Wilhelm Haas, Phd
Assistant Professor of Medicine
Harvard Medical School
Center for Cancer Research
Explore the Haas Lab
The Haas laboratory uses quantitative mass spectrometry-based proteomics to characterize cancer cells and their vulnerabilities in a comprehensive proteomewide manner. This is fueled by recent discoveries that have enhanced the depth and throughput of proteomics in quantifying proteins and their posttranslational 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.
We are specifically interested in mapping changes in the global landscape of protein-protein interactions - the interactome - that occur in cancer cells, and we have shown that dysregulations in the interactome are enabling the prediction of cancer vulnerabilities. Another focus in the lab is to develop high-throughput plasma proteome mapping technologies to enable early detection of cancer across multiple cancer types in an unbiased manner. We believe that our proteomics technologies have the potential to become a powerful tool in basic and clinical cancer research and may be used to diagnose cancer, predict its susceptibility, and monitor its progression.
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 number of proteins expressed in mammalian cells. Yet, the proteome holds enormous potential to improve our understanding of the basic principles underlying cancer to revolutionize the 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 and the interactome – the global map of proteinprotein interactions – has the potential to help us refine our approaches to drug design.
The core technology used in our research group is high-throughput quantitative proteomics enabled through multiplexed mass spectrometry. This technology allows us to map the proteome of a cancer cell line or tumor tissue at high throughput. Analyzing the proteome maps across a panel of cancer cell lines, we recently made the observation that the concentration of proteins in known complexes are accurately correlated across all analyzed cell lines. We showed that protein co-regulation analysis allows the genome-wide mapping of protein-protein interactions with an accuracy ten-times larger than when using co-expression analysis based on RNAseq data. We further found that deviations from co-regulation of two interacting proteins in specific cancer cell lines reflect perturbed cellular circuitry, and it remarkably predicts sensitization to therapeutics targeting regulatory modules in the associated pathway. We have termed this approach to fast, in-depth characterization of proteinprotein interaction landscapes interactome dysregulation (DysReg) mapping. This novel method has been developed in collaboration with the laboratory of Cyril Benes. It enables an interactome-wide mapping of proteinprotein interaction dysregulation and inferred cancer vulnerabilities of any cancer sample based on a proteome map that is acquired at high throughput.
We are further interested in the development and application of high-throughput proteomics methods to globally map protein phosphorylation dynamics in cancer samples and to use the data to specifically identify new kinase targets as cancer vulnerabilities.
Our goals are to apply these technologies to:
- identify novel cancer vulnerabilities that direct new treatment strategies
- to map cancer vulnerability dynamics, such as those occurring in the development of therapy resistance, to identify novel targets that enable to overcome the treatment resistance
- to use our technology in a clinical setting for mapping tumor vulnerabilities to inform treatment strategies in a patient specific manner
Another goal of our group is to develop a novel high-throughput proteomics platform to enable unbiased deep proteome mapping of plasma proteomes to enable early detection of cancer. Unbiased screening of more than 1000 plasma proteins rather than mapping a small of number of biomarkers will allow to enable a multi-biomarker assay for multiple cancer types that is constantly improved through adaptation to the detection accuracy.
Postdoctoral fellowships in cancer proteomics
The Haas Laboratory at the Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA, United States, is seeking highly-motivated candidates for two postdoctoral positions. We are leading experts in the field of quantitative mass spectrometry-based proteomics, and we are seeking candidates for two distinct projects.
Project 1: Identify cancer vulnerabilities suitable for personalized cancer treatment strategies. We have developed a method to use quantitative proteomics for high-throughput mapping of global protein-protein interactions landscapes (interactomes) (Lapek et al. (2017) Nat. Biotechnol. 35(10):983-989). Dysregulation of the interactome is a hallmark of cancer, but the specific functional consequences are poorly understood. This project aims to map dysregulations of specific protein-protein interactions in cancer to identify those that can be exploited for treatment. Candidates with a PhD or MD/PhD in biology, biochemistry, chemistry, oncology, immunology, or a related field are encouraged to apply. Strong technical expertise in biochemistry and molecular biology is required. Experience in working with mouse models is desired. Candidates with experience in mass spectrometry-based proteomics are preferred but the experience is not required. Candidates should show a strong interest in learning new techniques and an interest in working with big datasets.
Project 2: Early cancer detection through plasma proteomics. High-throughput proteomics has the potential to revolutionize early detection of cancer. We seek to develop new methods in high-throughput mass spectrometry-based proteomics for the mapping of blood plasma samples with the goal of identifying biomarkers for early detection of cancer. The optimal candidate has a PhD in chemistry, physics, engineering, machine learning, computational sciences, or a related field. Candidates should have a strong background in programming. Experience in working with mass spectrometers is desired. Candidates should show a strong interest in working with large datasets and on biological and clinical questions.
We are a multi-disciplinary team across biology, chemistry, and bioinformatics, and the lab is equipped with multiple state-of-the-art, high-performance mass spectrometers. The laboratory is supported by several funding sources from NIH and industry.
Kreuzer J, Edwards A, Haas W. Multiplexed quantitative phosphoproteomics of cell line and tissue samples. Methods Enzymol. 2019; 626, 41-65.
Lapek JD, Greninger P, Morris R, Amzallag A, Pruteanu-Malinici I, Benes CH*, Haas W*. (2017) Detection of dysregulated protein association networks by high-throughput proteomics predicts cancer vulnerabilities. Nat. Biotechnol. (in press)
Edwards A, Haas W. (2016) Multiplexed Quantitative Proteomics for High-Throughput Comprehensive Proteome Comparisons of Human Cell Lines. Methods Mol. Biol. 1394,1-13.
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. 87, 9855-9863.
McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL, Erickson BK, Rad R, Haas W, Gygi SP. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem. 2014; 86, 7150-7158.
Ting L, Rad R, Gygi SP*, Haas W*. (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937-940.
A Map of Protein-Protein Interactions Identified Using the IMAHP Technology Based on Protein Concentration Co-Regulation across Cancer Cell Lines.
Wilhelm Haas, PhdPrincipal Investigator
- Sambhavi Animesh, PhD
- Benedikt Clemens
- Soroush Hajizadeh, MSc
- Ashok Khatri, MS
- Johannes Kreuzer, PhD
- Robert Morris, PhD
- Xunqin “Elizabeth” Yin
- Eric Zaniewski
- Alice Zheng