Biology subjects

Korkola, J. E.

Publications and source records attributed to Korkola, J. E..

5 recordsLinked to original sources

bioRxiv2023

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes

Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes. In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival. We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2-receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel-resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

cell biology↗

bioRxiv2022

Systematic Replication Enables Normalization of High-throughput Imaging Assays

MotivationHigh-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work we consider how an experimental design incorporating multiple levels of replication enables removal of technical artifacts from such image-based platforms. ResultsWe develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application on MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. AvailabilityRaw data is on synapse (syn2862345), analysis code is on github (gjhunt/mema norm), a Docker image is available on dockerhub (gjhunt/memanorm). online.

bioinformatics↗

bioRxiv2021

Multiplex spatial systems analysis of local nanodose drug responses predicts effective treatment combinations of immunotherapies and targeted agents in mammary carcinoma

Better methods are needed to identify effective combinations of immunotherapies with chemotherapies and targeted anti-cancer agents. Here we present a Multiplex Implantable Microdevice Assay (MIMA) system for rapid in vivo assessment of the effects of multiple, spatially separate anticancer drugs directly in the complex tumor microenvironment. In prototypic experiments, olaparib, lenvatinib, palbociclib, venetoclax, panobinostat, doxorubicin, and paclitaxel and combinations thereof were administered simultaneously to murine mammary tumor models. Quantitative multiplex immunohistochemistry and spatial systems analyses of each local drug response defined cellular relations of fibroblasts, endothelial cells, immune lineages, immunogenic cell death, tumor proliferation and/or cancer stem cells that were used to predict effective drug combinations. A predicted combination of panobinostat, venetoclax and anti-CD40 showed long-term anti-tumor efficacy in multiple mouse models with no observable toxicity when administered systemically. Future MIMA use promises to design effective drug combinations for tumor cell control and immune activation on a personalized basis.

cancer biology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2019

Transformation and Integration of Microenvironment Microarray Data Improves Discovery of Latent Effects

The immediate physical and bio-chemical surroundings of a cell, the cellular microenvironment, is an important component of many fundamental cell and tissue level processes and is implicated in many diseases and dysfunctions. Thus understanding the interaction of cells with their microenvironment can further both basic research and aid the discovery of therapeutic agents. To study perturbations of cellular microenvironments a novel image-based cell-profiling technology called the microenvironment microarray (MEMA) has been recently employed. In this paper we explore the effect of preprocessing transformations for MEMA data on the discovery of biological and technical latent effects. We find that Gaussianizing the data and carefully removing outliers can enhance discovery of important biological effects. In particular, these transformations help reveal a relationship between cell morphological features and the extra-cellular-matrix protein THBS1 in MCF10A breast tissue. More broadly, MEMAs are part of a recent and wide-spread adoption of image-based cell-profiling technologies in the quantification of phenotypic differences among cell populations (Caicedo et al., 2017). Thus we anticipate that the advantages of the proposed preprocessing transformations will likely also be realized in the analysis of data from other highly-multiplexed technologies like Cyclic Immunofluorescence. All code and supplementary analysis for this paper is available at gjhunt.github.io/rr.

bioinformatics↗