Summary: Treatability of cancer was raised to over 80% by a new intelligent system that sifts through massive genetic datasets to pinpoint targets for cancer treatment, say these scientists. [This article first appeared on LongevityFacts. Author: Brady Hartman. ]
Scientists in Singapore have discovered a significantly improved way to treat cancer by listening to many different computer programs rather than just one.
Their new computer program reaches a consensus on how to treat a specific tumor, and it is significantly more accurate than existing predictive methods. The system isolates the Achilles heel of each individual tumor, helping doctors to choose the best treatment.
This work was jointly led by Dr. Denis Bertrand and Professor Niranjan Nagarajan from A*STAR’s Genome Institute of Singapore (GIS) and included researchers from the National Cancer Centre Singapore and the National University of Singapore. This is the first consensus algorithm that integrates various expert systems into a single accurate prediction for cancer treatment targets in individual tumors. The researchers published the results of their study on January 1, 2018, in the journal Cancer Research.
Professor Nagarajan, Associate Director and Senior Group Leader at GIS, said,
“It is remarkable that computer algorithms have become a new weapon in the battle against cancer. Instead of clubbing cancer cells with drugs indiscriminately, we are now trying to computationally pinpoint genetic weaknesses to target them with drugs more precisely.”
The Promise of Precision Oncology
The field of precision oncology aims to reduce cancer deaths by using genetic methods to pinpoint the best treatment for an individual’s cancer. Nearly 1 in 6 deaths worldwide is due to cancer reports the WHO; the global health organization adds that there were 8.8 million cancer deaths in 2015.
Precision cancer treatments differ from targeted cancer therapies, examples of which include the well-known cancer immunotherapies such as CAR T-cell therapy. Precision oncology, according to the NCI, is an approach that
“allows doctors to select treatments that are most likely to help patients based on a genetic understanding of their disease.”
Unlike most cancer treatments which target the organ where the tumor originated, precision cancer therapies are based on the genetic traits of the tumor and target specific genetic mutations.
Initial trials of the precision approach are surprisingly successful. For example, researchers conducting a clinical trial at Children’s Hospital Los Angeles just reported that three out of four adult and child cancer patients responded favorably to a new precision cancer treatment which targets a genetic mutation.
While precision cancer therapies have shown surprisingly good results, identifying the specific driver mutations is a major barrier to the widespread adoption of the precision oncology approach.
The Singapore team thinks they may have found a solution to this problem
Driver Mutations are the Main Triggers
Cancer cells have thousands of genetic mutations, but only a handful of these genetic lesions, known as driver mutations, will eventually grow into to a tumor. Pinpointing the driver mutations that lead to the uncontrolled growth of cancer cells is a principal goal of the field of precision oncology. Advancements in DNA sequencing have allowed scientists to determine the complete genetic makeup of cancers. However, up until now, researchers have not efficiently used these massive datasets to pinpoint the genetic culprits of an individual’s tumor. As GIS Executive Director Professor Ng Huck Hui notes,
“The complexity of cancer genetics is one of the biggest challenges that we face in treating it. By precisely identifying actionable mutations, and tailoring treatments to individuals, we are moving a step closer to precision medicine. I am delighted to note the ongoing development of new algorithms and technologies by GIS scientists to achieve this vision.”
Identifying the Best Cancer Treatment Targets
To better find the specific genetic mutations responsible for tumors, the scientists developed a new system, called ConsensusDriver that was able to identify the best treatment targets in nearly all patients studied, up to 81% of whom could be treated with existing drugs. As the authors of the new study say,
“We observed that the different methods provided largely nonoverlapping predictions, enabling the union to predict actionable driver genes for up to 81% of patients.”
Before the advent of ConsensusDriver, the existing ‘gold standard’ algorithms were able to identify the best treatment targets in only 9% to 68% percent of patients. However, these gold standard algorithms generally did a poor job of identifying the driver mutations, as the authors of the report say
“Most methods predict no driver genes for 10% of patients but many provide robust patient-specific predictions.”
To develop the new system, the team analyzed data from 3,400 tumors, across 15 different types of malignancies, including liver, colon, breast, lung, and stomach cancers. The scientists studied 18 different existing algorithms and found that each by itself could not identify the driver mutations in a significant proportion of the tumors.
While precision cancer therapies have shown startlingly good results, identifying the specific driver mutations has a major barrier to widespread adoption of the precision oncology approach. ConsensusDriver needs to be validated in further tests. However, if the system works as well as these initial results suggest, it could aid the field of precision oncology in finding cancer treatment targets.
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Cover Photo: Vitanovski (adapted) / Getty Images.
“NEW ALGORITHM PREDICTS TREATMENT TARGETS FOR CANCER USING ‘WISDOM OF THE CROWD’” Genome Institute of Singapore, A*STAR. January 02, 2018. Link to press release.
Denis Bertrand, Sibyl Drissler, Burton K. Chia, Jia Yu Koh, Chenhao Li, Chayaporn Suphavilai, Iain Beehuat Tan and Niranjan Nagarajan. “ConsensusDriver Improves upon Individual Algorithms for Predicting Driver Alterations in Different Cancer Types and Individual Patients.” Cancer Research. (78) (1) 290-301; January 1, 2018. Link to the journal article.
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