Associate Professor of Biology
"Many human cancers do not respond to treatment, and often times those that initially respond eventually acquire drug resistance. Our lab uses high-throughput screening technology in combination with tractable pre-clinical mouse models to investigate basic mechanisms of intrinsic and acquired drug resistance. Our goal is to identify novel cancer drug targets, as well as strategies for tailoring drug regimens to target protective mechanisms used by cancers to evade and escape cancer therapy. "
Michael Hemann is an associate professor in the MIT Department of Biology, a member of the Koch Institute, and an associate member of the Broad Institute. He earned his bachelor’s degree in molecular biology and biochemistry from Wesleyan University in 1993, and a doctorate in human genetics from the Johns Hopkins University School of Medicine in 2001. For his doctoral work, he was awarded the Harold M. Weintraub Graduate Student Award from the Fred Hutchinson Cancer Research Center. He was a post-doctoral fellow in the laboratory of Scott Lowe at Cold Spring Harbor Laboratory in New York, where he was supported by a Helen Hay Whitney Fellowship and a Lauri Strauss Leukemia Foundation Grant. In 2006, Professor Hemann joined the MIT faculty as an assistant professor. He has since been awarded a V Foundation Fellowship and was selected as a Rita Allan Fellow. In 2014, Professor Hemann became a co-director of the MIT graduate program in Biology.
Our lab uses adoptive transfer experiments in the mouse hematopoietic system to model tumor development. Initially, this work focused on gene overexpression studies involving deregulated oncogenes. Subsequently, we have expanded the utility of this adoptive transfer system using stable RNAi. Using retroviral infection of small hairpin RNAs (shRNAs) targeting the tumor suppressor p53 into Myc-overexpressing hematopoietic stem cells, we have successfully generated tumors which biochemically and phenotypically suppressed gene expression over extended periods in vivo. This technology has allowed us to accelerate and expand our analysis of the impact of defined lesions both on tumor onset and therapeutic response. Interestingly, different p53 shRNAs produced distinct phenotypes in vivo, ranging from benign lymphoid hyperplasia to highly disseminated lymphomas that paralleled the nullizygous setting. In all cases, the severity and type of disease correlated with the extent to which specific shRNAs inhibited p53 activity. Therefore, RNAi can stably suppress gene expression in stem cells and reconstituted organs derived from those cells. Moreover, intrinsic differences between individual shRNA expression vectors targeting the same gene could be used to create an 'epi-allelic series' for dissecting gene function and tumorigenesis in vivo. These experiments demonstrate the potential of RNAi as a tool to study gene function in vivo.
Following these initial proof of principle experiments, we have begun to use in vivo RNAi to investigate the role of putative tumor suppressors in the inhibition of Myc-induced lymphomagenesis. This includes the targeted analysis of candidate tumor-suppressive pathways, as well as the use of shRNA libraries to perform unbiased screens for novel tumor suppressors.
We have generated several shRNA vectors that mediate resistance to conventional chemotherapeutics in vivo. We are currently expanding this approach, through the use of targeted RNAi libraries, to assess the role of thousands of cancer-relevant genes in the response of diverse tumor types to a wide array of chemotherapeutics. This strategy allows us to perform genetic screens for mediators of chemotherapeutic response in a relevant therapeutic setting.
Importantly, this approach has broad flexibility. First, distinct tumor types can be examined to determine the effect of tumor genotype on mechanisms of chemotherapeutic resistance. Second, the pattern of shRNAs conferring drug resistance in an individual tumor treated with established chemotherapeutic can serve as a "treatment fingerprint", such that the mechanisms of action of novel chemotherapeutics may be deduced by comparison with these established patterns. Third, in addition to examining drug resistance, we can use representational approaches to identify shRNAs that promote sensitivity to specific drugs. In doing so, we hope to identify drug targets whose inactivation synergizes with existing anti-cancer therapies.
Genetic instability is a hallmark of advanced and chemoresistant human malignancy. However, using existing models, it is difficult to distinguish between the role of genetic instability in cell transformation versus chemotherapeutic response. Furthermore, it remains unclear whether genetic instability itself mediates chemoresistance or whether drug resistance arises as the consequence of an underlying lesion that simultaneously promotes both chemoresistance and instability. Our development of technology to acutely suppress genes in tractable tumor models provides a means of distinguishing between the genetics of cell transformation, acute chemoresistance and acquired chemoresistance.
We have generated a set of shRNAs targeting genomic stability pathways including DNA double-strand break repair, mismatch repair, homologous recombination, telomere function and mitotic checkpoints. These shRNAs are being used in combination with in vivo tumor transplantation systems to determine: 1) the effect of gene suppression on overall tumor growth in vivo, 2) the acute effect of gene suppression on tumor cell survival following treatment with cytotoxic chemotherapy and 3) the effect of long-term gene suppression and the consequent accumulation of genomic alterations on tumor cell survival following treatment with cytotoxic chemotherapy. Ideally, these experiments will clarify whether genetic instability is inherently advantageous or deleterious to an established tumor and whether the acute loss of DNA repair mechanisms or the acquired loss of genetic stability alters tumor drug response.
Learn more about the Hemann lab’s work in system biology and how they use high throughput genetics in model systems to screen for mechanisms of drug resistance by watching the video: "Inside the Lab: Michael H. Hemann, Ph.D."
Soto-Feliciano YM, Bartlebaugh JME, Liu Y, Sánchez-Rivera FJ, Bhutkar A, Weintraub AS, Buenrostro JD, Cheng CS, Regev A, Jacks TE, et al. 2017. PHF6 regulates phenotypic plasticity through chromatin organization within lineage-specific genes. Genes Dev 31: 973–989.
Bruno PM, Liu Y, Park GY, Murai J, Koch CE, Eisen TJ, Pritchard JR, Pommier Y, Lippard SJ, Hemann MT. 2017. A subset of platinum-containing chemotherapeutic agents kills cells by inducing ribosome biogenesis stress. Nat Med 23: 461–471.
Zhao B, Sedlak JC, Srinivas R, Creixell P, Pritchard JR, Tidor B, Lauffenburger DA, Hemann MT. 2016. Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution. Cell 165: 234–246.
Bent EH, Gilbert LA, Hemann MT. 2016. A senescence secretory switch mediated by PI3K/AKT/mTOR activation controls chemoprotective endothelial secretory responses. Genes Dev 30: 1811–1821.
Zhao B, Pritchard JR, Lauffenburger DA, Hemann MT. 2014. Addressing genetic tumor heterogeneity through computationally predictive combination therapy. Cancer Discov 4: 166–174.
Pallasch CP, Leskov I, Braun CJ, Vorholt D, Drake A, Soto-Feliciano YM, Bent EH, Schwamb J, Iliopoulou B, Kutsch N, et al. 2014. Sensitizing protective tumor microenvironments to antibody-mediated therapy. Cell 156: 590–602.
Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT. 2013. Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci USA 110: E170-179.
Gilbert LA, Hemann MT. 2012. Context-specific roles for paracrine IL-6 in lymphomagenesis. Genes Dev 26: 1758–1768.
Gilbert LA, Hemann MT. 2010. DNA damage-mediated induction of a chemoresistant niche. Cell 143: 355–366.
Jiang H, Reinhardt HC, Bartkova J, Tommiska J, Blomqvist C, Nevanlinna H, Bartek J, Yaffe MB, Hemann MT. 2009. The combined status of ATM and p53 link tumor development with therapeutic response. Genes Dev 23: 1895–1909.