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Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.
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Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.
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The SARS-CoV-2 is constantly mutating, and the new coronavirus such as Omicron has spread to many countries around the world. Anexelekto (AXL) is a transmembrane protein with biological functions such as promoting cell growth, migration, aggregation, metastasis and adhesion, and plays an important role in cancers and coronavirus disease 2019 (COVID-19). Unlike angiotensin-converting enzyme 2 (ACE2), AXL was highly expressed in respiratory system cells. In this study, we verified the AXL expression in cancer and normal tissues and found AXL expression was strongly correlated with cancer prognosis, tumor mutation burden (TMB), the microsatellite instability (MSI) in most tumor types. Immune infiltration analysis also demonstrated that there was an inextricable link between AXL expression and immune scores in cancer patients, especially in BLCA, BRCA and CESC. The NK-cells, plasmacytoid dendritic cells, myeloid dendritic cells, as one of the important components of the tumor microenvironment, were highly expressed AXL. In addition, AXL-related tumor neoantigens were identified and might provide the novel potential targets for tumor vaccines or SARS-Cov-2 vaccines research in cancer patients.
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Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.
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Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-â was inflammatory, proliferative, and immune-evasion; S-â ¡ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-â ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-â ¡; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.
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Background and Aims: Individuals of African (AFR) ancestry have a higher incidence of colorectal cancer (CRC) than those of European (EUR) ancestry and exhibit significant health disparities. Previous studies have noted differences in the tumor microenvironment between AFR and EUR patients with CRC. However, the molecular regulatory processes that underpin these immune differences remain largely unknown. Methods: Multiomics analysis was carried out for 55 AFR and 456 EUR patients with microsatellite-stable CRC using The Cancer Genome Atlas. We evaluated the tumor microenvironment by using gene expression and methylation data, transcription factor, and master transcriptional regulator analysis to identify the cell signaling pathways mediating the observed phenotypic differences. Results: We demonstrate that downregulated genes in AFR patients with CRC showed enrichment for canonical pathways, including chemokine signaling. Moreover, evaluation of the tumor microenvironment showed that cytotoxic lymphocytes and neutrophil cell populations are significantly decreased in AFR compared with EUR patients, suggesting AFR patients have an attenuated immune response. We further demonstrate that molecules called "master transcriptional regulators" (MTRs) play a critical role in regulating the expression of genes impacting key immune processes through an intricate signal transduction network mediated by disease-associated transcription factors (TFs). Furthermore, a core set of these MTRs and TFs showed a positive correlation with levels of cytotoxic lymphocytes and neutrophils across both AFR and EUR patients with CRC, thus suggesting their role in driving the immune infiltrate differences between the two ancestral groups. Conclusion: Our study provides an insight into the intricate regulatory landscape of MTRs and TFs that orchestrate the differences in the tumor microenvironment between patients with CRC of AFR and EUR ancestry.
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Tumor stemness is associated with tumor progression and therapy resistance. The recent advances in sequencing, genomics, and computational technologies have facilitated investigation into the tumor stemness cell-like characteristics. We identified subtypes of lung adenocarcinoma (LUAD) in bulk tumors or single cells based on the enrichment scores of 12 stemness signatures by clustering analysis of their transcriptomic profiles. Three stemness subtypes of LUAD were identified: St-H, St-M, and St-L, having high, medium, and low stemness signatures, respectively, consistently in six different datasets. Among the three subtypes, St-H was the most enriched in epithelial-mesenchymal transition, invasion, and metastasis signaling, genomically instable, irresponsive to immunotherapies and targeted therapies, and hence had the worst prognosis. We observed that intratumor heterogeneity was significantly higher in high-stemness than in low-stemness bulk tumors, but significantly lower in high-stemness than in low-stemness single cancer cells. Moreover, tumor immunity was stronger in high-stemness than in low-stemness cancer cells, but weaker in high-stemness than in low-stemness bulk tumors. These differences between bulk tumors and single cancer cells could be attributed to the non-tumor cells in bulk tumors that confounded the results of correlation analysis. Furthermore, pseudotime analysis showed that many St-H cells were at the beginning of the cell evolution trajectory, compared to most St-L cells in the terminal or later phase, suggesting that many low-stemness cells are originated from high-stemness cells. The stemness-based classification of LUAD may provide novel insights into the tumor biology as well as precise clinical management of this disease.
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Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)-0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)-0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.
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MicroRNA (miRNA) deregulation plays a critical role in the heterogeneous development of prostate cancer (PCa) by tuning mRNA levels. Herein, we aimed to characterize the molecular features of PCa by clustering the miRNA-regulated transcriptome with non-negative matrix factorization. Using 478 PCa samples from The Cancer Genome Atlas, four molecular subtypes (S-I, S-II, S-III, and S-IV) were identified and validated in two merged microarray and RNAseq datasets with 656 and 252 samples, respectively. Interestingly, the four subtypes showed distinct clinical and biological features after comprehensive analyses of clinical features, multiomic profiles, immune infiltration, and drug sensitivity. S-I is basal/stem/mesenchymal-like and immune-excluded with marked transforming growth factor ß, epithelial-mesenchymal transition and hypoxia signals, increased sensitivity to olaparib, and intermediate prognosis. S-II is luminal/metabolism-active and responsive to androgen deprivation therapy with frequent TMPRSS2-ERG fusion and a good prognosis. S-III is characterized by moderate proliferative and metabolic activity, sensitivity to taxane-based chemotherapy, and intermediate prognosis. S-IV is highly proliferative with moderate EMT and stemness, frequent deletions of TP53, PTEN and RB, and the poorest prognosis; it is also immune-inflamed and sensitive to anti-PD-L1 therapy. Overall, based on miRNA-regulated gene profiles, this study identified four distinct PCa subtypes that could improve risk stratification at diagnosis and provide therapeutic guidance.
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Because immune checkpoint inhibitors (ICIs) are effective for a subset of melanoma patients, identification of melanoma subtypes responsive to ICIs is crucial. We performed clustering analyses to identify immune subtypes of melanoma based on the enrichment levels of 28 immune cells using transcriptome datasets for six melanoma cohorts, including four cohorts not treated with ICIs and two cohorts treated with ICIs. We identified three immune subtypes (Im-H, Im-M, and Im-L), reproducible in these cohorts. Im-H displayed strong immune signatures, low stemness and proliferation potential, genomic stability, high immunotherapy response rate, and favorable prognosis. Im-L showed weak immune signatures, high stemness and proliferation potential, genomic instability, low immunotherapy response rate, and unfavorable prognosis. The pathways highly enriched in Im-H included immune, MAPK, apoptosis, calcium, VEGF, cell adhesion molecules, focal adhesion, gap junction, and PPAR. The pathways highly enriched in Im-L included Hippo, cell cycle, and ErbB. Copy number alterations correlated inversely with immune signatures in melanoma, while tumor mutation burden showed no significant correlation. The molecular features correlated with favorable immunotherapy response included immune-promoting signatures and pathways of PPAR, MAPK, VEGF, calcium, and glycolysis/gluconeogenesis. Our data recapture the immunological heterogeneity in melanoma and provide clinical implications for the immunotherapy of melanoma.
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Although immunotherapy has emerged as an effective therapeutic strategy for various cancers including head and neck squamous cell carcinomas (HNSCCs), only a subset of patients can benefit from such therapy. Hence, it is pressing to discover predictive biomarkers for cancer immunotherapy response. TP53 and HRAS mutations frequently occur in HNSCC and correlate with a worse prognosis in HNSCC. We extensively characterized the associations of TP53 mutations and HRAS mutations with HNSCC immunity based on multiple cancer genomics datasets. We compared the enrichment levels of 20 immune signatures between TP53-mutated and TP53-wildtype HNSCCs, and between HRAS-mutated and HRAS-wildtype HNSCCs, and found that TP53 mutations were associated with depressed immune signatures while HRAS mutations were associated with enhanced immune signatures in HNSCC. Moreover, we found multiple p53- and RAS-mediated pathways showing significant correlations with HNSCC immunity. Furthermore, we demonstrated that the association between TP53 mutation and tumor immunity was independent of the human papillomavirus (HPV) infection and smoking status in HNSCC. These data suggest that p53 and RAS may play important roles in regulating HNSCC immunity and that the TP53 and HRAS mutation status could be useful biomarkers for stratifying HNSCC patients responsive to immunotherapy.
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The programmed cell death protein 1 (PD-1) pathway has received considerable attention due to its role in eliciting the immune checkpoint response of T cells, resulting in tumor cells capable of evading immune surveillance and being highly refractory to conventional chemotherapy. Application of anti-PD-1/PD-L1 antibodies as checkpoint inhibitors is rapidly becoming a promising therapeutic approach in treating tumors, and some of them have successfully been commercialized in the past few years. However, not all patients show complete responses and adverse events have been noted, suggesting a better understanding of PD-1 pathway mediated immunosuppression is needed to predict patient response and improve treatment efficacy. Here, we review the progresses on the studies of the mechanistic role of PD-1 pathway in the tumor immune evasion, recent clinical development and commercialization of PD-1 pathway inhibitors, the toxicities associated with PD-1 blockade observed in clinical trials as well as how to improve therapeutic efficacy and safety of cancer immunotherapy.