Pharmacologic profiling of patient-derived xenograft models of primary treatment-naïve triple-negative breast cancer

BRCA1-Mediated Dual Regulation of Ferroptosis Exposes a Vulnerability to GPX4 and PARP Co-Inhibition in BRCA1-Deficient Cancers

Resistance to poly (ADP-ribose) polymerase inhibitors (PARPi) limits the therapeutic efficacy of PARP inhibition in treating breast cancer susceptibility gene 1 (BRCA1)-deficient cancers. Here we reveal that BRCA1 has a dual role in regulating ferroptosis. BRCA1 promotes the transcription of voltage-dependent anion channel 3 (VDAC3) and glutathione peroxidase 4 (GPX4); consequently, BRCA1 deficiency promotes cellular resistance to erastin-induced ferroptosis but sensitizes cancer cells to ferroptosis induced by GPX4 inhibitors (GPX4i). In addition, nuclear receptor coactivator 4 (NCOA4)-mediated ferritinophagy and defective GPX4 induction unleash potent ferroptosis in BRCA1-deficient cancer cells upon PARPi and GPX4i co-treatment. Finally, we show that xenograft tumors derived from patients with BRCA1-mutant breast cancer with PARPi resistance exhibit decreased GPX4 expression and high sensitivity to PARP and GPX4 co-inhibition. Our results show that BRCA1 deficiency induces a ferroptosis vulnerability to PARP and GPX4 co-inhibition and inform a therapeutic strategy for overcoming PARPi resistance in BRCA1-deficient cancers. Significance: BRCA1 deficiency promotes resistance to erastin-induced ferroptosis via blocking VDAC3 yet renders cancer cells vulnerable to GPX4i-induced ferroptosis via inhibiting GPX4. NCOA4 induction and defective GPX4 further synergizes GPX4i with PARPi to induce ferroptosis in BRCA1-deficient cancers and targeting GPX4 mitigates PARPi resistance in those cancers. See related commentary by Alborzinia and Friedmann Angeli, p. 1372.

Targeting neddylation and sumoylation in chemoresistant triple negative breast cancer

Triple negative breast cancer (TNBC) accounts for 15-20% of breast cancer cases in the United States. Systemic neoadjuvant chemotherapy (NACT), with or without immunotherapy, is the current standard of care for patients with early-stage TNBC. However, up to 70% of TNBC patients have significant residual disease once NACT is completed, which is associated with a high risk of developing recurrence within two to three years of surgical resection. To identify targetable vulnerabilities in chemoresistant TNBC, we generated longitudinal patient-derived xenograft (PDX) models from TNBC tumors before and after patients received NACT. We then compiled transcriptomes and drug response profiles for all models. Transcriptomic analysis identified the enrichment of aberrant protein homeostasis pathways in models from post-NACT tumors relative to pre-NACT tumors. This observation correlated with increased sensitivity in vitro to inhibitors targeting the proteasome, heat shock proteins, and neddylation pathways. Pevonedistat, a drug annotated as a NEDD8-activating enzyme (NAE) inhibitor, was prioritized for validation in vivo and demonstrated efficacy as a single agent in multiple PDX models of TNBC. Pharmacotranscriptomic analysis identified a pathway-level correlation between pevonedistat activity and post-translational modification (PTM) machinery, particularly involving neddylation and sumoylation targets. Elevated levels of both NEDD8 and SUMO1 were observed in models exhibiting a favorable response to pevonedistat compared to those with a less favorable response in vivo. Moreover, a correlation emerged between the expression of neddylation-regulated pathways and tumor response to pevonedistat, indicating that targeting these PTM pathways may prove effective in combating chemoresistant TNBC.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

XMR: an explainable multimodal neural network for drug response prediction

Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.

Subclonal heterogeneity and evolution in breast cancer

Subclonal heterogeneity and evolution are characteristics of breast cancer that play a fundamental role in tumour development, progression and resistance to current therapies. In this review, we focus on the recent advances in understanding the epigenetic and transcriptomic changes that occur within breast cancer and their importance in terms of cancer development, progression and therapy resistance with a particular focus on alterations at the single-cell level. Furthermore, we highlight the utility of using single-cell tracing and molecular barcoding methodologies in preclinical models to assess disease evolution and response to therapy. We discuss how the integration of single-cell profiling from patient samples can be used in conjunction with results from preclinical models to untangle the complexities of this disease and identify biomarkers of disease progression, including measures of intra-tumour heterogeneity themselves, and how enhancing this understanding has the potential to uncover new targetable vulnerabilities in breast cancer.

Identification of nuclear export inhibitor-based combination therapies in preclinical models of triple-negative breast cancer

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High-throughput drug screening reveals promising therapeutic candidates for TNBC.

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KPT-330, an XPO1 inhibitor, and GSK2126458 exhibit synergism in preclinical models of TNBC.

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XPO1 is overexpressed in basal-like breast tumors.

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XPO1 expression is associated with PIK3CA, MTOR, and MKI67 expression at the single-cell level.

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XPO1 overexpression in basal-like patients is associated with greater rates of metastases.

An estimated 284,000 Americans will be diagnosed with breast cancer in 2021. Of these individuals, 15–20% have basal-like triple-negative breast cancer (TNBC), which is known to be highly metastatic. Chemotherapy is standard of care for TNBC patients, but chemoresistance is a common clinical problem. There is currently a lack of alternative, targeted treatment strategies for TNBC; this study sought to identify novel therapeutic combinations to treat basal-like TNBCs. For these studies, four human basal-like TNBC cell lines were utilized to determine the cytotoxicity profile of 1363 clinically-used drugs. Ten promising therapeutic candidates were identified, and synergism studies were performed in vitro. Two drug combinations that included KPT-330, an XPO1 inhibitor, were synergistic in all four cell lines. In vivo testing of four basal-like patient-derived xenografts (PDX) identified one combination, KPT-330 and GSK2126458 (a PI3K/mTOR inhibitor), that decreased tumor burden in mice significantly more than monotherapy with either single agent. Bulk and single-cell RNA-sequencing, immunohistochemistry, and analysis of published genomic datasets found that XPO1 was abundantly expressed in human basal-like TNBC cell lines, PDXs, and patient tumor samples. Within basal-like PDXs, XPO1 overexpression was associated with increased proliferation at the cellular level. Within patient datasets, XPO1 overexpression was correlated with greater rates of metastasis in patients with basal-like tumors. These studies identify a promising potential new combination therapy for patients with basal-like breast cancer.

Laboratory Models for Investigating Breast Cancer Therapy Resistance and Metastasis

While numerous therapies are highly efficacious in early-stage breast cancers and in particular subsets of breast cancers, therapeutic resistance and metastasis unfortunately arise in many patients. In many cases, tumors that are resistant to standard of care therapies, as well as tumors that have metastasized, are treatable but incurable with existing clinical strategies. Both therapy resistance and metastasis are multi-step processes during which tumor cells must overcome diverse environmental and selective hurdles. Mechanisms by which tumor cells achieve this are numerous and include acquisition of invasive and migratory capabilities, cell-intrinsic genetic and/or epigenetic adaptations, clonal selection, immune evasion, interactions with stromal cells, entering a state of dormancy or senescence, and maintaining self-renewal capacity. To overcome therapy resistance and metastasis in breast cancer, the ability to effectively model each of these mechanisms in the laboratory is essential. Herein we review historic and the current state-of-the-art laboratory model systems and experimental approaches used to investigate breast cancer metastasis and resistance to standard of care therapeutics. While each model system has inherent limitations, they have provided invaluable insights, many of which have translated into regimens undergoing clinical evaluation. We will discuss the limitations and advantages of a variety of model systems that have been used to investigate breast cancer metastasis and therapy resistance and outline potential strategies to improve experimental modeling to further our knowledge of these processes, which will be crucial for the continued development of effective breast cancer treatments.