Biology subjects

Rosains, J.

Publications and source records attributed to Rosains, J..

3 recordsLinked to original sources

bioRxiv2019

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multi-region samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

cancer biology↗

bioRxiv2019

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

cancer biology↗

bioRxiv2017

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.\n\nHIGHLIGHTSO_LIA new gene expression profiling method, L1000, dramatically lowers cost\nC_LIO_LIThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles\nC_LIO_LIThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants\nC_LIO_LIThe work establishes feasibility and utility of a truly comprehensive Connectivity Map\nC_LI

genomics↗