Forestall Drug Resistance Research Articles

Abstract 3878: Targeting Hippo-YAP, BRD4 and RAS-MAPK interplay in lung cancer to forestall drug resistance

Abstract Introduction: Identifying parallel survival pathways or compensatory mechanisms that limit the response to targeted therapy is critical to discover effective combinatorial strategies for the treatment of lung cancer and to prevent emergence of drug resistance Results: Using combination drug screening in a panel of cell line models of non-small cell lung cancer (NSCLC), we identified the tumor suppressor STK11 (aka LKB1) as a molecular biomarker of sensitivity to BET and RAS-MAPK pathway inhibitors combination in NSCLC. We found a small molecule inhibitor of BET (bromodomain and extra terminal) family proteins (BRD4, 3, 2), PLX51107, suppressed BRAF and KRAS mutant NSCLC cell proliferation and in combination with RAS (KRAS G12C), RAF or MEK inhibitor led to pronounced loss in cell viability with potent MYC downregulation and synergistic apoptosis induction in vitro and potent tumor regression or stasis in vivo in several KRAS mutant CDX and PDX mouse models of NSCLC. However, BRAF and KRAS mutant NSCLC cells with co-occurring mutations in STK11 were resistant to BET and RAS, RAF or MEK co-inhibition. To unravel the underlying mechanism of resistance to BET and RAS-MAPK inhibitors upon STK11 loss we modeled STK11 deficiency in BRAF and KRAS mutant NSCLC cells and tumors. We found that STK11 loss creates an unprecedented dependence on YAP/TEAD TFs. STK11 silencing in BRAF and KRAS mutant NSCLC cells resulted in nuclear transfer/activation of the HIPPO pathway effector, YAP, upregulation of the YAP target BIRC5 (aka Survivin) and rescued the synthetic lethality due to BET and RAS, RAF or MEK co-inhibition. Phenocopying STK11 loss, constitutively active YAP (YAP5SA), but not transcriptionally inactive YAP (YAPS94A), mitigated the deleterious effects of BET and RAS, RAF or MEK co-suppression in BRAF and KRAS mutant NSCLC cells with potent upregulation of the pro-survival factor BIRC5. Conversely, we found that co-silencing YAP or TEADs with BETs re-sensitized BRAF and KRAS mutant NSCLC cells with co-occurring mutations in STK11 to BET and RAS-MAPK inhibitors combination and caused synthetic lethality. Conclusion: This study uncovered a functional interaction between BET proteins, STK11, HIPPO-YAP and RAS-MAPK signaling pathways in lung cancer. These results will help identify combinatorial strategies for treating aberrant RAS-MAPK pathway driven lung cancer more effectively and forestall drug resistance. Citation Format: Nilanjana Chatterjee, Victor Olivas, Wei Wu, Ben Powell, Trever Bivona. Targeting Hippo-YAP, BRD4 and RAS-MAPK interplay in lung cancer to forestall drug resistance. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3878.

Silencing PEX26 as an unconventional mode to kill drug-resistant cancer cells and forestall drug resistance

ABSTRACT Promoting the macroautophagy/autophagy-mediated degradation of specific proteins and organelles can potentially be utilized to induce apoptosis in cancer cells or sensitize tumor cells to therapy. To examine this concept, we enriched for autophagosomes from histone deacetylase inhibitor (HDACi)-sensitive U937 lymphoma cells and isogenic HDACi-resistant cells. Mass spectrometry on autophagosome-enriched fractions revealed that HDACi-resistant cells undergo elevated pexophagy, or autophagy of the peroxisome, an organelle that supports tumor growth. To disturb peroxisome homeostasis, we enhanced pexophagy in HDACi-resistant cells via genetic silencing of peroxisome exportomer complex components (PEX1, PEX6, or PEX26). This consequently sensitized resistant cells to HDACi-mediated apoptosis, which was rescued by inhibiting ATM/ataxia-telangiectasia mutated (ATM serine/threonine kinase), a mediator of pexophagy. We subsequently engineered melanoma cells to stably repress PEX26 using CRISPR interference (CRISPRi). Melanoma cells with repressed PEX26 expression showed evidence of both increased pexophagy and peroxisomal matrix protein import defects versus single guide scrambled (sgSCR) controls. In vivo studies showed that sgPEX26 melanoma xenografts recurred less compared to sgSCR xenografts, following the development of resistance to mitogen-activated protein kinase (MAPK)-targeted therapy. Finally, prognostic analysis of publicly available datasets showed that low expression levels of PEX26, PEX6 and MTOR, were significantly associated with prolonged patient survival in lymphoma, lung cancer and melanoma cohorts. Our work highlighted that drugs designed to disrupt peroxisome homeostasis may serve as unconventional therapies to combat therapy resistance in cancer. Abbreviations: ABCD3/PMP70: ATP binding cassette subfamily D member 3; ACOX1: acyl-CoA oxidase 1; AP: autophagosome; COX: cytochrome c oxidase; CQ: chloroquine; CRISPRi: clustered regularly interspaced short palindromic repeats interference; DLBCL: diffuse large B-cell lymphoma; GO: gene ontology; dCas9: Cas9 endonuclease dead, or dead Cas9; HDACi: histone deacetylase inhibitors; IHC: Immunohistochemistry; LAMP2: lysosomal associated membrane protein 2; LCFAs: long-chain fatty acids; LFQ-MS: label-free quantitation mass spectrometry; LPC: lysophoshatidylcholine; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MTOR: mechanistic target of rapamycin kinase; PBD: peroxisome biogenesis disorders; PTS1: peroxisomal targeting signal 1; ROS: reactive oxygen species; sgRNA: single guide RNA; VLCFAs: very-long chain fatty acids; Vor: vorinostat; WO: wash-off.

Abstract 954: Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles

Abstract Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and which patients to give them to based on the genetic or pathological features of their disease. However, progress towards this goal has been hindered by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts. We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the cell-viability profiles of single-agent small molecules, might be leveraged to nominate specific synergistic drug combinations and identify mechanisms of drug resistance, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy. To test this notion, we analyzed nearly 400,000 drug-sensitivity profiles in >800 cancer cell lines to identify candidate compound-gene pairs. We identified over 100 examples where outlier expression of a single transcript was correlated with resistance to a small molecule. Of these gene/drug pairs, 9 genes represented imminently druggable targets, including established clinically-relevant relationships between the alkylating agent temozolomide and MGMT expression, and between a subset of chemotherapeutics including paclitaxel and the efflux pump ABCB1. Inhibition of candidate “co-targets”, which included 3 previously characterized relationships and 6 novel relationships, resulted in cell-line-specific synergistic cell killing across multiple cell-line models. For validated compound-gene pairs, exogenous expression of the “co-target” was sufficient to confer resistance. For example, we found that high expression of MGLL, encoding monoglyceride lipase, was uniquely associated with lack of response to the histone lysine demethylase inhibitor GSK-J4. Endogenous or exogenous MGLL expression conferred resistance to GSK-J4, while MGLL-proficient cell lines could be sensitized to GSK-J4 up to 50-fold by co-treatment with an irreversible MGLL inhibitor. These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations. Citation Format: Matthew G. Rees, Lisa Brenan, Patrick Duggan, Cory M. Johannessen. Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 954.

Abstract A18: Predicting synergistic drug combinations from genomic features and single-agent response profiles

Abstract Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and whom to give them to based on the genetic or pathological features of their disease. However, accomplishing this goal has been precluded by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts. We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the response profiles of hundreds of single-agent small molecules, might be leveraged to nominate synergistic drug combinations, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy. To test this notion, we analyzed public cell-line drug-sensitivity data to identify candidate compound-gene pairs. We identified 7 examples in which outlier expression of a druggable candidate protein was associated with lack of single-agent response. Inhibition of 6/7 candidate co-targets resulted in cell-line-specific synergistic cell killing across multiple cell line models, validating the overall approach. For example, consistent with clinical findings, we found that high expression of the MGMT gene, encoding O-6-methylguanine-DNA methyltransferase, was uniquely associated with response to alkylating agents such as temozolomide, and that combination of the MGMT inhibitor O-6-benzylguanine with temozolomide resulted in synergistic killing. These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single-agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations. Citation Format: Matthew G. Rees, Lisa Brenan, Amanda Walker, Cory M. Johannessen. Predicting synergistic drug combinations from genomic features and single-agent response profiles [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A18.

MicroRNA pharmacogenomics based integrated model of miR-17-92 cluster in sorafenib resistant HCC cells reveals a strategy to forestall drug resistance

Among solid tumors, hepatocellular carcinoma (HCC) emerges as a prototypical therapy-resistant tumor. Considering the emerging sorafenib resistance crisis in HCC, future studies are urgently required to overcome resistance. Recently noncoding RNAs (ncRNAs) have emerged as significant regulators in signalling pathways involved in cancer drug resistance and pharmacologically targeting these ncRNAs might be a novel stratagem to reverse drug resistance. In the current study, using a hybrid Petri net based computational model, we have investigated the harmonious effect of miR-17-92 cluster inhibitors/mimics and circular RNAs on sorafenib resistant HCC cells in order to explore potential resistance mechanisms and to identify putative targets for sorafenib-resistant HCC cells. An integrated model was developed that incorporates seven miRNAs belonging to miR-17-92 cluster (hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-19a, hsa-miR-19b, hsa-miR-18a, hsa-miR-20a and hsa-miR-92) and crosstalk of two signaling pathways (EGFR and IL-6) that are differentially regulated by these miRNAs. The mechanistic connection was proposed by the correlation between members belonging to miR-17-92 cluster and corresponding changes in the protein levels of their targets in HCC, specifically those targets that have verified importance in sorafenib resistance. Current findings uncovered potential pathway features, underlining the significance of developing modulators of this cluster to combat drug resistance in HCC.

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Early HIV treatment to forestall drug resistance

Early HIV treatment to forestall drug resistance

Tumor Heterogeneity and Personalized Cancer Medicine: Are we Being Outnumbered?

Tumor heterogeneity is regarded as a major obstacle to successful personalized cancer medicine. The lack of reliable response assays reflective of in vivo tumor heterogeneity and associated resistance mechanisms hampers identification of reliable biomarkers. By contrast, oncogene addiction and paracrine signaling enable systemic responses despite tumor heterogeneity. This strengthens researchers in their efforts towards personalized cancer medicine. Given the fact that tumor heterogeneity is an integral part of cancer evolution, diagnostic tools need to be developed in order to better understand the dynamics within a tumor. Ultra-deep sequencing may reveal future resistant clones within a (liquid) tumor biopsy. On-treatment biopsies may provide insight into intrinsic or acquired drug resistance. Subsequently, upfront combinatorial treatment or sequential therapy strategies may forestall drug resistance and improve patient outcome. Finally, innovative response assays, such as organoid cultures or patient-derived tumor xenografts, provide an extra dimension to correlate molecular profiles with drug efficacy and control cancer growth.

Abstract LB-144: Modeling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance.

Abstract Mutational activation of BRAF is the most prevalent genetic alteration in human melanoma, with ≥50% of tumors expressing the BRAF (V600E) oncoprotein. Moreover, the impressive tumor regression and increased survival of late-stage BRAF-mutated melanoma patients in response to treatment with vemurafenib demonstrates the essential role of oncogenic BRAF in melanoma maintenance. Although vemurafenib therapy has transformed the treatment of late-stage melanoma patients, the durability of patient response to this drug is limited by the emergence of lethal drug resistant disease. Thus, understanding mechanisms and designing approaches to prevent the emergence of resistance are critical to providing improved therapy. Here we model the emergence of vemurafenib resistance using two early passage primary human-patient-derived xenograft (PDX) BRAF (T1799A)-mutated melanoma models, which were continuously treated with vemurafinib in immunocompromised mice. In one of these models vemurafenib resistance is due to selection of cells with elevated expression of the BRAF (V600E) oncoprotein. Remarkably, vemurafenib resistant tumors are not only drug resistant but also develop drug dependency such that tumors require exposure to vemurafenib for their continued proliferation. Indeed, cessation of drug administration in these PDX models leads to inhibition of tumor growth and, in many cases, tumor regression. This fitness deficit upon cessation of drug administration is based on an adaptive response to levels of MAPK pathway signaling output. Melanoma cells appear to proliferate best at a defined intermediate level of RAF/MEK/ERK signaling output. Hence, when drug resistant tumor cells are deprived of drug, the level of signaling output is increased leading to decreased proliferation. We sought to determine whether vemurafenib-resistant tumors in melanoma patients displayed similar evidence of drug dependency. In a small cohort of patients (n=42) with vemurafenib-resistant tumors for which CT scans were available following cessation of treatment (n=19), 14 out of 19 patients demonstrated evidence of reduced tumor growth velocity. Taken together, these data support the hypothesis that continuous treatment with vemurafenib can select for drug-resistant tumor cells that show a fitness deficit in the absence of the drug. Furthermore, we demonstrate in the preclinical setting that these observations can be leveraged therapeutically by using a discontinuous treatment regimen to forestall the emergence of resistance in two different PDX models. Consequently, these data may have important implications for the treatment of BRAF mutated melanoma patients with BRAF (V600E) inhibitors and suggest that altered dosing might delay or prevent the emergence of lethal drug resistant disease. Citation Format: Meghna Das Thakur, Rosalie Fisher, Fernando Salangsang, Allison Landman, William Sellers, Nancy Pryer, Mitchell Paul Levesque, Reinhard Dummer, Martin Gore, James Larkin, Martin McMahon, Darrin Stuart. Modeling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-144. doi:10.1158/1538-7445.AM2013-LB-144

Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance

Mutational activation of BRAF is the most prevalent genetic alteration in human melanoma, with ≥50% of tumours expressing the BRAF(V600E) oncoprotein. Moreover, the marked tumour regression and improved survival of late-stage BRAF-mutated melanoma patients in response to treatment with vemurafenib demonstrates the essential role of oncogenic BRAF in melanoma maintenance. However, as most patients relapse with lethal drug-resistant disease, understanding and preventing mechanism(s) of resistance is critical to providing improved therapy. Here we investigate the cause and consequences of vemurafenib resistance using two independently derived primary human melanoma xenograft models in which drug resistance is selected by continuous vemurafenib administration. In one of these models, resistant tumours show continued dependency on BRAF(V600E)→MEK→ERK signalling owing to elevated BRAF(V600E) expression. Most importantly, we demonstrate that vemurafenib-resistant melanomas become drug dependent for their continued proliferation, such that cessation of drug administration leads to regression of established drug-resistant tumours. We further demonstrate that a discontinuous dosing strategy, which exploits the fitness disadvantage displayed by drug-resistant cells in the absence of the drug, forestalls the onset of lethal drug-resistant disease. These data highlight the concept that drug-resistant cells may also display drug dependency, such that altered dosing may prevent the emergence of lethal drug resistance. Such observations may contribute to sustaining the durability of the vemurafenib response with the ultimate goal of curative therapy for the subset of melanoma patients with BRAF mutations.