Uridine-derived ribose fuels glucose-restricted pancreatic cancer

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease notoriously resistant to therapy1,2. This is mediated in part by a complex tumour microenvironment3, low vascularity4, and metabolic aberrations5,6. Although altered metabolism drives tumour progression, the spectrum of metabolites used as nutrients by PDA remains largely unknown. Here we identified uridine as a fuel for PDA in glucose-deprived conditions by assessing how more than 175 metabolites impacted metabolic activity in 21 pancreatic cell lines under nutrient restriction. Uridine utilization strongly correlated with the expression of uridine phosphorylase 1 (UPP1), which we demonstrate liberates uridine-derived ribose to fuel central carbon metabolism and thereby support redox balance, survival and proliferation in glucose-restricted PDA cells. In PDA, UPP1 is regulated by KRAS-MAPK signalling and is augmented by nutrient restriction. Consistently, tumours expressed high UPP1 compared with non-tumoural tissues, and UPP1 expression correlated with poor survival in cohorts of patients with PDA. Uridine is available in the tumour microenvironment, and we demonstrated that uridine-derived ribose is actively catabolized in tumours. Finally, UPP1 deletion restricted the ability of PDA cells to use uridine and blunted tumour growth in immunocompetent mouse models. Our data identify uridine utilization as an important compensatory metabolic process in nutrient-deprived PDA cells, suggesting a novel metabolic axis for PDA therapy.

Immune landscape, evolution, hypoxia-mediated viral mimicry pathways and therapeutic potential in molecular subtypes of pancreatic neuroendocrine tumours

OBJECTIVE

A comprehensive analysis of the immune landscape of pancreatic neuroendocrine tumours (PanNETs) was performed according to clinicopathological parameters and previously defined molecular subtypes to identify potential therapeutic vulnerabilities in this disease.

DESIGN

Differential expression analysis of 600 immune-related genes was performed on 207 PanNET samples, comprising a training cohort (n=72) and two validation cohorts (n=135) from multiple transcriptome profiling platforms. Different immune-related and subtype-related phenotypes, cell types and pathways were investigated using different in silico methods and were further validated using spatial multiplex immunofluorescence.

RESULTS

The study identified an immune signature of 132 genes segregating PanNETs (n=207) according to four previously defined molecular subtypes: metastasis-like primary (MLP)-1 and MLP-2, insulinoma-like and intermediate. The MLP-1 subtype (26%-31% samples across three cohorts) was strongly associated with elevated levels of immune-related genes, poor prognosis and a cascade of tumour evolutionary events: larger hypoxic and necroptotic tumours leading to increased damage-associated molecular patterns (viral mimicry), stimulator of interferon gene pathway, T cell-inflamed genes, immune checkpoint targets, and T cell-mediated and M1 macrophage-mediated immune escape mechanisms. Multiplex spatial profiling validated significantly increased macrophages in the MLP-1 subtype.

CONCLUSION

This study provides novel data on the immune microenvironment of PanNETs and identifies MLP-1 subtype as an immune-high phenotype featuring a broad and robust activation of immune-related genes. This study, with further refinement, paves the way for future precision immunotherapy studies in PanNETs to potentially select a subset of MLP-1 patients who may be more likely to respond.

Detection of postoperative plasma circulating tumour DNA and lack of CDX2 expression as markers of recurrence in patients with localised colon cancer

Background

Colon cancer (CC) is a heterogeneous disease. Novel prognostic factors beyond pathological staging are required to accurately identify patients at higher risk of relapse. Integrating these new biological factors, such as plasma circulating tumour DNA (ctDNA), CDX2 staining, inflammation-associated cytokines and transcriptomic consensus molecular subtypes (CMS) classification, into a multimodal approach may improve our accuracy in determining risk of recurrence.

Methods

One hundred and fifty patients consecutively diagnosed with localised CC were prospectively enrolled in our study. ctDNA was tracked to detect minimal residual disease by droplet digital PCR. CDX2 expression was analysed by immunostaining. Plasma levels of cytokines potentially involved in disease progression were measured using ELISAs. A 96 custom gene panel for nCounter assay was used to classify CC into colorectal cancer assigner and CMS.

Results

Most patients were classified into CMS4 (37%) and CMS2 (28%), followed by CMS1 (20%) and CMS3 (15%) groups. CDX2-negative tumours were enriched in CMS1 and CMS4 subtypes. In univariable analysis, prognosis was influenced by primary tumour location, stage, vascular and perineural invasion together with high interleukin-6 plasma levels at baseline, tumours belonging to CMS 1 vs CMS2 +CMS3, ctDNA presence in plasma and CDX2 loss. However, only positive ctDNA in plasma samples (HR 13.64; p=0.002) and lack of CDX2 expression (HR 23.12; p=0.001) were found to be independent prognostic factors for disease-free survival in the multivariable model.

Conclusions

ctDNA detection after surgery and lack of CDX2 expression identified patients at very high risk of recurrence in localised CC.

Differential and longitudinal immune gene patterns associated with reprogrammed microenvironment and viral mimicry in response to neoadjuvant radiotherapy in rectal cancer

BACKGROUND

Rectal cancers show a highly varied response to neoadjuvant radiotherapy/chemoradiation (RT/CRT) and the impact of the tumor immune microenvironment on this response is poorly understood. Current clinical tumor regression grading systems attempt to measure radiotherapy response but are subject to interobserver variation. An unbiased and unique histopathological quantification method (change in tumor cell density (ΔTCD)) may improve classification of RT/CRT response. Furthermore, immune gene expression profiling (GEP) may identify differences in expression levels of genes relevant to different radiotherapy responses: (1) at baseline between poor and good responders, and (2) longitudinally from preradiotherapy to postradiotherapy samples. Overall, this may inform novel therapeutic RT/CRT combination strategies in rectal cancer.

METHODS

We generated GEPs for 53 patients from biopsies taken prior to preoperative radiotherapy. TCD was used to assess rectal tumor response to neoadjuvant RT/CRT and ΔTCD was subjected to k-means clustering to classify patients into different response categories. Differential gene expression analysis was performed using statistical analysis of microarrays, pathway enrichment analysis and immune cell type analysis using single sample gene set enrichment analysis. Immunohistochemistry was performed to validate specific results. The results were validated using 220 pretreatment samples from publicly available datasets at metalevel of pathway and survival analyses.

RESULTS

ΔTCD scores ranged from 12.4% to -47.7% and stratified patients into three response categories. At baseline, 40 genes were significantly upregulated in poor (n=12) versus good responders (n=21), including myeloid and stromal cell genes. Of several pathways showing significant enrichment at baseline in poor responders, epithelial to mesenchymal transition, coagulation, complement activation and apical junction pathways were validated in external cohorts. Unlike poor responders, good responders showed longitudinal (preradiotherapy vs postradiotherapy samples) upregulation of 198 immune genes, reflecting an increased T-cell-inflamed GEP, type-I interferon and macrophage populations. Longitudinal pathway analysis suggested viral-like pathogen responses occurred in post-treatment resected samples compared with pretreatment biopsies in good responders.

CONCLUSION

This study suggests potentially druggable immune targets in poor responders at baseline and indicates that tumors with a good RT/CRT response reprogrammed from immune "cold" towards an immunologically "hot" phenotype on treatment with radiotherapy.

A Machine-Learning Tool Concurrently Models Single Omics and Phenome Data for Functional Subtyping and Personalized Cancer Medicine

One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique machine-learning method, phenotype mapping (PhenMap), which jointly integrates single omics data with phenotypic information using three published breast cancer datasets (n = 2045). The PhenMap framework uses a modified factor analysis method that is governed by a key assumption that, features from different omics data types are correlated due to specific "hidden/mapping" variables (context-specific mapping variables (CMV)). These variables can be simultaneously modeled with phenotypic data as covariates to yield functional subtypes and their associated features (e.g., genes) and phenotypes. In one example, we demonstrate the identification and validation of six novel "functional" (discrete) subtypes with differential responses to a cyclin-dependent kinase (CDK)4/6 inhibitor and etoposide by jointly integrating transcriptome profiles with four different drug response data from 37 breast cancer cell lines. These robust subtypes are also present in patient breast tumors with different prognosis. In another example, we modeled patient gene expression profiles and clinical covariates together to identify continuous subtypes with clinical/biological implications. Overall, this genome-phenome machine-learning integration tool, PhenMap identifies functional and phenotype-integrated discrete or continuous subtypes with clinical translational potential.

Intratumoral Transcriptome Heterogeneity Is Associated With Patient Prognosis and Sidedness in Patients With Colorectal Cancer Treated With Anti-EGFR Therapy From the CO.20 Trial

PURPOSE

Metastatic colorectal cancers (mCRCs) assigned to the transit-amplifying (TA) CRCAssigner subtype are more sensitive to anti-epidermal growth factor receptor (EGFR) therapy. We evaluated the association between the intratumoral presence of TA signature (TA-high/TA-low, dubbed as TA-ness classification) and outcomes in CRCs treated with anti-EGFR therapy.

PATIENTS AND METHODS

The TA-ness classes were defined in a discovery cohort (n = 84) and independently validated in a clinical trial (CO.20; cetuximab monotherapy arm; n = 121) and other samples using an established NanoString-based gene expression assay. Progression-free survival (PFS), overall survival (OS), and disease control rate (DCR) according to TA-ness classification were assessed by univariate and multivariate analyses.

RESULTS

The TA-ness was measured in 772 samples from 712 patients. Patients (treated with anti-EGFR therapy) with TA-high tumors had significantly longer PFS (discovery hazard ratio [HR], 0.40; 95% CI, 0.25 to 0.64; P < .001; validation HR, 0.65; 95% CI, 0.45 to 0.93; P = .018), longer OS (discovery HR, 0.48; 95% CI, 0.29 to 0.78; P = .003; validation HR, 0.67; 95% CI, 0.46 to 0.98; P = .04), and higher DCR (discovery odds ratio [OR]; 14.8; 95% CI, 4.30 to 59.54; P < .001; validation OR, 4.35; 95% CI, 2.00 to 9.09; P < .001). TA-ness classification and its association with anti-EGFR therapy outcomes were further confirmed using publicly available data (n = 80) from metastatic samples (PFS P < .001) and patient-derived xenografts (P = .042). In an exploratory analysis of 55 patients with RAS/BRAF wild-type and left-sided tumors, TA-high class was significantly associated with longer PFS and trend toward higher response rate (PFS HR, 0.53; 95% CI, 0.28 to 1.00; P = .049; OR, 5.88; 95% CI, 0.71 to 4.55; P = .09; response rate 33% in TA-high and 7.7% in TA-low).

CONCLUSION

TA-ness classification is associated with prognosis in patients with mCRC treated with anti-EGFR therapy and may further help understanding the value of sidedness in patients with RAS/BRAF wild-type tumors.

Consensus molecular subtypes and CRCassigner classifications in metastatic colorectal cancer (mCRC): Prognostic and predictive impact in the TRIBE2 study

4016

Background: The TRIBE2 study (NCT02339116) recently demonstrated the superiority of upfront FOLFOXIRI/bevacizumab (bev) when compared to a pre-planned strategy of doublets/bev in molecularly unselected but mostly (74%) RAS/ BRAF mutant mCRC patients. The Consensus Molecular Subtypes (CMS) and CRCAssigner (CRCA) demonstrated prognostic value in multiple studies, but their predictive role has not been established so far. Given the poor prognosis associated with early stage mesenchymal/stem-like subtypes, we hypothesized that the CMS/CRCA classifiers could predict benefit from the upfront intensified strategy in patients included in the TRIBE2 study. Methods: Untreated formalin-fixed paraffin-embedded samples were classified into CMS/CRCA subtypes using a custom nCounter assay (NanoString Technologies). The impact of subtypes on progression free survival (PFS), progression free survival 2 (PFS2, defined as the time from randomization until the second evidence of disease progression) or overall survival (OS) was evaluated in the profiled population. Results: 426 and 428 (63%) patients enrolled in the TRIBE2 study were profiled according to CMS and CRCA classifications, respectively. The distribution of CMS/CRCA subtypes differed according to primary tumour site (both p < 0.001 for CMS/CRCA) and RAS/ BRAF mutational status (both p < 0.001 for CMS/CRCA). Significant associations of both CMS/CRCA classifiers with PFS, PFS2 and OS were demonstrated (Table). The effect of treatment intensification was independent of CMS subtypes (p for interaction for PFS/PFS2/OS: ns). Significant interaction effect between CRCA subtypes and treatment arm was reported in terms of PFS (p = 0.017), PFS2 (p = 0.010) and OS (p = 0.008). The benefit from the intensification of the upfront chemotherapy seemed more relevant in the stem-like (PFS, HR = 0.60; p = 0.03) and mixed subtypes (HR = 0.44; p = 0.002). Conclusions: CMS subtypes have a prognostic role in mCRC independently of RAS/ BRAF status. CRCA classification may help identifying subgroups of patients who may derive a more substantial benefit from upfront FOLFOXIRI/bev. [Table: see text]

Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses

Breast cancer is a highly heterogeneous disease. Although differences between intrinsic breast cancer subtypes have been well studied, heterogeneity within each subtype, especially luminal-A cancers, requires further interrogation to personalize disease management. Here, we applied well-characterized and cancer-associated heterocellular signatures representing stem, mesenchymal, stromal, immune, and epithelial cell types to breast cancer. This analysis stratified the luminal-A breast cancer samples into five subtypes with a majority of them enriched for a subtype (stem-like) that has increased stem and stromal cell gene signatures, representing potential luminal progenitor origin. The enrichment of immune checkpoint genes and other immune cell types in two (including stem-like) of the five heterocellular subtypes of luminal-A tumors suggest their potential response to immunotherapy. These immune-enriched subtypes of luminal-A tumors (containing only estrogen receptor positive samples) showed good or intermediate prognosis along with the two other differentiated subtypes as assessed using recurrence-free and distant metastasis-free patient survival outcomes. On the other hand, a partially differentiated subtype of luminal-A breast cancer with transit-amplifying colon-crypt characteristics showed poor prognosis. Furthermore, published luminal-A subtypes associated with specific somatic copy number alterations and mutations shared similar cellular and mutational characteristics to colorectal cancer subtypes where the heterocellular signatures were derived. These heterocellular subtypes reveal transcriptome and cell-type based heterogeneity of luminal-A and other breast cancer subtypes that may be useful for additional understanding of the cancer type and potential patient stratification and personalized medicine.

Characterization of chemoradiation-induced changes in immune cells and targets for personalized therapy in locally advanced rectal cancer (LARC)

589

Background: Neoadjuvant radio/chemoradiotherapy (CRT) is a treatment milestone for LARC. The importance of immune response in CRT efficacy is increasingly realised. However immune cell changes associated with poor responders and their modulation with immune-CRT combinations is unclear. Methods: Matched archival pre-CRT biopsies and post-CRT resection specimens from patients (pts) treated with neoadjuvant CRT were retrieved. Delta-TCD (tumor cell density, estimated using quantitative point counting on virtual tissue H&E) and k-means clustering method were used to classify pts into good, intermediate and poor responders. Baseline expression and CRT-induced changes in 770 immune-related genes (plus 30 DNA damage response genes) were evaluated using NanoString Technologies. Results: At least 70 pts treated with short/long course radiotherapy (SCRT/LCRT) and matched tissues available were identified. To date, 27 pts evaluable for deltaTCD and gene expression were clustered into good (n:10), intermediate (n:7) and poor (n:10) responders. The expression of 14% (91/636) of immune genes was significantly affected by CRT (Bonferroni t-test, q-value < 0.05) overall, with significant increase in innate immunity and decrease in adaptive immunity across all pts (CIBERSORT and SSGSEA analyses). Between good and poor responders there were 6% (39/636) and 2% (15/636) of genes significantly affected by CRT (Bonferroni t-test, q-value < 0.05), respectively. CRT-induced increased CD8+ T cells expression in poor responders compared to good responders was seen. Increased baseline expression of resistance genes (including PD-L1, IDO1 and IL2RA) were seen in poor versus good responders. Validation with quantitative multiplex-immunofluorescence (Vectra) and correlation with SCRT/LCRT and time to surgery are on-going. Conclusions: The expression of immune-related genes is significantly modified by CRT in LARC. With the caveat of small numbers, we identified differentially expressed immune targets at baseline which may justify immune-CRT combinations in neoadjuvant setting in selected pts to modulate the CRT effect and ultimately increase response.

A seven-Gene Signature assay improves prognostic risk stratification of perioperative chemotherapy treated gastroesophageal cancer patients from the MAGIC trial

Background

Following neoadjuvant chemotherapy for operable gastroesophageal cancer, lymph node metastasis is the only validated prognostic variable; however, within lymph node groups there is still heterogeneity with risk of relapse. We hypothesized that gene profiles from neoadjuvant chemotherapy treated resection specimens from gastroesophageal cancer patients can be used to define prognostic risk groups to identify patients at risk for relapse.

Patients and methods

The Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial (n = 202 with high quality RNA) samples treated with perioperative chemotherapy were profiled for a custom gastric cancer gene panel using the NanoString platform. Genes associated with overall survival (OS) were identified using penalized and standard Cox regression, followed by generation of risk scores and development of a NanoString biomarker assay to stratify patients into risk groups associated with OS. An independent dataset served as a validation cohort.

Results

Regression and clustering analysis of MAGIC patients defined a seven-Gene Signature and two risk groups with different OS [hazard ratio (HR) 5.1; P < 0.0001]. The median OS of high- and low-risk groups were 10.2 [95% confidence interval (CI) of 6.5 and 13.2 months] and 80.9 months (CI: 43.0 months and not assessable), respectively. Risk groups were independently prognostic of lymph node metastasis by multivariate analysis (HR 3.6 in node positive group, P = 0.02; HR 3.6 in high-risk group, P = 0.0002), and not prognostic in surgery only patients (n = 118; log rank P = 0.2). A validation cohort independently confirmed these findings.

Conclusions

These results suggest that gene-based risk groups can independently predict prognosis in gastroesophageal cancer patients treated with neoadjuvant chemotherapy. This signature and associated assay may help risk stratify these patients for post-surgery chemotherapy in future perioperative chemotherapy-based clinical trials.

polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance

Background

To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce “polyClustR”, a tool that reconciles clusters identified by different methods into subtype “communities” using a hypergeometric test or a measure of relative proportion of common samples.

Results

The polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets were then utilised to identify and validate novel subtype communities with significant metastasis-free prognostic differences and associations with known chromosomal aberrations.

Conclusion

We demonstrate the value of the polyClustR approach of applying multiple consensus clustering algorithms and systematically reconciling the results in identifying novel subtype communities of two cancer types, which nevertheless are compatible with established understanding of these diseases. An R implementation of the pipeline is available at: https://github.com/syspremed/polyClustR

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2204-4) contains supplementary material, which is available to authorized users.

Molecular subtype assay to reveal anti-EGFR response sub-clones in colorectal cancer (CRC)

658

Background: We previously stratified CRC into five intrinsic gene expression subtypes (CRCAssigner) and four consensus molecular subtypes (CMS1-4) using expensive and time-consuming microarray/RNAseq platforms. Recently, we developed a low-cost (NanoCRC) assay using nCounter platform (NanoString Technology) and robust gene signatures to classify individual samples into both CRCAssigner and CMS subtypes. Here, we tested the assays using formalin-fixed paraffin-embedded (FFPE) patient samples from Royal Marsden Hospital. Given increased sensitivity to cetuximab in one of our CRCAssigner subtypes transit-amplifying (TA) that represents partly CMS2 subtype, we associated anti-EGFR response to the presence of TA sub-clones. Methods: FFPE from resected and biopsy CRC samples were profiled for our NanoCRC assay. Along with our single-sample subtyping method, we developed a tool to identify sub-clones of TA within individual samples. RAS/BRAF wild-type patients treated with single agent anti-EGFR therapy were statistically associated with sub-clonal molecular profiles, sidedness and single gene expression. Results: NanoCRC assay was highly reproducible with > 0.9 correlation coefficient. Among 76 samples (from 64 patients), all the five CRCAssigner, four CMS subtypes (including the mixed samples) and their associations with RAS/BRAF mutations were successfully identified. Subtype-mutational associations were similar to our previous publications. Among 15 KRAS/BRAF wild-type (wt) patients with single agent anti-EGFR therapy response data, TA sub-clone was identified in 7/9 with clinical benefit and 1/6 with no benefit (p < 0.05). The other sub-clones, sidedness and individual genes within the panels were not significantly associated. This TA sub-clone association was further confirmed using a publicly available cetuximab response data (39 KRAS wt; Khambata-Ford data; p=0.02). Conclusions: Overall, we developed a robust NanoCRC assay to classify CRC FFPE samples in to molecular subtypes. With caveat of small numbers, the presence of TA sub-clones is significantly associated with cetuximab response. Extensive validation is warranted.

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

Abstract 603: Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation

Background: Gene expression-based subtyping is widely accepted as a relevant source of disease stratification. Despite the widespread use, its translational and clinical utility is hampered by discrepant results, likely related to differences in data processing and algorithms applied to diverse patient cohorts, sample preparation methods, and gene expression platforms. In the absence of a clear methodological gold standard to perform such analyses, a more general framework that integrates and compares multiple strategies is needed to define common disease patterns in a principled, unbiased manner.

Methods: We formed a consortium of 6 independent experts groups - each with a previously published CRC classifier, ranging from 3 to 6 subtypes - to understand similarities and differences of their subtyping systems. Sage Bionetworks functioned as neutral party to aggregate public and proprietary data (Synapse platform) and perform meta-analysis. Each group applied its CRC subtyping signature to the collection of data sets with gene expression (n = 4,151, predominantly stage II and III). Using the resulting subtype labels, we developed a network-based model and applied a Markov cluster algorithm to detect robust network substructures that would indicate recurring subtype patterns and therefore a consensus subtyping system. Correlative analyses using clinico-pathological, genomic and epigenomic features was performed to robustly characterize the identified subtypes.

Results: This analytical framework revealed significant interconnectivity between the six independent classification systems, leading to the identification of four biologically distinct consensus molecular subtypes (CMS) enriched for key pathway traits: CMS1 (MSI Immune), hypermutated, microsatellite unstable, with strong immune activation; CMS2 (Canonical), epithelial, chromosomally unstable, with marked WNT and MYC signaling activation; CMS3 (Metabolic), epithelial, with evident metabolic dysregulation; and CMS4 (Mesenchymal), prominent TGFβ activation, angiogenesis, stromal invasion. Patients diagnosed with MSI Immune tumors had worse survival after relapse and those with mesenchymal tumors had increased risk of metastasis and worse overall survival.

Discussion: We describe a novel methodological paradigm for deriving benchmarks of disease subtyping. Our work represents the first example of a community of experts identifying and advocating for a single reproducible model for cancer subtyping, effectively unifying previous classifiers. In the CRC domain, the uniformity afforded by this new classification system and its application to a large data set revealed important subtype-specific biological associations that were previously unnoticed or marginally significant, supporting a new taxonomy of the disease.

Citation Format: Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurelien de Reynies, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian Bot, Jeffrey S. Morris, Iris Simon, Sarah Gerster, Evelyn Fessler, Felipe de Sousa e Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju Manyam, Bradley Broom, Valerie Boige, Ted Laderas, Ramon Salazar, Joe W. Gray, Josep Tabernero, Rene Bernards, Stephen Friend, Pierre Laurent-Puig, Jan P. Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar. Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 603. doi:10.1158/1538-7445.AM2015-603

Abstract 3083: Global gene expression profiling of mice tumor-derived organoids identifies key microRNAs and metabolic genes involved in CRC progression

Background. Progressive accumulation of mutations in oncogenic and tumor suppressor pathways are associated with colorectal cancer (CRC). Mutations in APC, KRAS and p53 represent key drivers events in CRC initiation and progression and are simultaneously mutated in about 30% of CRC. Tumor derived Organoids (TDO) are three-dimensional (3-D) structures composed of cells that are spatially organized like mini-guts and represent a useful ex vivo tool to study intestinal physiology and cancer progression. Here, we investigated the progressive deregulation of mRNA and microRNA (miRNA) genes in TDOs from intestines of three different Genetically Engineered Mouse models (GEMMs) harboring mutations in Apc, Apc plus Kras and Apc plus Kras and p53. Methods. Array analysis of protein coding and non-coding RNAs from mice TDOs was performed to identify mRNAs and miRNAs that were differentially expressed following Apc, Kras and p53 mutations. Agilent Feature Extraction software was used to analyze acquired array images and subsequent data processing was performed by the GeneSpring GX v11.5.1 software package. Results. Twenty-five% of mRNAs showed more than 2 fold changes in expression level and considered differentially expressed. Pathway analysis found that a great number of mRNAs, which were significantly differentially expressed (P-value 0.001), were involved in metabolic process. Of these, 10 were involved in metabolism of different amino acids or other compounds (e.g., arachidonic acid, ascorbate, alendrate, and linoleic acid) and 4 were encoding the cytochrome p450 family drug and xenobiotic metabolizing enzymes. In addition, 28 and 10% of miRNAs in Apc vs Apc/Kras and Apc/Kras vs Apc/Kras/p53 respectively were identified as differentially expressed miRNA. Twenty-two of these miRNAs were related to deregulated metabolic genes in this study. Selected metabolic genes (e.g., Pipox) were constantly down regulated from early (Apc only mutants) to advanced (Apc/Kras/p53 mutants) CRC, whereas their associated miRNAs (mir-883a-3p and mir-1943) were constantly up regulated. High expression level of mir-125 which was detected in Apc/Kras/p53 group was also associated to down regulation of associated metabolic genes: Pipox and Ggt7. Conclusions. Using 3-D TDOs, we have identified deregulated miRNAs that are involved in regulation of metabolic pathways associated with CRC progression. Further analysis of miRNA-mRNA interaction in these models may help identify metabolic vulnerabilities that may be exploited for CRC therapy.

Citation Format: Mahnaz Darvish Damavandi, Chiara Braconi, Luciano Cascione, Andrea Lampis, Jens Hahne, Claudio Murgia, Michele Ghidini, Gift Nyamundanda, Anguraj Sadanandam, Carlo Croce, Owen Sansom, Nicola Valeri. Global gene expression profiling of mice tumor-derived organoids identifies key microRNAs and metabolic genes involved in CRC progression. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3083. doi:10.1158/1538-7445.AM2015-3083

Probabilistic principal component analysis for metabolomic data

BACKGROUND

Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model.

RESULTS

Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data.

CONCLUSIONS

The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.