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Wilhelm Haas, PhdAssistant Professor of MedicineHarvard Medical School
The Haas laboratory uses quantitative mass spectrometry-based proteomics to study the cellular pathways that characterize cancer cells in a comprehensive proteome-wide manner. This is fueled by recent discoveries that have enhanced the depth and throughput of proteomics in quantifying proteins and their post-translational modiﬁcation. These improvements have put us at a pivotal point in the ﬁeld of mass spectrometry, where, for the ﬁrst time, we are able to handle the analysis of the large number of samples that have to be examined to generate the basis for understanding a disease that displays the heterogeneity found in cancer. Beyond trying to understand the global changes that occur in cancer cells, we are applying these methods to expand our understanding of how the proteome is altered when resistance emerges in response to treatment in individual patients. We believe that proteomics has the potential to become a diagnostic tool in cancer by identifying novel protein biomarkers that may be used to diagnose cancer, predict its susceptibility and monitor its progression.
Wilhelm Haas, PhdPrincipal Investigator
Cancer is based on dynamic changes of the genome that ultimately translate into an altered proteome, optimized for uncontrolled cell growth and division. In addition, many pathways initially causing cancer further promote the propagation of altered genetic information, accelerating the adaption of cancer cells to new environments. This dynamic process becomes even more complex if taking into account the dynamic state of the cellular proteome that is regulated by protein synthesis and degradation, posttranslational modiﬁcations, protein localization, and the interaction of proteins with other proteins as well as with different classes of biomolecules. While the “cancer genome” can now be easily accessed due to advances in DNA sequencing technology, the information contained in the “cancer proteome” has remained largely untapped due to technical challenges in quantifying the large amount of proteins expressed in mammalian cells. Yet, the proteome holds an enormous potential to improve our understanding of the basic principles underlying cancer to revolutionize early diagnosis of the disease and to improve patient care. Up to date, virtually all targeted therapeutics in cancer treatment are targeting proteins. Understanding how these drugs alter the proteome has the potential to help us reﬁne our approaches to drug design.
Despite the potentials of studying the proteome in order to improve our understanding of cancer, the proteome-contained information is substantially underused in cancer research. This is based on technical limitations of the proteomics technology, which for a long time did not match the capabilities of genetics tools already widely used in studying cancer. However, the past few years brought enormous improvements in all aspects of proteomics but especially in mass spectrometry, the main tool used in studying the proteome.
The level of high comprehensiveness in proteomics, which allows us to quantify almost all proteins and their post-translational modiﬁcations in a single experiment, was a ﬁrst step in increasing the technology’s competitiveness in comparison to genomics tools. A second and more recent improvement was the enhancement of the technology’s throughput, which now enables us to quantify up to 10 different samples in one experiment. In addition to applying these new methodologies to samples from primary tumor and cell culture models, my lab is continuing to work on improving both aspects by developing methods that will allow a more efficient monitoring of levels of post-translational modiﬁcations but also by increasing the throughput of proteomics through enhancing its multiplexing capacity. Both directions are aimed at improving proteomics as a tool in basic research but also pushing the technology’s capacity to enable its use in a clinical environment.
We are applying existing and new methods in two speciﬁc areas. By establishing quantitative maps of protein concentration and site speciﬁc protein phosphorylation levels from an extensive number of cancer cell lines and primary tumors, we are searching for proteome biomarkers in order to direct targeted therapies for individual patients. We are focusing these studies on lung cancer and are working in collaboration with the laboratories of Jeffrey Engelman and Cyril Benes to study cellular mechanisms that enable cancer cells to develop resistance against treatment by targeted therapeutics. We are working with cell line models and monitor changes in protein and phosphorylation levels while evoking resistance against the treatment with targeted therapeutics. We plan to manipulate levels of proteins or pathways found to be regulated using genetic tools (siRNA) to conﬁrm their role in overcoming the effect of drug treatment.
Braun, C.R.*, Bird, G.H., Wühr, M., Erickson, B.K., Rad, R., Walensky, L.D., Gygi, S.P.*, Haas, W.* (2015) Generation of Multiple Reporter Ions from a Single Isobaric Reagent Increases Multiplexing Capacity for Quantitative Proteomics. Anal. Chem. (in press, PMID: 26314710).
Minajigi A, Froberg JE, Wei C, Sunwoo H, Kesner B, Colognori D, Lessing D, Payer B, Boukhali M, Haas W, Lee JT. (2015) A compre-hensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation. Science 349, pii: aab2276.
Tolonen AC*, Haas W*. (2014) Quantitative proteomics using reductive dimethylation for stable isotope labeling. J. Vis. Exp. 89, doi: 10.3791/51416.
Ting L, Rad R, Gygi SP*, Haas W*. (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics, Nat. Methods 8, 937-940.
Tolonen AC*, Haas W*, Chilaka, AC, Aach J, Gygi SP, Church GM. (2011) Proteome-wide systems analysis of a cellulosic biofuel-producing microbe, Mol. Syst. Biol., 7, 461.
Haas W, Faherty BK, Gerber SA, Elias JE, Beausoleil SA, Bakalarski CE, Li X, Villen J, Gygi SP. (2006) Optimization and use of peptide mass measurement accuracy in shotgun proteomics. Mol. Cell. Proteomics 5, 1326-1337.
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