Lung cancer remains one of the hardest cancers to treat, despite major advances in targeted therapy and immunotherapy. At WCLC 2026, AbbVie is presenting new clinical and translational research across its lung cancer pipeline, including first-line Phase 1b data for the investigational PD-1/VEGF bispecific ABBV-1480 and new research around the SEZ6-targeted antibody-drug conjugate (ADC) ABBV-706.
Why pursue a pipeline that pairs bispecific antibodies with ADCs rather than committing fully to one modality in lung cancer?
Daejin Abidoye: What we’ve seen over the last decade, as our understanding of the biology of non-small cell lung cancer has evolved, is that this is a very heterogeneous disease. You have different oncogenic drivers, in addition to multiple mechanisms of resistance.
So, for us, the question isn’t really which modality is better. It’s which biological problem each modality is best placed to address.
ADCs give us a way to deliver targeted tumor-cell killing, while immunotherapies allow us to act on the immune mechanisms and tumor microenvironment around the cancer. Multimodality therapy has emerged as an important approach not only for achieving a response, but for trying to maintain durable remissions in patients with locally advanced or metastatic disease.
We already see the foundation for that with chemotherapy and PD-1 checkpoint inhibitors in non-small cell lung cancer. Where we think we can go further is by using ADCs to bring more targeted cytotoxic activity and then layering that with next-generation immunotherapies. We think those complementary approaches hold a lot of promise for patients.
How did the 90% objective response rate (ORR) you observed in squamous non-small cell lung cancer (NSCLC) shape your thinking on the Phase 3 design for ABBV-1480?
Daejin Abidoye: The 90% response rate was very encouraging, but we didn’t look at that number in isolation. What gave us confidence to move into Phase 3 was the combination of the activity we were seeing across both squamous and non-squamous disease, the durability we were beginning to observe, and the dose-optimization work.
At the recommended Phase 3 dose of 10 mg/kg, we saw an ORR of 90.0% in squamous NSCLC and 75.9% in non-squamous NSCLC. Progression-free survival was 11.8 months in squamous and 11.1 months in non-squamous NSCLC, and median overall survival had not been reached.
We also evaluated both 10 mg/kg and 20 mg/kg. At the lower dose of 10 mg/kg, we were seeing strong responses alongside a manageable safety profile, and that allowed us to feel very confident selecting it as the recommended dose for Phase 3.
What excites us about ABBV-1480 itself also comes down to some of its structural features. It’s built on what we believe is a potent PD-1 backbone. It has a silent Fc effector function, which we believe may help mitigate some of the immune-related effects we typically see with checkpoint inhibitors. And it has a unique VEGF epitope that we believe provides strong signal-blocking properties with regard to the anti-angiogenic interaction.
So, it wasn’t simply, “we saw a 90% response rate, let’s go to Phase 3.” It was the totality of what we were seeing around activity, durability, dose, and the profile of the molecule that gave us that confidence.
How does the breadth of SEZ6 expression in SCLC inform where ABBV-706 fits relative to other ADC targets being pursued in that disease?
Daejin Abidoye: ABBV-706 is our investigational SEZ6-directed ADC with a Top1 inhibitor payload. What excites us about it are two things: the activity we’ve seen with our proprietary Top1 payload, and what we’re learning about SEZ6 itself as a potential therapeutic target in small cell lung cancer (SCLC).
At WCLC, real-world research showed SEZ6 expression in 91% of patients with SCLC overall, and in more than 96% of patients with brain or liver metastases. What’s interesting to us about that is the breadth. It suggests SEZ6 may be relevant across a broad SCLC population rather than being confined to a very small subgroup.
We’ve also previously presented data from patients treated at 1.8 mg/kg, the recommended Phase 3 dose. In a second-line-only patient population, we saw response rates of more than 80%, with overall survival of 14.3 months.
The next question is where we can take it. We’ve already shared early data with ABBV-706 in combination with a PD-1 checkpoint inhibitor in a heavily pretreated population. That starts to give us a rationale for thinking about the molecule in earlier lines of therapy, where checkpoint inhibition is already part of the treatment landscape.
How do the second-line response data for ABBV-706 inform the case for eventually positioning an ADC as a front-line therapy?
Daejin Abidoye: In the second-line-plus patient population, we showed a confirmed objective response rate of 56%. But when we looked specifically at the second-line-only population, we saw a response rate of 82%.
To see that level of activity with a single-agent ADC in patients who have already been through treatment gives us a lot of confidence in thinking about what an ADC could potentially do earlier in the treatment pathway.
The way we think about it is that the ADC could drive a high level of tumor control and tumor reduction, and then when you combine that with an immunotherapy, the immunotherapy component may help extend the durability of those responses.
That’s been our strategy, we’ve been vocal about it, and it’s encouraging to see data now being generated that give us confidence in the direction we’re taking.
Why is tolerability, not just efficacy, the real competitive differentiator for these ADCs?
Daejin Abidoye: Tolerability has been key in our thinking from the start. It’s not just about putting patients on highly active therapies. It’s about whether patients can tolerate those therapies over time and stay on treatment long enough to benefit.
With traditional chemotherapy in NSCLC, the current paradigm often involves an induction phase followed by maintenance. The induction phase is designed to maximize the initial cytotoxic response, but one of the reasons you then move to a gentler maintenance regimen is that those more intensive therapies can be difficult for patients to stay on long term.
Now imagine if you could have a therapy that provides strong antitumor activity, but that patients can also tolerate for longer. For the patient, that potentially means fewer interruptions or dose modifications and more opportunity to remain on a treatment that is controlling their disease. And when we talk about improving outcomes, we also have to think about what treatment asks of the patient and the impact it has on their quality of life.
That’s one of the reasons we’ve thought so carefully about both molecular design and biomarker selection with our ADCs. With Temab-A, for example, we have seen response rates of 51% to 60% in c-Met-expressing patients with EGFR wild-type disease, depending on the c-Met expression threshold, and 70% in c-Met-expressing patients with EGFR-mutant disease.
For us, finding the right drug for the right patient is only part of the equation. We also want to develop treatments patients can remain on and potentially derive durable benefit from.
How does a fully realized biomarker-informed lung cancer program actually look in practice five years from now?
Daejin Abidoye: I think where this field evolves over time is toward much more individualized care, and we’re already getting at that through biomarker selection.
With traditional chemotherapy, you can treat a broad population, but not every patient responds, while every patient can potentially be exposed to toxicity. If you can use a biomarker to better identify the population most likely to benefit, you have an opportunity to improve that benefit-risk profile.
We’ve done that already with our first-generation c-Met-directed ADC by identifying a high c-Met-expressing patient population, and we’re building on that experience with Temab-A.
It also changes the way you can think about clinical trials. Rather than designing a very large study and then trying to work out afterward which patients benefited, you can increasingly use biomarkers at the front end to focus development on the patients you believe are most likely to respond. That has the potential to make development much more efficient.
And this isn’t just about targeted therapies. We’ve already seen in NSCLC that PD-L1 expression can help us understand the likelihood of benefit from checkpoint inhibitors. As we get better at bringing those pieces of information together, I think we’re going to become much more sophisticated about matching treatment to individual tumor biology.
The vision is a patient coming into the doctor’s office and the doctor being able to look at that patient’s biology and say, based on what we see in your tumor, this is the therapeutic approach that is most likely to benefit you.
That’s where I see this heading.
How could machine learning and artificial intelligence (AI) compress the drug development timeline, and where is the field already applying it?
Daejin Abidoye: In the era of AI and machine learning, how we think about patient treatment, patient management and drug development will continue to evolve.
One piece of that is identifying the right patient for the right drug at the right time, which we’ve already touched on. But another piece is predictability.
The more near-term question is how we get better at predicting patterns of resistance. If a patient is treated with a drug and then loses response, what is the next therapy that patient should go on? This is an area of active research across the field. Our understanding of the underlying biomarker can give us a better sense of what that patient may benefit from next.
Then there’s the broader drug-development timeline. Using targeted information, algorithms and machine learning to help us predict toxicity, safety, and the right dose more quickly. That could accelerate the path into Phase 3, where ultimately you have to answer whether a treatment can improve on the standard of care.
There’s also the potential to use aggregated real-world data and machine learning to better understand how the current standard of care performs outside a clinical trial. If we can use those tools appropriately, there is an opportunity to get to answers faster, make development more efficient and ultimately bring medicines to patients sooner.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
