Examining patient-specific responses to PARP inhibitors in a novel, human induced pluripotent stem cell-based model of breast cancer
Ontology highlight
ABSTRACT: Preclinical models of breast cancer that better predict patient-specific drug responses are critical for expanding the clinical utility of targeted therapies, including for inhibitors of poly(ADP-ribose) polymerase (PARP). Reprogramming primary cancer cells into human induced pluripotent stem cells (hiPSCs) recently emerged as a powerful tool to model drug response phenotypes, but its use to date has been limited to hematopoietic malignancies. We designed an optimized reprogramming methodology to generate breast cancer-derived hiPSCs (BC-hiPSCs) from nine patients representing all major subtypes of breast cancer. BC-hiPSCs retain patient-specific oncogenic variants, including variants unique to individual tumor subclones. Additionally, we developed a protocol to differentiate BC-hiPSCs into mammary epithelial cells and mammary-like organoids for in vitro disease modeling, including drug response phenotyping. Using these tools, we demonstrated that BC-hiPSCs can be used to screen for differential sensitivity to PARP inhibitors and mechanistically investigated the causal genetic variant driving drug sensitivity in one patient.
Project description:Breast Cancer (BC) patient stratification is mainly driven by tumour receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potential of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 32 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient’s tumour biopsy. We found that BC cell lines harbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (Drug Response Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows that transcriptional heterogeneity is common, dynamic and that its plasticity plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinic.
Project description:Breast cancer (BC), the most frequent tumor entity in women globally, shows a high therapeutic response in early and non-metastatic stages. However, triple-negative BC (TNBC), enriched with cancer stem cells (CSCs), presents significant challenges due to its chemoresistant and metastatic nature. Ubiquitin Specific Proteinase 22 (USP22) has emerged as a key player in promoting CSC functions, contributing to resistance to conventional therapies, tumor relapse, metastasis, and poor survival across various cancers, including BC. The specific role of USP22 in TNBC, however, remains underexplored. In this study, we employed the MMTV-cre, Usp22fl/fl transgenic mouse model to investigate USP22's influence on stem cell-like properties in mammary tissue. High-throughput transcriptomic analyses, combined with publicly available patient data and TNBC culture models, were utilized to elucidate USP22's role in CSC characteristics of TNBC. Our findings reveal that USP22 enhances CSC properties and drug tolerance by supporting oxidative phosphorylation, a key factor in the poor response to conventional therapies in aggressive BC subtypes. The study uncovers a novel tumor-supportive role of USP22 in sustaining cellular respiration, which contributes to the drug-tolerant behavior of HER2+-BC and TNBC cells. This highlights USP22 as a potential therapeutic target, offering new avenues to optimize standard treatments and address the aggressiveness of these malignancies.
Project description:Abstract Several endocrine therapy (ET) resistance mechanisms for ER-positive (ER+) breast cancer (BC) have been proposed, including acquired ESR1 (ERα gene) mutations. The two most common ESR1 mutations are Y537S and D538G, which give rise to a constitutively active receptor with reduced affinity for agonists and antagonists. The discovery of new effective therapies remains a significant challenge in treating mutated ER+ BC. In this context, Poly (ADP-ribose) polymerase-1 (PARP-1) has captured considerable interest as a target for therapeutic inhibitors in specific types of cancers. Here, we report that crosstalk between PARP-1 and ERα may represent a novel therapeutic approach for ER+ BC. We have demonstrated that the up-regulation of PARP-1 expression, stimulated by 17-β estradiol (E2), was blocked by treatment with fulvestrant (Ful), a potent ERα antagonist, or ESR1 siRNA in ER+ MCF7 and T47D BC cell models that express both wild type ERα and the Y537S mutation, indicating that ERα regulates the expression of PARP-1. In addition, ERα-mediated transcriptional activity depended on PARP-1 activity in these models, as confirmed by ERα target gene expression and ERE reporter gene analyses +/- niraparib (Nira). Notably, PARP-1 modulates the estrogen-dependent genomic binding of ERα and FoxA1, which play a crucial role in the proliferation of ER+ BC. We also showed that PARP-1 inhibition prevented proliferation and cell cycle activities of ERα WT and ERα Y537S cells upon ERα activation. In vivo, Nira and lasofoxifene (Laso), an ERα antagonist, significantly reduced primary tumor growth versus vehicle, both as single agents and in combination. Moreover, RNA-seq analyses demonstrated the downregulation of ERα signaling in the mammary glands of mice treated with Nira versus Vehicle. Our results provide novel insights into the molecular events through which PARP-1 may serve as a more comprehensive therapeutic approach to target ET BC resistance in women with advanced ER+ BC.
Project description:Breast cancer is among the most common malignancies and the leading cause of cancer-related deaths in women. SRSF1 proteins belong to an important splicing factor (SF) family and bind to different splicing regulatory elements (SREs) to promote or inhibit splicing. Cyperotundone (CYT) is the major bioactive component of sedge and reported to exhibit multiple biological functions. This study aimed to investigate the effects of CYT on breast cancer drug resistance and to explore the molecular mechanisms. CYT significantly suppressed the in vitro and in vivo growth of BC cells without affect the normal cells, induced cell apoptosis, and inhibited the migration and invasion of drug-resistant BC. In comparison with the mono treatment with CYT, combination of CYT and doxrubicin (Dox) enhanced the effects. CYT treatment regulated the RNA and protein levels of epithelial mesenchymal transition (EMT) biomarkers, suppressed the sphere formation ability and expression of cancer stem cell biomarkers in drug resistant BC cells. Results from transcriptome sequencing analysis and experiments identified significantly decreased SRSF1 level in drug resistant cells after CYT treatment. Knockdown of SRSF1 significantly decreased expression of full-length MYO1B protein in drug-resistant BC cells. Overexpression of SRSF1 and MYO1B revered the inhibitory effects of CYT. In conclusion, CYT repressed the growth and metastasis of BC cells and recovered drug sensitivity, via regulating the alternative splicing of RNAs.
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases. Breast cancer pleural effusion samples from triple negative patients. Compared samples that are computationally predicted to be sensitive to valproic acid and those that are not predicted to be sensitive.
Project description:The BRCA1 tumor suppressor gene encodes a multi-domain protein for which several functions have been described. These include a key role in homologous recombination repair (HRR) of DNA double-strand breaks (DSBs), which is shared with two other high-risk hereditary breast cancer suppressors, BRCA2 and PALB2. Although both BRCA1 and BRCA2 interact with PALB2, BRCA1 missense variants affecting its PALB2-interacting coiled-coil domain are considered sequence variants of uncertain clinical significance (VUS). Using genetically engineered mice, we now show that a BRCA1 coiled-coil domain VUS, Brca1 p.L1363P, disrupting the interaction with PALB2 leads to embryonic lethality and loss of breast cancer suppression. Brca1 p.L1363P mammary tumors develop with a similar latency as Brca1-null tumors, but show different histopathological features and more stable DNA copy number profiles. Nevertheless, Brca1 p.L1363P mammary tumors are HRR-incompetent and responsive to cisplatin and PARP inhibition.
Project description:The BRCA1 tumor suppressor gene encodes a multi-domain protein for which several functions have been described. These include a key role in homologous recombination repair (HRR) of DNA double-strand breaks (DSBs), which is shared with two other high-risk hereditary breast cancer suppressors, BRCA2 and PALB2. Although both BRCA1 and BRCA2 interact with PALB2, BRCA1 missense variants affecting its PALB2-interacting coiled-coil domain are considered sequence variants of uncertain clinical significance (VUS). Using genetically engineered mice, we now show that a BRCA1 coiled-coil domain VUS, Brca1 p.L1363P, disrupting the interaction with PALB2 leads to embryonic lethality and loss of breast cancer suppression. Brca1 p.L1363P mammary tumors develop with a similar latency as Brca1-null tumors, but show different histopathological features and more stable DNA copy number profiles. Nevertheless, Brca1 p.L1363P mammary tumors are HRR-incompetent and responsive to cisplatin and PARP inhibition.
Project description:Doxorubicin (DOX) is the most common chemotherapeutic drug used to treat breast cancer(BC), impairing DNA metabolism. We discovered that this drug induces, in breast cancer cells, the selective degradation of mitochondria suggesting that mitophagy could be a pro-survival path induced by DOX, hence favoring drug resistance. We anticipated that BC cells may avoid the effect of drug treatment by enhancing their mitophagy activity. Our goal was here to identify a novel relationship between mitophagy gene expressions and drug resistance in BC cells. This microarray analysis of MCF7 cells was performed to demonstrate a direct modulation of genes encoding key proteins regulating the mitophagy pathway in the cells treated with DOX compared to untreated control cells.
Project description:Metastatic melanoma is either intrinsically resistant or rapidly acquires resistance to targeted therapy treatments, such as MAPK inhibitors. A leading cause of resistance to targeted therapy is a dynamic transition of melanoma cells from a proliferative to a highly invasive state, a phenomenon called phenotype switching. Mechanisms regulating phenotype switching represent potential targets for improving treatment of melanoma patients. Using a drug screen targeting chromatin regulators in patient-derived 3D MAPK inhibitor-resistant melanoma cell cultures, we discovered that PARP inhibitors restore sensitivity to MAPK inhibitors, independent of DNA damage repair pathways. Integrated transcriptomic, proteomic, and epigenomic analyses demonstrated that PARP inhibitors induce lysosomal autophagic cell death, accompanied by enhanced mitochondrial lipid metabolism that ultimately increases antigen presentation and sensitivity to T-cell cytotoxicity. Moreover, transcriptomic and epigenetic rearrangements induced by PARP inhibition reversed EMT-like phenotype switching, which redirected melanoma cells toward a proliferative and MAPK inhibitor-sensitive state. The combination of PARP and MAPK inhibitors synergistically induced cancer cell death both in vitro and in vivo in patient-derived xenograft models. Therefore, this study provides a scientific rationale for treating melanoma patients with PARP inhibitors in combination with MAPK inhibitors to abrogate acquired therapy resistance.
Project description:This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.
Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins