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Hypoxia in prostate tumours has been associated with disease progression and metastasis. MicroRNAs are short noncoding RNA molecules that are important in several cell processes, but their role in hypoxic signalling is still poorly understood. miR-210 has been linked with hypoxic mechanisms, but this relationship has been poorly characterised in prostate cancer. In this report, the link between hypoxia and miR-210 in prostate cancer cells is investigated. Polymerase chain reaction analysis demonstrates that miR-210 is induced by hypoxia in prostate cancer cells using in vitro cell models and an in vivo prostate tumour xenograft model. Analysis of The Cancer Genome Atlas prostate biopsy datasets shows that miR-210 is significantly correlated with Gleason grade and other clinical markers of prostate cancer progression. Neural cell adhesion molecule (NCAM) is identified as a target of miR-210, providing a biological mechanism whereby hypoxia-induced miR-210 expression can contribute to prostate cancer. This study provides evidence that miR-210 is an important regulator of cell response to hypoxic stress and proposes that its regulation of NCAM may play an important role in the pathogenesis of prostate cancer.

The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade.Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones.OMm and OM (both with likelihood ratio statistic [LRS] P 0.001; C indexes = 0.84 and 0.85, respectively) were more prognostic for DRFS and provided significant additional prognostic information to all other assessment tools/factors assessed (all LRS P ≤ 0.002). In addition, the OM correctly classified more patients with distant recurrences (DRs) into the high-risk category than other risk classification tools. Similar results were observed for IDFS.Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions.

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of HE images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with particular risk for severe disease and mortality in the elderly population. SARS-CoV-2 infection is driven by a pathological hyperinflammatory response which results in a dysregulated immune response. Current advancements in aging research indicates that aging pathways have fundamental roles in dictating healthspan in addition to lifespan. Our review discusses the aging immune system and highlights that senescence and aging together, play a central role in COVID-19 pathogenesis. In our review, we primarily focus on the immune system response to SARS-CoV-2 infection, the interconnection between severe COVID-19, immunosenescence, aging, vaccination, and the emerging problem of Long-COVID. We hope to highlight the importance of identifying specific senescent endotypes (or “sendotypes”), which can used as determinants of COVID-19 severity and mortality. Indeed, identified sendotypes could be therapeutically exploited for therapeutic intervention. We highlight that senolytics, which eliminate senescent cells, can target aging-associated pathways and therefore are proving attractive as potential therapeutic options to alleviate symptoms, prevent severe infection, and reduce mortality burden in COVID-19 and thus ultimately enhance healthspan.

535 Background: Multigene prognostic signatures (MGPS) can identify early stage breast cancer patients who may require less aggressive treatment. To be clinically useful, MGPSs must provide additional prognostic information to clinicopathological information routinely used by clinicians. The OncoMasTR MGPS was discovered via a novel bioinformatic transcriptional network analysis. OncoMasTR consists of genes – Master Transcription Regulators (MTRs) – that regulate previously known prognostic genes and have identified functional roles in several hallmarks of cancer including proliferation, invasion and metastasis. The OncoMasTR Molecular Score (OM) consists of just 3 MTRs. The OncoMasTR Risk Score (OncoMasTR) combines OM with lymph node status and tumour size, and categorises patients as low or high risk. The OncoMasTR Test has been analytically and clinically validated. Methods: MTR expression was measured by RT-qPCR in tissue from 404 patients enrolled in an independent translational trial (NCT02050750) that collected tissue and clinical data from patients enrolled in TAILORx in Ireland. OM, OncoMasTR and Oncotype DX Recurrence Score (RS) were compared on the additional prognostic value they provided to Ki67, Nottingham Prognostic Index (NPI) and other clinicopathological information for distant recurrence (DR) and invasive disease (ID). Results: OM (LRχ2 = 20.3, p < 0.00001, c-index = 0.84) and OncoMasTR (LRχ2 = 22.6, p < 0.00001, c-index = 0.85) were significantly prognostic, and more prognostic than RS (LRχ2 = 8.4, p = 0.004, c-index = 0.73) for DR. OM and OncoMasTR provided more additional prognostic information than RS to Ki67, NPI, tumour size, tumour grade, and age for DR. Similar results were found when OM, OncoMasTR and RS were compared on prognostic performance for ID. Conclusions: OM and OncoMasTR were significantly prognostic for DR and ID and added significant prognostic value to Ki67, NPI, and other clinicopathological information. Furthermore, OM and OncoMasTR showed superior prognostic performance to RS.

Introduction Traditionally, many women with lymph node-negative breast cancer (LN-BC) are unnecessarily treated with chemotherapy. This highlights the importance of accurately predicting patient prognosis. We previously identified a novel prognostic signature encompassing a set of master transcriptional regulators (MTRs), termed OncoMasTR. At the mRNA level, OncoMasTR predicted recurrence risk for LN-BC better than current prognostic signatures and accurately classifies >50% of patients as ‘low-risk', thus potentially reducing overtreatment. Here, we describe ongoing validation of the OncoMasTR biomarker signature at the protein level. Methods Commercially available antibodies targeted against markers were validated by Western blotting and immunohistochemistry (IHC) using CRISPR/Cas9 knockout cell-lines and full-face BC sections. IHC was performed on tissue microarrays (TMAs) containing multiple tumour cores from BC patients. Visiopharm's Oncotopix algorithm was used for image analysis, with stained sections verified manually by a pathologist. Image analysis data was obtained, and cut-off points optimised by maximum Chi-square values were used to differentiate low and high expression of individual markers. This was combined with clinical data to generate Kaplan-Meier survival curves. Multivariate analysis was used to determine the optimal combination of biomarkers. Results Antibodies against eight components of the OncoMasTR signature have been validated. IHC analysis demonstrated highly-specific nuclear staining for seven of the markers (UHRF1, ATAD2, HMGB2, E2F1, TCF19, MYBL2, PTTG1), whilst one showed both nuclear and cytoplasmic staining (p16). Image analysis for UHRF1, ATAD2 and HMGB2, using both manual and automated histological scores, showed that high expression of these biomarkers is linked with reduced survival. Additionally, UHRF1 was confirmed to have the same survival correlation in another independent cohort. Interestingly, high nuclear expression of p16 is linked with prolonged survival whilst high cytoplasmic expression of p16 is associated with poorer survival, aligning with previous reports. Conclusions The OncoMasTR signature offers a more accurate stratification of low-vs-high risk, thus showing promise in improving prognosis prediction for early-stage BC. This will translate to reduced overtreatment, decreased costs, improved survival rates and enhanced quality of life for patients. Citation Format: Arman Rahman, Seodhna Lynch, Nebras Alattar, Niamh Niamh, Charles Weige,, Romina Silva, Claudia Aura, Fiona Lanigan, Adrian Bracken, Björn Nodin, Karin Jirström, William Gallagher. Refining treatment recommendations for lymph node-negative breast cancer patients using a novel protein-based prognostic signature: The OncoMasTR assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-230.

12074Background: Multigene prognostic signatures (MGPS) enable identification of early stage breast cancer (BC) patients requiring less aggressive treatment. OncoMasTR is a new MGPS found via a nov...

305 Background: Pancreatic cancer has a dismal prognosis and clinical protocols are still lacking predictive biomarkers. RNA-binding motif protein 3 (RBM3) has emerged as a promising biomarker in several solid cancers, including pancreatic cancer. High RBM3 expression in human tumors has been associated with good response to chemotherapy, as well as to confer increased chemosensitivity in vitro. The aim of this study was to identify RBM3-regulated genes in pancreatic cells in vitro, and further interrogate their potential utility as prognostic and predictive biomarkers in a translational setting. Methods: Next generation RNA-sequencing was applied to compare gene expression between MIAPaCa-2 cells with siRNA-downregulated RBM3 and control cells. Single genes with the strongest association to RBM3 were further selected by gene set enrichment analysis, and their prognostic value in pancreatic cancer was examined in The Cancer Genome Atlas (TCGA). The most promising biomarker candidates with well-validated antibodies were then analyzed by immunohistochemistry in tissue microarrays with tumors from a consecutive, retrospective cohort of 175 patients with periampullary and pancreatic adenocarcinoma. Results: MIAPaCa-2 cells with downregulated RBM3 displayed 21 differentially expressed genes (p < 0.01). One of the top downregulated genes was PDS cohesion associated factor A (PDS5A) encoding a protein involved in sister chromatid cohesion. PDS5A protein expression was reduced in siRBM3-treated MIAPaCa-2 cells compared to control cells. High PDS5A mRNA expression was significantly associated with a reduced survival in pancreatic cancer in the TCGA (n = 176, p = 0.026). High PDS5A protein expression in the separate cohort was significantly associated with a poor prognosis but predictive of improved response to adjuvant chemotherapy in KRAS-mutated, but not wild-type, pancreatobiliary-type tumors (p for interaction = 0.043). Conclusions: Here, we provide the first report of the expression of PDS5A in pancreatic and periampullary cancer, suggesting its potential utility as a prognostic and predictive biomarker. Further studies to unravel the underlying mechanisms are encouraged.