Gene Expression Profiling of Desmoid Tumors by Cdna Microarrays and Correlation with Progression-Free Survival

Published OnlineFirst April 15, 2015; DOI: 10.1158/1078-0432.CCR-14-2910

Biology of Human Tumors Clinical Cancer Research Gene Expression Proﬁling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival Sebastien Salas1,2, Celine Brulard3, Philippe Terrier4, Dominique Ranchere-Vince5, Agnes Neuville3, Louis Guillou6, Marick Lae7, Agnes Leroux8, Olivier Verola9, Kurtz Jean-Emmanuel10, Sylvie Bonvalot11, Jean-Yves Blay12, Axel Le Cesne13, Alain Aurias3, Jean-Michel Coindre3,14, and Frederic Chibon3,15

Abstract

Purpose: Because desmoid tumors exhibit an unpredictable itive predictive value (PPV) and negative predictive value clinical course, translational research is crucial to identify the (NPV). predictive factors of progression in addition to the clinical Results: Multivariate analysis showed that our molecular sig- parameters. The main issue is to detect patients who are at a nature had a significant impact on PFS while no clinical factor had higher risk of progression. The aim of this work was to identify any prognostic value. Among the 1,000 random signatures gen- molecular markers that can predict progression-free survival erated, 56.7% were significant and none was more significant than (PFS). our 36-gene molecular signature. PPV and NPV were high Experimental Design: Gene-expression screening was con- (75.58% and 81.82%, respectively). Finally, the top two genes ducted on 115 available independent untreated primary downregulated in no-recurrence were FECH and STOML2 and the desmoid tumors using cDNA microarray. We established a top gene upregulated in no-recurrence was TRIP6. prognostic gene-expression signature composed of 36 genes. Conclusions: By analyzing expression profiles, we have iden- To test robustness, we randomly generated 1,000 36-gene tified a gene-expression signature that is able to predict PFS. This signatures and compared their outcome association to our tool may be useful for prospective clinical studies. Clin Cancer Res; define 36-genes molecular signature and we calculated pos- 21(18); 4194–200. 2015 AACR.

Introduction (1, 2), but because desmoid tumors exhibit an unpredictable clinical course and an indistinguishable morphology, translation- Desmoid tumors are mesenchymal fibroblastic/myofibroblas- al research is crucial to identify the predictive factors of progres- tic proliferations. The major obstacle in the management of sion in addition to the clinical parameters. A significant improve- desmoid tumors is their high propensity for local recurrence ment would be to be able to detect patients who are at a higher risk even after complete surgical removal. Currently, an initial of progression and those with no risk of progression. "wait-and-see" policy is explored as a possible standard of care Biologically, alterations of the APC (mutation or loss of the entire locus) and CTNNB1 mutation might constitute an initial mutually exclusive alteration (3, 4). Moreover, Salas and collea- 1 2 Aix Marseille Univ, CRO2, INSERM U911, Marseille, France. APHM, gues described three recurrent and relevant alterations of chromo- Timone Hospital, Department of Medicine, Division of Adult Oncology, Marseille, France. 3Department of Pathology, INSERM U916, Bergonie somes 8, 20, and 6 by array comparative genomic hybridization Institute, Bordeaux, France. 4Department of Pathology, Gustave (CGH) array. These alterations could be involved in the same Roussy Institute, Villejuif, France. 5Department of Pathology, Leon 6 pathway or could confer a selective advantage. Patients harboring Berard Center, Lyon, France. University Institute of Pathology, Lau- CTNNB1 sanne, Switzerland. 7Department of Pathology, Institut Curie, Paris, mutations, in particular CTNNB1 (45F) mutations, are France. 8Department of Pathology, AlexisVautrin Center, Nancy, at risk of recurrence and the wild-type appears to be a good France. 9Department of Pathology, Saint-Louis Hospital, Paris, France. prognostic marker (5, 6). There have been only a few reports 10Department of Oncology and Hematology, University Hospital, Strasbourg, France. 11Department of Surgery, Gustave Roussy Insti- concerning gene-expression analysis in desmoid tumors. One of tute, Villejuif, France. 12Department of Medicine, Leon Berard Center, them demonstrated that a gene-expression signature could dis- Lyon, France. 13Department of Medicine, Gustave Roussy Institute, tinguish desmoid tumors from nodular fasciitis and suggested 14 Villejuif, France. Victor Segalen University Bordeaux, Bordeaux, that selected tyrosine kinases, transcription factors, and members France. 15Translational Research, Bergonie Institute, Bordeaux, France. of the Wnt, TGFb, IFN, and TNF signaling pathways could dis- Note: The data used in this article were provided by the French Sarcoma tinguish these two entities (7). Another study has shown that it Group database as part of the ConticaBase (www.conticabase.org). was identified genes, in particular ADAM12, WISP-1, and SOX 1, Corresponding Author: Sebastien Salas, Aix Marseille University, 13005 which were uniquely overexpressed in 12 cases of aggressive Marseille, France. Phone: 33-4-91-38-57-08; Fax: 33-4-91-38-76-58; E-mail: fibromatosis compared with expression in normal skeletal tissues [email protected] and a variety of normal tissues. The authors concluded that gene- doi: 10.1158/1078-0432.CCR-14-2910 expression patterns may be useful in the classification of subtypes 2015 American Association for Cancer Research. of aggressive fibromatosis (8). Finally, Colombo and colleagues

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dose of 50 Gray. Forty-three patients received radiotherapy. Sur- Translational Relevance gery was followed by radiotherapy in 16 patients. Clinical and The aim of this study was to identify molecular markers that histologic data were entered into a centralized computerized can predict progression-free-survival (PFS) and thus to distin- database (www.conticabase.org). All samples were obtained after guish those desmoid tumors for which the use of aggressive informed consent from patients. treatment is justified rather than a "wait-and-see" strategy. This study clearly demonstrates that there is prognostic molecular Pathology review signature of desmoid tumors that could benefit from different Histologic slides of all patients entered in this study were therapeutic strategies. The main question raised is how reviewed by the pathology subcommittee of the French Sarcoma patients should be managed. Should patients with poor prog- Group (GSF). This subcommittee included 20 pathologists and a nostic molecular signature be operated straightaway in a monthly slide review session was performed. For each tumor, one curative intent and/or benefit from early medical treatment? to eight slides were reviewed collegially. Histologic typing was This study is the starting point for prospective studies, the only based on the World Health Organization (WHO) histologic way to answer these questions and optimize the management typing of soft tissue tumors. Histopathologic diagnosis was con- of desmoid tumors. Prospective validation of the molecular firmed by the search for CTNNB mutations. All training samples signature is under way in France through a clinical trial had the CTNNB1 mutation. evaluating the "wait-and-see" strategy (ClinicalTrials.gov identifier NCT01801176). RNA extraction and cDNA array Total RNAs were extracted from frozen tumor samples with TRizol reagent (Life Technologies, Inc.) and purified using the RNeasy Min Elute TM Cleanup Kit (Qiagen) according to the manufacturer's procedures. RNA quality was checked on an Agi- (9) compared the gene-expression profiles of 14 sporadic lent 2100 bioanalyzer (Agilent Technologies). Samples were then desmoid tumors to those of five normal tissues acquired from analyzed on Human Genome U133 Plus 2.0 array (Affymetrix), corresponding desmoid tumor patients and six solitary fibrous according to the manufacturer's procedures. All microarray data tumor specimens. The protein products of three of the upre- were simultaneously normalized using the GCRMA algorithm gulated desmoid tumor genes, ADAM12, MMP2,andmidkine, (Wu J and Gentry (2014) RIwcfJMJ. GRCMA: Background Adjust- werefoundtobecommonlyexpressedinalargecohortof ment Using Sequence Information. R package version 2.40.0). human desmoid tumor samples assembled on a tissue micro- Minimum information about a microarray experiment–compliant array. Overexpression of midkine was significantly correlated data have been deposited at Gene Expression Omnibus under with decreased time to primary recurrence. Moreover, midkine accession number GSE58697. was found to enhance the migration and invasion of primary desmoid tumor cell cultures. These studies suggest the utility of Gene-expression analysis midkine as a clinical desmoid tumor molecular prognosticator Differential expression was established using the limma and a potential therapeutic target. Midkine could be a novel R package and P values were adjusted using the Benjamini– b-catenin transcriptional target (9). However, no relationship Hochberg procedure (10). Analysis of variance was performed was noted between gene-expression profiling and the clinical by using GeneSpring GX software (Agilent Technologies) one-way course of the disease. ANOVA test and P values were adjusted by using the Benjamini– In this study, molecular markers predictive for progression-free survival (PFS) were investigated using gene-expression screening Table 1. Patient and disease characteristics at baseline that was conducted on 115 available independent untreated Characteristics Cohort A Cohort B Cohorts A þ B primary desmoid tumors using cDNA microarray. (n ¼ 66) (n ¼ 49) (n ¼ 115) Median follow-up 2.36 (0.03–12.04) 1.43 (0.33–11.07) 1.82 (0.08–11.97) Materials and Methods (years) (IC 95) Age at diagnosis (%) Patients and samples 37 years 27 (41) 25 (50) 52 (45) From February 1, 1987 to March 6, 2008, 115 consecutive >37 years 37 (56) 24 (50) 61 (53) patients with sporadic aggressive fibromatosis were diagnosed for Nd 2 (3) 2 (2) their first tumoral event in 16 participating cancer centers. Among Male sex (%) 23 (35) 20 (41) 43 (37) Location (%) tumors in the 115 patients, 66 formed the training samples. The Intra-abdominal 4 (6) 8 (16) 12 (10) diagnosis of desmoid tumors was confirmed in each case by Abdominal wall 13 (20) 9 (18) 22 (18) collegial histologic analysis (mesenchymal fibroblastic/myofibro- Extra-abdominal 49 (74) 32 (66) 71 (62) blastic proliferations). The following clinical data were collected: Size (%) gender, age at diagnosis, location (intra-abdominal, abdominal 7 cm 35 (53) 20 (41) 55 (48) < wall, and extra-abdominal), size of tumor, and follow-up 7 cm 17 (26) 22 (45) 39 (34) Nd 14 (21) 7 (14) 21 (18) (Table 1). All patients had an initial surgical resection. Histologic Progression number (%) evaluation of surgical margins was available in 91 (79%) cases. 0 48 (72) 14 (29) 62 (54) Forty-seven patients (41%) had R0 resection, 32 (28%) had R1 1 0 30 (61) 30 (26) resection, and 12 (10%) had R2 resection (macroscopic incom- 2 13 (20) 1 (2) 14 (12) plete as R2 resection; microscopic incomplete resection as R1 3 5 (8) 1 (2) 6 (5) > resection; microscopic complete resection as R0 resection). Radio- 3 0 3(6) 3(3) Mutations CTNNB1 (%) 66 (100) 29 (60) 95(82) therapy generally included photons or electrons with a median

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Hochberg procedure. To assign prognosis, we applied the nearest Results centroid method. Centroids represent a centered mean of expres- Establishing molecular signature sion for the signature genes for each patient outcome (recurrence In unsupervised analysis, no molecular signature could be and no-recurrence) of the training sets. Each sample of the identified. To identify molecular markers that can predict PFS of validation sets was allocated to the prognostic class (centroid) desmoid tumors, we compared expression profiles in supervised with the highest Spearman correlation. Gene Ontology (GO) analyses of 48 patients without recurrence versus 18 patients analysis was performed to establish statistical enrichment in GO with two or three local recurrences. We identified 18 genes terms by using Gorilla software (11). upregulated in recurrence samples and 18 genes downregulated Statistical analysis in recurrence samples (Table 2). The supervised analysis compar- PFS is defined as time from the date of initial diagnosis to the ing patients without recurrence with those having one or more date of progression or recurrence or last follow-up. Local recurrences did not identify any prognostic molecular signature. recurrence-free survival (LRFS) is defined as time from the date Classification based on biologic process categories from GO of initial diagnosis to the date of recurrence or last follow-up. (www.geneontology.org) of the 36 genes demonstrated four We chose to study PFS because the validation cohort consisted enriched GO process terms: cellular component organization or of patients who underwent surgery that was not only R0 and R1 biogenesis (GO:0071840), cellular component organization but also R2. Survival curves were obtained by the Kaplan–Meier (GO:0016043), single-organism organelle organization method and compared with the log-rank test. All survival (GO:1902589), organelle organization (GO:0006996), which analyses were performed by using R software, version 2.14.1 include 16, 16, 10, and 12 genes, respectively (Table 3). The (R development Core Team, Vienna Austria, 2009) and survival interaction of the lists of genes from the ANOVA analysis com- package (Therneau T (2013). A Package for Survival Analysis in paring 0-versus-2, 0-versus-3, and 2-versus-3 recurrences identi- S. R package version 2.37-4). The Cox proportional hazards fied three genes as shown in the Venn diagram (Fig. 1): FECH, model was used to calculate adjusted hazard ratios (HR) and STOML2, and TRIP6. TRIP6 was overexpressed in the good out- their 95% confidence intervals (95% CI). Variables with a P come group unlike STOML2 and FECH, which were overexpressed valuelessthan0.05inunivariateanalysesweretestedinthe in the poor outcome group. multivariate analysis. Multivariate analysis was performed by using Cox regression with Firth's correction (R by Meinhard Prognostic factors for PFS and molecular signature validation Ploner and Fortran by Georg Heinze, 2012; coxphf: Cox regres- To test whether clinical factors can predict PFS, we performed sion with Firth's penalized likelihood. R package, version 1.09). univariate analysis for age at diagnosis (cutoff, 37 years), tumor The positive predictive value (PPV) is the ratio of the number of site (intra-abdominal versus abdominal wall versus extra- patients having neither a recurrence nor progression at the end abdominal) and tumor size (cutoff, 7 cm) in the whole cohort. of follow-up and classified in the group with a good prognostic Tumor site and tumor size were not significant but age at molecular signature over the total number of patients classified diagnosis (P ¼ 4.1e10 02)hadasignificant impact on PFS. as having a good prognostic signature. The negative predictive Survival analysis was also established for a 36-gene molecular value (NPV) is the ratio of the number of patients whose signature with centroid defined on cohort A (n ¼ 66) and disease recurred or progressed and were classified as having a classification and validation defined on the whole cohort poor prognostic signature over the total number of patients (n ¼ 115). Univariate analysis showed that the molecular classified as having a poor prognostic signature. Sensitivity, signature predicted PFS (P ¼ 1.7e10 07;Fig.2).Themolecular specificity, and accuracy were also calculated for the molecular signature also has a prognostic value in LRFS (only the signature. recurrence is considered as an event) in the validation cohort

Table 2. Final gene set containing 36 genes: 18 genes were upregulated in no-recurrence samples and 18 genes were downregulated in no-recurrence samples Downregulated genes in recurrence Upregulated genes in recurrence Probe set id Corrected P value Gene symbol Probe set id Corrected P value Gene symbol 209129_at 8.93e05 TRIP6 203116_s_at 3.23e07 FECH 226728_at 1.17e04 SLC27A1 215416_s_at 1.09e05 STOML2 204986_s_at 1.77e04 TAOK2 225439_at 2.49e05 NUDCD1 229377_at 2.55e04 GRTP1 200014_s_at 1.34e05 HNRNPC 206846_s_at 4.27e04 HDAC6 226312_at 2.26e05 RICTOR 220128_s_at 5.22e04 NPAL2 230465_at 2.73e05 HS2ST1 231767_at 6.35e04 HOXB4 202854_at 3.58e04 HPRT1 236229_at 8.01e04 Hs.661286 211727_s_at 3.66e04 COX11 203204_s_at 8.22e04 JMJD2A 227211_at 3.70e04 PHF19 214251_s_at 9.82e04 NUMA1 229253_at 7.60e04 THEM4 215692_s_at 9.93e04 MPPED2 200006_at 8.07e04 PARK7 236123_at 1.01e03 ST7L 226776_at 8.22e04 ENY2 228929_at 1.05e03 DNASE1 203312_x_at 8.27e04 ARF6 244614_at 1.48e03 TFG 203606_at 1.40e03 NDUFS6 232463_at 1.62e03 CXYorf10 200749_at 1.47e03 RAN 237317_at 1.68e03 Hs.127312.0 217880_at 2.69e03 CDC27 232331_at 2.26e03 Hs.25717.0 202121_s_at 3.17e03 CHMP2A 234106_s_at 2.43e03 FLYWCH1 226596_x_at 3.32e03 LOC729852

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Table 3. Classification based on biologic process categories from GO of the 36 ), T41 cases (P ¼ 1.3 10 4), and S45 and T41 cases (P ¼ 2.5 genes 10 7) but molecular signature has no prognostic value in P GO ID Corrected Z score Number of genes List of genes nonmutated cases (P ¼ 0.59). RAN HPRT1 TRIP6 36-gene signatures versus random signatures PHF19 To test its robustness, we randomly generated 1,000 36-gene THEM4 signatures and compared their outcome-association to our ENY2 defined 36-gene molecular signature. Exactly the same procedure KDM4A was used with centroid definition on cohort A and validation in GO:0071840 6.06e10 3 3.08 16 NUMA1 CHMP2A the whole cohort. Among the 1,000 random signatures generated, TAOK2 56.7% were significant (P < 0.05) and none was more significant STOML2 (P < 1.7e10 7) than our 36-gene molecular signature. Finally, the COX11 following values were obtained for our 36-gene molecular signa- NDUFS6 ture: PPV, 75.58%; NPV, 81.82%; specificity, 72.58%; sensitivity, RICTOR PARK7 81.82%; and accuracy, 78.26%. HDAC6 RAN Prognostic value of the signature in myxofibrosarcomas HPRT1 The signature was also tested in 42 myxofibrosarcomas. These TRIP6 PHF19 cases were previously published by Chibon and colleagues (12). THEM4 In this cohort, we found a prognostic value in metastasis-free ENY2 survival but not in LRFS (respectively P ¼ 0.007 and P ¼ 0.47). KDM4A GO:0016043 7.24e103 3.10 16 NUMA1 Discussion CHMP2A TAOK2 One of the main problems in managing desmoid tumors is STOML2 their locoregional aggressiveness and their propensity to recur COX11 after initial treatment. The aim of this study was to identify NDUFS6 RICTOR molecular markers that can predict PFS and thus to distinguish PARK7 those desmoid tumors for which the use of aggressive treatment HDAC6 is justified rather than a "wait-and-see" strategy. A few studies to NUMA1 date have tried to correlate biologic prognostic markers with RAN outcome, but the clinical impact was limited as a result of low STOML2 robustness (5, 6). Here, we performed the largest gene-expres- TAOK2 sion analysis of sporadic desmoid tumors from the French GO:1902589 8.97e10 3 6.11 10 NDUFS6 RICTOR Sarcoma Group tumor bank ever published. By analyzing THEM4 expression profiles, we have identified a gene-expression sig- ENY2 nature that is able to predict PFS. This molecular signature KDM4A identified two groups with clearly distinct PFS in the two sets of HDAC6 subjects. Patients in the good prognostic group had achieved a NUMA1 RAN 2-year PFS rate of 86% while those in the poor prognostic group STOML2 had a 2-year PFS rate of 44%. TAOK2 The top two genes upregulated in the recurrence group were NDUFS6 FECH and STOML2 and the top gene upregulated in the no- GO:0006996 1.62e10 2 4.70 12 PHF19 recurrence was TRIP6. These genes corresponded to the inter- RICTOR action of the lists of genes obtained from ANOVA analysis THEM4 PARK7 comparing 0-versus-2, 0-versus-3, and 2-versus-3 recurrences. ENY2 The function of these genes of interest is detailed below. KDM4A The last step of the heme synthetic pathway, which is essential HDAC6 for the cellular energy metabolism, is the incorporation of iron into protoporphyrin IX (PpIX) by the FECH enzyme. 5-Amino- levulinic acid (ALA) is a small-molecule precursor for the synthesis of (PpIX; ref. 13). Loss of FECH enzyme activity might be (P ¼ 1.e10 5). The prognostic value of the three-gene molec- responsible for enhanced PpIX accumulation in human carcino- ular signature (FECH, STOML2,andTRIP6) was lower than that mas (14). Van Hillegersberg and colleagues (15) have shown that of the 36-gene molecular signature (respectively P ¼ 0.025 and oral administration of ALA resulted in progressive accumulation P ¼ 1.7e10 07). Multivariate analysis comparing age at diag- of protoporphyrin in a rat colon carcinoma but not in the nosis to the 36-gene molecular signature demonstrated that the surrounding liver tissue. This team and others suggested that latter (P ¼ 7.9e10 07) was more predictive than age at diag- PpIX formation in human cancers, especially as a response to nosis, which was not significant (Table 4). The molecular ALA medication, might be related to lowered FECH enzyme signature had also prognostic value in mutated cases whatever activity. Moreover, Kemmner and colleagues (16) were able to thetypeofmutation(P ¼ 4 10 8), in S45 cases (P ¼ 3 10 3 demonstrate a significant downregulation of FECH mRNA

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Entity list 3 : One-way ANOVA Entity list 1 : One-way ANOVA custom selection 2 vs. 3 custom selection 0 vs. 2 3,797 entities 80 entities

5 25

Figure 1. 3 Venn diagram: interaction of the lists 3,770 33 of genes from ANOVA analysis comparing 0-versus-2, 0-versus-3, and 2-versus-3 recurrences.

1,179

Entity list 2 : One-way ANOVA custom selection 0 vs. 3 4,985 entities

expression in gastric, colonic, and rectal carcinomas. These data Ewing sarcoma cells. Although short-term proliferation was not were used as a rationale for the use of photodynamic therapy considerably affected by TRIP6 knockdown, silencing of the (PDT) supplemented with exogenously added ALA in particular protein significantly reduced migration, invasion, long-term on urothelial carcinoma (17). In our study, FECH was over- proliferation, and clonogenicity of Ewing sarcoma cells in vitro expressed in more aggressive forms of desmoid tumors. as well as tumorigenicity in vivo (30). Moreover, in nasopha- Recent evidence indicates that stomatin-like protein 2 ryngeal cancer cells (NPC), TRIP6 overexpression/knockdown (STOML2) regulates mitochondrial functions (i.e., energy pro- results in significant enhancement/inhibition of NPC cell duction, calcium buffering, and apoptosis) by organizing mito- migration, respectively (31). Finally, knockdown of TRIP6 in chondrial membranes into defined cardiolipin-enriched micro- glioblastoma or ovarian cancer xenografts restores nuclear p27 domains, which then facilitate the optimal assembly of mem- (KIP1) expression and impairs tumor proliferation and may brane-associated molecular complexes (18). STOML2 has been have a significant impact on enhanced NF-kBactivity,resistance identified as an oncogenic-related protein and found to be upre- to apoptosis, and Fas-mediated cell invasion in glioblastomas gulated in multi-cancers. First, STOML2 might serve as a prog- (29, 32). Paradoxically, in desmoid tumors, TRIP 6 was over- nostic marker especially in human gallbladder cancer, gastric expressed in the good outcome group. However, unlike cancer, glioma, colorectal cancer, breast cancer, and pulmonary STOML2 and FECH, TRIP6 is connected to b-catenin in the squamous cell carcinoma (19–24). STOML2 is also involved in literature, the main signaling pathway in desmoid tumors. invasion, regulating cell growth and cell adhesion in human Indeed, TRIP6 might compete with b-catenin for binding with esophageal squamous cell carcinoma, glioma, and human endo- the MAGI-1b/PTEN signalosome to destabilize E-cadherin metrial adenocarcinoma (21, 25–27). In desmoid tumor that junctional complexes and to promote cell motility through the behaves aggressively by infiltrating tissues deeply, STOML2 over- regulation of Akt/NF-kB targets and/or effectors of focal adhe- expression is clinically related to PFS. sion (33). STOML2 and FECH are likely b-catenin transcrip- Thyroid hormone receptor-interacting protein 6 (TRIP6) is a tional targets. zyxin-related adaptor protein and focal adhesion molecule (28). TRIP6 associates with a variety of molecules from the cell surface to the nucleus, regulates actin reorganization, focal adhesion assembly/disassembly, cell migration/invasion, antiapoptotic signaling, and transcriptional control. Notably, TRIP6 binds to lysophophatidic acid (LPA) receptor 2 (LPA2) and the Fas/CD95 receptor to promote LPA- and Fas ligand- induced cell migration in a c-Src-dependent manner. TRIP6 can also regulate prosurvival signaling via activation of NF-kB, extracellular signal–regulated kinase (ERK), and phosphatidy-

linositol 3-kinase (PI3K)/AKT, and nuclear TRIP6 acts as a 0.2 0.4 0.6 0.8 1.0

k survival Progression-free transcriptional coregulator of AP-1 and NF- B. These data P log rank = 1.7e–07

suggest that TRIP6 functions at a point of convergence of 0.0 multiple signaling pathways critical for cancer development 0 5 10 15 20 (29).Thus,TRIP6actsasanoncogene that partially accounts Years for the autonomous migratory, invasive, and proliferative Figure 2. properties of Ewing sarcoma cells independent of EWS/FLI1. Effect of molecular signature on PFS. Patients in good prognostic group had TRIP6 knockdown demonstrated that TRIP6 expression confers achieved a 2-year PFS rate of 86% while patients in poor prognostic a proproliferative and proinvasive transcriptional signature to group had a rate of 44%.

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Table 4. Univariate and multivariate analysis for prognostic factors in PFS Univariate Multivariate P HR (95% CI) P HR (95% CI) Molecular signature 1.7e1007 5.3 (2.64–10.58) 7.9e1007 4.8 (2.50–10.24) Age at diagnosis 4.1e1002 1.7 (1.02–3.08) 5.1e1001 1.2 (0.69–2.14)

Classification based on biologic process categories from GO of Prospective validation of the molecular signature is under way the 36 genes principally demonstrated four clusters composed of in France through a clinical trial evaluating the "wait-and-see" 16, 16, 10, and 12 genes, respectively. These clusters relate to strategy (ClinicalTrials.gov identifier NCT01801176). However, general biologic processes. our work concerns surgical patients and not those in a "wait-and- Validation of the molecular signature analysis was obtained see" strategy. Thus, we cannot affirm that this molecular signature from the whole population including a part of the training. Even who predicts progression after surgery, will predict progression though the validation cohort was not totally independent, the before surgery. prognostic value of this molecular signature was highly significant Finally, somatic markers are not the only factors to be in this rare disease. In multivariate analysis, only the 36-gene considered because desmoid tumors may completely shrink molecular signature was prognostic of PFS, unlike any classical spontaneously and undergo a change in size over time accord- clinical prognostic factor, such as tumor size, tumor site, or age. ing to external events (pregnancy, trauma, surgery performed Moreover, PPVs and NPVs were high, suggesting that this molec- at another site, etc.). The host environment is doubtlessly ular test could be used at the time of diagnosis to stratify patients involved. enrolled in randomized trials. Finally, to test its robustness, we compared its prognostic value to that of randomly generated Disclosure of Potential Conflicts of Interest signatures according to the methodology published in the article No potential conflicts of interest were disclosed. by Venet and colleagues. By comparing 47 published breast cancer outcome signatures to signatures of identical size constructed from random genes, they showed that 60% of the published Authors' Contributions signatures were not significantly better outcome predictors than Conception and design: S. Salas, J.-Y. Blay, J.-M. Coindre, F. Chibon random signatures (34). In soft tissue sarcoma, the CINSARC Development of methodology: S. Salas, A. Aurias, J.-M. Coindre, F. Chibon signature is particularly robust with only two random signatures Acquisition of data (provided animals, acquired and managed patients, more significantly associated with outcomes (35). In this study, provided facilities, etc.): S. Salas, P. Terrier, D. Ranchere-Vince, A. Neuville, L. Guillou, M. Lae, A. Leroux, O. Verola, K. Jean-Emmanuel, S. Bonvalot, J.-Y. our signature was always more significant than random signa- Blay, A. Le Cesne, J.-M. Coindre, F. Chibon tures, thus demonstrating the high reliability of our results. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, Moreover, the fact that the molecular signature had prognostic computational analysis): S. Salas, C. Brulard, L. Guillou, J.-Y. Blay, A. Le Cesne, value in another fibroblastic entity such as myxofibrosarcoma F. Chibon reinforces our idea that this signature is robust and that this Writing, review, and/or revision of the manuscript: S. Salas, C. Brulard, signature probably reflects a biologic properties. A. Neuville, M. Lae, A. Leroux, S. Bonvalot, J.-Y. Blay, J.-M. Coindre, F. Chibon Administrative, technical, or material support (i.e., reporting or organizing Although considered nonmalignant owing to their inability to data, constructing databases): S. Salas, P. Terrier, L. Guillou, J.-Y. Blay, metastasize, the locoregional recurrence rate of desmoid tumors J.-M. Coindre, F. Chibon after resection is nearly 50% and the likelihood of accelerating the Study supervision: S. Salas, A. Aurias, J.-M. Coindre, F. Chibon evolution of the disease with surgery is still debated. This shows that there is an unpredictable clinical course in desmoid tumors Acknowledgments and justifies a "wait-and-see" policy as initial strategy. It is there- The authors thank R. Cooke for editing the English. They also thank fore necessary to identify patients who are at a higher risk of the patient advocacy group, SOS desmoids, and NetSARC, RREPS, LYRIC, progression and the search for molecular markers may be a way DevWECAN, and EUROSARC networks for their support. forward (1, 2). This study clearly demonstrates that there is prognostic molecular signature of desmoid tumors that could benefit from different Grant Support therapeutic strategies. The main question raised is how patients This work has been financially supported by La Ligue National Contre le should be managed. Should patients with poor prognostic molec- Cancer. The costs of publication of this article were defrayed in part by the payment ular signature be operated straightaway in a curative intent and/or advertisement fi of page charges. This article must therefore be hereby marked bene t from early medical treatment? in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. This study is the starting point for prospective studies, the only way to answer these questions and optimize the management of Received December 16, 2014; revised March 19, 2015; accepted March 28, desmoid tumors. 2015; published OnlineFirst April 15, 2015.

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4200 Clin Cancer Res; 21(18) September 15, 2015 Clinical Cancer Research

Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival

Sébastien Salas, Celine Brulard, Philippe Terrier, et al.

Clin Cancer Res 2015;21:4194-4200. Published OnlineFirst April 15, 2015.

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