B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer.

This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors.

The ectonucleotidase CD39 identifies tumor-reactive CD8+ T cells predictive of immune checkpoint blockade efficacy in human lung cancer.

Improved identification of anti-tumor T cells is needed to advance cancer immunotherapies. CD39 expression is a promising surrogate of tumor-reactive CD8+ T cells. Here, we comprehensively profiled CD39 expression in human lung cancer. CD39 expression enriched for CD8+ T cells with features of exhaustion, tumor reactivity, and clonal expansion. Flow cytometry of 440 lung cancer biospecimens revealed weak association between CD39+ CD8+ T cells and tumoral features, such as programmed death-ligand 1 (PD-L1), tumor mutation burden, and driver mutations. Immune checkpoint blockade (ICB), but not cytotoxic chemotherapy, increased intratumoral CD39+ CD8+ T cells. Higher baseline frequency of CD39+ CD8+ T cells conferred improved clinical outcomes from ICB therapy. Furthermore, a gene signature of CD39+ CD8+ T cells predicted benefit from ICB, but not chemotherapy, in a phase III clinical trial of non-small cell lung cancer. These findings highlight CD39 as a proxy of tumor-reactive CD8+ T cells in human lung cancer.

Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab.

The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab's mechanism of action.

Construction of a prognostic model for breast cancer based on moonlighting genes.

Breast cancer (BRCA) is a highly heterogeneous disease, with significant differences in prognosis among patients. Existing biomarkers and prognostic models have limited ability to predict BRCA prognosis. Moonlighting genes regulate tumor progression and are associated with cancer prognosis. This study aimed to construct a moonlighting gene-based prognostic model for BRCA. We obtained differentially expressed genes (DEGs) in BRCA from The Cancer Genome Atlas and intersected them with moonlighting genes from MoonProt to acquire differential moonlighting genes. GO and KEGG results showed main enrichment of these genes in the response of BRCA cells to environmental stimuli and pentose phosphate pathway. Based on moonlighting genes, we conducted drug prediction and validated results through cellular experiments. After ABCB1 knockdown, viability and proliferation of BRCA cells were significantly enhanced. Based on differential moonlighting genes, BRCA was divided into three subgroups, among which cluster2 had the highest survival rate and immunophenoscore and relatively low tumor mutation burden. TP53 had the highest mutation frequency in cluster2 and cluster3, while PIK3CA had a higher mutation frequency in cluster1, with the majority being missense mutations. Subsequently, we established an 11-gene prognostic model in the training set based on DEGs among subgroups using univariate Cox regression, LASSO regression, and multivariable Cox regression analyses. Model prognostic performance was verified in GEO, METABRIC and ICGC validation sets. In summary, this study obtained three BRCA moonlighting gene-related subtypes and constructed an 11-gene prognostic model. The 11-gene BRCA prognostic model has good predictive performance, guiding BRCA prognosis for clinical doctors.

TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Defining the single base importance of human mRNAs and lncRNAs.

As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.

Comprehensive bioinformatics analysis of human cytomegalovirus pathway genes in pan-cancer.

BACKGROUND: Human cytomegalovirus (HCMV) is a herpesvirus that can infect various cell types and modulate host gene expression and immune response. It has been associated with the pathogenesis of various cancers, but its molecular mechanisms remain elusive. METHODS: We comprehensively analyzed the expression of HCMV pathway genes across 26 cancer types using the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We also used bioinformatics tools to study immune invasion and tumor microenvironment in pan-cancer. Cox regression and machine learning were used to analyze prognostic genes and their relationship with drug sensitivity. RESULTS: We found that HCMV pathway genes are widely expressed in various cancers. Immune infiltration and the tumor microenvironment revealed that HCMV is involved in complex immune processes. We obtained prognostic genes for 25 cancers and significantly found 23 key genes in the HCMV pathway, which are significantly enriched in cellular chemotaxis and synaptic function and may be involved in disease progression. Notably, CaM family genes were up-regulated and AC family genes were down-regulated in most tumors. These hub genes correlate with sensitivity or resistance to various drugs, suggesting their potential as therapeutic targets. CONCLUSIONS: Our study has revealed the role of the HCMV pathway in various cancers and provided insights into its molecular mechanism and therapeutic significance. It is worth noting that the key genes of the HCMV pathway may open up new doors for cancer prevention and treatment.

Automated Quantitative CD8+ Tumor-Infiltrating Lymphocytes and Tumor Mutation Burden as Independent Biomarkers in Melanoma Patients Receiving Front-Line Anti-PD-1 Immunotherapy.

BACKGROUND: CD8+ tumor-infiltrating lymphocyte (TIL) predicts response to anti-PD-(L)1 therapy. However, there remains no standardized method to assess CD8+ TIL in melanoma, and developing a specific, cost-effective, reproducible, and clinically actionable biomarker to anti-PD-(L)1 remains elusive. We report on the development of automatic CD8+ TIL density quantification via whole slide image (WSI) analysis in advanced melanoma patients treated with front-line anti-PD-1 blockade, and correlation immunotherapy response. METHODS: Seventy-eight patients treated with PD-1 inhibitors in the front-line setting between January 2015 and May 2023 at the University of Pittsburgh Cancer Institute were included. CD8+ TIL density was quantified using an image analysis algorithm on digitized WSI. Targeted next-generation sequencing (NGS) was performed to determine tumor mutation burden (TMB) in a subset of 62 patients. ROC curves were used to determine biomarker cutoffs and response to therapy. Correlation between CD8+ TIL density and TMB cutoffs and response to therapy was studied. RESULTS: Higher CD8+ TIL density was significantly associated with improved response to front-line anti-PD-1 across all time points measured. CD8+ TIL density ≥222.9 cells/mm2 reliably segregated responders and non-responders to front-line anti-PD-1 therapy regardless of when response was measured. In a multivariate analysis, patients with CD8+ TIL density exceeding cutoff had significantly improved PFS with a trend toward improved OS. Similarly, increasing TMB was associated with improved response to anti-PD-1, and a cutoff of 14.70 Mut/Mb was associated with improved odds of response. The correlation between TMB and CD8+ TIL density was low, suggesting that each represented independent predictive biomarkers of response. CONCLUSIONS: An automatic digital analysis algorithm provides a standardized method to quantify CD8+ TIL density, which predicts response to front-line anti-PD-1 therapy. CD8+ TIL density and TMB are independent predictors of response to anti-PD-1 blockade.

Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer.

BACKGROUND: In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses. METHODS: In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs. RESULTS: Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC50 values of 62 drugs. CONCLUSIONS: In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.

Genomic characterization and immunotherapy for microsatellite instability-high in cholangiocarcinoma.

BACKGROUND: Microsatellite 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. METHODS: We 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. RESULTS: Differential 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. CONCLUSIONS: MSI-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 REGISTRATION: This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577).

Concomitant genomic features stratify prognosis to patients with advanced EGFR mutant lung cancer.

This study aimed to explore the clinical significance of genomics features including tumor mutation burden (TMB) and copy number alteration (CNA) for advanced EGFR mutant lung cancer. We retrospectively identified 1378 patients with advanced EGFR mutant lung cancer and next-generation sequencing tests from three cohorts. Multiple co-occurring genomics alternations occurred in a large proportion (97%) of patients with advanced EGFR mutant lung cancers. Both TMB and CNA were predictive biomarkers for these patients. A joint analysis of TMB and CNA found that patients with high TMB and high CNA showed worse responses to EGFR-TKIs and predicted worse outcomes. TMBhighCNAhigh, as a high-risk genomic feature, showed predictive ability in most of the subgroups based on clinical characteristics. These patients had larger numbers of metastatic sites, and higher rates of EGFR copy number amplification, TP53 mutations, and cell-cycle gene alterations, which showed more potential survival gain from combination treatment. Furthermore, a nomogram based on genomic features and clinical features was developed to distinguish prognosis. Genomic features could stratify prognosis and guide clinical treatment for patients with advanced EGFR mutant lung cancer.

A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer.

BACKGROUND: Lactylation, a novel contributor to post-translational protein modifications, exhibits dysregulation across various tumors. Nevertheless, its intricate involvement in colorectal carcinoma, particularly for non-histone lactylation and its intersection with metabolism and immune evasion, remains enigmatic. METHODS: Employing immunohistochemistry on tissue microarray with clinical information and immunofluorescence on colorectal cell lines, we investigated the presence of global lactylation and its association with development and progression in colorectal cancer as well as its functional location. Leveraging the AUCell algorithm alongside correlation analysis in single-cell RNA sequencing data, as well as cox-regression and lasso-regression analysis in TCGA dataset and confirmed in GEO dataset, we identified a 23-gene signature predicting colorectal cancer prognosis. Subsequently, we analyzed the associations between the lactylation related gene risk and clinical characteristics, mutation landscapes, biological functions, immune cell infiltration, immunotherapy responses, and drug sensitivity. Core genes were further explored for deep biological insights through bioinformatics and in vitro experiments. RESULTS: Our study innovatively reveals a significant elevation of global lactylation in colorectal cancer, particularly in malignant tumors, confirming it as an independent prognostic factor for CRC. Through a comprehensive analysis integrating tumor tissue arrays, TCGA dataset, GEO dataset, combining in silico investigations and in vitro experiments, we identified a 23-gene Lactylation-Related Gene risk model capable of predicting the prognosis of colorectal cancer patients. Noteworthy variations were observed in clinical characteristics, biological functions, immune cell infiltration, immune checkpoint expression, immunotherapy responses and drug sensitivity among distinct risk groups. CONCLUSIONS: The Lactylation-Related Gene risk model exhibits significant potential for improving the management of colorectal cancer patients and enhancing therapeutic outcomes, particularly at the intersection of metabolism and immune evasion. This finding underscores the clinical relevance of global lactylation in CRC and lays the groundwork for mechanism investigation and targeted therapeutic strategies given the high lactate concentration in CRC.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.

BACKGROUND: Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC. METHOD: Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application. RESULTS: A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC. CONCLUSION: The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

Identification of a novel signature based on M6a modification regulators related LncRNA for stratification of the prognosis of prostate cancer.

Prostate cancer emerges as a life-threatening disease that affects approximately 1.3 million patients of male population globally. Various studies established lncRNAs as a critical role in prostate cancer progression by regulating multiple epigenetic pathways. Therefore, it is imperative to disclose the involvement of lncRNAs in prostate cancer and their usability as prognostic markers for the disease. The model was constructed using Cox and LASSO analysis. The accuracy of model was evaluated using various cohorts. Furthermore, the study assessed the correlative relationship of the model with tumor immunity, immunotherapy, SNV mutation, and drug sensitivity, among other factors. We developed an accurate and stable prognostic model for prostate cancer patients by screening out 11 m6A regulators related lncRNAs and integrating pathological features and age through a nomogram model. The model had satisfactory accuracy and stability in stratification of clinical outcomes of prostate cancer patients, as demonstrated by AUC values (higher than 0.7) at 3, 5, and 7 years in both internal and external cohorts. Moreover, we performed PCA analysis to confirm m6A-related lncRNAs as the best modeling strategy. We developed a prognosis predicting model based on 11 selected m6A modification related lncRNA, which displayed satisfactory potency in multiple cohorts.

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A 70-year-old man had radical surgery for colon cancer one year before the symptoms of memory loss and decreasing cognitive function. Subsequent magnetic resonance imaging revealed a brain mass, which was surgically resected and confirmed to be metastatic intestinal adenocarcinoma. Immunohistochemistry of the primary tumor and brain metastasis showed mismatch repair deficiency. The patient received adjuvant chemotherapy after surgery. However, the brain metastasis relapsed one month after the last chemotherapy. Genetic testing on the resected colon tumor samples confirmed microsatellite instability-high with a high tumor mutation burden by 77.7 muts/Mb. The patient was subsequently treated with programmed death-1 (PD-1) monoclonal antibody pembrolizumab (keytruda). The brain metastatic lesions were completely shrunk, and a complete clinical response was achieved.

Clinical plasma cells-related genes to aid therapy in colon cancer.

The tumor immune microenvironment (TIME) of colon cancer (CC) has been associated with extensive immune cell infiltration (IMI). Increasing evidence demonstrated that plasma cells (PC) have an extremely important role in advance of antitumor immunity. Nonetheless, there is a lack of comprehensive analyses of PC infiltration in clinical prognosis and immunotherapy in CC. This study systematically addressed the gene expression model and clinical information of CC patients. Clinical samples were obtained from the TCGA (The Cancer Genome Atlas) databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), GSVA, and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were employed to research the potential mechanism and pathways. Immunophenoscore (IPS) was obtained to evaluate the immunotherapeutic significance of risk score. Half maximal inhibitory concentration (IC50) of chemotherapeutic medicine was predicted by employing the pRRophetic algorithm. A total of 513 CC samples (including 472 tumor samples and 41 normal samples) were collected from the TCGA-GDC database. Significant black modules and 313 candidate genes were considered PC-related genes by accessing WGCNA. Five pivotal genes were established through multiple analyses, which revealed excellent prognostic. The underlying correlation between risk score with tumor mutation burden (TMB) was further explored. In addition, the risk score was obviously correlated with various tumor immune microenvironment (TIME). Also, risk CC samples showed various signaling pathways activity and different pivotal sensitivities to administering chemotherapy. Finally, the biological roles of the CD177 gene were uncovered in CC.

ERBB2-Mutant Gastrointestinal Tumors Represent Heterogeneous Molecular Biology, Particularly in Microsatellite Instability, Tumor Mutation Burden, and Co-Mutated Genes: An In Silico Study.

During recent years, activating mutations in ERBB2 have been reported in solid tumors of various organs, and clinical trials targeting ERBB2-mutant tumors have been conducted. However, no effective treatment has been established for gastrointestinal tumors targeting ERBB2 mutations. ERBB2-mutant tumors have a higher tumor mutation burden (TMB) and microsatellite instability (MSI) than ERBB2 non-mutant tumors, but not all ERBB2-mutant tumors are TMB- and MSI-high. Thus, a more detailed classification of ERBB2-mutant tumors based on the underlying molecular mechanisms is required. Herein, we classified ERBB2 mutations into three groups-group 1: both ERBB2 mutations and amplifications; group 2: ERBB2 mutations annotated as putative driver mutations but without amplifications; group 3: ERBB2 mutations annotated as non-driver mutations (passenger mutations or unknown significance) and those that were not amplified in gastrointestinal tumors. Esophageal adenocarcinoma, gastric cancer, and colorectal cancer presented significantly higher MSI and TMB in the ERBB2-mutant group than in the ERBB2-wild-type group. The proportions of TMB- and MSI-high tumors and frequency of co-mutated downstream genes differed among the groups. We identified TMB- and MSI-high groups; this classification is considered important for guiding the selection of drugs for ERBB2-mutant tumors with downstream genetic mutations.

Association of KMT2C/D loss-of-function variants with response to immune checkpoint blockades in colorectal cancer.

Immune checkpoint inhibitors (ICIs) have become important treatment strategies, yet responses vary among patients and predictive biomarkers are urgently needed. Mutations in KMT2C and KMT2D lead to increased levels of genomic instability. Therefore, we aimed to examine whether KMT2C/D mutations might be a predictor of immunotherapeutic efficacy. Here, we investigated the associations of KMT2C/D loss-of-function (LOF) variants with tumor mutation burden (TMB), MSI-H, PD-L1 expression, the levels of tumor-infiltrating leukocytes (TILs), and clinical response to ICIs. It was found that KMT2C/D LOF variants were associated with higher TMB. Compared with the non-LOF group, the proportion of patients with MSI-H tumors was larger in the LOF group. PD-L1 expression was higher in the LOF group only for colorectal cancer in both the Chinese and The Cancer Genome Atlas cohorts. Importantly, KMT2C/D LOF variants were associated with decreased regulatory T cells and increased levels of CD8+ T cells, activated NK cells, M1 macrophages, and M2 macrophages in colorectal cancer. However, there was no significant association between KMT2C/D LOF and TILs levels in other cancer types. Consistently, the results showed that KMT2C/D LOF variants were associated with prolonged overall survival only in colorectal cancer (p = 0.0485). We also presented that patients with KMT2C/D LOF mutations exhibited a better clinical response to anti-PD-1 therapy in a Chinese colorectal cancer cohort (p = 0.002). Taken together, these results suggested that KMT2C/D LOF variants could be a useful predictor for ICIs efficacy in colorectal cancer. In addition, the predictive value of KMT2C/D LOF variants was consistent with their association with TILs levels.

Tumor immune microenvironment predicts the pathologic response of neoadjuvant chemoimmunotherapy in non-small-cell lung cancer.

The clinical outcome of resectable non-small-cell lung cancer (NSCLC) patients receiving neoadjuvant chemoimmunotherapy is good but varies greatly. In addition, the pathological response after neoadjuvant chemoimmunotherapy is significantly associated with survival outcomes. The aim of this retrospective study was to identify which population of patients with locally advanced and oligometastatic NSCLC has a favorable pathological response after neoadjuvant chemoimmunotherapy. NSCLC patients treated with neoadjuvant chemoimmunotherapy were enrolled between February 2018 and April 2022. Data on clinicopathological features were collected and evaluated. Multiplex immunofluorescence was performed on pre-treatment puncture specimens and surgically resected specimens. In total, 29 patients with stages III and IV locally advanced or oligometastatic NSCLC who received neoadjuvant chemoimmunotherapy and R0 resection were enrolled. The results showed that 55% (16/29) of patients had a major pathological response (MPR) and 41% (12/29) of patients had a complete pathological response (pCR). In the stroma area of the pre-treatment specimen, the higher infiltration of CD3+ PD-L1+ tumor-infiltrating lymphocytes (TILs) and the lower infiltration of CD4+ and CD4+ FOXP3+ TILs were more likely to appear in patients with pCR. However, in the tumor area, the higher infiltration of CD8+ TILs was more likely to appear in patients with non-MPR. In the post-treatment specimen, we found increased infiltration of CD3+ CD8+ , CD8+ GZMB+ , and CD8+ CD69+ TILs and decreased infiltration of PD-1+ TILs both in the stroma and tumor areas. Neoadjuvant chemoimmunotherapy achieved an MPR rate of 55% and induced greater immune infiltration. In addition, we observed that the baseline TILs and their spatial distribution correlate to the pathological response.

Construction and validation of a bladder cancer risk model based on autophagy-related genes.

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.