A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies.

Genome projects now generate large-scale data often produced at various time points by different laboratories using multiple platforms. This increases the potential for batch effects. Currently there are several batch evaluation methods like principal component analysis (PCA; mostly based on visual inspection), and sometimes they fail to reveal all of the underlying batch effects. These methods can also lead to the risk of unintentionally correcting biologically interesting factors attributed to batch effects. Here we propose a novel statistical method, finding batch effect (findBATCH), to evaluate batch effect based on probabilistic principal component and covariates analysis (PPCCA). The same framework also provides a new approach to batch correction, correcting batch effect (correctBATCH), which we have shown to be a better approach to traditional PCA-based correction. We demonstrate the utility of these methods using two different examples (breast and colorectal cancers) by merging gene expression data from different studies after diagnosing and correcting for batch effects and retaining the biological effects. These methods, along with conventional visual inspection-based PCA, are available as a part of an R package exploring batch effect (exploBATCH; https://github.com/syspremed/exploBATCH )

Incidence of Infectious Diseases in Patients Suffering from Renal Diseases

Background: Infection is an invasion of an organism’s body tissues by disease-causing agents, their multiplication, and the reaction of host tissues to the infectious agents and the toxins they produce. Patients with renal compromised states are more susceptible to infection than normal individuals. In the pre-dialysis era, about 45% of patients with the renal compromised state suffering from infection required hospitalization, while a total of about 78% of the enrolled subjects needed hospitalization. It was assumed that the debility caused by the uremic state increased the risk of infection, and the reversal of uremia would reduce the risk of infection.Aim: The main aim of the study is to report the incidence of infectious diseases in patients with renal compromised state and appropriate measures to be considered to control infectious conditions.Materials and Methods: The study was carried out as prospective and cross-sectional studies. During the study period, a total of 195 subjects were examined with the renal compromised state, of which 108 subjects were suffering from infectious co-morbidity, and were enrolled based on inclusion and exclusion criteria, which includes in-patients, out-patients, and patients on regular dialysis.Results: This shows the percentage prevalence of infections in patients with the renal compromised state is 55.38. Patients were found to show various infectious states.Conclusion: The conclusion shows the probability of encountering a subject with renal compromised state along with co-morbid infection is 0.55. Evidence-based international guidelines are of great value and are instrumental in helping reduce health-care-associated infections.Keywords: Incidence of infectious diseases, Renal compromised state, Renal disease

Secreted semaphorin 5A suppressed pancreatic tumour burden but increased metastasis and endothelial cell proliferation.

BACKGROUND: Our earlier reports demonstrated that membrane-bound semaphorin 5A (SEMA5A) is expressed in aggressive pancreatic cancer cells and tumours, and promotes tumour growth and metastasis. In this study, we examine whether (1) pancreatic cancer cells secrete SEMA5A and (2) that secreted SEMA5A modulates certain phenotypes associated with tumour progression, angiogenesis and metastasis through various other molecular factors and signalling proteins. METHODS AND RESULTS: In this study, we show that human pancreatic cancer cell lines secrete the extracellular domain (ECD) of SEMA5A (SEMA5A-ECD) and overexpression of mouse Sema5A-ECD in Panc1 cells (not expressing SEMA5A; Panc1-Sema5A-ECD; control cells - Panc1-control) significantly increases their invasion in vitro via enhanced ERK phosphorylation. Interestingly, orthotopic injection of Panc1-Sema5A-ECD cells into athymic nude mice results in a lower primary tumour burden, but enhances the micrometastases to the liver as compared with Panc1-control cells. Furthermore, there is a significant increase in proliferation of endothelial cells treated with conditioned media (CM) from Panc1-Sema5A-ECD cells and a significant increase in microvessel density in Panc1-Sema5A-ECD orthotopic tumours compared with those from Panc1-control cells, suggesting that the increase in liver micrometastases is probably due to increased tumour angiogenesis. In addition, our data demonstrate that this increase in endothelial cell proliferation by Sema5A-ECD is mediated through the angiogenic molecules - interleukin-8 and vascular endothelial growth factor. CONCLUSION: Taken together, these results suggest that a bioactive, secreted form of Sema5A-ECD has an intriguing and potentially important role in its ability to enhance pancreatic tumour invasiveness, angiogenesis and micrometastases

Analytical Validation of Multiplex Biomarker Assay to Stratify Colorectal Cancer into Molecular Subtypes.

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond

Computational cancer biology: education is a natural key to many locks

BACKGROUND: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner. DISCUSSION: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research. SUMMARY: Here we argue that this imbalance, favoring ’wet lab-based activities’, will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization

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

Molecular Pathological Classification of Colorectal Cancer

Colorectal cancer (CRC) shows variable underlying molecular changes with two major mechanisms of genetic instability: chromosomal instability and microsatellite instability. This review aims to delineate the different pathways of colorectal carcinogenesis and provide an overview of the most recent advances in molecular pathological classification systems for colorectal cancer. Two molecular pathological classification systems for CRC have recently been proposed. Integrated molecular analysis by The Cancer Genome Atlas project is based on a wide-ranging genomic and transcriptomic characterisation study of CRC using array-based and sequencing technologies. This approach classified CRC into two major groups consistent with previous classification systems: (1) ∼16 % hypermutated cancers with either microsatellite instability (MSI) due to defective mismatch repair (∼13 %) or ultramutated cancers with DNA polymerase epsilon proofreading mutations (∼3 %); and (2) ∼84 % non-hypermutated, microsatellite stable (MSS) cancers with a high frequency of DNA somatic copy number alterations, which showed common mutations in APC, TP53, KRAS, SMAD4, and PIK3CA. The recent Consensus Molecular Subtypes (CMS) Consortium analysing CRC expression profiling data from multiple studies described four CMS groups: almost all hypermutated MSI cancers fell into the first category CMS1 (MSI-immune, 14 %) with the remaining MSS cancers subcategorised into three groups of CMS2 (canonical, 37 %), CMS3 (metabolic, 13 %) and CMS4 (mesenchymal, 23 %), with a residual unclassified group (mixed features, 13 %). Although further research is required to validate these two systems, they may be useful for clinical trial designs and future post-surgical adjuvant treatment decisions, particularly for tumours with aggressive features or predicted responsiveness to immune checkpoint blockade