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Sunday, June 12, 2016

Understanding how (serous) ovarian cancer spreads (B.C. Cancer Agency - link)



B.C. Cancer Agency
May 16, 2016 
 
BC Cancer Agency researchers are now providing critical insight into the spread of this deadly disease, for the first time mapping the composition of the cancer cell groups that have taken up residence within the patient’s abdomen and discovering two distinct patterns of cell migration in high grade serous ovarian cancer. This research is led by Dr. Sohrab Shah, senior scientist at the BC Cancer Agency and Canada Research Chair in Computational Cancer Genomics, and has been published in the world-leading, peer-reviewed scientific journal, Nature Genetics
Unlike most cancers that spread through blood or the lymph system, this study shows that high grade serous ovarian cancer cells have a unique opportunity to spread prolifically throughout the abdomen. In mapping the cell migration, Dr. Shah’s team has shown how these cells are able to settle and thrive in specific regions of the body, causing a widespread, life-threatening disease. 

This study has also confirmed that these tumours are made up of many different cancer cell types, explaining why some cells are susceptible to treatment while others are resistant, often leading to disease relapse after an initial response to treatment. Cell type migration patterns from ovary to other abdominal sites identified that specific ovary sites contained many more cell types relative to other sites, which could pinpoint ‘gateways’ of cell migration to other regions in the abdomen.

This new understanding of how high grade serous ovarian cancer cells migrate within the patient’s body provides insight that could inform future treatment selection. These results indicate that some cancer cells may have had pre-existing properties of resistance prior to the patient taking any treatment. This could indicate that a patient requires a much more aggressive, multi-treatment approach from the start of disease progression in order to prevent relapse.

To see an interactive info-graphic of this study, go here

A new approach to mapping the spread of cancer cells
shah nature paper tumour evolution.png
Situated within the tech hub of Vancouver, BC, Dr. Sohrab Shah’s bioinformatics lab at the BC Cancer Agency developed a new machine learning tool that enables study of the role of individual cancer cells in cancer progression. Published in Nature Methods, Shah’s team shows the power of digital cancer biology through computational analysis of mutations in individual ovarian cancer cells.
 
Developed by Dr. Andrew Roth, the open source software called Single Cell Genotyper (SCG) is a new statistical model and machine learning inference algorithm designed to determine the pattern of how DNA mutations are distributed in the genomes of individual tumour cells. This provides unprecedented digital resolution to identify the number of different types of cancer cells present in a tumour, and to track how they migrate when the disease spreads or relapses.  

Measurements of mutations in individual cancer cells are input into the SCG which is able to work through the ‘noise’ or ‘interference’ of competing or partially missing data to efficiently:
  • Estimate the number of cancer cell populations present in a tumour 
  • Identify the set of mutations that define each population 
  • Predict the abundance of each population in the tumour
This technology provides a new tool scientists can use to study the cell-population composition of all types of human cancer. This is a necessary first step to understand how cancers acquire resistance to treatment and spread beyond their site of origin.  

The SCG model allowed Shah and his team to reveal critical insight into the invasive spread of the most malignant form of ovarian cancer. This is a first in mapping two distinct patterns of cancer cell migration in the most deadly form of ovarian cancer.

The next steps are to apply SCG to define cell migration maps in ovarian cancer and breast cancer patients with a specific focus on determining which cells are resistant to treatment and what are their specific properties. This will allow researchers to build predictive tools to better inform future cancer care.

 

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