Drug-Sensitivity Screening and Genomic Characterization of 45

Published OnlineFirst July 3, 2018; DOI: 10.1158/1535-7163.MCT-17-0733

Companion Diagnostic, Pharmacogenomic, and Cancer Biomarkers Molecular Cancer Therapeutics Drug-Sensitivity Screening and Genomic Characterization of 45 HPV-Negative Head and Neck Carcinoma Cell Lines for Novel Biomarkers of Drug Efﬁcacy Tatiana Lepikhova1,2, Piia-Riitta Karhemo2, Riku Louhimo2, Bhagwan Yadav3, Astrid Murumagi€ 3, Evgeny Kulesskiy3, Mikko Kivento2, Harri Sihto1, Reidar Grenman 4, Stina M. Syrjanen€ 5, Olli Kallioniemi3, Tero Aittokallio3,6, Krister Wennerberg3, Heikki Joensuu1,7, and Outi Monni2

Abstract

There is an unmet need for effective targeted therapies showed some association with response to MEK and/or for patients with advanced head and neck squamous cell EGFR inhibitors. MYC amplification and FAM83H over- carcinoma (HNSCC). We correlated gene expression, expression associated with sensitivity to EGFR inhibitors, gene copy numbers, and point mutations in 45 human and PTPRD deletion with poor sensitivity to MEK inhibi- papillomavirus–negative HNSCC cell lines with the sen- tors. The connection between high FAM83H expression sitivity to 220 anticancer drugs to discover predictive and responsiveness to the EGFR inhibitor erlotinib was associations to genetic alterations. The drug response pro- validated by gene silencing and from the data set at the files revealed diverse efficacy of the tested drugs across the Cancer Cell Line Encyclopedia. The data provide several cell lines. Several genomic abnormalities and gene expres- novel genomic alterations that associated to the efficacy of sion differences were associated with response to mTOR, targeted drugs in HNSCC. These findings require further MEK, and EGFR inhibitors. NOTCH1 and FAT1 were the validation in experimental models and clinical series. most commonly mutated genes after TP53 and also Mol Cancer Ther; 17(9); 2060–71. 2018 AACR.

Introduction metastases invariably leads to death (2, 3). Systemic treatments for advanced HNSCC often consist of chemotherapeutic drugs, Head and neck squamous cell carcinoma (HNSCC) is a com- such as platinum salts and taxanes, but these drugs have only mon type of human cancer worldwide (1). Local HNSCCs are moderate efficacy (4). The only approved targeted drugs for often treated with surgery, radiotherapy, and/or chemoradiother- advanced HNSCC are cetuximab, a monoclonal antibody that apy, but cancer recurrence is frequent, and emergence of distant binds to the epidermal growth factor receptor (EGFR), and, recently, the anti-PD-1 antibodies nivolumab and pembrolizu- EGFR fi 1Research Programs Unit, Laboratory of Molecular Oncology, Translational mab (5). Despite is frequently ampli ed and overexpressed Cancer Biology Program, University of Helsinki, Helsinki, Finland. 2Research in HNSCC, cetuximab has shown only moderate efficacy (6, 7), Programs Unit, Genome-Scale Biology Program and Medicum, Biochemistry and and there are no clinically approved predictive biomarkers for Developmental Biology, University of Helsinki, Helsinki, Finland. 3Institute for cetuximab response. There is a clear unmet need for novel, Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. effective targeted treatments and accompanying biomarkers for 4Department of Otorhinolaryngology–Head and Neck Surgery, University of efficacy. Turku and Turku University Hospital, Turku, Finland. 5Department of Pathology, Institute of Dentistry, University of Turku, Turku, Finland. 6Department of Recent comprehensive genomic analyses of HNSCCs have Mathematics and Statistics, University of Turku, Turku, Finland. 7Department greatly advanced our understanding of the disease and the key of Oncology, Helsinki University Hospital, Helsinki, Finland. molecular alterations that drive these cancers (8–13). However, Note: Supplementary data for this article are available at Molecular Cancer the translation of the molecular aberrations into treatment Therapeutics Online (http://mct.aacrjournals.org/). strategies for patients is challenging as the efficacy of many fl T. Lepikhova and P.-R. Karhemo contributed equally to this article. anticancerdrugsisin uenced by the genomic and transcriptomic heterogeneity of tumors (14). Numerous novel antican- R. Louhimo and B. Yadav contributed equally to this article. cer drugs are entering the clinical phase of testing, and testing Current address for K. Wennerberg: BRIC—Biotech Research and Innovation all of them and their potential combinations in clinical trials Centre, University of Copenhagen, Copenhagen, Denmark. is challenging. Combining molecular and genetic analyses Corresponding Author: Outi Monni, Research Programs Unit, Genome-Scale with comprehensive preclinical compound screening in vitro Biology Program, University of Helsinki, Haartmaninkatu 8, Helsinki 00290, might reveal new drug targets, and could facilitate rational drug fi Finland. Phone: 358-40-7639302; E-mail: outi.monni@helsinki. testing in clinical trials. doi: 10.1158/1535-7163.MCT-17-0733 HNSCCs comprise both human papillomavirus (HPV)- 2018 American Association for Cancer Research. positive and HPV-negative cancers, the latter being more

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common. Patients with HPV-negative cancer have, in general, less patients whose cancer gave rise to a cell line were male, and favorable survival as compared with those with HPV-positive themeanagewas62yearsatthetimeoftissuesampling.A HNSCC (15). Recurrent or metastatic HPV-positive cancers majority(36of45,80%)ofthecell lines originated from a resemble genetically HPV-negative tumors (16). In the current primary tumor, 4 (9%) from a primary tumor that persisted study, we performed a large-scale drug screen on 45 HPV-negative after treatment, and 5 (11%) from recurrent cancer. Most HNSCC cell lines, and integrated the compound sensitivity land- of the cell lines (n ¼ 33, 73%) originated from an oral cavity scapes with detailed genomic and gene expression analyses to or oropharyngeal tumor, 11 (24%) from larynx cancers, and 1 identify novel drugs that might be effective for HNSCC along with (2%) from a maxillary sinus cancer. All cell lines tested predictive biomarkers for these drugs. negative for mycoplasma before the experiments using a VenorGeM Mycoplasma PCR Detection Kit (Minerva Biolabs GmbH). Materials and Methods The cells were cultured in Dulbecco's Modiﬁed Eagle Medium Cell lines (DMEM) supplemented with 2 mmol/L L-glutamine, 10% fetal The series consists of 45 HPV-negative HNSCC cell lines bovine serum, nonessential amino acids solution, penicillin established from HNSCC (Table 1). The cell lines were estab- (100 U/mL), and streptomycin (100 mg/mL) at 37Cinan lished at the Department of Otorhinolaryngology-Head and atmosphere of 5% CO2 (all reagents were purchased from Lonza Neck Surgery, Turku University Hospital (Turku, Finland) as BioWhittaker). Genotyping for HPV 6, 11, 16, 18, 26, 31, 33, 35, described elsewhere (17, 18). Thirty-one (69%) of the 45 39, 42, 43, 44, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73, and 82

Table 1. Clinical and pathologic characteristics of the HNSCC cell lines Cell line Sexa Ageb TNM Specimen site Typec Grade Passage UT-SCC-2 M 60 T4N1M0 Baseos oris pri G2 p10 UT-SCC-6A F 51 T2N1M0 Larynx rec G1 p31 UT-SCC-8 M 42 T2N0M0 Larynx pri G1 p35 UT-SCC-10 M 62 T1N0M0 Tongue pri G2 p6–7 UT-SCC-11 M 58 T1N0M0 Larynx rec G2 p18 UT-SCC-14 M 25 T3N1M0 Tongue pri (per) G2 p9 UT-SCC-16A F 77 T3N0M0 Tongue pri G3 p23 UT-SCC-19A M 44 T4N0M0 Larynx pri G2 p15 UT-SCC-21 M 79 T3N0M0 Tongue pri G2 p19 UT-SCC-24A M 41 T2N0M0 Tongue pri G2 p29 UT-SCC-27 M 71 T2N0M0 Gingiva of maxilla rec G3 p10 UT-SCC-28 F 58 T2N0M0 Floor of mouth pri (per) G1 p23 UT-SCC-29 M 82 T2N0M0 Larynx pri G1 p17 UT-SCC-30 F 77 T3N1M0 Oral tongue pri G1 p41 UT-SCC-36 M 46 T4N1M0 Floor of mouth pri G3 p20 UT-SCC-37 F 61 T2N0M0 Gingiva of mandib. pri G1 p25 UT-SCC-38 M 66 T2N0M0 Larynx pri G2 p5 UT-SCC-40 M 65 T3N0M0 Tongue pri G1 p6 UT-SCC-42A M 43 T4N3M0 Larynx pri G3 p23 UT-SCC-43 F 75 T4N1M0 Gingiva of mandib. pri G2 p10 UT-SCC-44 F 71 T4N2BM0 Gingiva of maxilla pri (per) G3 p24 UT-SCC-47 M 78 T2N0M0 Floor of mouth pri G3 p9 UT-SCC-49 M 76 T2N0M0 Larynx pri G2 p29 UT-SCC-53 M 76 T4N2cM0 Maxillary sinus pri G1 p19–20 UT-SCC-54A F 58 T2N0M0 Buchal mucosa pri G1 p35 UT-SCC-55 M 76 T4N1M0 Gingiva pri G2 p23–24 UT-SCC-60A M 59 T4N1M0 Tonsil (left) pri G1 p12 UT-SCC-65 M 68 T4N2CM0 Soft palate and tonsilla pri G2 p7–8 UT-SCC-67 F 80 T2N0M0 Tongue pri G2 p16 UT-SCC-72 M 50 T4N2M0 Gingiva pri (per) G2 p17–18 UT-SCC-73 F 86 T1N0M0 Tongue pri G2 p18 UT-SCC-74A M 31 T3N1M0 Tongue pri G1-G2 p44 UT-SCC-75 M 56 T2N2BM0 Larynx pri G2 p30þ UT-SCC-76A M 52 T3N0M0 Tongue pri G2 p17 UT-SCC-81 M 48 T2N0M0 Tongue pri G1 p18 UT-SCC-87 F 29 T3N1M0 Tongue pri G1 p31 UT-SCC-95 F 83 T1N0M0 Tongue pri G1 p25 UT-SCC-99 M 80 T3N0M0 Tongue pri G2 p5–6 UT-SCC-106A M 59 T1AN0M0 Larynx pri G2 p12 UT-SCC-107 M 46 T4N2CM0 Larynx pri G2 p24 UT-SCC-110A M 37 T4N0M0 Maxilla rec G3 p23 UT-SCC-112 F 94 T2N0M0 Gingivae mandib. pri G1 p16 UT-SCC-114 M 75 T3N0M0 Lower lip pri G1 p10 UT-SCC-122 M 36 T3NM0 Mucosae bucchae pri G2 p8 UT-SCC-126A F 81 T2N1M0 Lower lip rec G1 p8 aM ¼ male, F ¼ female; bage in years; cPri, primary tumor; pri (per), primary persistent tumor; rec ¼ recurrent tumor.

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strains was performed using a Multiplex HPV Genotyping Kit information about the gene sequence alterations in head and (DiaMex) in PCR-amplified samples of genomic DNA isolated neck cancers was searched from the PubMed. The genes with no from the cell lines. All cell lines were negative for all the HPV known mutations in head and neck cancer were removed from the strains tested. Normal skin fibroblasts were obtained from panel, and genes related to head and neck cancer were added, 3 patients whose tumors gave rise to the UT-SCC-19A, resulting in a head and neck cancer customized panel consisting of UT-SCC-29, and UT-SCC-45 cell lines, and they were cultured as 694 genes (Supplementary Table S1). The gene list of the 694 the cancer cells except that 20 mmol/L HEPES (Invitrogen) was genes was provided to Roche NimbleGen (Roche NimbleGen, added to the cell culture media. CCD 1106 KERTr (ATCC Inc) for a SeqCap EZ Choice custom panel design. The exon CRL-2309) keratinocytes were grown in keratinocyte-serum free locations were collated from ENSEMBL (v75), CCDS medium supplemented with epidermal growth factor and bovine (29Nov2013), VEGA (v54), and RefSeq (13Aug2013). Overlaps pituitary extract (Invitrogen). were collapsed, and a single unified list of target exons was generated. These coordinates were further adjusted by expanding Nucleic acid isolation any targets less than 100 bp to a minimum length of 100 bp, Genomic DNA was isolated from the cell lines using a DNeasy centered in the middle of the exon. Any overlaps were then Blood and Tissue Kit (Qiagen) according to the manufacturer's consolidated a second time. This coordinate file was used as protocol. The concentration and purity of the genomic DNA was the input for capture probe selection for a unique probe set estimated with a NanoDrop spectrophotometer (Thermo Scien- containing unique probes as determined by the SSAHA (Sequence tific). Total RNA was isolated using a Macherey-Nagel (Duren)€ Search and Alignment by Hashing Algorithm). A probe was Nucleospin RNA II Kit. The quality of total RNA was assessed with considered to match to the genome if there were five or fewer a 2100 Agilent Bioanalyzer (Agilent Technologies), and only single-base insertions, deletions, or substitutions between the samples of high quality (2100 Bioanalyzer RNA integrity value probe and the genome. >7) were included in the analyses. Next-generation sequencing Array comparative genomic hybridization (arrayCGH) The genomic DNA was sheared with an S2-focused ultra- An arrayCGH was carried out using an Agilent Human Genome sonicator (Covaris, Inc.). The library was prepared with a CGH 244A Oligo Microarray. The samples were preprocessed ThruPLEX DNA-seq Kit (Rubicon Genomics), and the targets according to an Agilent protocol (version 6.0, November 2008; were enriched using the custom SeqCap EZ Choice Library Agilent Oligonucleotide Array-Based CGH for Genomic DNA (Roche NimbleGen, Inc.). The samples were sequenced with Analysis). The gene copy-number data were processed as an Illumina HiSeq sequencing system (Illumina Inc.) using a described earlier (19). Briefly, the data were LOESS normalized, 100-nt paired ends read length. We reached an average coverage segmented with circular binary segmentation using an R-package of 197 at the target regions. DNAcopy (20), and the regions with segment means more than Thesampleswerealignedtohg19using"bwamem" two standard deviations from the mean (0.67) of three self- (v. 0.7.10) with default parameters (25) and sorted with the versus-self control cell lines were called altered. The arrayCGH Picard software (http://broadinstitute.github.io/picard). The data are available under a Gene Expression Omnibus (GEO) targeted sequencing protocol greatly increases the likelihood accession number GSE108062. The significance of frequent altera- of pseudoduplicated reads (i.e., reads that appear duplicated) tions was assessed with the GISTIC v.2.0.21 software (the para- and distinguishes real and false PCR duplicates. PCR duplicates meters used were q < 0.25, tamp ¼ 20.67, tdel ¼ 2 0.67, cap ¼ 2, were therefore not removed. Mutations were called using the join segment size ¼ 20). Copy-number polymorphic loci were VarScan v.2.3 software using parameters P ¼ 0.95, somatic excluded before data segmentation. The data were analyzed with P ¼ 0.05, strand-filter ¼ 1, and min-var-frequency ¼ 0.2 the Anduril software framework (21). As due to the stringent (26). Each cancer cell line was compared against the pooled criteria in arrayCGH analysis some previously reported alterations sequences from the three control cell lines (normal skin might be missed, the data were checked manually for consistency fibroblasts from the same patient as UT_SCC_19A, 29 and for selected regions with reported alterations. 45). The germline variants included in the dbSNP and the 1000Genomes databases were removed after calling. Gene expression microarray analysis Gene expression analysis was carried out on Illumina HT-12 Drug-sensitivity testing, scoring, and hierarchical clustering human microarrays (Illumina Inc.). The RNA samples from the 45 We used a compound library that consisted of 220 FDA- cell lines were processed according to the manufacturer's recom- approved drugs as well as investigational compounds. A large mendations. The gene expression data were quantile normalized proportion of the compounds were classified as signal trans- using the lumi R-package (22). The gene expression data are duction inhibitors (n ¼ 94), mostly targeting either receptor or available under a GEO accession number GSE108062. Down- cytoplasmic kinases (Supplementary Table S2). Other classes stream analysis of the gene expression data is described in detail that contained over 10 different compounds included alkylat- below. ing agents, hormonal agents, antimetabolites, angiogenesis inhibitors, and topoisomerase inhibitor. Plerixafor and plati- Custom panel design num salts including cisplatin and carboplatin were dissolved in A custom head and neck cancer panel was created using a Roche water as dimethyl sulfoxide (DMSO) inactivates them while the NimbleGen (Roche NimbleGen, Inc) Comprehensive Cancer rest of the compounds in the drug-sensitivity and resistance Design consisting of 578 genes as a baseline. We customized this testing (DSRT) screening platform were dissolved in DMSO. panel by analyzing these 578 genes in the cBioPortal (23, 24) for The DSRT was carried out as described previously (27). Briefly, published mutations in head and neck cancers. In addition, all compounds were tested over a 10,000-fold concentration

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range in five different concentrations in 384-well plates to Integrative analyses of drug sensitivity, gene expression, and generate quantitative and reliable dose-response data. Drugs gene copy numbers were dispensed on plates by acoustic liquid handler (Echo 550, The difference in the drug sensitivity between the cell lines with Labcyte) and dissolved in 5 mL of media. The cells were plated at or without a copy-number alteration (CNA) was tested using the a density of 1,000 cells/well in 20 mL volume by MultiDrop Wilcoxon rank-sum test, and the difference was considered sig- Combi peristaltic dispenser (Thermo Scientific) and incubated nificant at a false discovery rate (FDR) level q < 0.05 (Storey at 37 Cand5%CO2 for 72 hours. After 3 days cell viability was correction for multiple testing; ref. 31). The differences in drug measured by CellTiter-Glo luminescent assay (Promega). sensitivity were quantified by subtracting the median DSS of the DMSO and benzethonium chloride (100 mmol/L) were used samples with a CNA from the median DSS of the samples without as a negative and positive control, respectively. the CNA. The statistical testing was done for each drug separately. The dose-response curves were modeled using a four-parameter We further compared the gene expression data with the CNA data logistic function, defined by the top and bottom asymptotes, the to find the genes whose expression is modified due to a CNA. By slope, and the half-maximal inhibition constant (IC50). In the using the CNA data, we categorized the genes located in the same curve fitting, the top asymptote of the curve was fixed to 100% chromosome region into two groups, altered and nonaltered, and viability, while the bottom asymptote was allowed to float calculated the signal-to-noise ratio (32). The significance of the between 0 and 75% (i.e., compounds causing less than 25% difference in gene expression between the two groups was inhibition were considered inactive). To quantitatively profile the assessed with a permutation test and corrected for multiple individual HNSCC cells in terms of their compound responses, we hypothesis using the Benjamini–Hochberg correction (29). Genes calculated the model-based drug-sensitivity score (DSS), which with an FDR q < 0.05 were considered significantly altered. Only has been shown to improve the identification of cellular addic- CNAs that occurred in at least 10% of the cell lines were tested. tions and other druggable vulnerabilities in individual cancer Finally, the genes whose CNA status was significantly associated patients over IC50 and other univariate response parameters (27, with both gene expression and drug sensitivity were further tested 28). The differential DSS (dDSS) was calculated by comparing the for an association between gene expression and drug-sensitivity DSS of particular HNSCC cells to the mean DSS over all the cell profiles using the bootstrapCor function in the maigesPack R- lines. Unsupervised clustering of the DSS profiles across the tested package (0.3 |r| 0.3; ref. 33) and adjusted with the compounds and HNSCC cell lines was performed using the Ward Benjamini–Hochberg (29) method. hierarchical complete-linkage clustering algorithm using Spear- man and Manhattan distance measures for the drug and sample Real-time quantitative PCR (RT-qPCR) profiles, respectively. mRNA was reverse transcribed to cDNA using an iScript cDNA synthesis kit (Bio-Rad) following the manufacturer's Gene expression biomarker analysis for EGFR, MEK, and mTOR instructions. RT-qPCR analyses were done using the SYBR inhibitors Green dye (LightCycler 480 DNA SYBR Green I Master, Cat. Drug classes were defined as follows: EGFR inhibitors consisted No. 04707516001, Roche Diagnostics GmbH). Quantitation of afatinib, canertinib, gefitinib, and erlotinib; MEK inhibitors of was performed using LightCycler 480 384-multiwell plates and refametinib, binimetinib, selumetinib, trametinib, pimasertib, the LightCycler 480 instrument (Roche Diagnostics GmbH). and TAK-733; and mTOR inhibitors of OSI-027, AZD8055, sir- The analyses were done in a triplicate for each gene. The primers olimus, temsirolimus, ridaforolimus, and everolimus. For each of used are listed in Supplementary Table S3. The data were these drug classes, we grouped the six cell lines with the highest analyzed with a Basic relative quantification program of the class-specific DSS (median drug sensitivity calculated from the LightCycler 480 Software v.1.5 (Roche Diagnostics GbmH), compound-specific DSSs in the drug class) into a high-sensitivity based on the 2-DDCT-method. A keratinocyte cell line, CCD group, and the six cell lines with the lowest class-specific DSS into 1106 KERTr (ATCC CRL-2309), served as the reference sample a low-sensitivity group. We compared the difference in the gene for gene expression normalization. GAPDH was selected as the expression between the high and low-sensitivity groups, and the reference gene for normalization. genes with a fold change of > 2 (group medians) were considered significantly differentially expressed (t test, P < 0.01). The P values siRNA transfection/drug-response measurement of cancer cell for the significantly differentially expressed genes were adjusted lines by the Benjamini–Hochberg method (29) using "stats" R-package To validate selected results from the drug screen, we silenced (R Foundation for Statistical Computing). FAM83H (pool of siRNAs s50090, s50091, and s50092, Ambion) and RAB25 (pool of siRNAs SI03036544, Somatic mutations as biomarkers for drug response SI02644089, SI02644082, and SI00126084, Qiagen) in To assess the influence of a gene-specific mutation on drug UT-SCC-14 and UT-SCC-8 cell lines, respectively. A total of sensitivity, the cell lines with a mutation were compared with the 12 nmol/L pooled siRNAs were used in three replicate experi- rest of the cell lines using the Wilcoxon rank-sum test (two-sided). ments. Qiagen All Stars Negative (#1027281) served as a The difference between two groups was assessed with permuta- negative and Qiagen All Stars Hs Death Control (#1027299) tion test using "coin" R-package (30). The P values were adjusted as positive control. Cells were reverse transfected using with the Benjamini–Hochberg (29) method as described above. Lipofectamine RNAiMAX (Thermo Fisher Scientific), and 24 Due to the functional relevance of stop-gain mutations to protein hours later supplemented with nine different afatinib or erlo- product, the identical analysis was performed to assess the influ- tinib concentrations (0.1, 0.3, 1, 3.2, 10, 31.6, 100, 316.2, and ence of gene-specific stop-gain mutations on drug sensitivity. We 1,000 nmol/L or 1, 3.2, 10, 31.6, 100, 316.2, 1000, 3162.3, and restricted our analysis only to the most common mutations we 10,000 nmol/L, respectively). After 72 hours of drug treatment, observed in a gene panel of 694 genes. the number of viable cells was measured using the CellTiter-Glo

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luminescent cell viability assay (Promega). Percent inhibition frequently in TP53, NOTCH1, FAT1, CSMD3, and CDKN2A, and was calculated using the negative and positive controls using the commonly observed CNAs (amplifications at 3q26, 7p11.2, formula: 8q24, and 11q13 and deletions at 3p14, 8p23, 9p21, 9p23, and 18q), resemble those observed in clinical HNSCC (refs. 10–14, m : raw:intensity ¼ neg contols 34; Fig. 1; Supplementary Tables S5 and S6). In total, we detected %inhibition m m 100% neg: contols pos: contols 552 variants in 279 genes in the targeted HNSCC gene panel by next-generation sequencing (Supplementary Table S5). The num- To account for the siRNA effect on the cells, the percent ber of variants among the studied 694 genes differed between inhibition value at the lowest concentration of each dose response the cell lines (range, 3–29; mean, 12). The UT-SCC-60A cell line was treated as the siRNA background, and this value was sub- had the largest and the UT-SCC-99 and UT-SCC-36 the lowest tracted from values of the other concentrations. The percent number of mutations. The number of mutations did not correlate inhibition data for each drug were then curve fitted with Leven- with the primary tumor tumor–node–metastasis classification berg–Marquardt method. The replicates were averaged for each size category (P ¼ 0.379; unpaired t test) or with cancer regional P ¼ t concentration during the curve fitting. The IC50, slope, maximal lymph node involvement ( 0.887; unpaired test). and minimal response coming out of the curve fitting are then The genes that were mutated in at least five (10%) of the cell used to calculate the DSS (28). lines were compared with drug sensitivity (using the DSS) across the 45 cell lines. The results are shown in Supplementary Table S7. The analysis between CNA alterations and drug responses Results revealed 773 CNAs that were significantly associated with the HNSCC cell lines show selective responses to EGFR, MEK, and drug sensitivity (Wilcoxon rank-sum test, Q < 0.1; Supplementary mTOR inhibitors Table S8). A high-throughput DSRT platform of 220 compounds showed The influence of a CNA to gene expression was assessed by selective responses across the 45 HNSCC cell lines (Supplemen- expression profiling using Illumina HT-12 microarrays. Of the tary Table S4) as measured with the DSS, a modified form of the 4,298 genes (1,952 deleted, 2,347 gained) that were located in the area under the curve calculation that integrates multiple dose- regions altered in at least 10% of the cell lines, 472 showed response parameters for each drug. DSS therefore captures both significant downregulation and 445 significant upregulation the potency and the efficacy of the drug effect (Supplementary based on CNA cis effects at their loci (Supplementary Tables S9 Table S4). Unsupervised hierarchical clustering of drug sensitivity and S10). The amplified and upregulated genes included PIK3CA over all 45 cell lines yielded eight clusters (Supplementary at 3q26.3, EGFR at 7p11.2, CCND1, ORAOV1, PPFIA1, CTTN, and Fig. S1). Of these, group I was unresponsive to most of the 220 FADD at 11q13, and BIRC2, BIRC3, and YAP1 at 11q22. All of drugs tested. Three groups showed selectivity to EGFR and MEK these genes are known to be located in frequently amplified inhibitors (groups II, VI, and VIII) as single agents, proposing chromosomal regions in HNSCC (10–13). The deleted and synergistic effects for selected cell lines or in combination with an downregulated genes included CDKN2A at 9p21.3 and SMAD4 mTOR inhibitor (group VIII). Group V cell lines were responsive at the 18q21.2 region. A real-time quantitative PCR reaction to mTOR inhibitors and mostly unresponsive to EGFR and MEK of eight genes validated the gene expression microarray data inhibitors. (Supplementary Table S3). The activity of EGFR tyrosine kinase inhibitors afatinib, caner- Finally, to identify the genes that might be biomarkers for drug tinib, erlotinib, and gefitinib had a median DSS of >10, with response, within the amplified and deleted regions, we integrated canertinib (a pan-ErbB inhibitor) showing the highest activity drug-sensitivity data with the gene expression data. Only those (Supplementary Fig. S2; Supplementary Table S4). Many of the genes that showed marked CNAs in-cis and substantial changes in genomic alterations found in HNSCC may result in a persistent mRNA expression were included in this analysis. Significant drug- activation of the PI3K–AKT–mTOR pathway (11). In accordance sensitivity-gene expression-CNA triads are presented in Supple- with this, the cell lines often responded well to five of six tested mentary Table S11. The most common genetic alterations and mTOR inhibitors (AZD8055, everolimus, ridaforolimus, siroli- their association with drug response to EGFR, MEK and mTOR mus, and temsirolimus) and to all tested PI3K/mTOR inhibitors inhibitors for the 45 HNSCC cell lines are presented in Fig. 2. (apitolisib, dactolisib, omipalisib, and PF-04691502). The median DSS to these drugs was >10 (Supplementary Figs. S1 and S2; Systematic genomic analyses integrated with mTOR and MEK Supplementary Table S4). Interestingly, all the other PI3K inhi- inhibitor response reveal candidate biomarkers bitors except pictilisib did not show substantial efficacy (the To find potential biomarkers for mTOR and MEK inhibitor median DSS <10). MEK inhibitors had variable efficacy, with a sensitivity, we searched for somatic mutations and copy-number DSS >10 obtained with pimasertib, refametinib, TAK-733, and aberrations that associated with drug sensitivity from the analyses trametinib. described above. In addition, the gene expression patterns of the six most sensitive and the six most resistant cell lines for these HNSCC cell lines resemble clinical HNSCC tumors in terms of inhibitor classes were compared. These drugs were selected, genetic changes and display alterations that predict for drug because they showed high selectivity across all cell lines, and response they are of clinical interest in the treatment of HNSCC. The cell lines were subjected to a genome-wide analysis of gene A 24-gene signature discriminated the high and poorly respon- copy-number gains and losses using an Agilent Human Genome sive cell lines to six mTOR inhibitors (Fig. 3A; Supplementary CGH 244A Oligo Microarray and targeted to exome sequencing Table S12), from which everolimus and temsirolimus are cur- of 694 genes to correlate the genomic alterations with drug rently being evaluated as therapeutic options in combination with sensitivity. In general, the detected mutations, which occurred chemotherapy, radiotherapy, or other molecularly targeted drugs

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Figure 1. Genomic alterations in 45 HNSCC cell lines. A, Frequency plot of copy-number amplifications and deletions. Amplifications are shown in red and deletions in blue. The vertical axis shows the proportion of cell lines with a particular gain or loss. The horizontal black lines marks the 10% cutoffs that were used to include an alteration for further analyses. B, The most common mutations observed in the 45 HNSCC cell lines. The figure shows the genes that harbored 5 distinct mutations. The types of mutations are indicated with different shades of brown. The percentages (left) indicate the proportion of the cell lines with a mutation (one cell line may have 1 mutation). The proportion of HNSCCs with a mutation in the TCGA database is shown for comparison (the percentages on the right). Silent mutations were excluded. C, Coding mutations observed in NOTCH1 and FAT1 across the cell lines are shown. Splice-site mutations are excluded from the figure. Green indicates missense mutation, black truncating mutations, and violet other coding mutations. The image was created using MutationMapper at cBioPortal.

in HNSCC (35). One of these genes was oxysterol binding protein lines in our cohort, a larger series of samples is needed to further like 1A (OSBPL1A), a gene involved in lipid metabolism. The assess the statistical significance of the alterations. As an example, integrative analysis of CNA-gene expression and DSS revealed that UT-SCC cell lines that harbored a stop-gain mutation in FAT1 the expression of OSBPL1A was affected by deletion in a group of (present in 9% of the cell lines) showed a tendency for a higher poorly responsive cell lines. In comparison with the gene expres- sensitivity to temsirolimus as compared with the other cell lines sion profiles of the six most sensitive and resistant cell lines for six (Wilcoxon rank-sum test P < 0.05, Fig. 3D; Supplementary MEK inhibitors, 11 genes, including EFNB1 and STAT6, showed Table S7). higher levels of expression in the cell lines sensitive to MEK Mutations in NOTCH1, which is one of the most commonly inhibitors as compared with cell lines resistant to these drugs mutated genes in HNSCC, were associated with a poor response to (Fig. 3B; Supplementary Table S12). refametinib (Wilcoxon rank-sum test P < 0.05; Fig. 3C and D). Regarding CNAs, deletion of PTPRD at 9p24.1, present in nine Interestingly, mutations in NAV3 were associated with poorer cell lines, was statistically significantly associated with poor efficacy of four of the six MEK inhibitors tested (Wilcoxon rank- sensitivity to MEK inhibitor refametinib as assessed by Wilcoxon sum test P < 0.05; Supplementary Table S7; Fig. 3C). Cell lines rank-sum test (Q < 0.1; Supplementary Table S8; Fig. 3C). with CDKN2A mutation, which occurred in 20% of our cell lines, We found no significant associations between responses to had tendency for poor sensitivity to the MEK inhibitor trametinib mTOR or MEK inhibitors with mutations after correcting for (Wilcoxon rank-sum test P < 0.05, Fig. 3D; Supplementary Table multiple hypothesis testing (permutation test Q > 0.1). However, S7). On the other hand, CDKN2A deletions or decreased expres- as specific genes have mutations in relatively small number of cell sion did not show significant associations with drug response.

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Figure 2. Associations between sensitivity to EGFR, MEK, and mTOR inhibitors and selected genetic alterations in HNSCC cell lines. The cell lines investigated are shown on the horizontal axis. On the vertical axis are shown a total number of mutations from a panel of 694 genes, as well as the investigated drugs and the genes. The top panel depicts the numbers and the types of mutations in each cell line. The center panel shows a heatmap of the differential DSSs (dDSS) for selected drugs. The bottom panel shows selected genomic alterations for frequently mutated or copy-number-altered genes. The ordering of the cell lines is based on the dDSS clustering.

Novel biomarkers predict response to EGFR inhibitors gefitinib (r ¼ 0.49; permutation P < 0.001, Q > 0.2; Supplementary EGFR status has not been considered as a reliable biomarker for Table S11). This gene is amplified in 12% of HNSCCs (13). response to EGFR inhibitors in HNSCC (36). In our study, cell Moreover, FAM83H silencing made UT-SCC-14 cells more resis- lines with EGFR amplification and overexpression were weakly tant to afatinib and erlotinib (Supplementary Fig. S3E–S3I). associated with sensitivity to selected inhibitors, such as erlotinib To validate the gene expression results for RAB25, TMEM125, (r ¼ 0.36) and canertinib (r ¼ 0.30; Supplementary Table S11). and FAM83H in an independent data set, we determined drug- Most cell lines with increased EGFR copy number showed sensi- sensitivity calls (sensitive, intermediate, or resistant) in the Cancer tivity to EGFR inhibitors, but in line with previous studies, the Cell Line Encyclopedia (CCLE; ref. 37), which provides genetic result was not conclusive (Fig. 4A). and expression data for 490 cell lines together with their response Interestingly, when we compared the gene expression between to EGFR inhibitor erlotinib. We used 3-bin k-means clustering of cell lines responsive and resistant to EGFR inhibitors, the cell lines the logIC50 values. The sensitivity to erlotinib and expression of sensitive to EGFR inhibitors showed high mRNA levels for several RAB25, TMEM125, and FAM83H were significantly associated in genes associated with membrane trafficking and transport, such as the CCLE data set (Fig. 4E). RAB25, CLIC3, SYK, SLC31A2, LYPD3, TMEM125, and TSPAN1 Similar to MEK inhibitors, mutations in NAV3 and NOTCH1 (Fig. 4B and C). Knockdown of RAB25 slightly reduced the showed tendency with a poor response to EGFR inhibitors sensitivity of UT-SCC-8 cells to erlotinib and afatinib (Supple- (P < 0.05; Supplementary Table S7; Fig. 4D) discriminating a mentary Fig. S3A–S3D and S3I). group of cell lines with poorer responses to both EGFR and MEK In the integrative analysis of drug sensitivity, gene expression, inhibitors. In addition, several CNA alterations were associated and gene copy numbers, FAM83H overexpression, which was with response to EGFR inhibitors afatinib (Fig. 4D), canertinib, caused by gene amplification, correlated with enhanced sensitiv- gefitinib, and erlotinib (Supplementary Table S8). Cell lines with ity to EGFR inhibitors afatinib (r ¼ 0.53, permutation P ¼ 0.0001, MYC amplification showed tendency for better response to EGFR Q ¼ 0.2, Fig. 4D), erlotinib (r ¼ 0.53), canertinib (r ¼ 0.51), and inhibitors gefitinib, afatinib, and erlotinib as compared with the

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Figure 3. Selected genetic alterations that are associated with sensitivity to mTOR and MEK inhibitors. A and B, Unsupervised clustering of differentially expressed genes associated with sensitivity to mTOR inhibitors (OSI-027, AZD8055, sirolimus, temsirolimus, ridaforolimus, and everolimus) and MEK inhibitors (refametinib, binimetinib, selumetinib, trametinib, pimasertib, and TAK-733), respectively. Hierarchical clustering is based on the Euclidian distance calculated with an average linkage clustering algorithm. The color coding was scaled separately for each row to demonstrate the differences in gene expression. Red color indicates overexpression and green underexpression. The high and the low drug-sensitivity groups each contain six cell lines with the highest and the lowest class-speciﬁc DSS. C, An oncoprint of NOTCH1, NAV3, and PTPRD alterations across the 45 HNSCC cell lines. Individual genes are represented as rows, and the cell lines are represented as columns. The cell line order is based on sensitivity to refametinib. The oncoprint was created by using the cBioPortal. D, Sensitivity to temsirolimus, trametinib, and refametinib expressed as DSS value of cell lines, grouped according to FAT1, CDKN2A, and NOTCH1 mutation status. Wilcoxon rank-sum test P ¼ 0.02 (FAT1), 0.04 (CDKN2A), and 0.03 (NOTCH1).

other cell lines (Supplementary Table S8). FAM83H was coam- platform (DSRT) and combined the drug-sensitivity information plified with MYC (Fig. 4D), which might explain the association with detailed genomic and transcriptomic data, which allowed us between MYC amplification and EGFR inhibitor response. In line to find novel molecular associations for drug responses. We found with these results, there was also a significant correlation between several genomic alterations such as mutations in NOTCH1 and FAM83H and MYC amplification in The Cancer Genome Atlas FAT1 and deletions in PTPRD1, which showed tendency with (TCGA) HNSCC study (13) when we analyzed the cBioPortal poor efficacy of EGFR and MEK inhibitors. In addition, over- database (Fisher exact test, P < 0.001). expression of FAM83H and amplification of MYC showed tendency for improved sensitivity to EGFR inhibitors. To our knowl- Discussion edge, the present study is the largest study on HNSCC that integrates genetic information with drug responses. Apart from cetuximab and immune checkpoint inhibitors (6, 7, A limitation of this study is that only cell lines were tested, and 38), no other targeted drugs to treat advanced HNSCC are the number of single alterations was low, affecting the power of currently available. We studied 45 HNSCC cell lines established the statistical analyses. However, testing of a large number of from HPV-negative HNSCCs on a high-throughput drug-testing compounds and biomarkers is challenging in clinical trials or in

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Figure 4. Selected genetic alterations associated with response to EGFR inhibitors. A, A waterfall plot showing the cell line median sensitivity to EGFR inhibitors across the 45 HNSCC cell lines. The cell lines that harbor a CNA gain for EGFR are shaded. Cell lines have been ordered by the differential DSS (dDSS) calculated by comparing the DSS of particular HNSCC cells to the mean DSS over all the cell lines. B, Unsupervised clustering of differentially expressed genes associated with sensitivity to EGFR inhibitors (afatinib, canertinib, gefitinib, and erlotinib). Hierarchical clustering is based on a Euclidian distance obtained with an average linkage clustering algorithm. The color coding was scaled separately for each row to better show differences in gene expression. Red color indicates overexpression and green underexpression. The high and the low drug-sensitivity groups each contain six cell lines with the highest and the lowest DSSto EGFR inhibitors. C, Box plots showing expression of selected genes involved with cell membrane trafficking. Expression of each gene is associated with sensitivity to EGFR inhibitors. D, An oncoprint of NOTCH1, NAV3, MYC, and FAM83H alterations across the 45 HNSCC cell lines. Individual genes are represented as rows, and cell lines as columns. The cell line order is based on afatinib sensitivity. The oncoprint was created using the cBioPortal. E, Box plots showing expression of selected three genes in the CCLE data set related to erlotinib sensitivity. High expression of each gene (RAB25, TMEM125,andFAM83H) was significantly associated with sensitivity to erlotinib (Wilcoxon rank-sum test; ns, not significant; , P ¼ 0.003; , P < 0.001).

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xenograft models due to financial and other constraints. Repeated breast cancer cells to PI3K inhibitors (50). Only 3 of the 24 cell expansion of the cell lines likely results in adaptive changes in lines harboring mutations either in NOTCH1 or in the PI3K/ gene expression, but many of the cell lines used in this study had mTOR pathway had mutations in both, which is in line with low passage numbers. Prior studies on cancer cell lines generally previous clinical data showing no correlation between the PI3K/ agree with their value in drug discovery for molecularly targeted mTOR pathway and NOTCH1 mutations (11). Taken together, therapies (37, 39–43). Genomic characterization of the cell lines the results support an important role for NOTCH1 signaling in the corresponded well with the mutation spectrum observed in genesis of some HNSCC and as a potential drug target and human HNSCCs (13), suggesting that the cell lines recapitulate biomarker. oncogenic alterations observed in clinical tumor samples. Recog- In summary, the current comprehensive genomic analysis nizing that the translation of potential associations detected in integrated with high-throughput drug testing on a large panel preclinical models to clinically useful biomarkers may be chal- of HNSCC cell lines identified several biomarkers for EGFR, MEK, lenging and requires validation (42, 43), the integration of data and mTOR inhibitor efficacy in HNSCC. These biomarkers includ- obtained from DSRT and genomic characterization appears a ed genetic alterations such as NOTCH1 and NAV3 mutations, powerful strategy to identify systemic treatment options and MYC and FAM83H amplifications, and PTPRD deletion. These potential biomarkers (27, 28). potential biomarkers deserve further validation in clinical series, EGFR inhibitors are currently the only targeted kinase inhi- because robust biomarkers for EGFR, MEK, and mTOR inhibitor bitors that have been approved for the treatment of advanced efficacy are lacking. HNSCC and have modest efficacy. In HNSCC, EGFR expression or an increased EGFR gene copy number has been extensively Disclosure of Potential Conflicts of Interest studied as biomarkers for response to EGFR inhibitors, but they H. Joensuu is Vice President at and is a consultant/advisory board member are not predictive or prognostic (36). In our study, EGFR for Orion Pharma and has ownership interest (including stock, patents, etc.) in amplification or overexpression was weakly associated with Sartar Therapeutics. No potential conflicts of interest were disclosed by the other sensitivity to selected EGFR inhibitors, but in line with the authors. previous studies, the results were not conclusive. On the other hand, cell lines sensitive for EGFR inhibitors had high mRNA Authors' Contributions levels for genes involved in membrane trafficking and receptor Conception and design: T. Lepikhova, P.-R. Karhemo, H. Joensuu, Outi Monni and membrane transport, suggesting a role for the plasma Development of methodology: T. Lepikhova, R. Grenman, K. Wennerberg fi Acquisition of data (provided animals, acquired and managed patients, membrane protein organization and EGFR traf cking in drug provided facilities, etc.): T. Lepikhova, P.-R. Karhemo, A. Murum€agi, – response. For example, the EGFR inhibitor sensitive cell lines E. Kulesskiy, H. Sihto, R. Grenman, S.M. Syrj€anen, O. Kallioniemi, H. Joensuu, showed 8 times higher RAB25 mRNA levels as compared with Outi Monni the insensitive cell lines. In agreement with this finding, Rab25 Analysis and interpretation of data (e.g., statistical analysis, biostatistics, was found to influence EGFR endocytosis and gefitinib efficacy computational analysis): T. Lepikhova, P.-R. Karhemo, R. Louhimo, B. Yadav, (44). Interestingly, the cell lines with MYC amplification E. Kulesskiy, M. Kivento, H. Sihto, T. Aittokallio, K. Wennerberg, H. Joensuu, fi Outi Monni responded signi cantly better to EGFR inhibitors as compared Writing, review, and/or revision of the manuscript: T. Lepikhova, – with other cell lines. In a series with non smallcelllungcancer P.-R. Karhemo, R. Louhimo, B. Yadav, A. Murum€agi, E. Kulesskiy, M. Kivento, patients, a high MYC copy number correlated with high sen- H. Sihto, R. Grenman, S.M. Syrj€anen, O. Kallioniemi, T. Aittokallio, sitivity to EGFR inhibitors (45). K. Wennerberg, H. Joensuu, Outi Monni An example of a novel molecular association with EGFR Administrative, technical, or material support (i.e., reporting or organizing inhibitor response in the present study is amplification and data, constructing databases): P.-R. Karhemo, M. Kivento, O. Kallioniemi, FAM83H H. Joensuu, Outi Monni mRNA overexpression of .TheFAM83proteinswere Study supervision: H. Joensuu, Outi Monni fi recently identi ed as novel transforming oncogenes that pro- Other (coordination and detailed planning of the project): T. Lepikhova mote resistance to EGFR tyrosine kinase inhibitors in breast Other (data integration): B. Yadav cancer (46). However, suggesting that the effects of FAM83 Other (HPV genotyping): S.M. Syrj€anen proteins on drug sensitivity might vary for different FAM83 family members and might be tissue-type dependent, the Acknowledgments detailed molecular mechanisms that explain these associations We thank the Biomedicum Functional Genomics Unit (FuGU), FIMM High- remain to be elucidated. Throughput Biomedicine Unit, and FIMM Sequencing Unit at the University of Head and neck cancers harbor a wide spectrum of genetic Helsinki, Finland, for excellent technical assistance. This work was financially supported by Academy of Finland (O. Monni, alterations, and observations from several studies point to the H. Joensuu, T. Aittokallio, and K. Wennerberg; Center of Excellence for Trans- importance of mutations in TP53, NOTCH1, CDKN2A, PIK3CA, lational Cancer Biology, O. Kallioniemi, H. Joensuu), Jane & Aatos Erkko FBXW7, and HRAS in the molecular pathogenesis of HNSCC Foundation (O. Monni, H. Joensuu, and K. Wennerberg), Finnish (10–13). In the present study, NOTCH1 was the second most Cancer Society (O. Monni, H. Joensuu, T. Aittokallio, K. Wennerberg, and commonly mutated gene (present in 29% of the cell lines), and O. Kallioniemi), Sigrid Juselius Foundation (H. Joensuu, K. Wennerberg, the mutations were mostly found in cell lines with poor sensitivity and O. Kallioniemi), and Research Funds of Helsinki University Hospital (H. Joensuu). to EGFR and MEK inhibitors. NOTCH1 is regulated by TP53, and – it has both oncogenic and tumorigenic functions (47 49). The costs of publication of this article were defrayed in part by the payment of NOTCH1 is involved in squamous cell differentiation, and in page charges. This article must therefore be hereby marked advertisement in keratinocytes increased NOTCH activity reduces proliferation and accordance with 18 U.S.C. Section 1734 solely to indicate this fact. promotes differentiation, whereas loss of NOTCH1 promotes carcinogenesis (47–49). NOTCH1 activity and concurrent Received August 3, 2017; revised December 15, 2017; accepted June 21, 2018; downstream activation of MYC are associated with resistance of published first July 2, 2018.

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Drug-Sensitivity Screening and Genomic Characterization of 45 HPV-Negative Head and Neck Carcinoma Cell Lines for Novel Biomarkers of Drug Efficacy

Tatiana Lepikhova, Piia-Riitta Karhemo, Riku Louhimo, et al.

Mol Cancer Ther 2018;17:2060-2071. Published OnlineFirst July 3, 2018.

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