Tumor Mutational Burden Analysis Research Articles

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

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. 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. 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. 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.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

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

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. 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. Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES). 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.

Abstract 3651: Mutational analysis and spatial phenotyping to decipher racial disparities in pancreatic adenocarcinoma

Abstract Background: Pancreatic cancer has a 5-year survival of only 12%. This is due to the lack of effective treatments and virtually no early detection methods. To compound this already poor prognosis, African Americans face a 20% higher incidence and worse mortality compared to non-African American patients. With a substantial portion of the data and tools employed for studying pancreatic cancer failing to adequately represent this demographic we aim to gain more insight into the differences associated with worse outcomes for African Americans with pancreatic cancer. To achieve this, we conducted an in-depth analysis of tumor mutational burden using whole exome sequencing and the cutting-edge PhenoCycler®-Fusion 2.0 spatial biology platform, to uncover disparities in spatial immune and metabolic phenotypes. Methods: Using an ultrahigh-plex discovery panel of markers encompassing cell lineage, immune checkpoints, tissue architecture, activation, metabolism, proliferation, and stress, we sought to analyze the differences in spatial phenotypes, cellular neighborhoods and functional pathways across the two groups. Additionally, we performed laser capture microdissection on a cohort of patients containing equal numbers of African American and non-African American pancreatic cancer samples. Tumor, stroma, and adjacent normal areas were identified by a board-certified pathologist. These annotations were used to laser capture on 10-micron sections with 7 sections for each patient. We performed whole exome sequencing on these sections and different areas and compared between groups. Results: Through our investigation using these two technologies, we can find differences in the tumor microenvironment between African Americans and non-African Americans. We can see potential associations in the increased mortality and incidence of this group while also building a foundation of racially diverse data to be used for future studies. Whole exome sequencing has revealed distinct mutational patterns among African American patients, involving common genes that exhibit unique mutation profiles when compared to those previously observed in non-African American patients. Moreover, whole-slide spatial phenotyping reveals the differential tumor-immune landscapes and the key cellular and molecular niches that contribute to tumor progression and prognosis. These findings underscore the imperative need for a more systems biology exploration of the variations within these groups, with the aim of developing more effective and personalized treatment approaches. Citation Format: Daniel James Salas-Escabillas, Ning Ma, Bassem Ben Cheikh, Aditya Pratapa, Thais Pichardo, Kyra Langley, Julie Clark, Nina Steele, Niyati Jhaveri, David Kwon, Howard C. Crawford. Mutational analysis and spatial phenotyping to decipher racial disparities in pancreatic adenocarcinoma [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 3651.

Abstract 7612: Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular carcinoma

Abstract Introduction: Immunotherapy targeting PD-1/PD-L1 shows promise in treating hepatocellular carcinoma (HCC), but standard predictors like PD-L1 expression are unreliable for gauging treatment response. RNA sequencing has identified a BMS 4-gene inflammatory signature that could more accurately forecast clinical outcomes. However, genomic alterations and tumor mutational burden (TMB) haven't consistently predicted anti-PD(L)-1 therapy response in HCC. Therefore, the aim of this research is to identify potential genetic alternation that could serve as predictive marker for anti-PD(L)-1 therapy responses in advanced HCC. Methods: Formalin-fixed paraffin-embedded (FFPE) tumor tissues from 38 HCC patients were analyzed using targeted next-generation sequencing with the ACTOncoTM panel before anti-PD(L)-1 therapy. This analysis identified somatic variants across 440 genes and calculated TMB. In silico tools like NetMHC and IEDB predicted neo-peptide-HLA class I binding affinity. Additionally, peripheral blood mononuclear cells provided germline data for paired TMB calculations, with tumor responses assessed by RECIST criteria. Results: Within a cohort of 167 patients with HCC undergoing ICI therapy, 35 (20.9%) responded to treatment, while 132 (79.1%) did not. Available formalin-fixed paraffin-embedded (FFPE) samples from this population included those from 14 responders and 30 non-responders. After conducting quality control measures, which assessed tumor purity, DNA quantity and integrity, as well as sequencing quality, 13 samples from responders and 25 from non-responders qualified for further analysis. Gene alteration analysis identified mutations in TP53 (47%), MUC16 (34%), CTNNB1 (32%), USH2A (16%), ARID1A (13%), NOTCH3 (13%), and ADAMTS16 (11%) of the cases. A TP53 mutation was linked to poor overall survival, whereas mutations in CTNNB1 and the RTK/RAS/RAF pathway did not correlate with clinical outcomes. The median TMB, predicted at 3.2 ± 4.3 mutations per megabase (muts/Mb), was nominally higher in patients who achieved disease control compared to non-responders (4.6 vs. 3.7 muts/Mb, p=0.53). Utilizing paired TMB analysis, those with disease control exhibited a higher TMB trend than non-responders (6.0 vs. 2.2 muts/Mb, p=0.06). Notably, the 13 cases with MUC16 mutations were associated with a significantly higher predicted TMB (10.0 vs. 3.3 muts/Mb, p=0.0015). Conclusions: This study suggests that certain genetic alterations, especially TP53 mutations, may have predictive value for the response to anti-PD(L)-1 therapy in HCC. Moreover, an elevated TMB, particularly when associated with MUC16 mutations, seems to be correlated with improved disease control. Citation Format: San-Chi Chen, Nai-Jung Chiang, MingHuang Chen, Muh-Hwa Yang. Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular 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 7612.

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication.

Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

Identification of FLRT2 as a key prognostic gene through a comprehensive analysis of TMB and IRGPs in BLCA patients.

The tumor immune environment and immune-related genes are instrumental in the development, progression, and prognosis of bladder cancer (BLCA). This study sought to pinpoint key immune-related genes influencing BLCA prognosis and decipher their mechanisms of action. We analyzed differentially expressed genes (DEGs) between high- and low- tumor mutational burden (TMB) groups. Subsequently, we constructed a reliable prognostic model based on immune-related gene pairs (IRGPs) and analyzed DEGs between high- and low-risk groups. A total of 22 shared DEGs were identified across differential TMB and IRGPs-derived risk groups in BLCA patients. Through univariate Cox and multivariate Cox analyses, we highlighted five genes - FLRT2, NTRK2, CYTL1, ZNF683, PRSS41 - significantly correlated with BLCA patient prognosis. Notably, the FLRT2 gene emerged as an independent prognostic factor for BLCA, impacting patient prognosis via modulation of macrophage infiltration in immune microenvironment. Further investigation spotlighted methylation sites - cg25120290, cg02305242, and cg01832662 - as key regulators of FLRT2 expression. These findings identified pivotal prognostic genes in BLCA and illuminated the intricate mechanisms dictating patient prognosis. This study not only presents a novel prognostic marker but also carves out potential avenues for immunotherapy and targeted therapeutic strategies in BLCA. By demystifying the profound impact of immune-related genes and the tumor immune environment, this study augments the comprehension and prognostic management of bladder cancer.

Identification of immune cell-related prognostic genes characterized by a distinct microenvironment in hepatocellular carcinoma.

The development and progression of hepatocellular carcinoma (HCC) have been reported to be associated with immune-related genes and the tumor microenvironment. Nevertheless, there are not enough prognostic biomarkers and models available for clinical use. Based on seven prognostic genes, this study calculated overall survival in patients with HCC using a prognostic survival model and revealed the immune status of the tumor microenvironment (TME). To develop a novel immune cell-related prognostic model of HCC and depict the basic profile of the immune response in HCC. We obtained clinical information and gene expression data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. TCGA and ICGC datasets were used for screening prognostic genes along with developing and validating a seven-gene prognostic survival model by weighted gene coexpression network analysis and least absolute shrinkage and selection operator regression with Cox regression. The relative analysis of tumor mutation burden (TMB), TME cell infiltration, immune checkpoints, immune therapy, and functional pathways was also performed based on prognostic genes. Seven prognostic genes were identified for signature construction. Survival receiver operating characteristic curve analysis showed the good performance of survival prediction. TMB could be regarded as an independent factor in HCC survival prediction. There was a significant difference in stromal score, immune score, and estimate score between the high-risk and low-risk groups stratified based on the risk score derived from the seven-gene prognostic model. Several immune checkpoints, including VTCN1 and TNFSF9, were found to be associated with the seven prognostic genes and risk score. Different combinations of checkpoint blockade targeting inhibitory CTLA4 and PD1 receptors and potential chemotherapy drugs hold great promise for specific HCC therapies. Potential pathways, such as cell cycle regulation and metabolism of some amino acids, were also identified and analyzed. The novel seven-gene (CYTH3, ENG, HTRA3, PDZD4, SAMD14, PGF, and PLN) prognostic model showed high predictive efficiency. The TMB analysis based on the seven genes could depict the basic profile of the immune response in HCC, which might be worthy of clinical application.

Long noncoding RNA LINC00921 serves as a predictive biomarker for lung adenocarcinoma: An observational study.

Lung adenocarcinoma (LUAD) is usually diagnosed at advanced stages. Hence, there is an urgent need to seek an effective biomarker to predict LUAD status. Long noncoding RNAs (lncRNAs) play key roles in the development of tumors. However, the relationship between LINC00921 and LUAD remains unclear. The gene expression data of LUAD were downloaded from the Cancer Genome Atlas database to investigate the expression level of LINC00921 in LUAD. Diagnostic ability analysis, survival analysis, tumor mutational burden analysis, and immune cell infiltration analysis of LINC00921 in LUAD patients were performed simultaneously. According to the median expression value of LINC00921, patients were divided into LINC00921 high- and low-expression groups. The function of LINC00921 in LUAD was identified through difference analysis and enrichment analysis. Moreover, drugs that may be relevant to LUAD treatment were screened. Finally, blood samples were collected for real-time polymerase chain reaction. LINC00921 was significantly lower in LUAD tumor tissues. Notably, patients with low expression of LINC00921 had a shorter median survival time. Decreased immune cell infiltration in the tumor microenvironment in the low LINC00921 expression group may contribute to poorer patient outcomes. Tumor mutational burden was significantly different in survival between the LINC00921 high- and low-expression groups. In addition, LINC00921 may exert an influence on cancer development through its regulation of target genes transcription. Glyceraldehyde-3-phosphate dehydrogenase-related drugs may be more likely to be therapeutically effective in LUAD. LINC00921 was able to be used as the potential diagnostic indicator for LUAD.

Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma.

Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear. Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature. The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis. A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.

Development of a novel disulfidptosis-related lncRNA signature for prognostic and immune response prediction in clear cell renal cell carcinoma

Disulfidptosis, a novel form of regulated cell death, occurs due to the aberrant accumulation of intracellular cystine and other disulfides. Moreover, targeting disulfidptosis could identify promising approaches for cancer treatment. Long non-coding RNAs (lncRNAs) are known to be critically implicated in clear cell renal cell carcinoma (ccRCC) development. Currently, the involvement of disulfidptosis-related lncRNAs in ccRCC is yet to be elucidated. This study primarily dealt with identifying and validating a disulfidptosis-related lncRNAs-based signature for predicting the prognosis and immune landscape of individuals with ccRCC. Clinical and RNA sequencing data of ccRCC samples were accessed from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted for the identification of the disulfidptosis-related lncRNAs. Additionally, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator Cox regression, and stepwise multivariate Cox analysis were executed to develop a novel risk prognostic model. The prognosis-predictive capacity of the model was then assessed using an integrated method. Variation in biological function was noted using GO, KEGG, and GSEA. Additionally, immune cell infiltration, the tumor mutational burden (TMB), and tumor immune dysfunction and exclusion (TIDE) scores were calculated to investigate differences in the immune landscape. Finally, the expression of hub disulfidptosis-related lncRNAs was validated using qPCR. We established a novel signature comprised of eight lncRNAs that were associated with disulfidptosis (SPINT1-AS1, AL121944.1, AC131009.3, AC104088.3, AL035071.1, LINC00886, AL035587.2, and AC007743.1). Kaplan–Meier and receiver operating characteristic curves demonstrated the acceptable predictive potency of the model. The nomogram and C-index confirmed the strong correlation between the risk signature and clinical decision-making. Furthermore, immune cell infiltration analysis and ssGSEA revealed significantly different immune statuses among risk groups. TMB analysis revealed the link between the high-risk group and high TMB. It is worth noting that the cumulative effect of the patients belonging to the high-risk group and having elevated TMB led to decreased patient survival times. The high-risk group depicted greater TIDE scores in contrast with the low-risk group, indicating greater potential for immune escape. Finally, qPCR validated the hub disulfidptosis-related lncRNAs in cell lines. The established novel signature holds potential regarding the prognosis prediction of individuals with ccRCC as well as predicting their responses to immunotherapy.

A Lactic Acid Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironmental Status in Patients with Hepatocellular Carcinoma

This study aims to build a prognostic model based on lactic acid metabolism-related genes (LMRGs) to predict survival outcomes and tumor microenvironment status of Hepatocellular carcinoma (HCC) patients. The model was used to calculate riskscores of clinical samples. Survival analysis and Cox regression analysis were conducted to verify the independence and reliability of the riskscore to determine its clinical significance in prognosis evaluation of HCC. Additionally, we conducted a comprehensive analysis of tumor mutation burden (TMB), immune cell infiltration, and gene set molecular function in the high- and low-risk groups. We obtained 134 LMRGs mainly involved in cellular calcium homeostasis and calcium signaling pathways. The LMRGs in the risk assessment model included PFKFB4, SLC16A3, ADRA2B, SLC22A1, QRFPR, and PROK1. This study discovered much shorter overall survival and median survival time of patients with higher riskscores when compared to those with lower riskscores. It was indicated that for independent prediction of patients’ prognosis, the riskscore had a significant clinical value. A remarkable difference was also found regarding TMB between the two groups. Finally, cell experiments demonstrated that the knockout of PFKFB4 and SLC16A3 genes suppressed lactate. Our research demonstrated that the riskscore, established based on LMRGs, is a promising biomarker.

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.

Prognostic Significance of Tumor Mutation Burden among Patients with Non-small Cell Lung Cancer Who Received Platinum-based Adjuvant Chemotherapy: An Exploratory Study.

This study aimed to investigate the prognostic significance of tumor mutation burden (TMB) among patients with non-small cell lung cancer (NSCLC) who received platinum-based adjuvant chemotherapy. Tumor tissue specimens after surgical resection were collected for DNA extraction. Somatic mutation detection and TMB analysis were conducted using next-generation sequencing (NGS). Recurrence status of the patients was assessed in the hospital during the adjuvant chemotherapy period, and long-term survival data of patients were obtained by telephone follow-up. Univariate analysis between TMB status and prognosis was carried out by survival analysis. A retrospective review of 78 patients with non-squamous NSCLC who received platinum-based adjuvant chemotherapy showed a median disease-free survival of 3.6 years and median overall survival (OS) of 5.3 years. NGS analysis exhibited that the most common mutated somatic genes among the 78 patients were tumor suppressor protein p53 (TP53), epidermal growth factor receptor, low-density lipoprotein receptor related protein 1B, DNA methyltransferase 3 alpha and FAT atypical cadherin 3, and their prevalence was 56.4%, 48.7%, 37.2%, 30.7%, and 25.6%, respectively. TMB status was divided into TMB-L (≤ 4.5/Mb) and TMB-H (> 4.5/Mb) based on the median TMB threshold. Relevance of TMB to prognosis suggested that the median OS of patients with TMB-L was significantly longer than that of patients with TMB-H (NR vs. 4.6, P = 0.014). Higher TMB status conferred a worse implication on OS among patients with non-squamous NSCLC who received platinum-based adjuvant chemotherapy.

Pancancer analysis reveals the role of disulfidptosis in predicting prognosis, immune infiltration and immunotherapy response in tumors

Disulfidptosis has been reported as a novel cell death process, suggesting a therapeutic strategy for cancer treatment. Herein, we constructed a multiomics data analysis to reveal the effects of disulfidptosis in tumors. Data for 33 kinds of tumors were downloaded from UCSC Xene, and disulfidptosis-related genes (DRGs) were selected from a previous study. After finishing processing data by the R packages, the expression and coexpression of DRGs in different tumors were assessed as well as copy number variations. The interaction network was drawn by STRING, and the activity of disulfidptosis was compared to the single-sample gene set enrichment analysis algorithm. Subsequently, the differences in DRGs for prognosis and clinicopathological features were evaluated, and the tumor immune microenvironment was assessed by the TIMER and TISCH databases. Tumor mutation burden, stem cell features and microsatellite instability were applied to predict drug resistance, and the expression of checkpoints was identified for the prediction of immunotherapy. Moreover, the TCIA, CellMiner and Enrichr databases were also utilized for selecting potential agents. Ten DRGs were differentially expressed in tumors, and the plots of coexpression and interaction revealed their correlation. Survival analysis suggested SLC7A11 as the most prognosis-related DRG with the most significant results. Additionally, the comparison also reflected the differences in DRGs in the status of pathologic lymph node metastasis for 5 types of tumors. The tumor immune microenvironment showed commonality among tumors based on immune infiltration and single-cell sequencing, and the analysis of tumor mutation burden, stemness and microsatellite instability showed a mostly positive correlation with DRGs. Moreover, referring to the prediction about clinical treatment, most DRGs can enhance sensitivity to chemotherapeutic agents but decrease the response to immune inhibitors with increasing expression. In this study, a primarily synthetic landscape of disulfidptosis in tumors was established and provided guidance for further exploration and investigation.

Development of a breast cancer prognostic model based on vesicle-mediated transport-related genes to predict immune landscape and clinical drug therapy.

Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis. Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis. We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group. We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

Tumor Mutational Burden as a Biomarker of Immunotherapy Response: An Immunogram Approach in Onco-immunology

Abstract: Immune checkpoint inhibitors have revolutionized cancer treatment by allowing T cells to reactivate. Tumor mutational burden (TMB) is a biomarker that has emerged as a viable diagnostic for locating patients who would benefit from immunotherapy in particular cancer types. Greater neo-antigens mean more opportunities for T cell identification, and TMB is clinically linked to better immune checkpoint inhibitors. Tumor foreignness is a cancer immunogram, and TMB can be used as a substitute for foreignness. The role of TMB analysis as an independent predictor of immunotherapy response in the context of immune checkpoint inhibitor medications is the subject of this mini-review.

Development of an immunogenic cell death prognostic signature for predicting clinical outcome and immune infiltration characterization in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remain unclear. The MSigDB database collecting ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients. Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remains unclear. The MSigDB database collected ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients.

ENO2 as a Biomarker Regulating Energy Metabolism to Promote Tumor Progression in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and metastatic type of renal cell carcinoma. Despite significant advancements, the current diagnostic biomarkers for ccRCC lack the desired specificity and sensitivity, necessitating the identification of novel biomarkers and elucidation of their underlying mechanisms. Three gene expression profile datasets were obtained from the GEO database, and differentially expressed genes (DEGs) were screened. Gene Ontology and KEGG pathway analysis were conducted in ccRCC. To clarify the diagnosis and prognostic role of ENO2, Kaplan-Meier analysis and Cox proportional hazards regression analysis were performed. Functional experiments were also carried out to verify the significant role of ENO2 in ccRCC. Finally, tumor mutational burden analysis was utilized to investigate the potential role of ENO2 in gene mutations in ccRCC. The study showed that ENO2 is a potential biomarker for the diagnosis of ccRCC and can independently predict the clinical prognosis of ccRCC. Furthermore, we found that ENO2 can promote the occurrence and progression of ccRCC by affecting the glycolysis level of cells through the "Warburg effect". These findings provide new theories for the occurrence and development of ccRCC and can help formulate new strategies for its diagnosis and treatment.

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of theIPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB alsoexhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.