Biology subjects

Palmer, A. C.

Publications and source records attributed to Palmer, A. C..

10 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

An RNA Damage Response Network Mediates the Lethality of 5-FU in Clinically Relevant Tumor Types

5-fluorouracil (5-FU) is a successful and broadly used anti-cancer therapeutic. A major mechanism of action of 5-FU is thought to be through thymidylate synthase (TYMS) inhibition resulting in dTTP depletion and activation of the DNA damage response. This suggests that 5-FU should synergize with other DNA damaging agents. However, we found that combinations of 5-FU and oxaliplatin or irinotecan failed to display any evidence of synergy in clinical trials, and resulted in sub-additive killing in a panel of colorectal cancer (CRC) cell lines. In seeking to understand this antagonism, we unexpectedly found that an RNA damage response during ribosome biogenesis dominates the drugs efficacy in tumor types for which 5-FU shows clinical benefit. 5-FU has an inherent bias for RNA incorporation, and blocking this greatly reduced drug-induced lethality, indicating that accumulation of damaged RNA is more deleterious than the lack of new RNA synthesis. Using 5-FU metabolites that specifically incorporate into either RNA or DNA revealed that CRC cell lines and patient-derived colorectal cancer organoids are inherently more sensitive to RNA damage. This difference held true in cell lines from other tissues in which 5-FU has shown clinical utility, whereas cell lines from tumor tissues that lack clinical 5-FU responsiveness typically showed greater sensitivity to the drugs DNA damage effects. Analysis of changes in the phosphoproteome and ubiquitinome shows RNA damage triggers the selective ubiquitination of multiple ribosomal proteins leading to autophagy-dependent rRNA catabolism and proteasome-dependent degradation of ubiquitinated ribosome proteins. Further, RNA damage response to 5-FU is selectively enhanced by compounds that promote ribosome biogenesis, such as KDM2A inhibitors. These results demonstrate the presence of a strong RNA damage response linked to apoptotic cell death, with clear utility of combinatorially targeting this response in cancer therapy.

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↗

bioRxiv2022

Crosslinker Structure Modulates Bulk Mechanical Properties and Dictates hMSC Behavior on Hyaluronic Acid Hydrogels

Synthetic hydrogels are attractive platforms due in part to their highly tunable mechanics, which impact cell behavior and secretory profile. These mechanics are often controlled by altering the number of crosslinks or the total polymer concentration in the gel, leading to structure-property relationships that inherently couple network connectivity to the overall modulus. In contrast, the native extracellular matrix (ECM) contains structured biopolymers that enable stiff gels even at low polymer content, facilitating 3D cell culture and permeability of soluble factors. To mimic the hierarchical order of natural ECM, this work describes a synthetic hydrogel system in which mechanics are tuned using the structure of sequence-defined peptoid crosslinkers, while fixing network connectivity. Peptoid crosslinkers with different secondary structures are investigated: 1) a helical, molecularly stiff peptoid, 2) a non-helical, less stiff peptoid, and 3) an unstructured, relatively flexible peptoid. Bulk hydrogel storage modulus increases when crosslinkers of higher chain stiffness are used. In-vitro studies assess the viability, proliferation, cell morphology, and immunomodulatory activity of human mesenchymal stem cells (hMSCs) on each hydrogel substrate. Matrix mechanics regulate the morphology of hMSCs on the developed substrates, and all of the hydrogels studied upregulate IDO production over culture on TCP. Softer substrates further this upregulation to a plateau. Overall, this system offers a biomimetic strategy for decoupling hydrogel storage modulus from network connectivity, enabling systematic study of biomaterial properties on hMSC behavior and enhancement of cellular functionality for therapeutic applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/502671v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@11c5785org.highwire.dtl.DTLVardef@1b60763org.highwire.dtl.DTLVardef@19f2acdorg.highwire.dtl.DTLVardef@b31021_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2021

Cancer patient survival can be accurately parameterized, revealing time-dependent therapeutic effects and doubling the precision of small trials

Individual participant data (IPD) from completed oncology clinical trials are a valuable but rarely available source of information. A lack of minable survival distributions has made it difficult to identify factors determining the success and failure of clinical trials and improve trial design. We imputed survival IPD from [~]500 arms of phase III oncology trials (representing [~]220,000 events) and found that they are well fit by a two-parameter Weibull distribution. This makes it possible to use parametric statistics to substantially increase trial precision with small patient cohorts typical of phase I or II trials. For example, a 50-person trial parameterized using Weibull distributions is as precise as a 90-person trial evaluated using traditional statistics. Mining IPD also showed that frequent violations of the proportional hazards assumption, particularly in trials of immune checkpoint inhibitors (ICIs), arise from time-dependent therapeutic effects and hazard ratios. Thus, the duration of ICI trials has an underappreciated impact on the likelihood of their success.

cancer biology↗

bioRxiv2020

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotics mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that whether a drug acts as a bacteriostatic or bactericidal agent has little influence on resistance selection. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strains minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

pharmacology and toxicology↗

bioRxiv2020

Biomarker-guided treatment strategies for ovarian cancer identified from a heterogeneous panel of patient-derived tumor xenografts

Advanced ovarian cancers are a leading cause of cancer-related death in women. Such cancers are currently treated with surgery and chemotherapy which is often temporarily successful but exhibits a high rate of relapse after which treatment options are few. Here we assess the responses of a panel of patient-derived ovarian cancer xenografts (PDXs) to 19 mono and combination therapies, including small molecules and antibody-drug conjugates. The PDX panel aimed to mimic the heterogeneity of disease observed in patients, and exhibited a distribution of responsiveness to standard of care chemotherapy similar to human clinical data. Three monotherapies and one drug combination were found to be active in different subsets of PDXs. By analyzing gene expression data we identified gene expression biomarkers predictive of responsiveness to each of three novel targeted therapy regimens. While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. Biomarker frequency was similar in human patients, suggesting the possibility of a new therapeutic approach to ovarian cancer and demonstrating the potential power of PDX-based trials in broadening the reach of precision cancer medicine.

cancer biology↗

bioRxiv2018

Drugs in a curative combination therapy for lymphoma exhibit low cross-resistance but not pharmacological synergy

Curative cancer therapies are uncommon and nearly always involve multi-drug combinations developed by experimentation in humans; unfortunately, the mechanistic basis for the success of such combinations has rarely been investigated in detail, obscuring lessons learned. Here we use isobologram analysis to score pharmacological interaction, and clone tracing and CRISPR screening to measure cross-resistance among the five drugs comprising R-CHOP, a combination therapy that frequently cures Diffuse Large B-Cell Lymphomas. We find that drugs in R-CHOP exhibit very low cross-resistance but not synergistic interaction; together they achieve a greater fractional kill according to the null hypothesis for both the Loewe dose-additivity model and the Bliss effect-independence model. These data provide direct evidence for the 50-year old hypothesis that a curative cancer therapy can be constructed on the basis of independently effective drugs having non-overlapping mechanisms of resistance, without synergistic interaction, which has immediate significance for the design of new drug combinations.

cancer biology↗

bioRxiv2018

Comparing the efficacy of cancer therapies between subgroups in basket trials

An increase in the number of targeted anti-cancer drugs and growing genomic stratification of patients has led to the development of basket clinical trials in which a single drug is tested simultaneously in multiple tumor subtypes under a master protocol. Basket trials typically involve few patients per type, making it difficult to rigorously compare responses across types. We describe the use of permutation testing to analyze tumor volume changes and Progression Free Survival across subtypes in basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib. Permutation testing is a complement to the standard Simons two-stage binomial approach and can test for differences among subgroups using empirical null distributions while controlling for multiple hypothesis testing. This approach uncovers examples of therapeutic benefit missed by a binomial test; in the case of the SUMMIT trial, our analysis identifies an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations.

cancer biology↗

bioRxiv2018

Drug adaptation influences cardiotoxicity caused by tyrosine kinase inhibitors in iPSC-derived human cardiomyocytes

Cardiotoxicity induced by anti-cancer drugs is of increasing concern as the durability of therapeutic responses increases. The molecular basis of cardiotoxicity remains poorly understood, particularly when due to drug classes that do not inhibit the hERG potassium channel or cause the arrhythmias associated with long QT syndrome. This paper describes systematic molecular profiling of one such class of drugs, tyrosine kinase inhibitors (TKIs), which are widely used to treat solid tumors. Human cardiomyocytes differentiated from induced pluripotent stem cells (hiPSC-CMs) were exposed to one of four TKIs (Sunitinib, Sorafenib, Lapatinib and Erlotinib) observed to cause different levels of human cardiotoxicity and profiled by RNA sequencing (RNA-Seq) and mass spectroscopy-based proteomic analysis. We find that TKIs have diverse effects on hiPSC-CMs but genes involved in cardiac metabolism are particularly sensitive. In the case of Sorafenib, many genes involved in oxidative phosphorylation are down regulated resulting in a profound defect in mitochondrial metabolism. Cells adapt to this by upregulating aerobic glycolysis. Metabolic remodeling makes cells less acutely sensitive to Sorafenib and the effect is reversible upon drug withdrawal. Thus, the response of cardiomyocytes to Sorafenib is characterized by adaptive drug resistance previously described in tumor cells.

pharmacology and toxicology↗