3946 Stratification of Metastatic Colorectal Cancer
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#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* Introduction A B AstraZenica RAS signature gene expression CRC sample cohort KRAS mutation status vs. RAS scores from Affymetrix® microarray (log2) 0.16 DUSP6 PHLDA1 6 Colorectal cancer (CRC) is the third most common type of cancer in the United Fusion Frequency Fusion Frequency PROS1 SERPINB1 4 MAP2K3 States. Although chemotherapy, radiation and targeted therapies can improve S100A6 2 Ras signature scores calculated TRIB2 KANSL1:ARL17A 18 CCDC125:MAGED2 3 using FF samples ZFP106 0 SLCO4A1 0.12 survival rates, recent studies have shown the potential benefit of DUSP4 −2 Figure 1. Colorectal cancer samples cohort selection log2 intensity ELF1 SAMD5:SASH1 16 CEACAM5:CEACAM7 3 SPRY2 −4 ETV5 immunotherapies to improve outcomes for patients with advanced CRC. strategy. The cohort was selected by filtering out colorectal KANK1 −6 LZTS1 LMO7:EXT2 9 CMTM8:CMTM7 3 ETV4 cancer samples available as formalin-fixed, paraffin- FXYD5 Targeted therapies that use monoclonal antibodies (mAbs) to EGFR have been 0.08 LGALS3 DPP4:FAP 7 DLG5:DLG3 3 1 embedded (FFPE) and flash frozen (FF). Samples were RASscores Mean expression of all 18 genes shown to benefit some CRC patients. Until recently, KRAS has been the only Samples with high Samples with medium Samples with low TP53 Ras signature scores Ras signature scores Ras signature scores PIK3CA C10orf68:CCDC7 6 LPHN2:LPHN3 3 KRAS mutation status predictive biomarker for anti-EGFR therapy for metastatic CRC. However, then filtered for known RAS score obtained from p(aov)= 3.04e�03 APC 0.04 BRAF WT NOXA1:SLCO4A1 5 PDE4DIP:RP11-353N4.1 3 40% to 60% of patients with wild-type KRAS do not respond to anti-EGFR therapy. Affymetrix® array. Known RAS scores are divided into NRAS Mutant Select: NR3C2:NR3C1 5 RNF123:SERINC4 3 - samples that have both FF and FFPE 3 groups evenly: low (<33% percentile), medium 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 Therefore, to accurately predict patients’ responses to treatments and improve ut - about equal number from each Ras m OSBPL2:OSBPL1A 5 RP11-141M1.3:STARD13 3 score group (33%-66% percentile), high (>66% percentile). clinical outcomes, additional prediction and treatment methods are imperative. RAS signature score KRAS_wt Low High PRPF19:ABR 5 RP11-680G10.1:GSE1 3 One of the many efforts to improve prediction for CRC patients’ responses to the KRAS_ mutation call USP7:SPARC 5 SAMD5:RP11-307P5.1 3 anti-EGFR therapy is the development of gene expression based RAS signature PHF20L1:KIAA0753 4 SF3B2:PHF17 3 scores for identification of RAS activated tumors independent of mutations in the Figure 5. RAS signature scores versus mutation call. (A) Samples with KRAS mutation have significantly RP11-123O10.4:GRIP1 4 SRPK2:KMT2E 3 KRAS gene.2,3 In addition to passive immunotherapy using mAb, there have been 55 samples for the pilot study higher RAS signature scores compared with samples with wild type KRAS. (B) Combine information from gene WFDC10B:FA2H 4 TFG:GPR128 3 major advances in targeted active immunotherapy in other tumors, including expression, RAS signature score and mutation status. AKR1C1:AKR1E2 3 VTI1A:RP11-57H14.3 3 checkpoint inhibitors and cancer peptide vaccines.4,5 In melanoma, there have BIN2:MAN2A1 3 been preliminary clinical findings indicating that combined targeted therapies and simultaneous active immunotherapies such as blockade of multiple immune 55 FFPE colorectal cancer samples * Gene fusion events that occurred more than 3 times in the sample cohort. checkpoints could promote therapeutic synergy and improve clinical outcomes for KRAS mutation status vs. mutation number patients. In addition, chromosomal rearrangements have the potential to alter Figure 2. Multi-platform Table 2. Fusion Genes That Are Kinases gene function in many different ways. Recently there have been major advances comparison. Samples derived from 1500 DNA Analysis RNA Analysis in detecting these chromosomal rearrangements. Fusion genes such as BCR-ABL the same 55 FFPE blocks were Associated and EML4-ALK have become targets for therapy in cancer. There is considerable assayed across multiple platforms. Gene Name Description Whole Transcriptome Analysis Exome Analysis 1000 Figure 6. The number of mutation versus KRAS status. The method design to combine utNum effort being placed on combinatorial ways of tumor stratification to improve m ABR active BCR-related KRAS mutant samples have significantly higher number of RNA analysis (gene expression responses for these cancers. Similarly, since no single treatment can apply to RNA-seq AKT3 v-akt murine thymoma viral oncogene homolog 3 non synonymous mutations than KRAS wild type samples. Targeted Mutational signature scores) with DNA analysis all CRC patients, we aim to stratify patients using a combination of the 500 p(aov)= 2.5e�02 BAZ1A bromodomain adjacent to zinc finger domain 1A Analysis ® following methods: Affymetrix Microarray (i.e. mutation status) allows for BAZ1B bromodomain adjacent to zinc finger domain 1B comparison of RAS signature scores TruSight mutational panel ut m BLK BLK proto-oncogene, Src family tyrosine kinase 1. RAS signature score based on the expression profile of 18 genes. Targeted Transcriptome Analysis and overall gene expression from BLVRA biliverdin reductase A KRAS_wt This RAS signature score enables measurements of mitogen-activated different platforms. KRAS_ mutation call BMPR2 bone morphogenetic protein receptor type II protein/extracellular signal–regulated kinase (MEK) pathway functional Nanostring® CASK calcium/calmodulin-dependent serine protein kinase (MAGUK family) output independent of tumor genotype. Targeted RNA-seq CDK6 cyclin-dependent kinase 6 2. Expression profile of immune checkpoint inhibitor target genes, such as CDK9 cyclin-dependent kinase 9 A B CLK3 CDC like kinase 3 PD1 and PD-L1. Distribution of number of gene fusion events RAS scores vs. Number of fusion events DCLK2 doublecortin like kinase 2 0.16 FGFR1 fibroblast growth factor receptor 1 3. DNA mutational profiles of genes such as KRAS, APC, BRAF and NRAS. 6 Median = 17 FGFR4 fibroblast growth factor receptor 4 Further, we explored potential gene fusion events in colorectal cancer tumor 1FF and 5 FFPE Datasets Figure 3. Flowchart for 0.12 INSR insulin receptor samples and discovered potential association between RAS gene signature the analysis. 55 samples JAK2 Janus kinase 2 Affy, Affy, RNASeq-Acc, RNASeq- Targeted Nanostring, score and the level of chromosomal rearrangements. Data Source 4 FF FFPE FFPE rRNAdep, FFPE RNASeq,FFPE FFPE were gone through 5 0.08 LATS1 large tumor suppressor kinase 1 RAS scores different platforms for gene count LRRK1 leucine-rich repeat kinase 1 expression measurements – p(aov)= 5.79e�05 QC Quality Control and Data Normalization 0.04 ® 2 LRRK2 leucine-rich repeat kinase 2 Affymetrix , whole LYN LYN proto-oncogene, Src family tyrosine kinase um Affy, Affy, RNASeq-Acc, RNASeq- Targeted Nanostring, transcriptome RNA-Seq um n Methods and Results n FF FFPE FFPE rRNAdep, FFPE RNASeq,FFPE FFPE MAP3K7 mitogen-a by two different library ctivated protein kinase kinase kinase 7 0 MAPKAPK2 mitogen-activated protein kinase-activated protein kinase 2 55 FFPE samples were selected from a cohort of 468 samples with matching preparation methods, 0 10 20 30 40 w_fusion_ o Number of gene fusion events l FF samples. These 55 samples have about 1:1:1 ratio of high, medium and low Ras Signature Score Calculation targeted RNA-Seq, and high_fusion_ MAPKAPK5 mitogen-activated protein kinase-activated protein kinase 5 RAS scores. Here we showed our capability to obtain RAS signature scores with Nanostring®. Data went MARK3 MAP/microtubule affinity-regulating kinase 3 NEK9 NIMA-related kinase 9 concordant results using different platforms including whole transcriptome Correlation Analysis Regression Analysis through quality control and Gene Expr Signature Score Figure 7. RAS signature scores and the number of gene fusion events. (A) Distribution of gene ® PAK1 p21 protein (Cdc42/Rac)-activated kinase 1 RNA-seq, Affymetrix microarray (Affymetrix Inc.), targeted RNA-seq and normalization. For RAS fusion events of all samples. Only high and medium confidence gene fusion events based on results from ® PAN3 PAN3 poly(A) specific ribonuclease subunit Nanostring (Nanostring Technologies, Inc.). We discovered that samples that score calculation, 18 genes JAFFA7 were considered. (B) Samples with higher number of gene fusion events have significantly higher Data Integration for Tumor Stratification PKN2 protein kinase N2 have RAS activating mutations such as KRAS and BRAF have significantly were used based on RAS signature scores (p<0.001). 5 PLK2 polo-like kinase 2 higher RAS scores (p<0.001). On the contrary, expression of PD-L1 was previous study. RIOK2 RIO kinase 2 Gene Mutation, Mutation Burden Gene Fusion Events significantly lower in tumor samples harboring mutations of genes such as and mutational DNA information e.g. KRAS/BRAF/NRAS SCYL2 SCY1-like, kinase-like 2 MET, PTEN, NRAS, FBXW7 and GNAS.