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Background: Gene expression profiling has distinguished sporadic breast tumour classes with genetic and clinical differences. Less is known about the molecular classification of familial breast tumours, which are generally considered to be less heterogeneous. Here, we describe molecular signatures that define BRCA1 subclasses depending on the expression of the gene encoding for oestrogen receptor, ESR1.Methods: For this purpose, we have used the Oncochip v2, a cancer-related cDNA microarray to analyze 14 BRCA1-associated breast tumours.Results: Signatures were found to be molecularly associated with different biological processes and transcriptional regulatory programs. The signature of ESR1-positive tumours was mainly linked to cell proliferation and regulated by ER, whereas the signature of ESR1-negative tumours was mainly linked to the immune response and possibly regulated by transcription factors of the REL/NFkappaB family. These signatures were then verified in an independent series of familial and sporadic breast tumours, which revealed a possible prognostic value for each subclass. Over-expression of immune response genes seems to be a common feature of ER-negative sporadic and familial breast cancer and may be associated with good prognosis. Interestingly, the ESR1-negative tumours were substratified into two groups presenting slight differences in the magnitude of the expression of immune response transcripts and REL/NFkappaB transcription factors, which could be dependent on the type of BRCA1 germline mutation.Conclusion: This study reveals the molecular complexity of BRCA1 breast tumours, which are found to display similarities to sporadic tumours, and suggests possible prognostic implications. [ABSTRACT FROM AUTHOR]

Abstract Background Radiation-induced DNA double-strand break (DSB) repair can be tested by using pulsed-field gel electrophoresis (PFGE) in agarose-encapsulated cells. However, previous studies have reported that this assay is impaired by the spontaneous DNA breakage in this medium. We investigated the mechanisms of this fragmentation with the principal aim of eliminating it in order to improve the estimation of radiation-induced DNA repair. Methods Samples from cancer cell cultures or xenografted tumours were encapsulated in agarose plugs. The cell plugs were then irradiated, incubated to allow them to repair, and evaluated by PFGE, caspase-3, and histone H2AX activation (γH2AX). In addition, apoptosis inhibition was evaluated through chemical caspase inhibitors. Results We confirmed that spontaneous DNA fragmentation was associated with the process of encapsulation, regardless of whether cells were irradiated or not. This DNA fragmentation was also correlated to apoptosis activation in a fraction of the cells encapsulated in agarose, while non-apoptotic cell fraction could rejoin DNA fragments as was measured by γH2AX decrease and PFGE data. We were able to eliminate interference of apoptosis by applying specific caspase inhibitors, and improve the estimation of DNA repair, and apoptosis itself. Conclusions The estimation of radiation-induced DNA repair by PFGE may be improved by the use of apoptosis inhibitors. The ability to simultaneously determine DNA repair and apoptosis, which are involved in cell fate, provides new insights for using the PFGE methodology as functional assay.

Abstract Background Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally. Results This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC. Conclusion This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.

Abstract It is increasingly clear that complex networks of relationships between genes and/or proteins govern neoplastic processes. Our understanding of these networks is expanded by the use of functional genomic and proteomic approaches in addition to computational modeling. Concurrently, whole-genome association scans and mutational screens of cancer genomes identify novel cancer genes. Together, these analyses have vastly increased our knowledge of cancer, in terms of both "part lists" and their functional associations. However, genetic interactions have hitherto only been studied in depth in model organisms and remain largely unknown for human systems. Here, we discuss the importance and potential benefits of identifying genetic interactions at the human genome level for creating a better understanding of cancer susceptibility and progression and developing novel effective anticancer therapies. We examine gene expression profiles in the presence and absence of co-amplification of the 8q24 and 20q13 chromosomal regions in breast tumors to illustrate the molecular consequences and complexity of genetic interactions and their role in tumorigenesis. Finally, we highlight current strategies for targeting tumor dependencies and outline potential matrix screening designs for uncovering molecular vulnerabilities in cancer cells.

Abstract Background The consecutive acquisition of genetic alterations characterizes neoplastic processes. As a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. The recent identification of the collection of somatically mutated genes in breast tumors (breast cancer somatic "mutome") allows the comprehensive study of its function and organization in complex networks. Results We analyzed functional genomic data (loss of heterozygosity, copy number variation and gene expression in breast tumors) and protein binary interactions from public repositories to identify potential novel components of neoplastic processes, the functional relationships between them, and to examine their coordinated function in breast cancer pathogenesis. This analysis identified candidate tumor suppressors and oncogenes, and new genes whose expression level predicts survival rate in breast cancer patients. Mutome network modeling using different types of pathological and healthy functional relationships unveils functional modules significantly enriched in genes or proteins (genes/proteins) with related biological process Gene Ontology terms and containing known breast cancer-related genes/proteins. Conclusion This study presents a comprehensive analysis of the breast somatic mutome, highlighting those genes with a higher probability of playing a determinant role in tumorigenesis and better defining molecular interactions related to the neoplastic process.

Lung adenocarcinoma (LUAD) is a molecularly heterogeneous disease. In addition to genomic alterations, cancer transcriptional profiling can be helpful to tailor cancer treatment and to estimate each patient's outcome. Transcriptional activity levels of 50 molecular pathways were inferred in 4573 LUAD patients using Gene Set Variation Analysis (GSVA) method. Seven LUAD subtypes were defined and independently validated based on the combined behavior of the studied pathways: AD (adenocarcinoma subtype) 1-7. AD1, AD4, and AD5 subtypes were associated with better overall survival. AD1 and AD4 subtypes were enriched in epidermal growth factor receptor (EGFR) mutations, whereas AD2 and AD6 showed higher tumor protein p53 (TP53) alteration frequencies. AD2 and AD6 subtypes correlated with higher genome instability, proliferation-related pathway expression, and specific sensitivity to chemotherapy, based on data from LUAD cell lines. LUAD subtypes were able to predict immunotherapy response in addition to CD274 (PD-L1) gene expression and tumor mutational burden (TMB). AD2 and AD4 subtypes were associated with potential resistance and response to immunotherapy, respectively. Thus, analysis of transcriptomic data could improve patient stratification beyond genomics and single biomarkers (i.e., PD-L1 and TMB) and may lay the foundation for more personalized treatment avenues, especially in driver-negative LUAD., (© 2023 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Background and Aims: The NADPH oxidase NOX4 plays a tumor-suppressor function in HCC. Silencing NOX4 confers higher proliferative and migratory capacity to HCC cells and increases their in vivo tumorigenic potential in xenografts in mice. NOX4 gene deletions are frequent in HCC, correlating with higher tumor grade and worse recurrence-free and overall survival rates. However, despite the accumulating evidence of a protective regulatory role in HCC, the cellular processes governed by NOX4 are not yet understood. Accordingly, the aim of this work was to better understand the molecular mechanisms regulated by NOX4 in HCC in order to explain its tumor-suppressor action., Approach and Results: Experimental models: cell-based loss or gain of NOX4 function experiments, in vivo hepatocarcinogenesis induced by diethylnitrosamine in Nox4 -deficient mice, and analyses in human HCC samples. Methods include cellular and molecular biology analyses, proteomics, transcriptomics, and metabolomics, as well as histological and immunohistochemical analyses in tissues. Results identified MYC as being negatively regulated by NOX4. MYC mediated mitochondrial dynamics and a transcriptional program leading to increased oxidative metabolism, enhanced use of both glucose and fatty acids, and an overall higher energetic capacity and ATP level. NOX4 deletion induced a redox imbalance that augmented nuclear factor erythroid 2-related factor 2 (Nrf2) activity and was responsible for MYC up-regulation., Conclusions: Loss of NOX4 in HCC tumor cells induces metabolic reprogramming in a Nrf2/MYC-dependent manner to promote HCC progression., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI 95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration., (© 2022. The Author(s).)

Background: There is no effective therapy for patients with malignant pleural mesothelioma (MPM) who progressed to platinum-based chemotherapy and immunotherapy., Methods: We aimed to investigate the antitumor activity of CDK4/6 inhibitors using in vitro and in vivo preclinical models of MPM., Results: Based on publicly available transcriptomic data of MPM, patients with CDK4 or CDK6 overexpression had shorter overall survival. Treatment with abemaciclib or palbociclib at 100 nM significantly decreased cell proliferation in all cell models evaluated. Both CDK4/6 inhibitors significantly induced G1 cell cycle arrest, thereby increasing cell senescence and increased the expression of interferon signalling pathway and tumour antigen presentation process in culture models of MPM. In vivo preclinical studies showed that palbociclib significantly reduced tumour growth and prolonged overall survival using distinct xenograft models of MPM implanted in athymic mice., Conclusions: Treatment of MPM with CDK4/6 inhibitors decreased cell proliferation, mainly by promoting cell cycle arrest at G1 and by induction of cell senescence. Our preclinical studies provide evidence for evaluating CDK4/6 inhibitors in the clinic for the treatment of MPM., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Reactivation of dormant cancer cells can lead to cancer relapse, metastasis, and patient death. Dormancy is a nonproliferative state and is linked to late relapse and death. No targeted therapy is currently available to eliminate dormant cells, highlighting the need for a deeper understanding and reliable models. Here, we thoroughly characterize the dormant D2.OR and ZR-75-1, and proliferative D2A1 breast cancer cell line models in vivo and/or in vitro, and assess if there is overlap between a dormant and a senescent phenotype. We show that D2.OR but not D2A1 cells become dormant in the liver of an immunocompetent model. In vitro, we show that D2.OR and ZR-75-1 cells in response to a 3D environment or serum-free conditions are growth-arrested in G1, of which a subpopulation resides in a 4NG1 state. The dormancy state is reversible and not associated with a senescence phenotype. This will aid future research on breast cancer dormancy., (© 2021. The Author(s).)

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Background: Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasia affecting the lung mesothelium. Immune checkpoint inhibitors (ICI) in MPM have not been extremely successful, likely due to poor identification of suitable candidate patients for the therapy. We aimed to identify cellular immune fractions associated with clinical outcome and classify patients with MPM based on their immune contexture. For each defined group, we sought for molecular specificities that could help further define our MPM classification at the genomic and transcriptomic level, as well as identify differential therapeutic strategies based on transcriptional signatures predictive of drug response., Methods: The abundance of 20 immune cell fractions in 516 MPM samples from 7 gene expression datasets was inferred using gene set variation analysis. Identification of clinically relevant fractions was performed with Cox proportional-hazards models adjusted for age, stage, sex, and tumor histology. Immune-based groups were defined based on the identified fractions., Results: T-helper 2 (T H2 ) and cytotoxic T (T C ) cells were found to be consistently associated with overall survival. Three immune clusters (IG) were subsequently defined based on T H2 and T C immune infiltration levels: IG1 (54.5%) was characterized by high T H2 and low T C levels, IG2 (37%) had either low or high levels of both fractions, and IG3 (8.5%) was defined by low T H2 and high T C levels. IG1 and IG3 groups were associated with worse and better overall survival, respectively. While no differential genomic alterations were identified among immune groups, at the transcriptional level, IG1 samples showed upregulation of proliferation signatures, while IG3 samples presented upregulation of immune and inflammation-related pathways. Finally, the integration of gene expression with functional signatures of drug response showed that IG3 patients might be more likely to respond to ICI., Conclusions: This study identifies a novel immune-based signature with potential clinical relevance based on T H2 and T C levels, unveiling a fraction of patients with MPM with better prognosis and who might benefit from immune-based therapies. Molecular specificities of the different groups might be used to tailor specific potential therapies in the future., Competing Interests: Competing interests: EN participated in advisory boards from Bristol Myers Squibb, Merck Sharp & Dohme, Lilly, Roche, Pfizer, Takeda, Boehringer Ingelheim, Amgen, and AstraZeneca. VM is consultant to Bioiberica S.A.U. and Grupo Ferrer S.A., received research funds from Universal DX and is coinvestigator in grants with Aniling., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aim: Gain insight about the role of DNA methylation in the malignant growth of colon cancer. Patients & methods: Methylation and gene expression from 90 adjacent-tumor paired tissues and 48 healthy tissues were analyzed. Tumor genes whose change in expression was explained by changes in methylation were identified using linear models adjusted for tumor stromal content. Results: No differences in methylation were found between adjacent and healthy tissues, but clear differences were found between adjacent and tumor samples. We identified hypermethylated CpG islands located in promoter regions that drive differential gene expression of transcription factors and their target genes. Conclusion: Changes in methylation of a few genes provoke important changes in gene expression, by expanding the signal through transcription activation/repression.

Background & Aims: Upon ligand binding, tyrosine kinase receptors, such as epidermal growth factor receptor (EGFR), are recruited into clathrin-coated pits for internalization by endocytosis, which is relevant for signalling and/or receptor degradation. In liver cells, transforming growth factor-β (TGF-β) induces both pro- and anti-apoptotic signals; the latter are mediated by the EGFR pathway. Since EGFR mainly traffics via clathrin-coated vesicles, we aimed to analyse the potential role of clathrin in TGF-β-induced signalling in liver cells and its relevance in liver cancer., Methods: Real-Time PCR and immunohistochemistry were used to analyse clathrin heavy-chain expression in human (CLTC) and mice (Cltc) liver tumours. Transient knockdown (siRNA) or overexpression of CLTC were used to analyse its role on TGF-β and EGFR signalling in vitro. Bioinformatic analysis was used to determine the effect of CLTC and TGFB1 expression on prognosis and overall survival in patients with hepatocellular carcinoma (HCC)., Results: Clathrin expression increased during liver tumorigenesis in humans and mice. CLTC knockdown cells responded to TGF-β phosphorylating SMADs (canonical signalling) but showed impairment in the anti-apoptotic signals (EGFR transactivation). Experiments of loss or gain of function in HCC cells reveal an essential role for clathrin in inhibiting TGF-β-induced apoptosis and upregulation of its pro-apoptotic target NOX4. Autocrine TGF-β signalling in invasive HCC cells upregulates CLTC expression, switching its role to pro-tumorigenic. A positive correlation between TGFB1 and CLTC was found in HCC cells and patients. Patients expressing high levels of TGFB1 and CLTC had a worse prognosis and lower overall survival., Conclusions: This work describes a novel role for clathrin in liver tumorigenesis, favouring non-canonical pro-tumorigenic TGF-β pathways. CLTC expression in human HCC samples could help select patients that would benefit from TGF-β-targeted therapy., Lay Summary: Clathrin heavy-chain expression increases during liver tumorigenesis in humans (CLTC) and mice (Cltc), altering the cellular response to TGF-β in favour of anti-apoptotic/pro-tumorigenic signals. A positive correlation between TGFB1 and CLTC was found in HCC cells and patients. Patients expressing high levels of TGFB1 and CLTC had a worse prognosis and lower overall survival. CLTC expression in HCC human samples could help select patients that would benefit from therapies targeting TGF-β., (Copyright © 2019 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Background: Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs)., Methods: We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data., Results: The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported., Conclusions: This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci.

Human tumor growth depends on rapidly dividing cancer cells driving population expansion. Even advanced tumors, however, contain slowly proliferating cancer cells for reasons that remain unclear. Here, we selectively disrupt the ability of rapidly proliferating cancer cells to spawn AKT1 low daughter cells that are rare, slowly proliferating, tumor-initiating, and chemotherapy-resistant, using β1-integrin activation and the AKT1-E17K-mutant oncoprotein as experimental tools in vivo Surprisingly, we find that selective depletion of AKT1 low slow proliferators actually reduces the growth of a molecularly diverse panel of human cancer cell xenograft models without globally altering cell proliferation or survival in vivo Moreover, we find that unusual cancer patients with AKT1-E17K-mutant solid tumors also fail to produce AKT1 low quiescent cancer cells and that this correlates with significantly prolonged survival after adjuvant treatment compared with other patients. These findings support a model whereby human solid tumor growth depends on not only rapidly proliferating cancer cells but also on the continuous production of AKT1 low slow proliferators. Mol Cancer Ther; 17(1); 254-63. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Background: Absence of pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) correlates with poor long-term survival in patients with triple negative breast cancer (TNBC). These incomplete treatment responses are likely determined by mechanisms that enable cancer cells to resist being killed. However, the detailed characterization of a drug-resistant cancer cell state in residual TNBC tissue after NACT has remained elusive. AKT1 low quiescent cancer cells (QCCs) are a quiescent, epigenetically plastic, and chemotherapy-resistant subpopulation initially identified in experimental cancer models. Here, we asked whether QCCs exist in primary tumors from patients with TNBC and persist after treatment with NACT., Methods: We obtained pre-treatment biopsy, post-treatment mastectomy, and metastatic specimens from a retrospective cohort of TNBC patients treated with NACT at Massachusetts General Hospital (n = 25). Using quantitative automated immunofluorescence microscopy, QCCs were identified as AKT low /H3K9me2 low /HES1 high cancer cells using prespecified immunofluorescence intensity thresholds. QCCs were represented in 2D and 3D digital tumor maps and QCC percentage (QCC-P) and QCC cluster index (QCC-CI) were determined for each sample., Results: We showed that QCCs exist as non-random and heterogeneously distributed clusters within primary breast tumors. In addition, these QCC clusters persist after treatment with multi-agent, multi-cycle, neoadjuvant chemotherapy in both residual primary tumors and nodal and distant metastases in patients with triple negative breast cancer., Conclusions: These first-in-human data potentially qualify AKT1 low quiescent cancer cells as a non-genetic cell state that persists after neoadjuvant chemotherapy in triple negative breast cancer patients and warrants further study.

Background: Somatic copy number aberrations (CNAs) are common acquired changes in cancer cells having an important role in the progression of colon cancer (colorectal cancer, CRC). This study aimed to perform a characterisation of CNA and their impact in gene expression., Methods: Copy number aberrations were inferred from SNP array data in a series of 99 CRC. Copy number aberration events were calculated and used to assess the association between copy number dosage, clinical and molecular characteristics of the tumours, and gene expression changes. All analyses were adjusted for the quantity of stroma in each sample, which was inferred from gene expression data., Results: High heterogeneity among samples was observed; the proportion of altered genome ranged between 0.04 and 26.6%. Recurrent CNA regions with gains were frequent in chromosomes 7p, 8q, 13q, and 20, whereas 8p, 17p, and 18 cumulated losses. A significant positive correlation was observed between the number of somatic mutations and total CNA (Spearman's r=0.42, P=0.006). Approximately 37% of genes located in CNA regions changed their level of expression and the average partial correlation (adjusted for stromal content) with copy number was 0.54 (interquartile range 0.20 to 0.81). Altered genes showed enrichment in pathways relevant for CRC. Tumours classified as CMS2 and CMS4 by the consensus molecular subtyping showed higher frequency of CNA. Losses of one small region in 1p36.33, with gene CDK11B, were associated with poor prognosis. More than 66% of the recurrent CNA were validated in the The Cancer Genome Atlas (TCGA) data when analysed with the same procedure. Furthermore, 79% of the genes with altered expression in our data were validated in the TCGA., Conclusions: Although CNA are frequent events in microsatellite stable CRC, few focal recurrent regions were found. These aberrations have strong effects on gene expression and contribute to deregulate relevant cancer pathways. Owing to the diploid nature of stromal cells, it is important to consider the purity of tumour samples to accurately calculate CNA events in CRC.

Small molecule inhibitors of AKT (v-akt murine thymoma viral oncogene homolog) signaling are being evaluated in patients with various cancer types, but have so far proven therapeutically disappointing for reasons that remain unclear. Here, we treat cancer cells with subtherapeutic doses of Akti-1/2, an allosteric small molecule AKT inhibitor, in order to experimentally model pharmacologic inhibition of AKT signaling in vitro. We then apply a combined RNA, protein, and metabolite profiling approach to develop an integrated, multiscale, molecular snapshot of this "AKT(low)" cancer cell state. We find that AKT-inhibited cancer cells suppress thousands of mRNA transcripts, and proteins related to the cell cycle, ribosome, and protein translation. Surprisingly, however, these AKT-inhibited cells simultaneously upregulate a host of other proteins and metabolites posttranscriptionally, reflecting activation of their endo-vesiculo-membrane system, secretion of inflammatory proteins, and elaboration of extracellular microvesicles. Importantly, these microvesicles enable rapidly proliferating cancer cells of various types to better withstand different stress conditions, including serum deprivation, hypoxia, or cytotoxic chemotherapy in vitro and xenografting in vivo. These findings suggest a model whereby cancer cells experiencing a partial inhibition of AKT signaling may actually promote the survival of neighbors through non-cell autonomous communication., (©2015 American Association for Cancer Research.)

Background: Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon., Methods: Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors., Results: The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network., Conclusions: These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.

Background: Accurate detection of characteristic proteins secreted by colon cancer tumor cells in biological fluids could serve as a biomarker for the disease. The aim of the present study was to identify and validate new serum biomarkers and demonstrate their potential usefulness for early diagnosis of colon cancer., Methods: The study was organized in three sequential phases: 1) biomarker discovery, 2) technical and biological validation, and 3) proof of concept to test the potential clinical use of selected biomarkers. A prioritized subset of the differentially-expressed genes between tissue types (50 colon mucosa from cancer-free individuals and 100 normal-tumor pairs from colon cancer patients) was validated and further tested in a series of serum samples from 80 colon cancer cases, 23 patients with adenoma and 77 cancer-free controls., Results: In the discovery phase, 505 unique candidate biomarkers were identified, with highly significant results and high capacity to discriminate between the different tissue types. After a subsequent prioritization, all tested genes (N = 23) were successfully validated in tissue, and one of them, COL10A1, showed relevant differences in serum protein levels between controls, patients with adenoma (p = 0.0083) and colon cancer cases (p = 3.2e-6)., Conclusion: We present a sequential process for the identification and further validation of biomarkers for early detection of colon cancer that identifies COL10A1 protein levels in serum as a potential diagnostic candidate to detect both adenoma lesions and tumor., Impact: The use of a cheap serum test for colon cancer screening should improve its participation rates and contribute to decrease the burden of this disease.

In this study, we aim to identify the genes responsible for colorectal cancer risk behind the loci identified in genome-wide association studies (GWAS). These genes may be candidate targets for developing new strategies for prevention or therapy. We analyzed the association of genotypes for 26 GWAS single nucleotide polymorphisms (SNPs) with the expression of genes within a 2 Mb region (cis-eQTLs). Affymetrix Human Genome U219 expression arrays were used to assess gene expression in two series of samples, one of healthy colonic mucosa (n = 47) and other of normal mucosa adjacent to colon cancer (n = 97, total 144). Paired tumor tissues (n = 97) were also analyzed but did not provide additional findings. Partial Pearson correlation (r), adjusted for sample type, was used for the analysis. We have found Bonferroni-significant cis-eQTLs in three loci: rs3802842 in 11q23.1 associated to C11orf53, COLCA1 (C11orf92) and COLCA2 (C11orf93; r = 0.60); rs7136702 in 12q13.12 associated to DIP2B (r = 0.63) and rs5934683 in Xp22.3 associated to SHROOM2 and GPR143 (r = 0.47). For loci in chromosomes 11 and 12, we have found other SNPs in linkage disequilibrium that are more strongly associated with the expression of the identified genes and are better functional candidates: rs7130173 for 11q23.1 (r = 0.66) and rs61927768 for 12q13.12 (r = 0.86). These SNPs are located in DNA regions that may harbor enhancers or transcription factor binding sites. The analysis of trans-eQTLs has identified additional genes in these loci that may have common regulatory mechanisms as shown by the analysis of protein-protein interaction networks., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Introduction: The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets., Methods: A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples., Results: Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system., Conclusions: The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties.

Introduction: Changes in breast cancer cell biology following hormonal treatment have been claimed as promising predictor markers of clinical benefit even outperforming clinical response. From previous work we selected 10 genes showing both a well known regulation by oestrogen and a high level of early transcriptional regulation following therapy with aromatase inhibitors. Here we use an animal breast cancer model to explore the feasibility of the determination of their expression in minimally invasive samples and to further assess the magnitude of their regulation by letrozole. ANIMAL AND METHODS: Aromatase inhibitor sensitive breast cancer tumours were grown in athymic mice under supplement with androstenedione. Following initial tumour growth animals were assigned to a control group or to receive letrozole at two different dosages. Fine needle aspirates were obtained at the moment of treatment assignation and one week later. Expression of the following genes at both time points was determined: Ki-67, Cyclin D1, pS2, Trefoil Factor 3, PDZ domain containing 1, Ubiquitin-conjugating enzyme E2C, Stanniocalcin 2, Topoisomerase 2 alfa, MAN1A1 and FAS., Results: Fine needles aspirates were found to be a feasible and reproducible technique for RNA extraction. Trefoil Factor 3, pS2, Cyclin D1 and Stanniocalcin 2 were significantly downregulated by letrozole. Among them pS2 appears to be most sensitive to aromatase inhibitor treatment even differentiating sub-optimal from optimal letrozole dosage., Discussion: We present pre-clinical evidence to justify the exploration in clinical trials of pS2, Trefoil factor 3, Cyclin D1 and Stanniocalcin as dynamic markers of oestrogen-driven pathway activation.

A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.

Background: Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally., Results: This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC., Conclusion: This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.