There is a significant effort to implement personalized approaches to cancer treatment, tailoring therapeutic choices based on the genomic profile of each patient’s cancer. Scientists at the HudsonAlpha Institute for Biotechnology recently contributed to this effort by publishing a paper in Clinical Cancer Research describing the results of a study that explored gene expression and metabolic signatures in ovarian cancer to discover both markers of treatment response and potential therapeutic targets.
Ovarian cancer treatment limitations
Despite considerable progress in cancer treatments over the last 50 years, ovarian cancer remains the deadliest gynecological cancer in the United States. According to the American Cancer Society, an estimated 19,880 cases of ovarian cancer will be diagnosed in the U.S. in 2022. An estimated 12,810 women are expected to die from the disease due largely to inadequate early screening and a high incidence of disease recurrence after treatment.
Ovarian cancer originates from three types of ovarian cells that develop into different tumors—epithelial cells, germ cells, and stromal cells. There are also subtypes of tumors within each type of ovarian cancer, each with its own molecular, morphological, and clinical features. This makes it difficult for researchers and physicians to quickly and non-invasively determine the best therapy for a patient, monitor whether a treatment is working effectively, or predict if the patient will experience disease recurrence.
HudsonAlpha Institute for Biotechnology Faculty Investigator Sara Cooper, PhD, and her longtime clinical collaborator Rebecca Arend, MD, an assistant professor in the University of Alabama at Birmingham (UAB) Division of Gynecologic Oncology along with associate scientist in the Experimental Therapeutics Program at the UAB Comprehensive Cancer Center, have been working together for several years to identify new drugs, new drug targets, and new combinations of drugs to overcome current ovarian cancer treatment limitations.
Using genomic and metabolic analysis to inform treatment decisions
In their new study, the research team set out to integrate gene expression data and metabolic data from tumors and benign tissue to gain a more complete picture of a type of epithelial ovarian tumor called high-grade serous ovarian carcinoma. Cancer cells can alter their metabolism to support increased energy use due to their continuous growth and rapid proliferation. Metabolic reprogramming is a hallmark of cancer that contributes to chemoresistance, metastatic potential, and suppression of immune cells, but it has not been widely studied in ovarian cancer.
“Our study combined gene expression profiles with metabolic analysis in tumors to try to identify new pathways that can be targeted for treatment,” says Cooper. “We found diverse gene expression and metabolic differences across subtypes of high-grade serous tumors, which supports the use of genomic and metabolic information to help select treatments and predict treatment outcomes on an individualized basis.”
The group found that many patients had tumor gene expression profiles associated with known targetable pathways. In addition, they identified several promising biomarkers of response to treatment and new therapeutic targets.
Looking beyond primary tumors could help predict treatment resistance
The vast majority of genomics research interrogates primary tumor tissue, but important clues for how to treat patients, especially those with recurrent disease can be found in patient samples taken after treatment or from outside the primary tumor. The research team compared pre-treatment and post-treatment tumor tissue to study individual responses to chemotherapy. They found that chemotherapy caused changes in gene expression and activity of the WNT signaling pathway, which is involved in immune cell maintenance and renewal. The group found data to support that altered WNT activity in post-chemotherapy tumor tissue was connected to a weakened immune response in the tumor, suggesting that WNT signaling could be a promising therapeutic target to enhance immune function.
In advanced-stage ovarian cancer, fluid and cells called ascites fluid can build up in the patient’s abdomen. Ascites fluid is a known contributor to poor patient outcomes by facilitating metastasis and contributing to therapeutic resistance. Cooper and Arend’s groups set out to explore the possibility that markers of treatment resistance might be found in ascites, in addition to post-treatment tissue.
Their analysis comparing pre-chemotherapy tumor tissue and ascites cells from the same patient showed significant differences in gene expression profiles, including changes in tumor cell subtypes. Combined with previous studies, these findings indicate that ascites fluid contains a subset of resistant, more stem cell-like cells, which likely contribute to disease recurrence. Analysis of post-treatment tissue and ascites fluid could help better inform treatment decisions and monitor disease recurrence.
The study highlights some of the challenges in identifying precision medicine approaches for ovarian cancer patients, but it also supports the use of genomic and metabolic information to help select treatment, predict treatment outcomes, and identify potential new therapeutic targets to achieve better results for ovarian cancer patients.
Funding from the Tie the Ribbons event and other donations to HudsonAlpha’s ovarian cancer research program helped make this research possible.
Byline: Sarah Sharman, PhD, Science writer
Reference: Arend RC, et al. Metabolic Alterations and WNT Signaling Impact Immune Response in HGSOC. Clin Cancer Res. (2022) 28(7):1433-1445. DOI: 10.1158/1078-0432.CCR-21-2984.