Biology subjects

Pomeroy, A. E.

Publications and source records attributed to Pomeroy, A. E..

3 recordsLinked to original sources

bioRxiv2025

Integrating functional genomics and proteomics identifies Folate Carrier SLC19A1 as a predictor of pralatrexate sensitivity in T-cell lymphoma

Cancer therapies are typically effective in subsets of patients, reflecting the molecular diversity of cancers and motivating the need for predictive biomarkers of response. Biomarker-guided therapy is increasingly useful in oncology, yet biomarker discovery remains complicated by the large number of molecular features that make it difficult to distinguish causal determinants from spurious associations. To address this challenge, we combined functional genomic screening, proteomics, and drug sensitivity profiling to discover response biomarkers for a number of therapies used in the treatment of Peripheral T-Cell Lymphomas (PTCL). First, we used genome-wide CRISPR-dCas9 interference screens in PTCL cells under drug treatment to identify a shortlist of genes whose knockdown directly increases or decreases drug sensitivity. Next, we profiled drug responses across a diverse panel of 30 PTCL cultures and, from the shortlist, identified genes whose protein abundance correlated with drug sensitivity. Genes detected by both approaches are causal determinants of drug response and correlates of drug response across the panel of lymphoma cultures, making them promising candidates for predictive biomarkers. Basal expression of the reduced folate carrier SLC19A1 was a strong predictor of pralatrexate sensitivity, consistent with its role as the primary transporter for pralatrexate uptake. Simulated clinical trials predicted that biomarker-guided patient selection could improve the power to detect significant benefit of adding pralatrexate to frontline chemotherapy in PTCL. These findings illustrate how the causal insights of functional genetic screens can augment correlative studies to identify biomarkers of drug response, and suggest the potential for precise use of pralatrexate for PTCL.

cancer biology↗

bioRxiv2023

Ultrasensitive response explains the benefit of combination chemotherapy despite drug antagonism

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug CHOP regimen in Peripheral T-Cell Lymphoma (PTCL) cell lines, and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using month-long in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiation - the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a single hit, in order to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not reliant on positive drug-drug interactions.

pharmacology and toxicology↗

bioRxiv2020

A predictive model of gene expression reveals the role of regulatory motifs in the mating response of yeast

ABSTRACTCells use signaling pathways to receive and process information about their environment. These systems are nonlinear, relying on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models developed to describe signaling pathways often fail to show predictive power, because the models are not trained on data that probe the diverse time scales on which feedforward and feedback regulation operate. We addressed this limitation using microfluidics to expose cells to a broad range of dynamic environmental conditions. In particular, we focus on the well-characterized mating response pathway of S. cerevisiae (yeast). This pathway is activated by mating pheromone and initiates the transcriptional changes required for mating. Although much is known about the molecular components of the mating response pathway, less is known about how these components function as a dynamical system. Our experimental data revealed that pheromone-induced transcription persists following removal of pheromone and that long-term adaptation of the transcriptional response occurs when pheromone exposure is sustained. We developed a model of the regulatory network that captured both persistence and long-term adaptation of the mating response. We fit this model to experimental data using an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four regulatory motifs in coordinating pathway response to persistent and dynamic stimulation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗