Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland.

BACKGROUND: The ovarian hormones estrogen and progesterone (EP) are implicated in breast cancer causation. A specific consequence of progesterone exposure is the expansion of the mammary stem cell (MSC) and luminal progenitor (LP) compartments. We hypothesized that this effect, and its molecular facilitators, could be abrogated by progesterone receptor (PR) antagonists administered in a mouse model. METHODS: Ovariectomized FVB mice were randomized to 14 days of treatment: sham, EP, EP + telapristone (EP + TPA), EP + mifepristone (EP + MFP). Mice were then sacrificed, mammary glands harvested, and mammary epithelial cell lineages separated by flow cytometry using cell surface markers. RNA from each lineage was sequenced and differential gene expression was analyzed using DESeq. Quantitative PCR was performed to confirm the candidate genes discovered in RNA seq. ANOVA with Tukey post hoc analysis was performed to compare relative expression. Alternative splicing events were examined using the rMATs multivariate analysis tool. RESULTS: Significant increases in the MSC and luminal mature (LM) cell fractions were observed following EP treatment compared to control (p < 0.01 and p < 0.05, respectively), whereas the LP fraction was significantly reduced (p < 0.05). These hormone-induced effects were reversed upon exposure to TPA and MFP (p < 0.01 for both). Gene Ontology analysis of RNA-sequencing data showed EP-induced enrichment of several pathways, with the largest effect on Wnt signaling in MSC, significantly repressed by PR inhibitors. In LP cells, significant induction of Wnt4 and Rankl, and Wnt pathway intermediates Lrp2 and Axin2 (confirmed by qRTPCR) were reversed by TPA and MFP (p < 0.0001). Downstream signaling intermediates of these pathways (Lrp5, Mmp7) showed similar effects. Expression of markers of epithelial-mesenchymal transition (Cdh1, Cdh3) and the induction of EMT regulators (Zeb1, Zeb2, Gli3, Snai1, and Ptch2) were significantly responsive to progesterone. EP treatment was associated with large-scale alternative splicing events, with an enrichment of motifs associated with Srsf, Esrp, and Rbfox families. Exon skipping was observed in Cdh1, Enah, and Brd4. CONCLUSIONS: PR inhibition reverses known tumorigenic pathways in the mammary gland and suppresses a previously unknown effect of progesterone on RNA splicing events. In total, our results strengthen the case for reconsideration of PR inhibitors for breast cancer prevention.

CRIPTO antagonist ALK4L75A-Fc inhibits breast cancer cell plasticity and adaptation to stress.

BACKGROUND: CRIPTO is a multi-functional signaling protein that promotes stemness and oncogenesis. We previously developed a CRIPTO antagonist, ALK4L75A-Fc, and showed that it causes loss of the stem cell phenotype in normal mammary epithelia suggesting it may similarly inhibit CRIPTO-dependent plasticity in breast cancer cells. METHODS: We focused on two triple negative breast cancer cell lines (MDA-MB-231 and MDA-MB-468) to measure the effects of ALK4L75A-Fc on cancer cell behavior under nutrient deprivation and endoplasmic reticulum stress. We characterized the proliferation and migration of these cells in vitro using time-lapse microscopy and characterized stress-dependent changes in the levels and distribution of CRIPTO signaling mediators and cancer stem cell markers. We also assessed the effects of ALK4L75A-Fc on proliferation, EMT, and stem cell markers in vivo as well as on tumor growth and metastasis using inducible lentiviral delivery or systemic administration of purified ALK4L75A-Fc, which represents a candidate therapeutic approach. RESULTS: ALK4L75A-Fc inhibited adaptive responses of breast cancer cells under conditions of nutrient and ER stress and reduced their proliferation, migration, clonogenicity, and expression of EMT and cancer stem cell markers. ALK4L75A-Fc also inhibited proliferation of human breast cancer cells in stressed tumor microenvironments in xenografts and reduced both primary tumor size and metastatic burden. CONCLUSIONS: Cancer cell adaptation to stresses such as nutrient deprivation, hypoxia, and chemotherapy can critically contribute to dormancy, metastasis, therapy resistance, and recurrence. Identifying mechanisms that govern cellular adaptation, plasticity, and the emergence of stem-like cancer cells may be key to effective anticancer therapies. Results presented here indicate that targeting CRIPTO with ALK4L75A-Fc may have potential as such a therapy since it inhibits breast cancer cell adaptation to microenvironmental challenges and associated stem-like and EMT phenotypes.

PMLPR: A novel method for predicting subcellular localization based on recommender systems.

The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.