LSCC NGS informatics meeting – Marek Cmero: Evolution of cancer cell populations – 3pm Fri 21st Nov

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Please join us for the LSCC NGS informatics meeting.

Speaker: Marek Cmero
Title: Reconstructing the evolution of cancer cell populations – methods & challenges

When: 3pm, Friday 21st November
Where: Board room, VLSCI 187 Grattan Street, Carlton

Abstract:
Tumour evolution is a complex and multifaceted process that arises from the driving forces of carcinogenesis. As cancer progresses, distinct cellular populations with inheritable genetic characteristics that can be observed within a single tumour. Additionally, primary tumours can seed metastases in distant parts of the body from one or several cellular sub-populations. The ability to trace the progression of a cancer, by identifying sub-populations and inferring the relationships between them from shared genetic features, has only recently become feasible. Next-generation sequencing technologies are able to provide fine-grained genomic data which can quantify the relationships between intra-tumour populations as well as distant metastases. Understanding these relationships using methods of phylogenetic reconstruction can inform the evolution of invasive or metastatic genetic changes in the evolutionary history of a cancer. This information can assist in prognostication and prediction of cancer evolution in a clinical setting.
We discuss several approaches to this challenging problem, including single-nucleotide variations (SNVs) and copy-number variation (CNVs). Additionally, we present a method of reconstructing cancer phylogeny using large scale genomic aberrations, collectively known as structural variations (SVs.) We apply our method to prostate cancer – which is particularly SV-driven. By comparing multiple prostate cancer samples from the same patient, distinct cellular populations and their ancestral relationships can be de-convolved and the occurrence of a particular SV within a cancer’s evolution can be estimated. We demonstrate that tumour phylogenies are able to be reconstructed with SV data alone, and can provide clues in revealing which large-scale genomic variations are driving factors in prostate cancers.

 

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