(DALLAS) – New results from a landmark study suggest that there may be a way to predict treatment response in breast cancer patients before treatment begins. The study results, led by Susan G. Komen Brinker Prize winner Carlos Caldas, MD, FMedSCi, will be presented for the first time at the San Antonio Breast Cancer Symposium (SABCS) 2021, December 7-10.
During the Brinker Award lecture, in which Dr. Caldas presents the new findings, he will also receive the Susan G. Komen Brinker Award 2021 for Scientific Distinction in Basic Science, Komen’s highest scientific award. Dr. Caldas is recognized for his significant contributions to the field of breast cancer genomics and his leadership and work in the field of functional genomics of breast cancer.
The groundbreaking research study is slated for publication in the December 7th issue of Nature.
Under the direction of Dr. Caldas analyzed a research team from Cancer Research UK Cambridge Institute of the University of Cambridge and Addenbrooke’s Hospital breast cancer biopsies obtained at diagnosis before starting therapy with the idea that by profiling different components of the abnormal tumor tissue, a test could be developed who can predict response to treatment.
What they discovered was remarkable – the pre-treatment cancer landscape was highly predictive of response to therapy, and the performance of the predictor they developed improved significantly as clinical, tissue architecture, and molecular data were added.
“DR. Caldas’ latest study, which will be presented at SABCS, is an example of how his work advances our understanding of the DNA and RNA composition of human breast cancer, the genomic heterogeneity of breast cancer, and the relationship between the composition of a tumor and individual outcomes and responses has improved for breast cancer treatment, “said Komens Chief Scientific Advisor, Jennifer Pietenpol, Ph.D., Executive Vice President for Research at Vanderbilt University Medical Center, Director of the Vanderbilt-Ingram Cancer Center and BF Byrd Jr. Professor of Molecular Oncology.
“This work further defines molecular subtypes (or groups) of breast cancer and identifies the genomic changes that drive tumor growth,” said Dr. Pietenpol. “The discoveries of Dr. Caldas and his laboratory have redefined the breast cancer taxonomy, identified new subtypes and provided critical insights into the biology of the disease. “
About the Landmark Study
“It has been known for some time that cancer are complex tissues that contain not only malignant cells but also normal tissue cells (both of the immune system, such as lymphocytes, macrophages and other white blood cells) and other cell types, including fibroblasts and blood vessels,” said Dr. Caldas. “These form an abnormal tissue ecosystem that we call a tumor.”
“We carefully analyzed breast cancer biopsies obtained at diagnosis before starting therapy, with the idea that profiling different components of the abnormal tumor tissue could create a robust test that could predict response to treatment,” said Dr. Caldas.
By applying machine learning algorithms to the digitized tissue images obtained from the biopsies, the researchers were able to characterize the architecture and cellular composition of the cancerous tissue. In order to gain a detailed understanding of the molecular composition of the tumor, the researchers created a profile of the DNA and RNA of the cancer and the surrounding normal cells using next-generation sequencing. This enabled them to catalog all genetic mutations and chromosome aberrations in the cancer cells and measure the expression of all genes both in the tumor cells and in the surrounding immune and other normal cells.
The data obtained from 168 tumor biopsies were multidimensional and complex. Therefore, the researchers used a step-by-step process to create a test that predicts response to therapy. In the first step, they determined which parameters (extracted from clinical, pathological, digital tissue images and DNA / RNA data) were associated with the response to an 18-week chemotherapy. In a second step, they combined these parameters using a machine learning framework and built a model that predicted how well a tumor would respond to therapy.
Researchers rigorously validated their predictive test by applying it to an additional 75 independently profiled tumors, including tumors from a clinical study conducted in collaboration with the Edinburgh Cancer Center and the University of Warwick. The most important cancer parameters of the predictive test included the number and type of mutations in the cancer cells, the rate at which the cancer cells grow, and the extent to which the immune system attacks the tumor before starting therapy.
In the future, the test could be used to determine which women can be treated with the therapies currently available in the NHS if the test says they are likely to respond or, alternatively, can be treated with novel therapies in clinical trials if the test this predicts the tumor is likely to be resistant. Perhaps more importantly, this approach has shown a new way of thinking about predicting therapy response that could be adapted for other cancers.
“The ability to predict treatment response based on characterization of the tumor ecosystem will transform the practice of oncology. This research began nine years ago and was only possible because of the persistence and dedication of the clinical and research teams and the generous funding of the research, ”said Dr. Caldas.
About the Susan G. Komen Brinker Awards for Scientific Distinction
The prestigious Brinker Awards for Scientific Distinction, established by Komen in 1992, recognize leading scientists for their significant contributions to our understanding of the mechanisms underlying breast cancer (basic science) and advances in breast cancer treatment (clinical research), both of which are essential Are important to fight the disease.
Research method
Data / statistics analysis
Research subject
Human tissue samples
Publication date of the article
December 7, 2021
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https://www.eurekalert.org/news-releases/937220
