Solving problem of overtreatment in breast cancer

Solving problem of overtreatment in breast cancer

OncoStem has raised a total of $9 million from two investors (Sequoia Capital and Artiman Ventures) in seed and Series A rounds\

Representative image: iStock Photo

It was when Manjiri Bakre was pursuing her Ph.D. at the Indian Institute of Science that she lost a friend to breast cancer. The event made her envisage the need for people to be more aware of the course of cancer to be able to plan their treatment accordingly. She recognised the problem of overdiagnosis or overtreatment of breast cancer in the country and decided to combat it with a prognostic test. 

It is with this idea in mind, she founded OncoStem Diagnostics, a Bengaluru-based health-tech startup that uses AI to solve the ‘overtreatment’ problem, in 2011. OncoStem’s flagship product CanAssist Breast, an AI-based test that predicts the risk of breast cancer recurrence and helps a patient decide whether there is a need for chemotherapy, was launched in 2018.

“I noticed that there are other companies in the West that have developed tests that can predict the recurrence of cancer. However, these tests were out of reach for our patients here because of the cost and also because of the different biology of the disease in Indian patients. The Western tests were validated on patients from the West and the biology of the patients in the west is different from that of patients in the east. This is when I decided to develop a new test that will help in beneficial treatment planning for our patients,” says Bakre, a cell biologist and CEO & Founder, OncoStem.

How it works

CanAssist Breast (CAB) is a machine learning-based prognostic test that helps to personalise treatment for early-stage breast cancer patients with hormone receptor positive (HR+) and HER2 negative (HER2-) tumours.

On the rate of overdiagnosis or overtreatment in the country, she says 95% of these patients in India get chemotherapy while only 15% need/ benefit from it. OncoStem’s CanAssist-Breast can spare potentially over 60,000 breast cancer patients in India and about 1 million patients worldwide every year from the severe side effects and unnecessary costs of chemotherapy, she adds.

“This implies most patients are being overtreated with chemotherapy, which has toxic side effects and lowers the quality of life in addition to being a financial burden. There is a need to identify exactly which patients need and benefit from chemotherapy, helping the remaining 80% of patients avoid this toxic treatment and its side-effects,” she says.

In the West, such tests are used routinely (ex: Oncotype Dx and Mammaprint) but they are prohibitively expensive for patients in Asia plus the turnaround time is also long (3 weeks), Bakre mentions, saying CAB  serves with a cost-effective and quick solution within 8-10 days. The approximate cost of the test is Rs 60,000.

OncoStem currently works with 300 oncologists, 10+ hospitals and diagnostics chains such as SRL, Oncquest and Dr Lal PathLabs. Till date, 1.000 patients have availed the test. 

One of its kind

CanAssist Breast is the only such test developed and validated on Indian patients, according to Bakre. “The tests developed by companies in the US and Europe are developed on Caucasian patients who tend to be, at least, a decade older than the average Indian breast cancer patient. This is also the only test performed in a laboratory in India. Tests developed in the West are marketed by distributors in India but the samples are shipped to
the US/Europe for testing.”

OncoStem has raised a total of $9 million from two investors (Sequoia Capital and Artiman Ventures) in seed and Series A rounds.

The startup is currently working on a second test for breast cancer and a similar test for ovarian cancer.

“Research is underway towards identifying and characterising novel drug targets for breast and ovarian cancer. We are working on the full automation of our test CanAssist Breast. We are working on digital pathology solutions that will allow complete automation and also decentralisation. This will increase throughput and also allow any hospital in the world to conduct the testing in their own laboratory,” says Bakre.

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