CDC25B partners with PP2A to induce AMPK activation and tumor suppression in triple negative breast cancer

Cell division cycle 25 (CDC25) dual specificity phosphatases positively regulate the cell cycle by activating cyclin-dependent kinase/cyclin complexes. Here, we demonstrate that in addition to its role in cell cycle regulation, CDC25B functions as a regulator of protein phosphatase 2A (PP2A), a major cellular Ser/Thr phosphatase, through its direct interaction with PP2A catalytic subunit. Importantly, CDC25B alters the regulation of AMP-activated protein kinase signaling (AMPK) by PP2A, increasing AMPK activity by inhibiting PP2A to dephosphorylate AMPK. CDC25B depletion leads to metformin resistance by inhibiting metformin-induced AMPK activation. Furthermore, dual inhibition of CDC25B and PP2A further inhibits growth of 3D organoids isolated from patient derived xenograft model of breast cancer compared to CDC25B inhibition alone. Our study identifies CDC25B as a regulator of PP2A, and uncovers a mechanism of controlling the activity of a key energy metabolism marker, AMPK

Anastrozole has an association between degree of estrogen suppression and outcomes in early breast cancer and is a ligand for estrogen receptor α

Purpose: To determine if the degree of estrogen suppression with aromatase inhibitors (AI: anastrozole, exemestane, letrozole) is associated with efficacy in early-stage breast cancer, and to examine for differences in the mechanism of action between the three AIs. Experimental design: Matched case-control studies [247 matched sets from MA.27 (anastrozole vs. exemestane) and PreFace (letrozole) trials] were undertaken to assess whether estrone (E1) or estradiol (E2) concentrations after 6 months of adjuvant therapy were associated with risk of an early breast cancer event (EBCE). Preclinical laboratory studies included luciferase activity, cell proliferation, radio-labeled ligand estrogen receptor binding, surface plasmon resonance ligand receptor binding, and nuclear magnetic resonance assays. Results: Women with E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL after 6 months of AI treatment had a 2.2-fold increase in risk (P = 0.0005) of an EBCE, and in the anastrozole subgroup, the increase in risk of an EBCE was 3.0-fold (P = 0.001). Preclinical laboratory studies examined mechanisms of action in addition to aromatase inhibition and showed that only anastrozole could directly bind to estrogen receptor α (ERα), activate estrogen response element-dependent transcription, and stimulate growth of an aromatase-deficient CYP19A1-/- T47D breast cancer cell line. Conclusions: This matched case-control clinical study revealed that levels of estrone and estradiol above identified thresholds after 6 months of adjuvant anastrozole treatment were associated with increased risk of an EBCE. Preclinical laboratory studies revealed that anastrozole, but not exemestane or letrozole, is a ligand for ERα. These findings represent potential steps towards individualized anastrozole therapy

Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

Abstract Background Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. Results We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein–protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Conclusions Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl

Identification of pathways associated with chemosensitivity through network embedding.

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions

Bora downregulation results in radioresistance by promoting repair of double strand breaks.

Following DNA double-strand breaks cells activate several DNA-damage response protein kinases, which then trigger histone H2AX phosphorylation and the accumulation of proteins such as MDC1, p53-binding protein 1, and breast cancer gene 1 at the damage site to promote DNA double-strand breaks repair. We identified a novel biomarker, Bora (previously called C13orf34), that is associated with radiosensitivity. In the current study, we set out to investigate how Bora might be involved in response to irradiation. We found a novel function of Bora in DNA damage repair response. Bora down-regulation increased colony formation in cells exposed to irradiation. This increased resistance to irradiation in Bora-deficient cells is likely due to a faster rate of double-strand breaks repair. After irradiation, Bora-knockdown cells displayed increased G2-M cell cycle arrest and increased Chk2 phosphorylation. Furthermore, Bora specifically interacted with the tandem breast cancer gene 1 C-terminal domain of MDC1 in a phosphorylation dependent manner, and overexpression of Bora could abolish irradiation induced MDC1 foci formation. In summary, Bora may play a significant role in radiosensitivity through the regulation of MDC1 and DNA repair

CERN Computer Newsletter: APRIL-JUNE 1994

List of 137 genes that were among the top 500 Robust-ProGENI-identified genes for at least 40 (over a quarter of 139 studied) treatments in the GDSC dataset (sheet 1). Also includes pathway and Gene Ontology enrichment analysis results of this set (sheet 2). (XLSX 126 kb

Effect of Bora on radiosensitivity and cell cycle in human tumor cell lines.

<p>A. Downregulation of Bora desensitizes cells to IR. Knockdown in HupT3 and Hela cells was performed using two Bora specific siRNAs, followed by MTS assay (upper panel) and colongenic assays (lower panel) to determine cell survival after treatment with increasing doses of IR. ** indicates p<0.05. Western blot was performed to determine the knockdown efficiency (left panel). B. Overexpression of Bora in HupT3 and Hela cells sensitizes cells to IR treatment as determined by MTS assay (upper panel), and clonogenic assays (lower panel). Western blot was performed to determine the overexpression efficiency (left panel). All experiments were performed in triplicate and error bars represent SEM of three independent experiments. Significance was defined by comparing the AUC values of radiation cytotoxicity curves between control and Bora knockdown cells. The x-axis indicates the radiation dose, and the y-axis indicates the surviving fraction after radiation exposure. ** indicates p<0.05. C. Bora affects radiosensitivity independent of its role in PLK1 pathway. HupT3 and Hela cells were transfected with indicated siRNAs, followed by MTS assay with increasing dosage of IR. Western blot was performed to determine the knockdown efficiency (left panel). All experiments were performed in triplicate and error bars represent SEM of three independent experiments. D. Down regulation of Bora slightly increased cells in S and G/M. HupT3 cells transfected with negative or Bora siRNAs were subjected to flow cytometry. Proportion of cells in each cell cycle was quantified. Western blot was performed to determine the knockdown efficiency (left panel). Error bar represents 3 independent experiments.</p

Bora prevents MDC1 and 53BP1 foci formation but not γH2AX.

<p>A. HupT3 cells were transfected with FLAG-tagged Bora, irradiated by 10 Gy and 0.5 h later stained with anti-FLAG antibody and γH2AX antibody, respectively. γH2AX foci were counted. At least 100 cells were counted in each experiment. Results are represented as number of foci per cell. Westernblot was performed to determine knockdown efficiency. B. MDC1 and 53BP1 foci formation in control HupT3 cells or cells with Bora overexpression. Cells were immunostained with anti- FLAG (red), MDC1 or 53BP1 (green) and DAPI (blue) antibodies. Quantification of MDC1 and 53BP1 foci. MDC1 and 53BP1 foci were counted and at least 100 cells were counted in each experiment. Results are represented as number of foci per cell. *** indicates p<0.01. Overexpression efficiency was indicated by Western blot analysis using anti-FLAG and anti-Bora antibody. C. Quantification of MDC1and 53BP1foci formation in control HupT3 cells or cells with Bora knockdown. Cells were immunostained with anti- FLAG (red), MDC1 or 53BP1 (green) and DAPI (blue) antibodies. Quantification of MDC1 and 53BP1 foci was performed in a similar fashion as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119208#pone.0119208.g003" target="_blank">Fig. 3B</a>, and *** indicates p<0.01. Knockdown efficiency was determined by Western blot analysis using anti-Bora antibody.</p