New high-throughput analytic enables differentiation of tumor cells within its microenvironment

Illustration of white blood cells attacking a cancer cell

One of the paramount goals in the treatment and eradication of cancer is understanding what causes cancer. To this end, it is crucial to be able to study tumor cells, their genetics, and epigenetics in isolation. Houston Methodist experts from the Center for Bioinformatics and Computational Biology have devised a new technology that enables clinicians to distinguish the tumor cells from among the non-cancerous components of the microenvironment, which may lead to more informed cancer treatment approaches. Rather than existing in isolation, tumor cells are always part of the local microenvironment along with stromal cells, immune cells, and components of the extracellular matrix. The ability to discern tumor cells from normal cells within the tumor microenvironment and to delineate the clonal substructure within tumors has been a challenge in the field. Various methodologies are available that aim to study human tumors by analyzing single-cell transcriptomes. The ability to estimate genomic copy numbers from RNA read counts has been demonstrated previously by techniques such as inferCNV and HoneyBadger. However, these are equipped to only analyze data from low throughput single-cell RNA sequencing methods such as Fluidigm and SMART-seq2. Recent breakthroughs in transcriptomic technologies allow high throughput-sequencing with the generation of single-cell transcriptomic data and measurement of gene expression in thousands of singular cells simultaneously. These high-throughput sequencing technologies such as Drop-Seq, Indrop, 10X Chromium (microdroplet systems) and Wafergen iCELL8, SeqWell, CelSee (nanowells) provide a major cost advantage by allowing sequencing of thousands of individual cells simultaneously for less than 1 USD per cell.

CopyKAT has several noteworthy applications and advantages over previous methodologies. First, it is compatible with both high-throughput and first-generation single-cell RNA sequencing data. This makes it superior to previous methodologies such as inferCNV and HoneyBadger. Unlike inferCNV and HoneyBadger, CopyKAT is capable of accurately resolving chromosome breakpoints and is compatible with extensively used 3’ and 5’ single-cell RNA sequencing methods including 10X Genomics, Drop-Seq, and InDrop. Second, it demonstrates a unique ability to identify tumor cells in unbiased single-cell RNA sequencing data that is composed of various cell types of the tumor microenvironment. Moreover, CopyKAT maintains this ability even in cases of extremely low (less than 15%) or extremely high (greater than 90%) tumor purity. Third, CopyKAT can delineate clonal substructures within the tumor microenvironment based on copy number aberrations. Of particular importance is its ability to distinguish malignant cells from normal epithelial cells since these two cell types often express the same biomarkers. Notably, secondary analyses such as correlation studies between gene expression profiles and phenotypes and/or signaling pathways are feasible once the clonal substructure is identified within the tumor mass. Aberrations of gene expression, as seen in different subclones, can result in distinct cancerous phenotypes. Understanding and unraveling these cellular and molecular mechanisms may aid in the identification of novel therapeutics and diagnostics to fight cancer.

Related Articles