REGULAR ARTICLE Peripheral T-cell lymphoma: molecular profiling recognizes subclasses and identifies prognostic markers Marta Rodrıguez,1,2,* Ruth Alonso-Alonso,1,2,* Laura Tomas-Roca,1,* Socorro M. Rodrıguez-Pinilla,1,2 Rebeca Manso-Alonso,1 1,2 Laura Cereceda, Jennifer Borrego n, Teresa Villaescusa,3 Ra 1 rdoba,2,3 Margarita S ul Co anchez-Beato,2,4 Ismael Fern andez-Miranda,4 Isabel Betancor, Carmen Barcena, Juan F. Garcıa, Manuela Mollejo, Mo 1 5 2,6 2,7 nica Garcıa-Cosio, Paloma Martin-Acosta,2,9 2,8 10 11 Fina Climent, Dolores Caballero, Lorena de la Fuente, 12,13 Pablo Mınguez,12-14 Linda Kessler,15 Catherine Scholz,15 15 Antonio Gualberto, Rufino Monde jar,2,16 and Miguel A. Piris 1,2 1 Pathology Department, Instituto de Investigaci on Sanitaria Fundaci on Jimenez Dıaz, Madrid, Spain; 2Centro de Investigaci on Biom edica en Red de Cancer (CIBERONC), ISCIII, Madrid, Spain; 3Lymphoma Unit, Department of Hematology, Fundaci enez Dıaz University Hospital, Health Research Institute IIS-FJD, Madrid, Spain; 4Lymphoma on Jim Research Group, Medical Oncology Department, Instituto de Investigaci on Sanitaria Puerta de Hierro-Segovia de Arana, Madrid, Spain; 5Pathology Department, Hospital Universitario 12 de Octubre, Madrid, Spain; 6Pathology Department, Hospital MD Anderson Cancer Center, Madrid, Spain; 7Pathology Department, Hospital Virgen de la Salud, Toledo, Spain; 8Pathology Department, Hospital Universitario Ram on y Cajal, Madrid, Spain; 9Pathology Department, Hospital Universitario Puerta de Hierro-Segovia de Arana, Madrid, Spain; 10Pathology Department, Hospital Universitari de Bellvitge, IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain; 11Haematology Department, Hospitalario Universitario de Salamanca (HUS/IBSAL), Salamanca, Spain; 12Bioinformatics Unit, Instituto de Investigaci on Sanitaria-Fundaci on Jim enez Dıaz University Hospital, Universidad Autonoma de Madrid (IIS-FJD, UAM), Madrid, Spain; 13Department of Genetics, Instituto de Investigaci on Sanitaria-Fundacion Jimenez Dıaz University Hospital, Universidad Autonoma de Madrid (IIS-FJD, UAM), Madrid, Spain; 14Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain; 15Kura Oncology Inc., San Diego, CA; and 16UGC Laboratorios, Hospital Universitario de Puerto Real, Cadiz, Spain Key Points Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of approximately 30% after 5 years. We have  Gene expression and analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString mutational analysis platform (NanoString Technologies, Seattle, WA) that includes 208 genes associated with confirm the differences among the T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways, and 3 peripheral TCL stromal cell subpopulations. A comparative analysis of the various histological types of subclasses: AITL, PTCL (angioimmunoblastic T-cell lymphoma [AITL]; PTCL with T follicular helper [TFH] PTCL-NOS, and phenotype; PTCL not otherwise specified [NOS]) showed that specific sets of genes were PTCL-TFH. associated with each of the diagnoses. These included TFH markers, cytotoxic markers,  The expression of a and genes whose expression was a surrogate for specific cellular subpopulations, includ- gene set, including ing follicular dendritic cells, mast cells, and genes belonging to precise survival (NF-kB) B-cell genes, is an and other pathways. Furthermore, the mutational profile was analyzed using a custom IPI-independent prog- panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH, and PTCL-NOS. nostic factor for AITL The main differences among the 3 nodal PTCL classes involved the RHOAG17V mutations cases. (P , .0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL- TFH (16.66%) cases but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these 3 categories, identi- fies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL. Submitted 17 May 2021; accepted 13 August 2021; prepublished online on Blood Data associated with this study are available upon request by emailing marta. Advances First Edition 30 September 2021; final verstion published online 20 December rodriguezm@quironsalud.es. 2021. DOI 10.1182/bloodadvances.2021005171. The full-text version of this article contains a data supplement. *M.R., R.A.-A., and L.T.-R. contributed equally to this article. ß 2021 by The American Society of Hematology. Licensed under Creative Commons The microarray data have been deposited in BioProject (accession number Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permit- PRJNA752277); https://www.ncbi.nlm.nih.gov/bioproject/752277. ting only noncommercial, nonderivative use with attribution. All other rights reserved. 5588 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 Introduction Formalin-fixed, paraffin-embedded (FFPE) tissue sections from diag- nostic biopsies were collected at the time of diagnosis before CHOP (cyclophosphamide, doxorubicin, vincristine, and predni- Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, sone), CHOP-like, or CHOEP (cyclophosphamide, doxorubicin, eto- with a poor response to therapy and an approximate 5-year overall poside, vincristine, and prednisone, for treating patients younger survival (OS) of only 30%.1 Nodal PTCL is not considered a single than 60 years) regimens. We analyzed 66 patients with AITL, 21 disease but rather a group of distinct disorders, including angioim- patients with PTCL-NOS, and 18 patients with PTCL-TFH. The munoblastic T-cell lymphoma (AITL), PTCL not otherwise specified main clinical characteristics of the patients are shown in supplemen- (NOS), and PTCL with T follicular helper (TFH) phenotype. Gene tal Table 1. Diagnoses were made using whole sections. All cases expression profiling studies have identified proliferation as a prog- were reviewed, and a consensus diagnosis was made by 2 expert nostic marker2 and delineated biological and prognostic subgroups hematopathologists (S.M.R.-P., M.A.P.) following the 2017 World within PTCL-NOS (PTCL-GATA3 and PTCL-TBX21).3 Health Organization Guidelines. All cases were nodal PTCL. Histological study of PTCL has revealed significant stromal partici- The validation series of 54 patients with PTCL comprised 27 AITL, pation, with the presence of follicular dendritic cells (FDCs), eosino- 18 PTCL-NOS, and 9 PTCL-TFH cases. Clinical and molecular fea- phils, plasma cells, and high endothelial venules, in which B cells tures, follow-up time, and current status are summarized in supple- and Epstein-Barr virus play a role in PTCL pathogenesis. Molecular mental Table 1. Samples were obtained following the same studies have confirmed that monoclonal mutated T cells comprise a requirements as those described for the discovery series. minor part of the tumor mass in most cases and, definitively, in AITL cases.4,5 Moreover, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in 44 patients with Differential diagnosis of these 3 main subtypes of PTCL involves a AITL, 18 patients with PTCL-TFH, and 14 patients with PTCL-NOS combination of clinical, histological, and immunohistochemical (supplemental Appendix). markers but frequently requires more specific markers and has rela- tively low reproducibility.6 Several projects have since evaluated nCounter gene-expression assay whether gene expression analysis could improve diagnostic accu- racy by delineating specific T-cell lymphoma classes or by identify- Gene-expression profiling was performed with nCounter Technology ing more precise prognostic models.2,3,7-10 Nevertheless, the need (NanoString Technologies). Total RNA from 105 FFPE sections for reliable diagnostic and prognostic markers in PTCL/AITL remains from diagnostic samples was isolated using a truXTRAC FFPE total partially unmet. NA kit (Covaris Inc., Woburn, MA) following the manufacturer's instructions (supplemental Appendix). We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform that includes 208 genes Targeted next-generation sequencing related to T-cell differentiation, oncogenes and tumor suppressor Genomic DNA was extracted from FFPE using a truXTRAC FFPE genes, deregulated pathways, and stromal cell subpopulations. DNA Kit (Covaris Inc.) following the manufacturer's instructions. Specifically, the platform includes genes expressed by the cell types present in PTCL and AITL specimens, together with normal The SureSelect target enrichment custom panel (3110861) was T-cell populations, for the purposes of facilitating the deconvolu- designed using the SureDesign (Agilent Technologies, Santa Clara, tion of the T-cell lymphoma microenvironment, and developing an CA) web-based tool. The 62 genes included have previously been integrated view of the cell composition of PTCL tumoral speci- described as mutated in PTCL (supplemental Table 3). The designs mens. We have also analyzed the mutational profile using a cus- covered coding exons of the selected genes. The targeted regions tom panel targeting 62 genes in 76 cases distributed in AITL, (according to Human Assembly GRCh37/hg19) were captured PTCL-TFH, and PTCL-NOS. using a SureSelect Target Enrichment Low Input System (Agilent Technologies), as described in the manufacturer’s instructions. Cap- The data generated in this manner were used to identify the signa- tured libraries were diluted to 1.3 pM for Illumina clustering, and ture characteristics of the different types of PTCL (AITL, PTCL- paired-end sequencing was performed on a NextSeq500 sequencer NOS, and PTCL-TFH) and identify prognostic markers. (Illumina Inc., San Diego, CA). Results were validated using a separate group of 54 patients with Somatic single nucleotide variants (SNVs) were identified in each PTCL. sample following the best practices GATK workflow for calling somatic mutations, which involves aligning reads with the Burrows- Methods Wheeler Aligner (version 0.7.17) and SNV detection and filtering Study design and patients using Mutect211 (supplemental Appendix). One hundred five patients with PTCL were included in the study, In general, we excluded variants with a variant alelle frequency which was developed in collaboration with several Spanish hospitals (VAF) of ,5% because we expect these variants to be artifacts due whose work complied with the clinical protocols of the Grupo Espa- to the sensitivity of the NGS technique. In addition, we have ~ol de Linfomas y Trasplante Auto n logo de Me  dula Osea (GEL- excluded variants with a VAF of .40% because earlier published TAMO) Group, under the supervision of the Fundacio n Jimenez findings led us to expect them to be germline mutations. Previously Dıaz (FJD) local ethics committee, and in accordance with the Dec- identified specific mutations, such as RHOAG17V and IDH2 (R172), laration of Helsinki. were accepted with a threshold of 1% VAF. 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 MOLECULAR PROFILING IDENTIFIES PROGNOSTIC MARKERS 5589 Tissue microarrays and immunostaining Results Immunohistochemistry with specific antibodies was performed for General features of the PTCL series clinical diagnosis (antibodies listed in supplemental Table 4). Specif- ically, we used PD1, CXCL13, and ICOS as TFH markers. All The clinical characteristics of the discovery series are summarized in PTCL-TFH cases expressed at least 2 TFH cell markers. supplemental Table 1. The series includes 105 patients diagnosed with PTCL, subdivided into 66 patients with AITL, 21 patients with Tissue microarrays were designed as previously described, using PTCL-NOS, and 18 patients with PTCL with the TFH phenotype, all two 0.6-mm tissue cores per case taken from archival FFPE tumor of whom were treated with curative intention following the guidelines blocks (supplemental Appendix). of the GELTAMO group.12 Survival analysis Most cases were diagnosed as AITL (n 5 66), the others being roughly equally split between PTCL-NOS (n 5 21) and PTCL-TFH First, we divided the data of the 159 patients into a discovery (train- (n 5 18). The median age for all patients was 67 years (range: ing) set (n 5 105) and a validation set (n 5 54) at random in a 2:1 26-88 years) (supplemental Table 1). Mostly, the initial therapeutic ratio. Survival data were available for 105 patients with PTCL from approaches were CHOP, CHOP-like, and CHOEP regimens for the the discovery series and for 54 patients with PTCL from the valida- most common PTCL subtypes. tion series (supplemental Appendix). Survival data were available for 75 of the 76 patients sequenced. Survival probability for the whole series was 47.92% at 32 months, with a median follow-up of 32 months (range: 1-177 months). Statistical analysis Immunohistochemical analyses were performed in whole sections of All statistical analyses were performed using R 3.5.2 (R Foundation all 105 samples. The tissue microarrays and immunohistochemistry for Statistical Computing, Vienna, Austria), IBM SPSS Statistics for were performed as a part of the histopathological review. We did Windows version 21 (IBM Corp., Armonk, NY), and GraphPad not find any significant differences between histological diagnosis Prism 8.4.0 (San Diego, CA) (supplemental Appendix). and either OS or time to progression (TTP) (data not shown). AITL (N=44) PTCL-TFH (N=18) PTCL-NOS (N=14) TET2 IDH2 DNMT3A EZH2 Epigenetic/ ARID1A chromatine remodelling SMARCA4 SMARCB1 ARID1B BCOR NCOR1 RHOA RHOAG17V NOTCH1 ZEB1 CARD11 CD28 T-cell differentiation VAV1 PTPRN2 RARA ICOS FYN LCK MTOR PRKCB NRAS PLCG1 PI3K/AKT PRKCQ signaling BRAF mTOR MAPK1 SYK TSC1 SIK3 JAK1 STAT3 JAK/STAT pathway JAK3 STAT5B JAK2 TP53 DNA repair/TP53 pathway/tumor ATM suppressors KRAS PRDM1 CDKN2A IRF4 BCR/NF-KB CCR4 CD274 Immune surveillance CD58 CXCL12 EPHB6 EFHC1 FOXM1 DDX3X Others TNFRSF1B ZHX3 KPNA7 Missense Splice site Synonymous UTR Frameshift In frame Stop gained Start gained Figure 1. Distribution of mutations in the 62 analyzed genes in the whole series of 76 tumor samples. Analysis of genomic alterations by target sequencing panel for primary lymphomas in patients with AITL, PTCL-TFH, and PTCL-NOS. A total of 46 368,287 mapped reads were generated, and 81.8% of the total bases were aligned to the complete human genome (UCSC hg19, GRCh37, February 2009). The mean coverage was 993X (range: 31-1379) for tumor samples. Rows correspond to sequenced genes; columns represent individual PTCL patients. Color coding: blue, missense; green, synonymous; purple, frameshift; red, stop gained; yellow, splice sit; pink, UTR; orange, in frame; gray, start gained. 5590 RODRIGUEZ et al 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 100 AITL PTCL-TFH PTCL-NOS (N=44) (N=18) (N=14) AITL 80 PTCL-TFH PTCL-NOS 60 40 20 0 RHOA G17V TNFRSF1B SMARCA4 RHOA_all NOTCH1 DNMT3A IDH2_all IDH2172 CARD11 PTPRN2 CXCL12 ARID1B ARID1A PRKCQ NCOR1 DDX3X PRKCB PLCG1 EFHC1 STAT3 MTOR RHOA BCOR KRAS TET2 VAV1 ZEB1 EZH2 TSC1 CD28 RARA CD58 IDH2 JAK1 TP53 JAK3 JAK2 ATM IRF4 SIK3 Figure 2. Most frequently mutated genes (%) in the whole cohort. They are subdivided as AITL (orange), PTCL-TFH (green), and PTCL-NOS (purple). Mutational analysis NOTCH1 (13.15%), and ZEB1 (10.52%), although the order of Of the 76 sequenced cases, 44 were classified as AITL, 18 as their frequencies differed between tumor groups. Mutations in the PTCL-TFH, and 14 as PTCL-NOS. A total of 46 368,287 mapped DNA methylation regulator TET2 gene were the most common reads were generated, and 81.8% of all the bases were aligned to among the 3 nodal T-cell lymphoma types. the complete human genome (UCSC hg19, GRCh37, February The main difference among the 3 nodal PTCL classes was the 2009). The mean coverage was 993X (range: 31-1379) for tumor higher frequency of RHOAG17V mutations (P , .0001). They were samples. The mean coverage depth for each tumor sample was cal- present approximately twice as frequently in cases of AITL culated by HsMetrics (Picard Tools, Broad Institute, Cambridge, (34.09%) as in PTCL-TFH (16.66%) but could not be detected in MA). This tool was used to analyze the results of the target-capture PTCL-NOS. Unlike previous results,15 the DNMT3AR882 variant was sequencing experiments. Twenty-seven of the 76 sequenced tumor identified in the 3 subgroups although at low frequencies in AITL cases from the entire series (35.53%) had values greater than the (3/44), PTCL-TFH (1/18), and PTCL-NOS (1/14). mean. The mean coverage in the coding region was the same as We ascribed the mutated genes to 10 pathways (Figure 1). The the mean coverage, and the same number of samples was greater epigenetic/chromatin remodeling pathway was mutated in 77.22% than the mean. of all cases, and the T-cell differentiation and PI3K/AKT signaling Figure 1 and supplemental Table 5 summarize the details of the mTOR pathways were mutated in 60.52% and 31.57% of cases, SNVs. After filtering, we observed a complex genomic landscape, respectively. With respect to the lymphoma-type subdivision, epige- defined by the presence of an average of 4 SNVs per sample netic/chromatin remodeling was the most frequently affected path- (range: 1-20). We identified SNVs in 52 of the 62 genes (83.87%) way in PTCL-NOS (85.71%), AITL (77.27%), and PTCL-TFH analyzed. As described previously,3,13-15 some cases have more (72.22%). However, the T-cell differentiation pathway was more fre- than 1 SNV per gene, this phenomenon being most frequently quently altered in AITL and PTCL-TFH (68.18% and 66.66%, observed in TET2 gene, although we also observed multiple muta- respectively) than in PTCL-NOS subtype (28.57%). In the PTCL- tions in VAV1, RHOA, NOTCH1, MTOR, JAK1, ZEB1, ATM, NOS subtype, genetic alterations were also present in the immune DNMT3A, and ARID1B. Only 2 AITL samples had no SNVs remain- surveillance pathway (21.42%) but were absent from the other sub- ing after filtering. types. Additional data are shown in Figure 2 and supplemental Table 5. The most recurrently mutated genes in the overall series were TET2 (61.84%), DNMT3A (30.26%), RHOAG17V (23.68%), IDH2 To study the prognostic impact of gene mutations on the PTCL (21.05%), ARID1B (17.1%), VAV1 (15.78%), MTOR (14.47%), series, we analyzed the data by Cox proportional hazards 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 MOLECULAR PROFILING IDENTIFIES PROGNOSTIC MARKERS 5591 Heatmap Group B2M B2M CD63 HLA-DRB1 Group HLA-DRB1 HLA-DQA1 HLA-DQA1 4 AITL HLA-DQB1 NOS CXCL13 HLA-DPA1 HLA-DQB1 TFH HLA-DPB1 HLA-DPA1 2 LAG3 GZMB PRF1 HLA-DPB1 GZMB IRF4 CD68 0 PRF1 TGFB1 SH2D1A ITK ENTPD1 –2 GYPC TRAF1 VCAM1 PIM1 ITGAX –4 CD84 HIF1A PTEN PTRPC –6 FYN TMEM173 TNFRSF1B CD4 IDH2 GZMA NKG7 TRBC1/2* CXCR4 TRBC1/2 PIM2 CD3D CD3E CD3D DOX3X RHOA CD3E CR2 CD19 MS4A1 IL21 TNFRSF8 FAM30A FAM30A TNFRSF17 FCER2 TCL1A TNFRSF17 BLK FCRL2 TCL1A CD209 CXCL12 BLK EOMES GNLY HBB CD163 FCGR38 CCR5 GZMH NT5E POGFRA GZMM CTSW BATF3 CSF3R FPR1 S100B TRGC1/2* MTA3 SIK3 MIB1 TET2 CD274 IL3RA HLA-DPB2 DNMT3A POCD1LG2 KDR VEGFC SLC2A1 TBX21 EGR2 ITGAM E2F1 FOXM1 CDK2 CDK6 CD27 CDK4 CDK2 CDK4 KLRK1 CDK6 CD38 ICAM1 MS4A4A IL2RA CMKLR1 IDH1 CUL3 RASGRP1 VAV1 PLCG1 NOTCH NOTCH1 TIA1 BCL6 CD7 BCL6 MTOR MTOR NRP1 GATA3 PRDM1 AKT1 CXCR3 TNF PTGER4 CARD11 IKBKB IL21R MYC CD2 MK167 MKi67 PCNA GPR18 PCNA SYK SP1 IKBKG PI3KCA CXCR5 CD5 CXCR5 ICOS ICOS SPIB CD8A HLA-DQB2 CCR7 CD69 MAP3K14 CD6 CD3G TRAT1 K1RB1 TPSAB1/B2 ALK LMP1 ARG1 LMP1 LMP2 BZLF1 BMLF1 BZLF1 LMP2 DNTT IL17A BMLF1 env tax IL4 IL13 IL17F CCR3 RNASE3 HBG ENPP3 MPO TMCO1 B3GAT1 CEACAM3 EPB42 S100A12 CPA3 HDC KIT MS4A2 MUC1 TEK NOS2 RORC HLA-DQA2 CR1 NANOG CD244 FCAR KIR2DL3 KIR2DL3 KIR2DS2 KIR3DL2 KIR2DS2 HSD11B1 CCL13 KIR3DL2 MME KIR3DL1 PNOC SDC1 XCL1/2 KLRD1 ASCL2 FPR2 ADGRE1 ANGPT1 CD1A NCR1 TP63 SIGLEC8 PTGS2 NCAM1 SIGLEC5 EDN1 NOS3 EFHC1 TET1 FOXP3 IL10 MUM1 PDCD1 IL6 CD34 FLT3 5592 RODRIGUEZ et al 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 A B C Adj. p-value < 0.01 Adj. p-value < 0.01 Adj. p-value < 0.01 Adj. p-value < 0.05 New.annotation: differential mRNA Adj. p-value < 0.05 New.Annotation: differential Adj. p-value < 0.05 New.annotation: differential mRNA mRNA Adj. p-value < 0.10 expression in AITL vs. baseline Adj. p-value < 0.10 expression in AITL vs. baseline Adj. p-value < 0.10 expression in PTCL-TFH Adj. p-value < 0.50 of PTCL-NOS Adj. p-value < 0.50 of PTCL-TFH Adj. p-value < 0.50 vs. baseline of PTCL-NOS Upregulated Upregulated 5 EOMES genes Upregulated genes 5 Upregulated genes CXCL13 HDC genes CD63 in AITL GZMB in PTCL-NOS in PTCL-NOS in AITL CD34 4 NKG7 FLT3 CD34 FLT3 MAP3K14 CCR7 4 TGFB1 4 IL10 NKG7 CCR7 ADGRE1 BATF3 -Log10(p-value) -Log10(p-value) -Log10(p-value) GZMB PRF1 TGFB1 CD8A HDC 3 IL10 Upregulated genes in PTCL-TFH 3 CD63 3 CR2 GATA3 MME TNF B2M RORC PDCD1 LAG3 CD69 Upregulated genes CD244 FCGR3S ZMH HLA-DQA S4A2 in PTCL-TFH CDK2 MAP3K14 PTOS2 TNFRSE CD209 2 TET1 CXCL13 2 BATF3 ???? TETJ NT5E CMES VCAM1 GATA3CD209 HLA-DQB1 2 GZMA NOAM1 NRP1EDN1FCAR HBB ???? LA CD87 CXCR3 NRP1 HLA-DQA FPR2 KIR2DL3 ENTPD1 IDH2 ICOS ICOS NCAM1 NOS2 PDGFRA HLA-DQB1 FCGR GEMH SPIB CPA3 VCAM1 RORC CXCR4 KLRD1 IL21 R1 KIR2DL3 ???? TPD1 1 HSD11B1 1 KLRB1 MUC1 DDX3 ???? CD69 S1 CCL13 1 EOMES ???? TNF CD163 TBX21 IL2RA NT5E ???? NOS2 0 0 0 –1 0 1 2 3 4 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -2 -1 0 1 2 3 4 Log2(fold change) Log2(fold change) Log2(fold change) D E T-cell lymphoma genes AITL T-cells Th17 Cytotoxic T-cells B-cells 0.5 ALCL Cell type scores (centered) HTLV-1 Eosinophils Vascular Th2 cells 115 0.0 Neutrophils Mast cells Stem cells Follicular DCs MHC-associated DCs 31 49 –0.5 Treg M2 Cell cycle 13 pDCs Metabolic pathways Calcium homeostasis 148 16 130 NK CD56dim cells –1.0 Proliferation Immune response T-cell follicular helper (Tfh) PTCL NOS PTCL Tfh Reticulocytes Oncogenes PI3KCA Non-canonical NF-kB pathway Exhausted CD8+ T-cells CXCR4 pathway PTCL-NOS AITL PTCL-TFH Th1 cells M1 CD45 Figure 4. Volcano plots, Venn diagram and trend plot. The upregulated/unchanged/downregulated genes for (A) AITL vs PTCL-NOS, (B) AITL vs PTCL-TFH, and (C) PTCL-TFH vs PTCL-NOS represented by volcano plots. (D) Venn diagram of the 3 entities. (E) Trend plot of cell type scores. regression. We found no significant differences within the overall TFH cells (CXCR5 and ICOS) series or when specific tumor types were analyzed separately. Epstein-Barr virus (BMLF1, BZLF1, LMP1, and LMP2) Natural killer cells (KIR2DS2, KIR2DL2, and KIR2DL3) Unsupervised gene expression analysis Other coregulated genes of interest were those defining FDCs, We studied the gene expression profile using the NanoString Tech- macrophages, and the NF-kB pathway (classic and alternative), nologies platform. The unsupervised analysis revealed clusters of among others (Figure 3). coregulated genes, which corresponded to the tumor stroma or the neoplastic cells, as shown in Figure 3. Unsupervised analysis did not reveal any well-defined clusters related to the tumor subtypes of patients with PTCL. However, most Some of the clusters of coregulated genes corresponded to the of the AITL cases were members of 2 main clusters, with cases of following: PTCL-NOS and PTCL-TFH interspersed among the other groups Major histocompatibility complex–associated genes (B2M, (Figure 3). HLA-DRB1, HLA-DPA1, HLA-DPB1, HLA-DQA1, and HLA- Supervised gene expression analysis and genes DQB1) differentially expressed in PTCL entities Cytotoxic T cells (PRF1 and GZMB) T cells (CD3D, CD3E, and TRBC1/2) We compared the gene signatures of the different types of PTCL B cells (FAM30A, TNFRSF17, TCL1A, and BLK) (AITL, PTCL-NOS, and PTCL-TFH). Proliferation genes (CDK2, CDK4, CDK6, MKi67, and Comparison between AITL and PTCL-NOS identified 21 differen- PCNA) tially expressed genes (FDR , 0.05 and log2 FC . 2). Differential Figure 3. Co-regulated genes, as classified by unsupervised hierarchical clustering analysis, based on the genes included in the NanoString platform. Heatmap depicting gene clusters of coregulated genes that allow the recognition of signatures expressed by tumoral and stromal cells. Each column represents sample categories (AITL in red, PTCL-NOS in blue, and PTCL-TFH in green). Each row represents the expression level of a gene, depicted according to the color scale shown. 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 MOLECULAR PROFILING IDENTIFIES PROGNOSTIC MARKERS 5593 expression analyses showed 8 significantly upregulated and 4 signif- shows the number of genes that overlapped and differed icantly downregulated genes in AITL (FDR , 0.01 and log2 FC . among these 3 entities (Figure 4D). All 3 conditions seem to 2; Figure 4A). Supplemental Table 6 shows the top 20 genes. have a relatively large group of genes (AITL: 115 genes; Genes defining AITL in this study are those that recognize the TFH PTCL-NOS: 148 genes; PTCL-TFH: 130 genes) that was spe- phenotype (CXCL13, CD10 [MME]), FDCs (CR2), mast cells cific to each entity. (HDC), alternative NF-kB (MAP3K14 [NIK]), and others, whereas Genes found to be differentially expressed among the PTCL lym- PTCL-NOS shows a higher level of expression of cytotoxic genes phoma types identified signatures for cell types that characterize (NKG7, GZMB, PRF1) and important immunoregulators, such as each of these tumors. For example, AITL cases had enhanced IL-10. expression of FDCs, DCs, mast cells, and major histocompatibility Eleven genes were differentially expressed (FDR , 0.05 and log2 complex–associated gene sets, whereas elevated expression of a FC . 2) between AITL and PTCL-TFH subtypes when we per- neutrophil-associated gene set was found in the PTCL-NOS group formed a differential gene expression analysis using solver (Figure 4E). Advanced Data Analysis (NanoString Technologies). We found Box plot comparisons of cell types in the AITL, PTCL-NOS, and 8 significantly upregulated genes in the AITL subtype (FDR , 0.01 PTCL-TFH sample sets showed significantly higher B-cell, T-cell, and log2 FC . 2; Figure 4B). Supplemental Table 7 indicates the and vascular scores in the AITL cases (Figure 5A-C). AITL cases top 20 genes. The analysis showed that AITL cases were character- ized by a higher level of expression of genes defining specific T-cell and the other PTCL subtypes also showed notably variable expres- subpopulations such as CD8A, EOMES, CD63, CD34, TGFB1, sion of this B-cell gene set (FAM30A, TNFRSF17, TCL1A, and and CCR7. BLK). The level of expression of a proliferation-associated gene set was higher in the PTCL-TFH subtype (Figure 5D). Finally, a TFH When comparing PTCL-TFH and PTCL-NOS, we found 4 genes gene set was enriched in AITL and PTCL-TFH (Figure 5E). with a higher level of expression in PTCL-NOS (GZMB, NKG7, BATF3, and IL-10 with FDR , 0.05 and log2 FC . 2), whereas Prognostic significance of the gene the genes upregulated in the PTCL-TFH group identified the TFH expression signatures phenotype (PD1, CXCL13, and ICOS) and GATA3 (Figure 4C). To study the prognostic impact of the genes and clinical variables Supplemental Table 8 shows the top 20 genes. on the PTCL series, we analyzed the data by Cox proportional haz- Differential expression analysis identified significant differ- ards regression. More informative models emerged when specific ences among the 3 main tumor subtypes. The Venn diagram tumor types were analyzed separately (Model 2, Model 3, and A B C 13 14 12 12 11 11 13 Vascular score B-cells score T-cells score 10 10 12 9 9 8 11 8 7 6 10 7 PTCL-NOS AITL PTCL-TFH PTCL-NOS AITL PTCL-TFH PTCL-NOS AITL PTCL-TFH D E 12 14 T-cell follicular helper (Tfh) score 13 Proliferation score 11 12 11 10 10 9 9 8 8 7 PTCL-NOS AITL PTCL-TFH PTCL-NOS AITL PTCL-TFH Figure 5. Box plots of cell type scores of AITL, PTCL-NOS, and PTCL-TFH. Scores for (A) B cells, (B) T cells, and (C) vascular, (D) proliferation, and (E) T-cell follicular helper (TFH) cells. 5594 RODRIGUEZ et al 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 A C E AITL Low-risk PTCL-NOS Low-risk PTCL-TFH Low-risk 100 High-risk 100 High-risk 100 High-risk OS probability (%) OS probability (%) OS probability (%) 75 75 75 50 50 50 25 25 25 p = 0.003 p = 0.002 p = 0.019 0 0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 0 25 50 75 100 125 150 Time (months) Time (months) Time (months) Low 33 30 27 27 27 27 27 Low 10 10 7 7 7 7 7 Low 9 7 5 5 5 5 4 High 33 15 5 3 3 3 3 High 11 2 2 2 1 1 1 High 9 3 0 0 0 0 0 B D F AITL Low-risk PTCL-NOS Low-risk PTCL-TFH Low-risk 100 High-risk 100 High-risk 100 High-risk TTP probability (%) TTP probability (%) TTP probability (%) 75 75 75 50 50 50 25 25 p = 0.001 25 p = 0.004 p = 0.009 0 0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 0 25 50 75 100 125 150 Time (months) Time (months) Time (months) Low 33 30 27 27 27 27 27 Low 10 8 6 4 4 4 4 Low 9 6 4 4 4 4 4 High 33 13 5 1 1 0 0 High 11 2 1 1 1 1 0 High 9 1 0 0 0 0 0 Figure 6. Kaplan-Meier survival analysis in relation to each prognostic model for OS and TTP. Red and blues lines indicate the high- and low-risk groups, respectively. The vertical bar represents the OS and the TTP probability (%), whereas the horizontal bar represents the follow-up time in months. Patients at risk at the corresponding time point are shown. Statistical significance (P) was that associated with the log-rank test. (A-B) AITL, (C-D) PTCL-NOS, and (E-F) PTCL-TFH. Model 4 for each tumor subtype) than when analyzed together In a subsequent analysis, taking the median risk score as the cut- (Model 1; P 5 .055) (supplemental Table 9). point, 66 patients with AITL in the discovery set were divided into high-risk (greater than the median risk score) and low-risk (less than In AITL cases, analyzing the univariate Cox proportional hazards or equal to the median) groups. The 21 patients with PTCL-NOS ratios identified International Prognostic Index (IPI) and age and 5 and 18 PTCL-TFH cases from the discovery series were analyzed in genes whose expression was significantly associated with differ- the same way. Kaplan-Meier (K-M) survival curve analyses were con- ences in OS and TTP probability (supplemental Tables 10 and 11, ducted for each patient group, which showed that both the OS and respectively). These correspond to genes expressed by B cells the TTP of the 3 groups of lymphoma types in the high-risk group (BLK, CD19, MS4A1, and SDC1) and IDH1 (P , .05). The Cox were significantly lower than for the low-risk group (P , .05; Figure multivariate analysis yielded an integrated model including IPI, BLK, 6A-F). OS probabilities for AITL cases, stratified using the integrated CD19, and MS4A1. model, were 75% (HR, 0.35; 95% CI, 0.14-0.85) for the low-risk In the PTCL-NOS subtype, analyzing the univariate Cox proportional group and 30% (HR, 2.73; 95% CI, 1.37-6.86) for the high-risk hazards ratios identified IPI and 6 genes whose expression was sig- group. TTP survival probability was 70% (HR, 0.32; 95% CI, 0.16- nificantly associated with differences in OS and TTP probability 0.92) for the low-risk group and 20% (HR, 2.08; 95% CI, 1.42- (supplemental Tables 12 and 13, respectively). These were genes 6.68) for the high-risk group. K-M survival curves showed 75% and involved in proliferation (MIB1, EZF1, and SP1), expressed by 30% OS for the low-risk and high-risk groups, respectively. The monocytes (ARG1), related to the NF-kB pathway (IKBKB), and of TTP survival probabilities were 70% for the low-risk group and 20% importance in T-cell lymphoma pathogenesis (CARD11) (P , .05). for the high-risk group. These percentages were selected using the The Cox multivariate analysis yielded an integrated model that differences between the log-rank test typically reported for the Cox included MIB1 and EZF1. regression model, which is estimated by maximizing likelihood or partial likelihood. In the PTCL-TFH subgroup, the univariate Cox proportional hazards ratio analysis identified IPI and 6 genes whose expression was sig- Separate K-M survival curve analyses were performed for prognostic nificantly associated with differences in OS and TTP probability models, including B-cell genes and IPI in the AITL group. Taking the (supplemental Tables 14 and 15, respectively). These were genes median risk score as the cut-point, 21 patients with PTCL-NOS in expressed by cytotoxic T cells (GZMA, PRF1, KLRB1, and NKG7), the discovery series were divided into high-risk (11 samples, greater monocytes (CD163), and TFH (CXCR5) cells (P , .05). The Cox than the median risk score) and low-risk (10 samples, less than or multivariate analysis yielded an integrated model that included IPI, equal to the median) groups. Expression of these genes and IPI GZMA, and KLRB1. identified significantly shorter OS and TTP in the high-risk group 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 MOLECULAR PROFILING IDENTIFIES PROGNOSTIC MARKERS 5595 A B AITL (validation series) AITL (validation series) Low-risk TTP probability (%) 100 Low-risk OS probability (%) 100 High-risk High-risk 75 75 50 50 25 25 p < 0.0001 p < 0.0001 0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 Time (months) Time (months) Low 13 13 11 10 9 9 9 Low 13 11 9 9 9 9 9 High 14 7 6 6 6 6 6 High 14 7 6 6 6 6 6 Figure 7. Kaplan-Meier survival analysis for OS and TTP in the validation series. Red and blues lines indicate the high- and low-risk groups, respectively. The verti- cal bar represents the TTP probability (%), whereas the horizontal bar represents the follow-up time in months. Patients at risk at the corresponding time point are shown. Statistical significance (P) was that associated with the log-rank test. (A-B) AITL. than in the low-risk group (P , .01) (Figure 6A-B). Analyses were groups was analyzed with a 2-sided log-rank test, which gave also performed for models of proliferation genes in the PTCL-NOS roughly similar significant results for OS and TTP (P , .0001) (Fig- subtype. Taking the median risk score as the cut-point, 18 patients ure 7A-B, respectively) from a K-M survival analysis. However, we with PTCL-TFH in the discovery series were divided into high-risk (9 could not validate the specific prognostic models in the PTCL-NOS cases) and low-risk (9 samples) groups. Expression of these genes and PTCL-TFH validation series (data not shown). Taken together, identified significantly shorter OS and TTP in the high-risk than in we found robust evidence that validated the prognostic model in an the low-risk group (P , .01) (Figure 6C-D. Finally, IPI and cytotoxic independent group of patients with AITL. T-cell genes in PTCL-TFH cases were analyzed. Expression of these genes identified significantly shorter OS and TTP in the high-risk Discussion than in the low-risk group (P , .01) (Figure 6E-F). PTCL subclassification and prognostication still lack biologically We also specifically analyzed the potential prognostic value of sound, reproducible markers and instead rely mainly on clinical and GATA3 and TBX21 expression in the cases in the PTCL-NOS cate- analytical characteristics.16,17 The exceptions are Ki6718 and the gory. No significant association was found with differences in sur- subclassification into GATA3/TBX21 PTCL-NOS.9,19 This study vival probability (log-rank test, P 5 .082 for OS and P 5 .087 for identifies a set of genes that may be useful for stratifying patients TTP). However, the number of patients with this diagnosis was rela- with PTCL and AITL according to their specific risks, thereby aiding tively small (21). the clinical management of these patients. The study was performed Mutational data were not found to be associated with differences in in paraffin-embedded samples and thus could be routinely applied. either OS or TTP. Unsupervised clustering (Figure 3) shows the existence of coregu- lated gene sets that define specific components of the stroma and Validation of the prognostic models in an phenotypic features of the neoplastic cells, enabling the deconvolu- independent PTCL series tion of the complex tumor samples in terms that allow individual dis- To validate the clinical outcome prediction models, a validation crete cell subpopulations or subtle changes in the neoplastic series including FFPE samples from 54 patients with PTCL (27 phenotype to be identified. AITL, 18 PTCL-NOS, and 9 PTC-TFH cases) was analyzed. Our results provide a degree of validation of the existence of consis- The characteristics of the validation set are summarized in supple- tent differences among the 3 major lymphoma subtypes, thus con- mental Table 1. All patients were treated with CHOP, CHOP-like, or firming the subclassification proposed by the World Health CHOEP regimens. The median age of the patients was 64.5 years Organization,20 corroborated through molecular analysis.3 They also (range: 31-88 years). The proportional survival of the whole series identify specific gene sets that may be used for differential diagnosis was 44.46% at 83 months, with a median follow-up of 83 months and reflect differences in the neoplastic cells and/or the tumor (range: 1-237 months). microenvironment. Mutational data also confirm differences among the 3 subclasses. There was a higher frequency of RHOAG17V The prognostic models were then applied to the validation series. mutations (P , .0001) in AITL cases, as was also reported recently To assess whether each model can be validated, we first scored 54 by Yoon et al.21 patients and divided them into high- and low-risk groups according to the scores, with the median risk score as the cut-point. In the It was also possible to identify a distinct subgroup of AITL cases, AITL group, analysis of OS identified the following HRs: low risk, enriched in B cells, with a more favorable prognosis (75% at 60 0.29 (95% CI, 0.12-0.71) and high risk, 3.15 (95% CI, 1.39-5.05). months). A subset of AITL cases with a more indolent clinical The TTP group had the following HR values: low risk, 0.27 (95% course had been demonstrated in previous clinical and molecular CI, 0.11-0.51) and high risk, 3.17 (95% CI, 1.25-6.19). In the AITL studies.9,22,23 A favorable role for the presence of B cells in T-cell entity, the difference in survival between the high- and low-risk lymphoma prognosis was initially proposed by Iqbal and coworkers9 5596 RODRIGUEZ et al 28 DECEMBER 2021 • VOLUME 5, NUMBER 24 after their use of Affymetrix HG-U133 chips in a series of fresh AITL Center (CNIO) in Madrid and Ignacio Mahillo for his statistical specimens. Our study confirmed these observations using a tool support at the Biostatistics and Epidemiology Unit of Instituto de that can be applied in routine FFPE specimens based on the recog- Investigacion Sanitaria-Fundaci enez Dıaz University Hospi- on Jim nition of a B-cell signature that includes TCL1A, PNOC, CD19, tal in Madrid. The authors especially thank all the Biobanks and CD20, and SPIB genes. An attempt to reproduce this finding, Pathology Departments for their exceptional work collecting and which was present in 49 of 105 (46.7%) cases, using immunohisto- organizing samples. Additionally, the authors are indebted to the chemistry for CD20 in paraffin-embedded sections was not patients who contributed to this study. successful. This work was supported by grants from Instituto de Salud Of the other PTCL subtypes, PTCL-NOS analysis showed that the Carlos III, from Ministerio de Economıa, Industria y Competitivi- expression of a set of proliferation-related genes was associated dad, Asociacio n Espan ~ola Contra el Cancer (AECC), Comuni- with shorter OS and TTP, as revealed by univariate and multivariate dad Auto noma de Madrid and Centre for Biomedical Network analyses. This was not confirmed in the validation group because Research on Cancer (CIBERONC): SAF2013-47416-R, CIBER- the difference was too small to be statistically significant for the low ONC-ISCIII (CB16/12/00291), ISCIII-MINECO-AES-FEDER number of cases, but the result was nevertheless consistent with (Plan Estatal I 1 D 1 I 2013-2016), AECC PROYE18054PIRI, those of other researchers.18,24,25 CAM B2017/BMD-3778, PIC97/2017_FJD, PIE15/0081, PIE16/01294, and PI19/00715. R.A.-A. is the recipient of PFIS PTCL-TFH group survival analysis showed that in contrast to the predoctoral fellowship. L.T.-R. is funded by Marie Skłodowska- findings in AITL, the presence of a B-cell gene signature was not Curie Individual Fellowship (No 882597). M.R. is supported by associated with changes in survival probability. In contrast, the CIBERONC (CB16/12/00291) P.M. has a Miguel Servet con- expression of cytotoxic/natural killer markers (GZMA, PRF1, and tract funded by the ISCIII (CP16/00116). L.d.l.F. was supported KLRB1) was associated with shorter OS and TTP although these by the ISCIII contract CA18/00017. findings were not confirmed in the validation series. These series of cases show that the expression of GATA3/TBX21 Authorship is not associated with changes in survival probability, in contrast with the findings of Iqbal and coworkers.9,19,21 Our findings are con- Contribution: M.R., R.A.-A., R.M., and M.A.P. conceived and designed sistent with those of Drieux et al.,7 although both studies featured the study; M.R., R.A.-A., S.M.R.-P., L.C., T.V., R.C., M.S.-B., I.B., C.B., relatively small numbers of cases with a diagnosis of PTCL-NOS. J.F.G., M.M., M.G.-C., P.M.-A., F.C., D.C., and R.M. acquired the data; R.A.-A., J.B., and M.R. conducted experiments; M.R., L.T.-R., R.A.-A., The mutational analysis of the gene panel containing 62 selected L.d.l.F., and P.M. performed data analysis; M.R., L.T.-R., R.A.-A., genes allowed us to confirm already known variants in PTCL. The S.M.R.-P., R.M.-A., I.F.-M., L.T.-R., M.S.-B., L.K., C.S., A.G., R.M., P.M., mutational profile of AITL, PTCL-NOS, and PTCL-TFH identified var- and M.A.P. interpreted data; R.A.-A., J.B., L.C., I.B., L.d.l.F., and P.M. iants in TET2, DNMT3A, and RHOA, in line with results of previous provided technical assistance; M.R., L.T.-R., and M.A.P. wrote the studies.15,21 However, unlike some previous publications,15,21,26-28 manuscript. All authors critically read and approved the final we observed IDH2 variants in all PTCL groups, albeit with different manuscript. frequencies, and the DNMT3AR882 variant was identified in the 3 subgroups but at low frequencies in patients with AITL (3/44), Conflict-of-interest disclosure: M.A.P. declares having received PTCL-TFH (1/18), and PTCL-NOS (1/14). The main difference lecture fees and advisory board fees from Millenium/Takeda, Jansen, among the 3 nodal PTCL classes was the higher frequency of NanoString, Kyowa Kirin, Gilead, and Celgene. The remaining RHOAG17V mutations. They were present approximately twice as authors declare no competing financial interests. frequently in AITL cases (34.09%) as in PTCL-TFH (16.66%) but ORCID profiles: M.R., 0000-0001-8543-6767; R.A-A., 0000- could not be detected in cases of PTCL-NOS.21,29,30 0001-9503-7268; S.M.R-P., 0000-0002-2191-1327; R.C., 0000- In summary, this study identifies gene clusters expressed by the dif- 0002-7654-8836; M.S.-B., 0000-0002-0058-7599; I.F-M., 0000- ferent microenvironmental and neoplastic components of PTCL- 0002-5136-7270; P.M-A., 0000-0002-3274-4595; P.M., 0000- NOS, PTCL-TFH, and AITL, including specific gene sets, which can 0003-4099-9421; L.K., 0000-0002-8677-3252; M.A.P., 0000- be used with routinely processed paraffin-embedded samples to 0001-5839-3634. provide a more specific diagnosis and prognosis. Correspondence: Miguel A. 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