Systems analysis of dynamic transcription factor activity identifies targets for treatment in Olaparib resistant cancer cells

Abstract

The development of resistance to targeted therapeutics is a challenging issue for the treatment of cancer. Cancers that have mutations in BRCA, a DNA repair protein, have been treated with poly (ADP-ribose) polymerase (PARP) inhibitors, which target a second DNA repair mechanism with the aim of inducing synthetic lethality. While these inhibitors have shown promise clinically, the development of resistance can limit their effectiveness as a therapy. This study investigated mechanisms of resistance in BRCA-mutated cancer cells (HCC1937) to Olaparib (AZD2281) using TRACER, a technique for measuring dynamics of transcription factor (TF) activity in living cells. TF activity was monitored in the parental HCC1937 cell line and two distinct resistant cell lines, one with restored wild-type BRCA1 and one with acquired resistance independent of BRCA1 for 48 hours during treatment with Olaparib. Partial least squares discriminant analysis (PLSDA) was used to categorize the three cell types based on TF activity, and network analysis was used to investigate the mechanism of early response to Olaparib in the study cells. NOTCH signaling was identified as a common pathway linked to resistance in both Olaparib-resistant cell types. Western blotting confirmed upregulation of NOTCH protein, and sensitivity to Olaparib was restored through co-treatment with a gamma secretase inhibitor. The identification of NOTCH signaling as a common pathway contributing to PARP inhibitor resistance by TRACER indicates the efficacy of transcription factor dynamics in identifying targets for intervention in treatment-resistant cancer and provides a new method for determining effective strategies for directed chemotherapy.

Dynamic microRNA activity identifies therapeutic targets in trastuzumab‐resistant HER2+ breast cancer

“…We advanced our previously described TRACER technology to encompass dynamic monitoring of miRNA activity and used this array to investigate the dynamics of miRNA activity in HER2 + breast cancer to identify potential miRNA‐mediated mechanisms for resistance. Both supervised and unsupervised classification could identify treated and untreated cells from both the resistant and responsive phenotypes, consistent with our previous observations using this method (Decker et al, ). Downregulation of miR‐21 activity was identified through the multivariate analysis as a potential driving factor for the resistant phenotype in HER2 + breast cancer cells.…”

“…PLS‐DA was used to identify a multivariate signature for the time‐course changes in miRNA activity in the sensitive and resistant cell lines and also to identify how these cells lines differed from untreated control cells (Figure ). We have previously demonstrated that this method provided superior classification for dynamic transcription factor activity data (Decker et al, ), and as such used this method to identify the linear combinations of miRNAs that best delineated treated and untreated sensitive and resistant cells. The time‐course PLS‐DA classified the cells into three groups (untreated aggregate, treated BT474, treated BT474R) with 98.9% accuracy from 10‐fold cross validation using two components with 10 selected variables for each loading vector.…”

“…We generated a library of miRNA reporters with 3′ UTRs that are exact matches to endogenous miRNAs from miRbase (Kozomara & Griffiths-Jones, 2013), which provides reporting for activity with maximum sensitivity and specificity. Our miRNA activity array expands upon our previous work developing a transcription factor activity array (Bellis et al, 2011;Bernabé et al, 2016;Decker et al, 2017) to measure the dynamic regulome in living cells. Both arrays utilize activity measurements of regulatory factors to provide a unique perspective on the regulome of the cell that can be missed through other techniques.…”

“…We utilized both unsupervised (PCA) as well as supervised (PLS-DA) classification methods to determine a combination of variables that PLS-DA was used to identify a multivariate signature for the time-course changes in miRNA activity in the sensitive and resistant cell lines and also to identify how these cells lines differed from untreated control cells ( Figure 5). We have previously demonstrated that this method provided superior classification for dynamic transcription factor activity data (Decker et al, 2017), and as such used this method to identify the linear combinations of miRNAs that best delineated treated and untreated sensitive and resistant cells. experiments, the VIP scores were similar for both LV1 and LV2, with the highest score assigned to miR-21.…”

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

“…Cancer-host interactions have been implicated in the poor (and sometimes surprising) clinical outcomes of existing and new treatments. Chemotherapies fail when molecular-scale processes (e.g., DNA repair failures, mutations, or epigenetic alterations) cause resistant tumor clones to emerge (multicellular-scale birth-death processes) which can survive the treatment [6,7,8,9,10,11]. Anti-angiogenic therapies that target blood vessels were expected to be potent agents against cancer [12], but disrupting tissue perfusion inhibits drug delivery and increases hypoxia, which was subsequently shown to select for more aggressive tumor phenotypes, including alternative metabolism and increased tissue invasion [13,14,15].…”