Copy number alterations
Copy-number alterations (CNAs) are changes in the allelic quantity of genes: from 2N copies to 1N, 0N (losses), or 3N to 200+N (gains). They are often formed through entire chromosome gains or losses. CNAs, which complement single-nucleotide variants and short insertion-deletions (often simply referred to as “mutations”), are the most common genetic alterations in cancer cells. However, CNAs are challenging to study. While mutations can readily be tested for biological effects by mutating the normal gene on a plasmid, copy number alterations often consist of pathway-level changes affected dozens or hundreds of genes, often on multiple chromosomes.
To assist in the challenge of discovering which copy number alterations drive disease, we utilize bioinformatics to prioritize which copy-number-altered genes are most likely affecting the biology of a cancer cell. We then can use normal molecular biology techniques to raise or lower the dosage of those genes within the cell to discover which aspects of biology are affected by those genes. The cutting-edge aspects of the research involve these phenotypic readouts, which are best described by the example of autophagy.
To assist in the challenge of discovering which copy number alterations drive disease, we utilize bioinformatics to prioritize which copy-number-altered genes are most likely affecting the biology of a cancer cell. We then can use normal molecular biology techniques to raise or lower the dosage of those genes within the cell to discover which aspects of biology are affected by those genes. The cutting-edge aspects of the research involve these phenotypic readouts, which are best described by the example of autophagy.
Autophagy
Is it “auto-phagy” or “ah-TAW-phagy”? That is up to you. “Auto” describes ‘self’ and “phagy” describes ‘eating’ in Greek, and this is exactly what autophagy refers to: the cellular recycling machinery.
Our bioinformatics identified the genes involved in Autophagy as one of the most significantly deleted pathways in serous ovarian cancer – a cancer type heavily affected by copy-number alterations. Looking at those genes which initiate autophagy, they predominantly lay on regions selectively deleted within the ovarian cancer genome:
Blue = deleted region across patients, Red = gained region across patients
Our bioinformatics identified the genes involved in Autophagy as one of the most significantly deleted pathways in serous ovarian cancer – a cancer type heavily affected by copy-number alterations. Looking at those genes which initiate autophagy, they predominantly lay on regions selectively deleted within the ovarian cancer genome:
Blue = deleted region across patients, Red = gained region across patients
This suggests that autophagy may act as a tumor suppressor pathway, as initially proposed by Dr. Levine and Dr. Heintz.
Regardless of whether or not autophagy acts as a tumor suppressor, which is debated and likely depends on context, the fact that so many autophagy genes are deleted suggests that these ovarian cancer cells are impaired in their ability to induce autophagy upon stress. With this impairment in homeostasis, it was reasonable to hypothesize that a pharmacologic stress may imbue a selective stress on ovarian cancer cells that normal cells can overcome. That is to say, a therapeutic window may exist.
This turns out to be the case. A half-dozen cell culture models and four mouse models of ovarian cancer succumbed to autophagy-disruption therapy. We term this “COAST” therapy: Combination Of Autophagy Selective Therapeutics. We hope that this type of therapy will help patients suffering from ovarian cancer in the near future.
Movie of ovarian cancer cells perishing during COAST therapy:
Regardless of whether or not autophagy acts as a tumor suppressor, which is debated and likely depends on context, the fact that so many autophagy genes are deleted suggests that these ovarian cancer cells are impaired in their ability to induce autophagy upon stress. With this impairment in homeostasis, it was reasonable to hypothesize that a pharmacologic stress may imbue a selective stress on ovarian cancer cells that normal cells can overcome. That is to say, a therapeutic window may exist.
This turns out to be the case. A half-dozen cell culture models and four mouse models of ovarian cancer succumbed to autophagy-disruption therapy. We term this “COAST” therapy: Combination Of Autophagy Selective Therapeutics. We hope that this type of therapy will help patients suffering from ovarian cancer in the near future.
Movie of ovarian cancer cells perishing during COAST therapy:
This finding was discovered by careful manipulation and observation of specific autophagy genes in cell line models, using lentiviral constructs and modern molecular biological techniques. We continue to expand on how autophagy manipulation affects cancer cell biology from a standpoint of genetic and metabolic heterogeneity.
We are also developing new tools to study copy number alterations directly using CRISPR-Cas9 tools and single-DNA-alteration-capture sequencing techniques.
We are also developing new tools to study copy number alterations directly using CRISPR-Cas9 tools and single-DNA-alteration-capture sequencing techniques.
Bioinformatics
Biology is so darn complicated. Of course, this is the beauty of biological research. While the vast majority of scientific study of biology manipulates a single gene or molecule at a time, the reality of disease is that a multitude of genetic, epigenetic, and environmental changes all collaborate to produce a phenotypic change.
We create tools to help bioinformaticians and bench-scientists alike better understand complex biology. Since our wet-lab research is focused on copy number alterations, the most sophisticated tools from the lab relate to this topic.
Visit delaneyapps.com to try out our current public bioinformatic tools.
We create tools to help bioinformaticians and bench-scientists alike better understand complex biology. Since our wet-lab research is focused on copy number alterations, the most sophisticated tools from the lab relate to this topic.
Visit delaneyapps.com to try out our current public bioinformatic tools.
