Biology subjects

Korn, J. M.

Publications and source records attributed to Korn, J. M..

3 recordsLinked to original sources

bioRxiv2020

PISCES: a package for rapid quantitation and quality control of large scale mRNA-seq datasets

PISCES eases processing of large mRNA-seq experiments by encouraging capture of metadata using simple textual file formats, processing samples on either a single machine or in parallel on a high performance computing cluster (HPC), validating sample identity using genetic fingerprinting, and summarizing all outputs in analysis-ready data matrices. PISCES consists of two modules: 1) compute cluster-aware analysis of individual mRNA-seq libraries including species detection, SNP genotyping, library geometry detection, and quantitation using salmon, and 2) gene-level transcript aggregation, transcriptional and read-based QC, TMM normalization and differential expression analysis of multiple libraries to produce data ready for visualization and further analysis. PISCES is implemented as a python3 package and is bundled with all necessary dependencies to enable reproducible analysis and easy deployment. JSON configuration files are used to build and identify transcriptome indices, and CSV files are used to supply sample metadata and to define comparison groups for differential expression analysis using DEseq2. PISCES builds on many existing open-source tools, and releases of PISCES are available on GitHub or the python package index (PyPI).

bioinformatics↗

bioRxiv2020

Biomarker-guided treatment strategies for ovarian cancer identified from a heterogeneous panel of patient-derived tumor xenografts

Advanced ovarian cancers are a leading cause of cancer-related death in women. Such cancers are currently treated with surgery and chemotherapy which is often temporarily successful but exhibits a high rate of relapse after which treatment options are few. Here we assess the responses of a panel of patient-derived ovarian cancer xenografts (PDXs) to 19 mono and combination therapies, including small molecules and antibody-drug conjugates. The PDX panel aimed to mimic the heterogeneity of disease observed in patients, and exhibited a distribution of responsiveness to standard of care chemotherapy similar to human clinical data. Three monotherapies and one drug combination were found to be active in different subsets of PDXs. By analyzing gene expression data we identified gene expression biomarkers predictive of responsiveness to each of three novel targeted therapy regimens. While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. Biomarker frequency was similar in human patients, suggesting the possibility of a new therapeutic approach to ovarian cancer and demonstrating the potential power of PDX-based trials in broadening the reach of precision cancer medicine.

cancer biology↗

bioRxiv2017

Correction of copy number induced false positives in CRISPR screens

Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70-80% decrease of false positive hits in regions of high copy number with either method.

bioinformatics↗