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The former ASCO president shares his insights on breast cancer breakthroughs, AI in pathology, and what it really takes to move the needle in precision oncology.

Discover Pharma caught up with Dr George Sledge executive vice president and chief medical officer at Caris Life Sciences, to reflect on the company’s ASCO 2025 data and discuss the evolving role of multi-omic data and equity in testing.

In this exclusive Q&A, the Caris CMO explains how AI-powered digital pathology, real-world datasets and expanded molecular testing could drive more personalized, equitable cancer care.

What were the biggest takeaways from Caris’ ASCO 2025 data portfolio?

The big takeaway is the increasing value of large, combined datasets in answering important scientific questions across many cancer types. When you integrate whole exome and whole transcriptome NGS data with a broad set of protein-based IHC assays and large clinical datasets (mainly from claims data), you gain a unique ability to interrogate large patient populations. That kind of multi-layered data allows us to explore both biologic and therapeutic questions in much greater depth.

How are multi-omic approaches changing what we understand about response predictors in breast and CNS tumors?

One key example we presented this year focused on antibody-drug conjugates (ADCs). There are several FDA-approved ADCs for breast cancer with overlapping indications, but questions remain: Which one should be used first? Are there subsets of patients who respond better to a particular ADC?

Our advanced breast cancer database suggests that combining NGS and protein data can help answer that. Specifically, estrogen receptor–negative, HER2-null patients appeared to benefit most from sequencing sacituzumab govitecan followed by trastuzumab deruxtecan.

What stood out in the equity-focused research — and what still needs to change?

Equity in molecular testing remains a pressing challenge. The “usual suspects” that drive broader healthcare disparities—poverty, race and ethnicity, rural geography, and older age—also apply here. These groups are less likely to receive modern NGS testing, which can have devastating consequences.

The encouraging part is that when testing does happen, outcomes appear to be equal across racial lines. That underscores how critical access is.

How are AI and ctDNA moving from buzzwords to meaningful clinical tools?

What excites me most is the potential for AI to generate signatures that guide patient care. At this year’s ASCO, Caris—working with NSABP—developed an AI-based digital pathology signature that predicts late recurrence risk in ER-positive, early-stage breast cancer.

What’s particularly promising is that this model works from a standard H&E slide. That means globally, a slide could be uploaded, the algorithm applied, and results delivered on the same day. This could be a real game-changer for personalized follow-up care.

What kinds of conversations are you having with biopharma partners about using this data strategically?

Biopharma partners come to us with many data-driven questions. Some of the most common include:

• Where are the optimal patient populations for new therapies?

• Can we use multi-omic data to identify novel targets, such as new cell surface markers for ADCs?

• Can we pinpoint candidates for personalized cancer vaccines?

• Can we uncover mechanisms of drug resistance that inform next-gen therapies?

In all these cases, the answer is yes—our data can help provide those insights.

From your perspective as a former ASCO President — are we truly shifting toward multi-omic personalization, or is there still resistance?

Oncologists want to use what works. Like Caris, they’re platform-agnostic. If a technology improves outcomes, it will eventually be adopted. The pace of that adoption depends on the strength of the evidence and how effectively we communicate it.

We all want to do the right thing—but we also need to understand what the right thing is.

Finally, what are your hopes for how this data will actually move the needle in clinical practice?

There’s an old saying about the stock market: in the short run, it’s a voting machine; in the long run, a weighing machine.

The data we’re generating falls into the “weighing machine” category—it adds substance to the collective fight against cancer. You don’t judge ASCO on the Tuesday after the meeting. You judge it a year or two later, when that data has helped shape real change.