smgroves@virginia.edu

Charlottesville, VA, 22911

Welcome to my site! I am currently a postdoctoral researcher studying cancer systems biology at the University of Virginia in the Janes Lab. My work is part of the Center for Systems Analysis of Stress-adapted Organelles (SASCO) that combines systems biology with mathematical modeling and data science to solve complex biological problems. I focus on computational modeling of dysregulated of chromosomal segregation during mitosis in cancer.

After receiving a B.S. in Physics and Mathematics from The College of William & Mary in 2016, I entered Vanderbilt via their Quantitative and Chemical Biology program and completed my PhD in the Quaranta Lab in March 2022, where I modeled regulatory networks and phenotypic transitions (cell state plasticity) in Small Cell Lung Cancer. I consider myself a physicist by training and a computational biologist by practice. My research experiences in undergrad, graduate school, and my post-doc allow me to combine these identities to approach complex biological problems through the analysis and modeling of high-dimensional data.

I am also passionate about science communication, particular in the visual form. I’ve been learning motion design to make educational video content in my role as the SASCO Outreach Core Education Scholar. Follow along with my journey here!

funding awards

2022-2023: NIH F32 Post-doctoral Training Grant Awardee
2017-2021: NSF Graduate Research Fellowships Program Fellow
2016-2017: BIDS (Vanderbilt Training Program in Big Biomedical Data Science) Trainee

news

Jul 3, 2023	I started as a postdoc in the Janes lab at UVA. I will be working on Project 1 of the U54 Center for Systems Analysis of Stress-adapted Organelles (SASCO). My goal will be to build a computational, physics-based model of the chromosome passenger complex (CPC) that controls chromosome segregation during mitosis. In cancer, it is unknown how the CPC is affected by dysregulated mitotic transcription factors. Using a reaction-diffusion model built in VCell, we will investigate the phase separation behavior of the CPC during mitosis in cancer systems.
Jun 3, 2023	I published a paper in Molecular Cancer with a group of collaborators at Vanderbilt. I did a WGCNA analysis of melanoma RNA-seq data that provided supporting evidence for the importance of key transcription factors, such as Tfcp2l1, that regulate phenotype after loss of Cxcr2. The Richmond lab and other collaborators did a great job with this work, and I am grateful I was asked to help with the computational analysis for the project!
Apr 19, 2023	I was selected as the Education Scholar for the SASCO Outreach Core at UVA. This means I will be making a 25 minute video to educate clinicians on systems biology.

selected publications

1. Cell Sys

   Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

   Sarah M. Groves, Geena V. Ildefonso, Caitlin O. McAtee, and 23 more authors

   Cell Systems Aug 2022

   Abs Bib HTML

   Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper’s Transparent Peer Review process is included in the supplemental information.

   @article{10.1016/j.cels.2022.07.006,
     year = {2022},
     title = {{Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer}},
     author = {Groves, Sarah M. and Ildefonso, Geena V. and McAtee, Caitlin O. and Ozawa, Patricia M.M. and Ireland, Abbie S. and Stauffer, Philip E. and Wasdin, Perry T. and Huang, Xiaomeng and Qiao, Yi and Lim, Jing Shan and Bader, Jackie and Liu, Qi and Simmons, Alan J. and Lau, Ken S. and Iams, Wade T. and Hardin, Doug P. and Saff, Edward B. and Holmes, William R. and Tyson, Darren R. and Lovly, Christine M. and Rathmell, Jeffrey C. and Marth, Gabor and Sage, Julien and Oliver, Trudy G. and Weaver, Alissa M. and Quaranta, Vito},
     journal = {Cell Systems},
     issn = {2405-4712},
     doi = {10.1016/j.cels.2022.07.006},
     url = {https://www.cell.com/cell-systems/fulltext/S2405-4712(22)00313-1?\_returnURL=https\%3A\%2F\%2Flinkinghub.elsevier.com\%2Fretrieve\%2Fpii\%2FS2405471222003131\%3Fshowall\%3Dtrue},
     keywords = {},
     month = aug
   }

2. PLOS Comp Bio

   Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

   David J. Wooten, Sarah M. Groves, Darren R. Tyson, and 6 more authors

   PLOS Computational Biology Oct 2019

   Abs Bib HTML

   Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

   @article{10.1371/journal.pcbi.1007343,
     year = {2019},
     title = {{Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers}},
     author = {Wooten, David J. and Groves, Sarah M. and Tyson, Darren R. and Liu, Qi and Lim, Jing S. and Albert, Réka and Lopez, Carlos F. and Sage, Julien and Quaranta, Vito},
     journal = {PLOS Computational Biology},
     issn = {1553-734X},
     doi = {10.1371/journal.pcbi.1007343},
     pmid = {31671086},
     url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007343},
     pages = {e1007343},
     number = {10},
     volume = {15},
     keywords = {},
     month = oct
   }

Email is the best way to contact me.