Last updated : June 18, 2026 9:11 am
One of our major priorities in next 3-5 years is the introduction of a kit-based version of CanAssist Breast, enabling hospitals conduct the test locally
In an exclusive interview with Rahul Koul, Editor, Indian Pharma Post, Dr. Manjiri Bakre, Founder and CEO, OncoStem Diagnostics, shares insights about her journey from cancer researcher to entrepreneur, the science behind CanAssist Breast, the growing role of precision medicine in India, and the need to make advanced cancer diagnostics more accessible and affordable.
What inspired you to establish OncoStem Diagnostics, and what unmet need in cancer care were you aiming to address? Looking back, what have been OncoStem's biggest milestones so far?
The journey began during my PhD when I lost a close friend to breast cancer. She had been diagnosed with early-stage breast cancer and underwent treatment, but unfortunately the disease recurred. That experience remained with me and triggered a question that would eventually shape my career: can we predict which cancers are likely to come back and which are not?
As I continued my work as a research scientist and engaged with oncologists across India, I realised there was a significant unmet need in cancer care. Many clinicians were forced to make treatment decisions based largely on conventional clinical parameters. In the absence of a reliable and affordable way to assess recurrence risk in greater detail, chemotherapy was often prescribed as a precautionary measure.
I founded OncoStem to bridge this gap. Through our proprietary tumour biology-based prognostic tests, we sought to provide insights that go beyond conventional clinical assessment and help determine the likelihood of cancer recurrence. Our objective has always been to support clinicians in making more informed treatment decisions while reducing unnecessary chemotherapy without compromising patient outcomes.
Precision medicine is transforming cancer treatment globally. How do you see its adoption evolving in India, and what role is OncoStem playing in helping clinicians make more informed treatment decisions for breast cancer patients?
Precision medicine is steadily becoming an integral part of cancer care in India across several cancers, including breast cancer. In reality, the need for precision medicine has existed for much longer than many people realise.
When I first started interacting with oncologists more than a decade ago, many of them already understood the value of prognostic testing and were keen to use such tools to guide treatment decisions. They recognised that not every patient with early-stage breast cancer derives benefit from chemotherapy and that tumour biology must be considered alongside traditional clinical parameters.
The challenge, however, has always been accessibility. Most prognostic tests available in the market were developed in Western countries, require samples to be shipped overseas, involve long turnaround times, and remain prohibitively expensive for routine use in India. As a result, despite being aware of the risks associated with overtreatment, clinicians often had little practical choice but to recommend chemotherapy.
Today, that landscape is changing. There is greater awareness among clinicians, stronger evidence supporting biology-driven treatment decisions, and more importantly, the emergence of affordable solutions that are relevant to Indian patients.
At OncoStem, our mission has been to bridge this accessibility gap. CanAssist Breast was developed using Indian patient data to offer an affordable and accessible prognostic test that assesses recurrence risk based on tumour biology. By identifying which patients are likely to benefit from chemotherapy and which can safely avoid it, we help clinicians move beyond a one-size-fits-all approach and make more personalised treatment decisions.
Could you explain the science behind OncoStem's prognostic testing platform, CanAssist Breast, and how it differs from conventional diagnostic approaches?
CanAssist Breast, or CAB, is designed for a specific group of early-stage breast cancer patients. It is important to understand that it is not a diagnostic or screening test. Once a patient has been diagnosed with breast cancer and the primary tumour has been surgically removed, one of the most important questions for both the doctor and the patient is whether the cancer is likely to return or metastasise. This is precisely where CAB plays a role.
The test predicts the risk of recurrence by combining two critical components. The first involves understanding the underlying tumour biology. Every tumour is unique, just as every patient is unique. By understanding how aggressive the tumour biology is, we can gain deeper insights into its behaviour and more accurately assess recurrence risk.
To do this, we analyse five biomarkers, or proteins, using immunohistochemistry (IHC), which is considered a gold-standard technology in oncology for evaluating protein expression. Once these biomarkers are assessed using a standardised Roche platform, the information is integrated into our AI-based algorithm along with three clinical parameters: tumour size, tumour grade, and lymph node status, which reflects the extent of cancer spread to lymph nodes at diagnosis.
Traditional diagnostic approaches primarily focus on identifying the type of cancer and evaluating characteristics such as tumour size, grade, and lymph node involvement. While these factors provide valuable information, they do not always capture the biological behaviour of the tumour.
Two patients of the same age with tumours of identical size, grade, and node status can still experience very different outcomes because their tumours may behave differently at a biological level. CanAssist Breast helps bridge this gap by providing additional prognostic information that can support more informed treatment planning.
The test was developed using Indian patient data and has since been validated across multiple international patient cohorts. This combination of biology-driven assessment and real-world validation makes it a valuable tool for clinical decision-making.
What are the key factors driving demand for genomic and molecular diagnostics in oncology today? How can personalised cancer treatment improve patient outcomes while reducing unnecessary therapies?
There is growing recognition of the physical, emotional, and financial burden associated with cancer treatment. Both clinicians and patients are increasingly seeking tools that can support more precise and evidence-based treatment decisions.
Personalised treatment enables doctors to identify which patients are likely to benefit from specific therapies and which may not. In early-stage breast cancer, prognostic testing can identify patients who have a low risk of recurrence and may safely avoid chemotherapy.
Such an approach improves quality of life by reducing exposure to unnecessary treatments and their associated side effects, while also helping optimise healthcare resources. Ultimately, the goal is not to provide less treatment but to provide the right treatment to the right patient.
What role does real-world patient data play in refining predictive cancer diagnostics? Are there any emerging technologies that you believe will significantly impact oncology over the next decade?
Real-world patient data is becoming increasingly important in refining predictive diagnostics. While clinical trials help us understand how a test performs under controlled conditions, real-world evidence provides insights into how it performs across diverse patient populations, how it influences actual treatment decisions, and whether it ultimately improves patient outcomes.
Real-world data helps recalibrate risk models for local populations, identify differences among patient subgroups based on factors such as age, ethnicity, and access to care, and validate whether predictive outcomes translate into meaningful clinical utility.
This is particularly important in India, where disease biology, treatment practices, and outcomes can differ significantly from those observed in Western populations. In many ways, real-world data transforms a model from being statistically accurate to being clinically meaningful and trusted.
Looking ahead, several emerging technologies are expected to reshape cancer diagnosis and treatment. Among the most significant are liquid biopsy, spatial biology and tumour microenvironment analysis, causal AI and treatment-effect prediction, and digital pathology.
Despite advances in cancer care, affordability remains a challenge. How can innovative diagnostics become more accessible to patients?
Affordability remains one of the biggest challenges in healthcare. Improving access will require a combination of indigenous innovation, greater awareness, and supportive policy measures.
Locally developed diagnostics can play an important role because they are often designed around local patient populations and healthcare realities. Reimbursement is another critical factor. While treatment costs are increasingly covered through insurance schemes, advanced diagnostics are not always consistently included.
Expanding reimbursement frameworks to cover clinically validated prognostic tests would significantly improve access. As adoption increases and testing volumes grow, costs are also likely to come down, much like in any other industry.
Equally important is integrating these tests into routine clinical workflows and increasing awareness among both doctors and patients regarding their value.
What growth opportunities do you see in Tier-2 and Tier-3 cities for precision oncology solutions? How important are collaborations with hospitals, oncologists, and research institutions in scaling your business?
One of the key challenges faced by patients in Tier-2 and Tier-3 cities relates to access, logistics, and specialised expertise. There is a clear need for solutions that are affordable, practical, and easy to implement.
Collaboration is essential to achieving this. Hospitals, oncologists, pathology laboratories, and research institutions all have a critical role to play in embracing scientifically validated technologies and expanding access to precision oncology.
Strong partnerships across the healthcare ecosystem will continue to be crucial in ensuring that patients benefit from personalised cancer care irrespective of where they live.
What are your key strategic priorities for OncoStem over the next three to five years?
One of our major priorities is the introduction of a kit-based version of CanAssist Breast. At present, tumour samples are typically sent to our laboratory in Bengaluru for analysis. The kit-based format will enable hospitals with the required infrastructure to conduct the test locally, reducing logistical delays, shortening turnaround times, and making prognostic testing more accessible and seamlessly integrated into existing clinical workflows.
We believe this approach can significantly improve access for patients across the country. In parallel, OncoStem is also working on expanding its portfolio by developing prognostic tests for additional cancer types in the coming years.