Download #4842 Pharmacodynamic Stratification of Metastatic Colorectal

#4842 Pharmacodynamic Stratification of Metastatic Colorectal Cancer Patients Using Genomic Datasets
Sharon Austin1, Kellie Howard1, Fang Yin Lo1, Mollie McWhorter1, Heather Collins1, Amanda Leonti1, Lindsey Maassel1, Christopher Subia1, Tuuli Saloranta1,
Nicole Heying1, Leila Ritter1, Kerry Deutsch1, James Cox1, Timothy Yeatman2, Steven Anderson3 and Anup Madan1
1Laboratory Corporation of America® Holdings, Seattle, WA; 2Gibbs Cancer Center, Spartanburg, SC; 3Laboratory Corporation of America® Holdings, Research Triangle Park, NC
Results
Abstract
Previously, the mutation status of KRAS was the only validated predictive biomarker for
metastatic colorectal cancer (CRC). While KRAS mutated tumors demonstrated resistance to
epidermal growth factor (EGFR) inhibitors like cetuximab, KRAS WT and EGFR-expressing
tumors were predicted to be responsive. However, KRAS WT metastatic colorectal cancer
(CRC) patients have a poor prognosis even with EGFR inhibitor therapy as not all KRAS WT
CRCs are responsive to such targeted agents. A gene expression based RAS signature score
was developed based on multiple tumor tissue samples to identify RAS activated tumors
independent of mutations in the KRAS gene1, 2. To further refine this score and define
technologies that can be used on FFPE samples isolated in a clinical setting, we analyzed
DNA and RNA derived from fifty-five (55) FFPE preserved colorectal cancer tumor biopsies
using multiple sequencing, digital and array-based technologies. These samples were selected
from a CRC cohort in which the initial gene expression-based RAS signature score was
calculated utilizing data compiled from fresh frozen (FF) tumor samples from the same 55
patients. The 55 samples were selected for this study as they had representative samples with
Background
▶ Colorectal cancer (CRC) is the
3rd
most common human
malignancy and is a major cause of cancer mortality in the
Western world
high, medium and low RAS signature scores. Transcriptomic analyses (RNA-Seq, Affymetrix®
microarrays, NanoString® and Targeted RNA-Seq) were performed on all 55 FFPE samples
and three new RAS scores were calculated from the gene expression datasets. These RAS
scores were based on different gene signatures; (1) an 18 gene signature, (2) a 13 gene
signature, and (3) a 147 gene signature. A significant correlation was identified between RAS
scores calculated from the 18 and 13 gene signatures (correlation coefficient ~ 0.88 and ~0.76
respectively, p-value < 0.0001). To further refine gene expression signatures, samples were
grouped based upon their mutation status obtained by whole exome sequencing (WES) and
targeted DNA sequencing data (Illumina® TruSight® and LifeTech Cancer Panels). In our
sample set, the 18 gene RAS score was found to be dependent on the mutation status of
KRAS. Further analysis is being carried out to better understand the relationship between the
calculated RAS signature scores and the mutation status of other genes. This analysis will lead
to the development of a novel genomic signature for better pharmacodynamic stratification of
colorectal carcinoma patients.
Methods
RAS Score (log10) 55 FFPE Samples
References
Figure 4. RAS score correlation with Affymetrix array.
(A) RAS scores highly correlate between platforms. For datasets comparing RNA Seq and Affymetrix®, RAS
scores were determined by normalizing to all genes. (B) For datasets comparing NanoString® and Affymetrix®,
RAS scores were determined by normalizing to 11 housekeeping genes.
Combine Gene Expression with Mutation Status
Sample Cohort
Cross Platform Comparison
Figure 2. Sample cohort.
The cohort was selected by filtering out colorectal cancer
samples available as formalin-fixed, paraffin-embedded (FFPE)
and flash frozen (FF). Samples were then filtered for known
RAS score obtained from Affymetrix® array. Known RAS scores
are divided into 3 groups evenly: low (<33% percentile), medium
(33%-66% percentile), high (>66% percentile).
Figure 3. Cross platform comparison.
Samples derived from the same 55 FFPE blocks were assayed across multiple
platforms. The method design to combine RNA analysis (gene expression
signature scores) with DNA analysis (i.e., mutation status) allows for comparison
of RAS signature scores and overall gene expression from different platforms.
▶ Previously the mutation status of KRAS was the only
validated predictive biomarker3
▶ Prognosis of metastatic CRC is still poor
▶ Measuring independent biomarkers is unlikely to capture
the complexity of RAS signaling pathway dependence2,3,4
▶ Recent studies have developed RAS signature scores
based on a specific gene expression profile2,4
RAS Scores Developed from Gene Expression
Signatures
Figure 5. RAS score signature gene expression.
Combining gene expression with mutation stats across 55 CRC samples.
Figure 6. RAS score signature gene
expression. Correlation of RAS scores with
mutation status of KRAS, NRAS or BRAF.
Future Directions
RAS Score Calculation
Normalized by Expression of 18 Signature Genes with the Exception of NanoString®
▶ RNAseq: Mean of log2(FPKM) of signature genes
▶ Affymetrix® microarray: Mean of log2 (intensity) of signature genes
▶ NanoString®: Mean of positive adjusted counts (normalized to 11 housekeeping genes)
Figure 1. Previous reports on RAS signature scores2,4.
Different normalization methods can be used to generate RAS scores to predict
drug response2,4.
References
1.
Amado RG, Wolf M, Peeters M, Van Cutsem E, Siena S, Freeman DJ, Juan T, Sikorski R, Suggs S,
Radinsky R, Patterson SD, Chang DD. Wild-type KRAS is required for panitumumab efficacy in
patients with metastatic colorectal cancer. J Clin Oncol. 2008 Apr 1;26(10):1626-34. doi: 10.1200/
JCO.2007.14.7116. Epub 2008 Mar 3. PubMed PMID: 18316791.
3.
Heinemann V, Stintzing S, Kirchner T, Boeck S, Jung A. Clinical relevance of EGFR- and KRASstatus in colorectal cancer patients treated with monoclonal antibodies directed against the
EGFR. Cancer Treat Rev. 2009 May;35(3):262-71. doi: 10.1016/j.ctrv.2008.11.005. Epub 2008 Dec 30.
Review. PubMed PMID: 19117687.
2.
Dry JR, Pavey S, Pratilas CA, Harbron C, Runswick S, Hodgson D, Chresta C, McCormack R, Byrne N,
Cockerill M, Graham A, Beran G, Cassidy A, Haggerty C, Brown H, Ellison G, Dering J, Taylor BS,
Stark M, Bonazzi V, Ravishankar S, Packer L, Xing F, Solit DB, Finn RS, Rosen N, Hayward NK,
French T, Smith PD. Transcriptional pathway signatures predict MEK addiction and response to
selumetinib (AZD6244). Cancer Res. 2010 Mar 15;70(6):2264-73. doi: 10.1158/0008-5472.CAN-091577. Epub 2010 Mar 9. PubMed PMID: 20215513; PubMed Central PMCID: PMC3166660.
4.
Loboda A, Nebozhyn M, Klinghoffer R, Frazier J, Chastain M, Arthur W, Roberts B, Zhang T, Chenard
M, Haines B, Andersen J, Nagashima K, Paweletz C, Lynch B, Feldman I, Dai H, Huang P, Watters J. A
gene expression signature of RAS pathway dependence predicts response to PI3K and RAS
pathway inhibitors and expands the population of RAS pathway activated tumors. BMC Med
Genomics. 2010 Jun 30;3:26. doi: 10.1186/1755-8794-3-26. PubMed PMID: 20591134; PubMed Central
PMCID: PMC2911390.
Presented at AACR 2015
Figure 7. Combine genomic aberrations.
Future directions will include comprehensive analysis incorporating whole genome sequencing, transcriptome and
epigenome. Continual investigation of off the shelf and custom panels.
Covance is the drug development business of Laboratory Corporation of America® Holdings (LabCorp®). Findings in this poster were
developed by the scientist who at the time was affiliated with the LabCorp Clinical Trials or Tandem Labs brands, now part of Covance.