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Abstract Background Bidirectional crosstalk between HER2 and estrogen receptor (ER) pathways may influence outcomes and the efficacy of endocrine therapy (ET). Low HER2 expression levels (HER2-low) have emerged as a predictive biomarker in patients with breast cancer (BC). Methods PALLAS is an open, international, phase 3 study evaluating the addition of palbociclib for 2 years to adjuvant ET in patients with stage II-III ER-positive/HER2-negative BC. To assess the impact of HER2 expression on patient outcomes in the phase III PALLAS trial, we analyzed (1) the association between rate of HER2-low with demographic and clinicopathological parameters, (2) the prognostic value of HER2-low status on invasive disease-free survival (iDFS), distant relapse-free survival (DRFS), and overall survival (OS) and (3) HER2 expression’s value as a predictive biomarker of response to palbociclib. HER2-low was defined as HER2 immunohistochemistry (IHC) 1 + or IHC 2 + with negative in situ hybridization (ISH). All pathologic evaluation was performed locally. Prognostic and predictive power of HER2 were assessed with Cox models. Results From the original PALLAS intention-to-treat population (N = 5753), 5304 patients (92.2%) were included in this analysis. Among these, 2254 patients (42.5%) were classified as having HER2 IHC 0 (HER2-0), and 3050 (57.5%) as having HER2-low disease (1838 with IHC 1 + and 1212 with IHC 2 +). Median follow-up was 59.8 months. HER2-low prevalence varied significantly across 21 participating countries (range 16.7% to 75.6%; p

Abstract Background Patients with inflammatory breast cancer (IBC) have overall poor clinical outcomes, with triple-negative IBC (TN-IBC) being associated with the worst survival, warranting the investigation of novel therapies. Preclinical studies implied that ruxolitinib (RUX), a JAK1/2 inhibitor, may be an effective therapy for TN-IBC. Methods We conducted a randomized phase II study with nested window-of-opportunity in TN-IBC. Treatment-naïve patients received a 7-day run-in of RUX alone or RUX plus paclitaxel (PAC). After the run-in, those who received RUX alone proceeded to neoadjuvant therapy with either RUX + PAC or PAC alone for 12 weeks; those who had received RUX + PAC continued treatment for 12 weeks. All patients subsequently received 4 cycles of doxorubicin plus cyclophosphamide prior to surgery. Research tumor biopsies were performed at baseline (pre-run-in) and after run-in therapy. Tumors were evaluated for phosphorylated STAT3 (pSTAT3) by immunostaining, and a subset was also analyzed by RNA-seq. The primary endpoint was the percent of pSTAT3-positive pre-run-in tumors that became pSTAT3-negative. Secondary endpoints included pathologic complete response (pCR). Results Overall, 23 patients were enrolled, of whom 21 completed preoperative therapy. Two patients achieved pCR (8.7%). pSTAT3 and IL-6/JAK/STAT3 signaling decreased in post-run-in biopsies of RUX-treated samples, while sustained treatment with RUX + PAC upregulated IL-6/JAK/STAT3 signaling compared to RUX alone. Both treatments decreased GZMB+ T cells implying immune suppression. RUX alone effectively inhibited JAK/STAT3 signaling but its combination with PAC led to incomplete inhibition. The immune suppressive effects of RUX alone and in combination may negate its growth inhibitory effects on cancer cells. Conclusion In summary, the use of RUX in TN-IBC was associated with a decrease in pSTAT3 levels despite lack of clinical benefit. Cancer cell-specific-targeting of JAK2/STAT3 or combinations with immunotherapy may be required for further evaluation of JAK2/STAT3 signaling as a cancer therapeutic target. Trial registration www.clinicaltrials.gov , NCT02876302. Registered 23 August 2016.

Abstract Aim Deregulated signaling pathways are a hallmark feature of oncogenesis and driver of tumor progression. Dual specificity protein phosphatase 4 (DUSP4) is a critical negative regulator of the mitogen-activated protein kinase (MAPK) pathway and is often deleted or epigenetically silenced in tumors. DUSP4 alterations lead to hyperactivation of MAPK signaling in many cancers, including breast cancer, which often harbor mutations in cell cycle checkpoint genes, particularly in TP53. Methods Using a genetically engineered mouse model, we generated mammary-specific Dusp4-deleted primary epithelial cells to investigate the necessary conditions in which DUSP4 loss may drive breast cancer oncogenesis. Results We found that Dusp4 loss alone is insufficient in mediating tumorigenesis, but alternatively converges with loss in Trp53 and MYC amplification to induce tumorigenesis primarily through chromosome 5 amplification, which specifically upregulates Dbf4, a cell cycle gene that promotes cellular replication by mediating cell cycle checkpoint escape. Conclusions This study identifies a novel mechanism for breast tumorigenesis implicating Dusp4 loss and p53 mutations in cellular acquisition of Dbf4 upregulation as a driver of cellular replication and cell cycle checkpoint escape.

Abstract Background Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients.

Abstract Background During pregnancy, as the mammary gland prepares for synthesis and delivery of milk to newborns, a luminal mammary epithelial cell (MEC) subpopulation proliferates rapidly in response to systemic hormonal cues that activate STAT5A. While the receptor tyrosine kinase ErbB4 is required for STAT5A activation in MECs during pregnancy, it is unclear how ErbB3, a heterodimeric partner of ErbB4 and activator of phosphatidyl inositol-3 kinase (PI3K) signaling, contributes to lactogenic expansion of the mammary gland. Methods We assessed mRNA expression levels by expression microarray of mouse mammary glands harvested throughout pregnancy and lactation. To study the role of ErbB3 in mammary gland lactogenesis, we used transgenic mice expressing WAP-driven Cre recombinase to generate a mouse model in which conditional ErbB3 ablation occurred specifically in alveolar mammary epithelial cells (aMECs). Results Profiling of RNA from mouse MECs isolated throughout pregnancy revealed robust Erbb3 induction during mid-to-late pregnancy, a time point when aMECs proliferate rapidly and undergo differentiation to support milk production. Litters nursed by ErbB3 KO dams weighed significantly less when compared to litters nursed by ErbB3 WT dams. Further analysis revealed substantially reduced epithelial content, decreased aMEC proliferation, and increased aMEC cell death during late pregnancy. Consistent with the potent ability of ErbB3 to activate cell survival through the PI3K/Akt pathway, we found impaired Akt phosphorylation in ErbB3 KO samples, as well as impaired expression of STAT5A, a master regulator of lactogenesis. Constitutively active Akt rescued cell survival in ErbB3-depleted aMECs, but failed to restore STAT5A expression or activity. Interestingly, defects in growth and survival of ErbB3 KO aMECs as well as Akt phosphorylation, STAT5A activity, and expression of milk-encoding genes observed in ErbB3 KO MECs progressively improved between late pregnancy and lactation day 5. We found a compensatory upregulation of ErbB4 activity in ErbB3 KO mammary glands. Enforced ErbB4 expression alleviated the consequences of ErbB3 ablation in aMECs, while combined ablation of both ErbB3 and ErbB4 exaggerated the phenotype. Conclusions These studies demonstrate that ErbB3, like ErbB4, enhances lactogenic expansion and differentiation of the mammary gland during pregnancy, through activation of Akt and STAT5A, two targets crucial for lactation.