Avatars For Personalized Cancer Treatment

Researchers have grown micro-tumor models using patients’ cancer cells to identify more effective treatments for drug-resistant tumors.

AsianScientist (Sep. 14, 2017) – In a study published in Nature Communications, researchers in Singapore have grown micro-tumor models outside the human body to identify alternative treatment options for cancer patients with drug-resistant tumors.

Cancer remains a major cause for disease-related mortality worldwide. It accounted for 8.8 million deaths in 2015, and nearly one in six global deaths was due to cancer. 70 percent of all cancer deaths occur in the developing world, including countries in Africa, Asia, Central and South America.

Despite the many anticancer treatments available, some patients fail to respond to first-line or standard-of-care cancer drugs. Hence, there is a need to identify alternative courses of treatment in such patients.

In this study, scientist grew the patients’ own tumor material in a dish, successfully generating 24 patient-derived ‘avatar-models.’ These micro-tumor models were then screened for therapeutic and genetic vulnerabilities against a panel of clinically relevant drugs, including standard-of-care treatments such as chemotherapy, radiotherapy or targeted anti-cancer drugs. This approach is known as phenotype-based treatment.

In addition, genomic characterization of these models helped identify new biomarkers that could predict tumor progression as well as the patients’ response to different classes of drugs.

“Our approach is unique in the sense that we are using phenotype-driven approaches prospectively to define real-time clinical management of disease, and then investigating the genomic basis for response to identify novel prognostic biomarkers. The ultimate goal would be for this innovative strategy to be available to every cancer patient in future,” explained the study’s lead author, Dr. Ramanuj DasGupta, Group Leader, Cancer Therapeutics and Stratified Oncology at the Genome Institute of Singapore (GIS).

“To truly assess what treatments may or may not work for a given patient, we need to let the patient’s own cancer cells tell us the genetic basis for their response or lack thereof to certain treatments, instead of using historically existing cell lines. This approach would be exceptionally useful where there is limited treatment options, for instance, in late-stage patients, or in cases where there is no clear biomarker-driven treatment strategy,” DasGupta added.

Among the group of patients involved in the study, two of them went on to receive the phenotype-based treatment in the clinic and responded remarkably well to the treatment options that were identified. The tumor volume shrank by more than 95 percent in one patient within six weeks of treatment. In the other patient, the expression of a cancer antigen marker in the blood was reduced by over 90 percent.

The researchers plan to subsequently scale this effort to reveal specific predictive gene expression and mutation signatures that could be used to stratify different patient groups and corresponding treatment options. This would help to guide future treatment strategies validated through larger clinical trials.

This approach shows the potential to predict treatment response to drugs and help clinicians identify appropriate therapeutic options for cancer patients.

“Tumor heterogeneity poses a major challenge in cancer therapies because different clonal populations of cells within a tumor can respond very differently to standard-of-care treatments, often resulting in treatment failure or relapse for unknown reasons,” said Executive Director of GIS, Professor Ng Huck Hui. “I am delighted this multi-institutional collaborative effort has resulted in a novel approach that can help us advance precision medicine.”

The article can be found at: Chia et al. (2017) Phenotype-driven Precision Oncology as a Guide for Clinical Decisions One Patient at a Time.


Source: A*STAR.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.

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