How AI and Patient-Level Data Help Oncology Teams Define New Markets

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Oncology markets are no longer defined only by tumor type or line of therapy. AI, patient-level data, and primary research now help teams define markets by biology, care pathways, and access barriers. That shift can shape development strategy, trial design, and commercialization plans much earlier in the asset lifecycle.

Why Label-Based Market Definitions Miss Opportunity

For years, oncology markets have been described through broad labels such as indication, line of therapy, and ICD code. Those definitions are useful, but they often miss where unmet need and competitive separation actually sit. Two tumors with the same name can behave like different diseases, and two patients in the same indication can follow very different treatment paths.

That matters because many of the most valuable oncology opportunities sit inside narrower segments. A broad label may describe the market on paper, but it rarely shows where testing gaps, pathway delays, access friction, or unmet need are concentrated. Teams that define markets more precisely can build stronger evidence plans and inform commercialization strategies. In oncology, markets are not fixed by the label alone. They are defined by the populations, pathways, and access dynamics teams choose to focus on.

Why This Shift Is Happening Now

Oncology teams now have wider access to claims, EHR, lab, genomic, biomarker, and registry data. That data makes it easier to reconstruct patient pathways and segment populations by biology and behavior, not just by label. It also makes it easier to test whether a broad commercial category contains smaller, more actionable markets.

AI adds speed and pattern detection to that process. It can identify under-diagnosed cohorts, uncover treatment gaps, and highlight patterns that manual analysis might miss. Primary research then helps validate those findings by showing how physicians and patients make decisions in real practice.

The key question is no longer just, “How large is this existing market?” It is also, “Which clinically and biologically coherent market should we define and build around?” For oncology teams, that shift can change how an asset is developed, positioned, and launched.

Four Boundaries Define a Defensible Market

A defensible oncology market often sits at the intersection of four boundaries:

• Biological boundary: phenotype, biomarker status, resistance mechanism, severity, and progression pattern.
• Clinical pathway boundary: where patients are diagnosed, referred, treated, delayed, or lost along the real-world pathway.
• Economic boundary: payer rules, coverage criteria, and value thresholds that shape access.
• Behavioral boundary: physician and patient decisions that data alone cannot fully explain.

Taken together, these four boundaries define a defensible, evidence-backed market you choose to play in—one you can explain to leadership, justify to investors, and defend against competitors.

The Method: Triangulating AI, Data, and Primary Research

Defining a market is not one analysis. It is a repeatable loop that pulls together three layers of insight. The AI and analytics layer handles pattern detection, predictive segmentation, and white-space identification within patient-level data. It helps teams generate hypotheses about where under-recognized or under-served populations may exist.

The primary research layer adds qualitative depth and quantitative validation. It helps confirm the willingness, drivers, and barriers that AI cannot observe on its own, and it grounds the data signal in real clinical and commercial behavior.

The synthesis loop is where those inputs come together. AI generates hypotheses, primary market research tests them, and patient-level data quantifies the resulting opportunity. With each pass, the market boundary becomes sharper and more defensible.

Technology advances have made this process more practical over the past few years. Gains in speed, cost, and scale now let teams run this loop continuously instead of once per planning cycle. That shift turns market definition into an ongoing capability rather than a one-off project. When teams treat this triangulation loop as an upstream discipline, market definition stops being a downstream sizing exercise and starts shaping decisions across the asset lifecycle.

Adapting Existing Markets vs. Creating New Ones

Not every opportunity means inventing a new category. This approach supports two different plays, and knowing which one to pursue is a strategic decision.

Adapting an existing market means re-segmenting, repositioning, or expanding eligibility within a category that already exists. In many cases, that means identifying a better-defined sub-population within a familiar indication and building a more precise strategy around it.

Creating a new market means surfacing an under-diagnosed or mis-served population and building the category around it. In those cases, the opportunity is not simply to compete within an established market, but to define one that competitors may not yet be measuring clearly.

The decision comes down to three questions: how strong the biological signal is, how defensible the market boundary is, and whether the asset can credibly own that segment. The goal is to know when to redraw the boundary and when to expand within it.

Oncology in Practice

Oncology is a strong proving ground for this approach because biology already fragments many tumor types into distinct, addressable diseases. That makes it easier to see how market definition can move beyond the label and toward more precise, evidence-backed boundaries. In practice, those opportunities often emerge across solid tumors and hematologic malignancies in different ways.

Solid Tumors: Redefining Markets by Mutation and Testing Gaps

In solid tumors, biology can reveal narrower markets inside broad and crowded indications. In lung cancer, for example, multiple PD-1 and PD-L1 therapies compete across a large NSCLC population. But the more actionable opportunity may sit inside the gap between patients who appear eligible on paper and patients who actually receive timely biomarker testing and treatment adjustment in practice.

That gap can define a market of its own. Rather than competing across the full metastatic NSCLC population, organizations can define, size, and build strategy around patients who are not being identified or treated according to their full biomarker profile.

FGFR-altered urothelial carcinoma is a clear biology-defined segment within a broad tumor type. Real-world data also show that many eligible patients are not tested or treated with a matched FGFR inhibitor.

Even with an approved targeted therapy and companion diagnostic in place, much of the addressable population remains unidentified. In this case, the testing and treatment gap is the market. It is a clear example of how patient-level data can help teams define a more defensible opportunity than the tumor label alone.

Hematologic Malignancies: Unmet Need Inside Crowded Indications

Hematologic malignancies show a different version of the same pattern. In CLL, physicians now choose between continuous BTKi, fixed-duration BCL2i, and limited chemoimmunotherapy by segmenting patients based on biology, fitness and comorbidities, prior exposure, and patient preferences.

That segmentation can include factors such as del17p or TP53 status, IGHV status, complex karyotype, and treatment history. Trial data and real-world evidence can help clarify which subgroups gain the most benefit and tolerability from BTKi-first versus venetoclax-first strategies, and how outcomes differ when switching classes at relapse.

This level of segmentation creates specific commercial opportunities. Organizations can target well-defined biological and clinical niches, optimize sequencing positions, and build HEOR and real-world evidence that show differentiated value in those subpopulations.

A similar dynamic exists in MM, which remains the largest hematologic malignancy by market size. The growth of triplets and quadruplets has expanded treatment options, but it has not eliminated unmet need. Outcomes, tolerability, and access still vary across molecular and clinical subgroups.

High-risk cytogenetic patients, early-relapse patients, and post-CAR-T or post-bispecific patients can each represent distinct markets with different evidence, positioning, and access needs. These are the kinds of biologically and clinically defined segments that can remain hidden inside what is often described as a crowded indication.

The opportunity is not to compete only in the broad all-comers space. It is to define where a therapy can address a more specific unmet need, whether through a regimen designed for high-risk biology, a better option for heavily pretreated or frail patients, or a new mechanism aimed at a segment underserved by current combinations.

Early Identification: Building the Market Upstream

By combining registry, lab, and EHR data with primary research, teams can find under-diagnosed or late-diagnosed patients and build the market upstream of current treatment. The FGFR example above is a good illustration: a large share of patients with a targetable alteration are never tested, so shifting value toward earlier biomarker testing and identification becomes an opportunity in itself.

Pathway Repositioning: From Acute to Prevention

Using claims and patient-journey data, teams can expose pathway gaps and reposition value, for example, around monitoring for and preventing progression or serious treatment-related events, rather than only treating them once they have occurred. In these cases, the market is defined not just by who receives a therapy, but by where the pathway breaks down and where earlier intervention can change outcomes.

From Market Definition to a Differentiated Value Proposition

A market definition is only as useful as the strategy it enables. Once the boundary is clear, it needs to translate into a differentiated value proposition and an evidence plan that can support it.

That means anchoring the value proposition to the population, clinical pathway, and payer logic that define the market. It means quantifying value with patient-level evidence, including the outcomes, burden, and cost implications that matter for that segment. It also means sequencing launch and evidence-generation plans in ways that help defend the boundary against competitors over time.

When done well, market definition does not sit apart from commercialization strategy. It shapes the story an organization tells about where it can win, why that segment matters, and what evidence is needed to support that claim.

Implications and Where to Go From Here

The larger shift is organizational. Market definition becomes a cross-functional, continuous capability that spans commercial, analytics, real-world evidence, medical, and development. It stops being a one-time sizing exercise and becomes part of how teams shape strategy across the asset lifecycle.

Building that capability requires more than data alone. It depends on data strategy, fit-for-purpose infrastructure, AI governance, and integrated primary research that can make the synthesis loop repeatable and credible.

The organizations that do this well will be better positioned to define markets by biology rather than label alone. For oncology teams, that can lead to more focused evidence generation, clearer differentiation, and stronger commercialization strategy. In oncology most of all, the teams that define their markets by biology, not simply by indication, will set the boundaries that others have to compete inside.

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