Diverse, Biologically Relevant, and Targetable Gene ...by EcoRI/SalI restriction digest and ligation (New England BioLabs). The complete pBABE-puro-TMEM87B-MERTK ex-pression construct

Therapeutics, Targets, and Chemical Biology

Diverse, Biologically Relevant, and TargetableGene Rearrangements in Triple-Negative BreastCancer and Other MalignanciesTimothy M. Shaver1,2, Brian D. Lehmann1,2, J. Scott Beeler1,2, Chung-I Li3, Zhu Li1,2,Hailing Jin1,2, Thomas P. Stricker4, Yu Shyr5,6, and Jennifer A. Pietenpol1,2

Abstract

Triple-negative breast cancer (TNBC) and other molecularlyheterogeneous malignancies present a significant clinical chal-lenge due to a lack of high-frequency "driver" alterations amena-ble to therapeutic intervention. These cancers often exhibit geno-mic instability, resulting in chromosomal rearrangements thataffect the structure and expression of protein-coding genes. How-ever, identification of these rearrangements remains technicallychallenging. Using a newly developed approach that quantita-tively predicts gene rearrangements in tumor-derived geneticmaterial,we identified and characterized anovel oncogenic fusioninvolving the MER proto-oncogene tyrosine kinase (MERTK) and

discovered a clinical occurrence and cell line model of the target-able FGFR3–TACC3 fusion in TNBC. Expanding our analysis toother malignancies, we identified a diverse array of novel andknown hybrid transcripts, including rearrangements betweennoncoding regions and clinically relevant genes such as ALK,CSF1R, and CD274/PD-L1. The over 1,000 genetic alterationswe identified highlight the importance of considering noncod-ing gene rearrangement partners, and the targetable genefusions identified in TNBC demonstrate the need to advancegene fusion detection for molecularly heterogeneous cancers.Cancer Res; 76(16); 4850–60. �2016 AACR.

IntroductionDespite advances in precision medicine, the treatment of

many molecularly heterogeneous cancers remains challengingdue to a lack of recurrent alterations amenable to therapeuticintervention. To make significant clinical advances againstthese recalcitrant cancers, integrative genomic and molecularanalyses are required to understand their complexity and toidentify targetable features arising from lower-frequency genet-ic events. Our laboratory has identified six molecular subtypesof one such heterogeneous disease, triple-negative breast cancer(TNBC), with each subtype displaying unique ontologies anddifferential response to standard-of-care chemotherapy (1, 2).Ongoing genomic analysis of TNBC has identified a low fre-

quency and widely varying clonality of therapeutically action-able alterations across subtypes, including mutations inPIK3CA and BRAF, amplification of EGFR, loss of PTEN, andexpression of androgen receptor (AR; refs. 3–5). However, asignificant proportion of TNBC cases lack somatic alterations ofany established "driver" gene, highlighting the importance ofcontinued genomic analysis and discovery integrated withmolecular profiling of the tumor microenvironment (3).

A defining feature of TNBC and other clinically challenging,molecularly heterogeneous cancers such as ovarian carcinomaand lung squamous cell carcinoma is copy number alteration(CNA), which is frequently accompanied by mutation or inac-tivation of the p53 tumor suppressor (4, 6–8). The genomicinstability that gives rise to CNA can also result in chromo-somal rearrangements and gene fusions, which have long beenrecognized as oncogenic drivers and effective drug targets in avariety of hematological and solid malignancies (9). In recentyears, the advent of next-generation sequencing (NGS) hasenabled the identification of a number of gene fusion eventsin epithelial cancers, with varying frequency and therapeuticrelevance (10–14).

In order to broaden our understanding of the somatic altera-tions underlying TNBC, we sought to identify known and novelgene rearrangements affecting the structure and/or expressionlevel of protein-coding transcripts. While a number of compu-tational approaches have been developed to identify hybridRNA and DNA sequences using NGS data, numerous technicalhurdles complicate accurate and efficient gene rearrangementdetection (15). For instance, widespread regions of sequencehomology and current limitations in read length result insometimes-ambiguous alignment and a large number of falsepositives. To combat this, many detection methodologies use

1Department of Biochemistry,Vanderbilt University, Nashville,Tennes-see. 2Vanderbilt-Ingram Cancer Center,Vanderbilt University MedicalCenter, Nashville, Tennessee. 3Department of Statistics, NationalCheng Kung University, Tainan, Taiwan. 4Department of Pathology,Microbiology, and Immunology,Vanderbilt University Medical Center,Nashville, Tennessee. 5Center for Quantitative Sciences, VanderbiltUniversity Medical Center, Nashville, Tennessee. 6Department of Bio-statistics, Vanderbilt University Medical Center, Nashville, Tennessee.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

T.M. Shaver and B.D. Lehmann contributed equally to this article.

Corresponding Authors: Jennifer A. Pietenpol, Vanderbilt-Ingram CancerCenter, 652 Preston Research Building, 2200 Pierce Avenue, Nashville,TN 37232-6838. Phone: 615-936-1512; Fax: 615-936-1790; E-mail:[email protected]; and Brian D. Lehmann,[email protected]

doi: 10.1158/0008-5472.CAN-16-0058

�2016 American Association for Cancer Research.

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on February 8, 2021. © 2016 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from

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filters designed to enrich for biologically relevant gene rearran-gements, such as restricting both potential fusion partners toprotein-coding loci (16). While these filters enrich for currentlyknown gene fusions, they are not effective for the discovery ofless canonical events, such as rearrangements between codingand noncoding regions of the genome. We developed a newalgorithm, Segmental Transcript Analysis (STA), which usesexon-level expression estimates to rank a population of sam-ples based on their likelihood of harboring a rearrangement ina given gene. We identified a number of known and novelrearrangements involving functionally diverse gene partners inTNBC, and expanded our analysis and discovery to a wider,multicancer cohort from The Cancer Genome Atlas (TCGA).The DNA-validated rearrangements that we identified includenoncoding portions of the genome acting as both 50 and 30

partners in clinically relevant hybrid transcripts.

Materials and MethodsCell culture

SUM185PE (Asterand, March 2010) andMCF10A cells (ATCC,June 2012) were cultured as previously described (1). Ba/F3 cells(provided by Dr. Christine Lovly, Vanderbilt University, Novem-ber 2014) were cultured in RPMI þ GlutaMAX (Gibco 61870)with 1 ng/mL IL3 (Life Technologies) and 5% (v/v) FBS(Gemini). All cell lines were maintained in 100 U/mL peni-cillin and 100 mg/mL streptomycin (Gemini) and tested neg-ative for mycoplasma (Lonza). Cell Line Genetics performedpositive short tandem repeat DNA fingerprinting analysis onSUM185PE (March 2011); cells were cultured for fewer than2 months after identification. We also verified SUM185PE bymanual identification of unique variants in NGS data. DNAfingerprinting analysis was not performed on MCF10A or Ba/F3 cells, but the Ba/F3 cell line displayed the previouslypublished IL3 dependence phenotype (17).

FGFR3/TACC3 siRNA transfectionSUM185PE cells (8,000 cells/well) were reverse transfected

in a 96-well format with RNAiMax (0.1 mL, Life Technologies)and siRNAs (1.25 pmole) targeting the FGFR3 exon 4/5 junc-tion (Dharmacon D-003133-06), FGFR3 exon 11 (D-003133-05), TACC3 exon 13 (D-004155-03), TACC3 30 UTR (D-004155- 04), TACC3 exon 4 (D-004155-01), or TACC3 exon5 (D-004155-02; siRNA #1-6, respectively). AllStars NegativeControl siRNA and AllStars Hs Cell Death Control siRNA(Qiagen) were included as experimental controls. Viability wasassessed at 72 hours by incubating cells with alamarBlue(Invitrogen). For each experiment, the mean fluorescence value(Ex/Em: 560/590 nm) of four wells per condition was normal-ized to the negative control.

IC50 determinationSUM185PE cells were seeded in quadruplicate (8,000 cells/

well) in 96-well plates. After overnight attachment, growth medi-um was replaced with medium (control) or medium containinghalf-log serial dilutions of the FGFR inhibitor PD173074 (Sell-eckchem). Viability was assessed at 72 hours by incubating cellswith alamarBlue (Invitrogen). Half-maximal inhibitory con-centration (IC50) values were determined after normalization tountreated wells and double-log transformation of dose responsecurves as previously described (18).

Ba/F3 IL3 withdrawalStably transfected Ba/F3 cells were seeded in 6-well plates

(40,000 cells/well) in growth medium � 1 ng/mL IL3. Live-cellcounts were manually determined at three sequential 24-hourtimepoints by visual inspection using a hemocytometer andTrypan blue (Bio-Rad).

Cloning and generation of stable cell linesThe TMEM87B-MERTK expression construct, corresponding

to amino acids 1-55 of TMEM87B (NM_032824) and aminoacids 433-1000 of MERTK (NM_006343), was synthesized in apMK-RQ-Bb vector using GeneOptimizer and GeneArt (LifeTechnologies) and cloned into pBABE-puro (19) (Addgene)by EcoRI/SalI restriction digest and ligation (New EnglandBioLabs). The complete pBABE-puro-TMEM87B-MERTK ex-pression construct sequence is available upon request. Togenerate stably transfected cell lines, pBABE-puro-TMEM87B-MERTK or pBABE-puro empty vector retroviruses were pack-aged in Phoenix cells (Orbigen) and Ba/F3 and MCF10Acells were transduced with virus for two 24-hour intervals with8 mg/mL polybrene (Sigma), then selected and maintainedwith 1.5 mg/mL (Ba/F3) or 0.5 mg/mL (MCF10A) puromycin(Sigma).

ImmunoblottingSUM185PE cells were lysed 72 hours after siRNA transfection.

Ba/F3 cells were grown in suspension and incubated for 90minutes in the indicatedmedia before lysis. MCF10A cells seededin 10-cm plates were incubated for 180 minutes in the indicatedgrowth media before in-well lysis. All cells were lysed in RIPAbuffer supplemented with protease and phosphatase inhibitors.Cell lysates [40 mg (SUM185PE) or 30 mg (Ba/F3 and MCF10A)]were separated on polyacrylamide gels and transferred to poly-vinyl difluoride membranes (Millipore). Immunoblotting wasperformed using FGFR3 B-9 (1:200, Santa Cruz), GAPDHMAB374 (1:1000, Millipore), MERTK D21F11 (1:1000, CellSignaling Technology), phospho-Akt Ser473 D9E (1:2000, CellSignaling Technology), total Akt (1:1000, Cell Signaling 9272),phospho-Erk1/2 Thr202/Tyr204D.13.14.4E (1:2000, Cell Signal-ing Technology), and total Erk1/2 3A7 (1:2000, Cell SignalingTechnology).

Segmental Transcript Analysis algorithmThe median-normalized, exon-level RPKM vector for sample

i in gene k is denoted as gRPKMik. The Fscore for sample i in gene k isdefined as

Fscoreik ¼ dGikDi

stdev8i

dGikDi

where dGik is the geometric mean of dð gRPKM ik; gRPKM i'kÞ, 8i „ i'and d(x, y) is a distance measurement (i.e., Euclidean distance)between x and y.Di is the biggest difference of RPKMwith lag 1 insample i and stdev8idGikDi is the standarddeviationof dGikDi for genek. For ranking Fscore across genes, the normalized Fscore isdefined as

Gscoreik ¼Fscoreik �min

8i;8kFscoreik

max8i;8k

Fscoreik �min8i;8k

Fscoreik:

Diverse and Targetable Gene Rearrangements in Cancer

www.aacrjournals.org Cancer Res; 76(16) August 15, 2016 4851




For ranking Gscore between samples, the Segmental TranscriptAnalysis score is defined as

STAscoreik ¼ log2

Gscoreik �mean8k

Gscoreik

stdev8k

Gscoreikþ 2

0@

1A;

wheremean8kGscoreik is themean ofGscore for all genes in samplei and stdev8kGscoreik is the standard deviation of Gscore for allgenes in sample i.

Corresponding R code is included as a supplementary file.Detailed descriptions of file sources, processing, and filtering areavailable in the Supplementary Methods.

Transcript and protein annotationGene locus and exon data were determined by GENCODE and

RefGene annotations as described in the SupplementaryMethods.For exon-level expression plots, the exon order is based onsequential order of exons in theRefGene annotation. Thepresenceof isoforms may cause deviation from the normal exon number-ing scheme. For hybrid transcript frame calls, the RefGene anno-tation was processed to generate starting and ending frame valuesof 0, 1, 2 (CDS) or �1 (UTR) for each exon. Due to the potentialfor multiple reading frames at a given coordinate, a hybridtranscript between two exon boundaries featuring any concor-dance of starting and ending CDS frame was annotated as in-frame. A hybrid transcript between two exon boundaries withnon-overlapping CDS frames was annotated as out-of-frame. Anexon boundary with exclusively UTR status was annotatedaccordingly.

The domains and protein features depicted in schematics wereobtained fromUniProtKB (20). Features were exclusively selectedfrom annotations with "Reviewed" status, and all coding featuresare to scale.

SUM185PE RNA sequencingTotal RNA was isolated from SUM185PE cells using RNeasy

(Qiagen). RNA quality was assessed by NanoDrop (Thermo)and Bioanalyzer (Agilent). Two micrograms of total RNA wasused for the TruSeq Stranded Total RNA Library Prep Kit(Illumina). Libraries were quantified by Qubit (Life Techno-logies) and qPCR, and library size and quality were assessedby Bioanalyzer (Agilent). The constructed RNA-seq librarywas sequenced at the Vanderbilt Technologies for AdvancedGenomics (VANTAGE) core on an Illumina HiSeq 2500 usinga paired-end 100-bp protocol. Reads were de-multiplexedand trimmed using SeqPrep. FASTQ files are deposited inthe NCBI Read Sequence Archive under accession numberSRP077076.

SUM185PE RNA-seq reads were aligned using the TCGA RNA-seq v2 pipeline (https://cghub.ucsc.edu/, July 31, 2013 revision)to ensure compatibility with TCGA data. Reference data andcustom scripts for exon-level expression quantification weredownloaded from the UNC database as referenced in the pro-tocol. Alignment was conducted using the default TCGA work-flow and MapSplice v12_07, RSEM v1.1.13, and UBU v1.2. Cellline RNA-seq data were grouped with the TCGA BRCA dataset forSTA input and processed using the STA discovery pipelinedescribed in the Supplementary Methods.

Shah and colleagues data acquisitionRNA-seq data for 80 TNBC clinical specimens (3) were down-

loaded from the European Genome-phenome Archive asEGAS00001000132 on May 18, 2014. Aligned BAM files wereconverted to fastq using bedtools v2.17.0 and were aligned andprocessed as described in the Supplemental Methods.

Statistical analysisAll analyses and graphical representations were performed

using R v3.2.0. Post-siRNA viability ratios were plotted as themean� SEM of four independent experiments, and P values werecalculated using the Wilcoxon rank-sum test with Bonferronimultiple comparison adjustment. PD173074 IC50 values wereplotted as the mean � SEM of three independent experiments.Ba/F3 IL3 withdrawal data were plotted as the mean � SD oftwo conditions with three independent experiments across threedays, and P values were calculated using the restricted maximumlikelihood (REML)-based mixed effects model.

Supplementary informationAdditional data (Supplementary Figs. S1–S9), their corre-

sponding legends, and expanded details of sequence file proces-sing are found as supplementary files.

ResultsGene rearrangement prediction by STA

In order to prioritize downstream computation and minimizefilters restricting the genomic location of putative rearrangementpartners, we developed an algorithm known as Segmental Tran-script Analysis (STA) that uses a distance matrix approach togenerate aberrant transcript scores for a population of samples(Materials and Methods). Based on exon-level expression valuesacross a gene, STA is used to assign a normalized score for eachsample by quantifying deviation from the population in bothmagnitude and directionality of expression. This approach allowsthe detection of different structural classes of rearrangements—general up- or downregulation of a transcript might accompanypromoter or untranslated region (UTR) swapping, for instance,while abrupt gain or loss of expression from one exon to the nextcould result fromabreakpointwithin the interveningDNA.Whilepast discovery approaches using microarray datasets have lever-aged exon-level expression comparison in individual transcripts(21), the novel population-based comparison method used inSTA effectively controls for confounding issues such as alternativesplicing and normalization artifacts that cause uneven exon-levelexpression values across genes and facilitates detection of modestbut biologically relevant changes in transcript levels.

To evaluate the utility of STA as a prediction tool for bothknown and novel gene rearrangements, we developed a verticallyintegrated NGS analysis pipeline (Fig. 1). Briefly, exon-levelexpression data were collated for a given tumor type and STAscores were calculated for each sample on a per-gene basis (Fig. 1Aand B). RNA-seq data for each aberrant transcript passing adefined STA score threshold were assessed for evidence of rear-rangement between the candidate gene and another genomicregion (Fig. 1C), followed by analysis and realignment of nearbywhole-genome sequencing reads (WGS) to identify a DNA break-point with unique sequence spanning both rearrangement part-ners (Fig. 1D and E; ref. 22). Samples displaying a discretebreakpoint upon realignment were classified as DNA-validated

Shaver et al.

Cancer Res; 76(16) August 15, 2016 Cancer Research4852




rearrangements. While reliant upon the availability and adequatesequencing depth of WGS data, this approach provides indepen-dent structural evidence for the validity of any detected rearrange-ments. As a consequence, rearrangements that might be omittedin an RNA sequencing-only approach due to concerns abouthomology and false positivity, such as hybrid transcripts betweencoding and noncoding regions of the genome, can be identifiedwith greater confidence.

Using test RNA-seq and WGS data from TCGA, we confirmedthe ability of STA to identify known gene fusions across multiplecancer types, including CD74–ROS1 in lung adenocarcinoma,NFASC–NTRK1 in glioblastoma, and TMPRSS2–ETV4 in prostateadenocarcinoma (Fig. 1 and Supplementary Fig. S1A and S1B;refs. 23–25). The algorithm also reliably identified therapeuticallyactionable anaplastic lymphoma kinase (ALK) fusions in lung

adenocarcinoma, although DNA validation was not available inall cases due to incomplete availability of WGS data (Supplemen-tary Fig. S1C; ref. 11). Numerous aberrant transcripts were detectedfor the RET receptor tyrosine kinase, which undergoes rearrange-ment in 10% to 20% of sporadic papillary thyroid cancers (Sup-plementary Fig. S1D; ref. 26). When possible, we obtained DNAvalidation for RET fusions in these samples, including knownrearrangements with CCDC6, ERC1, and NCOA4 (SupplementaryTable S1). To evaluate the ability of STA to predict fusions previ-ously identified in TCGA RNA-seq, we compared the STA scoredistribution of fusion transcripts across all genes (SupplementaryFig. S2A; ref. 27) and recurrently fused kinases (SupplementaryFig. S2B; ref. 28) to the distribution for all genes and samplesevaluated. In both cases, STA scores were significantly higher inthe rearrangement-harboring transcripts (P < 2.2 � 10�16).

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Figure 1.

Quantitative prediction by STA facilitates an integrated fusion detection pipeline. A–E, stepwise description of STA discovery pipeline with accompanyingschematics and example data. A, exon-level expression values for a population of samples plotted as continuous lines. Samples passing STA scorethreshold (B) are plotted in red and denoted by asterisks; samples below the threshold are plotted in black. B, ROS1 STA scores for each sampleplotted in descending order. Samples with an STA score of 2 or above are plotted in red; samples with an STA score below 2 are plotted in black.C, schematic of RNA-seq reads. Dotted lines, continuous read segments. D, schematic of discordant WGS read pairs. Thin lines, read pairs.E, schematic of breakpoint-spanning WGS reads identified after realignment. In C–E, colors indicate alignment location of individual read segments, asdepicted in the description at left. Schematics are not to scale.






Due to the ability of STA to detect aberrant loss of expression inaddition to gain, we hypothesized that inactivation of tumorsuppressors would constitute a substantial portion of STA-pre-dicted rearrangements. Indeed, the algorithm displayed a robustability to identify intragenic loss of expression of known tumorsuppressors. In a lung squamous cell carcinoma sample, weidentified a rearrangement resulting in early truncation of theSWI/SNF subunit ARID1A (Supplementary Fig. S3A; ref. 29). Weadditionally validated rearrangements with noncoding DNA inbladder and endometrial carcinoma resulting in the loss offunctional domains of the PI3K-negative regulator PTEN and thep300 histone acetyltransferase, respectively (Supplementary Fig.S3B and S3C; refs. 30, 31). In a kidney chromophobe tumor, weidentified a rearrangement between a noncoding portion ofchromosome 5 and the gene encoding the miRNA-processingenzyme DROSHA that results in its early truncation (Supplemen-tary Fig. S3D; ref. 32). Interestingly, inactivating DROSHA muta-tions have recently been reported in over 10% of cases of Wilmstumor, a pediatric kidney cancer (33).

A novel oncogenic kinase fusion in TNBCTo discover known and novel gene rearrangements in clinical

TNBC cases, we used NGS data from TCGA and performed STAprediction on 173 TNBC tumors. We identified and validated atthe DNA level a previously uncharacterized fusion involving theMER proto-oncogene tyrosine kinase (MERTK; ref. 34). In thisrearrangement, a nearby gene encoding the transmembrane pro-tein TMEM87B acts as 50 partner, breaking shortly after its signalpeptide and fusing with the late extracellular domain-codingportion of MERTK (Fig. 2A). The resulting fusion transcript dis-plays increased expression and retains the full transmembraneand intracellular kinase domains of MERTK, which is overex-pressed or ectopically expressed in numerous cancers (Fig. 2B;ref. 35). In order to assess if this truncated form of MERTK retainsits ability to activate the oncogenic MAPK/Erk and Akt signalingpathways (36), we engineered a retroviral expression constructencoding the tumor-derived TMEM87B–MERTK fusion. In theIL3-dependent Ba/F3 mouse lymphocyte cell line, stable expres-sion of TMEM87B–MERTK led to constitutively elevated levels of

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The TMEM87B–MERTK gene fusion in TNBC promotes constitutive oncogenic signaling and cell survival. A, diagram of the TMEM87B and MERTK proteins and theDNA-validated gene fusion protein product. Protein features are labeled. B, protein schematics indicating membrane topology (not to scale). Colors indicateprotein sequences as indicated. In A and B, dotted lines represent protein regions encoded by the gene fusion transcript. C, immunoblot analysis of theindicated proteins from Ba/F3 cells transfected with an empty vector or one expressing the TMEM87B–MERTK fusion gene. Cell lines were grown in thecontinuous presence of 5% FBS and 1 ng/mL IL3 (þ) or switched to 0.5% FBS and no IL3 (�) for 90 minutes. D and E, graphs depicting growth curves of Ba/F3 cellstransfected with the TMEM87B–MERTK fusion gene (solid line) or empty vector (dotted line). Cells were grown in the continuous presence of 1 ng/mL IL3(D) or switched to no-IL3 media at day 0 (E) and viable cell counts were obtained by hemocytometer with trypan blue exclusion at the indicated time points.Error bars, SD of three replicates and P values comparing the two conditions are specified at top left. F, immunoblot analysis of the indicated proteins fromMCF10Acells transfected with constructs identical to C. Cells were grown in complete growth media with 2.5% horse serum (þ) or switched to base media with 0.5%horse serum and no growth factor additives for 180 minutes (�). aa, amino acid; BRCA, breast invasive carcinoma; SP, signal peptide; TM, transmembrane.

Shaver et al.





phospho-Akt and retention of robust Erk and Akt signaling evenafter serum starvation and withdrawal of IL3 (Fig. 2C; ref. 17).Accordingly, while the TMEM87B–MERTK and empty vectorcontrol cells grew similarly in the presence of IL3, the fusionprotein conferred a clear survival advantage after IL3 withdrawal(Fig. 2D and E). Whereas the control cells died by day 3 after IL3withdrawal, the TMEM87B–MERTK-expressing cells proliferatedunder the same conditions (Fig. 2E) and could be cultured inthe absence of IL3 for at least 1 month (data not shown). Theseresults are consistent with the survival-promoting role of full-length MERTK in melanoma, glioblastoma, and other cancers(36–38). To verify that the fusion protein-modulated signalingcould be replicated in breast-derived, basal epithelial cells, weexpressed TMEM87B–MERTK in immortalizedMCF10A cells andobserved similar activation of Erk and Akt after serum starvationand growth factor withdrawal (Fig. 2F). Of note, we identified anidentical TMEM87B–MERTK fusion in the lung squamous cellcarcinoma RNA-seq data set from TCGA, along with a BCL2L11–

MERTK RNA transcript with the same breakpoint in bladdercarcinoma, but WGS data were not available for DNA validation(data not shown). An independent RNA-seq fusion analysis ofTCGA samples corroborated the TMEM87B–MERTK fusion inTNBC and identified identical rearrangements in cervical carci-noma and lung adenocarcinoma (27), demonstrating selectionfor a recurrently truncated form of MERTK in multiple cancertypes.

FGFR3–TACC3 gene fusion is a targetable driver alterationin TNBC

In order to identify and evaluate an endogenousmodel of onco-genic gene fusions in TNBC, we expanded our analysis to RNA-seqdata from 80 additional TNBC tumors (3) and 28 TNBC cellline models. In both a tumor specimen and the SUM185PEcell line, we discovered FGFR3–TACC3 fusions similar to theoncogenic rearrangements recently observed in glioblastoma andbladder carcinoma, which result in the fusion of the FGFR3

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Figure 3.

The FGFR3–TACC3 gene fusion is atargetable driver alteration in TNBC.A and B, diagram of the proteinproducts of the FGFR3–TACC3 genefusions found in a tumor sample fromTNBC patient TTR0001024(3) (A) andthe SUM185PE TNBC cell line (B).Protein features are labeled, anddotted lines outline protein regionsencoded by the gene fusion transcript.Numbers, amino acid position in thewild-type proteins; arrows, targetinglocations of the siRNAs used in theexperiments. C and D, immunoblotanalysis of the indicated proteins fromSUM185PE lysate (C) and relativeviability of the cells (D) after 72-hourtreatment with the indicated siRNAs(depicted inB), a nontargeting control(NT), or a cell death-inducing positivecontrol (CD). InC, the legend indicatesproteins expected to undergoknockdown based on siRNA targetlocation. Wild-type (WT) and fusedforms of FGFR3 are denoted by a filledand hollow arrow, respectively. In D,viability as assessed by alamarBlue isnormalized to the nontargetingcontrol. Error bars, SEM of fourindependent experiments. Asterisks,P < 0.001 when compared with NTcontrol. E, half-maximal inhibitoryconcentrations (IC50) of the FGFRinhibitor PD173074 for the indicatedcell lines as assessed by alamarBlueassay. Error bars, SEM of threeindependent experiments.






kinase domain to the coiled-coil domain of TACC3 (Fig. 3A and B;refs. 12, 39). To determine if FGFR3–TACC3 is a targetable"driver alteration" in TNBC, we conducted knockdown andpharmaceutical inhibition of the fusion protein in SUM185PEcells. Immunoblotting for FGFR3 in cell lysates produceddistinct bands consistent with the predicted molecular weightsof the wild-type and fused proteins (Fig. 3C). Two siRNAstargeting FGFR3 (Fig. 3B, siRNA #1-2) reduced expression ofboth forms of the protein and decreased cell viability after 72hours to levels comparable to a cell-death control (Fig. 3C andD). To verify that the viability decrease was due to the loss offused FGFR3 rather than wild-type, we assessed two siRNAstargeting the fused portion of TACC3 (Fig. 3B, siRNA #3-4) andtwo siRNAs targeting sequences outside the recombined region(Fig. 3B, siRNA #5-6). The two TACC3 siRNAs targeting aportion of the transcript contained within the fusion decreasedexpression of the resulting hybrid protein and reduced viabilityto a level similar to FGFR3 knockdown, whereas addition of thesiRNAs targeting a portion of TACC3 outside the fused regiondid not significantly decrease viability or fusion protein expres-sion (Fig. 3C and D). Additionally, SUM185PE cells displayedpronounced sensitivity to the FGFR inhibitor PD173074 (IC50

¼ 48 � 13 nmol/L), whereas the majority of other cell linestested displayed micromolar or greater half-maximal inhibitoryconcentrations (Fig. 3E; ref. 40). The only other line withsimilar sensitivity, MFM223, harbors a previously reportedamplification of FGFR2 (41).

Novel and noncanonical rearrangements in TNBCAdditional STA predictions from the TCGA TNBC clinical data

set led to the identification and DNA validation of a structurallyand functionally diverse array of gene rearrangements. In onesample, dual 50 UTR breakpoints result in promoter swappingbetween the myosin heavy-chain gene MYH9 and the histonemodifier gene NFYC, which was recently identified as an onco-gene in choroid plexus carcinoma (42). The resulting fusiontranscript is highly expressed and retains the entire NFYC openreading frame (Fig. 4A). In another rearrangement, the 55-kDaisoform of the transmembrane glycoprotein neuroplastin isfused to the C-terminus of the cilia-associated transcript CLUAP1,leading to a transcript encoding the signal peptide and a singleextracellular Ig-like domain from neuroplastin (Fig. 4B). Impor-tantly, a small portion of the retained Ig-like domain was previ-ously demonstrated to be sufficient for the FGFR1 activationexhibited by the full-length protein (43), implying that theNPTN–CLUAP1 gene fusion may lead to the secretion of a para-crine FGFR1 activator.

We also identified rearrangements in TNBC involvingimmune-related proteins. In one case, FBXO3 undergoes rear-rangement with the gene encoding the membrane attack com-plex inhibitor CD59, with resultant expression of a transcriptencoding the functional domains of CD59. Interestingly, theRNA breakpoint for CD59 occurs at a non-annotated splice sitewithin the coding sequence that precisely mimics the cleavagesite of the mature protein from a glycosylphosphatidylinositol

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TNBCs harbor a functionally diverse array of gene rearrangements. A–D, four examples of STA-predicted rearrangements in TNBCs from TCGA. Each panelfeatures an exon-level expression diagram and STA score plot for the gene and cancer type analyzed. Red, the representative DNA-validatedrearrangement that is depicted at the bottom of each panel as a schematic of the resulting aberrant protein. Blue, additional aberrant transcripts meeting STAscore threshold. Black, background population below threshold. Protein features and UTRs are labeled, and dotted lines indicate hybrid transcriptjunctions. aa, amino acid; BRCA, breast invasive carcinoma; Cyto, cytoplasmic domain; nt, nucleotide; SP, signal peptide; TM, transmembrane.

Shaver et al.





(GPI) anchor addition signal (Fig. 4C; ref. 44). The conse-quences of GPI anchor loss and the retention of a portion ofFBXO at the C-terminus were not assessed; however, solubleforms of CD59 have been previously noted to retain theircomplement-mediated cytotoxicity-suppressive function (45).In a final example, the gene encoding the interleukin 6 receptor(IL6R) breaks at the junction between its transmembrane andcytoplasmic domains and undergoes rearrangement with thenoncoding pseudogene RPL29P7 (Fig. 4D). Intriguingly, previ-ous studies have demonstrated that the cytoplasmic domainof IL6R is dispensable for its interaction with the gp130 trans-activator (46), and the resulting fusion transcript is highlyexpressed. We speculate that the IL6R–RPL29P7 fusion proteinretains the IL6-binding and transactivation capacity of wild-typeIL6R, and is overexpressed due to loss of the negative-regulatoryIL6R 30 UTR. This rearrangement illustrates a mechanism bywhich 30 hybrid transcript formation with noncoding regionsof the genome can lead to increased expression of tumor-promoting genes, similar to the increased expression resultingfrom 30 UTR loss in the FGFR3–TACC3 gene fusion (47) andconfirming findings from previous gene fusion discovery effortsin breast cancer tumors and cell lines (48, 49).

Functionally and structurally diverse rearrangements acrosscancer

Given the ability of STA to identify novel classes of rearran-gements in TNBC, we broadened our discovery efforts using 14additional NGS datasets from TCGA. In total, we analyzed 5461tumor samples with RNA-seq across 14 cancer types, of which1264 (23%) had accompanying WGS available (Supplemen-tary Fig. S4). We validated 1,178 gene rearrangements at theRNA and DNA levels (Supplementary Table S1). Of note, ourattempts to validate newly discovered rearrangements at theDNA level were confounded in part by variable WGS availabil-ity and sequencing depth across TCGA studies. We found a clearcorrelation between both the validation rate and frequency ofrearrangements detected per sample and WGS file size (Sup-plementary Figs. S5 and S6).

Fusions between two protein-coding genes constituted 40%of the validated rearrangements. Among the remaining rear-rangements of coding genes with noncoding DNA, a majorityfeatured the protein-coding gene as the 50 partner, as inferred byRNA breakpoints. For 3% of total rearrangements detected,however, noncoding regions of the genome acted as 50 hybridtranscript partners and caused deregulated expression of codinggenes (Supplementary Fig. S7A). To characterize the function ofgenes undergoing each rearrangement type, we classified rear-rangement partners according to a previously published onco-gene and tumor suppressor prediction method (50). Theenrichment pattern of genes in these categories was consistentwith tumor-promoting gain or loss of function (SupplementaryFig. S7B and S7C). Coding genes acting as the 30 transcriptpartner in out-of-frame fusions included a higher proportion oftumor suppressors, for example, whereas in-frame fusions andrearrangement at UTRs included more oncogenes (Supplemen-tary Fig. S7C).

Across tumor types, we noticed a trend of tumor-promotinggene overexpression by a structurally diverse set of gene rearran-gements. In a thyroid carcinoma sample, we identified a rear-rangement between a 50 portion of the long noncoding RNA(lncRNA)MALAT1 and the recurrently fused ALK (11), leading to

extremely high expression of a transcript retaining the ALK kinasedomain (Fig. 5A). Of note, the MALAT1–ALK rearrangementoccurs upstream of ALK exon 16 rather than the most commonexon 19 and 20breakpoints, but numerous in-framemethioninesin the exon 16 to 19 region could allow translation initiation afterthe noncoding, 50 MALAT1 portion of the hybrid transcript (datanot shown).We also identified abnormally high expression of theMAPK activator HRAS in a head and neck squamous cell carci-noma sample. WGS analysis revealed a rearrangement betweenRNH1 and a DNA breakpoint upstream of the HRAS transcrip-tional start site. The 50 UTR of RNH1 is subsequently spliced toexon 2 of HRAS, resulting in elevated transcription of a completeHRAS open reading frame (Fig. 5B). We noted a similar over-expression of embryonic stem cell–expressed Ras (ERAS) by thePQBP1 promoter in lung squamous cell carcinoma, but the DNAbreakpoint for that sampleoccurredwithin thefirst intronofERAS(Supplementary Table S1). Although KRAS fusions have beendescribed in metastatic prostate cancer (10), we believe these arethe first DNA-validated fusions involving HRAS and ERAS to bereported. A similar rearrangement leading to overexpression ofthe entire HRAS coding sequence was identified in a murineMoloney leukemia virus–induced cell line and showed the abilityto transform NIH 3T3 fibroblasts (51).

DNA breakpoints upstream of the transcriptional start site, asseen in RNH1–HRAS, can lead to extreme overexpression that iseasily detectable by STA. In an estrogen receptor (ER)–positivebreast cancer sample, the ER-responsive gene RARA displayedrearrangement with a region upstream of PRR11, leading to morethan 10-fold increase of PRR11 transcript compared with thepopulation average (Fig. 5C). While PRR11 is a relatively under-studied cell cycle progression gene with normally periodic expres-sion, it is overexpressed in breast and lung cancer and PRR11knockdown inhibits cancer cell proliferation (52, 53). A similarrearrangement and consequent increase in transcript levelsoccurred in a thyroid carcinoma, where PAX8, the 50 partner inthe recurrent PAX8–PPARG thyroid fusion, is spliced to the 50 UTRof GLIS1, a transcription factor whose expression was previouslycorrelated with Wnt pathway activation and epithelial-to-mesen-chymal transition in a mouse model of breast cancer (Fig. 5D;refs. 54, 55).

The noncanonical rearrangements identified using STA alsoinclude clinically relevant immune-modulatory genes. In ahead and neck squamous cell carcinoma sample, we identifieda hybrid transcript in which a noncoding portion of chromo-some 16 is fused to the 50 UTR of the gene encoding the colonystimulating factor 1 receptor (CSF1R), leading to overexpres-sion of a complete CSF1R-coding transcript (Fig. 5E; ref. 56).Additionally, a colorectal cancer sample with abnormally highexpression of CD274, which encodes the T-cell suppressor PD-L1, harbors a rearrangement between the second-to-last exonof CD274 and a noncoding region of chromosome 9 (Fig. 5F;ref. 57). The resulting hybrid transcript encodes a near-com-plete copy of PD-L1; as in the previous IL6R–RPL29P7 rear-rangement, we speculate that loss of the endogenous negative-regulatory 30 UTR of CD274 leads to the increase in transcriptlevels.

DiscussionThe wide spectrum of rearrangements identified using STA

demonstrates the ability of oncogenic selection to exploit the






modular architecture of the human genome to ensure tumorproliferation and survival. While some novel rearrangementsemerging from our analysis consisted of classic receptor tyrosinekinase fusions, such as TMEM87B–MERTK in TNBC (Fig. 2), wealso observed overexpression of entire coding transcripts resultingfrom promoter and UTR swapping, including oncogenicRas family members (Fig. 5B). Importantly, our ability to assessrearrangements with noncoding regions led to the detectionof not only inactivating truncations in tumor suppressors,but also gain-of-function events. For instance, the gene encodingthe tumor suppressor PD-L1, which has emerged as a primetarget in the rapidly advancing field of cancer immunotherapy,underwent rearrangement and overexpression of a transcriptharboring a noncoding sequence in place of its endogenous,negative-regulatory 30 UTR (Fig. 5F; ref. 58). We observedsimilar rearrangements multiple times in our analyses, includingthe IL6R–RPL29P7 fusion in TNBC (Fig. 4D), and we hypothesizethat overexpression by this mechanism may explain many of

the rearrangements occurring between oncogenes and noncodingregions of the genome (Supplementary Fig. S7B). The MALAT1–ALK lncRNA gene fusion and the increased expression of tumor-associated macrophage drug target CSF1R by a noncoding 50

hybrid transcript partner additionally demonstrate the clinicalrelevance of gene rearrangements involving noncoding regions(Fig. 5A and E; ref. 56). Further consideration of these nonca-nonical events in future gene rearrangement discovery will becritical for our understanding and treatment of TNBC and othermolecularly heterogeneous cancers.

The targetable gene fusions we identified across many cancertypes demonstrate the need to rapidly advance gene fusiondetection for molecularly heterogeneous cancers. Although thefrequency of individual gene rearrangements in these cancersmaybe low, the two rearrangements we validated with biologicalrelevance for TNBC involve specificmolecular targets for therapiesalready in clinical investigation (14) or development (36). Ifreadily detectable, several of the gene fusions we identified would

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Overexpression of oncogenic transcripts across cancer types results from gene rearrangement with coding and noncoding DNA. A–F, six examplesof STA-predicted rearrangements from additional tumor types in TCGA, representing the categories described at left. Each panel features anexon-level expression diagram and STA score plot for the gene and cancer type analyzed. Red, the representative DNA-validated rearrangement that isdepicted at the bottom of each panel as a schematic of the resulting aberrant protein. Blue, additional aberrant transcripts meeting STA scorethreshold. Black, background population below threshold. Protein features and UTRs are labeled, and dotted lines indicate hybrid transcript junctions.aa, amino acid; BRCA, breast invasive carcinoma; COADREAD, colorectal carcinoma; Cyto, cytoplasmic domain; HNSC, head and neck squamous cellcarcinoma; nt, nucleotide; SP, signal peptide; THCA, thyroid carcinoma; TM, transmembrane.

Shaver et al.





provide an immediate opportunity for patient alignment totargeted therapy and would serve as biomarkers for patientselection in basket trials such as the NCI-MATCH (MolecularAnalysis of Therapy Choice) Program.

Our results provide evidence for the effectiveness of STA as aquantitative prediction tool for both known and novel generearrangements. Although our analysis made use of RNA andDNA sequencing files already processed by the TCGA ResearchNetwork, a pipeline based on focused assembly of STA-pre-dicted transcripts could increase the sensitivity, efficiency, andbreadth of rearrangement detection, accelerating discovery ofdiagnostic and prognostic markers. As tumor sequencing effortscontinue, we look forward to optimizing and expanding theuse of STA to comprehensively catalogue gene rearrangementsacross cancer.

The novel approach used in STA enabled the discovery andanalysis of known and novel gene rearrangements on agenome-wide scale, which has clear relevance to the develop-ment and repurposing of targeted therapies. Moreover, analysisof the rearrangements we identified provides insight into themultimodular architecture of proteins and the diverse func-tional and regulatory domains selected for and against duringtumorigenesis. Continued advances in detection methods suchas STA will be critical for the treatment of diseases such as TNBCand the ability of the field to apply mechanistic insight from"exceptional responders" to individual tumor genomes con-taining unique mutations and rearrangements.

Disclosure of Potential Conflicts of InterestY. Shyr is a consultant/advisory board member for Novartis (formerly

GSK), Roche, and Janssen. No potential conflicts of interest were disclosedby the other authors.

Authors' ContributionsConception and design: T.M. Shaver, B.D. Lehmann, J.A. PietenpolDevelopment of methodology: T.M. Shaver, B.D. Lehmann, J.S. Beeler,J.A. PietenpolAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): T.M. Shaver, B.D. Lehmann, Z. Li, H. Jin, T.P. Stricker,J.A. PietenpolAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): T.M. Shaver, B.D. Lehmann, J.S. Beeler, C.-I. Li,Y. Shyr, J.A. PietenpolWriting, review, and/or revision of themanuscript: T.M. Shaver, B.D. Lehmann,J.S. Beeler, T.P. Stricker, J.A. PietenpolAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): T.M. Shaver, B.D. Lehmann, J.A. PietenpolStudy supervision: J.A. Pietenpol

AcknowledgmentsThe authors thank members of the Pietenpol lab for their critical review of

the manuscript and members of the Lovly lab for guidance with the Ba/F3cell line model. This work was conducted in part using the resources of theAdvanced Computing Center for Research and Education at VanderbiltUniversity. The results published here are in part based upon data generatedby TCGA Research Network: http://cancergenome.nih.gov.

Grant SupportT.M. Shaver was supported by grants CA183531, GM008554, and HHMI

MIG56006779. B.D. Lehmann was supported by Komen CCR13262005. J.A.Pietenpol was supported by CA098131, CA105436, and Komen SAC110030.Shared resources were provided by CA068485.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received January 14, 2016; revised March 14, 2016; accepted May 11, 2016;published OnlineFirst May 26, 2016.

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Shaver et al.




2016;76:4850-4860. Published OnlineFirst May 26, 2016.Cancer Res Timothy M. Shaver, Brian D. Lehmann, J. Scott Beeler, et al. MalignanciesRearrangements in Triple-Negative Breast Cancer and Other Diverse, Biologically Relevant, and Targetable Gene

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