Image from PNAS
A study published in Cell has shown that patient-derived cancer cell lines harbor most of the same genetic changes found in patients’ tumors and could therefore be used to learn how cancers are likely to respond to new drugs.
Researchers believe this discovery could help advance personalized cancer medicine by leading to results that help doctors predict the best available drugs or the most suitable clinical trials for each individual patient.
“We need better ways to figure out which groups of patients are more likely to respond to a new drug before we run complex and expensive clinical trials,” said study author Ultan McDermott, MD, PhD, of the Wellcome Trust Sanger Institute in Cambridge, UK.
“Our research shows that cancer cell lines do capture the molecular alterations found in tumors and so can be predictive of how a tumor will respond to a drug. This means the cell lines could tell us much more about how a tumor is likely to respond to a new drug before we try to test it in patients. We hope this information will ultimately help in the design of clinical trials that target those patients with the greatest likelihood of benefiting from treatment.”
The researchers said this is the first systematic, large-scale study to combine molecular data from patients, cancer cell lines, and drug sensitivity.
For the study, the team looked at genetic mutations known to cause cancer in more than 11,000 patient samples of 29 different cancer types, including acute lymphoblastic leukemia, acute myeloid leukemia, chronic lymphocytic leukemia, chronic myelogenous leukemia, diffuse large B-cell lymphoma, and multiple myeloma.
The researchers built a catalogue of the genetic changes that cause cancer in patients and mapped these alterations onto 1000 cancer cell lines. Next, they tested the cell lines for sensitivity to 265 different cancer drugs to understand which of these changes affect sensitivity.
This revealed that the majority of molecular abnormalities found in patients’ cancers are also found in cancer cells in the laboratory.
The work also showed that many of the molecular abnormalities detected in the thousands of patient samples can, both individually and in combination, have a strong effect on whether a particular drug affects a cancer cell’s survival.
The results suggest cancer cell lines could be better exploited to learn which drugs offer the most effective treatment to which patients.
“If a cell line has the same genetic features as a patient’s tumor, and that cell line responded to a specific drug, we can focus new research on this finding,” said study author Francesco Iorio, PhD, of the European Bioinformatics Institute in Cambridge, UK.
“This could ultimately help assign cancer patients into more precise groups based on how likely they are to respond to therapy. This resource can really help cancer research. Most importantly, it can be used to create tools for doctors to select a clinical trial which is most promising for their cancer patient. That is still a way off, but we are heading in the right direction.”
