DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls

Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer

Background

Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Methods

Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210.

Results

The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens.

Conclusions

The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a tumor. ITH was involved in the cancer progression, aggressiveness, therapy resistance and cancer recurrence. Integrative machine learning procedure including 10 methods was conducted to develop an ITH-related signature (IRS) in The Cancer Genome Atlas (TCGA), GSE54467, GSE59455 and GSE65904 cohort. Several scores, including tumor immune dysfunction and exclusion (TIDE) score, tumor mutation burden (TMB) score and immunophenoscore (IPS), were used to evaluate the role of IRS in predicting immunotherapy benefits. Two immunotherapy datasets (GSE91061 and GSE78220) were utilized to the role of IRS in predicting immunotherapy benefits of skin cutaneous melanoma (SKCM) patients. The optimal prognostic IRS constructed by Lasso method acted as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in SKCM, with the area under the curve of 2-, 3- and 4-year receiver operating characteristic curve being 0.722, 0.722 and 0.737 in TCGA cohort. We also constructed a nomogram and the actual 1-, 3- and 5-year survival times were highly consistent with the predicted survival times. SKCM patients with low IRS scores had a lower TIDE score, lower immune escape score and higher TMB score, higher PD1&CTLA4 IPS. Moreover, SKCM patients with low IRS scores had a lower gene sets score involved in DNA repair, angiogenesis, glycolysis, hypoxia, IL2-STAT5 signaling, MTORC1 signaling, NOTCH signaling and P53 pathway. The current study constructed a novel IRS in SKCM using 10 machine learning methods. This IRS acted as an indicator for predicting the prognosis and immunotherapy benefits of SKCM patients.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood.

METHODS

We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity.

RESULTS

A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient.

CONCLUSION

Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

INTRODUCTION

Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported.

METHODS

We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database.

RESULTS

We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295.

CONCLUSION

Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.

Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis

Despite immune checkpoint inhibitors (ICIs) have shown the greatest success in melanoma treatment, only a subset of melanoma patients responds well to ICIs. Thus, identifying predictive biomarkers for immunotherapy response is crucial. In this study, we took complementary advantages of immunotherapy data and The Cancer Genome Atlas (TCGA) multi-omics data to explore the predictive biomarkers for the response to immunotherapy in melanoma. We first predicted responsive and non-responsive melanomas in the TCGA skin cutaneous melanoma (SKCM) cohort based on both somatic mutation and transcriptome datasets which involved immunotherapy data for melanoma. This method identified 170 responsive and 56 non-responsive melanomas in TCGA-SKCM. Based on the TCGA-SKCM data, we performed a comprehensive comparison of multi-omics molecular features between responsive and non-responsive melanomas. We identified the molecular features significantly associated with immunotherapy response in melanoma at the genome, transcriptome, epigenome, and proteome levels, respectively. Our analysis confirmed certain immunotherapy response-associated biomarkers, such as tumor mutation burden (TMB), copy number alteration (CNA), intratumor heterogeneity (ITH), PD-L1 expression, and tumor immunity. Moreover, we identified some novel molecular features associated with immunotherapy response: (1) the activation of mast cells and dendritic cells correlating negatively with immunotherapy response; (2) the enrichment of many oncogenic pathways correlating positively with immunotherapy response, such as JAK-STAT, RAS, MAPK, HIF-1, PI3K-Akt, and VEGF pathways; and (3) a number of microRNAs and proteins whose expression correlates with immunotherapy response. In addition, the mTOR signaling pathway has a negative association with immunotherapy response. The novel biomarkers have potential predictive values in immunotherapy response and warrant further investigation.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a frequent malignancy of the digestive system with a poor prognosis and high mortality rate worldwide. Intratumor heterogeneity (ITH) is associated with tumor progression, poor prognosis, immunosuppression, and therapy resistance. However, the relationship between ITH and prognosis, the immune microenvironment, and the chemotherapy response in COAD patients remains unknown, and this knowledge is urgently needed.

Methods

We obtained clinical information and gene expression data for COAD patients from The Cancer Genome Atlas (TCGA) database. The DEPTH2 algorithm was utilized to evaluate the ITH score. X-tile software was used to determine the optimal cutoff value of the ITH score. The COAD patients were divided into high- and low-ITH groups based on the cutoff value. We analyzed prognosis, tumor mutation burden (TMB), gene mutations, and immune checkpoint expression between the high- and low-ITH groups. Differentially expressed genes (DEGs) in the high- and low-ITH groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to screen the prognosis-related genes for the construction of an ITH-related prognostic signature. The nomogram was used to predict the overall survival (OS) of COAD patients. The protein–protein interaction (PPI) network was constructed by using the GeneMANIA database. Principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were employed to explore the differences in biological pathway activation status between the high- and low-risk groups. The proportion and type of tumor-infiltrating immune cells were evaluated by the CIBERSORT and ESTIMATE algorithms. Additionally, we assessed the chemotherapy response and predicted small-molecule drugs for treatment. Finally, the expression of the prognosis-related genes was validated by using the UALCAN database and Human Protein Atlas (HPA) database.

Results

The OS of the high-ITH group was worse than that of the low-ITH group. A positive correlation between ITH and TMB was identified. In subgroups stratified by age, gender, and tumor stage, the OS of the low-ITH group remained better than that of the high-ITH group. There were dramatic differences in the mutated genes, single nucleotide variant classes, variant types, immune checkpoints and cooccurring and mutually exclusive mutations of the DEGs between the high- and low-ITH groups. Based on the DEGs between the high- and low-ITH groups, we constructed a five-gene signature consisting of CEACAM5, ENO2, GABBR1, MC1R, and SLC44A4. The COAD patients were divided into high- and low-risk groups according to the median risk score. The OS of the high-risk group was worse than that of the low-risk group. The nomogram was used to accurately predict the 1-, 3- and 5-year OS of COAD patients and showed good calibration and moderate discrimination ability. The stromal score, immune score, and ESTIMATE score of the high-risk group were significantly higher than those of the low-risk group, whereas tumor purity showed the opposite trend. The patients classified by the risk score had distinguishable sensitivity to chemotherapeutic drugs. Finally, two public databases confirmed that CEACAM5 and SLC44A4 were upregulated in normal tissues compared with COAD tissues, and ENO2, GABBR1, and MC1R were upregulated in COAD tissues compared with normal tissues.

Conclusion

Overall, we identified an ITH-related prognostic signature for COAD that was closely related to the tumor microenvironment and chemotherapy response. This signature may help clinicians make more personalized and precise treatment decisions for COAD patients.