Cancer is being redefined through the lens of genetics, data science, and the body’s biological clock, ushering in a new era of precision oncology. In this exclusive conversation with Shinto Kallattu of Medical Tourism Today, DR. TAPASREE ROY SARKAR, Assistant Professor in the Department of Biology (Genetics & Genomics) at Texas A&M University, USA; and Co-Director of the Center for Statistical Bioinformatics and the Center for Biological Clock Research (CBCR), explores how cancer bioinformatics, artificial intelligence, metabolism, and nanotechnology are transforming research into real-world clinical impact. Bridging cutting-edge science with patient-centered care, she offers rare insights into how personalized, data-driven approaches are shaping the future of cancer diagnosis and treatment.
Dr. Tapasree, can you tell us about your career journey and what motivated you to work in genetics and cancer research?
From a very young age, I was drawn to cancer research after witnessing the fierce and devastating nature of the disease and its profound impact on patients and families. Those early experiences stayed with me and shaped my determination to understand cancer at its roots. As I progressed through my training, genetics emerged as a powerful lens through which to study cancer—revealing how subtle molecular changes can drive disease initiation, progression, and therapeutic resistance. My career journey has therefore been guided by a desire to connect fundamental genetic mechanisms with real-world clinical challenges, with the long-term goal of translating biological insights into more effective and durable cancer therapies.
In simple terms, what does cancer bioinformatics mean, and why is it important for modern healthcare?
Cancer bioinformatics is the application of computational and statistical approaches to analyze large-scale biological and clinical data—such as genomics, transcriptomics, proteomics, metabolomics, and spatial profiling—to understand how cancers develop, evolve, and respond to therapy. It is crucial for modern healthcare because it enables us to translate complex data into actionable insights, helping identify disease subtypes, predict treatment response, and uncover new therapeutic targets. By integrating molecular data with clinical information, cancer bioinformatics supports precision medicine, allowing treatments to be tailored to individual patients rather than relying on one-size-fits-all approaches.
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CANCER IS HIGHLY DIVERSE, EVEN WITHIN THE SAME TUMOR TYPE, WHICH IS WHY TREATMENTS WORK DIFFERENTLY FOR DIFFERENT PATIENTS
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How does studying cancer metabolism help doctors better understand how cancer grows and spreads?
Studying cancer metabolism reveals how tumor cells rewire their energy use and nutrient pathways to support rapid growth, survive stress, and evade the immune system. These metabolic changes also influence how cancer cells interact with surrounding tissues and spread to distant organs. By understanding these metabolic adaptations, doctors can better identify aggressive disease, predict treatment response, and develop therapies that target the unique metabolic vulnerabilities of cancer cells.
From a patient’s perspective, how close are we to seeing bioinformatics-based cancer research translate into everyday clinical care?
We are already seeing bioinformatics-based cancer research impact routine clinical care, particularly through genomic testing that guides diagnosis and treatment selection. As technologies become more accessible and datasets grow, bioinformatics is increasingly helping clinicians predict treatment response, monitor disease progression, and identify resistance earlier. While challenges remain in data integration and implementation, the transition from research to everyday clinical practice is well underway, and patients are beginning to benefit from more precise, personalized cancer care.
Cancer is a major concern for patients and families. What common misconceptions about cancer would you like to clarify for the general public?
One common misconception is that cancer is a single disease with a single solution. In reality, cancer is highly diverse, even within the same tumor type, which is why treatments work differently for different patients. Another misconception is that a cancer diagnosis always means a poor outcome; advances in early detection, targeted therapies, and immunotherapy have significantly improved survival and quality of life for many patients. Finally, people often underestimate the importance of prevention and early diagnosis—lifestyle factors, screening, and timely medical care can make a substantial difference in outcomes.
Nanotechnology sounds complex. How could it make cancer diagnosis and treatment safer or more precise for patients?
Nanotechnology allows doctors to design extremely small particles that can deliver drugs or imaging agents directly to cancer cells while sparing healthy tissue. This targeted approach can reduce side effects and improve treatment effectiveness by concentrating therapies where they are needed most. Nanotechnology can also enhance cancer diagnosis by enabling earlier and more accurate detection, helping clinicians tailor treatments more precisely to each patient’s disease.
The biological clock is an emerging area of research. How does our body clock affect cancer risk and treatment outcomes?
Our biological clock, or circadian rhythm, regulates many essential processes such as hormone production, metabolism, immune function, and DNA repair. When this clock is chronically disrupted—by factors like shift work, irregular sleep, or jet lag—these tightly controlled processes can become dysregulated, increasing cancer risk and influencing how tumors grow and respond to therapy. Our recent work showed that disruption of the biological clock leads to abnormal mammary gland morphology and accelerates aggressive mammary tumorigenesis (Ogunlusi et al., 2025, Oncogene), highlighting how circadian misalignment can directly shape the tissue environment in ways that promote cancer development.
How is artificial intelligence helping researchers analyze cancer data more quickly and accurately?
Artificial intelligence enables researchers to rapidly analyze vast and complex cancer datasets that would be impossible to interpret manually. AI-driven algorithms can identify patterns in genomic, imaging, and clinical data, uncover hidden disease subtypes, and predict treatment responses with high accuracy. By accelerating data analysis and reducing human bias, AI is helping translate large-scale cancer data into actionable insights that can ultimately improve diagnosis, treatment selection, and patient outcomes.
What challenges remain in using big data for cancer research, and how can the healthcare community address them?
One major challenge is integrating diverse data types—such as genomic, clinical, imaging, and lifestyle data—into coherent and reliable models. Data quality, standardization, and patient privacy are also ongoing concerns. Addressing these challenges will require close collaboration between clinicians, researchers, and data scientists, along with improved data-sharing frameworks, robust analytical tools, and responsible governance to ensure that big data insights translate into meaningful and equitable benefits for patients.
What key message would you like patients and healthcare providers to take away from recent advances in cancer genomics and bioinformatics?
The key message is that cancer care is becoming increasingly precise and personalized. Advances in cancer genomics and bioinformatics are helping us understand that every tumor is biologically unique, allowing treatments to be better matched to each patient’s disease. For patients and healthcare providers, this means more informed decision-making, improved treatment outcomes, and a future where cancer therapy is guided not only by where a tumor arises, but by its underlying molecular and genetic features.