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439 Background: The benefit of checkpoint blockade in advanced gastric cancer is limited and biomarkers related to response are needed. Novel gene expression analysis software was used to identify Hallmarks of Cancer associated with clinical benefit following nivolumab treatment in >2nd line advanced gastroesophageal adenocarcinoma (GEA). Methods: RNA-sequencing data from baseline GEA patient diagnostic tumor samples (103 from NCT02862535; 5 from NCT02862535) were analyzed using the claraT platform (V2.0.0, Almac Diagnostic Services). 62 gene signatures were quantified representing 6 key Hallmarks of Cancer (Avoiding Immune Destruction, Activating Invasion and Metastases, Sustaining Proliferative Signaling, Inducing Angiogenesis, Resisting Cell Death and Genome Instability and Mutation). Clinical benefit (CB) was defined as tumor response or overall survival (OS) > 1 year. HER2 status was from medical records. Survival analyses used cox proportional hazards models. Results: Gene expression signatures (GES) identified 5 molecular subgroups (C1-C5). Rate of CB in each molecular subtype are outlined in Table. C3 and C4 had significantly improved OS compared to C2, (HR = 0.45; p= 0.02 and HR = 0.42; p= 0.02). Greater proportions of HER2+ subjects were present in C4 and C3 vs. C2, with C3 statistically significant (60% vs. 14%; p= 0.012). Gene expression characterized by chromosomal instability (CIN) and homologous recombination repair deficiency (HRD) were associated with HER2(+) (wilcox p= < 0.05). Patients selected by only using CIN & HRD had significant improvement in OS (HR = 0.63; p= 0.03). Conclusions: Interferon-based GES did not predict benefit from immune checkpoint blockade. GES representing HRD and activation of HER2, EGFR and MAPK (each enriched in CIN) were associated with improved survival upon checkpoint blockade in advanced GEA patients. Clinical trial information: NCT02862535 . [Table: see text]

e21045 Background: Traditionally gene expression signatures (GES) are used individually to classify patients into subgroups. Signatures targeting the same biology are often developed independently and may not classify identically. We developed the claraT software tool that uses consensus between multiple published GES categorised by the Hallmarks of Cancer (Hanahan & Weinberg, 2011) to classify cancers. As metastatic melanoma represents poor prognostic disease (5-yr survival 15-20%), we applied claraT to the TCGA melanoma dataset to identify targetable biologies, validated in a cohort of melanoma patients treated with Ipilimumab. Methods: TCGA RNA-seq data ( n= 472) was analysed using the claraT platform including GES for immune ( n= 14), angiogenesis ( n= 9) and epithelial-mesenchymal transition (EMT) ( n= 12) Hallmarks. Samples were clustered for the combined and individual Hallmarks. Median progression-free (PFS) and overall-survival (OS) differences were analysed across identified subgroups. Analysis was validated in an Ipilimumab treated melanoma dataset ( n= 42) (Van Allen, 2015). Results: Clustering the combined Hallmarks identified 4 subgroups in the TCGA cohort: 1) Immune active, 2) Immune-EMT active, 3) EMT-Angiogenesis active, 4) All inactive. Groups 1&2 had significantly improved OS compared to Groups 3&4 (HR = 0.50, p< 0.0001). Clustering using single Hallmarks revealed that immune-positive tumours had significantly improved OS (HR = 0.53, p< 0.0001) compared to immune-negative tumours. Angiogenesis-negative tumours displayed improved PFS (HR = 0.73, p= 0.03) and OS (HR = 0.53, p

11 Background: Approximately 20% of patients with organ confined prostate cancer (PCa) will develop disease recurrence following radical treatment (surgery or external beam radiotherapy (EBRT)). We hypothesized that a molecular subgroup of early PCa may have metastatic potential at presentation, resulting in disease recurrence. Methods: Using unsupervised hierarchical clustering of gene expression from a PCa dataset we identified a novel molecular subgroup with a transcriptional profile similar to metastatic disease. We developed a 70 gene expression assay to prospectively identify patients within the subgroup from formalin fixed and paraffin embedded tissue (FFPE). Initial assessment found the assay to be prognostic in three independent publicly available prostatectomy datasets. We therefore assessed the prognostic value of the assay in FFPE clinical samples collected from multiple international sites. FFPE tumor resections and tumor biopsy specimens were obtained from 322 surgical patients and 248 patients treated with EBRT. Regions of highest Gleason grade were identified for macrodissection, RNA extraction and gene expression analysis. Samples were dichotomized as metastatic biology assay positive or negative using a pre-specified cut-off. The association of assay results with biochemical failure (BF) and distant metastases (DM) was tested on multivariate (MVA). Results: The assay was significantly associated with BF on MVA (HR 1.67 [1.16-2.38]; p = 0.0059), (HR 2.26 [1.26-4.04]; p = 0.0062) and DM on MVA (HR 3.39 [1.88-6.12]; p = 0.0001), (HR 3.26 [1.27-8.30]; p = 0.0137) for surgery and EBRT cohorts respectively. Importantly, in a combined model, the assay demonstrated additional information to the commonly used CAPRA clinical tool for prediction of DM, HR 2.72 [2.10-3.51]; p < 0.0001, and HR 2.72 [1.42-5.20]; p = 0.0026 (Prostate Metastatic Assay combined with CAPRA-S and CAPRA respectively). Conclusions: The metastatic biology assay predicts BF and DM in PCa patients treated with surgery or EBRT. The assay may help to select patients at risk of metastatic disease for additional treatment aimed at preventing disease recurrence.