“A growing problem in cancer research is figuring out how to analyze the many kinds of big genomic data for different cancers. The overwhelming quantity and complexity of the data has created an analysis bottleneck that has slowed the translation of the knowledge within the data to the clinic,” said researcher Gordon Okimoto.
“We have figured out a way to mine these data for the benefit of cancer patients,” he added.
Okimoto and collaborators developed a computational algorithm called the Joint Analysis of Many Matrices by ITeration (JAMMIT).
JAMMIT uses advanced mathematics to identify different patterns across multiple molecular data types such as gene expression and genetic mutations that when taken together accurately predicts what treatments would be best for a given cancer patient.
“The algorithm could accelerate the approval of powerful treatments for many cancers, improve clinical outcomes, and reduce costs for treating cancer. I believe this discovery can open a path to more precision medicine clinical trials that could be initiated and run locally in Hawai’i,” said researcher Randall Holcombe.
The analysis identified small sets of genes that accurately predict which patients would benefit most from chemotherapy. These same signatures also suggest that many of the ovarian and liver cancer patients studied would benefit from combining chemotherapy with immunotherapy.
The study has been published in Biodata Mining.