Poor Outcome in Postpartum Breast Cancer Patients Is Associated with Distinct Molecular and Immunologic Features.

PURPOSE: Patients with postpartum breast cancer diagnosed after cessation of breastfeeding (postweaning, PP-BCPW) have a particularly poor prognosis compared with patients diagnosed during lactation (PP-BCDL), or to pregnant (Pr-BC) and nulliparous (NP-BC) patients, regardless of standard prognostic characteristics. Animal studies point to a role of the involution process in stimulation of tumor growth in the mammary gland. However, in women, the molecular mechanisms that underlie this poor prognosis of patients with PP-BCPW remain vastly underexplored, due to of lack of adequate patient numbers and outcome data. EXPERIMENTAL DESIGN: We explored whether distinct prognostic features, common to all breast cancer molecular subtypes, exist in postpartum tumor tissue. Using detailed breastfeeding data, we delineated the postweaning period in PP-BC as a surrogate for mammary gland involution and performed whole transcriptome sequencing, immunohistochemical, and (multiplex) immunofluorescent analyses on tumor tissue of patients with PP-BCPW, PP-BCDL, Pr-BC, and NP-BC. RESULTS: We found that patients with PP-BCPW having a low expression level of an immunoglobulin gene signature, but high infiltration of plasma B cells, have an increased risk for metastasis and death. Although PP-BCPW tumor tissue was also characterized by an increase in CD8+ cytotoxic T cells and reduced distance among these cell types, these parameters were not associated with differential clinical outcomes among groups. CONCLUSIONS: These data point to the importance of plasma B cells in the postweaning mammary tumor microenvironment regarding the poor prognosis of PP-BCPW patients. Future prospective and in-depth research needs to further explore the role of B-cell immunobiology in this specific group of young patients with breast cancer.

Tumour-educated platelets for breast cancer detection: biological and technical insights.

BACKGROUND: Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier. METHODS: Platelet mRNA was sequenced from 266 women with stage I-IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses. RESULTS: Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals. CONCLUSIONS: We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

The RNA-Binding Protein ATXN2 is Expressed during Megakaryopoiesis and May Control Timing of Gene Expression.

Megakaryopoiesis is the process during which megakaryoblasts differentiate to polyploid megakaryocytes that can subsequently shed thousands of platelets in the circulation. Megakaryocytes accumulate mRNA during their maturation, which is required for the correct spatio-temporal production of cytoskeletal proteins, membranes and platelet-specific granules, and for the subsequent shedding of thousands of platelets per cell. Gene expression profiling identified the RNA binding protein ATAXIN2 (ATXN2) as a putative novel regulator of megakaryopoiesis. ATXN2 expression is high in CD34+/CD41+ megakaryoblasts and sharply decreases upon maturation to megakaryocytes. ATXN2 associates with DDX6 suggesting that it may mediate repression of mRNA translation during early megakaryopoiesis. Comparative transcriptome and proteome analysis on megakaryoid cells (MEG-01) with differential ATXN2 expression identified ATXN2 dependent gene expression of mRNA and protein involved in processes linked to hemostasis. Mice deficient for Atxn2 did not display differences in bleeding times, but the expression of key surface receptors on platelets, such as ITGB3 (carries the CD61 antigen) and CD31 (PECAM1), was deregulated and platelet aggregation upon specific triggers was reduced.

Pancreatic cancer organoids recapitulate disease and allow personalized drug screening.

We report the derivation of 30 patient-derived organoid lines (PDOs) from tumors arising in the pancreas and distal bile duct. PDOs recapitulate tumor histology and contain genetic alterations typical of pancreatic cancer. In vitro testing of a panel of 76 therapeutic agents revealed sensitivities currently not exploited in the clinic, and underscores the importance of personalized approaches for effective cancer treatment. The PRMT5 inhibitor EZP015556, shown to target MTAP (a gene commonly lost in pancreatic cancer)-negative tumors, was validated as such, but also appeared to constitute an effective therapy for a subset of MTAP-positive tumors. Taken together, the work presented here provides a platform to identify novel therapeutics to target pancreatic tumor cells using PDOs.

Computational approaches for the analysis of RNA-protein interactions: A primer for biologists.

RNA-binding proteins (RBPs) play important roles in the control of gene expression and the coordination of different layers of post-transcriptional regulation. Interactions between certain RBPs and mRNA transcripts are notoriously difficult to predict, as any given protein-RNA interaction may rely not only on RNA sequence, but also on three-dimensional RNA structures, competitive inhibition from other RBPs, and input from cellular signaling pathways. Advanced and high-throughput technologies for the identification of RNA-protein interactions have come to the rescue, but the identification of binding sites and downstream functional effects of RBPs from the resulting data can be challenging. In this review, we discuss statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA-protein interaction datasets. This primer is intended for life scientists who are interested in incorporating these tools into their own research. We begin with the demystification of regression models, as used in the analysis of next-generation sequencing data, and progress to a discussion of Hidden Markov Models, which are of particular value in analyzing cross-linking followed by immunoprecipitation data. We then continue with examples of machine learning techniques, such as support vector machines and gradient tree boosting. We close with a brief discussion of current trends in the field, including deep learning architectures.

RNA Binding Proteins and Regulation of mRNA Translation in Erythropoiesis.

Control of gene expression in erythropoiesis has to respond to signals that may emerge from intracellular processes or environmental factors. Control of mRNA translation allows for relatively rapid modulation of protein synthesis from the existing transcriptome. For instance, the protein synthesis rate needs to be reduced when reactive oxygen species or unfolded proteins accumulate in the cells, but also when iron supply is low or when growth factors are lacking in the environment. In addition, regulation of mRNA translation can be important as an additional layer of control on top of gene transcription, in which RNA binding proteins (RBPs) can modify translation of a set of transcripts to the cell's actual protein requirement. The 5' and 3' untranslated regions of mRNA (5'UTR, 3'UTR) contain binding sites for general and sequence specific translation factors. They also contain secondary structures that may hamper scanning of the 5'UTR by translation complexes or may help to recruit translation factors. In addition, the term 5'UTR is not fully correct because many transcripts contain small open reading frames in their 5'UTR that are translated and contribute to regulation of mRNA translation. It is becoming increasingly clear that the transcriptome only partly predicts the proteome. The aim of this review is (i) to summarize how the availability of general translation initiation factors can selectively regulate transcripts because the 5'UTR contains secondary structures or short translated sequences, (ii) to discuss mechanisms that control the length of the mRNA poly(A) tail in relation to mRNA translation, and (iii) to give examples of sequence specific RBPs and their targets. We focused on transcripts and RBPs required for erythropoiesis. Whereas differentiation of erythroblasts to erythrocytes is orchestrated by erythroid transcription factors, the production of erythrocytes needs to respond to the availability of growth factors and nutrients, particularly the availability of iron.

Strap associates with Csde1 and affects expression of select Csde1-bound transcripts.

Erythropoiesis is regulated at many levels, including control of mRNA translation. Changing environmental conditions, such as hypoxia or the availability of nutrients and growth factors, require a rapid response enacted by the enhanced or repressed translation of existing transcripts. Cold shock domain protein e1 (Csde1/Unr) is an RNA-binding protein required for erythropoiesis and strongly upregulated in erythroblasts relative to other hematopoietic progenitors. The aim of this study is to identify the Csde1-containing protein complexes and investigate their role in post-transcriptional expression control of Csde1-bound transcripts. We show that Serine/Threonine kinase receptor-associated protein (Strap/Unrip), was the protein most strongly associated with Csde1 in erythroblasts. Strap is a WD40 protein involved in signaling and RNA splicing, but its role when associated with Csde1 is unknown. Reduced expression of Strap did not alter the pool of transcripts bound by Csde1. Instead, it altered the mRNA and/or protein expression of several Csde1-bound transcripts that encode for proteins essential for translational regulation during hypoxia, such as Hmbs, eIF4g3 and Pabpc4. Also affected by Strap knockdown were Vim, a Gata-1 target crucial for erythrocyte enucleation, and Elavl1, which stabilizes Gata-1 mRNA. The major cellular processes affected by both Csde1 and Strap were ribosome function and cell cycle control.

Ribosome profiling uncovers selective mRNA translation associated with eIF2 phosphorylation in erythroid progenitors.

The regulation of translation initiation factor 2 (eIF2) is important for erythroid survival and differentiation. Lack of iron, a critical component of heme and hemoglobin, activates Heme Regulated Inhibitor (HRI). This results in phosphorylation of eIF2 and reduced eIF2 availability, which inhibits protein synthesis. Translation of specific transcripts such as Atf4, however, is enhanced. Upstream open reading frames (uORFs) are key to this regulation. The aim of this study is to investigate how tunicamycin treatment, that induces eIF2 phosphorylation, affects mRNA translation in erythroblasts. Ribosome profiling combined with RNA sequencing was used to determine translation initiation sites and ribosome density on individual transcripts. Treatment of erythroblasts with Tunicamycin (Tm) increased phosphorylation of eIF2 2-fold. At a false discovery rate of 1%, ribosome density was increased for 147 transcripts, among which transcriptional regulators such as Atf4, Tis7/Ifrd1, Pnrc2, Gtf2h, Mbd3, JunB and Kmt2e. Translation of 337 transcripts decreased more than average, among which Dym and Csde1. Ribosome profiling following Harringtonine treatment uncovered novel translation initiation sites and uORFs. Surprisingly, translated uORFs did not predict the sensitivity of transcripts to altered ribosome recruitment in presence or absence of Tm. The regulation of transcription and translation factors in reponse to eIF2 phosphorylation may explain the large overall response to iron deficiency in erythroblasts.

Csde1 binds transcripts involved in protein homeostasis and controls their expression in an erythroid cell line.

Expression of the RNA-binding protein Csde1 (Cold shock domain protein e1) is strongly upregulated during erythropoiesis compared to other hematopoietic lineages. Csde1 expression is impaired in the severe congenital anemia Diamond Blackfan Anemia (DBA), and reduced expression of Csde1 in healthy erythroblasts impaired their proliferation and differentiation. To investigate the cellular pathways controlled by Csde1 in erythropoiesis, we identified the transcripts that physically associate with Csde1 in erythroid cells. These mainly encoded proteins involved in ribogenesis, mRNA translation and protein degradation, but also proteins associated with the mitochondrial respiratory chain and mitosis. Crispr/Cas9-mediated deletion of the first cold shock domain of Csde1 affected RNA expression and/or protein expression of Csde1-bound transcripts. For instance, protein expression of Pabpc1 was enhanced while Pabpc1 mRNA expression was reduced indicating more efficient translation of Pabpc1 followed by negative feedback on mRNA stability. Overall, the effect of reduced Csde1 function on mRNA stability and translation of Csde1-bound transcripts was modest. Clones with complete loss of Csde1, however, could not be generated. We suggest that Csde1 is involved in feed-back control in protein homeostasis and that it dampens stochastic changes in mRNA expression.

TLR4 and C5aR crosstalk in dendritic cells induces a core regulatory network of RSK2, PI3Kß, SGK1, and FOXO transcription factors.

Crosstalk between complement component 5a receptors (C5aRs) and TLRs in dendritic cells (DCs) occurs upon pathogen invasion; however, studies on C5aR and TLR crosstalk mainly focused on the modulating effect of C5a on TLR-induced cytokine production. To elucidate the breadth of C5aR and TLR4 crosstalk, the effect of simultaneous treatment with C5a and LPS was investigated in human monocyte-derived DCs (moDCs) 2 h after stimulation using whole transcriptome sequencing analysis. Although the effect of C5a on hallmark genes defining TLR4-induced DC maturation was limited at this time point, RNA sequencing analysis revealed a great variety of novel C5a targets, of which many interfere with TLR4-mediated immune activation. Analysis of functional relationships among these genes uncovered induction of a central immune regulatory network upon C5aR and TLR4 crosstalk, involving the transcription factors forkhead box (FOX)O1 and FOXO3 and the signaling molecules serum- and glucocorticoid-inducible kinase (SGK1), ribosomal S6 kinase 2 (RSK2), and PI3Kß. C5aR and TLR crosstalk, furthermore, yielded down-regulation of mainly proinflammatory network branches, including IL-12B, IL-2Rα (IL-2RA), and jagged 1 (JAG1) and cooperative induction of predominantly anti-inflammatory network branches, including sphingosine kinase 1 (SPHK1), ß2 adrenergic receptor (ADRB2), gastric inhibitory polypeptide receptor (GIPR), and four-and-a-half Lin11, Isl-1, and Mec-3 domains protein 2 (FHL2). Together, these data point toward induction of generalized immune regulation of DC function. Motif enrichment analysis indicate a prominent role for basic leucine zipper (bZIP) and IFN regulatory factor 4 (IRF4) transcription factors upon C5aR and TLR4 crosstalk. Additionally, differences were observed in the modulating capacity of C5a on DCs in the absence or presence of a pathogen (TLR stimulus). Our findings shed new light on the depth and complexity of C5aR and TLR4 crosstalk and provide new foci of research for future studies.