Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks

<p>Abstract</p> <p>Background</p> <p>One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data.</p> <p>Results</p> <p>When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and <it>F</it>-score over a range of values. The maximal <it>F</it>-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier.</p> <p>Conclusions</p> <p>Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.</p

Analysis of cancer metabolism with high-throughput technologies

<p>Abstract</p> <p>Background</p> <p>Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect – a long-standing puzzle in cancer energy metabolism – at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.</p> <p>Results</p> <p>We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (<it>ACO2</it>) in the brain exceeded both citrate synthase (<it>CS</it>) and isocitrate dehydrogenase 2 (<it>IDH2</it>), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (<it>GAPDH</it>) and enolase (<it>ENO</it>) were considerably increased when compared to the brain sample.</p> <p>Conclusions</p> <p>The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.</p

Osimertinib as first-line treatment of EGFR mutation-positive advanced non-small-cell lung cancer

Purpose The AURA study (ClinicalTrials.gov identifier: NCT01802632) included two cohorts of treatment-naive patients to examine clinical activity and safety of osimertinib (an epidermal growth factor receptor [EGFR] -tyrosine kinase inhibitor selective for EGFR-tyrosine kinase inhibitor sensitizing [EGFRm] and EGFRT790M resistance mutations) as first-line treatment of EGFR-mutated advanced non-small-cell lung cancer (NSCLC). Patients and Methods Sixty treatment-naive patients with locally advanced or metastatic EGFRm NSCLC received osimertinib 80 or 160 mg once daily (30 patients per cohort). End points included investigator-assessed objective response rate (ORR), progression-free survival (PFS), and safety evaluation. Plasma samples were collected at or after patients experienced disease progression, as defined by Response Evaluation Criteria in Solid Tumors (RECIST), to investigate osimertinib resistance mechanisms. Results At data cutoff (November 1, 2016), median follow-up was 19.1 months. Overall ORR was 67% (95% CI, 47% to 83%) in the 80-mg group, 87% (95% CI, 69% to 96%) in the 160-mg group, and 77% (95% CI, 64% to 87%) across doses. Median PFS time was 22.1 months (95% CI, 13.7 to 30.2 months) in the 80-mg group, 19.3 months (95% CI, 13.7 to 26.0 months) in the 160-mg group, and 20.5 months (95% CI, 15.0 to 26.1 months) across doses. Of 38 patients with postprogression plasma samples, 50% had no detectable circulating tumor DNA. Nine of 19 patients had putative resistance mechanisms, including amplification of MET (n = 1); amplification of EGFR and KRAS (n = 1); MEK1, KRAS, or PIK3CA mutation (n = 1 each); EGFR C797S mutation (n = 2); JAK2 mutation (n = 1); and HER2 exon 20 insertion (n = 1). Acquired EGFRT790M was not detected. Conclusion Osimertinib demonstrated a robust ORR and prolonged PFS in treatment-naive patients with EGFRm advanced NSCLC. There was no evidence of acquired EGFRT790M mutation in postprogression plasma samples. (C) 2017 by American Society of Clinical Oncology

Supplementary Figure S6 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

Kaplan–Meier estimates showing investigator-assessed PFS in FLAURA trial patients by clearance or non-clearance of plasma EGFRm status at Weeks 3 or 6 in patients who had baseline detectable plasma EGFRm. For comparison, patients with baseline non-detectable plasma EGFRm are included. A, Osimertinib arm by Week 3 plasma EGFRm status (n = 238). B, Comparator EGFR-TKI arm by Week 3 plasma EGFRm status (n = 243). C, Osimertinib arm by Week 6 plasma EGFRm status (n = 240). D, Comparator EGFR-TKI arm by Week 6 plasma EGFRm status (n = 235). Censored data are indicated by tick marks. Abbreviations: CI, confidence interval; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R); NC, not calculable; mPFS, median PFS; TKI, tyrosine kinase inhibitor.</p

Supplementary Figure S4 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

Assessment of correlation between EGFRm VAF in baseline plasma and investigator-assessed PFS in A, patients from the AURA3 trial, by treatment arm and B, patients from the FLAURA trial, by treatment arm. Abbreviations: CR, complete response; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R); NE, not evaluable; PD, progressive disease; PFS, progression-free survival; PR, partial response; SD, stable disease; VAF, variant allelic fraction.</p

Supplementary Table S1 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

Representativeness of study participants</p

Supplementary Figure S1 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

AURA3 flow diagram for ctDNA analysis (n = 291). Clearance refers to non-detectable plasma EGFRm, where EGFRm was detectable at baseline. *There were 9 patients in the osimertinib arm and 1 patient in the platinum-pemetrexed arm without a valid ddPCR result at Weeks 3 and 6; these samples were excluded from the ctDNA clearance analysis. Abbreviations: ctDNA, circulating tumor DNA; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R).</p

Supplementary Figure S2 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

Correlation between EGFRm allelic fraction at baseline by ddPCR (Biodesix) and NGS (Guardant Health) analyses in A, patients from the AURA3 trial (n = 202) and B, patients from the FLAURA trial (n = 347). Abbreviations: ddPCR, droplet digital PCR; EGFRm, epidermal growth factor receptor mutation (ex19del or L858R); NGS, next-generation sequencing.</p

Supplementary Table S2 from Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA

Univariable and multivariable analyses for OS from the AURA3 trial (ctDNA evaluable population, n = 291) and FLAURA trial (ctDNA evaluable population, n = 499).</p