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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.
Dr. Sohrab Shah-Scientists Map Spread of Deadliest Ovarian cancer from BC Cancer Foundation on Vimeo.
A new approach to mapping the spread of cancer cells
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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