One, evaluating the target engagement. Has the drug interdicted the target in the manner we anticipate? And through pharmacodynamic (PD) biomarker assessment, we can also, alongside our clinical pharmacology colleagues, use findings to support dosing schedule estimations.

Two, interrogating the impact of disease-associated biomarkers to evaluate the mechanism of action and combination potential. We use biomarkers to reflect dysregulated pathways, and we look for early reads on disease modifying activities and reversion of some of those dysregulated pathways to normal. When we see pathways not impacted, this gives us an idea of what other drugs might be able to be used in combination.

Three, examining the heterogeneity of the clinically defined patient population as we identify baseline, or even on-treatment biomarkers, that show us which patients may or may not benefit from our therapies.

Our Biomarker Sciences group truly spans R&D and commercial as we engage with programs two to three years before they enter the clinic, and then through all stages of development into post-marketing. At the heart of our mission is the desire to better understand human pathobiology. We strive to stay informed of new technologies and approaches that could help us answer questions posed in our biomarker strategy and that included building a new domain of expertise, a molecular epidemiology function.

Our Biomarker leads, aligned by TA, constitute more than half of the department and are accountable for the development of the Biomarker Strategy in concert with our Research and Clinical colleagues. To support biomarker discovery and the need for rapid assay prototyping and development across therapeutic areas, about a quarter of the team is a lab-based group that are experts across all assay formats. We also have a biobanking sample management group, to handle sample procurement, and the use of clinical samples in support of these efforts. We established a dedicated in vitro diagnostics (IVD) group for biomarkers that reveal themselves as diagnostic candidates, which can occur at any stage through the development process. And finally, we have our molecular epidemiology function, led by Shahed, to support all therapeutic areas by providing expertise to successfully deploy RWD and multimodal clinico-genomic datasets, in support of both our biomarker and translational efforts. RWD expertise was a capability that I had wanted to add to the department for some time.

Clinical trials provide the ability to observe outcomes in very controlled settings. But the estimate of patients who participate in clinical trials is about 4 percent — so what about the other 96 percent? By leveraging RWD, we can include treatment information, lab data, and outcomes together with multimodal data to explore a broad patient population, including indications not yet explored in clinical development. There’s an opportunity to expand beyond what can be done within the clinical trial arena.

Even with data from clinical trials, access to additional real-world datasets is advantageous. Having worked on the translational side for 20 years and, in particular, seeking to identify biomarkers that can predict response or non-response to a therapeutic regimen, and despite generating biomarker hypotheses from well-controlled trials, there are always unknown unknowns. Having access to larger, independent datasets is critical to de-risk a hypothesis prior to a prospective trial, which hasn’t been easy in the past.

There are few large genomic DNA-based datasets that also include extensive transcript data and clinical lab data. This is a key element of our strategy. How does a patient whose tumor has a specific profile respond to a therapeutic regimen? If there is a response, what’s the duration?

Our first major foray into this field was assembling a cohort in advanced non-small cell lung cancer (aNSCLC) to compare checkpoint inhibitor therapy, checkpoint inhibitor plus chemo, or chemo-alone cohorts to test patient stratification hypotheses. We examined specific target expression and inferred pathway activity from the transcript data, together with DNA profiling. Our initial aim was a pragmatic approach to explore the molecular profiles of tumors that display primary resistance to a specific therapeutic regimen.

Time to next treatment and time-to-treatment discontinuation are valuable markers of primary resistance. Access to Tempus’ dataset has allowed us to test published stratification hypotheses, both DNA- and RNA-based. Results around signatures, such as tumor mutational burden, CD8 infiltration, looking at interferon gamma signatures, as well as PD-1 and PD-L1 expression for those genes, confirmed the validity of the dataset, and allowed us to see how our current range of targets measured up.

We chose aNSCLC due to its importance as an indication in oncology, but also, at the time, we had no internal studies from which to explore these hypotheses and our exploratory targets. Procuring a large sample set with the appropriate outcomes data was going to take too long, and would be expensive. Our initial foray into a reasonably-sized real-world, multi-omics dataset was a learning exercise for us. But it was critical to establish the trust and use of RWD by our key stakeholders.

We can use multimodal data in earlier stages of drug development than we do today. This is a nascent space so there are challenges to using these datasets and integration into existing processes, but we can use them in target validation work, translational biomarker efforts, indication expansion, predictive biomarker discovery, combination strategy, and many other opportunities.

From a diagnostic perspective, I also believe data may help us understand the patient journey when particular lab tests or diagnostics are utilized, to inform future IVD development programs, but also to see if development of EMR-based clinical decision support tools may be beneficial. We’re seeing more and more of this.

I think use of RWD in the pathology setting will continue to expand. This is currently something we are exploring at Gilead. Having access to scanned images for all patients in a real-world dataset would be ideal, and this is clearly an area where artificial intelligence (AI) is making an impact.

Effort should also be made to increase the diversity of patients represented in these datasets, so they’re inclusive of patients from a wide cross-section of the patient community we want to serve. As a community, we need to think about how to expand these datasets in this way. It will provide an enormous benefit when developing drugs for patients.

What’s critical is to start with efforts that you believe can demonstrate value, and carefully design the questions to ask, to ensure that, if they’re successfully addressed, they’re going to add value to the programs in support of the patients we serve.

All of this requires multiple iterations and continuous communication. As Scott described, it is necessary to demonstrate value through projects, and build up from there. And you will also need people who can speak languages that can resonate across different disciplines.

I also believe the application of liquid biopsy, molecular residual disease, and residual disease monitoring will become more important. And, of course, the use of multi-cancer early detection methodologies. For all of these, large datasets that include outcomes and treatment information will be needed to prove their value to health care systems and patients by directing therapy where it can have most benefit. Expanded use of RWD will help capture the ninety-plus percent of patients not participating in clinical trials.

Another challenge comes about when sample sizes become too small. But there is power in descriptive statistics. Stratifying the patient population, looking at different variables and descriptive purposes can be inputs to informatics when working with smaller sample sizes.