ARTICLE IN PRESS. Cancer Genetics (2016)

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1 Cancer Genetics (2016) ORIGINAL ARTICLE Chromosomal microarray provides enhanced targetable gene aberration detection when paired with next generation sequencing panel in profiling lung and colorectal tumors S. Mukherjee a, Z. Ma a, S. Wheeler a, M. Sathanoori a, C. Coldren a, J.L. Prescott a, N. Kozyr b, M. Bouzyk b, M. Correll c, H. Ho a, P.K. Chandra a, P.A. Lennon a, * a PathGroup, Nashville, TN, USA; b Akesogen, Norcross, GA, USA; c GenoSpace, Cambridge, MA, USA The development of targeted therapies based on specific genomic alterations has altered the treatment and management of lung and colorectal cancers. Chromosomal microarray (CMA) has allowed identification of copy number variations (CNVs) in lung and colorectal cancers in great detail, and next-generation sequencing (NGS) is used extensively to analyze the genome of cancers for molecular subtyping and use of molecularly guided therapies. The main objective of this study was to evaluate the utility of combining CMA and NGS for a comprehensive genomic assessment of lung and colorectal adenocarcinomas, especially for detecting drug targets. We compared the results from NGS and CMA data from 60 lung and 51 colorectal tumors. From CMA analysis, 33% were amplified, 89% showed gains, 75% showed losses and 41% demonstrated loss of heterozygosity; pathogenic variants were identified in 81% of colon and 67% lung specimens through NGS. KRAS mutations commonly occurred with loss in TP53 and there was significant loss of BRCA1 and NF1 among male patients with lung cancer. For clinically actionable targets, 23% had targetable CNVs when no pathogenic variants were detected by NGS. The data thus indicate that combining the two approaches provides significant benefit in a routine clinical setting not available by NGS alone. Keywords CMA, NGS, lung, colon, profiling 2015 Elsevier Inc. All rights reserved. Lung cancer is the leading cause of cancer death worldwide (1). Currently, the overall 5-year survival rate for all lung cancer patients is about 15% (2); this low rate is primarily due to diagnosis at a late stage of the disease. Malignant lung tumors are classified into two major histological subtypes that differ in biology and therapeutic response: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) (3). Although about one third of NSCLC patients are diagnosed with the tumor still localized in the lung, and hence amenable to surgical resection, about 30 60% of these patients develop Received September 4, 2015; received in revised form December 24, 2015; accepted December 27, * Corresponding author. address: palennon@pathgroup.com metastatic recurrences within 5 years after surgery (4). Colorectal cancer (CRC) is also fairly common among cancers and represents 15% of all malignancies. Based on the National Cancer Institute s SEER database, the 5-year relative survival rate for colorectal cancers is about 11% for stage IV cancers; while early stage CRC (stage I and II) has a high cure rate after surgery, the recurrence rate is about 50% for stage III CRC after surgery alone and most patients with metastatic disease will ultimately succumb to their cancer. DNA sequencing panels using next-generation sequencing (NGS) are widely used to identify single nucleotide variants in relevant genes, but NGS technology that can identify chromosomelevel changes in copy number is not yet widely available. This may leave gaps in clinical characterization of patient specimen leading to missed opportunities for therapeutic interventions /$ - see front matter 2015 Elsevier Inc. All rights reserved.

2 2 S. Mukherjee et al. The development of targeted therapies has altered the landscape of lung and colorectal cancer treatment and management, driven by the identification of mutations in EGFR (5,6) that confer sensitivity to epidermal growth factor receptor (EGFR) inhibitors and EML4-ALK fusions that make tumors susceptible to ALK inhibition (7). The concept of using information from a patient s tumor to make therapeutic and treatment decisions has found further support with the characterization of rearrangements in ROS1 (8), also associated with sensitivity to ALK inhibitors, and ongoing research in targeted therapies for KRAS (9) and MET (10). EGFR and VEGF inhibitors also represent targeted therapies in CRC, based on retrospective analyses from several large trials to show that patients with tumors bearing KRAS mutations do not respond to either cetuximab- or panitumumab-based therapy (11). Next generation sequencing (NGS), which performs massively parallel sequencing of millions of DNA fragments, is now used extensively to analyze the genome of thousands of cancers across many disease types, including lung and colorectal cancers. It is used to monitor disease progression and has enabled subtyping and use of molecularly guided therapies. Many of the abnormalities in tumor suppressor genes, such as TP53, STK11, CDKN2A, KEAP1, and SMARCA4 (12 15) are common in lung cancer but not currently targetable for therapy. At present, genes with known or potential targetable mutations include EGFR, KRAS, FGFR1, ERBB2, PIK3CA, ALK, BRAF, ROS1, MAP2K1, RET, NRAS and AKT1 (16). In CRC, genome wide sequencing analyses have identified an average of 80 mutations per tumor (17). Consistent with the model of tumor progression in CRC, WNT pathway genes are mutated in ~90% (18). Other important contributors are mutations in the TGF-β pathway, which include SMAD2, SMAD3, and SMAD4. Several molecules targeting members of the WNT pathway are being developed (19). Activating mutations identified in BRAF or NRAS have been shown to reduce the effectiveness of anti-egfr therapy in CRC (20). Although there are no currently available KRAS inhibitors, efforts have focused on proteins downstream of EGFR including RAF and MEK. Trials are ongoing with the MEK inhibitors trametinib, cobimetinib, pimasertib, and MEK-162 which show some efficacy in RAS-mutated cancers (21). Copy number variations (CNVs), which result in genomic structural variation due to abnormal gene copy numbers, including gene amplification, gain, and deletion, arise as a result of selection pressures that favor cancer development. CNVs can drive expression of both protein-coding and non-coding genes, and targeting these alterations provides opportunities for personalized therapy. Chromosomal microarray (CMA) and other array-based methods for CNV detection are widely used because of their resolution and throughput (22), and have allowed identification and analysis of CNVs in lung and colorectal cancers in great detail (23 25). The most commonly amplified regions in lung cancer include MYC, TERT, CCND1, and EGFR (22). Gains of 8q and loss of 13q have been associated with poorer disease-free survival in adenocarcinoma, and with distant metastases in small cell carcinoma (26,27). The FGFR1 gene is amplified at the 8p11.2 chromosomal region in ~20% of squamous cell lung cancer patients; clinical trials with FGFR inhibitors are currently ongoing (28). MET amplification occurs in approximately 4% of NSCLC patients not previously exposed to systemic therapies and in up to 20% of patients with acquired resistance to EGFR tyrosine kinase inhibitors. Several agents that target MET amplification have entered in clinical trials for patients with advanced disease (29). Many reports have correlated copy number changes in CRC with prognosis and treatment (30 33). For example, loss of chromosome 18q has been associated with poor outcome after adjuvant chemotherapy with fluorouracil-based regimens, gains at the EGFR locus with response to cetuximab and panitumumab, and losses in chromosome 4 with lymph node metastasis and relapse. Clinical benefit has been observed in patients with targeted therapy in patients that exhibit amplification at the FLT3 gene in metastatic colorectal cancer (34). Thus, there appears to be an important role for genomic characterization of all somatic mutation types including CNVs, point mutations, and short insertions and deletions to identify gene defects in tumors. This could enhance the ability to diagnose tumor subtypes and provide more prognostic and therapeutic information to clinicians and their patients. In our practice, the majority of lung and colon tumors are tested using both an NGS-based hot spot panel and CMA analysis. To begin to address the question of how the identification of different types of variants effects patient care, we compared the results from DNA sequencing and CMA data for all lung and colorectal tumors received for SmartGenomics testing in Methods Patients and tumor samples All patients were referred for SmartGenomics Solid Tumor Profile colon and lung testing to Pathgroup Labs, Inc between March 2014 and January SmartGenomics Tumor Profiling includes performing genetic testing recommended by NCCN guidelines as well as additional testing by chromosomal microarray (Illumina CytoSNP-850K BeadChip) and a 62 gene next generation sequencing panel (see Appendix A for a list of the genes) to find additional prognostic and therapy related targets. 51 colon and 60 lung specimens were included in this cohort that spanned the entire spectrum of diagnostic and refractory tumor specimens; colon and lung specimens are the highest volume specimens that arrive at our practice for SmartGenomics Solid Tumor Profiling. An internal pathologist reviewed multiple H&E sections for each specimen to assess the tumor percentage, define areas for enrichment by macrodissection and verify adequate tumor throughout the tissue area examined. A minimum of 20% tumor cell nuclei was required for study. DNA extractions and QC For CMA analysis, after macrodissection of FFPE sections which were formalin fixed for 6 40 hours, DNA was extracted using the Maxwell 16 FFPE Tissue LEV DNA Purification Kit (Promega, Madison WI). DNA quality was assessed using NanoDrop spectrophotometry (Thermo Scientific), 260/280 ratio between 1.8 and 2.0 were acceptable, and DNA quantity by Qubit (Life Technologies). In addition, the Infinium QC and Restore kits (Illumina) were used to identify poor quality specimens and repair samples for downstream use. For DNA sequencing using next generation sequencing, tissue was deparaffinized using xylene and areas of interest were

3 CMA complements NGS in detecting drug targets 3 macrodissected, digested with proteinase K at 56 C for 1 hour and heated to 90 C for 1 hour to lyse cells. DNA was isolated from the lysis using the QIAsymphony DSP DNA Mini Kit (Qiagen) and concentrated using the Zymo Research DNA Clean & Concentrator-5 (Zymo Research). Amplifiable DNA quantity (haploid genome copy number) was determined using a quantitative Taqman assay directed to the single copy gene FTH1 (Life Technologies). Of note, two separate extraction methods were used as CMA was performed in conjunction with an outside vendor, to which pathologist marked slides from formalin fixed paraffin embedded tissues were sent from the same blocks on which internal NGS was performed (Akesogen, Norcross, GA). Illumina Infinium CNV processing and analysis by chromosomal microarray (CMA) DNA samples were enzymatically fragmented, hybridized to an Illumina CytoSNP-850K BeadChip, stained using the Xstain HD BeadChip process and washed (35). BeadChips were scanned using the iscan system, and raw data normalization, SNP clustering, CNV identification and SNP calling were performed using the GenomeStudio v3.3 Genotyping Module to generate genotype calls, B-allele frequency (BAF) and logr ratio (Illumina, San Diego, CA). Systematic correction of probe distribution was performed using the quadratic correction algorithm of the Nexus Copy Number 7.5 software, and copy number variation analysis was performed using SNPRank Segmentation algorithm. Each sample is compared to a pooled normal in silico control generated from 106 FFPE samples using the same technology and arrays as our clinical samples. The log ratio thresholds were set as follows gain: 0.09, loss: 0.135, amplification: 0.3, homozygous loss: The remaining parameters were set as follows homozygous frequency threshold: 0.9, homozygous value threshold: 0.8, heterozygous imbalance threshold: For quality control, only samples with quality scores (which represent robust probe to probe variance) less than 0.1 were considered for downstream cytogenetic data analysis. The segmented data were analyzed using Nexus Copy Number software. Of note, as a result our own clinical validation, only gains and losses of 200 kb or more in size which include 16 or more consecutive SNP probes deflected beyond threshold, and containing RefSeq genes, were considered true calls. Those true calls known to be normal copy number variants were considered benign and not included in the data analyzed in this project. Finally, terminal regions greater than 8 MB and interstitial regions greater than 10 MB which demonstrated absence of heterozygosity (AOH) were included in the analysis. Next-generation sequencing (NGS) and analysis A minimum of 600 haploid genomes from each specimen were preamplified, cleaned and used, in duplicate, as template for multiplex PCR enrichment using the AccessArray system (Fluidigm) for the genes listed in Appendix A. Each resulting library was barcoded, pooled with other libraries and quantified using the Library Quantification Kit (KAPA Biosystems). Diluted library pools were denatured and sequenced on a MiSeq (Illumina) using MiSeq Reagent Kit v2 with cycle paired end reads. Trimming, alignment, variant calling and variant annotation were conducted using the MiSeq Reporter Custom Amplicon workflow (version 2.3) and Genome Analysis Toolkit (GATK, version 2.3-9). Pathogenic variants were called if the observed variant met the following criteria: present in at least 5% of the reads at that locus, present in both replicates, predicted to modify the sequence of the encoded protein and not present in a database of common non-pathogenic inherited variants (dbsnp version 138). The analytic sensitivity of the assay is 5% allele frequency and average coverage is greater than 1,000 (ranging from 200 to 10,000 ). Variants with low read coverage were confirmed using Sanger sequencing. Results As part of SmartGenomics tumor profiling, 51 colon and 55 lung specimens were analyzed for CNVs using CMA. Overall, 33% of specimens showed amplifications, 89% showed gains, 75% showed losses and 41% showed absence of heterozygosity (AOH) (Figure 1A). The most frequently observed CNVs were copy number gains and losses making up 44% and 38% of all observed CNVs (Figure 1). The profile of CNVs was remarkably similar for both colon and lung specimen. This includes the frequency of CNV types (Figure 1) and the number of CNVs identified per specimen (13.98 for colon and for lung). These data indicate that both colon and lung tumors contain a large number of CNVs potentially affecting the expression and function of many genes. In order to understand how identification of CNVs aids in the treatment of patients with colon and lung tumors, we performed a separate analysis of CNVs which was restricted to only those loci that contained medically relevant genes (ie, MR-CNVs): these are genes that are targets of FDA approved drugs, both on and off-label, and those involved in pathways that are the subjects of clinical trials (see Appendix B). In 61% (31/51) of colon specimens, a total of 162 MR-CNVs were identified with an average of 5.2 MR-CNVs per affected specimen. In lung specimens, 60% (36/60) contained 249 MR-CNVs with an average of 6.9 per affected specimen. For colon specimens, genes within the highly represented MR- CNV loci included WNT pathway regulators like TP53, APC and FBXW7 (Table 1). While CNVs at these loci were also found in the lung, the distribution was different with high levels of MR-CNVs containing the genes BRCA2, CDKN2A and RB1 (Table 1). These identified genes reflect the different etiologies of colon and lung tumors. Much of the benefit of precision medicine comes from the results obtained from DNA sequencing panels. As part of SmartGenomics profiling, both CMA and DNA sequencing by NGS for genes commonly mutated in solid tumors are performed. To determine the added benefit of CNV analysis, specimens that had both types of analysis were compared. From DNA sequencing, pathogenic variants were identified in 81% (41/51) of colon and 67% (40/55) lung specimens. Pathogenic variants were identified in BRAF, KRAS, NRAS, PIK3CA, TP53 and other medically relevant and commonly mutated genes (Figure 2). These included substitution, frameshift and splice site variants that would not be detected by CMA analysis. Restricting the analysis to medically relevant loci, 24 (23%) specimens had an identified CNV, but did not have an identified pathogenic variant from DNA sequencing (Figure 3). In contrast, for 7% of specimen variants were only identified

4 4 S. Mukherjee et al. Figure 1 CNV detection by CMA. (A) The percent of specimens that had at least one of the specified type of CNV: all CNVs, amplifications, gains, losses and AOH. Beneath the graph is the number of positive and total specimens for each specimen type and each CNV type. Overall, CNVs were detected in 90% of specimen with gains and losses identified at the highest levels. (B) The percent of CNVs that were identified in each category: amplification, gain, loss or AOH. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) by DNA sequencing. These data indicate that the combination of CMA and DNA sequencing provides benefit that is not currently provided by either technology alone. In colon cancers, losses in TP53, APC, SMARCB1, PTEN, NF2 and FBXW7 were prominent, whereas in the lung cancer group, BAP1, TP53, CDKN2A, STK11, RB1 and BRCA2 were predominantly lost. When individual genes were analyzed for associations of loss or gain/amplification with somatic hot spot mutations detected by NGS, the strongest association we observed was, not surprisingly, the coexistence of TP53 mutations with loss of TP53 gene segments, mostly in the colon cohort (10/31). In colon cancers, the most prevalent mutations were observed in KRAS, TP53, BRAF, and PIK3CA. KRAS mutations commonly occurred with loss in TP53, APC, PTEN; TP53 mutations were also associated with FBXW7, SMARCB1 and PTEN loss. In our lung cancer cohort, the most prevalent mutations again are observed in KRAS, TP53, BRAF, PIK3CA, Table 1 Identified copy number changes in medically relevant (targetable) genes Gene Number of colon specimens Gain/ Amplifications Loss Number of lung specimens Gain/ Amplifications FLT3 7 1 EGFR 2 1 CCND2 1 1 KRAS 1 2 KMT2A 1 BRAF 1 1 CCNE1 1 1 FGFR1 1 3 TP APC SMARCB PTEN NF FBXW ATM 9 6 BAP STK BRCA1 6 6 CEBPA 6 2 NF1 6 7 SMARCA CDKN2A 5 16 RB TSC PTCH BRCA PIK3CA 5 NTRK1 4 MDM4 4 CDK6 4 BCL2 2 NOTCH2 2 MET 2 CCND3 2 NRAS 2 AKT2 2 JAK1 2 SMAD4 2 PIK3R1 1 CRTC1 1 XPO1 1 PDGFRB 1 ALK 1 JAK2 1 DNMT3A 1 JAK3 1 ERBB2 1 MAP2K2 1 MDM2 1 FGFR3 1 AKT1 1 Loss

5 CMA complements NGS in detecting drug targets 5 A Colon B Lung Number of Cases KRAS TP53 BRAF PIK3CA NRAS KIT FBXW7 PIK3R1 FGFR4 GNAS MET RUNX1 ALK MAP2K1 Number of Cases TP53 KRAS BRAF PIK3CA EGFR NRAS RUNX1 MYC AKT1 CDKN2A PIK3R1 FGFR3 STK11 HRAS ABL1 KIT Figure 2 Variants detected by gene using DNA sequencing. The number of specimens with identified variants in commonly mutated genes is shown for (A) colon and (B) lung specimens. Differences in the patterns of genes mutated are consistent with the individual tumor types. with KRAS mutations occurring in conjunction with TP53 loss. Two statistically significant associations we observed were the loss of BRCA1 (two-tailed p = ) and NF1 (two-tailed p = ) in male patients, in the lung cancer cohort (using Fisher s exact test), which may increase surveillance of these losses in male lung cancer patients as they may respond to PARP and MEK inhibitors, respectively. All our observations were trends we detected in our small group of patients; the A DNA Sequencing Variant Not Detected Detected CNV Not Detected 1(2%) 3(6%) CNV Detected 9(18%) 38(75%) Colon B DNA Sequencing Variant Not Detected Detected CNV Not Detected 3(5%) 4(7%) CNV Detected 15(27%) 33(60%) Lung Figure 3 Comparison of CMA and DNA sequencing in colon and lung specimens. Matrices comparing the number of (A) colon and (B) lung specimens with identified medically relevant CNVs and DNA sequencing variants. (A) In colon specimens, 18% of specimens had identified CNV, but no variants by DNA sequencing, 6% had variants identified by DNA sequencing, but not CMA and 2% had no CNV or sequencing variants. In lung, 27% had only CNV detection, 7% had only identified variants by DNA sequencing and 5% had neither CNV nor variant by DNA sequencing detected. small number of patients in this dataset limits the statistical power of the stratification and association analyses, and a largescale validation is necessary to confirm and extend our observations. Of note, all primary data can be seen in Appendix C. Discussion One of the main goals of this study was to evaluate the utility of translating genomic insights obtained from a combination of both CMA and NGS for a comprehensive genomic assessment of the most common aberrations in lung and colorectal adenocarcinomas. We have shown that mutation analysis of multiple genes by NGS in combination with establishing copy number status yields more information about relevant changes in the tumor genome to guide treatment decisions. Adoption of genome-based selection of anti-cancer therapy is expected to increase rapidly, and advances in sequencing technology and data management are expected to drive this change, to eventually encompass large-scale parallel sequencing of hundreds of genes simultaneously. It is expected that in the near future, complete sequence coverage of all relevant genomic treatment targets will be readily available. Largescale rearrangements and copy number changes, although feasible, are not sufficiently refined and streamlined at present to be performed routinely in a clinical setting by NGS, and alternative molecular methods such as single nucleotide polymorphism (SNP) arrays and CMA are better suited for this purpose. Genotyping at single nucleotide resolution is more effectively performed by NGS, although genome-wide scans of CNVs have progressed significantly in resolution. The data presented here indicate that combining CMA and NGS yields more actionable information about relevant changes in the tumor genome and may better guide treatment decisions than either technology alone.

6 6 S. Mukherjee et al. The largest exome sequencing study of lung and colon tumor genomes to date was published by the Cancer Genome Atlas (36,37). Consistent with this and numerous other studies (38 40), most of the significantly recurrent actionable genomic alterations detected by both DNA sequencing and CMA in the colon cohort were observed at known cancer-related genes, such as APC, TP53, KRAS, PIK3CA, FBXW7, SMAD4, APC, SMARCB1, and PTEN. Similarly, in the lung cohort, common recurrent driver mutations were observed in multiple oncogenes, including TP53, KRAS, BRAF, EGFR, PIK3CA, BAP1, CDKN2A, APC, and BRCA2. Therefore, the data derived from DNA sequencing and CMA in this study are consistent with the current understanding of colon and lung tumor genomic alterations. CMA testing effectively transitioned into the clinical laboratory over the past decade and is used for CNV assessment in patients undergoing diagnostic testing in a variety of clinical scenarios due to its ability to assess DNA copy number across the entire genome at a higher resolution than karyotyping or fluorescence in situ hybridization (FISH). For example, the American College of Medical Genetics and Genomics (ACMG) Practice Guidelines recommend that CMA be used as first-tier testing for the postnatal evaluation of individuals with developmental delay/intellectual disability, and notes its application in prenatal diagnosis as well (41). CMAs have also been used extensively as a high throughput tool for the analysis of genetic alterations in complex cancer genomes, applied in a clinical research setting to the analysis of solid tumors as well as leukemias and lymphomas. One of the early clinical applications in oncology was the detection of genomic imbalances in chronic lymphocytic leukemia (CLL), where losses and gains of specific chromosomes and loci are independent predictors of prognosis and disease progression (42). Importantly, CMA offers a comprehensive view of the whole genome and does not require dividing cells allowing use of fresh frozen or formalin-fixed paraffin-embedded tissue. Currently, tests based on this technology are considered as adjunctive assessments in the evaluation of lymphomas and leukemias, although conventional cytogenetics and FISH analyses are still necessary for detection of balanced translocations. However, it is neither established nor recommended for post-therapy follow-up or minimal residual disease detection (43). Even with current limitations, its ability to detect smaller CNVs previously undetectable by conventional techniques has been critical in identifying genes present in somatic aberrations which aid in diagnosis, prognosis and therapy determinations, as is just beginning to emerge in the literature (44 48). At present, DNA sequencing is well-suited for genotyping with single nucleotide resolution. However, data analysis is not sufficiently refined or widely available to characterize CNVs. An important observation in the current study is that in 23% of the specimens, CMA analysis provided medically relevant targets in the absence of variants identified by DNA sequencing. The high prevalence of CNVs is likely due to large scale chromosomal instability that has been described for these tumor types and presents an opportunity to identify affected relevant genes in patient specimens. For example, in a patient with metastatic non-small cell carcinoma of lung origin, CMA analysis detected CNVs including amplification of CDK6, MET (7q) and CDK4, MDM2 (12q) genes. Based on the CMA data, this patient could be eligible for one of several ongoing clinical trials targeting the CDK4/6 activated pathway, MET-inhibitors or novel dual EGFR/c-MET inhibitors (41). Similarly, a patient with metastatic colonic adenocarcinoma showed several cytogenomic aberrations, including amplification of FGFR1 (8p11.2) and MYC (8q24). Targeted therapies for these CNVs are available as part of ongoing clinical trials for which this patient may qualify (48,49). Thus, CMA analysis provided potential treatment options for these patients, neither of which had variants identified by DNA sequencing. Further studies will be needed to determine the effect of the combined approach on overall survival statistics. When the data were analyzed for associations of loss or gain/amplification with somatic hot spot mutations, coexistence of TP53 mutations with loss of TP53 gene segments was relatively common in the colon cohort. Also, KRAS mutations commonly occurred with loss in TP53, APC, PTEN in CRC, in conjunction with TP53 loss in lung cancers as well. We observed significant loss of tumor suppressors BRCA1 and NF1 among male patients in the lung cancer cohort. Importantly, loss of tumor suppressors has been a recent focus of targeted therapy (50). Loss of BRCA1 and NF1 may provide male lung cancers an additional treatment option, including PARP inhibitors and MEK inhibitors, respectively (see Appendix B). A key distinction of our study is the complementary use of both sequencing and array techniques to better inform patient management in a clinical setting, leading to the increased detection of actionable alterations, since many of these aberrations are associated with therapies or ongoing clinical trials, as well as providing diagnostic and prognostic information. It is well known that genomic instability leading to copy number alterations in lung and colon tumors is common, and sequencing platforms in routine clinical use are currently not sufficient to accurately analyze and score copy number alterations; we are thus using array-based comparative genomic hybridization to complement NGS testing. In the near future, adoption of genome-based selection of anti-cancer therapies is expected to increase rapidly. This change will be driven by advances in technology, data management and complete sequence coverage of all relevant genomic treatment targets. Advances in NGS technology to extend the read length could have several benefits over the current short read lengths, such as allele phasing (51), improved balanced rearrangement detection (52), improved insertion and deletion mapping (52), and spanning repeat sequences (53). However, there will need to be innovative solutions developed to address the genomic DNA fragmentation seen in FFPE solid tumor specimens for longer read chemistries to be applied to these clinical specimens. Multiple types of data such as from DNA sequencing, CMA or expression profiling can be combined not only to elucidate biological causes and mechanisms of cancer but also to tailor diagnostic tests, therapeutics and disease-monitoring to the tumor genetic profile. This will likely result in a more refined approach to cancer treatment to maximize therapeutic efficacy and minimize side effects. Acknowledgments All authors are employed by commercial clinical laboratories or commercial bioinformatics companies which bill for providing array-cgh, DNA sequencing and or informatics services.

7 CMA complements NGS in detecting drug targets 7 Appendix A: List of 62 genes interrogated via next generation sequencing AKT1 FGFR2 KRAS PTEN ALK FGFR3 MAP2K1 PTPN11 ARAF FGFR4 MET RAF1 ATRX FLT3 MPL RET BRAF GATA3 MYC ROS1 CDKN2A GNA11 NOTCH1 RUNX1 CHEK2 GNAQ NPM1 SMAD4 CSF1R GNAS NRAS SMARCB1 CTNNB1 H3F3A NTRK1 SMO DDR2 HRAS NTRK2 SRC EGFR IDH1 NTRK3 STK11 ERBB2 IDH2 PDGFRA TP53 ESR1 JAK2 PIK3CA TSHR EZH2 JAK3 PIK3R1 VHL FBXW7 KDR PTCH1 WT1 FGFR1 KIT Appendix B: List of genes which are targets of FDA approved drugs, both on and off-label, and those involved in pathways that are the subjects of clinical trials, thus used in designating medically-relevant copy number variations (MR-CNVs) (from Gene gain/ Amplification Targeted therapy Gene gain/amplification Targeted therapy Gene loss/ Homozygous loss Targeted therapy ABL1-3 Imatinib JAK1-3 Pacritinib APC PKF AKT1-3 Ipatasertib KIT Dasatinib ATR Olaparib ALK Crizotinib KMT2A (MLL) EPZ-5676 ATM Veliparib AR Andarine KRAS Trametinib BAP1 Vorinostat ARAF Encorafenib LYN Saracatinib BRCA1-2 Olaparib AURORA A-C Tozasertib MAP2K1,2K2,3K1 (MEK) Selemetinib CEBPA Panobinostat BCL2 ABT-737 MAPK1 (ERK2) Ulixertinib CDKN2A-2B Flavopiridol BRAF Vemurafenib MCL1 Obatoclax mesylate CDKN1A-1B Dinaciclib BRD2-4 RVX-208 MDM2,4 MI-773 FBXW7 Everolimus BTK Ibrutinib MET Golvatinib NF1-2 Alpelisib CCND1-3 Sirolimus MST1R BMS PTCH1 Taladegib CCNE1 Flavopiridol mtor Sirolimus PTEN Ipatasertib CDK1,4,5,6,7,9 Flavopiridol NOTCH1,2 Semagacestat RB1 PD CRKL Dasatinib NRAS Cetuximab SMARCA4 Entinostat CSF1R PF NTRK1-3 LOXO-101 SMARCB1 Taladegib CTNNB1 icrt3 PDGFRA Ponatinib STK11 Dasatinib DDR2 Dasatinib PDGFRB Axitinib TP53 RG-7112; Nutlin-3 DNMT3A Azacitidine PIK3CA Sirolimus TSC1 Sirolimus EGFR Erlotinib PIK3R1 Selemetinib EPHA3-5 Dovitinib RET Regorafenib ERBB2 (HER2) Trastuzumab ROS1 Crizotinib ERBB3 (HER4) Sapitinib SMAD4 Galunisertib ERBB4 Lapatinib SRC Dasatinib ERG Degrasyn SYK Fostamatinib ESR1 Tamoxifen TBK1 GSK EZH2 Tazemetostat TEK Dovitinib FGFR1-3 Lucitanib TGRB1-2 LY FLT3 Dovitinib TMPRSS2 Vorinostat HDAC9-11 Panobinostat TNFSF11 Denosumab HRAS Lonafarnib TORC1-2(CRTC1-CRTC2) Omipalisib IDH1-2 AG-120 VEGFR1-3 PF IGF1R Linsitinib XPO1 Selinexor

8 8 S. Mukherjee et al. Appendix C: List of samples per disease type with medically relevant primary data of NGS mutations and CMA copy number events Accession Disease NGS mutation CMA medically relevant CNVs Case 1 Colon NRAS Amplification: FLT3; Loss: FBXW7, APC, PTEN, TP53 Case 2 Colon KRAS Loss: APC Case 3 Colon NRAS Case 4 Colon KRAS Case 5 Colon Loss: BAP1, FBXW7, CDKN2A, PTCH1, TSC1, ATM, TP53, NF2, SMARCB1 Case 6 Colon KRAS Case 7 Colon KRAS Case 8 Colon PIK3CA, BRAF Case 9 Colon Loss: ATM Case 10 Colon KRAS Loss: APC Case 11 Colon Amplification: FGFR1; Loss: APC, TP53 Case 12 Colon BRAF Case 13 Colon KRAS Amplification: FLT3; Loss: BAP1, FBXW7, APC, PTEN, ATM, NF1, BRCA1, TP53, CEBPA, SMARCA4, NF2, SMARCB1, PTEN Case 14 Colon MAP2K1 Case 15 Colon GS Case 16 Colon KIT, KIT, PIK3CA Loss: BAP1, FBXW7, APC, CDKN2A, PTEN, TP53, NF1, BRCA1, CEBPA, SMARCA4, STK11, NF2, SMARCB1, ATM Case 17 Colon KRAS Case 18 Colon KRAS Case 19 Colon Amplification: KRAS, CCND2, FLT3 Case 20 Colon Case 21 Colon KRAS Loss: BAP1, FBXW7, TP53 Case 22 Colon ALK, FGFR4, PIK3R1 Case 23 Colon KRAS Loss: PTEN, TP53, NF2, SMARCB1 Case 24 Colon KRAS, MET, RUNX1 Loss: APC, PTEN Case 25 Colon NRAS Case 26 Colon BRAF Case 27 Colon KRAS Case 28 Colon Amplification: CCNE1; Loss: TP53, SMARCA4, STK11, CEBPA, NF2, SMARCB1 Case 29 Colon Amplification: FLT3; Loss: APC, CEBPA, SMARCA4, STK11 Case 30 Colon KRAS Loss: APC, PTEN, TP53, NF2, SMARCB1 Case 31 Colon KRAS Loss: RB1 Case 32 Colon Loss: PTEN, NF1, BRCA1, TP53, NF2, SMARCB1 Case 33 Colon Amplification: EGFR, FLT3 Case 34 Colon BRAF, FBXW7 Case 35 Colon TP53 Loss: BAP1, FBXW7, PTEN, ATM, NF1, BRCA1, TP53, CEBPA, SMARCA4, STK11, NF2, SMARCB1, APC Case 36 Colon KRAS, TP53 Loss: TP53, NF2, SMARCB1 Case 37 Colon KRAS, PIK3CA, TP53 Loss: TP53 Case 38 Colon BRAF, TP53 Loss: BAP1, FBXW7, CDKN2A, PTEN, RB1, BRCA2, TP53, NF2, SMARCB1 Case 39 Colon TP53 Amplification: EGFR, KMT2A Case 40 Colon TP53 Loss: FBXW7, APC, PTCH1, TSC1, ATM, RB1, TP53 Case 41 Colon KRAS Case 42 Colon KRAS, PIK3CA, TP53 Amplification: FLT3; Loss: FBXW7, PTEN, ATM, TP53 Case 43 Colon Case 44 Colon BRAF, PIK3CA, TP53 Amplification: BRAF; Loss: TP53, NF2, SMARCB1 Case 45 Colon KRAS, TP53 Loss: BAP1, FBXW7, APC, CDKN2A, PTCH1, TSC1, PTEN, ATM, NF1, TP53, BRCA1, STK11 Case 46 Colon FBXW7, KRAS, PIK3CA, TP53 Loss: CDKN2A, TP53 Case 47 Colon KRAS, PIK3CA (continued on next page)

9 Appendix C: (continued ) ARTICLE IN PRESS CMA complements NGS in detecting drug targets 9 Accession Disease NGS mutation CMA medically relevant CNVs Case 48 Colon BRAF, TP53 Amplification: FLT3 Case 49 Colon TP53 Loss: BAP1, FBXW7, APC, PTEN, TP53, STK11, CEBPA, NF2, SMARCB1 Case 50 Colon KRAS, TP53 Case 51 Colon KRAS, TP53 Loss: FBXW7, APC, PTEN, ATM, TP53, NF1, BRCA1, NF2, SMARCB1 Case 52 Lung RUNX1, BRAF Case 53 Lung AKT1 Amplification: PIK3CA, FGFR3, PIK3R1, PDGFRB, FGFR1, JAK2, SMAD4, BCL2; Loss: BAP1, PTCH1, TSC1, PTEN, BRCA2, RB1, TP53, NF1, BRCA1 Case 54 Lung KRAS Amplification: KRAS; Loss: BAP1, CDKN2A, PTCH1, TSC1, TP53 Case 55 Lung Case 56 Lung EGFR, EGFR Loss: CDKN2A, PTEN, BRCA2, RB1, SMARCB1, NF2 Case 57 Lung Amplification: CDK6, MET; Loss: NF1 Case 58 Lung KIT Loss: BAP1, FBXW7, APC, PTEN, NF1, BRCA1, TP53, NF2, SMARCB1 Case 59 Lung Case 60 Lung Amplification: AKT2; Loss: BAP1, FBXW7, APC, CDKN2A, PTEN, ATM, RB1, BRCA2, NF1, BRCA1, TP53, SMARCA4, STK11, NF2, SMARCB1 Case 61 Lung Case 62 Lung MYC Amplification: JAK1, MDM4, NTRK1, EGFR, AKT2, CCNE1; Loss: BAP1, CDKN2A, TSC1, RB1, BRCA2, TP53 Case 63 Lung PIK3CA, BRAF Case 64 Lung KRAS Case 65 Lung Loss: CDKN2A Case 66 Lung Case 67 Lung EGFR Amplification: MDM2; Loss: STK11 Case 68 Lung FGFR3 Case 69 Lung ABL1, KRAS Loss: TP53 Case 70 Lung Loss: APC, STK11 Case 71 Lung Amplification: CDK6, MET, BRAF, FGFR1, FLT3; Loss: PTEN Case 72 Lung KRAS Loss: SMARCA4, STK11 Case 73 Lung Loss: CDKN2A, PTCH1, TSC1, RB1, BRCA2, NF1, BRCA1, TP53, SMARCA4, STK11 Case 74 Lung KRAS Case 75 Lung Case 76 Lung Loss: CDKN2A, PTCH1, TSC1, PTEN, ATM, RB1, BRCA2, CEBPA, SMARCA4, STK11, APC Case 77 Lung Amplification: CCND3; Loss: BAP1, FBXW7, APC, PTCH1, TSC1, NF1, BRCA1, TP53, SMARCA4, STK11 Case 78 Lung Amplification: PIK3CA; Loss: BAP1, FBXW7, CDKN2A, PTCH1, TSC1, TP53 Case 79 Lung STK11 Loss: BAP1, FBXW7, CDKN2A, PTCH1, TSC1, RB1, BRCA2, TP53, SMARCA4, STK11 Case 80 Lung Case 81 Lung KRAS Case 82 Lung KRAS Loss: ATM Case 83 Lung Loss: BAP1, FBXW7, APC, CDKN2A, PTCH1, TSC1, RB1, BRCA2, SMARCA4, STK11, NF2, SMARCB1 Case 84 Lung Loss: APC, CDKN2A Case 85 Lung TP53 Loss: FBXW7, APC, CDKN2A, PTCH1, TSC1, RB1, BRCA2, BAP1 Case 86 Lung TP53 Case 87 Lung KRAS Loss: CDKN2A, TP53, NF2, SMARCB1 Case 88 Lung TP53, TP53 Loss: PTCH1, ATM, SMARCA4, STK11 Case 89 Lung BRAF, PIK3CA Case 90 Lung TP53 Amplification: NTRK1; Loss: PTEN Case 91 Lung NRAS, TP53 Case 92 Lung TP53 Amplification: MDM4, NOTCH2, NRAS, NTRK1, PIK3CA, KRAS, CCND2; Loss: CDKN2A, STK11 Case 93 Lung TP53 Amplification: SMAD4, BCL2; Loss: BAP1, RB1, TP53, NF2, SMARCB1 (continued on next page)

10 Appendix C: (continued ) ARTICLE IN PRESS 10 S. Mukherjee et al. Accession Disease NGS mutation CMA medically relevant CNVs Case 94 Lung PIK3R1,TP53 Loss: BAP1, FBXW7, CDKN2A, RB1, BRCA2, NF1, BRCA1, TP53, CEBPA, SMARCA4, STK11 Case 95 Lung CDKN2A,TP53 Amplification: CDK6 Case 96 Lung KRAS,RUNX1,TP53 Case 97 Lung PIK3CA,TP53 Amplification: PIK3CA, FGFR1; Loss: BAP1, FBXW7, CDKN2A, PTCH1, TSC1, TP53, SMARCB1 Case 98 Lung TP53 Case 99 Lung BRAF,HRAS Case 100 Lung KRAS Amplification: MDM4, DNMT3A, ALK, XPO1, CCND3, CDK6, CRTC1, JAK3 Case 101 Lung KRAS Case 102 Lung TP53 Loss: BAP1, APC, PTEN, RB1, BRCA2, TP53 Case 103 Lung TP53 Amplification: JAK1, MDM4, NOTCH2, NRAS, NTRK1, PIK3CA, AKT1, ERBB2, MAP2K2; Loss: BAP1 Case 104 Lung KRAS Case 105 Lung Loss: BAP1, FBXW7, PTEN, ATM, RB1, BRCA2, RB1, TP53, SMARCA4, STK11, NF2, SMARCB1 Case 106 Lung TP53 References 1. Govindan R, Page N, Morgensztern D, et al. Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database. J Clin Oncol 2006;24: Jemal A, Siegel R, Xu J, et al. Cancer statistics, CA Cancer J Clin 2010;60: Ag TI. Pathology and genetics of tumours of the lung, pleura, thymus and heart. France: WHO Publications, Goldstraw P, Crowley J, Chansky K, et al. The IASLC lung cancer staging project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J Thorac Oncol 2007;2: Lynch TJ, Bell DW, Sordella R, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004;350: Paez JG, Jänne PA, Lee JC, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004;304: Kwak EL, Bang YJ, Camidge DR, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med 2010;363: Bergethon K, Shaw AT, Ou SH, et al. ROS1 rearrangements define a unique molecular class of lung cancers. J Clin Oncol 2012;30: De Castro Carpeno J, Belda-Iniesta C. KRAS mutant NSCLC, a new opportunity for the synthetic lethality therapeutic approach. Transl Lung Cancer Res 2013;2: Ou SH, Kwak EL, Siwak-Tapp C, et al. Activity of crizotinib (PF ), a dual mesenchymal-epithelial transition (MET) and anaplastic lymphoma kinase (ALK) inhibitor, in a non-small cell lung cancer patient with de novo MET amplification. J Thorac Oncol 2011;6: Shanmugam V, Ramanathan RK, Lavender NA, et al. Whole genome sequencing reveals potential targets for therapy in patients with refractory KRAS mutated metastatic colorectal cancer. BMC Med Genomics 2014;7: Takahashi T, Nau MM, Chiba I, et al. p53: a frequent target for genetic abnormalities in lung cancer. Science 1989;246: Sanchez-Cespedes M, Parrella P, Esteller M, et al. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res 2002;62: Singh A, Misra V, Thimmulappa RK, et al. Dysfunctional KEAP1 NRF2 interaction in non-small-cell lung cancer. PLoS Med 2006;3:e Medina PP, Romero OA, Kohno T, et al. Frequent BRG1/ SMARCA4-inactivating mutations in human lung cancer cell lines. Hum Mutat 2008;29: Levy MA, Lovly CM, Pao W. Translating genomic information into clinical medicine: lung cancer as a paradigm. Genome Res 2012;22: Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancers. Science 2007;318: van de Wetering M, Sancho E, Verweij C, et al. The beta-catenin/ TCF-4 complex imposes a crypt progenitor phenotype on colorectal cancer cells. Cell 2002;111: Emma Hitt, PhD, Wnt Signaling Inhibition: Will Decades of Effort Be Fruitful at Last? Available at: -Signaling-Inhibition-Will-Decades-of-Effort-Be-Fruitful-at -Last#sthash.ILk29XW8.dpuf. 20. Douillard JY, Oliner KS, Siena S, et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med 2013;369: doi: /nejmoa Grimaldi AM, Simeone E, Ascierto PA. The role of MEK inhibitors in the treatment of metastatic melanoma. Curr Opin Oncol 2014;26: Davies JJ, Wilson IM, Lam WL. Array CGH technologies and their applications to cancer genomes. Chromosome Res 2005;13: Tonon G, Wong KK, Maulik G, et al. High-resolution genomic profiles of human lung cancer. Proc Natl Acad Sci U S A 2005;102: Weir BA, Woo MS, Getz G, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature 2007;450: Ried T, Knutzen R, Steinbeck R, et al. Comparative genomic hybridization reveals a specific pattern of chromosomal gains and

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