Gregory W. Schwartz

I am a Scientist at the Princess Margaret Cancer Centre and an Assistant Professor at the University of Toronto in the Department of Medical Biophysics.

Life is a complex system of interconnecting networks: from the transcriptome to the proteome, from a single cell to clonal populations, each layer of information can be interpreted as a network where each molecule, cell, or tissue in some way affects the overall system. Systems biology attempts to take into account as many of these levels as possible in order to answer biological questions pertaining to not only the organization of life but also separating signal from noise to discover key components of disease or adverse clinical outcomes. While sampling technologies give partial insight to these issues, the other half of the solution, namely mathematical and computational analyses of biological data, are needed more than ever in this high-throughput era.

The need for such analyses drives my research. In my efforts to elucidate the relationships between genotype and phenotype, clonal diversity and selection, and integration of multiple modalities of information (transcriptome, proteome, phosphoproteome, etc.), I have developed algorithms, programs, and mathematical measures.

More specifically, I am interested in clonal behavior and evolution (12, 7, 10, 5), pattern recognition of the interactome through integration (6), network structure similarity, data visualization (10), and everything in-between.

While single-cell RNA sequencing can reveal much about the transcriptomic state of individual cells, these states are largely set by the interaction of transcription factors with regulatory elements within accessible chromatin regions. To understand the relationship between cells with similar or unique chromatin states, we developed TooManyPeaks, part of the TooManyCells suite, for end-to-end analysis of single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data (1).

Single-cell analyses enable measurement of the transcriptome, chromatin accessibility, and more at single-cell resolution. However, grouping similar cells at different resolution cutoffs severely limits the exploration of both common and rare heterogeneous populations. We have developed TooManyCells, a suite of novel tools focused on simultaneous visualization of single-cell clade relationships at all resolutions (2).

Poor prognosis of patients with acute myeloid leukemia can be predicted based on the presence of internal tandem duplications (ITDs) in Fms-like tyrosine kinase 3 (FLT3). We discovered new classes of FLT3-ITDs that predict patient outcomes by creating HeatITup, an algorithm that identifies, characterizes, and visualizes these classes of FLT3-ITDs (5).

While we can measure different modalities, such as the transcriptome and proteome, it is difficult to analytically relate each level of information in a systematic manner. We developed inteGREAT, an algorithm to use integration and differential integration between conditions to identify biomarkers across modalities (6).

• Schwartz GW, Zhou Y, Petrovic J, and Faryabi RB. TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes. Cell Rep. 36, (2021). doi:10.1016/j.celrep.2021.109575
• Schwartz GW, Petrovic J, Fasolino M, Zhou Y, Cai S, Xu L, Pear WS, Vahedi G, and Faryabi RB. TooManyCells identifies and visualizes relationships of single-cell clades. Nat. Methods 17 405–413 (2020). doi:10.1038/s41592-020-0748-5
• Rome KS, Stein, SJ, Kurachi M, Petrovic J, Schwartz GW, Mack EA, Uljon S, Wu WW, DeHart AG, McClory SE, Xu L, Gimotty PA, Blacklow SC, Faryabi RB, Wherry EJ, Jordan MS, Pear WS. Trib1 regulates T cell differentiation during chronic infection by restraining the effector program. J. Exp. Med. 217 e20190888 (2020). doi:10.1084/jem.20190888
• Petrovic J, Zhou Y, Fasolino M, Goldman N, Schwartz GW, Mumbach MR, Nguyen SC, Rome KS, Sela Y, Zapataro Z, Blacklow SC, Kruhlak MJ, Shi J, Aster JC, Joyce EF, Little SC, Vahedi G, Pear WS, Faryabi RB. Oncogenic Notch Promotes Long-Range Regulatory Interactions within Hyperconnected 3D Cliques. Mol. Cell. 73 1174-1190.e12. (2019). doi:10.1016/j.molcel.2019.01.006
• Schwartz GW, Manning BS, Zhou Y, Velu PD, Bigdeli A, Astles R, Lehman AW, Morrissette JJD, Perl AE, Li M, Carroll M, and Faryabi RB. Classes of ITD predict outcomes in AML patients treated with FLT3 inhibitors. Clin. Cancer Res. (2019). doi:10.1158/1078-0432.CCR-18-0655 Commentary: Prognostic Models Turn the Heat(IT)up on FLT3^ITD-Mutated AML. doi:10.1158/1078-0432.CCR-18-3146
• Schwartz GW, Petrovic J, Zhou Y, and Faryabi RB. Differential integration of transcriptome and proteome identifies pan-cancer prognostic biomarkers. Front. Genet. 9, 205 (2018). doi:10.3389/fgene.2018.00205
• Meng W*, Zhang B*, Schwartz GW, Rosenfeld AM, Ren D, Thome J JC, Carpenter DJ, Matsuoka N, Lerner H, Friedman AL, Granot T, Farber DL, Shlomchik MJ, Hershberg U and Luning Prak ET. An atlas of B cell clonal distribution in the human body. Nat. Biotechnol. 35, 879-884 (2017). doi:10.1038/nbt.3942
• Antell GC, Dampier W, Aiamkitsumrit B, Nonnemacher MR, Jacobson JM, Pirrone V, Zhong W, Kercher K, and Passic S, Williams JW, Schwartz G, Hershberg U, Krebs FC, and Wigdahl B. Utilization of HIV-1 envelope V3 to identify X4- and R5-specific Tat and LTR sequence signatures. Retrovirology 13, 32 (2016). doi:10.1186/s12977-016-0266-9
• Schwartz GW, Shauli T, Linial M, and Hershberg U. Serine substitutions are linked to codon usage and differ for variable and conserved protein regions. Sci. Rep. 9, 1 (2019). doi:10.1038/s41598-019-53452-3
• Schwartz GW, Shokoufandeh A, Ontañón S, and Hershberg U. Using a novel clumpiness measure to unite data with metadata: finding common sequence patterns in immune receptor germline V genes. Pattern Recogn. Lett. 74, 24-29 (2016). doi:10.1016/j.patrec.2016.01.011
• Meng W, Jayaraman S, Zhang B, Schwartz GW, Daber RD, Hershberg U, Garfall AL, Carlson CS and Luning Prak ET. Trials and tribulations with VH replacement. Front. Immunol. 5, 10 (2014). doi:10.3389/fimmu.2014.00010
• Schwartz GW, Hershberg U. Germline Amino Acid Diversity in B Cell Receptors is a Good Predictor of Somatic Selection Pressures. Front. Immunol. 4, 357 (2013). doi:10.3389/fimmu.2013.00357
• Schwartz GW, Hershberg U. Conserved variation: identifying patterns of stability and variability in BCR and TCR V genes with different diversity and richness metrics. Phys. Biol. 10, 035005 (2013). doi:10.1088/1478-3975/10/3/035005

https://github.com/jpulgarin/canvaslife

gregory.schwartz@uhnresearch.ca

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