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Posted: Mar 21, 2017
Single-cell analysis pave way for more accurate cancer prognosis
(Nanowerk News) For the first time, researchers have applied single-cell transcriptomics to colorectal cancer (CRC) – the third most common cancer in the world – and discovered that this method could lead to improved patient stratification and eventually, a more accurate prognosis of CRC patients. Jointly led by scientists from A*STAR’s Genome Institute of Singapore (GIS) and National Cancer Centre Singapore (NCCS), the team performed an unbiased analysis of functional heterogeneity in colorectal tumours and their microenvironment using RNA-seq profiling of unsorted single cells from 11 primary tumours and normal tissue.
As there are no existing algorithms that can accurately identify cell-type groups from the new data type, the researchers developed their own and compared it to existing single-cell clustering algorithms using a novel benchmark data set.
Called Reference Component Analysis (RCA), this novel method incorporates reference-based cell-cell similarity scores and substantially improves clustering accuracy, outperforming existing algorithms to robustly cluster single-cells. It is also the first time anyone has systematically benchmarked such an algorithm, which will serve as an important foundation for future studies.
A key conclusion of this study was that of epithelial-mesenchymal transition (EMT), which is thought to contribute to the spread of cancer cells throughout the body, or metastasis. Looking at the single-cell level, the researchers found that the mesenchymal genes that were expressed in the tumour and supposedly signatures of EMT, were not expressed in the cancer cells. Instead, these genes were expressed in the non-cancer cells – a finding that would not have been possible based on traditional bulk transcriptomics.
“The biggest advantage of single-cell analysis is that we are able to look at one cell at a time, which allows us to know exactly in which cell each gene was expressed. While we didn’t disprove EMT, we certainly found no evidence for it in the cancer cells we analysed. In fact, it appears that it’s the non-cancer cells that express mesenchymal genes in our colon tumour samples,” said Dr Shyam Prabhakar, the study’s co-corresponding author and Associate Director of Integrative Genomics at the GIS. “Our findings are consistent with some papers published recently that reached a similar conclusion. Nevertheless, this is a shift from the dominant paradigm – it really challenges our view of how cancer cells spread in the body.”
Although bulk transcriptomics is well established as a way of classifying CRC patients, the more fine-grained single-cell analysis could potentially support even more precise stratification.
“Using the data from our study, we found that our single-cell signatures were able to classify patients more accurately, enabling an improved prognosis of how likely they are to survive over a five-year window. The tumour microenvironment is critical to the biological behaviour of cancers. Using the technologies in our study, we have elucidated, identified and characterised different cell populations that comprise the tumour microenvironment and are now moving to further studies to experimentally characterise the interactions between these cells,” said co-corresponding author, Dr Iain Tan, Senior Consultant, Medical Oncologist at NCCS and clinician scientist at the GIS.
GIS Executive Director Prof Ng Huck Hui added, “This is a first step towards understanding how patients with the same diagnosis are actually very different. We hope methods like RCA can be used to obtain a more accurate prognosis of patient survival rate and help us understand why the patients’ prognoses are different from one another. This will eventually lead to individualised and more targeted treatments for these different, further stratified groups of patients.”
Targeting to reach a million single-cell transcriptomes, the research team will next scale up and expand their research by applying single-cell analyses to other domains, such as lung cancer, leukemias, ageing tissues and host response to infection.