VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

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

Targeting melanoma’s MCL1 bias unleashes the apoptotic potential of BRAF and ERK1/2 pathway inhibitors

Funder: AstraZenecaAbstract: BRAF and MEK1/2 inhibitors are effective in melanoma but resistance inevitably develops. Despite increasing the abundance of pro-apoptotic BIM and BMF, ERK1/2 pathway inhibition is predominantly cytostatic, reflecting residual pro-survival BCL2 family activity. Here, we show that uniquely low BCL-XL expression in melanoma biases the pro-survival pool towards MCL1. Consequently, BRAF or MEK1/2 inhibitors are synthetic lethal with the MCL1 inhibitor AZD5991, driving profound tumour cell death that requires BAK/BAX, BIM and BMF, and inhibiting tumour growth in vivo. Combination of ERK1/2 pathway inhibitors with BCL2/BCL-w/BCL-XL inhibitors is stronger in CRC, correlating with a low MCL1:BCL-XL ratio; indeed the MCL1:BCL-XL ratio is predictive of ERK1/2 pathway inhibitor synergy with MCL1 or BCL2/BCL-w/BCL-XL inhibitors. Finally, AZD5991 delays acquired BRAFi/MEKi resistance and enhances the efficacy of an ERK1/2 inhibitor in a model of acquired BRAFi + MEKi resistance. Thus combining ERK1/2 pathway inhibitors with MCL1 antagonists in melanoma could improve therapeutic index and patient outcomes

Programmed Cell Death Ligand 1 Expression in Untreated EGFR Mutated Advanced NSCLC and Response to Osimertinib Versus Comparator in FLAURA.

EGFR mutated (EGFRm) NSCLC tumors occasionally express programmed cell death ligand 1 (PD-L1), although frequency and clinical relevance are not fully characterized. We report PD-L1 expression in patients with EGFRm advanced NSCLC and association with clinical outcomes following treatment with osimertinib or comparator EGFR tyrosine kinase inhibitors in the FLAURA trial (phase III, NCT02296125). Of 231 tissue blocks available from the screened population (including EGFRm-positive and -negative samples), 197 had sufficient tissue for PD-L1 testing using the SP263 (Ventana, Tucson, Arizona) immunohistochemical assay. Tumor cell (TC) staining thresholds of PD-L1 TC greater than or equal to 1%, TC greater than or equal to 25%, and TC greater than or equal to 50% were applied. Progression-free survival (PFS) was investigator-assessed, per Response Evaluation Criteria in Solid Tumor, version 1.1, according to PD-L1 expressors (TC ≥ 1%) or negatives (TC PD-L1 staining was successful in 193 of 197 patient formalin-fixed paraffin-embedded blocks; of these, 128 of 193 were EGFRm-positive and 106 of 128 patients were randomized to treatment (osimertinib: 54; comparator: 52). At the PD-L1 TC greater than or equal to 25% threshold, 8% (10 of 128) of EGFRm-positive tumors expressed PD-L1 versus 35% (23 of 65) of EGFRm-negative tumors. With the TC greater than or equal to 1% threshold, 51% (65 of 128) versus 68% (44 of 65) were mutation-positive and -negative, respectively, and with the TC greater than or equal to 50% threshold, 5% (7 of 128) versus 28% (18 of 65), were mutation-positive and -negative, respectively. For PD-L1 expressors (TC ≥ 1%), median PFS was 18.4 months with osimertinib and 6.9 months with comparator (hazard ratio = 0.30; 95% confidence interval: 0.15-0.60). For PD-L1-negative patients (TC Clinical benefit with osimertinib was unaffected by PD-L1 expression status

Candidate mechanisms of acquired resistance to first-line osimertinib in EGFR-mutated advanced non-small cell lung cancer

Abstract Osimertinib, an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), potently and selectively inhibits EGFR-TKI-sensitizing and EGFR T790M resistance mutations. In the Phase III FLAURA study (NCT02296125), first-line osimertinib improved outcomes vs comparator EGFR-TKIs in EGFRm advanced non-small cell lung cancer. This analysis identifies acquired resistance mechanisms to first-line osimertinib. Next-generation sequencing assesses circulating-tumor DNA from paired plasma samples (baseline and disease progression/treatment discontinuation) in patients with baseline EGFRm. No EGFR T790M-mediated acquired resistance are observed; most frequent resistance mechanisms are MET amplification (n = 17; 16%) and EGFR C797S mutations (n = 7; 6%). Future research investigating non-genetic acquired resistance mechanisms is warranted

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