Substantial heterogeneity exists within cervical cancer that is generally infected by human papillomavirus (HPV). However, the most common histological subtype of cervical cancer, cervical squamous cell carcinoma (CSCC), is poorly characterized regarding the association between its heterogeneity and HPV oncoprotein expression. We filtered out 138 CSCC samples with infection of HPV16 only as the first step; then we compressed HPV16 E6/E7 expression as HPV pca and correlated HPV pca with the immunological profiling of CSCC based on supervised clustering to discover subtypes and to characterize the differences between subgroups in terms of the HPV pca level, pathway activity, epigenetic dysregulation, somatic mutation frequencies, and likelihood of responding to chemo/immunotherapies. Supervised clustering of immune signatures revealed two HPV16 subtypes (namely, HPV16-IMM and HPV16-KRT) that correlated with HPV pca and clinical outcomes. HPV16-KRT is characterized by elevated expression of genes in keratinization, biological oxidation, and Wnt signaling, whereas HPV16-IMM has a strong immune response and mesenchymal features. HPV16-IMM exhibited much more epigenetic silencing and significant mutation at FBXW7, while MUC4 and PIK3CA were mutated frequently for HPV16-KRT. We also imputed that HPV16-IMM is much more sensitive to chemo/immunotherapy than is HPV16-KRT. Our characterization tightly links the expression of HPV16 E6/E7 with biological and clinical outcomes of CSCC, providing valuable molecular-level information that points to decoding heterogeneity. Together, these results shed light on stratifications of CSCC infected by HPV16 and shall help to guide personalized management and treatment of patients.
The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2 , and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status ( p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.
Tumors are closely related to the tumor microenvironment (TME). The complex interaction between tumor cells and the TME plays an indisputable role in tumor development. Tumor cells can affect the TME, promote tumor angiogenesis and induce immune tolerance by releasing cell signaling molecules. Immune cell infiltration (ICI) in the TME can affect the prognosis of patients with bladder cancer. However, the pattern of ICI of the TME in bladder cancer has not yet been elucidated. Herein, we identified three distinct ICI subtypes based on the TME immune infiltration pattern of 584 bladder cancer patients using the ESTIMATE and CIBERSORT algorithms. Then, we identified three gene clusters based on the differentially expressed genes (DEGs) between the three ICI subtypes. In addition, the ICI score was determined using single sample gene set enrichment analysis (ssGSEA). The results suggested that patients in the high ICI score subgroup had a favorable prognosis and higher expression of checkpoint-related and immune activity-related genes. The high ICI score subgroup was also linked to increased tumor mutation burden (TMB) and neoantigen burden. A cohort treated with anti-PD-L1 immunotherapy confirmed the therapeutic advantage and clinical benefit of patients with higher ICI scores. In the end, our study also shows that the ICI score represents an effective prognostic predictor for evaluating the response to immunotherapy. In conclusion, our study deepened the understanding of the TME, and it provides new ideas for improving patients’ response to immunotherapy and promoting individualized tumor immunotherapy in the future.
Objective: Although gynecologic and breast (Pan-Gyn) cancers share a variety of similar characteristics, their response to immunotherapy is different. Immune checkpoint inhibitor therapy is not effective in all patients, while neoantigen load (NAL) may be a predictive biomarker. However, the selection of a NAL cutoff point and its predictive effect remain to be elucidated. Methods: We divided 812 Pan-Gyn cancer samples from The Cancer Genome Atlas into three groups based on 60 and 80% of their load percentile. We then correlated the identified NAL subgroups with gene expression, somatic mutation, DNA methylation, and clinicopathological information. We also characterized each subgroup by distinct immune cell enrichment, PD-1 signaling, and cytolytic activity. Finally, we predicted the response of each subgroup to chemotherapy and immunotherapy. Results: Across Pan-Gyn cancers, we identified three distinct NAL subgroups. These subgroups showed differences in biological function, genetic information, clinical variables, and immune infiltration. Eighty percent was identified as a meaningful cutoff point for NAL. In all patients, a higher NAL (top 20%) was associated with better overall survival as well as high immune infiltration and low intra-tumor heterogeneity. Furthermore, an interesting lncRNA named AC092580.4 was found, which was associated with two significantly different immune genes (CXCL9 and CXCL13). Conclusions: Our novel findings provide further insights into the NAL of Pan-Gyn cancers and may open up novel opportunities for their exploitation toward personalized treatment with immunotherapy.
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