Abstract
BackgroundCancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.ResultsTo investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10−33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10−3, n = 348, IMvigor210 cohort) in the tumor patients.ConclusionsHerein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.
Highlights
Cancer biology has reached a point where it is well understood that cancer cells interact with their microenvironment, which determines whether it will respond to treatment, develop resistance, recur or metastasize
2.2.5 Functional Gene Set-Based Prognostic Model To accurately predict the prognosis and therapeutic benefits of breast cancer (BC) patients, we proposed a functional gene set-based prognostic model, the construction of which consisted of three main steps: converting gene expression into activation scores of functional gene sets, identifying functional gene sets significantly associated with cellular proportions, and establishing the prognostic model based on the identified functional gene sets in the previous step
A similar trend was observed in the GSE75688 scRNA-Seq dataset [27] (Supplementary Figures 2A, B and Supplementary Table S1). These results indicate that our BCspecific RGEP can accurately predict the cellular compositions in BC-tumor microenvironment (TME), and with better predictive performance compared to other non-specific references
Summary
Cancer biology has reached a point where it is well understood that cancer cells interact with their microenvironment, which determines whether it will respond to treatment, develop resistance, recur or metastasize. Despite the partial success of conventional therapies (surgery, chemotherapy, radiotherapy, and targeted therapy) and other ongoing therapeutic advances (immunotherapy), it remains a concern why some patients eventually develop metastases and others respond poorly to treatment. Since TILs comprise a heterogeneous population of cells with different physiological/ pathological effects in the tumor microenvironment (TME), new emerging technologies (e.g., single-cell RNA sequencing: scRNA-seq) have gained an advantage in resolving their functional interpretation in BC [4]. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive
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