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Simple Summary Identification of effective therapies for clinically aggressive, treatment-resistant colorectal cancer (CRC) remains an unmet clinical need. Targeted therapies against the epidermal growth factor receptor (EGFR) signaling axis lead to clinical benefits only in a small fraction of patients due to primary and acquired resistance. We previously showed that the NEDD8 pathway inhibitor pevonedistat induced tumor stabilization in preclinical models of aggressive CRC. Here, through synthetic lethality screenings, we found that pevonedistat could be successfully combined with EGFR pathway-targeted treatments in BRAF-mutant and RAS-RAF wild-type CRCs originally resistant to BRAF and EGFR blockade. We found that combined blockade of NEDD8 and EGFR pathways reverted compensatory feedback loops that reduced the efficacy of single treatments. Our results provide preclinical validation of a promising therapeutic strategy for clinically aggressive CRC resistant to EGFR and BRAF-targeted treatments. Abstract Colorectal cancer (CRC) is a heterogeneous disease showing significant variability in clinical aggressiveness. Primary and acquired resistance limits the efficacy of available treatments, and identification of effective drug combinations is needed to further improve patients’ outcomes. We previously found that the NEDD8-activating enzyme inhibitor pevonedistat induced tumor stabilization in preclinical models of poorly differentiated, clinically aggressive CRC resistant to available therapies. To identify drugs that can be effectively combined with pevonedistat, we performed a “drop-out” loss-of-function synthetic lethality screening with an shRNA library covering 200 drug-target genes in four different CRC cell lines. Multiple screening hits were found to be involved in the EGFR signaling pathway, suggesting that, rather than inhibition of a specific gene, interference with the EGFR pathway at any level could be effectively leveraged for combination therapies based on pevonedistat. Exploiting both BRAF-mutant and RAS/RAF wild-type CRC models, we validated the therapeutic relevance of our findings by showing that combined blockade of NEDD8 and EGFR pathways led to increased growth arrest and apoptosis both in vitro and in vivo. Pathway modulation analysis showed that compensatory feedback loops induced by single treatments were blunted by the combinations. These results unveil possible therapeutic opportunities in specific CRC clinical settings.

Glioblastomas (GBM) can be classified into three major transcriptional subgroups (proneural, mesenchymal, classical), underlying different molecular alterations, prognosis, and response to therapy. However, transcriptional analysis is not routinely feasible and assessment of a simplified method for glioblastoma subclassification is required. We propose an integrated molecular and immunohistochemical approach aimed at identifying GBM subtypes in routine paraffin-embedded material. RNA-sequencing analysis was performed on representative samples (n = 51) by means of a “glioblastoma transcriptional subtypes (GliTS) redux” custom gene signature including a restricted number (n = 90) of upregulated genes validated on the TCGA dataset. With this dataset, immunohistochemical profiles, based on expression of a restricted panel of gene classifiers, were integrated by a machine-learning approach to generate a GliTS based on protein quantification that allowed an efficient GliTS assignment when applied to an extended cohort (n = 197). GliTS redux maintained high levels of correspondence with the original GliTS classification using the TCGA dataset. The machine-learning approach designed an immunohistochemical (IHC)-based classification, whose concordance was 79.5% with the transcriptional- based classification, and reached 90% for the mesenchymal subgroup. Distribution and survival of GliTS were in line with reported data, with the mesenchymal subgroup given the worst prognosis. Notably, the algorithm allowed the identification of cases with comparable probability to be assigned to different GliTS, thus falling within overlapping regions and reflecting an extreme heterogeneous phenotype that mirrors the underlying genetic and biological tumor heterogeneity. Indeed, while mesenchymal and classical subgroups were well segregated, the proneural types frequently showed a mixed proneural/classical phenotype, predicted as proneural by the algorithm, but with comparable probability of being assigned to the classical subtype. These cases, characterized by concomitant high expression of EGFR and proneural biomarkers, showed lower survival. Collectively, these data indicate that a restricted panel of highly sensitive immunohistochemical markers can efficiently predict GliTS with high accuracy and significant association with different clinical outcomes. The authors developed a novel simplified assay for glioblastoma transcriptional classification on formalin-fixed-paraffin-embedded tissue samples. On such dataset, immunohistochemical profiles, based on expression of a restricted panel of gene classifiers, were integrated by machine learning approach to generate a glioblastoma transcriptional signature based on protein quantification that allowed to efficiently assign transcriptional subgroups to an extended cohort. Correlations with both histopathological features and clinical outcome have been also performed.

Patient-derived xenografts (PDXs) are obtained by transplanting fragments of a patient's tumour into immunodeficient mice. Growth and propagation of PDXs allows correlating therapeutic response in vivo with extensive, multi-dimensional molecular annotation, leading to identification of predictive biomarkers. PDXs are increasingly recognised as clinically relevant models of cancer for several reasons, of which the main is the possibility of studying the behaviour of cancer cells in a natural microenvironment, where they interact with stromal components accrued from the mouse host. PDXs maintain close similarities with the tumour of origin, in terms of tissue architecture, molecular features and response to treatments. Indeed, preclinical trials in PDXs have been shown to match and also anticipate data obtained in patients. Exploration of more complex processes like metastatic evolution and antitumour immune responses is actively pursued with PDXs, as new generations of host models emerge on the horizon.

In recent years, cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) have substantially improved the clinical outcome of pseudomyxoma peritonei (PMP) originating from mucinous appendiceal cancer. However, current histopathological grading of appendiceal PMP frequently fails in predicting disease outcome. We recently observed that the integration of cancer cell transcriptional traits with those of cancer-associated fibroblasts (CAFs) improves prognostic prediction for tumors of the large intestine. We therefore generated global expression profiles on a consecutive series of 24 PMP patients treated with CRS plus HIPEC. Multiple lesions were profiled for nine patients. We then used expression data to stratify the samples by a previously published “high-risk appendiceal cancer” (HRAC) signature and by a CAF signature that we previously developed for colorectal cancer, or by a combination of both. The prognostic value of the HRAC signature was confirmed in our cohort and further improved by integration of the CAF signature. Classification of cases profiled for multiple lesions revealed the existence of outlier samples and highlighted the need of profiling multiple PMP lesions to select representative samples for optimal performances. The integrated predictor was subsequently validated in an independent PMP cohort. These results provide new insights into PMP biology, revealing a previously unrecognized prognostic role of the stromal component and supporting integration of standard pathological grade with the HRAC and CAF transcriptional signatures to better predict disease outcome.

Angiogenesis requires the temporal coordination of the proliferation and the migration of endothelial cells. Here, we investigated the regulatory role of microRNAs (miRNAs) in harmonizing angiogenesis processes in a three-dimensional in vitro model. We described a microRNA network which contributes to the observed down- and upregulation of proliferative and migratory genes, respectively. Global analysis of miRNA–target gene interactions identified two sub-network modules, the first organized in upregulated miRNAs connected with downregulated target genes and the second with opposite features. miR-424–5p and miR-29a-3p were selected for the network validation. Gain- and loss-of-function approaches targeting these microRNAs impaired angiogenesis, suggesting that these modules are instrumental to the temporal coordination of endothelial migration and proliferation. Interestingly, miR-29a-3p and its targets belong to a selective biomarker that is able to identify colorectal cancer patients who are responding to anti-angiogenic treatments. Our results provide a view of higher-order interactions in angiogenesis that has potential to provide diagnostic and therapeutic insights.

Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC “intrinsic subtypes”. We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.

Patient-Derived Xenografts (PDXs), entailing implantation of cancer specimens in immunocompromised mice, are emerging as a valuable translational model that could help validate biologically relevant targets and assist the clinical development of novel therapeutic strategies for gastric cancer. More than 30% of PDXs generated from gastric carcinoma samples developed human B-cell lymphomas instead of gastric cancer. These lymphomas were monoclonal, Epstein Barr Virus (EBV) positive, originated tumorigenic cell cultures and displayed a mutational burden and an expression profile distinct from gastric adenocarcinomas. The ability of grafted samples to develop lymphomas did not correlate with patient outcome, nor with the histotype, the lymphocyte infiltration level, or the EBV status of the original gastric tumor, impeding from foreseeing lymphoma onset. Interestingly, lymphoma development was significantly more frequent when primary rather than metastatic samples were grafted. Notably, the development of such lympho-proliferative disease could be prevented by a short rituximab treatment upon mice implant, without negatively affecting gastric carcinoma engraftment. Due to the high frequency of human lymphoma onset, our data show that a careful histologic analysis is mandatory when generating gastric cancer PDXs. Such care would avoid misleading results that could occur if testing of putative gastric cancer therapies is performed in lymphoma PDXs. We propose rituximab treatment of mice to prevent lymphoma development in PDX models, averting the loss of human-derived samples.

BackgroundNeoantigens that arise as a consequence of tumour-specific mutations can be recognized by T lymphocytes leading to effective immune surveillance. In colorectal cancer (CRC) and other tumour types, a high number of neoantigens is associated with patient response to immune therapies. The molecular processes governing the generation of neoantigens and their turnover in cancer cells are poorly understood. We exploited CRC as a model system to understand how alterations in DNA repair pathways modulate neoantigen profiles over time.MethodsWe performed Whole Exome Sequencing (WES) and RNA sequencing (RNAseq) in CRC cell lines,in vitroandvivo, and in CRC patient-derived xenografts (PDXs) to track longitudinally genomic profiles, clonal evolution, mutational signatures and predicted neoantigens.ResultsThe majority of CRC models showed remarkably stable mutational and neoantigen profiles, however those carrying defects in DNA repair genes continuously diversified. Rapidly evolving and evolutionary stable CRCs displayed characteristic genomic signatures, and transcriptional profiles. Downregulation of molecules implicated in antigen presentation occurred selectively in highly mutated and rapidly-evolving CRC.ConclusionsThese results indicate that CRC carrying alterations in DNA repair pathways display dynamic neoantigen patterns that fluctuate over time. We define CRC subsets characterized by slow and fast evolvability and link this phenotype to downregulation of antigen-presenting cellular mechanisms. Longitudinal monitoring of the neoantigen landscape could be relevant in the context of precision medicine.

Purpose: Patient-derived xenograft (PDX) models accurately recapitulate the tumor of origin in terms of histopathology, genomic landscape, and therapeutic response, but some limitations due to costs associated with their maintenance and restricted amenability for large-scale screenings still exist. To overcome these issues, we established a platform of 2D cell lines (xeno-cell lines, XL), derived from PDXs of colorectal cancer with matched patient germline gDNA available. Experimental Design: Whole-exome and transcriptome sequencing analyses were performed. Biomarkers of response and resistance to anti-HER therapy were annotated. Dependency on the WRN helicase gene was assessed in MSS, MSI-H, and MSI-like XLs using a reverse genetics functional approach. Results: XLs recapitulated the entire spectrum of colorectal cancer transcriptional subtypes. Exome and RNA-seq analyses delineated several molecular biomarkers of response and resistance to EGFR and HER2 blockade. Genotype-driven responses observed in vitro in XLs were confirmed in vivo in the matched PDXs. MSI-H models were dependent upon WRN gene expression, while loss of WRN did not affect MSS XLs growth. Interestingly, one MSS XL with transcriptional MSI-like traits was sensitive to WRN depletion. Conclusions: The XL platform represents a preclinical tool for functional gene validation and proof-of-concept studies to identify novel druggable vulnerabilities in colorectal cancer.

Cancer cell dependence on activated oncogenes is therapeutically targeted, but acquired resistance is virtually unavoidable. Here we show that the treatment of addicted melanoma cells with BRAF inhibitors, and of breast cancer cells with HER2-targeted drugs, led to an adaptive rise in neuropilin-1 (NRP1) expression, which is crucial for the onset of acquired resistance to therapy. Moreover, NRP1 levels dictated the efficacy of MET oncogene inhibitors in addicted stomach and lung carcinoma cells. Mechanistically, NRP1 induced a JNK-dependent signaling cascade leading to the upregulation of alternative effector kinases EGFR or IGF1R, which in turn sustained cancer cell growth and mediated acquired resistance to BRAF, HER2, or MET inhibitors. Notably, the combination with NRP1-interfering molecules improved the efficacy of oncogene-targeted drugs and prevented or even reversed the onset of resistance in cancer cells and tumor models. Our study provides the rationale for targeting the NRP1-dependent upregulation of tyrosine kinases, which are responsible for loss of responsiveness to oncogene-targeted therapies.

BRAF V600E mutations occur in ∼10% of colorectal cancer cases, are associated with poor survival, and have limited responses to BRAF/MEK inhibition with or without EGFR inhibition. There is an unmet need to understand the biology of poor prognostic BRAFMT colorectal cancer. We have used differential gene expression and pathway analyses of untreated stage II and stage III BRAFMT (discovery set: n = 31; validation set: n = 26) colorectal cancer, and an siRNA screen to characterize the biology underpinning the BRAFMT subgroup with poorest outcome. These analyses identified the unfolded protein response (UPR) as a novel and druggable pathway associated with the BRAFMT colorectal cancer subgroup with poorest outcome. We also found that oncogenic BRAF drives endoplasmic reticulum (ER) stress and UPR pathway activation through MEK/ERK. Furthermore, inhibition of GRP78, the master regulator of the UPR, using siRNA or small molecule inhibition, resulted in acute ER stress and apoptosis, in particular in BRAFMT colorectal cancer cells. In addition, dual targeting of protein degradation using combined Carfilzomib (proteasome inhibitor) and ACY-1215 (HDAC6-selective inhibitor) treatment resulted in marked accumulation of protein aggregates, acute ER stress, apoptosis, and therapeutic efficacy in BRAFMT in vitro and xenograft models. Mechanistically, we found that the apoptosis following combined Carfilzomib/ACY-1215 treatment is mediated through increased CHOP expression. Taken together, our findings indicate that oncogenic BRAF induces chronic ER stress and that inducers of acute ER stress could be a novel treatment strategy for poor prognostic BRAFMT colorectal cancer. Mol Cancer Ther; 17(6); 1280–90. ©2018 AACR.

Summary The microenvironment influences cancer drug response and sustains resistance to therapies targeting receptor-tyrosine kinases. However, if and how the tumor microenvironment can be altered during treatment, contributing to resistance onset, is not known. We show that, under prolonged treatment with tyrosine kinase inhibitors (TKIs), EGFR- or MET-addicted cancer cells displayed a metabolic shift toward increased glycolysis and lactate production. We identified secreted lactate as the key molecule instructing cancer-associated fibroblasts to produce hepatocyte growth factor (HGF) in a nuclear factor κB-dependent manner. Increased HGF, activating MET-dependent signaling in cancer cells, sustained resistance to TKIs. Functional or pharmacological targeting of molecules involved in the lactate axis abrogated in vivo resistance, demonstrating the crucial role of this metabolite in the adaptive process. This adaptive resistance mechanism was observed in lung cancer patients progressed on EGFR TKIs, demonstrating the clinical relevance of our findings and opening novel scenarios in the challenge to drug resistance.

Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis ( MaREA ), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.

Stromal content heavily impacts the transcriptional classification of colorectal cancer (CRC), with clinical and biological implications. Lineage-dependent stromal transcriptional components could therefore dominate over more subtle expression traits inherent to cancer cells. Since in patient-derived xenografts (PDXs) stromal cells of the human tumour are substituted by murine counterparts, here we deploy human-specific expression profiling of CRC PDXs to assess cancer-cell intrinsic transcriptional features. Through this approach, we identify five CRC intrinsic subtypes (CRIS) endowed with distinctive molecular, functional and phenotypic peculiarities: (i) CRIS-A: mucinous, glycolytic, enriched for microsatellite instability or KRAS mutations; (ii) CRIS-B: TGF-β pathway activity, epithelial–mesenchymal transition, poor prognosis; (iii) CRIS-C: elevated EGFR signalling, sensitivity to EGFR inhibitors; (iv) CRIS-D: WNT activation, IGF2 gene overexpression and amplification; and (v) CRIS-E: Paneth cell-like phenotype, TP53 mutations. CRIS subtypes successfully categorize independent sets of primary and metastatic CRCs, with limited overlap on existing transcriptional classes and unprecedented predictive and prognostic performances., Stromal cells contribute to the gene expression profiles based on which colorectal cancer (CRC) molecular subtypes are classified. Here, patient-derived xenografts enable the authors to obtain cancer cell-specific transcriptomes by excluding transcripts from murine stromal cells, based on which they define CRC intrinsic subtypes (CRIS) and evaluate their prognostic and predictive potential.

MicroRNA (miRNA) expression profile can be used as prognostic marker for human cancers. We aim to explore the significance of miRNAs in colorectal cancer (CRC) metastasis.We performed miRNA microarrays using primary CRC tissues from patients with and without metastasis, and validated selected candidates in 85 CRC samples by quantitative real-time PCR (qRT-PCR). We tested metastatic activity of selected miRNAs and identified miRNA targets by prediction algorithms, qRT-PCR, western blot and luciferase assays. Clinical outcomes were analysed in six sets of CRC cases (n=449), including The Cancer Genome Atlas (TCGA) consortium and correlated with miR-224 status. We used the Kaplan-Meier method and log-rank test to assess the difference in survival between patients with low or high levels of miR-224 expression.MiR-224 expression increases consistently with tumour burden and microsatellite stable status, and miR-224 enhances CRC metastasis in vitro and in vivo. We identified SMAD4 as a miR-224 target and observed negative correlation (Spearman Rs=-0.44, p0.0001) between SMAD4 and miR-224 expression in clinical samples. Patients with high miR-224 levels display shorter overall survival in multiple CRC cohorts (p=0.0259, 0.0137, 0.0207, 0.0181, 0.0331 and 0.0037, respectively), and shorter metastasis-free survival (HR 6.51, 95% CI 1.97 to 21.51, p=0.0008). In the TCGA set, combined analysis of miR-224 with SMAD4 expression enhanced correlation with survival (HR 4.12, 95% CI 1.1 to 15.41, p=0.0175).MiR-224 promotes CRC metastasis, at least in part, through the regulation of SMAD4. MiR-224 expression in primary CRC, alone or combined with its targets, may have prognostic value for survival of patients with CRC.

Recent studies identified a poor-prognosis stem/serrated/mesenchymal (SSM) transcriptional subtype of colorectal cancer (CRC). We noted that genes upregulated in this subtype are also prominently expressed by stromal cells, suggesting that SSM transcripts could derive from stromal rather than epithelial cancer cells. To test this hypothesis, we analyzed CRC expression data from patient-derived xenografts, where mouse stroma supports human cancer cells. Species-specific expression analysis showed that the mRNA levels of SSM genes were mostly due to stromal expression. Transcriptional signatures built to specifically report the abundance of cancer-associated fibroblasts (CAFs), leukocytes or endothelial cells all had significantly higher expression in human CRC samples of the SSM subtype. High expression of the CAF signature was associated with poor prognosis in untreated CRC, and joint high expression of the stromal signatures predicted resistance to radiotherapy in rectal cancer. These data show that the distinctive transcriptional and clinical features of the SSM subtype can be ascribed to its particularly abundant stromal component.

The development of molecularly targeted anticancer agents relies on large panels of tumour-specific preclinical models closely recapitulating the molecular heterogeneity observed in patients. Here we describe the mutational and gene expression analyses of 151 colorectal cancer (CRC) cell lines. We find that the whole spectrum of CRC molecular and transcriptional subtypes, previously defined in patients, is represented in this cell line compendium. Transcriptional outlier analysis identifies RAS/BRAF wild-type cells, resistant to EGFR blockade, functionally and pharmacologically addicted to kinase genes including ALK, FGFR2, NTRK1/2 and RET. The same genes are present as expression outliers in CRC patient samples. Genomic rearrangements (translocations) involving the ALK and NTRK1 genes are associated with the overexpression of the corresponding proteins in CRC specimens. The approach described here can be used to pinpoint CRCs with exquisite dependencies to individual kinases for which clinically approved drugs are already available.

Background: Bone metastases represent a common and severe complication in breast cancer, and the involvement of cancer stem cells (CSCs) in the promotion of bone metastasis is currently under discussion. Here, we used a human-in-mice model to study bone metastasis formation due to primary breast CSCs-like colonisation. Methods: Primary CD44+CD24− breast CSCs-like were transduced by a luciferase-lentiviral vector and injected through subcutaneous and intracardiac (IC) routes in non-obese/severe-combined immunodeficient (NOD/SCID) mice carrying subcutaneous human bone implants. The CSCs-like localisation was monitored by in vivo luciferase imaging. Bone metastatic CSCs-like were analysed through immunohistochemistry and flow cytometry, and gene expression analyses were performed by microarray techniques. Results: Breast CSCs-like colonised the human-implanted bone, resulting in bone remodelling. Bone metastatic lesions were histologically apparent by tumour cell expression of epithelial markers and vimentin. The bone-isolated CSCs-like were CD44−CD24+ and showed tumorigenic abilities after injection in secondary mice. CD44−CD24+ CSCs-like displayed a distinct bone tropism signature that was enriched in genes that discriminate bone metastases of breast cancer from metastases at other organs. Conclusion: Breast CSCs-like promote bone metastasis and display a CSCs-like bone tropism signature. This signature has clinical prognostic relevance, because it efficiently discriminates osteotropic breast cancers from tumour metastases at other sites.

Background Many microarray experiments search for genes with differential expression between a common "reference" group and multiple "test" groups. In such cases currently employed statistical approaches based on t-tests or close derivatives have limited efficacy, mainly because estimation of the standard error is done on only two groups at a time. Alternative approaches based on ANOVA correctly capture within-group variance from all the groups, but then do not confront single test groups with the reference. Ideally, a t-test better suited for this type of data would compare each test group with the reference, but use within-group variance calculated from all the groups. Results We implemented an R-Bioconductor package named Mulcom, with a statistical test derived from the Dunnett's t-test, designed to compare multiple test groups individually against a common reference. Interestingly, the Dunnett's test uses for the denominator of each comparison a within-group standard error aggregated from all the experimental groups. In addition to the basic Dunnett's t value, the package includes an optional minimal fold-change threshold, m. Due to the automated, permutation-based estimation of False Discovery Rate (FDR), the package also permits fast optimization of the test, to obtain the maximum number of significant genes at a given FDR value. When applied to a time-course experiment profiled in parallel on two microarray platforms, and compared with two commonly used tests, Mulcom displayed better concordance of significant genes in the two array platforms (39% vs. 26% or 15%), and higher enrichment in functional annotation to categories related to the biology of the experiment (p value < 0.001 in 4 categories vs. 3). Conclusions The Mulcom package provides a powerful tool for the identification of differentially expressed genes when several experimental conditions are compared against a common reference. The results of the practical example presented here show that lists of differentially expressed genes generated by Mulcom are particularly consistent across microarray platforms and enriched in genes belonging to functionally significant groups.

Prior data suggest pre-diagnostic aspirin use impacts breast tumour biology and patient outcome. Here, we employed faithful surgical resection models of HER2+ and triple-negative breast cancer (TNBC), to study outcome and response mechanisms across breast cancer subtypes.NOD/SCID mice were implanted with HER2+ MDA-MB-231/LN/2-4/H2N, trastuzumab-resistant HER2+ HCC1954 or a TNBC patient-derived xenograft (PDX). A daily low-dose aspirin regimen commenced until primary tumours reached ~250 mmAspirin delayed time to metastasis in MDA-MB-231/LN/2-4/H2N xenografts and decreased growth of HER2Our data employing clinically relevant models demonstrate that aspirin alters breast tumour biology. However, aspirin may not represent a robust chemo-preventative agent in the HER2

Background Systematic search for genes whose gain-of-function by exogenous expression confers an advantage in cell-based selective screenings is a powerful method for unbiased functional exploration of the genome, and has the potential to disclose new targets for cancer therapy. A major limit of this approach resides in the labor-intensive cloning of resistant cells, identification of the integrated genes and validation of their ability to confer a selective advantage. Moreover, the selection has to be drastic and genes conferring a limited advantage are typically missed. Results We developed a new functional screening strategy based on transduction of mammalian cells of a given species with an expression library from another species, followed by one-shot quantitative tracing with DNA microarrays of all library-derived transcripts before and after selection. In this way, exogenous transcripts enriched after selection, and therefore likely to confer resistance, are readily detected. We transduced a retroviral cDNA expression library from mouse testis into human and canine cells, and optimized the use of commercial murine gene expression arrays for species-specific detection of library-derived transcripts. We then conducted a functional screening by growing library-transduced canine MDCK cells in suspension, to enrich for cDNAs conferring anchorage independence. Notably, these cells show partial resistance to loss of anchorage, and the selection can be of limited stringency, compromising approaches based on clonal selection or anyway requiring high stringency. Microarray analysis revealed reproducible enrichment after three weeks of growth on polyhema for seven genes, among which the Hras proto-oncogene and Sox5. When individually transduced into MDCK cells, Sox5 specifically promoted anchorage-independent growth, thereby confirming the validity and specificity of the approach. Conclusion The procedure described here brings substantial advantages to the field of expression cloning, being faster, more systematic and more sensitive. Indeed, this strategy allowed identification and validation of genes promoting anchorage-independent growth of epithelial cells under selection conditions not amenable to conventional expression cloning.