Overview
With the advent of high throughput technologies, the quantity of ‘omics’ data has rapidly increased, creating the need for methodologies that
can analyze complex datasets and provide interpretations that assist in decision making. We have developed PANOPLY; a novel computational approach to integrate both germline and somatic data obtained from multi-omics platforms for an individual of interest and analyze that data in the context matched-control samples. This approach is used to summarize knowledge into informative and predictive models to narrow appropriate treatment options and to aid clinician/researchers in decision making. The objective of PANOPLY is to process a variety of “omics” data along with molecular, pathological and clinical data from cancer patients with the ultimate goal of “individualizing therapy. Specifically, PANOPLY takes pre-processed data such as germline DNA sequence, somatic single nucleotide variants (SNVs), small insertions and deletions (INDELs), copy number variants (CNVs), fusion transcripts, along with RNA seq gene expression data to build an integrated network which ranks the cancer genesets for a given patient.
From these data, the PANOPLY system identifies the most promising drug targets.
We have applied our algorithm to the clinical and genomic data from breast cancer patients treated with neoadjuvant chemotherapy in a recent prospective clinical study and have identified the most promising druggable targets and pathways that are activated. We are now in the process of prioritizing and validating these findings using patient derived xenografts (PDXs) from the same patients. PANOPLY is a workflow that was developed using bash, LaTeX, and R. The workflow works mainly through an R package available for download below and shared on github. PANOPLY provides comprehensive reports that are useful for clinicians and researchers.
Authors:
Krishna R. Kalari, PhD; Jason P. Sinnwell, MS; Kevin J. Thompson, PhD; Xiaojia Tang, PhD; Erin Carlson, BA; Peter T. Vedell, PhD; Jean-Pierre Kocher, PhD; Richard Weinshilboum, MD; Liewei Wang, MD, PhD; Judy C. Boughey, MD; Matthew P. Goetz, MD; and Vera J. Suman, PhD.
Co-corresponding authors:
Krishna R Kalari and Vera J Suman
Package Downloads
Comprehensive Reports:
BEAUTY (TNBC) Patient:
TCGA Colon Cancer Patient
TCGA Breast Cancer Patient:
User Manuals:
