Preclinical efficacy of a HER2 synNotch/CEA-CAR combinatorial immunotherapy against colorectal cancer with HER2 amplification.

HER2 amplification occurs in approximately 5% of colorectal cancer (CRC) cases and is associated only partially with clinical response to combined human epidermal growth factor receptor 2 (HER2)/epidermal growth factor receptor (EGFR)-targeted treatment. An alternative approach based on adoptive cell therapy using T cells engineered with anti-HER2 chimeric antigen receptor (CAR) proved to be toxic due to on-target/off-tumor activity. Here we describe a combinatorial strategy to safely target HER2 amplification and carcinoembryonic antigen (CEA) expression in CRC using a synNotch-CAR-based artificial regulatory network. The natural killer (NK) cell line NK-92 was engineered with an anti-HER2 synNotch receptor driving the expression of a CAR against CEA only when engaged. After being transduced and sorted for HER2-driven CAR expression, cells were cloned. The clone with optimal performances in terms of specificity and amplitude of CAR induction demonstrated significant activity in vitro and in vivo specifically against HER2-amplified (HER2amp)/CEA+ CRC models, with no effects on cells with physiological HER2 levels. The HER2-synNotch/CEA-CAR-NK system provides an innovative, scalable, and safe off-the-shelf cell therapy approach with potential against HER2amp CRC resistant or partially responsive to HER2/EGFR blockade.

The EurOPDX Data Portal: an open platform for patient-derived cancer xenograft data sharing and visualization.

BACKGROUND: Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons - ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium. MAIN BODY: In this paper we describe the main features of the EurOPDX Data Portal ( https://dataportal.europdx.eu/ ), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views. (SHORT) CONCLUSION: EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users' interests.

A simplified integrated molecular and immunohistochemistry-based algorithm allows high accuracy prediction of glioblastoma transcriptional subtypes.

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.

Prospective patient stratification into robust cancer-cell intrinsic subtypes from colorectal cancer biopsies.

Colorectal cancer (CRC) biopsies underpin accurate diagnosis, but are also relevant for patient stratification in molecularly-guided clinical trials. The consensus molecular subtypes (CMSs) and colorectal cancer intrinsic subtypes (CRISs) transcriptional signatures have potential clinical utility for improving prognostic/predictive patient assignment. However, their ability to provide robust classification, particularly in pretreatment biopsies from multiple regions or at different time points, remains untested. In this study, we undertook a comprehensive assessment of the robustness of CRC transcriptional signatures, including CRIS and CMS, using a range of tumour sampling methodologies currently employed in clinical and translational research. These include analyses using (i) laser-capture microdissected CRC tissue, (ii) eight publically available rectal cancer biopsy data sets (n = 543), (iii) serial biopsies (from AXEBeam trial, NCT00828672; n = 10), (iv) multi-regional biopsies from colon tumours (n = 29 biopsies, n = 7 tumours), and (v) pretreatment biopsies from the phase II rectal cancer trial COPERNCIUS (NCT01263171; n = 44). Compared to previous results obtained using CRC resection material, we demonstrate that CMS classification in biopsy tissue is significantly less capable of reliably classifying patient subtype (43% unknown in biopsy versus 13% unknown in resections, p = 0.0001). In contrast, there was no significant difference in classification rate between biopsies and resections when using the CRIS classifier. Additionally, we demonstrated that CRIS provides significantly better spatially- and temporally- robust classification of molecular subtypes in CRC primary tumour tissue compared to CMS (p = 0.003 and p = 0.02, respectively). These findings have potential to inform ongoing biopsy-based patient stratification in CRC, enabling robust and stable assignment of patients into clinically-informative arms of prospective multi-arm, multi-stage clinical trials. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

FOXA1 and AR in invasive breast cancer: new findings on their co-expression and impact on prognosis in ER-positive patients.

BACKGROUND: The role of forkhead-box A1 (FOXA1) and Androgen receptor (AR) in breast cancer (BC) has been extensively studied. However, the prognostic role of their co-expression in Estrogen receptor positive (ER+) BC has not been investigated so far. The aim of the present study was thus to assess the co-expression (protein and mRNA) of FOXA1 and AR in BC patients, in order to evaluate their prognostic impact according to ER status. METHODS: Immunohistochemical expression of AR and FOXA1 was evaluated on 479 consecutive BC, with complete clinical-pathological and follow up data. Fresh-frozen tissues from 65 cases were available. The expression of AR and FOXA1 with ER was validated using mRNA analyses. Survival and Cox proportional hazard analyses were used to evaluate the relationship between FOXA1, AR and prognosis. RESULTS: Expression of ER, AR and FOXA1 was observed in 78, 60 and 85% of cases respectively. Most AR+ cases (97%) were also FOXA1+. The level of FOXA1 mRNA positively correlated with level of both AR mRNA (r = 0.8975; P < 0.001) and ER mRNA (r = 0.7326; P < 0.001). In ER+ BC, FOXA1 was associated with a good prognosis independently of AR expression in the three subgroups analyzed (FOXA1+/AR+; FOXA1+/AR-; FOXA1-/AR-). Multivariate analyses confirmed that FOXA1 may provide more information than AR in Disease-Free Interval (DFI) of ER+ BC patients. CONCLUSION: Our results suggest that in BC the expression of FOXA1 is directly related to the expression of AR. Despite that, FOXA1 is found as superior predicting marker of recurrences compared to AR in ER+ BC patients.

Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power.

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.

The clinical and biological significance of MIR-224 expression in colorectal cancer metastasis.

OBJECTIVE: 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. DESIGN: 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. RESULTS: 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, p<0.0001) 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). CONCLUSIONS: 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.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

The miR-126 regulates angiopoietin-1 signaling and vessel maturation by targeting p85ß.

Blood vessel formation depends on the highly coordinated actions of a variety of angiogenic regulators. Vascular endothelial growth factor (VEGF) and Angiopoietin-1 (Ang-1) are both potent and essential proangiogenic factors with complementary roles in vascular development and function. Whereas VEGF is required for the formation of the initial vascular plexus, Ang-1 contributes to the stabilization and maturation of growing blood vessels. Here, we provide evidence of a novel microRNA (miRNA)-dependent molecular mechanism of Ang-1 signalling modulation aimed at stabilizing adult vasculature. MiRNAs are short non-coding RNA molecules that post-trascriptionally regulate gene expression by translational suppression or in some instances by cleavage of the respective mRNA target. Our data indicate that endothelial cells of mature vessels express high levels of miR-126, which primarily targets phosphoinositide-3-kinase regulatory subunit 2 (p85ß). Down-regulation of miR-126 and over-expression of p85ß in endothelial cells inhibit the biological functions of Ang-1. Additionally, knockdown of miR-126 in zebrafish resulted in vascular remodelling and maturation defects, reminiscent of the Ang-1 loss-of-function phenotype. Our findings suggest that miR-126-mediated phosphoinositide-3-kinase regulation, not only fine-tunes VEGF-signaling, but it strongly enhances the activities of Ang-1 on vessel stabilization and maturation.

MACC1 mRNA levels predict cancer recurrence after resection of colorectal cancer liver metastases.

OBJECTIVE: Upon colon cancer metastasis resection in liver, disease outcome is heterogeneous, ranging from indolent to very aggressive, with early recurrence. The aim of this study is to investigate the capability of metastasis associated in colon cancer 1 (MACC1) levels measured in liver metastasis specimens to predict further recurrence of the disease. METHODS: Gene expression and gene dosage of MACC1, hepatocyte growth factor (HGF), and hepatocyte growth factor receptor (MET) were assessed using quantitative realtime polymerase chain reaction on a cohort of 64 liver metastasis samples from patients with complete follow-up of 36 months and detailed clinical annotation. The most relevant mutations associated to prognosis in colorectal cancer, KRAS, and PIK3CA were assessed on the same specimens with Sanger sequencing. RESULTS: Receiver operating characteristic (ROC) analysis revealed that MACC1 mRNA abundance is a good indicator of metastatic recurrence (AUC = 0.65, P < 0.05), whereas no such results were obtained with MET and HGF, nor with gene dosage. Generation of MACC1-based risk classes was capable of successfully separating patients into poor and good prognosis subgroups [hazard ratio (HR) = 5.236, 95% confidence interval (CI) = 1.2068-22.715, P < 0.05]. Also KRAS mutation was significantly associated with higher risk of recurrence (HR = 2.07, 95% CI = 1.048-4.09, P < 0.05). Cox regression multivariate analysis supported the independence of MACC1, but not KRAS, from known prognostic clinical information (Node Size HR = 3.155, 95% CI = 1.4418-6.905, P < 0.001, Preoperative carcinoembryonic antigen HR = 2.359, 95% CI = 1.0203-5.452, P < 0.05, MACC1 HR = 7.2739, 95% CI = 1.6584-31.905, P < 0.01). CONCLUSIONS: MACC1, a new easily detectable biomarker in cancer, is an independent prognostic factor of recurrence after liver resection of colorectal cancer metastasis.

Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity.

BACKGROUND: Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information. METHODS: A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification. RESULTS: Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification. CONCLUSIONS: These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Dual VEGFA/BRAF targeting boosts PD-1 blockade in melanoma through GM-CSF-mediated infiltration of M1 macrophages.

The introduction of targeted therapies represented one of the most significant advances in the treatment of BRAFV600E melanoma. However, the onset of acquired resistance remains a challenge. Previously, we showed in mouse xenografts that vascular endothelial growth factor (VEGFA) removal enhanced the antitumor effect of BRAF inhibition through the recruitment of M1 macrophages. In this work, we explored the strategy of VEGFA/BRAF inhibition in immunocompetent melanoma murine models. In BRAF mutant D4M melanoma tumors, VEGFA/BRAF targeting reshaped the tumor microenvironment, largely by stimulating infiltration of M1 macrophages and CD8+ T cells, and sensitized tumors to immune checkpoint blockade (ICB). Furthermore, we reported that the association of VEGFA/BRAF targeting with anti-PD-1 antibody (triple therapy) resulted in a durable response and enabled complete tumor eradication in 50% of the mice, establishing immunological memory. Neutralization and CRISPR-Cas-mediated editing of granulocyte-macrophage colony-stimulating factor (GM-CSF) abrogated antitumor response prompted by triple therapy and identified GM-CSF as the cytokine instrumental in M1-macrophage recruitment. Our data suggest that VEGFA/BRAF targeting in melanoma induces the activation of innate and adaptive immunity and prepares tumors for ICB. Our study contributes to understanding the tumor biology of BRAFV600E melanoma and suggests VEGFA as therapeutic target.

Coexisting cancer stem cells with heterogeneous gene amplifications, transcriptional profiles, and malignancy are isolated from single glioblastomas.

Glioblastoma (GBM) is known as an intractable, highly heterogeneous tumor encompassing multiple subclones, each supported by a distinct glioblastoma stem cell (GSC). The contribution of GSC genetic and transcriptional heterogeneity to tumor subclonal properties is debated. In this study, we describe the systematic derivation, propagation, and characterization of multiple distinct GSCs from single, treatment-naive GBMs (GSC families). The tumorigenic potential of each GSC better correlates with its transcriptional profile than its genetic make-up, with classical GSCs being inherently more aggressive and mesenchymal more dependent on exogenous growth factors across multiple GBMs. These GSCs can segregate and recapitulate different histopathological aspects of the same GBM, as shown in a paradigmatic tumor with two histopathologically distinct components, including a conventional GBM and a more aggressive primitive neuronal component. This study provides a resource for investigating how GSCs with distinct genetic and/or phenotypic features contribute to individual GBM heterogeneity and malignant escalation.

Transcriptional hallmarks of Noonan syndrome and Noonan-like syndrome with loose anagen hair.

Noonan syndrome (NS) is among the most common nonchromosomal disorders affecting development and growth. NS is genetically heterogeneous, being caused by germline mutations affecting various genes implicated in the RAS signaling network. This network transduces extracellular signals into intracellular biochemical and transcriptional responses controlling cell proliferation, differentiation, metabolism, and senescence. To explore the transcriptional consequences of NS-causing mutations, we performed global mRNA expression profiling on peripheral blood mononuclear cells obtained from 23 NS patients carrying heterozygous mutations in PTPN11 or SOS1. Gene expression profiling was also resolved in five subjects with Noonan-like syndrome with loose anagen hair (NS/LAH), a condition clinically related to NS and caused by an invariant mutation in SHOC2. Robust transcriptional signatures were found to specifically discriminate each of the three mutation groups from 21 age- and sex-matched controls. Despite the only partial overlap in terms of gene composition, the three signatures showed a notable concordance in terms of biological processes and regulatory circuits affected. These data establish expression profiling of peripheral blood mononuclear cells as a powerful tool to appreciate differential perturbations driven by germline mutations of transducers involved in RAS signaling and to dissect molecular mechanisms underlying NS and other RASopathies.

The neuronal protein Neuroligin 1 promotes colorectal cancer progression by modulating the APC/ß-catenin pathway.

BACKGROUND: Colorectal cancer (CRC) remains largely incurable when diagnosed at the metastatic stage. Despite some advances in precision medicine for this disease in recent years, new molecular targets, as well as prognostic/predictive markers, are highly needed. Neuroligin 1 (NLGN1) is a transmembrane protein that interacts at the synapse with the tumor suppressor adenomatous polyposis Coli (APC), which is heavily involved in the pathogenesis of CRC and is a key player in the WNT/ß-catenin pathway. METHODS: After performing expression studies of NLGN1 on human CRC samples, in this paper we used in vitro and in vivo approaches to study CRC cells extravasation and metastasis formation capabilities. At the molecular level, the functional link between APC and NLGN1 in the cancer context was studied. RESULTS: Here we show that NLGN1 is expressed in human colorectal tumors, including clusters of aggressive migrating (budding) single tumor cells and vascular emboli. We found that NLGN1 promotes CRC cells crossing of an endothelial monolayer (i.e. Trans-Endothelial Migration or TEM) in vitro, as well as cell extravasation/lung invasion and differential organ metastatization in two mouse models. Mechanistically, NLGN1 promotes APC localization to the cell membrane and co-immunoprecipitates with some isoforms of this protein stimulates ß-catenin translocation to the nucleus, upregulates mesenchymal markers and WNT target genes and induces an "EMT phenotype" in CRC cell lines CONCLUSIONS: In conclusion, we have uncovered a novel modulator of CRC aggressiveness which impacts on a critical pathogenetic pathway of this disease, and may represent a novel therapeutic target, with the added benefit of carrying over substantial knowledge from the neurobiology field.

Implementing subtype-specific pre-clinical models of breast cancer to study pre-treatment aspirin effects.

BACKGORUND: 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. METHOD: 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 mm3 and subsequently resected. MDA-MB-231/LN/2-4/H2N mice were monitored for metastasis utilising imaging. To interrogate the survival benefit of pre-treatment aspirin, 3 weeks post-resection, HCC1954/TNBC animals received standard-of-care (SOC) chemotherapy for 6 weeks. Primary tumour response to aspirin was interrogated using immunohistochemistry. RESULTS: Aspirin delayed time to metastasis in MDA-MB-231/LN/2-4/H2N xenografts and decreased growth of HER2+ /TNBC primary tumours. Lymphangiogenic factors and lymph vessels number were decreased in HER2+ tumours. However, no survival benefit was seen in aspirin pre-treated animals (HCC1954/TNBC) that further received adjuvant SOC, compared with animals treated with SOC alone. In an effort to study mechanisms responsible for the observed reduction in lymphangiogenesis in HER2+ BC we utilised an in vitro co-culture system of HCC1954 tumour cells and mesenchymal stromal cells (MSC). Aspirin abrogated the secretion of VEGF-C in MSCs and also decreased the lymph/angiogenic potential of the MSCs and HCC1954 by tubule formation assay. Furthermore, aspirin decreased the secretion of uPA in HCC1954 cells potentially diminishing its metastatic capability. CONCLUSION: Our 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+ or TNBC setting.

DNA damage response and repair genes in advanced bone and soft tissue sarcomas: An 8-gene signature as a candidate predictive biomarker of response to trabectedin and olaparib combination.

Background: Advanced and unresectable bone and soft tissue sarcomas (BSTS) still represent an unmet medical need. We demonstrated that the alkylating agent trabectedin and the PARP1-inhibitor olaparib display antitumor activity in BSTS preclinical models. Moreover, in a phase Ib clinical trial (NCT02398058), feasibility, tolerability and encouraging results have been observed and the treatment combination is currently under study in a phase II trial (NCT03838744). Methods: Differential expression of genes involved in DNA Damage Response and Repair was evaluated by Nanostring® technology, extracting RNA from pre-treatment tumor samples of 16 responder (≥6-month progression free survival) and 16 non-responder patients. Data validation was performed by quantitative real-time PCR, RNA in situ hybridization, and immunohistochemistry. The correlation between the identified candidate genes and both progression-free survival and overall survival was investigated in the publicly available dataset "Sarcoma (TCGA, The Cancer Genome Atlas)". Results: Differential RNA expression analysis revealed an 8-gene signature (CDKN2A, PIK3R1, SLFN11, ATM, APEX2, BLM, XRCC2, MAD2L2) defining patients with better outcome upon trabectedin+olaparib treatment. In responder vs. non-responder patients, a significant differential expression of these genes was further confirmed by RNA in situ hybridization and by qRT-PCR and immunohistochemistry in selected experiments. Correlation between survival outcomes and genetic alterations in the identified genes was shown in the TCGA sarcoma dataset. Conclusions: This work identified an 8-gene expression signature to improve prediction of response to trabectedin+olaparib combination in BSTS. The predictive role of these potential biomarkers warrants further investigation.

A non-dividing cell population with high pyruvate dehydrogenase kinase activity regulates metabolic heterogeneity and tumorigenesis in the intestine.

Although reprogramming of cellular metabolism is a hallmark of cancer, little is known about how metabolic reprogramming contributes to early stages of transformation. Here, we show that the histone deacetylase SIRT6 regulates tumor initiation during intestinal cancer by controlling glucose metabolism. Loss of SIRT6 results in an increase in the number of intestinal stem cells (ISCs), which translates into enhanced tumor initiating potential in APCmin mice. By tracking down the connection between glucose metabolism and tumor initiation, we find a metabolic compartmentalization within the intestinal epithelium and adenomas, where a rare population of cells exhibit features of Warburg-like metabolism characterized by high pyruvate dehydrogenase kinase (PDK) activity. Our results show that these cells are quiescent cells expressing +4 ISCs and enteroendocrine markers. Active glycolysis in these cells suppresses ROS accumulation and enhances their stem cell and tumorigenic potential. Our studies reveal that aerobic glycolysis represents a heterogeneous feature of cancer, and indicate that this metabolic adaptation can occur in non-dividing cells, suggesting a role for the Warburg effect beyond biomass production in tumors.

Mulcom: a multiple comparison statistical test for microarray data in Bioconductor.

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.

Synthetic Lethality Screening Highlights Colorectal Cancer Vulnerability to Concomitant Blockade of NEDD8 and EGFR Pathways.

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.