Accuracy of Postoperative Risk Scores for Survival Prediction in Interagency Registry for Mechanically Assisted Circulatory Support Profile 1 Continuous-Flow Left Ventricular Assist Device Recipients.

In this study, we sought to determine the accuracy of several critical care risk scores for predicting survival of Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) Profile 1 patients after continuous-flow left ventricular assist device (CF-LVAD) placement. We retrospectively analyzed the records of 605 patients who underwent CF-LVAD implantation between 2003 and 2016. We calculated the preoperative HeartMate II Risk Score (HMRS) and preoperative Right Ventricular Failure Risk Score (RVFRS) and the following risk scores for postoperative days 1-5: HMRS, RVFRS, Model for End-stage Liver Disease (MELD), MELD-eXcluding International Normalized Ratio, Post Cardiac Surgery (POCAS) risk score, Sequential Organ Failure Assessment (SOFA) risk score, and Acute Physiology and Chronic Health Evaluation III. The preoperative scores and the postoperative day 1, 5-day mean, and 5-day maximum scores were entered into a receiver operating characteristic curve analysis to examine accuracy for predicting 30-day, 90-day, and 1-year survival. The mean POCAS score was the best predictor of 30-day and 90-day survival (area under the curve [AUC] = 0.869 and 0.816). The postoperative mean RVFRS was the best predictor of 1-year survival (AUC = 0.7908). The postoperative maximum and mean RVFRS and HMRS were more accurate than the preoperative scores. Both of these risk score measurements of acuity in the postoperative intensive care unit setting help predict early mortality after LVAD implantation.

Genomic profiling is predictive of response to cisplatin treatment but not to PI3K inhibition in bladder cancer patient-derived xenografts.

PURPOSE: Effective systemic therapeutic options are limited for bladder cancer. In this preclinical study we tested whether bladder cancer gene alterations may be predictive of treatment response. EXPERIMENTAL DESIGN: We performed genomic profiling of two bladder cancer patient derived tumor xenografts (PDX). We optimized the exome sequence analysis method to overcome the mouse genome interference. RESULTS: We identified a number of somatic mutations, mostly shared by the primary tumors and PDX. In particular, BLCAb001, which is less responsive to cisplatin than BLCAb002, carried non-sense mutations in several genes associated with cisplatin resistance, including MLH1, BRCA2, and CASP8. Furthermore, RNA-Seq analysis revealed the overexpression of cisplatin resistance associated genes such as SLC7A11, TLE4, and IL1A in BLCAb001. Two different PIK3CA mutations, E542K and E545K, were identified in BLCAb001 and BLCAb002, respectively. Thus, we tested whether the genomic profiling was predictive of response to a dual PI3K/mTOR targeting agent, LY3023414. Despite harboring similar PIK3CA mutations, BLCAb001 and BLCAb002 exhibited differential response, both in vitro and in vivo. Sustained target modulation was observed in the sensitive model BLCAb002 but not in BLCAb001, as well as decreased autophagy. Interestingly, computational modelling of mutant structures and affinity binding to PI3K revealed that E542K mutation was associated with weaker drug binding than E545K. CONCLUSIONS: Our results suggest that the presence of activating PIK3CA mutations may not necessarily predict in vivo treatment response to PI3K targeted therapies, while specific gene alterations may be predictive for cisplatin response in bladder cancer models and, potentially, in patients as well.

MAC: identifying and correcting annotation for multi-nucleotide variations.

BACKGROUND: Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome. RESULTS: Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls. CONCLUSIONS: MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.

Whole-genome sequencing of a malignant granular cell tumor with metabolic response to pazopanib.

Granular cell tumors are an uncommon soft tissue neoplasm. Malignant granular cell tumors comprise <2% of all granular cell tumors, are associated with aggressive behavior and poor clinical outcome, and are poorly understood in terms of tumor etiology and systematic treatment. Because of its rarity, the genetic basis of malignant granular cell tumor remains unknown. We performed whole-genome sequencing of one malignant granular cell tumor with metabolic response to pazopanib. This tumor exhibited a very low mutation rate and an overall stable genome with local complex rearrangements. The mutation signature was dominated by C>T transitions, particularly when immediately preceded by a 5' G. A loss-of-function mutation was detected in a newly recognized tumor suppressor candidate, BRD7. No mutations were found in known targets of pazopanib. However, we identified a receptor tyrosine kinase pathway mutation in GFRA2 that warrants further evaluation. To the best of our knowledge, this is only the second reported case of a malignant granular cell tumor exhibiting a response to pazopanib, and the first whole-genome sequencing of this uncommon tumor type. The findings provide insight into the genetic basis of malignant granular cell tumors and identify potential targets for further investigation.