Abstract LB-216: Tumor heterogeneity and therapy resistance analyzed at the single-cell level

Abstract

Every tumor, either primary or metastatic is composed of many types of cancer cells, each with their own genomic and transcriptional makeup. Together with normal cells, mostly stroma, and immune cells, defining the tumor microenvironment, both populations create pathologic organ of unique spatial heterogeneity. Cancer therapies are selected based on analysis of the tumor cells historically using pathology evaluation, and more recently supported by mutational analyses through high throughput sequencing of panels of cancer genes or whole exomes. However, in many cases, less prominent and therapy-resistant cancer cell populations are missed due to bulk sequencing, and as such rare clones are not selected for, and can become resistant over time and create tumor spread. Here, we analyzed single-cell transcriptomic (scRNA-Seq) datasets of the tumor microenvironment of primary and metastatic samples of six tumor types: breast, colon, glioma, melanoma, head and neck, and kidney cancers. Using scRNA-Seq data from tumor and normal cells, we performed comprehensive analyses of tumor hallmarks, focusing on cancer stemness, molecular subtypes, and tumor heterogeneity. When available, we analyzed single-cells from longitudinal studies to understand mechanisms of resistance responsible for the failure of neo-adjuvant and adjuvant therapies. In total, we have performed an integrative analysis of gene expression of primary and metastatic tumors of 4202 bulk samples from The Cancer Genome Atlas (TCGA) and 96k cells belonging to 105 tumor samples from published single cell RNA-Seq datasets. TCGA datasets were analyzed by combining a recent BREW-based methodology and OCLR machine learning algorithm. OCLR trained on TCGA cancer specific samples estimated weights for each subtype and assigned the subtype to the highest correlated group for each individual cell. Following a similar procedure, we derived mRNAsi, a stemness index based on RNA-Seq data, for each individual single cell. Publicly available single-cell datasets and drug response data were utilized to test the association between stemness scores and drug sensitivity. Results confirmed that cell lines exposed to drug treatment resulted in lower stemness score compared to resistant cell lines. We detected intra-tumor stemness heterogeneity, and the presence of distinct molecular subtypes, suggesting that a unique target therapy might not be effective to eradicate all tumor cells. In case of breast cancer, therapy resistance was associated with higher mRNAsi. For low-grade glioma (LGG) we observed that most of the cells resulted in a codel subtype. In particular, mRNAsi score was able to stratify tumor subtypes; with higher scores corresponding to subtype G-CIMP-LOW and a lower mRNAsi score corresponding to Classic Like or LGm6-GBM. Moonlight dynamic recognition analysis showed upregulation of MYC Target and DNA_repair, while down-regulation of epithelial to mesenchymal (EMT) and TGF_beta signaling in cancer cells with higher mRNAsi. The results of our studies will help us to identify cancer cell phenotypes and tumor microenvironment composition that confer resistance to cancer therapies and may pave the way to implement scRNA-Seq analyses as diagnostic tools in clinical oncology. Citation Format: Antonio Colaprico, Francesca Petralia, Elena Papaleo, Olivier Gavaert, Xi Chen, Karol Szkudlarek, Maciej Wiznerowicz. Tumor heterogeneity and therapy resistance analyzed at the single-cell level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-216.