Sarabjot Pabla: My latest article exploring the fascinating world of precision oncology and its inherent challenges

Sarabjot Pabla shared on LinkedIn:

“New Article Alert: ‘The Goldilocks Dilemma: When Cancer Treatment Is Just Right… For One’

I’m excited to share my latest article exploring the fascinating world of precision oncology and its inherent challenges. Thanks to Janice Cutinho for inspiring this discussion at the Labcorp global R&D summit.

Key points:

Precision oncology promises tailored cancer treatments based on individual molecular profiles. The very specificity that makes it effective also creates scaling challenges. New efforts to classify tumors by molecular markers add another layer of complexity.

As we push the boundaries of personalized medicine, we’re faced with a paradox: treatments become more effective for individuals but harder to apply broadly. It’s a classic case of ‘perfect for one, difficult for many.’

What are your thoughts on this dilemma? How can we balance personalization with scalability in healthcare?

The Goldilocks Dilemma: When Cancer Treatment Is Just Right… For One

Precision oncology, while hailed as a revolutionary approach to cancer treatment, inherently carries a paradox that challenges its widespread application. At its core, precision oncology aims to tailor cancer therapies to the specific genetic and molecular characteristics of each patient’s tumor.

This individualized approach stands in stark contrast to traditional ‘one-size-fits-all’ cancer treatments, promising more effective outcomes with fewer side effects. However, this very strength of personalization becomes a significant hurdle when attempting to scale precision oncology to larger populations.

The fundamental premise of precision oncology relies on identifying unique biomarkers and genetic mutations within each patient’s cancer cells. These specific targets then inform the selection of tailored therapies, often in the form of targeted drugs or immunotherapies.

While this approach can lead to remarkable results for individual patients, it inherently resists standardization. Each patient’s cancer is, in essence, a unique disease, requiring a bespoke treatment plan. This level of customization makes it challenging to develop therapies that can be broadly applied across a diverse patient population, limiting the potential for large-scale clinical trials and widespread adoption.

Moreover, the highly individualized nature of precision oncology creates significant logistical and economic challenges. The process of analyzing each patient’s tumor, identifying relevant biomarkers, and developing or selecting appropriate therapies is time-consuming and resource-intensive. This complexity not only increases the cost of treatment but also requires specialized expertise and advanced technological infrastructure, which may not be readily available in many healthcare settings.

As a result, the scalability of precision oncology is further constrained, creating a tension between the promise of personalized medicine and the practical realities of delivering healthcare to large populations. This inherent contradiction poses a significant challenge to the widespread implementation of precision oncology, despite its potential to revolutionize cancer treatment.

The field of precision oncology is indeed evolving, with a significant shift towards reclassifying tumors based on molecular markers rather than their tissue of origin. This new approach adds another layer of complexity to the inherent challenges of scaling precision oncology. Let me expand on this aspect:

The move towards molecular classification of tumors represents a paradigm shift in oncology. Traditionally, cancers have been categorized and treated based on their tissue of origin – breast cancer, lung cancer, colon cancer, etc.

However, precision oncology is pushing for a more nuanced understanding, where tumors are classified according to their molecular profiles, regardless of where they originate in the body. This approach recognizes that tumors in different organs can share similar genetic mutations or molecular characteristics, potentially responding to the same targeted therapies.

While this molecular reclassification offers exciting possibilities for more accurate diagnosis and targeted treatment, it further complicates the scalability of precision oncology. By fragmenting cancer types into numerous molecular subtypes, it creates even smaller, more specific patient populations for each potential treatment. This granularity, while scientifically sound, presents significant challenges for drug development, clinical trials, and treatment protocols.

Clinical trials, which are crucial for developing and validating new cancer therapies, become increasingly difficult to conduct with this molecular approach. Finding enough patients with the same molecular profile to participate in a trial becomes a major hurdle, potentially slowing down the drug development process and making it more expensive.

Furthermore, this molecular reclassification may lead to scenarios where a drug approved for a specific molecular subtype of one cancer could potentially be effective for the same subtype in a different cancer. This cross-cancer applicability, while promising, further complicates regulatory approval processes and treatment guidelines.

Precision oncology stands at a crossroads. Its promise of personalized, effective cancer treatment is undeniable, yet its path to widespread implementation remains fraught with challenges. The shift towards molecular classification adds a new dimension to these difficulties, further fragmenting an already complex landscape.

As the field progresses, finding a balance between personalization and scalability will be crucial. The future of cancer treatment may well depend on our ability to navigate this delicate equilibrium, leveraging the power of precision medicine while developing strategies to make it accessible to broader populations. Only by addressing these inherent contradictions can precision oncology truly revolutionize cancer care on a global scale.”

Source: Sarabjot Pabla/LinkedIn

Sarabjot Pabla is the Director of Laboratory and Bioinformatics Research and Development in Oncology at Labcorp. He leads a team of scientists and engineers focused on advancing oncology diagnostics and therapeutics through comprehensive genomic and immune profiling. Before joining Labcorp, Sarabjot served as the Head of Clinical and Research Bioinformatics at OmniSeq, where he worked on developing precision oncology solutions.