Genomics of Hematological Malignances Lab

The challenge

Despite accomplishment of genomic characterization of hematological neoplasm, clinicians knew they still lacked a clear understanding of the biological basis of leukemias and lymphomas, and how to translate that understanding into more effective prevention, diagnosis, and treatment. To reach these goals, it is clear that a new type of hematological research network had to be created. The traditional academic model of individual laboratories working within their specific disciplines was not designed to meet the emerging challenges of biomedicine. To gain a comprehensive view of the human genome and biological systems, they instead had to work in a highly integrated fashion. That meant working in nimble teams that combined biology, genomics, mathematics, computation, artificial intelligence and engineering with medical science and clinical research. It also meant working at a scale usually seen in industry, with access to world-class infrastructure.

Main research areas

Genomics of myeloid neoplasms

Myeloid neoplasms (MN, including acute myeloid leukemias, myelodysplastic syndromes and myeloproliferative neoplasms) are rare hematological cancers that include a significant proportion of patients with unmet clinical needs. MN are caused by somatic mutations. These abnormalities may be generated by inherited predispositions, environmental factors, or random mutations arising during DNA replication in normal stem cells. The identification of recurrent genetic events represents one of the most direct approaches to understand cancer biology. Our mission is to study mechanisms of disease in MN, and rapidly translate findings of these investigations into clinical tools within the clinical practice. We combine genomic profiling (RNA/DNA sequencing and innovative single cell analysis) with clinical studies to develop innovative personalized medicine strategies for MN.

Artificial Intelligence in hematology

We aim to advance the use of artificial intelligence (AI) in hematology. We focus on developing innovative clinical applications of AI and leveraging machine-learning technology in hopes of improving hematological healthcare delivery in areas such as diagnostics, disease prediction and treatment planning. We aim to foster collaboration and communication between physicians, researchers and data-scientists; offer programmatic and technology support for AI initiatives and conduct research in hematological malignancies.

Real World Hematology

Real world evidence (RWE, that is, scientific evidence derived from the clinical ‘footprints’ of real patients as they access their routine medical care) is the new-paradigm of value-based healthcare, as a basis for the clinical implementation of personalized medicine programs. Real-world evidence can provide answers on burden of disease, healthcare utilization (in terms of appropriateness with respect to evidence-based guidelines), post-marketing drugs’ safety, effectiveness and cost-effectiveness profiles of drug therapies and healthcare pathways.

Innovative educational and training opportunities

The post-genomic era is leading to a revolution in the understanding of human diseases and in the designing of the related therapies. However, the possibility of elucidating cellular and molecular mechanisms that regulate complex diseases such as leukemias and lymphomas needs a deeply interaction between basic and applied research. This requires the formation of “translational” professional profiles with a robust background in basic research and data analysis, equipped to bridge the gap between bench and clinical investigation. Such skills are crucial in orienting research to efficiently meet current diagnostic and therapeutic need in hematology.