NIH grant: Artificial intelligence-based integration of genomic and histopathologic data to improve precision cancer medicine (1K99CA315594-01)

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NIH grant 1K99CA315594-01: Artificial intelligence-based integration of genomic and histopathologic data to improve precision cancer medicine
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: Kevin Michael Boehm. Award: $140400.
Start: 2026-07-17T00:00:00.
Project Abstract
Microscopic images of tumors profile disease biology in a way that complements disaggregated genomic
sequencing. Digital pathology images and tumor genomics are increasingly available together in clinical
settings, and there is thus an unmet need for artificial intelligence (AI) methods to integrate them toward clinical
and biological insights. This proposal will develop multimodal, interpretable AI models that fuse spatial and
molecular tumor profiles with prior biological knowledge to sharpen diagnosis and improve selection of
systemic therapy, focusing on breast cancer, non-small cell lung cancer, and muscle-invasive urothelial bladder
cancer. In the K99 phase, I will study the added value of multimodal self-supervised learning to fuse pathology
and genomics data for diagnosis and prediction of response to neoadjuvant therapy, with a research focus on
using graphical methods to incorporate structured biological knowledge into high-performing black-box models.
I will go beyond estimating prognosis with causal-inference analyses to estimate treatment effect by regimen.
In the R00 phase, I will deepen the integration of structured biological knowledge into the models, especially
the causal inference methods, and extend them into a treatment recommender system. Also during the
independent phase, I will work to translate these models toward deployment via federated multi-institution
validation, testing on newly accrued cases, and human-factors studies with pathologists/oncologists to assess
interpretability, calibration, and decision impact. Building on recent successes in AI, the work will develop
optimized, interpretable architectures for oncology to integrate complementary tumor profiles with pre-existing
knowledge for actionable insights in cancer data science. Concretely, resultant models could eventually be
developed toward de-escalating ineffective therapy, expediting effective treatment for cancers of unknown
primary, and informing trial design for biomarker-guided neoadjuvant care. My career development plan builds
on my prior training in computational pathology and clinical oncology to develop skills in multimodal
self-supervised learning, causal inference, graph theory, and cancer biology, along with leadership and
communication for team science. I will take targeted coursework, workshops, and grant-writing seminars;
present at major meetings; and lead collaborative manuscripts. During the K99 phase, my training will be well
supported by Dr. Sohrab Shah (Chief, Computational Oncology) and Dr. Nikolaus Schultz (Director, Cancer
Data Science Initiative) and a panel of experts including pathologists, molecular biologists, and
physician-scientists, with access to already-curated, world-class cohorts and the resources that I need to
transition to independence.

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