Elucidating Drug Resistance Mechanisms Research Articles

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Development and in-depth characterization of BRAFi-resistant melanoma cell lines in vitro and in vivo

Regardless of the clinical response and improved patient survival observed following treatment with BRAFi like Vemurafenib (Vem), rapid development of resistance still remains as a major obstacle in melanoma therapy. In this context, we developed and characterized two acquired Vem-resistant melanoma cell lines, A375V and SK-MEL-28V, and an intrinsically Vem-resistant cell line, RPMI-7951. Altered morphology and growth rate of the resistant cell lines displayed spindle-shaped cells with filopodia formation and enhanced proliferation rate as compared to parental cells. Further in vitro characterization in 2D models confirmed the emergence of a resistant phenotype in melanoma cells. To mimic the in vivo tumor microenvironment, spheroids were developed for both parental and resistant cell lines to recognize materialization of invadopodia structures demonstrating elevated invasiveness and proliferation of resistant cells-based spheroids, especially A375V. Importantly, we validated A375V cell line in vivo to prove its tumorigenicity and drug resistance in tumor xenograft model. Taken together, our established clinically relevant Vem-resistant tumor model could be beneficial to elucidate drug resistance mechanisms, screen and identify novel anticancer therapies to overcome BRAFi resistance in melanoma.

Exploring the Spatial Landscape of the Estrogen Receptor Proximal Proteome With Antibody-Based Proximity Labeling

Estrogen receptor α (ERα) drives the transcription of genes involved in breast cancer (BC) progression, relying on coregulatory protein recruitment for its transcriptional and biological activities. Mutation of ERα as well as aberrant recruitment of its regulatory proteins contribute to tumor adaptation and drug resistance. Therefore, understanding the dynamic changes in ERα protein interaction networks is crucial for elucidating drug resistance mechanisms in BC. Despite progress in studying ERα-associated proteins, capturing subcellular transient interactions remains challenging and, as a result, significant number of important interactions remain undiscovered. In this study, we employed biotinylation by antibody recognition (BAR), an innovative antibody-based proximity labeling (PL) approach, coupled with mass spectrometry to investigate the ERα proximal proteome and its changes associated with resistance to aromatase inhibition, a key therapy used in the treatment of ERα-positive BC. We show that BAR successfully detected most of the known ERα interactors and mainly identified nuclear proteins, using either an epitope tag or endogenous antibody to target ERα. We further describe the ERα proximal proteome rewiring associated with resistance applying BAR to a panel of isogenic cell lines modeling tumor adaptation in the clinic. Interestingly, we find that ERα associates with some of the canonical cofactors in resistant cells and several proximal proteome changes are due to increased expression of ERα. Resistant models also show decreased levels of estrogen-regulated genes. Sensitive and resistant cells harboring a mutation in the ERα (Y537C) revealed a similar proximal proteome. We provide an ERα proximal protein network covering several novel ERα-proximal partners. These include proteins involved in highly dynamic processes such as sumoylation and ubiquitination difficult to detect with traditional protein interaction approaches. Overall, we present BAR as an effective approach to investigate the ERα proximal proteome in a spatial context and demonstrate its application in different experimental conditions.

Bioinformatics toolbox for exploring target mutation-induced drug resistance.

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Abstract 1421: Targeting cell-adaptive mechanisms of drug resistance in lethal prostate cancer

Abstract Precision oncology therapies are significantly hindered by the emergence of genetically or epigenetically-distinct cellular sub-populations, presenting either pre-existing or acquired drug resistance. For example, patients affected by advanced prostate cancer (PC), which is initially driven by aberrant activation of androgen receptor (AR) signals, often respond to Androgen Deprivation Therapy (ADT). Unfortunately, long-term treatment leads to the emergence of ADT-resistant tumors, termed Castration Resistant Prostate Cancer (CRPC), and poor outcome. Next-generation AR inhibitors extend CRPC patient survival, but frequently lead to the emergence of an even more aggressive Neuroendocrine PC (NEPC) phenotype, with exceedingly poor prognosis. Progression to NEPC is often determined by epigenetic-mediated reprogramming, rather than genomic alterations, resulting in cell-adaptive resistance, driven by aberrant activation of a handful of Master Regulator (MR) proteins. Consequently, better and more mechanistic understanding of cell-adaptive resistance is necessary to develop effective therapeutic strategies for CRPC. To address this challenge, we devised an RNA-based, high-throughput methodology, to systematically elucidate drug resistance mechanisms with single-cell resolution, and we applied it to study diverse, clonal populations that emerge following long-term treatment of PC LnCAP cells with next-generation AR inhibitors. Specifically, we identified Master Regulator (MR) proteins leading to the emergence of drug resistance, as well as clinically-available inhibitors able to reversing their activity, which were experimentally validated. We used PLATE-Seq, a low-cost, high-throughput RNA-Seq technology to profile more than 80 clonal, ADT-resistant populations, with single-cell resolution, and the VIPER algorithm to elucidate the MR proteins that mechanistically regulate their cell-adaptation programs. Our analysis revealed distinct cell-adaptive responses, associated with profound epigenetic reprogramming of prostate cancer cells. We used OncoTreat to prioritize a list of more than 400 FDA-approved and late-stage investigational drugs. Sensitivity to predicted drugs was confirmed in vitro and in vivo studies, in xenograft models derived from ADT- resistant clones are underway. We propose that this framework may represent a relevant advance to the study of cell-adaptive mechanism of drug resistance. Critically, this methodology is not limited to prostate cancer and can be applied to study arbitrary drug-resistance phenotypes. Citation Format: Prem Subramaniam, Alessandro Vasciaveo, Juan Arriaga, Jordan Kesner, Cory Abate-Shen, Andrea Califano. Targeting cell-adaptive mechanisms of drug resistance in lethal prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1421.

Abstract PD5-08: Neoadjuvant chemotherapy alters the genomic landscape and immune microenvironment of breast cancers

Abstract Understanding how standard-of-care drug treatments affect tumor intrinsic biology and microenvironment is critical for elucidating drug resistance mechanisms and developing better combination therapies as well as new therapies. To characterize the effects of neoadjuvant chemotherapy (NAC) on the genome, transcriptome and tumor infiltrating leukocytes (TILs), we have conducted whole exome and whole transcriptome sequencing of a large longitudinal breast cancer cohort consisting of 146 cases and 281 paired tumor samples. In total, 52 (38%) patients achieved pathologic complete response (pCR) while 85 patients (62%) had residual disease with standard chemotherapy regimen. Tumor biopsies were collected for each patient at three time points – pre-treatment, three weeks after the first cycle of anthracycline and cyclophosphamide (AC) and at the time of surgery after 3 more cycles of AC followed by 4 cycles of taxane or taxane plus Herceptin in case of HER2+ subtype. We detected 5,955 protein-altering somatic mutations affecting 4,414 genes in pretreatment samples and 502 acquired mutations in surgery samples affecting 477 genes including 19recurrently mutated genes such as TP53 and NOTCH1. Across all subtypes, 4,346 genes were differentially expressed (DE) following NAC treatment and significantly enriched in pathways such as cell cycle, ER signaling, PI3K/mTOR, immune and metabolism. Expression-based virtual microdissection analysis indicated that NAC treatment induced an increase in the fractions of stromal and adjacent normal tissue compartment, consistent with observed reduction in tumor cellularity. To assess the NAC induced changes in the molecular landscape of these tumors, we compared molecular features including gene expression signatures, mutation prevalence and copy number alteration between three time points while adjusting for confounding effects of molecular subtype and tumor cellularity. We found that NAC induced dynamic changes in gene expression signatures associated with proliferation and immunomodulatory treatment response. We further validated the observed pattern of change in TILs through histopathology and digital imaging analyses. In pretreatment tumors, 116 genes were DE between patients with pCR vs. those with residual disease with significant enrichment in immune/inflammatory pathways. Further, pre-treatment TIL levels were found to be significantly associated with pCR, echoing previous reports in breast cancers that implicated anti-tumor immunity in mediating the efficacy of chemotherapies. Our analyses also revealed associations between NAC response and baseline genomic attributes such as genomic alterations that affect DNA damage repair pathways. Taken together, these results suggest that NAC induced a multitude of changes on the genomic landscape and immune microenvironment of breast cancers, some of which point to combination strategies with immunomodulatory therapies and therapies that target DNA damage repair. Citation Format: Kan Z, Lal S, Ding Y, Lee JE, Lee S-H, Lee SK, Yu JH, Choi Y-l, Kim SW, Nam SJ, Kim J-Y, Ram S, Powell E, Ching K, Cho SY, Bonato V, Deng S, Park W-Y, Rejto P, Bienkowska J, Park Y-H. Neoadjuvant chemotherapy alters the genomic landscape and immune microenvironment of breast cancers [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD5-08.

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

BackgroundMechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.ResultsHere we present a comprehensive network, and discrete dynamic model, of signal transduction in ER+ breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.ConclusionsThe use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.

Abstract 2442: Simplified sequencing of full-length isoforms in cancer on the PacBio Sequel System

Abstract Tremendous flexibility is maintained in the human proteome via alternative splicing, and cancer genomes often subvert this flexibility to promote survival. Identification and annotation of cancer-specific mRNA isoforms is critical to understanding how mutations in the genome affect the biology of cancer cells. While microarrays and other NGS-based methods have become useful for studying transcriptomes, these technologies yield short, fragmented transcripts that remain a challenge for accurate, complete reconstruction of splice variants. In cancer proteomics studies, the identification of biomarkers from mass spectroscopy data is often limited by incomplete gene isoform expression information to support protein to transcript mapping. The Iso-Seq™ protocol developed at PacBio offers the only solution for direct sequencing of full-length, single-molecule cDNA sequences needed to discover biomarkers for early detection and cancer stratification, to fully characterize gene fusion events, and to elucidate drug resistance mechanisms. Knowledge of the complete isoform repertoire is also key for accurate quantification of isoform abundance. As most transcripts range from 1 - 10 kb, fully intact RNA molecules can be sequenced using SMRT® Sequencing without requiring fragmentation or post-sequencing assembly. However, some cancer research applications have presented a challenge for the Iso-Seq protocol, due to the combination of limited sample input and the need to deeply sequence heterogenous samples. Here we report the optimization of the Iso-Seq library preparation protocol for the PacBio Sequel platform and its application to cancer cell lines and tumor samples. We demonstrate how loading enhancements on the higher-throughput Sequel instrument have decreased the need for size fractionation steps, reducing sample input requirements while simultaneously simplifying the sample preparation workflow and increasing the number of full-length transcripts per SMRT Cell. The results highlight the potential for broader application of the Iso-Seq method to more comprehensively characterize alternative splicing in cancer. Citation Format: Meredith Ashby, Ting Hon, Elizabeth Tseng, Aparna Vedula, Tyson A. Clark. Simplified sequencing of full-length isoforms in cancer on the PacBio Sequel System [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2442. doi:10.1158/1538-7445.AM2017-2442

Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.

74P Elucidating drug resistance mechanisms using 2D and 3D culture systems

74P Elucidating drug resistance mechanisms using 2D and 3D culture systems

Abstract 4078: Investigating chemoresistance mechanisms in colorectal cancer stem cells

Abstract Treatment of advanced colorectal cancer (CRC) is impacted by a lack of selectivity, the development of drug resistance and overall tumour heterogeneity. Cancer stem cells (CSCs) are a subpopulation of tumour cells believed to be responsible for tumour recurrence due to their ability to self-renew, differentiate and evade current treatments. This project explores the responses of CRC CSCs to chemotherapy and aims to identify therapeutic targets of the CSC population. We used serial colonosphere culture to enrich for CSCs from two human CRC cell lines, HCT116 and SW480. Limiting dilution analysis showed that CSC enriched cell lines have significantly higher colonosphere formation ability compared to their respective parental cells. Further characterization of CSC cell lines was performed via qPCR and flow cytometry to determine the expression of colorectal CSC markers. A significant upregulation in CD166 and EpCAM gene expression was seen in HCT-CSC and SW-CSCs compared to parental cell lines. In vitro assays were used to determine cellular responses to three commonly employed chemotherapeutics, 5-fluorouracil, cisplatin and epirubicin. A limiting dilution analysis demonstrated that cells treated with chemotherapy for 48 hours had reduced colonosphere forming potential. Cell viability in response to chemotherapy was analyzed via PrestoBlue assays in both monolayer and 3D culture. CSC and parental cell lines responded similarly in a dose-dependent manner to treatment in monolayer, but CSC colonospheres were more resistant to high doses of chemotherapy than their respective parental cells. ABC transporter expression plays a major role in the development of multi-drug resistance so the expression of ABCB1, ABCC1 and ABCG2 was examined via qPCR. There was a significant upregulation in ABCB1 and ABCG2 in HCT-CSCs compared to HCT-P cells. SW-CSCs also had significant upregulation in ABCB1 compared to SW-P cells. Other resistance mechanisms may also be key targets for therapeutic intervention including the functional CSC marker aldehyde dehydrogenase (ALDH). The detoxification effects of ALDH could have a protective effect against the surges of reactive oxygen species resulting from chemotherapy treatment. The expression of ALDH and ABC transporter genes was also explored in response to chemotherapy. The goal of this project is to elucidate drug resistance mechanisms in colorectal CSCs, in order to target and eradicate the tumour cells responsible for metastases and recurrent disease. Our analysis demonstrates that colorectal CSCs possess heightened protective mechanisms in comparison to other cancer cells originating from the same tumour. Future experiments will further investigate these resistance mechanisms to determine their therapeutic potential. Note: This abstract was not presented at the meeting. Citation Format: Stacey J. Butler, Lisa Richardson, Nathan Farias, Brenda L. Coomber. Investigating chemoresistance mechanisms in colorectal cancer stem cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4078. doi:10.1158/1538-7445.AM2015-4078

Lysosomal drug sequestration as a mechanism of drug resistance in vascular sarcoma cells marked by high CSF-1R expression.

BackgroundHuman angiosarcoma and canine hemangiosarcoma are thought to arise from vascular tissue or vascular forming cells based upon their histological appearance. However, recent evidence indicates a hematopoietic or angioblastic cell of origin for these tumors. In support of this idea, we previously identified an endothelial-myeloid progenitor cell population with high expression of endothelial cell markers and the myeloid cell marker, colony stimulating factor 1 receptor (CSF-1R). Here, we further characterized these cells to better understand how their cellular characteristics may impact current therapeutic applications.MethodsWe performed cell enrichment studies from canine hemangiosarcoma and human angiosarcoma cell lines to generate cell populations with high or low CSF-1R expression. We then utilized flow cytometry, side population and cell viability assays, and fluorescence based approaches to elucidate drug resistance mechanisms and to determine the expression of hematopoietic and endothelial progenitor cell markers.ResultsWe demonstrated that cells with high CSF-1R expression enriched from hemangiosarcoma and angiosarcoma cell lines are more drug resistant than cells with little or no CSF-1R expression. We determined that the increased drug resistance may be due to increased ABC transporter expression in hemangiosarcoma and increased drug sequestration within cellular lysosomes in both hemangiosarcoma and angiosarcoma.ConclusionsWe identified drug sequestration within cellular lysosomes as a shared drug resistance mechanism in human and canine vascular sarcomas marked by high CSF-1R expression. Taken together, our results demonstrate that studies in highly prevalent canine hemangiosarcoma may be especially relevant to understanding and addressing drug resistance mechanisms in both the canine and human forms of this disease.

Abstract 3209: Elucidating drug resistance mechanisms in melanoma with quantitative proteomics.

Abstract Introduction: Exciting breakthroughs have been made in targeted therapy for melanoma; however, the development of drug resistance remains a critical problem for patients due to the speed of onset and the numerous complex mechanisms. Parallel development of platform for quantitative protein expression enables biomarker monitoring to evaluate the multiple pathways through which tumor cells can acquire drug resistance. We have developed quantitative assays using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) to assess a diverse panel of >70 proteins involved in signaling pathways, including apoptosis, receptor tyrosine kinases, PI3K/Akt/MTOR, and MAP kinases. Assays quantify changes in response to targeted therapeutic agents and examine subsequent resistance mechanisms in cell lines, xenografts, and biopsies from patients. Experimental procedures: Naïve and BRAF V600E inhibitor-resistant melanoma cell lines were treated with vehicle control, BRAF V600E inhibitor (PLX4720), HSP90 inhibitor (XL-888, Exelixis), or MEK inhibitor (AZD6244). Immunoblotting and LC-MRM were used to evaluate protein expression levels. Heat shock proteins can be measured directly from whole cell lysates or tumor homogenates using a few micrograms of protein. For expression of cancer signaling proteins (many of which are HSP90 clients), 50 micrograms of total protein were fractionated by SDS-PAGE prior to digestion and LC-MRM quantification. Similar experiments were conducted with mouse xenograft tumors from treated and control mice; these measurements have also been applied to fine needle aspirates (FNAs) and core biopsies from patients in the context of a clinical trial. Data summary: LC-MRM quantification is effective in quantifying these proteins in cell lines, xenografts, and biopsies or FNAs. The LC-MRM panel assay was able to screen for a diverse range of proteins revealing previously undescribed cellular responses to treatment and elucidate potential mechanisms of resistance. As an example, both LC-MRM data and Westerns showed consistent responses to HSP90 inhibition, including increased expression of HSP71 and reduction of HSP90 client levels. Proteins were also modulated in response and resistance to BRAF and MEK inhibitors. Conclusion: LC-MRM quantifies biomarker panels and provides an assessment tool that can translated in a straightforward way from cell line models to patient specimens. This emerging technique holds great promise for biopsy-mandated clinical trials, particularly in diseases like melanoma that can be readily sampled. Measurements using this LC-MRM platform can be used to assess drug performance and patient response to evaluate treatment, detect drug resistance, and suggest rational combination therapies. Citation Format: Elizabeth Remily-Wood, Kim Paraiso, Eirik Haarberg, Y. Ann Chen, Vito Rebecca, Keiran Smalley, John M. Koomen. Elucidating drug resistance mechanisms in melanoma with quantitative proteomics. [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 3209. doi:10.1158/1538-7445.AM2013-3209

Abstract 4966: Influence of the microenvironment on drug sensitivity in breast cancer using a three-dimensional cell culture model.

Abstract Three-dimensional (3D) cell culture is an important research tool and an invaluable alternative to two-dimensional (2D) cell culture as it is more suitable to represent in vivo conditions. The tumor microenvironment influences cellular activity and using 3D cell culture allows these interactions to be studied in vitro. We have evaluated the efficacy of a novel 3D cell culture model for breast cancer and determined cellular responses caused by drug activity. We have recently developed and optimized a miniaturized 3D breast cancer culture model. Our model uses reconstituted basement membrane from Growth Factor Reduced Matrigel™ as a scaffold and is suitable for semi-automated early stage drug discovery. The newly developed 3D culture model has been extensively characterized for reproducibility and the activity of known anti-breast cancer therapies. Notably, anthracyclines and taxanes showed significantly altered drug sensitivity in 3D when compared to monolayer cell cultures of breast cancer cell lines. To determine the mechanisms underlying the altered sensitivity in 3D cell culture, we sought to identify the differences between untreated and drug treated spheroids, as well as the differences between 2D and 3D cell culture responses to anthracyclines and taxanes. We evaluated proliferation, penetration, cellular architecture and signaling of major cellular pathways in our 3D model. We will discuss the proliferation rate comparing 2D and 3D cell culture models and drug penetration (exemplified by doxorubicin) in 3D cell culture. The effect of anthracyclines and taxanes on the 3D cellular architecture, without the influence of extracellular matrix proteins in the microenvironment, will also be presented. In addition, we have characterized the signaling pathways in 2D and 3D cell culture commonly associated with enhanced drug resistance and other relevant proteins including those associated with apoptosis. Deconstructing the cellular processes in 3D breast cancer cell culture in response to breast cancer therapies has the potential to elucidate drug resistance mechanisms. The results obtained from this research illustrate the characteristics and mechanisms of both monolayer and 3D cell culture with respect to anthracycline and taxane drug activity. These results demonstrate the benefits of 3D cell culture in early stage novel compound evaluation for breast cancer. Citation Format: Carrie J. Lovitt, Vicky M. Avery. Influence of the microenvironment on drug sensitivity in breast cancer using a three-dimensional cell culture model. [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 4966. doi:10.1158/1538-7445.AM2013-4966