PAQR8 promotes breast cancer recurrence and confers resistance to multiple therapies.

BACKGROUND: Breast cancer mortality is principally due to recurrent disease that becomes resistant to therapy. We recently identified copy number (CN) gain of the putative membrane progesterone receptor PAQR8 as one of four focal CN alterations that preferentially occurred in recurrent metastatic tumors compared to primary tumors in breast cancer patients. Whether PAQR8 plays a functional role in cancer is unknown. Notably, PAQR8 CN gain in recurrent tumors was mutually exclusive with activating ESR1 mutations in patients treated with anti-estrogen therapies and occurred in > 50% of both patients treated with anti-estrogen therapies and those treated with chemotherapy or anti-Her2 agents. METHODS: We used orthotopic mouse models to determine whether PAQR8 overexpression or deletion alters breast cancer dormancy or recurrence following therapy. In vitro studies, including assays for colony formation, cell viability, and relative cell fitness, were employed to identify effects of PAQR8 in the context of therapy. Cell survival and proliferation were quantified by immunofluorescence staining for markers of apoptosis and proliferation. Sphingolipids were quantified by liquid chromatography-high resolution mass spectrometry. RESULTS: We show that PAQR8 is necessary and sufficient for efficient mammary tumor recurrence in mice, spontaneously upregulated and CN gained in recurrent tumors that arise following therapy in multiple mouse models, and associated with poor survival following recurrence as well as poor overall survival in breast cancer patients. PAQR8 promoted resistance to therapy by enhancing tumor cell survival following estrogen receptor pathway inhibition by fulvestrant or estrogen deprivation, Her2 pathway blockade by lapatinib or Her2 downregulation, and treatment with chemotherapeutic agents. Pro-survival effects of PAQR8 were mediated by a Gi protein-dependent reduction in cAMP levels, did not require progesterone, and involved a PAQR8-dependent decrease in ceramide levels and increase in sphingosine-1-phosphate levels, suggesting that PAQR8 may possess ceramidase activity. CONCLUSIONS: Our data provide in vivo evidence that PAQR8 plays a functional role in cancer, implicate PAQR8, cAMP, and ceramide metabolism in breast cancer recurrence, and identify a novel mechanism that may commonly contribute to the acquisition of treatment resistance in breast cancer patients.

Genomic landscape of metastatic breast cancer identifies preferentially dysregulated pathways and targets.

Nearly all breast cancer deaths result from metastatic disease. Despite this, the genomic events that drive metastatic recurrence are poorly understood. We performed whole-exome and shallow whole-genome sequencing to identify genes and pathways preferentially mutated or copy-number altered in metastases compared with the paired primary tumors from which they arose. Seven genes were preferentially mutated in metastases - MYLK, PEAK1, SLC2A4RG, EVC2, XIRP2, PALB2, and ESR1 - 5 of which are not significantly mutated in any type of human primary cancer. Four regions were preferentially copy-number altered: loss of STK11 and CDKN2A/B, as well as gain of PTK6 and the membrane-bound progesterone receptor, PAQR8. PAQR8 gain was mutually exclusive with mutations in the nuclear estrogen and progesterone receptors, suggesting a role in treatment resistance. Several pathways were preferentially mutated or altered in metastases, including mTOR, CDK/RB, cAMP/PKA, WNT, HKMT, and focal adhesion. Immunohistochemical analyses revealed that metastases preferentially inactivate pRB, upregulate the mTORC1 and WNT signaling pathways, and exhibit nuclear localization of activated PKA. Our findings identify multiple therapeutic targets in metastatic recurrence that are not significantly mutated in primary cancers, implicate membrane progesterone signaling and nuclear PKA in metastatic recurrence, and provide genomic bases for the efficacy of mTORC1, CDK4/6, and PARP inhibitors in metastatic breast cancer.

Comparative clinical utility of tumor genomic testing and cell-free DNA in metastatic breast cancer.

PURPOSE: Breast cancer metastases differ biologically from primary disease; therefore, metastatic biopsies may assist in treatment decision making. Commercial genomic testing of both tumor and circulating tumor DNA have become available clinically, but utility of these tests in breast cancer management remains unclear. METHODS: Patients undergoing a clinically indicated metastatic tumor biopsy were consented to the ongoing METAMORPH registry. Tumor and blood were collected at the time of disease progression before subsequent therapy, and patients were followed for response on subsequent treatment. Tumor testing (n = 53) and concurrent cell-free DNA (n = 32) in a subset of patients was performed using CLIA-approved assays. RESULTS: The proportion of patients with a genomic alteration was lower in tumor than in blood (69 vs. 91%; p = 0.06). After restricting analysis to alterations covered on both platforms, 83% of tumor alterations were detected in blood, while 90% of blood alterations were detected in tumor. Mutational load specific for the panel genes was calculated for both tumor and blood. Time to progression on subsequent treatment was significantly shorter for patients whose tumors had high panel-specific mutational load (HR 0.31, 95% CI 0.12-0.78) or a TP53 mutation (HR 0.35, 95% CI 0.20-0.79), after adjusting for stage at presentation, hormone receptor status, prior treatment type, and number of lines of metastatic treatment. CONCLUSIONS: Treating oncologists must distinguish platform differences from true biological heterogeneity when comparing tumor and cfDNA genomic testing results. Tumor and concurrent cfDNA contribute unique genomic information in metastatic breast cancer patients, providing potentially useful biomarkers for aggressive metastatic disease.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA.

BACKGROUND: It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation. RESULTS: The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS). CONCLUSION: The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.