From Signal To Strategy: What Oncology Basket Trials Can Teach Clinical Development Teams

A conversation between AbbVie Vice President, Global Medical Affairs, Oncology Svetlana Kobina and Clinical Leader Executive Editor Abby Proch

Basket trials offer oncology development teams a way to evaluate whether a therapy’s biological rationale holds across multiple tumor types.

In this interview, Svetlana Kobina, vice president of global medical affairs, oncology at AbbVie, discusses how these studies can help clinical development teams move from early clinical signals to a more disciplined strategy. She explores the role of biomarkers, cohort selection, operational complexity, and evidence generation in shaping decisions about where to invest, expand, refine, or stop development.

Drawing on AbbVie’s experience with Temab-A, Kobina explains why the value of a basket study is its ability to generate clearer, more actionable insight into which patients may be most likely to benefit.

Clinical Leader: Basket studies have become increasingly common in oncology development. From your perspective, what are the biggest advantages of evaluating the same therapy across multiple tumor types rather than pursuing a traditional indication-by-indication approach?

Svetlana Kobina: Traditionally, oncology development has progressed one cancer type at a time. This approach remains important; however, there are many advantages to basket/umbrella studies. These types of studies allow us to be more efficient by streamlining the path from early signals to decision-making, which can help us bring medicines to patients more quickly.

From a development perspective, many newer therapies are designed around specific biological features that can be present across multiple cancers, such as a protein on the surface of a tumor cell, a genetic alteration, or a signaling pathway that drives tumor growth. Basket studies give us a structured way to ask whether the same biological feature in different cancers suggests the same treatment may work across those settings. They are not about grouping tumor types without rationale; rather, patients are grouped based on characteristics relevant to the therapy’s mechanism of action, with tumor type as one of several variables under evaluation. That allows us to better assess whether the target is predictive of response and where that association is strong enough to support further development.

For development teams, this leads to more focused decision-making, helping prioritize the indications where there is both clear biological rationale and meaningful clinical activity.

For patients, this approach can result in medicines becoming available sooner and helps direct research toward settings where therapies have the greatest potential to deliver benefit, particularly in cancers where treatment options remain limited.

AbbVie has evaluated Temab-A across multiple solid tumor cohorts. What factors influence decisions around which tumor types to include and which biological hypotheses are worth testing?

We are focused on developing therapies for difficult-to-treat cancers, and Temab-A targets c-Met, which has been associated, in certain settings, with tumor growth, progression, and poor prognosis. That broad relevance is what enables us to evaluate Temab-A across a range of tumor types, including ovarian cancer, head and neck squamous cell carcinoma, pancreatic cancer, hepatocellular carcinoma, esophageal cancer, and others, to understand where the biology translates into meaningful clinical activity.

Several factors influence which tumor cohorts to include — target biology, patient need, what the study can teach us, and whether the results will inform the next development decision. The goal is not to include as many tumor types as possible but to include the right ones, where the results are most likely to generate actionable learnings.

Platinum-resistant ovarian cancer (PROC) data presented at ASCO 2026 illustrate this approach. One hypothesis was that higher c-Met expression would be associated with a greater likelihood of response. The data showed an objective response rate of 80% in c-Met–positive patients, compared with 34% in patients below the biomarker cutoff.

These results are informative not only because of the response rates, but because they suggest that c-Met expression may help identify patients more likely to benefit, directly informing future study design and patient selection strategies. Fundamentally, this approach is about efficient learning, identifying where biology is most relevant, where unmet need is greatest, and where the therapy has the strongest potential to benefit patients.

One of the challenges in oncology development is distinguishing between a promising signal and a meaningful opportunity. How do basket studies help inform those decisions?

In early oncology studies, a signal might be tumor shrinkage, disease control, or a response that lasts longer than expected. Those findings are encouraging but on their own they are not enough. A single response may reflect something unusual about one patient’s cancer.

What builds confidence is pattern and consistency — responses seen across multiple patients or tumor types, durability of response, stronger activity in biomarker-defined populations. Basket studies are valuable because they help to show not just whether activity exists but where it’s most consistent and aligned with the biology we’re targeting.

That’s what turns a signal into a meaningful opportunity. If activity is strongest in patients whose tumors express that protein at higher levels, it becomes actionable, helping define a clearer patient population and next step.

The goal in cross-tumor studies isn’t uniform results but identifying where biology is most relevant. We see that with the Temab-A program, where activity in PROC and head and neck cancer appear stronger in tumors with higher c-Met protein expression. Basket studies give teams a clearer way to judge when a signal is strong and consistent enough to move forward — and when to refine or redirect.

Biomarkers often play a central role in these studies. What have you learned about the opportunities and limitations of using biomarkers to guide development decisions across different disease settings?

Biomarkers can help us understand which patients are most likely to benefit from a particular treatment. If activity is stronger in patients whose tumors express the target at higher levels, that can shift the question from “is this working?” to “which patients should we focus on?”

This creates a significant opportunity to improve patient selection, optimize enrollment, and enhance the clinical relevance of subsequent studies. For antibody-drug conjugates, or ADCs, this is critical because the target is central to the therapy’s mechanism of action.

The challenge is that biomarkers don’t behave the same way in every disease setting. A signal that’s clear in one tumor type may not translate to another due to differences in biology, prior treatments, or the tumor environment.

When that happens, the answer is not to abandon the biomarker or blindly apply it everywhere. It is to refine how it is used, whether that’s adjusting thresholds, improving assays, or understanding where they’re most predictive. Cross-tumor studies support this process by revealing where a biomarker is consistent, where it is not, and how its interpretation should vary across settings.

Biomarkers are powerful tools, but they need to be interpreted as part of a broader clinical and biological assessment to guide smarter development decisions.

Basket studies are sometimes viewed as a way to generate insights more quickly, but speed and evidence generation are not the same thing. How do you balance the need for rapid learning with the need for robust decision-making?

The key to balancing evidence generation with speed lies in study design. Before a basket study begins, teams need to be clear about what decisions it’s meant to inform, what evidence would be persuasive enough to act on, and what questions remain.

In cross-tumor studies, that may mean determining whether to expand a particular tumor type, refine a biomarker strategy, or justify a more focused follow-up study. Those decisions should shape the design and interpretation from the outset.

The value of this approach is that it can quickly refine an initial hypothesis. A broad idea that a therapy may work across several tumor types often becomes more specific as data emerge, showing where activity is strongest or where other factors may be influencing outcomes.

The key is to look at the full picture: response, durability, safety, biomarker findings, disease context, and patient need. A single encouraging result may justify more exploration but not a conclusion. At the same time, mixed data may still be highly informative if they clarify where the therapy fits best.

Speed and rigor should not be treated as opposing forces. When well-designed, these studies can accelerate learning while still supporting careful, evidence-based decisions that can ultimately help bring the right therapies to the right patients sooner.

Running studies across multiple tumor types can create both scientific and operational complexity. What are some of the practical challenges development teams need to navigate?

There are many complexities in executing a story across multiple tumor types. Clinical variability represents a primary challenge. Each tumor type has different treatment pathways, referral patterns, and prior therapies, which affect how patients are identified and enrolled.

This variability introduces operational complexity, as sites managing different cancers often follow different workflows. Protocols must therefore balance consistency with flexibility to function effectively across diverse clinical environments.

Biomarker testing adds another layer. If the study depends on measuring target expression, everything from tissue collection to assay quality and turnaround time becomes critical.

Delays or variability in testing can directly affect enrollment and data interpretation.

Safety monitoring is also more nuanced. Different patient populations come in with different treatment histories and baseline risks, so teams need a consistent framework for monitoring safety across the study, while still accounting for those differences.

The key takeaway is that these studies require more than a good hypothesis. They require strong execution. When protocols, testing, and site communication are aligned, these studies can generate strong insights that guide development.

Looking back at AbbVie's experience, what lessons have been most valuable in shaping decisions about where to invest, expand, or stop development?

For AbbVie, the most important decisions come from looking at the full picture: clinical activity, safety, durability, biomarker patterns, disease biology, feasibility, and unmet patient need. When those factors align, the path forward becomes clearer. When they don’t, that’s equally informative.

This is especially important in oncology early development because the choices made at this stage shape what patients may eventually have access to.

For example, with Temab-A, the starting point was a clear biological rationale: c-Met is expressed across multiple solid tumors, so we evaluated activity across different cohorts to understand where the mechanism was most relevant.

That’s important, because in early oncology development, it’s easy to treat any encouraging signal as a reason to keep expanding. But decisions have to be more disciplined. We need to determine whether the data support further evaluation, a more refined patient population, or taking a step back from a particular setting.

The goal isn’t to pursue every signal but to follow the strongest evidence.

In that sense, focus is what enables progress. These studies can help development teams make sharper choices earlier — not by eliminating uncertainty but by showing which uncertainties are worth resolving next.

For clinical development leaders considering a basket-study approach, what advice would you offer?

My advice is to be very clear on the question the study is meant to answer — not the outcome you hope to see.

A strong design should define up front what evidence would be persuasive enough to expand, what would require refining the patient population, and what would suggest stopping.

As good tools and data become more advanced, that discipline matters even more. Faster insights don’t replace good evidence — and without consistent biomarker testing, thoughtful cohort selection, and clear interpretation, speed can amplify uncertainty.

The most effective studies are adaptive. That means building in planned moments to review emerging data, refine enrollment, adjust biomarker thresholds if justified, expand the most informative cohorts, or stop investing in areas where the evidence is not developing.

For AbbVie, that mindset fits with the way we think about oncology development: Start with a strong biological rationale, test it in the settings where it has a credible basis, and allow the data to make the path more precise over time.

The future of this approach will not be defined by how many tumor types can be placed into one protocol. It will be defined by how well those studies help us make better decisions sooner. The real measure of success is not a bigger basket. It is a clearer path from biological insight to a study design that can bring the right therapy to the right patients with greater confidence.

Editor’s note: This transcript has been edited for clarity.

About The Expert:

With over two decades of leadership in R&D and medical affairs within the pharmaceutical sector, Svetlana Kobina currently serves as the vice president of global and U.S. medical affairs oncology at AbbVie. Holding both MD and Ph.D. degrees, Svetlana leverages her unique expertise in oncology and hematology in her work at AbbVie. Svetlana’s work spans across global medical affairs, R&D, and integrated data science functions, ensuring unparalleled standards of quality, safety, and efficacy.

Prior to her work at AbbVie, Svetlana held senior vice president and vice president roles within Bayer’s global medical affairs and data science oncology division for 12+ years. In addition to Bayer, she also worked at Sanofi, leading their EU and U.S. medical affairs group within oncology and hematology for more than five years.