (HR vs. A: 0.35, 95% CI 0.16 to 0.76).

CONCLUSIONS

In patients with unresectable metastatic melanoma, the treatment sequence of immune checkpoint inhibition followed by BRAF/MEK inhibitors was associated with longer periods of new brain metastases-free survival than the reverse sequence. A regimen in which immune checkpoint inhibition was sandwiched between BRAF/MEK inhibition also appeared to be protective against brain metastases. (ClinicalTrials.gov number NCT02631447.).

Whole-Exome Sequencing of Vulvar Squamous Cell Carcinomas Reveals an Impaired Prognosis in Patients With TP53 Mutations and Concurrent CCND1 Gains

Very little information is available on the mutational landscape of vulvar squamous cell carcinoma (VSCC), a disease that mainly affects older women. Studies focusing on the mutational patterns of the currently recognized etiopathogenic types of this tumor (human papillomavirus [HPV]-associated [HPV-A], HPV-independent [HPV-I] with TP53 mutation [HPV-I/TP53mut], and HPV-I with wild-type TP53 [HPV-I/TP53wt]) are particularly rare, and there is almost no information on the prognostic implications of these abnormalities.Whole-exome DNA sequencing of 60 VSCC and matched normal tissues from each patient was performed. HPV detection, immunohistochemistry (IHC) for p16, p53, and mismatch repair proteins were also performed. Ten tumors (16.7%) were classified as HPV-A, 37 (61.7%) as HPV-I/TP53mut, and 13 (21.6%) as HPV-I/TP53wt. TP53 was the most frequently mutated gene (66.7%), followed by FAT1 (28.3%), CDKN2A (25.0%), RNF213 (23.3%), NFE2L2 (20%) and PIK3CA (20%). All the 60 tumors (100%) were DNA mismatch repair proficient. Seventeen tumors (28.3%) showed CCND1 gain. Bivariate analysis, adjusted for International Federation of Gynecology and Obstetrics stage, revealed that TP53 mutation, CCND1 gain, and the combination of the 2 alterations were strongly associated with impaired recurrence-free survival (hazard ratio, 4.4; P < .001) and disease-specific survival (hazard ratio, 6.1; P = .002). Similar results were obtained when p53 IHC status was used instead of TP53 status and when considering only HPV-I VSCC. However, in the latter category, p53 IHC maintained its prognostic impact only in combination with CCND1 gains. All tumors carried at least one potentially actionable genomic alteration. In conclusion, VSCCs with CCND1 gain represent a prognostically adverse category among HPV-I/TP53mut tumors. All patients with VSCCs are potential candidates for targeted therapy.

The neoantigens derived from transposable elements - A hidden treasure for cancer immunotherapy

Neoantigen-based therapy is a promising approach that selectively activates the immune system of the host to recognize and eradicate cancer cells. Preliminary clinical trials have validated the feasibility, safety, and immunogenicity of personalized neoantigen-directed vaccines, enhancing their effectiveness and broad applicability in immunotherapy. While many ongoing oncological trials concentrate on neoantigens derived from mutations, these targets do not consistently provoke an immune response in all patients harboring the mutations. Additionally, tumors like ovarian cancer, which have a low tumor mutational burden (TMB), may be less amenable to mutation-based neoantigen therapies. Recent advancements in next-generation sequencing and bioinformatics have uncovered a rich source of neoantigens from non-canonical RNAs associated with transposable elements (TEs). Considering the substantial presence of TEs in the human genome and the proven immunogenicity of TE-derived neoantigens in various tumor types, this review investigates the latest findings on TE-derived neoantigens, examining their clinical implications, challenges, and unique advantages in enhancing tumor immunotherapy.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Pathways to hypermutation in high-grade gliomas: Mechanisms, syndromes, and opportunities for immunotherapy

Despite rapid advances in the field of immunotherapy, including the success of immune checkpoint inhibition in treating multiple cancer types, clinical response in high-grade gliomas (HGGs) has been disappointing. This has been in part attributed to the low tumor mutational burden (TMB) of the majority of HGGs. Hypermutation is a recently characterized glioma signature that occurs in a small subset of cases, which may open an avenue to immunotherapy. The substantially elevated TMB of these tumors most commonly results from alterations in the DNA mismatch repair pathway in the setting of extensive exposure to temozolomide or, less frequently, from inherited cancer predisposition syndromes. In this review, we discuss the genetics and etiology of hypermutation in HGGs, with an emphasis on the resulting genomic signatures, and the state and future directions of immuno-oncology research in these patient populations.

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

A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.