Download #3946 Stratification of Metastatic Colorectal Cancer Patients Using

#3946 Stratification of Metastatic Colorectal Cancer Patients Using DNA and RNA Sequencing
Fang Yin Lo1, Sharon Austin1, Kellie Howard1, Mollie McWhorter1, Heather Collins1, Amanda Leonti1, Lindsey Maassel1, Christopher Subia1, Tuuli Saloranta1, Nicole Christopherson1, Kathryn Shiji1,
Shradha Patil1, Saman Tahir1, Sally Dow1, Evan Anderson1, Jon Oblad1, Kerry Deutsch1, Timothy Yeatman2, Steven Anderson3 and Anup Madan1
1Covance, Seattle, WA; 2Gibbs Cancer Center, Spartanburg, SC; 3Covance, Durham, NC
Table 1. Frequency of Gene Fusion Events*
B
AstraZenica RAS signature gene expression
from Affymetrix® microarray (log2)
Select:
- samples that have both FF and FFPE
- about equal number from each Ras
score group
0.08
6
RAS signature score
Low
High
mutation call
Figure 5. RAS signature scores versus mutation call. (A) Samples with KRAS mutation have significantly
higher RAS signature scores compared with samples with wild type KRAS. (B) Combine information from gene
expression, RAS signature score and mutation status.
55 samples for the pilot study
Whole Transcriptome Analysis
Exome Analysis
RNA-seq
Targeted Mutational
Analysis
Affymetrix® Microarray
TruSight mutational panel
Targeted Transcriptome Analysis
Figure 2. Multi-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.
CCDC125:MAGED2
CEACAM5:CEACAM7
CMTM8:CMTM7
DLG5:DLG3
LPHN2:LPHN3
PDE4DIP:RP11-353N4.1
RNF123:SERINC4
RP11-141M1.3:STARD13
RP11-680G10.1:GSE1
SAMD5:RP11-307P5.1
SF3B2:PHF17
SRPK2:KMT2E
TFG:GPR128
VTI1A:RP11-57H14.3
Frequency
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Table 2. Fusion Genes That Are Kinases
Figure 6. The number of mutation versus KRAS status.
KRAS mutant samples have significantly higher number of
non synonymous mutations than KRAS wild type samples.
1000
p(aov)= 2.5e�02
500
mutation call
Nanostring®
Targeted RNA-seq
A
Distribution of number of gene fusion events
B
0.16
6
Targeted
RNASeq,FFPE
Nanostring,
FFPE
Gene Expr Signature Score
Quality Control and Data Normalization
Affy,
FF
RNASeq-Acc,
FFPE
Affy,
FFPE
RNASeqrRNAdep, FFPE
Targeted
RNASeq,FFPE
Nanostring,
FFPE
Ras Signature Score Calculation
DNA and mutational
information
Correlation Analysis
Regression Analysis
Data Integration for Tumor Stratification
Gene Mutation,
e.g. KRAS/BRAF/NRAS
Mutation Burden
0.12
4
0.08
p(aov)= 5.79e�05
0.04
2
0
0
10
20
30
Number of gene fusion events
40
low_fusion_num
RNASeqrRNAdep, FFPE
high_fusion_num
RNASeq-Acc,
FFPE
Affy,
FFPE
Median = 17
RAS scores
Affy,
FF
Figure 3. Flowchart for
the analysis. 55 samples
were gone through 5
different platforms for gene
expression measurements –
Affymetrix®, whole
transcriptome RNA-Seq
by two different library
preparation methods,
targeted RNA-Seq, and
Nanostring®. Data went
through quality control and
normalization. For RAS
score calculation, 18 genes
were used based on
previous study.5
count
Data Source
1FF and 5 FFPE Datasets
QC
Presented at AACR 2016
Fusion
1500
KRAS_wt
RNA Analysis
KRAS_mut
DNA Analysis
RAS scores vs. Number of fusion events
Figure 7. RAS signature scores and the number of gene fusion events. (A) Distribution of gene
fusion events of all samples. Only high and medium confidence gene fusion events based on results from
JAFFA7 were considered. (B) Samples with higher number of gene fusion events have significantly higher
RAS signature scores (p<0.001).
Gene Fusion Events
Number of fusion events vs. RAS scores
RNASeq.RNAAccess
Affymetrix®
0.5
0.6
0.8
Nanostring®
RNASeq.rRNAdepletion
Affymetrix®
0.7
RNASeq.RNAAccess
targeted RNAseq
R=0.88
0.3
0.25
0.2
0.1
0.05
2. EGFR gene copy number as a prognostic marker in colorectal cancer patients treated with cetuximab or panitumumab: a systematic review
and meta-analysis
p(aov)= 9.97e�06
3. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Lièvre A, Bachet JB, Le Corre D, Boige V, Landi B,
Emile JF, Côté JF, Tomasic G, Penna C, Ducreux M, Rougier P, Penault-Llorca F, Laurent-Puig P. Cancer Res. 2006 Apr 15; 66(8):3992-5.
4. Loboda A et al. 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.
5. BMC Medical Genomics 2010, 3:26Dry JR et al. Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib
(AZD6244). Cancer Res. 2010 Mar 15;70(6):2264-73.
0
0
0.05
0.1
0.15
0.2
RAS scores from
RNASeq-RNAAccess
0.9
1. Gallagher DJ, Kemeny N. Metastatic colorectal cancer: from improved survival to potential cure. Oncology. 2010;78:237–248.
10
0
0.15
References
20
hi_rasscore
RNASeq.rRNAdepletion
0.35
1
Pearson correlation coefficients
Figure 4. Correlation of RAS signature scores between different platforms. (A) RAS scores calculated
from multiple platforms are significantly correlated. All pairwise comparison has rho>=0.5 and p < 0.05.
(B) Correlation of RAS scores calculated from RNA-seq and Affymetrix®. Scores between different
platforms are highly correlated.
Associated
Gene Name Description
ABR
active BCR-related
AKT3
v-akt murine thymoma viral oncogene homolog 3
BAZ1A
bromodomain adjacent to zinc finger domain 1A
BAZ1B
bromodomain adjacent to zinc finger domain 1B
BLK
BLK proto-oncogene, Src family tyrosine kinase
BLVRA
biliverdin reductase A
BMPR2
bone morphogenetic protein receptor type II
CASK
calcium/calmodulin-dependent serine protein kinase (MAGUK family)
CDK6
cyclin-dependent kinase 6
CDK9
cyclin-dependent kinase 9
CLK3
CDC like kinase 3
DCLK2
doublecortin like kinase 2
FGFR1
fibroblast growth factor receptor 1
FGFR4
fibroblast growth factor receptor 4
INSR
insulin receptor
JAK2
Janus kinase 2
LATS1
large tumor suppressor kinase 1
LRRK1
leucine-rich repeat kinase 1
LRRK2
leucine-rich repeat kinase 2
LYN
LYN proto-oncogene, Src family tyrosine kinase
MAP3K7
mitogen-activated protein kinase kinase kinase 7
MAPKAPK2 mitogen-activated protein kinase-activated protein kinase 2
MAPKAPK5 mitogen-activated protein kinase-activated protein kinase 5
MARK3
MAP/microtubule affinity-regulating kinase 3
NEK9
NIMA-related kinase 9
PAK1
p21 protein (Cdc42/Rac)-activated kinase 1
PAN3
PAN3 poly(A) specific ribonuclease subunit
PKN2
protein kinase N2
PLK2
polo-like kinase 2
RIOK2
RIO kinase 2
SCYL2
SCY1-like, kinase-like 2
SRPK2
SRSF protein kinase 2
TNIK
TRAF2 and NCK interacting kinase
30
mid_rasscore
Nanostring®
0.4
low_rasscore
B
RAS scores from Affymetrix®
A
Number of fusion events
40
targeted RNAseq
55 FFPE samples were selected from a cohort of 468 samples with matching
FF samples. These 55 samples have about 1:1:1 ratio of high, medium and low
RAS scores. Here we showed our capability to obtain RAS signature scores with
concordant results using different platforms including whole transcriptome
RNA-seq, Affymetrix® microarray (Affymetrix Inc.), targeted RNA-seq and
Nanostring® (Nanostring Technologies, Inc.). We discovered that samples that
have RAS activating mutations such as KRAS and BRAF have significantly
higher RAS scores (p<0.001). On the contrary, expression of PD-L1 was
significantly lower in tumor samples harboring mutations of genes such as
MET, PTEN, NRAS, FBXW7 and GNAS. Kruskal-Wallis test showed that the
expression of PD-L1 was significantly lower in samples with higher RAS
signature scores (p<0.05). Furthermore, using the RNA-sequencing data, we
were able to detect gene fusion events in these tumor samples. After filtering out
low confidence results, a total of 730 gene fusion events were detected among
the 55 tumor samples. While most of the gene fusion events were only detected
once within the sample cohost, some were detected in multiple samples. For
example, the fusion between KANSL1 and ARL17A was detected in 18 of the
55 samples. This is a relatively new discovery that had just started being
mentioned in other cancer research institute reports.6 Other fusions that
appeared multiple times include SAMD5 and SASH1. Interestingly, we
discovered that significantly fewer fusion events were detected in samples with
lower RAS signature scores than samples with higher RAS scores (p < 10-5).
1045 genes are involved in these fusion events. GO enrichment analysis shows
that many of the cell cycle and phosphorylation associated pathways are
significantly over-represented within these 1045 genes. Further analysis is being
carried out for the implication of association between gene fusion events and
RAS gene signature scores. Our analysis will lead to the development a
combinatorial method for stratifying metastatic CRC patients.
18
16
9
7
6
5
5
5
5
5
4
4
4
3
3
KRAS mutation status vs. mutation number
2. Expression profile of immune checkpoint inhibitor target genes, such as
PD1 and PD-L1.
Methods and Results
KANSL1:ARL17A
SAMD5:SASH1
LMO7:EXT2
DPP4:FAP
C10orf68:CCDC7
NOXA1:SLCO4A1
NR3C2:NR3C1
OSBPL2:OSBPL1A
PRPF19:ABR
USP7:SPARC
PHF20L1:KIAA0753
RP11-123O10.4:GRIP1
WFDC10B:FA2H
AKR1C1:AKR1E2
BIN2:MAN2A1
*Gene fusion events that occurred more than 3 times in the sample cohort.
55 FFPE colorectal cancer samples
1. RAS signature score based on the expression profile of 18 genes.
This RAS signature score enables measurements of mitogen-activated
protein/extracellular signal–regulated kinase (MEK) pathway functional
output independent of tumor genotype.
3. DNA mutational profiles of genes such as KRAS, APC, BRAF and NRAS.
Further, we explored potential gene fusion events in colorectal cancer tumor
samples and discovered potential association between RAS gene signature
score and the level of chromosomal rearrangements.
Frequency
4
2
TP53
PIK3CA
KRAS mutation status
APC
WT
BRAF
Mutant
NRAS
p(aov)= 3.04e�03
0.04
Fusion
DS−56293
DS−52681
DS−60296
DS−51803
DS−53114
DS−60252
DS−54129
DS−52838
DS−51982
DS−51941
DS−54503
DS−40199
DS−54363
DS−54564
DS−33635
DS−48764
DS−52790
DS−51997
DS−53453
DS−49639
DS−70294
DS−54041
DS−48893
DS−53191
DS−52210
DS−47369
DS−54783
DS−49315
DS−48055
DS−58258
DS−52151
DS−48607
DS−58341
DS−54589
DS−67980
DS−56858
DS−44878
DS−50687
DS−56963
DS−54051
DS−51977
DS−51652
DS−48857
DS−56376
DS−52853
DS−51043
DS−52320
DS−56326
DS−53211
DS−49796
DS−50925
DS−51797
DS−49826
DS−60353
DS−67955
Samples with low
Ras signature scores
KRAS_wt
Samples with medium
Ras signature scores
0.12
KRAS_mut
Samples with high
Ras signature scores
Figure 1. Colorectal cancer samples cohort selection
strategy. The cohort was selected by filtering out colorectal
cancer samples available as formalin-fixed, paraffinembedded (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).
RASscores
Ras signature scores calculated
using FF samples
0
DUSP6
PHLDA1
PROS1
SERPINB1
MAP2K3
S100A6
TRIB2
ZFP106
SLCO4A1
DUSP4
ELF1
SPRY2
ETV5
KANK1
LZTS1
ETV4
FXYD5
LGALS3
Mean expression of all 18 genes
log2 intensity
KRAS mutation status vs. RAS scores
0.16
mutNum
Colorectal cancer (CRC) is the third most common type of cancer in the United
States. Although chemotherapy, radiation and targeted therapies can improve
survival rates, recent studies have shown the potential benefit of
immunotherapies to improve outcomes for patients with advanced CRC.
Targeted therapies that use monoclonal antibodies (mAbs) to EGFR have been
shown to benefit some CRC patients.1 Until recently, KRAS has been the only
predictive biomarker for anti-EGFR therapy for metastatic CRC. However,
40% to 60% of patients with wild-type KRAS do not respond to anti-EGFR therapy.
Therefore, to accurately predict patients’ responses to treatments and improve
clinical outcomes, additional prediction and treatment methods are imperative.
One of the many efforts to improve prediction for CRC patients’ responses to the
anti-EGFR therapy is the development of gene expression based RAS signature
scores for identification of RAS activated tumors independent of mutations in the
KRAS gene.2,3 In addition to passive immunotherapy using mAb, there have been
major advances in targeted active immunotherapy in other tumors, including
checkpoint inhibitors and cancer peptide vaccines.4,5 In melanoma, there have
been preliminary clinical findings indicating that combined targeted therapies and
simultaneous active immunotherapies such as blockade of multiple immune
checkpoints could promote therapeutic synergy and improve clinical outcomes for
patients. In addition, chromosomal rearrangements have the potential to alter
gene function in many different ways. Recently there have been major advances
in detecting these chromosomal rearrangements. Fusion genes such as BCR-ABL
and EML4-ALK have become targets for therapy in cancer. There is considerable
effort being placed on combinatorial ways of tumor stratification to improve
responses for these cancers. Similarly, since no single treatment can apply to
all CRC patients, we aim to stratify patients using a combination of the
following methods:
A
CRC sample cohort
−6 −4 −2
Introduction
Figure 8. Number of gene fusion
events and RAS signature scores.
Samples with lower RAS scores have
significantly fewer gene fusion events
detected than samples with higher
RAS scores.
Figure 9. Scatter plot of enriched GO
cluster representatives.
Multidimensional scaling is applied to
the list of significantly enriched GO
terms in fusion genes found in the CRC
samples.6
6. http://newswise.com/articles/rutgers-cancer-researchers-examine-gene-fusion-and-treatment-implications-for-breast-cancer
7. Genome Medicine 20157:43.
Covance is the drug development business of Laboratory Corporation of America Holdings (LabCorp). Content of this material
was developed by scientists who at the time were affiliated with LabCorp Clinical Trials or Tandem Labs, now part of Covance.