Collective intelligence for translational medicine: Crowdsourcing insights and innovation from an interdisciplinary biomedical research community.

This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.3109/07853890.2015.1091945Translational medicine bridges the gap between discoveries in biomedical science and their safe and effective clinical application. Despite the gross opportunity afforded by modern research for unparalleled advances in this field, the process of translation remains protracted. Efforts to expedite science translation have included the facilitation of interdisciplinary collaboration within both academic and clinical environments in order to generate integrated working platforms fuelling the sharing of knowledge, expertise, and tools to align biomedical research with clinical need. However, barriers to scientific translation remain, and further progress is urgently required. Collective intelligence and crowdsourcing applications offer the potential for global online networks, allowing connection and collaboration between a wide variety of fields. This would drive the alignment of biomedical science with biotechnology, clinical need, and patient experience, in order to deliver evidence-based innovation which can revolutionize medical care worldwide. Here we discuss the critical steps towards implementing collective intelligence in translational medicine using the experience of those in other fields of science and public health

Circulating Cell-Free Tumour DNA for Early Detection of Pancreatic Cancer.

Pancreatic cancer is a lethal disease, with mortality rates negatively associated with the stage at which the disease is detected. Early detection is therefore critical to improving survival outcomes. A recent focus of research for early detection is the use of circulating cell-free tumour DNA (ctDNA). The detection of ctDNA offers potential as a relatively non-invasive method of diagnosing pancreatic cancer by using genetic sequencing technology to detect tumour-specific mutational signatures in blood samples before symptoms manifest. These technologies are limited by a number of factors that lower sensitivity and specificity, including low levels of detectable ctDNA in early stage disease and contamination with non-cancer circulating cell-free DNA. However, genetic and epigenetic analysis of ctDNA in combination with other standard diagnostic tests may improve early detection rates. In this review, we evaluate the genetic and epigenetic methods under investigation in diagnosing pancreatic cancer and provide a perspective for future developments

Evaluation of pre-analytical factors affecting plasma DNA analysis.

Pre-analytical factors can significantly affect circulating cell-free DNA (cfDNA) analysis. However, there are few robust methods to rapidly assess sample quality and the impact of pre-analytical processing. To address this gap and to evaluate effects of DNA extraction methods and blood collection tubes on cfDNA yield and fragment size, we developed a multiplexed droplet digital PCR (ddPCR) assay with 5 short and 4 long amplicons targeting single copy genomic loci. Using this assay, we compared 7 cfDNA extraction kits and found cfDNA yield and fragment size vary significantly. We also compared 3 blood collection protocols using plasma samples from 23 healthy volunteers (EDTA tubes processed within 1 hour and Cell-free DNA Blood Collection Tubes processed within 24 and 72 hours) and found no significant differences in cfDNA yield, fragment size and background noise between these protocols. In 219 clinical samples, cfDNA fragments were shorter in plasma samples processed immediately after venipuncture compared to archived samples, suggesting contribution of background DNA by lysed peripheral blood cells. In summary, we have described a multiplexed ddPCR assay to assess quality of cfDNA samples prior to downstream molecular analyses and we have evaluated potential sources of pre-analytical variation in cfDNA studies

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.This study was supported by the NIHR Oxford Biomedical Research Centre. CY is supported by a UK Medical Research Council Research Grant (MR/P02646X/1). MLD is funded by Wellcome Trust grant 100262Z/12/Z. SS is funded by a NIHR Academic Clinical Lecturership

Temporality of body mass index, blood tests, comorbidities and medication use as early markers for pancreatic ductal adenocarcinoma (PDAC): a nested case–control study

Objective Prior studies identified clinical factors associated with increased risk of pancreatic ductal adenocarcinoma (PDAC). However, little is known regarding their time-varying nature, which could inform earlier diagnosis. This study assessed temporality of body mass index (BMI), blood-based markers, comorbidities and medication use with PDAC risk .Design We performed a population-based nested case–control study of 28 137 PDAC cases and 261 219 matched-controls in England. We described the associations of biomarkers with risk of PDAC using fractional polynomials and 5-year time trends using joinpoint regression. Associations with comorbidities and medication use were evaluated using conditional logistic regression.Results Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while following a U-shaped relationship for BMI and haemoglobin. Five-year trends showed biphasic BMI decrease and HbA1c increase prior to PDAC; early-gradual changes 2–3 years prior, followed by late-rapid changes 1–2 years prior. Liver markers and blood counts (white blood cell, platelets) showed monophasic rapid-increase approximately 1 year prior. Recent diagnosis of pancreatic cyst, pancreatitis, type 2 diabetes and initiation of certain glucose-lowering and acid-regulating therapies were associated with highest risk of PDAC.Conclusion Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while followed a U-shaped relationship for BMI and haemoglobin. BMI and HbA1c derange biphasically approximately 3 years prior while liver markers and blood counts (white blood cell, platelets) derange monophasically approximately 1 year prior to PDAC. Profiling these in combination with their temporality could inform earlier PDAC diagnosis

Biomarkers of response to PD-1 pathway blockade

The binding of T cell immune checkpoint proteins programmed death 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to their ligands allows immune evasion by tumours. The development of therapeutic antibodies, termed checkpoint inhibitors, that bind these molecules or their ligands, has provided a means to release this brake on the host anti-tumour immune response. However, these drugs are costly, are associated with potentially severe side effects, and only benefit a small subset of patients. It is therefore important to identify biomarkers that discriminate between responders and non-responders. This review discusses the determinants for a successful response to antibodies that bind PD-1 or its ligand PD-L1, dividing them into markers found in the tumour biopsy and those in non-tumour samples. It provides an update on the established predictive biomarkers (tumour PD-L1 expression, tumour mismatch repair deficiency and tumour mutational burden) and assesses the evidence for new potential biomarkers

Clustering of master regulators into different functional groups.

<p>Heat maps showing the similarity between the samples in the <b>A</b>. ICGC and <b>B</b>. TCGA cohorts as measured by “signature distance” between the MRs activity profiles [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002223#pmed.1002223.ref027" target="_blank">27</a>]. Unsupervised analysis identified three classes of tumours with differential activities of the three identified disease processes: cell cycle (pink), Hedgehog/Wnt (blue), and Notch (green).</p

Oncogenic <i>KRAS</i> is regulated by three groups of master regulators.

<p><b>A.</b> Volcano plot showing the magnitude of the differential gene expression between murine mock ductal cells and murine cre ductal cells (with activated oncogenic <i>Kras</i>). Each dot represents one probe with detectable expression in both conditions. The coloured dots mark the threshold (<i>p</i> < 0.05 and log2 fold-change > 1) for defining a gene as differentially expressed. <b>B.</b> Ras GTPase assay shows increased GTPase activity in cre cells blotted with pan-Ras antibody (M, mock; c, cre). <b>C.</b> Visual representation of master regulators (MRs) identified with msVIPER analysis (<i>p</i> < 0.01). The nodes in the networks represent the 55 master regulators (large dots) and the corresponding inferred targets (smaller dots). The edges in the network represent the regulatory relationship between regulators and the inferred targets. The colours highlight the community structure of the network identified via greedy optimization of modularity. The three groups of nodes correspond to a total of 27 master regulators and represent three distinct disease processes enriched for cell cycle (pink), Hedgehog/Wnt signalling (blue), and Notch signalling (green) pathways. <b>D.</b> For the 27 MRs in the three core processes, the heat map shows their activity (first column) and differential expression in the <i>KRAS</i> signature (second column) as obtained by Virtual Inference of Protein-activity by Enriched Regulon (VIPER) analysis. “Expression” refers to the differential expression value after <i>KRAS</i> induction (cell line experiment). The colour code of in the heat map corresponds to the t-statistic value obtained after limma differential expression analysis, with blue representing down-regulated genes and red representing up-regulated genes after <i>KRAS</i> activation. “Activity” refers to the differential protein activity value after <i>KRAS</i> induction with red or blue representing activation or inactivation, respectively. The protein activity score is quantitatively inferred by the aREA algorithm in VIPER by systematically analysing expression of genes coexpressed with the transcription factor (TF).</p