Harvard Catalyst Profiles

Stanford University	BA	06/1999	Economics
Stanford University	MS	03/2001	Computer Science
Johns Hopkins School of Public Health	MPH	06/2010	Biostatistics and Epidemiology
Johns Hopkins University School of Medicine	MD	05/2011	Medicine
Johns Hopkins Hospital		06/2014	Internal Medicine Residency
Dana-Farber Cancer Institute/Harvard CancerCare		08/2018	Hematology/Oncology Fellowship

Dr. Liu is a medical oncologist and computational biologist at the Dana-Farber Cancer Institute and Brigham & Women's Hospital, with a background in computer science, biostatistics, and epidemiology. The Liu lab bridges multiple disciplines, and operates at the intersection of clinical oncology, cancer biology, and computational biology, developing computational approaches to dissect therapeutic resistance and susceptibility to chemo-, targeted-, and immuno-therapies in patient tumors. A focus is on development of rigorous methodologies using statistics and machine-learning techniques applied to specific clinical contexts to discover biologically relevant molecular correlates of response/resistance (for novel therapeutic targets and rational combination therapies) and build clinically relevant predictive models of therapeutic response/resistance. Examples of past work include

(1) development of predictive models of response to PD-1 immune checkpoint blockade in metastatic melanoma integrating clinical, genomic, and transcriptomic features (Liu, Schilling et al Nat Medicine 2019)
(2) validation of mutations in ERCC2 (gene necessary for nucleotide excision DNA repair) as marker of susceptibility to cisplatin-based chemotherapy in muscle-invasive bladder cancer, leading to clinical trials in progress (Liu et al JAMA Onc 2016; Li et al Clin Canc Res 2019)
(3) discovering a novel class of chromatin-modulating gene mutations as drivers of oncogenesis in a standardized meta-analysis of over 1000 prostate cancers (Armenia*, Wankowic*, Liu* et al Nat Gen 2018) *co-first author
(4) de novo discovery of a cisplatin-induced mutational signature in pre- and post-cisplatin treated bladder cancers, later validated in platinum-treated lung and ovarian cancers (Liu et al Nat Comm 2017)
(5) in the same cohort, discovery of intratumoral heterogeneity and multiple gene mutations as molecular correlates of survival and therapeutic resistance (Liu et al Nat Comm 2017)
(6) dissection of immunological correlates to a signature of mismatch repair deficiency in prostate cancer (Nava Rodrigues*, Rescigno*, Liu* et al JCI 2018) *co-first author
(7) elucidating evolution of therapeutic resistance and tumor heterogeneity from 23 tumor samples from a melanoma delayed exceptional responder to sequential immune checkpoint blockade collected over 6 years (Liu et al, under review)

Two broad areas of inquiry exist in the lab:

1. Development of parsimonious, biologically-informed predictive models utilizing statistical and machine learning approaches and integrating clinical and molecular data to guide clinical management.

2. Mapping of clinical tumor heterogeneity and evolution of resistance and metastasis, using longitudinally collected tumor samples from patients over time, treatments, development of resistance, and from different metastatic sites to develop insight into drivers of resistance and metastasis.

Current list of publications:
https://www.ncbi.nlm.nih.gov/myncbi/david.liu.4/bibliography/public/