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Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.

Ultrasound-powered implants (UPIs) represent cutting edge power sources for implantable medical devices (IMDs), as their powering strategy allows for extended functional lifetime, decreased size, increased implant depth, and improved biocompatibility. IMDs are limited by their reliance on batteries. While batteries proved a stable power supply, batteries feature relatively large sizes, limited life spans, and toxic material compositions. Accordingly, energy harvesting and wireless power transfer (WPT) strategies are attracting increasing attention by researchers as alternative reliable power sources. Piezoelectric energy scavenging has shown promise for low power applications. However, energy scavenging devices need be located near sources of movement, and the power stream may suffer from occasional interruptions. WPT overcomes such challenges by more stable, on-demand power to IMDs. Among the various forms of WPT, ultrasound powering offers distinct advantages such as low tissue-mediated attenuation, a higher approved safe dose (720 mW cm-2 ), and improved efficiency at smaller device sizes. This study presents and discusses the state-of-the-art in UPIs by reviewing piezoelectric materials and harvesting devices including lead-based inorganic, lead-free inorganic, and organic polymers. A comparative discussion is also presented of the functional material properties, architecture, and performance metrics, together with an overview of the applications where UPIs are being deployed.

In this study, we developed an image analysis algorithm for quantification of two potential apoptotic biomarkers in non-small-cell lung cancer (NSCLC): FLIP and procaspase-8. Immunohistochemical expression of FLIP and procaspase-8 in 184 NSCLC tumors were assessed. Individual patient cores were segmented and classified as tumor and stroma using the Definiens Tissue Studio. Subsequently, chromogenic expression of each biomarker was measured separately in the nucleus and cytoplasm and reported as a quantitative histological score. The software package pROC was applied to define biomarker thresholds. Cox proportional hazards analysis was applied to generate hazard ratios (HRs) and associated 95% CI for survival. High cytoplasmic expression of tumoral (but not stromal) FLIP was associated with a 2.5-fold increased risk of death in lung adenocarcinoma patients, even when adjusted for known confounders (HR 2.47, 95% CI 1.14–5.35). Neither nuclear nor cytoplasmic tumoral procaspase-8 expression was associated with overall survival in lung adenocarcinoma patients; however, there was a significant trend (P for trend=0.03) for patients with adenocarcinomas with both high cytoplasmic FLIP and high cytoplasmic procaspase-8 to have a multiplicative increased risk of death. Notably, high stromal nuclear procaspase-8 expression was associated with a reduced risk of death in lung adenocarcinoma patients (adjusted HR 0.31, 95% CI 0.15–0.66). On further examination, the cells with high nuclear procaspase-8 were found to be of lymphoid origin, suggesting that the better prognosis of patients with tumors with high stromal nuclear procaspase-8 is related to immune infiltration, a known favorable prognostic factor. No significant associations were detected in analysis of lung squamous cell carcinoma patients. Our results suggest that cytoplasmic expression of FLIP in the tumor and nuclear expression of procaspase-8 in the stroma are prognostically relevant in non-small-cell adenocarcinomas but not in squamous cell carcinomas of the lung.

Technology advancements have enhanced our abilities to gain greater insight to the tumor environment. However, such emergent methods have brought new considerations in data storage, access and analysis. Modern large data projects and clinical trial materials could be explored to a greater degree if appropriate infrastructure could be built to support efforts. Such a scaffold would be an integrated and dynamic framework where novel hypotheses could be investigated in modern big data collections. Placing discovery back in the hands of the researcher through a reactive and supportive framework will enhance our understanding of cancer aetiology. The Cancer Integromics Research Application Framework (CIRAFm) was created to emulate current platforms that have a rigid analytical interface. We designed a robust architecture that supports a reactive user-friendly interface blended to several cross-platform coding technologies. It has been developed to accommodate an individualised framework where we can create an ‘app-store' of key software to fit research questions. In efforts to encapsulate and accelerate differing data types it sits astride of two NoSQL based database management systems, minimising data redundancy. Initial requirements have begun to create algorithms to investigate alignment-free applications on next generation sequencing (NGS) data enhancing analysis of spatial and temporal heterogeneity in cancer. Key drivers can be explored by assisting software to build correlative marker associations using Darwinian approaches such as genetic algorithms. Information requirements from external platforms are assisted through a novel domain specific language (DSL) to enable a singular interface. The modularised architecture of the platform is enabled through an Angular framework supported by interactive and dynamic data visualisation software, D3.js. Data analytics can be explored through the ‘apps' created, investigating new markers in large data and enhancing our understanding of tumor heterogeneity. Alignment-free phylogenetics of NGS data harnesses our capabilities to display fully sequence evolution in patient data. This highlights possible sub-types, markers of interest and maps to therapeutic compounds via the DSL to our externally developed drug discovery software, QUADrATiC. Future scientific endeavours further defining new tumor subtypes are paramount in efforts to help uncover treatment strategies. Unburdened by legacy pipelines and data, flexible and robust models of architecture provide an effective and efficient framework for research on our ever increasing search for precision medicine. Citation Format: Darragh G. McArt, Seedevi Senevirathne, Aideen Roddy, Jessica Black, Alan Gilmore, Suneil Jain, Philip Dunne, David Waugh. Integrative analytics: A framework for precision medicine [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 290.