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 )

Molecular or Metabolic Reprograming: What Triggers Tumor Subtypes?

Tumor heterogeneity is reflected and influenced by genetic, epigenetic, and metabolic differences in cancer cells and their interactions with a complex microenvironment. This heterogeneity has resulted in the stratification of tumors into subtypes, mainly based on cancer-specific genomic or transcriptomic profiles. Subtyping can lead to biomarker identification for personalized diagnosis and therapy, but stratification alone does not explain the origins of tumor heterogeneity. Heterogeneity has traditionally been thought to arise from distinct mutations/aberrations in "driver" oncogenes. However, certain subtypes appear to be the result of adaptation to the disrupted microenvironment caused by abnormal tumor vasculature triggering metabolic switches. Moreover, heterogeneity persists despite the predominance of single oncogenic driver mutations, perhaps due to second metabolic or genetic "hits." In certain cancer types, existing subtypes have metabolic and transcriptomic phenotypes that are reminiscent of normal differentiated cells, whereas others reflect the phenotypes of stem or mesenchymal cells. The cell-of-origin may, therefore, play a role in tumor heterogeneity. In this review, we focus on how cancer cell-specific heterogeneity is driven by different genetic or metabolic factors alone or in combination using specific cancers to illustrate these concepts. Cancer Res; 76(18); 5195-200. ©2016 AACR

Micropropagation of White Palash tree (Butea monosperma (Lam.) Taub. Var. lutea (Witt.)).

An efficient and reproducible protocol is established for rapid in vitro multiplication of an endangered, valuable medicinal plant, Butea monosperma (Lam.) Taub. Var. lutea, through cotyledonary nodes of mature seeds. Among various cytokinins tested, high frequency of direct shoot regeneration was induced on Murashige and skoog (MS) medium supplemented with BAP, which found to be more effective and showed optimal response at 2 mg/L with a maximum number of 8.35±0.32 multiple shoots per explant. Proliferation of shoots was established by repeated subculturing on to same regeneration medium with 2-3 weeks of time interval. Rooting of regenerated shoots was achieved after 3 weeks of culture on MS medium containing 1 mg/L IBA. In vitro raised plantlets were transferred to pots containing sterilized soil and vermiculate mixture in 1:1 ratio and then shifted to greenhouse. Well established plantlets exhibited 75% survival rate

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

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

A Case Report on Longitudinal Collection of Tumour Biopsies for Gene Expression-Based Tumour Microenvironment Analysis from Pancreatic Cancer Patients Treated with Endoscopic Ultrasound Guided Radiofrequency Ablation.

BACKGROUND: Most patients with pancreatic ductal adenocarcinoma (PDAC) are metastatic at presentation with dismal prognosis warranting improved systemic therapy options. Longitudinal sampling for the assessment of treatment response poses a challenge for validating novel therapies. In this case study, we evaluate the feasibility of collecting endoscopic ultrasound (EUS)-guided longitudinal fine-needle aspiration biopsies (FNABs) from two PDAC patients and conduct gene expression studies associated with tumour microenvironment changes associated with radiofrequency ablation (RFA). METHODS: EUS-guided serial/longitudinal FNABs of tumour were collected before and after treatment from two stage III inoperable gemcitabine-treated PDAC patients treated with targeted RFA three times. Biopsies were analysed using a custom NanoString panel (144 genes) consisting of cancer and cancer-associated fibroblast (CAFs) subtypes and immune changes. CAF culture was established from one FNAB and characterised by immunofluorescence and immunoblotting. RESULTS: Two-course RFA led to the upregulation of the CD1E gene (involved in antigen presentation) in both patients 1 and 2 (4.5 and 3.9-fold changes) compared to baseline. Patient 1 showed increased T cell genes (CD4-8.7-fold change, CD8-35.7-fold change), cytolytic function (6.4-fold change) and inflammatory response (8-fold change). A greater than 2-fold upregulation of immune checkpoint genes was observed post-second RFA in both patients. Further, two-course RFA led to increased PDGFRα (4.5-fold change) and CAF subtypes B and C genes in patient 1 and subtypes A, B and D genes in patient 2. Patient 2-derived CAFs post-first RFA showed expression of PDGFRα, POSTN and MYH11 proteins. Finally, RFA led to the downregulation of classical PDAC subtype-specific genes in both patients. CONCLUSIONS: This case study suggests longitudinal EUS-FNAB as a potential resource to study tumour and microenvironmental changes associated with RFA treatment. A large sample size is required in the future to assess the efficacy and safety of the treatment and perform comprehensive statistical analysis of EUS-RFA-based molecular changes in PDAC

Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas

RNA polymerase II mediates the transcription of all protein-coding genes in eukaryotic cells, a process that is fundamental to life. Genomic mutations altering this enzyme have not previously been linked to any pathology in humans, which is a testament to its indispensable role in cell biology. On the basis of a combination of next-generation genomic analyses of 775 meningiomas, we report that recurrent somatic p.Gln403Lys or p.Leu438_His439del mutations in POLR2A, which encodes the catalytic subunit of RNA polymerase II (ref. 1), hijack this essential enzyme and drive neoplasia. POLR2A mutant tumors show dysregulation of key meningeal identity genes including WNT6 and ZIC1/ZIC4. In addition to mutations in POLR2A, NF2, SMARCB1, TRAF7, KLF4, AKT1, PIK3CA, and SMO4 we also report somatic mutations in AKT3, PIK3R1, PRKAR1A, and SUFU in meningiomas. Our results identify a role for essential transcriptional machinery in driving tumorigenesis and define mutually exclusive meningioma subgroups with distinct clinical and pathological features

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

Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses.

Breast cancer is a highly heterogeneous disease. Although differences between intrinsic breast cancer subtypes have been well studied, heterogeneity within each subtype, especially luminal-A cancers, requires further interrogation to personalize disease management. Here, we applied well-characterized and cancer-associated heterocellular signatures representing stem, mesenchymal, stromal, immune, and epithelial cell types to breast cancer. This analysis stratified the luminal-A breast cancer samples into five subtypes with a majority of them enriched for a subtype (stem-like) that has increased stem and stromal cell gene signatures, representing potential luminal progenitor origin. The enrichment of immune checkpoint genes and other immune cell types in two (including stem-like) of the five heterocellular subtypes of luminal-A tumors suggest their potential response to immunotherapy. These immune-enriched subtypes of luminal-A tumors (containing only estrogen receptor positive samples) showed good or intermediate prognosis along with the two other differentiated subtypes as assessed using recurrence-free and distant metastasis-free patient survival outcomes. On the other hand, a partially differentiated subtype of luminal-A breast cancer with transit-amplifying colon-crypt characteristics showed poor prognosis. Furthermore, published luminal-A subtypes associated with specific somatic copy number alterations and mutations shared similar cellular and mutational characteristics to colorectal cancer subtypes where the heterocellular signatures were derived. These heterocellular subtypes reveal transcriptome and cell-type based heterogeneity of luminal-A and other breast cancer subtypes that may be useful for additional understanding of the cancer type and potential patient stratification and personalized medicine