95 research outputs found

    PI3K inhibition enhances the anti-tumor effect of eribulin in triple negative breast cancer

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    Loss of the tumor suppressor phosphatase and tensin homolog (PTEN) is commonly observed in triple negative breast cancer (TNBC), leading to activation of the phosphoinositide 3-kinase (PI3K) signaling to promote tumor cell growth and chemotherapy resistance. In this study, we investigated whether adding a pan-PI3K inhibitor could improve the cytotoxic effect of eribulin, a non-taxane microtubule inhibitor, in TNBC patient-derived xenograft models (PDX) with loss of PTEN, and the underlying molecular mechanisms. Three TNBC-PDX models (WHIM6, WHIM12 and WHIM21), all with loss of PTEN expression, were tested for their response to BKM120 and eribulin, alone or in combinatio

    RON signalling promotes therapeutic resistance in ESR1 mutant breast cancer

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    BACKGROUND: Oestrogen Receptor 1 (ESR1) mutations are frequently acquired in oestrogen receptor (ER)-positive metastatic breast cancer (MBC) patients who were treated with aromatase inhibitors (AI) in the metastatic setting. Acquired ESR1 mutations are associated with poor prognosis and there is a lack of effective therapies that selectively target these cancers. METHODS: We performed a proteomic kinome analysis in ESR1 Y537S mutant cells to identify hyperactivated kinases in ESR1 mutant cells. We validated Recepteur d\u27Origine Nantais (RON) and PI3K hyperactivity through phospho-immunoblot analysis, organoid growth assays, and in an in vivo patient-derived xenograft (PDX) metastatic model. RESULTS: We demonstrated that RON was hyperactivated in ESR1 mutant models, and in acquired palbociclib-resistant (PalbR) models. RON and insulin-like growth factor 1 receptor (IGF-1R) interacted as shown through pharmacological and genetic inhibition and were regulated by the mutant ER as demonstrated by reduced phospho-protein expression with endocrine therapies (ET). We show that ET in combination with a RON inhibitor (RONi) decreased ex vivo organoid growth of ESR1 mutant models, and as a monotherapy in PalbR models, demonstrating its therapeutic efficacy. Significantly, ET in combination with the RONi reduced metastasis of an ESR1 Y537S mutant PDX model. CONCLUSIONS: Our results demonstrate that RON/PI3K pathway inhibition may be an effective treatment strategy in ESR1 mutant and PalbR MBC patients. Clinically our data predict that ET resistance mechanisms can also contribute to CDK4/6 inhibitor resistance

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

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    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

    PDXNet portal: Patient-derived Xenograft model, data, workflow and tool discovery

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    We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI\u27s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials

    Proteogenomic markers of chemotherapy resistance and response in triple-negative breast cancer

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    UNLABELLED: Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2-M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31-33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. SIGNIFICANCE: Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31-33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483

    Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

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    PURPOSE: We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. METHODS: TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV RESULTS: Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV CONCLUSIONS: We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial
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