Data-driven approach may help personalize cancer treatment
- Big-data pipeline identifies cancer-specific changes in metabolic pathways
- Team successfully profiled metabolic reprogramming in 26 major cancer types
- Their approach can identify patients more likely to respond to cancer therapies
BUFFALO, N.Y. — Researchers from Roswell Park Comprehensive Cancer Center have developed a new bioinformatics-based approach for monitoring key changes in cancer cells. They describe their work to develop this data-driven method and how it can be used to enhance and personalize cancer treatment in a recent article in the journal Nature Communications.
Cancer cells reprogram their metabolism to redirect nutrient resources in ways that support their rapid proliferation and spread to other sites in the body. Cancer drugs that target these altered metabolic pathways are largely effective, but they can also be highly toxic and difficult to tolerate, and determining how an individual will respond to these drugs has proven challenging. A better understanding of how these metabolic pathways are changed in specific cancer types could help scientists design more effective and personalized therapies.
A research team led by Dominic Smiraglia, PhD, Associate Professor of Oncology in the Department of Cancer Genetics and Genomics at Roswell Park, examined the similarities and differences in cell metabolism among 10,704 tumors representing 26 major cancer types, including breast, prostate, colon, lung, liver and skin cancer. Using transcriptomic data from The Cancer Genome Atlas, a major public database of information about genes and cancer genetics, the researchers were able to distinguish cancerous tumors from healthy tissue based on the expression patterns of genes within 114 metabolic pathways regulating various aspects of cell metabolism. Further, they identified “master metabolic transcriptional regulators” (MMTRs) controlling groups of genes within the affected metabolic pathways that could explain why these metabolic differences exist and how they are similar or different among various cancer types.
“We found that gene expression of master metabolic transcriptional regulators can be used to explain why metabolic differences exist in different disease sites and then identify patients who are potential responders to metabolic therapeutics,” says Dr. Smiraglia, who is senior author of the study. “Our data pipeline identifies a strong link between differences in the way these MMTRs are expressed, the degree of dysregulation of a cancer metabolic pathway, and a patient’s sensitivity to drugs targeting those pathways.”
Using this approach, the team successfully profiled metabolic reprogramming in the 26 major cancer types studied, revealing both common and unique patterns of metabolic dysregulation across multiple types of cancer as well as within individual cancer types. Additionally, their studies identified metabolic pathways uniquely dysregulated among different subtypes of breast cancer. These observations are consistent with clinical knowledge in breast cancer, where different subtypes have different treatment regimens and outcomes. Their findings suggest that metabolic-based therapies can be specifically tailored to certain subtypes of breast cancer.
“Our research offers an analytical pipeline that can identify dysregulated metabolic pathways in cancer as well as the patients who will best respond to metabolic-targeted therapy,” says Spencer Rosario, first author of the study and a doctoral student in Dr. Smiraglia’s lab. “The magnitude of distortion of a particular metabolic pathway can accurately predict drug sensitivity, and clinicians can use this information to develop novel and personalized metabolic-based therapeutic strategies that decrease the number and severity of side effects related to drug toxicity.”
In addition to accurately predicting sensitivity to metabolic therapy and providing insights into the mechanisms underlying disease-site specific metabolic dysregulation, the pipeline can also be used to discover future targets for pharmacologic intervention, the authors note.
The study, “Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas,” was supported by the National Cancer Institute (project nos. R01CA197996, F99CA212455/4K00CA212455 and P30CA016056). The article is available at nature.com.
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Annie Deck-Miller, Senior Media Relations Manager