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Christos Hatzis, PhD
Associate Professor of Medicine
Director Bioinformatics, Breast Medical Oncology
Yale Cancer Center
Yale University School of Medicine
New Haven, Connecticut
- Seeking to understand variations in individual response to anti-cancer treatments in order to guide more effective therapies.
A bioinformatics approach is employed to analyze mutations in breast tumors and gain insight into why treatments work for some patients but not others.
Dr. Hatzis is applying new methods to decipher the genetic complexity of cancer and response to treatment to identify more effective and personalized treatment strategies.
The goal of Dr. Hatzis' work is to understand how breast cancers are genetically different, not only in regard to the unique genetic profile of individual tumors, but also the genetic diversity of tumor cells within a tumor, and how this diversity may influence response to anti-cancer treatments. Linking tumor mutations to specific therapeutic strategies for an individual patient remains a critical challenge in realizing the vision of precision medicine.
Dr. Hatzis' team developed a bioinformatics tool to organize all the mutations detected in a tumor and to map the biological functions that may be affected by the mutations. This work led to the identification of a subset of triple negative breast cancer patients that are extremely likely to benefit from standard chemotherapy. This year, the team will analyze about 2000 cancer cases. This could lead to better understanding of how tumors emerge and may suggest new treatment strategies.
An additional challenge to precision medicine is drug resistance, either acquired or inherent, to targeted and standard therapies. This year, Dr. Hatzis and his team will study the molecular characteristics of biopsies before and after treatment in patients with HER2-postive breast cancer from the NeoALTTO trial. They will determine whether specific tumor mutations or other genetic alterations are related to resistance to treatment. This will be the first such study in HER2-positive breast cancer and could inform future treatment strategies.
They are additionally looking at drug resistance in triple negative breast cancer and believe that the sequence of therapies may provide clues. Results from these studies may lead to more effective therapeutic approaches that will reduce drug resistance and provide durable responses.
Collectively, Dr. Hatzis' studies will advance the goals of precision medicine in delivering the right drug at the right time.
Dr. Hatzis is an Assistant Professor of Medicine and Director of Bioinformatics in Breast Medical Oncology at the Yale School of Medicine. He received his PhD in Biochemical Engineering at the University of Minnesota and held several senior research roles in the biotechnology industry. He has been the cofounder of two startup companies specializing in bioinformatics tools development and in clinical diagnostics. Dr. Hatzis had been an active member of FDA's MAQC and SEQC programs and co-investigator on the NCI Cancer Biospecimen Integrity program. Among his most significant contributions are the co-development with colleagues from MD Anderson of the RCB index, a continuous index of residual disease after neoadjuvant treatment for prognosis in breast cancer patients, and the development of gene-expression based prognostic signatures for patients treated with standard chemotherapy, which account for subtype differences and integrate endocrine sensitivity, and chemotherapy response and resistance endpoints.
Since joining the faculty of the Yale School of Medicine in 2013, Dr. Hatzis continues to be involved in the design of biomarker validation clinical studies and development of strategies for translating genomic diagnostic assays to clinical practice. His current research interests focus on developing methods to characterize the genetic and molecular heterogeneity of breast cancer subtypes and the implications that this might have on response and resistance to treatment. A key area of interest is to develop methodology that integrates genomic level information of individual patients to lead to more focused treatment decisions tailored for the individual tumor.
BCRF Investigator Since
The Play for P.I.N.K. Award