Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as PDL1, PD1, FOXP3, IDO1, and CTLA4, was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of TP53 were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of MAP3K1 and MAP2K4 mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity.

Chromosomal translocations that generate in-frame oncogenic gene fusions are powerful examples of success of targeted cancer therapies1–3. We discovered FGFR3-TACC3 (F3-T3) gene fusions in 3% of human glioblastoma4. Subsequent studies reported similar frequencies of F3-T3 in many other cancers, thus qualifying F3-T3 as one of the most recurrent fusions across all tumor types5,6. F3-T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors but the downstream oncogenic signaling remains largely unknown2,4–6. Here, we report that tumors harboring F3-T3 cluster within transcriptional subgroups characterized by activation of mitochondrial functions. F3-T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. We show that phosphorylation of PIN4 is the signaling intermediate for the activation of mitochondrial metabolism. The F3-T3-PIN4 axis triggers peroxisome biogenesis and new protein synthesis. The anabolic response converges on PGC1α through intracellular ROS, enabling mitochondrial respiration and tumor growth. Our analyses uncover the oncogenic circuit engaged by F3-T3, expose reliance on mitochondrial respiration as unexpected therapeutic opportunity for F3-T3-positive tumors and provide a clue to the genetic alterations that initiate the chain of metabolic responses driving mitochondrial metabolism in cancer.

Abstract

Code cloning has been very often indicated as a bad software development practice. However, many studies appearing in the literature indicate that this is not always the case. In fact, either changes occurring in cloned code are consistently propagated, or cloning is used as a sort of templating strategy, where cloned source code fragments evolve independently. This paper (i) proposes an automatic approach to classify the evolution of source code clone fragments, and (ii) reports a fine-grained analysis of clone evolution in four different Java and C software systems, aimed at investigating to what extent clones are consistently propagated or they evolve independently. Also, the paper investigates the relationship between the presence of clone evolution patterns and other characteristics such as clone raSuresh Thummalapenta North Carolina State University, Raleigh, USA E-mail: sthumma@ncsu.edu Luigi Cerulo, Lerina Aversano, Massimiliano Di Penta Department of Engineering -University of Sannio, Benevento, Italy E-mail: lcerulo@unisannio.it, aversano@unisannio.it, dipenta@unisannio.it 2 dius, clone size and the kind of change the clones underwent, i.e., corrective maintenance or enhancement.

No abstract

BackgroundRecently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.ResultsA recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement.ConclusionsCompared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

Software repositories, such as CVS and Bugzilla, provide a huge amount of data regarding, respectively, source code and change request history. In this paper we propose a study on how change requests have been assigned to developers involved in an open source project and a method to suggest the set of best candidate developers to resolve a new change request. The method is based on the hypothesis that, given a new change request, developers that have resolved similar change requests in the past are the best candidates to resolve the new one. The suggestion can be useful for project managers in order to choose the best candidate to resolve a particular change request and/or to construct a competence database of developers working on software projects. We use the textual description of change requests stored in software repositories to index developers as documents in an information retrieval system. An Information Retrieval method is then applied to retrieve the candidate developers using the textual description of a new change request as a query.Case and evaluation study of the analysis and the methods introduced in this paper has been conducted on two large open source projects, Mozilla and KDE.

BackgroundCancer-related immune antigens in the tumor microenvironment could represent an obstacle to agents targeting EGFR “cetuximab” or VEGF “bevacizumab” in metastatic colorectal cancer (mCRC) patients.MethodsInfiltrating immune cells into tumor tissues, cancer-related expression of immune antigens (CD3, CD8, CD68, CD73, MPO, CD15/FUT4) from 102 mCRC patients receiving first-line Cetuximab or Bevacizumab plus chemotherapy were assessed by immunohistochemistry and validated in an independent tissue microarrays of 140 patients. Genome-wide expression profiles from 436 patients and 60 colon cancer cell lines were investigated using bioinformatics analysis. In vitro kinase assays of target genes activated by chemokines or growth factors were performed.ResultsHere, we report that cancer-related CD15/FUT4 is overexpressed in most of mCRCs patients (43 %) and associates with lower intratumoral CD3+ and CD8+ T cells, higher systemic inflammation (NLR at diagnosis >5) and poorer outcomes, in terms of response and progression-free survival than those CD15/FUT4-low or negative ones (adjusted hazard ratio (HR) = 2.92; 95 % CI = 1.86–4.41; P < 0.001). Overexpression of CD15/FUT4 is induced through RAF-MEK-ERK kinase cascade, suppressed by MEK inhibitors and exhibits a close connection with constitutive oncogenic signalling pathways that respond to ERBB3 or FGFR4 activation (P < 0.001). CD15/FUT4-high expressing colon cancer cells with primary resistance to cetuximab or bevacizumab are significantly more sensitive to MEK inhibitors than CD15/FUT4-low counterparts.ConclusionCancer-related CD15/FUT4 overexpression participates in cetuximab or bevacizumab mechanisms of resistance in mCRC patients. CD15/FUT4 as a potential target of the antitumor immune response requires further evaluation in clinical studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-015-0225-7) contains supplementary material, which is available to authorized users.