Tumor Mutational Burden Values Research Articles

Therapeutic potential of immune-cold solid tumors through homologous recombination deficiency.

e14585 Background: Genome-wide homologous recombination deficiency (HRD) score is an important parameter to select patients for HRR pathway inhibitors. HRD scores are independent therapeutic predictors unlike tumor mutation burden (TMB), and microsatellite instability (MSI) values to stratify tumors for predicting immunogenicity. Since solid tumors often choose the hallmark of immune evasion to disseminate to distant tissues, their identification and treatment through genomic signatures for immunotherapy is essential. Low TMB and MSI values are often associated with immune evasion and immune cold tumors. For such patients HRD scores can be important determinants to inform on the therapeutic potential of HRR pathway inhibitors and immune checkpoint inhibitors (ICI). In this study, we retrospectively investigated solid tumors with metastatic phenotypes exhibiting microsatellite stable (MSS), MSI-Low and TMB low phenotypes. The presence of HRR pathway deficiency in these patients may lead to chromosomal instabilities and cell surface neo-antigens thereby offering potential therapeutic benefit to PARP inhibitors and ICI. Methods: 53 metastatic cancer patients were retrospectively analysed for genome-wide HRD, TMB and MSI scores based on OncoIndx comprehensive gene panel (CGP). OncoIndx CGP targets 1080 cancer related genes along with 47 HRR genes and 1600 MSI signature sites. HRD, TMB and MSI scores were determined by sequencing the individual sample libraries in pair end mode on Illumina Nextseq platform (depth 5500X). Cutoff scores HRD high (≥50), TMB high (≥10 Mutations/MB), MSI high (≥20). Results: A cohort of 53 solid tumor patients were evaluated retrospectively of which 71.6% (n=38) patients showed metastasis (brain, Popliteal fossa, skeletal, and nodal metastasis). Of these, 9.4% (n=5) patients were detected with MSI-high, 56.6% (n=30) were MSI-Low and 1.8% (n=1) were MSS. 13.2% (n=7) patients were TMB-High, 3.7% (n=2) were TMB intermediate and 41.0% (n=22) were TMB-Low. Most importantly, 41.0% (n=22) patients were both MSI and TMB low thus transforming the tumor suggestively immune-cold. Only 22.7% (n=5) patients showed PD-L1 expression on IHC with scores ranging from TPS: 2% to 20%. In this dominantly immune-cold patient cohort, 40.9% (n=9) patients showed high/intermediate HRD (22.7% high (n=5); 18.18% Intermediate (n=4)). Conclusions: The presence of HRD in 40.9% patients in the dominant immune-cold patients was observed with increased chromosomal instability and cell surface neo-antigens. Thus, HRD could potentially transform tumors to become immune-hot to benefit from PARP inhibitors and ICI.

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.

Abstract 6456: The utility of clinical sequencing in the diagnosis and treatment of soft tissue sarcomas; Real world data based on nation-wide database

Abstract Purpose: Soft tissue sarcomas (STS) are rare and heterogeneous tumors arising from mesenchymal connective tissue, accounting for approximately 1%-2% of all malignancies. Due to their rarity and heterogeneity, it is challenging for accurate diagnosis, prognosis, and treatment of sarcoma. Recently, next-generation sequencing (NGS)-based comprehensive cancer genome profiling (CGP) has increasingly become a routine practice for patients with solid cancers worldwide. To collect genomic information and clinical characteristics of patients who underwent CGP, the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) has also been established since 2018 in Japan. Almost all the clinical and genomic data of patients who received CGP tests the had been collected in C-CAT. Here, we examined the utility of CGP test in the diagnosis and treatment of STS using this nation-wide database. Patients and Methods: We retrospectively evaluated the records of patients with STS who received F1CDx between 2019 and 2022 and registered in C-CAT. This assay carries 324 genes and determines nucleotide substitutions, insertions/deletions, and copy number (CN) alterations of 309 genes, fusions of 36 genes, tumor mutation burden (TMB) and microsatellite instability (MSI). High TMB (TMB-H) was defined as a TMB value >10 muts/Mb. Ranges of the MSI score were assigned MSI-High (MSI-H) or microsatellite stable (MSS). Results: From 2019 to 2022, 1387 patients with STS were registered in C-CAT. The histological types included leiomyosarcoma in 357 patients, dedifferentiated liposarcoma in 178 patients, and undifferentiated pleomorphic sarcoma in 82 patients, and the others. The most commonly altered genes included TP53, CDKN2A, Rb1, and CDKN2B. Among the 1387 cases, we observed 110 fusion events in 108 cases (7.7%), and 2 cases showed 2 types of fusion transcripts. Fifteen (2.8%) patients were re-classified based on the detection of highly histology-specific translocations. Among them, an initial diagnosis of sarcoma NOS was classified as Ewing Sarcoma (EWSR1-FLI1 in 2 patients), NTRK-rearranged spindle cell neoplasm (NTRK fusion in 3 patients), CIC-rearranged sarcoma (CIC-DUX4 in 3 patients), and sarcoma with BCOR genetic alterations (BCOR-ZC3H7B in 1 patient) as these gene fusions were identified. Druggable gene alterations was detected in 347 patients (25%) and 55 patients (4%) who received genotype-matched therapy. In 38 patients for whom the effect of treatment could be evaluated, complete response and partial response were achieved in 2 (5%) and 10 (26%) patients, respectively. Discussion: In conclusion, the comprehensive genomic profiling test appears to be an important tool in the diagnosis and treatment of sarcomas. Citation Format: Eiji Nakada, Tomohiro Fujiwara, Toshi Kunisada, Shota Takihira, Daisuke Ennishi, Hideki Yamamoto, Kiichiro Ninomiya, Shuta Tomida, Mashu Futagawa, Akira Hirasawa, Shinichi Toyooka, Toshifumi Ozaki. The utility of clinical sequencing in the diagnosis and treatment of soft tissue sarcomas; Real world data based on nation-wide database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6456.

Abstract 2501: High mutation burden and ultraviolet light signature in conjunctival squamous cell carcinoma

Abstract Purpose: Conjunctival squamous cell carcinoma (conjSCC) is endemic in sub-Saharan Africa and is a major cause of ocular morbidity and mortality. This study aims to evaluate Tumor Mutation Burden (TMB), genomic signature, and histologic characteristics of conjunctival squamous cell carcinoma (conjSCC) in two cohorts of patients; one from the USA and one from Ethiopia. Methods: Whole exome sequencing data, PD-L1 expression, histologic features, and human papillomavirus (HPV) status were analyzed in conj SCC tumors of 25 patients treated at an Ethiopian (ETH) cancer center and 29 patients treated at a US cancer center. Genomic alterations in conjSCC were compared with SCCs from other anatomic sites using data from The Cancer Genome Atlas (TCGA). Results: TMB was found to be relatively high in conjunctival SCC (3.01/Mb, log10), was comparable to TMB values associated with cutaneous SCC, and was higher than most other SCC types available in the TCGA database. ETH samples had higher tumor mutational burden compared to the US cohort (3.34 vs 2.73). Solar elastosis was seen in a higher percentage of tumors from Ethiopia compared with the US cohort (78% vs. 10%). HPV status was similar between the cohorts (ETH: 21%, MDA: 28%). PD-L1 positivity defined as > 1% of tumor cells was seen in 33 of 53 tumors (62%). Mutations associated with ultraviolet light (UV) signature were the most frequently encountered signatures in both cohorts (SBS7b: 74% and SBS7a: 72%). UV signatures were more prevalent in the ETH cohort compared with the US cohort. Conclusions: Conjunctival SCC is associated with a high TMB providing the opportunity for immune checkpoint inhibitor therapy for patients with locally advanced and/or metastatic conjunctival SCC. Clinical trials are needed to assess the efficacy of immune checkpoint inhibitors in patients with conjunctival SCC, particularly in sub-Saharan Africa where this cancer is more frequently seen, and is associated with significant morbidity and mortality. Citation Format: Bita Esmaeli, Frederico O. Gleber-Netto, Oded Sagiv, Curtis R. Pickering, Neil Gross, Jing Ning, Melisachew M. Yeshi, Jonas Mitku, Michael T. Tetzlaff, Priyadharsini Nagarajan. High mutation burden and ultraviolet light signature in conjunctival squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2501.

Tumor mutational burden assessment and standardized bioinformatics approach using custom NGS panels in clinical routine

BackgroundHigh tumor mutational burden (TMB) was reported to predict the efficacy of immune checkpoint inhibitors (ICIs). Pembrolizumab, an anti-PD-1, received FDA-approval for the treatment of unresectable/metastatic tumors with high TMB as determined by the FoundationOne®CDx test. It remains to be determined how TMB can also be calculated using other tests.ResultsFFPE/frozen tumor samples from various origins were sequenced in the frame of the Institut Curie (IC) Molecular Tumor Board using an in-house next-generation sequencing (NGS) panel. A TMB calculation method was developed at IC (IC algorithm) and compared to the FoundationOne® (FO) algorithm.Using IC algorithm, an optimal 10% variant allele frequency (VAF) cut-off was established for TMB evaluation on FFPE samples, compared to 5% on frozen samples. The median TMB score for MSS/POLE WT tumors was 8.8 mut/Mb versus 45 mut/Mb for MSI/POLE-mutated tumors. When focusing on MSS/POLE WT tumor samples, the highest median TMB scores were observed in lymphoma, lung, endometrial, and cervical cancers. After biological manual curation of these cases, 21% of them could be reclassified as MSI/POLE tumors and considered as “true TMB high.” Higher TMB values were obtained using FO algorithm on FFPE samples compared to IC algorithm (40 mut/Mb [10–3927] versus 8.2 mut/Mb [2.5–897], p < 0.001).ConclusionsWe herein propose a TMB calculation method and a bioinformatics tool that is customizable to different NGS panels and sample types. We were not able to retrieve TMB values from FO algorithm using our own algorithm and NGS panel.

Harmonization of tumor mutation burden testing with comprehensive genomic profiling assays: an IQN Path initiative

BackgroundAlthough conflicting results emerged from different studies, the tumor mutational burden (TMB) appears as one of most reliable biomarkers of sensitivity to immune checkpoint inhibitors. Several laboratories are reporting TMB...

Genomic characterization and immunotherapy for microsatellite instability-high in cholangiocarcinoma

BackgroundMicrosatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA.MethodsWe enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA.ResultsDifferential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P = 0.001) and progression-free survival (PFS, HR = 0.14, P < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy.ConclusionsMSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS.Trial registrationThis study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577).

Tumor mutational burden as a predictive biomarker for non-small cell lung cancer treated with immune checkpoint inhibitors of PD-1/PD-L1.

The significant clinical benefits of PD-1/PD-L1 immune checkpoint inhibitors (ICIP) in non-small cell lung cancer (NSCLC) have been widely recognized, emphasizing the urgent need for a reliable biomarker. In this study, we find the remarkable capacity of tumor mutational burden (TMB) to serve as an accessible and streamlined indicator. We designed a retrospective cohort study, consisting of 600 NSCLC patients treated with ICIP. Association between TMB and overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) has been explored. A strong positive correlation between TMB levels and OS, PFS rates, clinical benefit has been found when TMB > = 16(TMB > = 16 mutations/megabase (mut/Mb)). However, when TMB < 16, increasing TMB values did not exhibit a gradual stepwise increase in OS and PFS rates. The median months of OS in the TMB > = 16 and < 16 are 35.58, and 10.71months respectively with average 12.39months (p < 0.0001). The median months of PFS in the TMB > = 16 and < 16 are not-obtained, and 2.79months respectively with an average of 3.32months (p < 0.0001). The DCR in the TMB > = 16 and < 16 are 71.4% and 44.2% respectively with an average of 47.7% (p < 0.0001). The ORR in the TMB > = 16 and < 16 are 49.4% and 20.8% respectively with an average of 24.5% (p < 0.0001). The TMB > = 16 shows significantly associated with optimal ICIP treatment outcomes, including higher patient survival rates, delayed disease progression, and significant clinical benefits. These results present the potential of TMB as a promising biomarker candidate for NSCLC patients undergoing ICIP treatment.

Analysis of tumor mutational burden and mutational landscape comparing whole-exome sequencing and comprehensive genomic profiling in patients with resectable early-stage non-small-cell lung cancer

Background: Identifying actionable driver mutations via tissue-based comprehensive genomic profiling (CGP) is paramount in treatment decisions for metastatic non-squamous, non-small-cell lung cancer (NSCLC). However, the role of CGP remains elusive in resectable NSCLC. Here, we elucidate the feasibility of CGP in early-stage NSCLC Korean patients and compare the tumor mutational burden (TMB) and mutation landscape using three different platforms. Methods: All surgically resected NSCLC samples ( N = 96) were analyzed to assess the concordance in TMB calculation and targetable mutations using whole-exome sequencing (WES) and TruSight Oncology 500 (TSO500). In all, 26 samples were analyzed with Foundation One CDx Assay (F1CDx). Programmed death-ligand 1 (PD-L1) expression was evaluated using Vectra Polaris. Results: Stage distribution post-surgery was 80% I ( N = 77) and 20% II ( N = 19). Ninety-nine percent ( N = 95) were adenocarcinoma. The median TMB with WES and TSO500 was 1.6 and 4.7 mut/Mb, respectively ( p &lt; 0.05). Using all three platforms, the median TMB was 1.9, 5.5, and 4 mut/Mb for WES, TSO500, and F1CDx, respectively ( p = 0.0048). Linear regression analysis of TMB values calculated between WES and TSO500 resulted in a concordance correlation coefficient of 0.83. For the PD-L1 tumor proportion score of &lt;1% (negative, N = 18), 1–49% (low, N = 68), and ⩾50% (high, N = 10), the R2 values were 0.075, 0.79, and 0.95, respectively. The R2 values for TMB concordance were variable between the three platforms. Mutation landscape revealed EGFR mutation (51%, N = 49) as the most common actionable driver mutation, comprising L858R ( N = 22), E19del ( N = 20), and other non-common EGFR mutations ( N = 7). Conclusion: TSO500 and F1CDx showed robust analytical performance for TMB assessment with TSO500 showing stronger concordance of TMB with high PD-L1 expression. As the paradigm for the management of early-resected NSCLC continues to evolve, understanding TMB and the mutation landscape may help advance clinical outcomes for this subset of patients.

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

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

Prognostic Value of Tumor Mutational Burden in Follicular Lymphoma Patients Treated with Immunochemotherapy

INTRODUCTION: Follicular lymphoma (FL) is the most prevalent indolent non-Hodgkin lymphoma subtype. It is characterized by frequent relapses, and patients who progress or relapse within 24 months (POD24) of front-line immunochemotherapy (ICT) have a poor outcome. Several prognostic indices have been proposed to identify high-risk patients, but none of them are currently used in clinical practice to guide treatment. Tumor mutational burden (TMB), defined as the number of somatic mutations/megabase (mut/Mb), has been proposed as a biomarker for predicting response to immune checkpoint inhibitors in several types of cancer. Therefore, this study aimed to explore the role of TMB in FL patients treated with frontline ICT. METHODS: The study included a total of 103 patients with FL grade 1-3a treated with frontline ICT. TMB was determined by Next Generation Sequencing (NGS) in 98 diagnostic lymph node biopsies with enough material available, with the Oncomine Tumor Mutational Load Assay (Thermo Fisher). This assay covers 1.65Mb across 409 genes frequently mutated in cancer, allowing an accurate estimation of TMB. Sequencing was performed on the Ion GeneStudio S5 system with &amp;gt;300X mean read depth. Only exonic non-synonymous variants remaining after manual variant refinement to exclude artefacts were considered. In addition, the mutational profile of 56 of the 98 patients was analyzed using a QIAgen custom DNA panel which covered 64 genes frequently mutated in FL. Sequencing was performed using NextSeq (Illumina) with &amp;gt;3000X mean read depth. Variants were analyzed and interpreted for pathogenicity using several databases, and only pathogenic or presumed pathogenic variants (P/PP) were considered. Statistical analysis was performed using SPSS and R. RESULTS: The final cohort included 94 patients, as in 4/98 patients (4%) TMB could not be accurately estimated due to a high frequency of artefacts caused by cytosine deamination. Median age at diagnosis of 61 years (range 24-86), 51% males and 49% females. Patients with advanced disease stage at diagnosis represented 87% of the cohort. Median TMB was 5.08mut/Mb (range 1.69-13.80 mut/Mb), and all cases genetically profiled presented at least one P/PP variant. The number of P/PP mutations correlated with TMB at diagnosis (p=0.004). TMB did not correlate with clinical parameters, such as age at diagnosis (p=0.891), histologic grade (p=0.243), Ann Arbor stage (p=0.200), ECOG (p=0.190), FLIPI (p=0.363), FLIPI2 (p=0.693), or LDH levels (p=0.483). However, lower TMB at diagnosis was associated with the absence of the t(14;18)(q32;q21) translocation (mean 3.93 vs 5.42 mut/Mb, p=0.006). The analysis of the mutational status of the 64 genes frequently mutated in FL revealed that mutations in genes involved in cellular migration ( GNA13, GNAI2) were associated with higher TMB values (4.50 vs 7.56 mut/Mb, p=0.001). An statistically significant association between low TMB and shorter progression-free survival (PFS) (p=0.011) was found when stratifying patients into two groups: high basal TMB (&amp;gt;2.55mut/Mb) and low basal TMB (≤2.55mut/Mb) (Figure 1). 2-year PFS was 54.5% (95%CI: 33.2-89.5%) in low-TMB patients and 84.9% (95%CI: 77.3-93.7%) in high-TMB patients. 5-year PFS was 39.0% (95%CI: 19.7-77.0%) in low-TMB patients and 70.5% (95%CI: 60.0-82.9%) in high-TMB patients. The proportion of POD24 cases was higher in the low-TMB group (46.1% vs 16.9%, p=0.039), none of them being refractory to treatment (7/70 in the high-TMB group). Genetically, the low-TMB group was enriched in patients with mutations affecting genes involved in the mTOR signaling pathway ( ATP6AP1, ATP6AP2, ATP6V1B2, RRAGC) (50.0% vs 8.7%, p=0.006). CONCLUSIONS: FL patients harboring t(14;18) or mutations in genes involved in migration have higher TMB values at diagnosis. Patient stratification based on TMB values allows the identification of a subgroup of FL patients with ≤2.55 mut/Mb with shorter PFS after treatment with frontline ICT. The prognostic usefulness of TMB should also be explored at relapse, in particular in patients receiving novel immunotherapies such as bispecific antibodies and CAR T-cell therapies, that are being approved for relapsed/refractory FL. ACKNOWLEDGMENTS: Work supported by Institut de Salud Carlos III-Federación Española de Enfermedades Raras (ISCIII-FEDER) grant PI19/00034, GLD18/00117, 2017SGR205, PT20/00023, and Xarxa de Banc de Tumors de Catalunya.

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer.

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman's correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method. Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85-0.94] and 0.86 (95% CI: 0.74-0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6-16 mut/Mb in both sets. Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.

Multi-classification prediction model of lung cancer tumor mutation burden based on residual network

Medical studies have found that tumor mutation burden (TMB) is positively correlated with the efficacy of immunotherapy for non-small cell lung cancer (NSCLC), and TMB value can be used to predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the whole exon sequencing (WES) technology, which usually costs too much time and expenses. To deal with above problem, this paper studies the correlation between TMB and slice images by taking advantage of digital pathological slices commonly used in clinic and then predicts the patient TMB level accordingly. This paper proposes a deep learning model (RCA-MSAG) based on residual coordinate attention (RCA) structure and combined with multi-scale attention guidance (MSAG) module. The model takes ResNet-50 as the basic model and integrates coordinate attention (CA) into bottleneck module to capture the direction-aware and position-sensitive information, which makes the model able to locate and identify the interesting positions more accurately. And then, MSAG module is embedded into the network, which makes the model able to extract the deep features of lung cancer pathological sections and the interactive information between channels. The cancer genome map (TCGA) open dataset is adopted in the experiment, which consists of 200 pathological sections of lung adenocarcinoma, including 80 data samples with high TMB value, 77 data samples with medium TMB value and 43 data samples with low TMB value. Experimental results demonstrate that the accuracy, precision, recall and F1 score of the proposed model are 96.2%, 96.4%, 96.2% and 96.3%, respectively, which are superior to the existing mainstream deep learning models. The model proposed in this paper can promote clinical auxiliary diagnosis and has certain theoretical guiding significance for TMB prediction.

MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study.

To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS). We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA). Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977). The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

Identification and verification of microtubule associated genes in lung adenocarcinoma

Associated with high morbidity and mortality, lung adenocarcinoma (LUAD) is lacking in effective prognostic prediction and treatment. As chemotherapy drugs commonly used in clinics, microtubule-targeting agents (MTAs) are limited by high toxicity and drug resistance. This research aimed to analyze the expression profile of microtubule-associated genes (MAGs) in LUAD and explore their therapy efficiency and impact on prognosis. Key MAGs were identified as novel molecular targets for targeting microtubules. The LUAD project in The Cancer Genome Atlas (TCGA) database was used to identify differently expressed MAGs. On the one hand, a microtubule-related prognostic signature was constructed and validated, and its links with clinical characteristics and the immune microenvironment were analyzed. On the other hand, hub MAGs were obtained by a protein–protein interaction (PPI) network. Following the expression of hub MAGs, patients with LUAD were classified into two molecular subtypes. A comparison was made of the differences in half-maximal drug inhibitory concentration (IC50) and tumor mutational burden (TMB) between groups. In addition, the influence of MAGs on the anticancer efficacy of different therapies was explored. MAGs, which were included in both the prognosis signature and hub genes, were considered to have great value in prognosis and targeted therapy. They were identified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 154 differently expressed MAGs were discovered. For one thing, a microtubule-related prognostic signature based on 14 MAGs was created and identified in an external validation cohort. The prognostic signature was used as an independent prognostic factor. For another, 45 hub MAGs were obtained. In accordance with the expression profile of 45 MAGs, patients with LUAD were divided into two subtypes. Distinct differences were observed in TMB and IC50 values of popular chemotherapy and targeted drugs between subtypes. Finally, five genes were included in both the prognosis signature and hub genes, and identified by qRT-PCR. A microtubule-related prognosis signature that can serve as an independent prognostic factor was constructed. Microtubule subtype influenced the efficacy of different treatments and could be used to guide therapy selection. In this research, five key MAGs, including MYB proto-oncogene like 2 (MYBL2), nucleolar and spindle-associated protein 1 (NUSAP1), kinesin family member 4A (KIF4A), KIF15 and KIF20A, were verified and identified. They are promising biomarkers and therapeutic targets in LUAD.

Higher 13-Gene-Estimated TMB Detected from Plasma ctDNA is Associated with Worse Outcome for T-Cell Lymphoma Patients.

Exome sequencing of in situ tumor samples reveals that mutated genes can predict the prognosis of patients with T-cell lymphoma (TCL). However, how tumor mutation burden (TMB) derived from circulating tumor DNA (ctDNA) may stratify TCL patients remains unclear.The plasma ctDNA of 79 newly diagnosed TCL patients from the clinical center is used for targeted exome sequencing, and the exome data of 4035 TCL patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) database is obtained for comparison analysis.TCL patients with higher TMB, as evaluated with a panel of 120 genes (panel-TMB120), are associated with poor prognosis. More importantly, COX regression analysis identifies a subset of 13 genes in panel-TMB120, including AP3B1 (Adaptor related protein complex 3 subunit beta 1), ATM (Ataxia-telangiectasia mutated), BCL6 (B cell lymphoma 6), BRAF (B-Raf proto-oncogene, serine/threonine kinase), CDKN2B (Cyclin dependent kinase inhibitor 2B), EPCAM (Epithelial cell adhesion molecule), FBXO11 (F-box protein 11), JAK1 (Janus kinase 1), MDM2 (Murine double minute 2), NF1 (Neurofibromin 1), STAT5B (Signal transducer and activator of transcription 5B), STAT6 (Signal transducer and activator of transcription 6), and TET2 (Tet methylcytosine dioxygenase 2), which are significantly associated with prognosis. Specifically, higher TMB values calculated with these 13 genes (panel-TMB13) are able to significantly predict unfavorable prognosis for these patients. Together, panel-TMB13 and the International Prognostic Index (IPI) are used for risk stratification.Panel-TMB13 is identified, which can predict poor prognosis for TCL patients carrying higher panel-TMB13 scores and suggest that panel-TMB13 may be a potential biomarker for supplement risk stratification of TCL patients.

Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas.

Background: Glycosyltransferase participates in glycosylation modification, and glycosyltransferase alterations are involved in carcinogenesis, progression, and immune evasion, leading to poor outcomes. However, in-depth studies on the influence of glycosyltransferase on clinical outcomes and treatments are lacking. Methods: The analysis of differentially expressed genes was performed using the Gene Expression Profiling Interactive Analysis 2 database. A total of 10 machine learning algorithms were introduced, namely, random survival forest, elastic network, least absolute shrinkage and selection operator, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine. Gene Set Enrichment Analysis was performed to explore signaling pathways regulated by the signature. Cell-type identification by estimating relative subsets of RNA transcripts was used for estimating the fractions of immune cell types. Results: Here, we analyzed the genomic and expressive alterations in glycosyltransferase-related genes in gliomas. A combination of 80 machine learning algorithms was introduced to establish the glycosyltransferase-related mRNA signature (GRMS) based on 2,030 glioma samples from The Cancer Genome Atlas Program, Chinese Glioma Genome Atlas, Rembrandt, Gravendeel, and Kamoun cohorts. The GRMS was identified as an independent hazardous factor for overall survival and exhibited stable and robust performance. Notably, gliomas in the high-GRMS subgroup exhibited abundant tumor-infiltrating lymphocytes and tumor mutation burden values, increased expressive levels of hepatitis A virus cellular receptor 2 and CD274, and improved progression-free survival when subjected to anti-tumor immunotherapy. Conclusion: The GRMS may act as a powerful and promising biomarker for improving the clinical prognosis of glioma patients.

Tumor mutation burden in gastro-entero-pancreatic-neuroendocrine neoplasms.

As rare tumors, there are limited treatment options for neuroendocrine neoplasms (NENs). Recently, microsatellite instability (MSI) and tumor mutation burden (TMB) have been emerging as potential biomarkers in various tumors. However, there is a lack of research on the use of these biomarkers in gastro-entero-pancreatic (GEP)-NENs. We analyzed 31 patients diagnosed with GEP-NEN between 2013 to 2022. The TMB and MSI analyses using next-generation sequencing (NGS) were performed for all patients. The TruSightTM Oncology 500 assay from Illumina was used as the NGS panel. Out of the 31 patients analyzed, the most frequent primary origin was the pancreas (12 patients, 38.7%), followed by the stomach (4 patients, 12.9%), gallbladder (4 patients, 12.9%), rectum (7 patients, 22.6%), small bowel (2 patients, 6.5%), and bile duct (1 patient, 3.2%). Among these patients, 19 (61.3%) were diagnosed with well-differentiated neuroendocrine tumors, with grade 2 being the most common (15 patients, 48.4%), followed by grade 3 (3 patients, 9.7%) and grade 1 (1 patient, 3.2%). Neuroendocrine carcinoma was confirmed in 12 patients (38.7%). The median number of metastases was 2.0 [interquartile range (IQR), 1.0-3.0], and the liver was the most common site of metastasis (23 patients, 74.2%). The median TMB was 4.7 (IQR, 3.1-6.3) mutations/Mb, and all tumors were classified as microsatellite stability (MSS). Only one patient had a high TMB (266.4 mutations/Mb), which was a grade 3 neuroendocrine tumor originating from the pancreas. The TMB value did not vary depending on the primary tumor site or World Health Organization (WHO) grade. This analysis showed that, despite very low incidence, there are GEP-NENs with high TMB. For precision medicine, testing for MSI and TMB is needed for this tumor type.

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.

Breast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR). Breast cancer genomic and transcriptomic data were obtained from The Cancer Genome Atlas (TCGA). Breast cancer samples were dichotomized into high- and low-TMB groups according to TMB values. Differentially expressed DDR genes between high- and low-TMB groups were incorporated into univariate and multivariate cox regression model to build prognosis model. Performance of the prognosis model was validated in an independently new GEO dataset and evaluated by time-dependent ROC curves. Between high- and low-TMB groups, there were 6,424 differentially expressed genes, including 67 DDR genes. Ten genes associated with prognosis were selected by univariate cox regression analysis, among which seven genes constituted a panel to predict breast cancer prognosis. The seven-gene prognostic model, as well as the gene copy numbers are closely associated with tumor-infiltrating immune cells. We established a seven-gene prognostic model comprising MDC1, PARP3, PSMB1, PSMB9, PSMD2, PSMD7, and PSMD14 genes, which provides a basis for further exploration of a population-based prediction of prognosis and immunotherapy response in patients with breast cancer.

Distributional range of tumor mutational burden (TMB) across multiple cancer types emphasizing the importance of different cutoff values in different tumor types for clinical interpretation: A consensus statement from the SEQUOIA consortium.

2615 Background: The measurement of tumor mutational burden (TMB) is critical to select patients who are expected to respond to immune checkpoint inhibitors or immune-oncology (IO). The majority of the clinical guidelines on the interpretation &amp; utility of TMB values are based on studies performed on lung cancer, setting a cutoff of 10. Laboratories that are using comprehensive genomic profiling (CGP) for tumor testing are reporting TMB values &amp;/or interpretations (high/low) beyond lung cancer. Multiple studies have shown that the TMB signature is not universal, but rather tumor &amp;/or even patient-specific. However, the pan-cancer trial for IO (KEYNOTE-158) failed to consider setting different cutoffs per tumor type. Currently, no major guidelines or consensus statements address the issue of reporting TMB as a biomarker beyond lung cancer. In the current study, the membership of the SEQUOIA (Sequencing &amp; Oncology Informatics Academic Team) Consortium investigates the landscape of TMB values across multiple cancer types. Methods: We investigated TMB values in 11,670 patient samples (M = 6225; F= 5445) across 13 different tumor types &amp; histological subtypes, including cancers with lung, colorectal, gastrointestinal, brain, prostate, pancreatic, biliary, endometrioid, ovarian, skin, hematological (CLL myeloma) &amp; lymphoid origin. All samples were processed by a CGP panel of 523 genes. Multiple specimen types were included in the study consisting of tissue, blood, marrow, &amp; lymph node. Results: The range of TMB values varied significantly across different cancer types &amp; histological subtypes. The colorectal, melanoma &amp; endometrial cancers have a wider range of TMB values (2-400 mutations/MB) relative to prostate, breast, myeloma, &amp; serous cancers (0-30 mutations/MB). Along with the range, the mean, &amp; median TMB values are also significantly varied across these tumor types. We also identified that a subset of patients exhibits high TMB (&gt;30 mutations/MB) values across almost all types of cancer. Conclusions: TMB values &amp; their association with response to immune checkpoint inhibitors are not very well studied in all cancer types, although a TMB cut-off of 10 mutations/MB was defined as TMB-high for FDA-approved IO in patients with unresectable or metastatic solid tumors. The range, mean, median, cut-offs &amp; clinical interpretations of TMB-high &amp; low for IO based therapies for different tumor types has not been well established. Our data confirm the wide range, mean, &amp; median across different tumor types, &amp; thus laboratories &amp; oncologists need to be cautious in the clinical interpretation of TMB values for patient management. Future studies should assess responsiveness to IO in specific tumor types based on TMB value to determine tumor-specific TMB cutoff values.