The new department will consolidate elements of Weill Cornell Medicine’s Department of Physiology and Biophysics, the Institute for Computational Biomedicine and other systems biology and computation-focused programs. Its creation follows a multiyear reorganization intended to strengthen connections between basic science, clinical medicine and technology development. Rather than treating computational biology as a supporting service, the department will position it as a central research discipline. Its scientists will work with clinicians and experimental biologists to interpret complex measurements generated from patients, including genomic sequences, medical images, electronic health records and physiological data collected through mobile and wearable devices. The goal is to transform these disparate streams into coherent biological models that can reveal why diseases arise and why patients respond differently to the same treatment.

Landau’s research background reflects the kind of interdisciplinary science the department is intended to promote. He is the Bibliowicz Family Professor of Medicine and a professor of medicine and systems and computational biomedicine at Weill Cornell Medicine. He is also affiliated with the Sandra and Edward Meyer Cancer Center, the Englander Institute for Precision Medicine and the New York Genome Center, and practices oncology at NewYork-Presbyterian/Weill Cornell Medical Center. His work has focused on cancer evolution, somatic mutations and the molecular changes that allow malignant cells to emerge and survive. In particular, he has advanced single-cell profiling methods, which examine genetic and molecular features in individual cells rather than averaging signals across an entire tumor. This distinction is critical because tumors are rarely uniform; they contain diverse cell populations with different mutations, behaviors and vulnerabilities.

One of the department’s central ambitions is to construct large datasets derived directly from human patients. These datasets could combine DNA sequencing, RNA and other molecular measurements with longitudinal clinical records, imaging, treatment histories and continuous physiological monitoring. Properly integrated, such information could allow researchers to trace disease over time instead of examining it at isolated clinical moments. Artificial intelligence models trained on these data may eventually identify molecular patterns associated with elevated risk, treatment resistance or impending relapse. Such systems would not replace clinical judgment, but could provide physicians with dynamic risk assessments that are more individualized than conventional population-based statistics. The same datasets could also help researchers identify previously unrecognized disease subtypes and uncover biological targets for new therapies.

The technical challenge is substantial. Patient-derived information is produced by instruments and institutions using different standards, sampling schedules and measurement technologies. Genomic data may be incomplete, clinical records may contain inconsistencies and wearable devices can generate noisy signals influenced by behavior or environment. Computational biomedicine must therefore address data harmonization, privacy, bias, statistical validation and the interpretability of machine-learning models. A prediction is not clinically useful simply because it is accurate in a research dataset; it must also be reliable across populations and understandable enough to support decisions about screening or treatment. The new department is expected to combine computational expertise with experimental validation and clinical evaluation, creating a feedback loop in which predictions can be tested in the laboratory and at the bedside.

Landau also plans to establish an innovation laboratory devoted to molecular measurement technologies for the artificial-intelligence era. The premise is that future computational systems will be limited by the quality and scale of the biological information available to them. New technologies capable of measuring many molecular features simultaneously, repeatedly and at lower cost could provide the dense datasets needed to model disease with greater precision. Single-cell and multi-omic approaches are especially important because they can connect genetic alterations with gene activity, cellular states and interactions among cells. By developing measurement platforms within the department, Weill Cornell Medicine hopes to generate its own high-dimensional datasets rather than relying exclusively on existing technologies. Those tools could become a foundation for both fundamental discoveries and clinically deployable diagnostics.

Landau’s research has already moved from laboratory investigation into commercial applications. He helped launch C2i Genomics in 2019, a company that combined whole-genome sequencing with machine-learning methods to monitor cancer. The platform was designed to detect minimal residual disease, meaning small numbers of cancer cells that remain after treatment and may later lead to recurrence. In 2024, C2i Genomics was acquired by diagnostics company Veracyte, which has expanded its capabilities in cancer care. Veracyte has since launched the TrueMRD test for muscle-invasive bladder cancer, with additional applications planned. Landau also helped found Montage Bio, which licensed computational, cell-free DNA sequencing and single-cell multi-omic technologies from Cornell to develop precision medicines against novel somatic targets. These efforts illustrate how research on cancer genomes can be translated into tools for monitoring patients and designing therapies.

The new department will be built through recruitment as well as institutional consolidation. Landau expects to recruit five to seven junior faculty members and one to two mid-career investigators specializing in computational biology and artificial intelligence. Some appointments may be jointly organized with existing academic departments, allowing computational researchers to work closely with experts in cancer, physiology, genetics and clinical medicine. The department will also add computational staff and laboratory resources, while providing dedicated space for collaborative research. Weill Cornell Medicine has committed new laboratory areas in recently acquired floors of 1334 York Ave., which will serve as a physical center for the department and its molecular innovation laboratory. The design reflects a broader shift in biomedical research toward teams that unite experimental scientists, data scientists, engineers and physicians.

The initiative is supported in part by a $20 million gift to Cornell University from philanthropists Andrew and Ann Tisch. The funding includes an endowed professorship for the department’s chair, which Landau will hold as the Andrew H. and Ann R. Tisch Professor of Systems and Computational Biomedicine. The gift is intended to encourage collaboration among Weill Cornell Medicine, Cornell Tech and Cornell’s Ithaca campus. Landau has described partnerships beyond individual laboratories as essential because artificial-intelligence systems generally improve when trained on larger and more diverse datasets. He has also emphasized connections with hospitals, biotechnology companies and frontier AI laboratories in New York and elsewhere. The strategy reflects an emerging reality in biomedical science: understanding complex disease increasingly requires resources, expertise and patient data on a scale that no single laboratory can assemble alone.

Landau earned bachelor’s and medical degrees from Tel Aviv University and a doctorate in cancer biology from Paris Diderot University. After serving as a flight surgeon in the Israeli Air Force, he trained in internal medicine and oncology at Yale, Dana-Farber Cancer Institute and Harvard Medical School before joining Weill Cornell Medicine in 2016. He has published extensively in leading scientific journals and received honors including a Sontag Foundation Distinguished Scientist Award, a Pershing Square Sohn Prize for Young Investigators in Cancer Research and an NIH Director’s New Innovator Award. His appointment signals Weill Cornell Medicine’s intention to make computational and systems-based research a defining component of its scientific future. If the department succeeds, its most significant contribution may be a new model of biomedical discovery—one in which molecular measurements, clinical observations and artificial intelligence continuously inform one another to detect disease earlier, explain its complexity and make treatment more precisely responsive to each patient.

Subject of Research: Systems and computational biomedicine, artificial intelligence, genomics, cancer biology, single-cell profiling and liquid biopsy technologies.

Article Title: Dan Landau Named Inaugural Chair of Weill Cornell Medicine’s Department of Systems and Computational Biomedicine

Web References: https://news.weill.cornell.edu/news/2022/12/20m-gift-to-boost-innovation-in-health-and-technology

Image Credits: Weill Cornell Medicine

Keywords: Dan Landau, Weill Cornell Medicine, computational biomedicine, artificial intelligence, cancer genomics, single-cell profiling, liquid biopsy, minimal residual disease, precision medicine, systems biology, Cornell University, molecular measurement technologies

The impetus behind these fellowships lies in the rapidly expanding availability of large-scale biological datasets and the increasing necessity for sophisticated computational frameworks to interpret them. Yung S. Lie, PhD, President and CEO of the Damon Runyon Cancer Research Foundation, emphasizes the crucial role of computational expertise in precision medicine, where modeling and data integration are vital for dissecting tumor heterogeneity and treatment responses. The selected fellows epitomize this interdisciplinary approach, bridging “dry” lab quantitative sciences with “wet” lab biological insights to pioneer novel avenues in cancer understanding and intervention.

Among the fellowship recipients is Dr. Minsoo Kim, who focuses on the enigmatic presence of aneuploid cells—cells with abnormal chromosome numbers—in ostensibly healthy breast tissue. Challenging long-held assumptions that normal cells uniformly maintain chromosomal integrity, Dr. Kim’s research investigates these rare aneuploid populations as potential early harbingers of breast cancer. By developing a heterogeneous graph neural network (GNN), his work will jointly model single-cell copy number variations and gene expression data, representing genes, cells, and chromosome segments as distinct nodes. This nuanced modeling approach aims to disentangle gene expression changes driven by chromosomal gains or losses from other transcriptional variations.

Crucially, Dr. Kim intends to extend this computational framework into spatial transcriptomics, which retains the spatial context of gene expression within tissue architecture. This enhancement is designed to illuminate how the microenvironment influences aneuploid cell behavior and interactions, potentially revealing biomarkers for early detection and mechanisms of cancer risk stratification. By applying these analyses to longitudinal breast tissue samples from patients monitored over years, where some subsequently developed cancer, the project aspires to not only refine predictive diagnostics but also offer clinicians tools for earlier, more targeted intervention strategies.

Dr. Sahana Kuthyar’s research addresses a pressing clinical challenge: the elevated risk of severe lung infections in cancer patients undergoing immunosuppressive therapies like chemotherapy and radiation. These treatments, while efficacious against tumors, impair myeloid immune components critical for combating bacterial pathogens, leaving patients vulnerable to conditions such as pneumonia. Moreover, the common clinical practice of providing supplemental oxygen further complicates this risk by altering the pulmonary environment to favor aggressive bacterial proliferation. Dr. Kuthyar’s investigation bridges human and murine models to unravel this complex interplay.

Her computational strategy leverages hierarchical network modeling to integrate gene expression profiles with metabolomic data, applying multi-omics factor analysis for a holistic view of microbial and host immune dynamics under hyperoxic conditions. By cross-validating predictive models between human patients and mouse models, the study aims to iteratively refine understanding of how bacterial adaptation and immune suppression converge to create critical infection vulnerabilities. The insights garnered here may pave the way for predictive diagnostics and novel therapeutic approaches to mitigate life-threatening infections in immunocompromised cancer populations.

Matthew Leventhal, PhD, embarks on a pioneering inquiry into sex chromosome biology within cancer, focusing on the differential roles of active and inactive X chromosomes in females—a subject deeply intertwined with oncogenic potential. Given that females carry two X chromosomes with one subjected to early developmental silencing, mutations impacting the active X chromosome may have outsized consequences on cellular function and tumor progression. Dr. Leventhal’s work centers on developing computational tools capable of resolving the haplotype-specific copy number of chromosomes from bulk whole-genome sequencing data, correcting phasing errors that have historically obscured distinctions between active and inactive X chromosome alterations.

Integrating DNA sequencing with RNA-seq expression data, this methodology will allow for the first pan-cancer analysis of X chromosome dynamics across more than 8,500 tumors spanning 31 cancer types. The goal is to identify recurrent copy number alterations preferentially affecting either the active or inactive X, potentially uncovering novel oncogenic drivers or vulnerabilities previously masked due to analytical limitations. Additionally, determining whether such chromosomal alterations exist in precancerous cells could have transformative implications for early detection and intervention strategies tailored to sex chromosome biology.

The innovations promised by these fellows are testament to the evolving landscape of cancer research, where computational advancements are indispensable to dissecting biological complexity. The utilization of graph neural networks, multi-omics integration, and sophisticated haplotype phasing models exemplifies the next frontier of oncological inquiry, promising heightened precision in diagnosis, prognosis, and treatment. Beyond their individual research agendas, these scientists exemplify the Damon Runyon Foundation’s vision of cultivating interdisciplinary talent equipped to unravel cancer’s multifaceted biology.

Since 1946, the Damon Runyon Cancer Research Foundation has championed early-career investigators, recognizing that the initial years of scientific pursuit are critical for unleashing transformative discoveries. Over $491 million invested and nearly 4,100 funded scientists reflect an enduring commitment to nurturing high-risk, high-reward research. The foundation’s outstanding track record, highlighted by thirteen Nobel laureates among its alumni, underscores its impact on the global cancer research community.

These current fellowships reinforce the need to blur conventional boundaries between computational and biological sciences, reinforcing a paradigm where machine learning algorithms and spatial data are indispensable complements to experimental biology. As the biological sciences grapple with data of unprecedented scale and complexity, the fusion of quantitative expertise and biological insight will catalyze breakthroughs in understanding cancer’s origins, progression, and treatment resistance.

The relevance of this fellow-supported research extends to personalized and precision medicine, where patient-specific molecular data can guide tailored therapeutic regimens. Detecting early aneuploid cell populations, predicting infection risks in susceptible patients, and elucidating sex chromosome influences represent concrete ways in which computational biology is reshaping cancer care. Through these fellowships, the Damon Runyon Foundation equips young scientists with not only resources but also mentorship from leaders in computational and biological cancer research, creating a fertile environment for interdisciplinary innovation.

As these fellows progress, their work is poised to impact fundamental understanding and clinical strategies alike. Whether refining early detection algorithms for breast cancer, unearthing microbial-immune crosstalk in cancer-associated pneumonia, or decoding X chromosome alterations across cancers, these efforts embody a new wave of cancer research empowered by computational sophistication. The field awaits the ripple effects of their discoveries as they translate complex biological data into actionable knowledge with the potential to save lives.

In sum, the 2026 Damon Runyon Quantitative Biology Fellows symbolize a convergence of technology and biology at a pivotal moment in cancer research. Their ambitious projects harness state-of-the-art computational methodologies to tackle profound questions about cancer initiation, progression, and patient vulnerability. Supported by visionary funding and mentorship, these scholars exemplify the future of biomedical research, where multidisciplinary collaboration and quantitative prowess unlock mysteries once deemed impenetrable.

Subject of Research: Computational approaches to cancer biology focusing on early detection, infection risk in immunocompromised patients, and sex chromosome genomics in cancer.

Article Title: Unlocking Cancer’s Complexities: How Computational Pioneers are Shaping the Future of Oncology

News Publication Date: 2026

Web References: http://damonrunyon.org/

Keywords: cancer research, computational biology, machine learning, graph neural networks, spatial transcriptomics, multi-omics analysis, cancer immunology, X chromosome, aneuploidy, precision medicine, early cancer detection, network modeling