molecular
tumor
boards
Successful Clinical Sequencing by MolecularTumor Board in an Elderly Patient With RefractorySézary SyndromeYasuki Hijikata, MD, PhD1; Kazuaki Yokoyama, MD, PhD2; Nozomi Yokoyama, MT3; Yasuo Matsubara, MD, PhD1; Eigo Shimizu4;Makoto Nakashima, PhD5; Makoto Yamagishi, PhD5; Yasunori Ota, MD, PhD7; Lay Ahyoung Lim, MD, PhD1; Rui Yamaguchi, PhD4;Mika Ito6; Yukihisa Tanaka, MT7; Tamami Denda, MS7; Kenzaburo Tani, MD1; Hiroshi Yotsuyanagi, MD1; Seiya Imoto, PhD8;Satoru Miyano, PhD4; Kaoru Uchimaru, MD, PhD5; and Arinobu Tojo, MD, PhD2,8
INTRODUCTION
Sézary syndrome (SS), a rare, aggressive, leukemicvariant of cutaneous T-cell lymphoma (CTCL), com-prises the malignant clonal proliferation of T lym-phocytes, and is prone to skin involvement.1 Mostpatients with SS are elderly, with erythroderma presenton . 80% of the body. SS is rarely curable and hasa poor prognosis; the median survival of patients withstage IV SS is approximately 2 years.2 Recent SSgenomic profiling with next-generation sequencing(NGS) demonstrated impaired normal signaling, whichsuggests the possibility of new treatment targets.3
NGS-based genomic testing for cancer is becomingmore widespread as a clinical tool for accurate di-agnosis and proper treatment in clinical oncology.4
However, the use of various NGS techniques toguide cancer therapy has created challenges in an-alyzing large volumes of genomic data and reportingresults to patients and caregivers.5 To resolve this, weorganized a clinical sequencing team called the mo-lecular tumor board (MTB) at the Institute of MedicalScience, The University of Tokyo. Clinical sequencingis associated with several potential challenges inanalysis, interpretation, and drug development for rarecancers such as SS. Here, we report the use of clinicalsequencing to achieve a complete response in anelderly patient with SS refractory to standard treatment.
CASE REPORT
A 75-year-old male was diagnosed with SS (T4N3M0B1b;stage IVA2) in 2015. He had received varioustreatments in multiple hospitals, including pred-nisone, psoralen and ultraviolet A, extracorporealphotopheresis, bexarotene, romidepsin, total skinelectron beam therapy, brentuximab vedotin, in-terferon alfa-2b, methotrexate, and pembrolizumab,but skin lesions did not respond adequately. Severepneumonia also occurred duringmethotrexate therapyand was resolved by intensive care. The patient wasadmitted to our hospital in November 2016. Gener-alized erythroderma, clinical stage IIIA, was observed.He received mogalizumab, low-dose VP-16, and local
radiation therapy. Each resulted in temporal clinicalresponses, but with limited effects.
Our study protocol (No. 26-112-270402/28-17-0708;Appendix) was approved by our institutional reviewboard and was in accordance with the Declarationof Helsinki. After obtaining written informed consent,whole-exome sequencing (WES) and RNA sequencingwere performed on skin tumor biopsy samples onFebruary 1, 2017, for comparisons with normal tissue,followed by analysis by our hospital curators (AppendixFigs A1 and A2). Specifically, from WES, 77 codingvariants were identified (Appendix Table A1). Amongthese, the MTB inferred only one driver mutation inTP53 (c.560-1G.A, splicing mutation, AppendixTable A1) from which no actionable molecular targetswere identified. However, from RNA sequencing,actionable molecular targets were identified. Weregarded the following gene upregulations as directlytargetable: CCR4 (mogalizumab), LCK (dasatinib),CD274 as PD-L1 (PD-L1 inhibitors, pembrolizumab),CTLA4 (ipilimumab), IL2RA as CD25 (denileukin dif-titox), TNFRSF8 as CD30 (brentuximab vedotin), andnuclear factor-κB (NF-κB) signaling pathway (Table 1).Of note, we also inferred lenalidomide as a potentialtherapeutic option on the basis of the following tworeasons: The tumor was enriched in pathways that wereall indirectly targetable by lenalidomide (eg, KyotoEncyclopedia of Genes and Genomes [KEGG]-NF-κBsignaling [Appendix Fig A7], KEGG-cytokine-cytokinereceptor interaction [Appendix Fig A9], andKEGG-naturalkiller [NK]-cell–mediated cytotoxicity6 [Appendix Fig A8],and KEGG-tumor necrosis factor [TNF] signal-ing [Appendix Fig A10] on the basis of the KEGGDRUGDatabase,7 Therapeutic Target Database8), anddocumented efficacy of lenalidomide was reported inpatients with CTCL with overall response rates of28% in a phase II trial of monotherapy for refractorySS9 and a phase III trial of maintenance therapy.10
Mogalizumab, pembrolizumab, and brentuximabvedotin had already been used during the patient’sprotracted clinical course. Denileukin diftitox wasexcluded because the latest immunohistochemical
ASSOCIATEDCONTENT
Appendix
Author affiliationsand supportinformation (ifapplicable) appear atthe end of thisarticle.
Accepted onDecember 14, 2019and published atascopubs.org/journal/po on May 15, 2020:DOI https://doi.org/10.1200/PO.19.00254
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analysis of the patient’s lesions showed that they wereCD25−. The two remaining candidates were ipilimumaband lenalidomide. The patient was reluctant to receiveipilimumab because its mechanism of action is similar tothat of pembrolizumab. Therefore, we focused on lenali-domide as a therapeutic option.
To identify any potential predictive signatures of responseto lenalidomide therapy in this patient, we investigatedwhether there could be any overlapping lenalidomide-related gene signatures in our in-house, independentRNA sequencing data set (Appendix Figs A2-A5). Spe-cifically, we focused on available data from patients withadult T-cell leukemia/lymphoma (ATL), another mature/peripheral T-cell neoplasm in which lenalidomide dem-onstrated clinical efficacy with an overall response rate of42% in a phase II study (ATLL-002 [ClinicialTrials.govidentifier: NCT01724177]). Gene expression data fromtumors of two patients with ATL before lenalidomidetherapy were available for this purpose; one patient (uniquepatient number [UPN] 1) had an objective response tolenalidomide therapy, whereas the other patient (UPN2)was primarily refractory to lenalidomide (the patient char-acteristics are listed in Appendix Table A2). To identifymolecular signatures associated with lenalidomide re-sponse and to reduce bias derived from different dis-eases, such as ATL, we extracted differentially expressedgenes from the tumor of UPN1 (responsive ATL case)compared with expression levels in the tumor from UPN2(refractory ATL case). Overall, we found 1,250 and 1,197genes upregulated in CTCL and the current ATL case,respectively.
KEGG pathway enrichment on the basis of the DAVIDannotation tool revealed that 73 and 88 pathways wereenriched in CTCL and ATL, respectively (Appendix Fig A3;Appendix Table A3). Among common pathways (n = 52),we identified HTLV-1 and lenalidomide target signatures(Appendix Fig A3, A7-A10; Appendix Table A3). Gene setenrichment analysis (GSEA) of panels of curated gene setsrelated to lenalidomide confirmed the enrichment of asignature predictive of response to lenalidomide in both
CTCL and ATL. In contrast, neither signature of resistancewas enriched in CTCL and ATL (Table 2). On the basis ofthese predictive signatures and documented efficacy inpatients with CTCL,9,10 we chose lenalidomide.
Beginning on September 11, 2017, lenalidomide was orallyadministered at a daily dose of 25 mg for 21 days of a28-day cycle while the patient had clinical stage IIB disease(Fig 1A). After the first course of treatment, all skin tumorsdisappeared except for one on his sternal region (Fig 1B). Inaddition, a transient flare reaction (TFR) that presented aserythema around skin tumors was observed during the firstcourse (Fig 1C). Aspiration pneumonia developed at theend of the first course, and thus, the dosage of the secondcourse was reduced to 10 mg daily; it was subsequentlyincreased by 5 mg every cycle to a maximum of 20 mgdaily. We extended the interval course from 1 to 2 weeksaccording to the patient’s status. Although new lesionsdisappeared after three courses of treatment, a 9-cmsternal tumor remained, but it was smaller than its pre-vious size. It was later irradiated with 20 Gy in December2017. The fifth course of lenalidomide was completed onFebruary 3, 2017, and all tumors, including sternal lesions,disappeared completely (Fig 1D).
Upon immunohistochemical analysis of tumor specimensbefore and after lenalidomide treatment, a remarkablenumber of CD8+ cells and few CD25+ and Foxp3+ cells wereobserved in each skin tumor (Appendix Table A4; AppendixFig A6). Especially, the number of CD8+ cells after the firstcourse increased compared with that before the treatmentbut decreased in the treatment-resistant lesion after thethird course of lenalidomide.
DISCUSSION
To our knowledge, this report is the first of successfulclinical results in a patient with refractory SS treated withlenalidomide, which was selected on the basis of clinicalsequencing. Advances in clinical sequencing have enabledthe discovery of actionable alterations that yield clinicalbenefit.11 Patients with advanced-stage SS have an un-favorable prognosis and an unmet clinical need for effective
TABLE 1. Actionable Alterations With DrugUpregulated Gene Drug Reason of the Exclusion
CCR4 Mogalizumab Drug resistance
LCK Dasatinib Lack of clinical evidence
PDL1 PD-L1 inhibitor Drug resistance
CTLA4 Ipilimumab Lack of consent
CD25 Denileukin diftitox Lack of expression by immunohistochemistry
CD30 Brentuximab vedotin Drug resistance
Activated signaling pathway
NF-κB Lenalidomide MTB recommendation
NOTE. See also Table 2. Candidate genes and drugs selected by the MTB proposed the activated NF-κB pathway on the basis of gene set enrichmentanalysis results. The attending physician chose lenalidomide (off-label use) from this report.Abbreviations: MTB, molecular tumor board; NF-κB, nuclear factor-κB.
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treatment. Overall survival has not been shown to benefitfrom aggressive intervention with cytotoxic agents becauseof serious treatment-associated toxicities, including pro-longed myelosuppression. An increased understanding ofthe pathogenesis of SS, with the identification of new tar-gets, has led to new therapeutic approaches with biologicagents to reduce treatment-related toxicities and improveoutcomes.1
The rationale for lenalidomide selection by the MTB wasbased on five factors. First, lenalidomide binds the cullin4-RING-E3 ubiquitin ligase cereblon complex and de-grades lymphoid transcription factors IKZF1 and IKZF3,leading to a decrease in NF-κB.12 Second, activated tumornecrosis factor signaling, known as a lenalidomide-targetpathway in the KEGG DRUG database, was detected in thetumor, which is also considered targetable by lenalidomideon the basis of our in-house data, UPN1. Third, a phase IItrial of lenalidomide monotherapy for advanced refractoryCTCL reported reliable and meaningful clinical benefits; inthat trial, lenalidomide-induced TFR was correlated with
good clinical response in patients with SS, which suggeststhat mechanisms that underlie this reaction might repre-sent specific anticancer immune responses.9 Indeed, in thecurrent patient, immune response to cancer was consid-ered to be induced by lenalidomide as shown by the ap-pearance of TFR (Fig 1C). Immunohistochemical stainingshowed that CD8+ cells in the tumor after the appearance ofTFR were increased after lenalidomide treatment. Fourth,lenalidomide exerts immunomodulatory effects, such asNK- and T-cell activation and the induction of T-helper1 cytokine production and cytotoxic activity; it also altersthe tumor microenvironment through its anti-angiogenic,antiproliferative, and pro-apoptotic properties.13 These char-acteristics provided the rationale for using this agent inour elderly patient with SS with susceptibility to infection.Actually, NK-cell–mediated cytotoxicity and cytokine-cytokine receptor interactions were enriched in the tu-mor before lenalidomide treatment. In addition, the fre-quencies of circulating NK cells and CD3+CD8+ T cellsincreased after the administration of lenalidomide compared
TABLE 2. Lenalidomide-Related Gene Sets and Annotations
Lenalidomide-Related Gene Sets and Annotations Current Patient
A Lenalidomide-Responsive Patient
With ATL
Gene SetGeneSize Signature Reference NES P FDR q NES P FDR q
HINATA_NFKB_IMMU_INF 17 Lenalidomide-responsive/target pathway
TTD8, KEGG DRUG/pathway7
1.88 .004 0.008 1.55 .027 0.013
TIAN_TNF_SIGNALING_VIA_NFKB 25 Lenalidomide-responsive/target pathway
TTD8, KEGG DRUG/pathway7
1.67 .005 0.025 1.36 .029 0.046
KEGG_NF-kappa_B_signaling_pathway 102 Lenalidomide-responsive/target pathway
TTD8, KEGG DRUG/pathway7
1.93 .000 0.005 1.38 .017 0.033
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
249 Lenalidomide-responsive/target pathway
TTD8, KEGG DRUG/pathway7
1.75 .000 0.005 1.58 .000 0.009
KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY
130 Lenalidomide-responsive/target pathway
TTD8, KEGG DRUG/pathway7
1.73 .000 0.005 1.45 .003 0.018
KEGG_APOPTOSIS 86 Lenalidomide-responsive/target pathway
TTD, KEGG DRUG/pathway
1.44 .004 0.023 1.18 .118 0.114
Aue_CLL_lenalidomide_target_Th1immunity_TableS1
155 Lenalidomide-responsive/target pathway
Aue17 1.93 .000 0.009 1.94 .000 0
Bhutani_myeloma_lenalidomide_target_genes_TableS2
147 Lenalidomide-responsive/target pathway
Bhutani18 1.22 .035 0.104 1.60 .000 0.009
E2F3_UP.V1_UP 186 Lenalidomide resistance Neri19 1.23 .029 0.284 NE
KRAS.600.LUNG.BREAST_UP.V1_UP 271 Lenalidomide resistance Neri19 1.23 .320 0.220 1.42 0.062
KRAS.KIDNEY_UP.V1_UP 139 Lenalidomide resistance Neri19 NE NE
KRAS.LUNG_UP.V1_UP 136 Lenalidomide resistance Neri19 1.44 .005 0.206 1.00 .452 0.486
RAF_UP.V1_DN 183 Lenalidomide resistance Neri19 NE 1.12 .181 0.358
MEK_UP.V1_DN 182 Lenalidomide resistance Neri19 NE 1.03 .388 0.445
RPS14_DN.V1_DN 179 Lenalidomide resistance Neri19 1.12 .109 0.333 NE
AKT_UP_MTOR_DN.V1_DN 173 Lenalidomide resistance Neri19 NE 1.12 .217 0.334
STK33_DN 256 Lenalidomide resistance Neri19 NE 1.33 .004 0.103
Abbreviations: ATL, adult T-cell leukemia/lymphoma; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; NE, not enriched; NES,normalized enrichment score; TTD, Therapeutic Target Database.
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with those at first treatment (Appendix Fig A11). Incontrast, the frequency of effector regulatory T cells didnot increase (Appendix Fig A11). These findings weresimilar to those described in the immunohistochemistry(Appendix Fig A6). These immunologic changes mighthave induced anticancer effects9 and prevented severeinfection.14 Finally, the Japanese national health in-surance system has already approved lenalidomide for thetreatment of myelodysplastic syndrome, refractory/relapsemultiple myeloma, and ATL.
Lenalidomide did not adequately treat the patient’s bulkysternal mass. Acquired lenalidomide resistance is con-sidered to be caused by reduced levels of tumor cellcereblon protein because of epigenetic modifications ofthe cereblon promotor region, cereblon gene mutations or
chromosomal deletions, or acquired cereblon pathway mu-tations and detects in other components of the E3 ligasecomplex.15 Another possible reason is that the delivery oflenalidomide to the tumor was hindered by the abundantsurrounding interstitial tissue and the immunosuppressivetumor microenvironment.16
In the near future, clinical sequencing will support clinicaldecisions with regard to appropriate treatments for patientswith rare and refractory cancers. In addition, clinical se-quencing on the basis of a combination of several NGStechniques might be particularly useful in identifying ap-propriate treatment options. This case report is the first toour knowledge of the use of clinical sequencing to iden-tify a successful treatment strategy for a patient withrefractory SS.
AFFILIATIONS1Department of General Medicine, IMSUT Hospital, Institute of MedicalScience, The University of Tokyo, Tokyo, Japan2Department of Hematology/Oncology, IMSUT Hospital, Institute ofMedical Science, The University of Tokyo, Tokyo, Japan3Department of Applied Genomics, Research Hospital, Institute ofMedical Science, The University of Tokyo, Tokyo, Japan4Laboratory of DNA Information Analysis, Human Genome Center,Institute of Medical Science, The University of Tokyo, Tokyo, Japan5Laboratory of Tumor Cell Biology, Department of Computational Biologyand Medical Sciences, Graduate School of Frontier Sciences, TheUniversity of Tokyo, Tokyo, Japan6Division of Molecular Therapy, Advanced Clinical Research Center,Institute of Medical Science, The University of Tokyo, Tokyo, Japan
7Department of Diagnostic Pathology, IMSUT Hospital, Institute ofMedical Science, The University of Tokyo, Tokyo, Japan8Division of Health Medical Data Science, Health Intelligence Center,Institute of Medical Science, The University of Tokyo, Tokyo, Japan
CORRESPONDING AUTHORArinobu Tojo, MD, PhD, Institute of Medical Science, The University ofTokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan; e-mail:[email protected].
EQUAL CONTRIBUTIONY.H. and K.Y. contributed equally as co-first authors.K.Y. and A.T. contributed equally as senior authors.
A B
DC
FIG 1. Changes in the patient’s skin lesions during lenalidomide treatment. (A) The skin lesions on the trunk andarms before lenalidomide treatment. (B) After the first treatment course, almost all skin lesions except for that onthe patient’s sternal region disappeared. (C) Documented transient flare reaction presented as erythema aroundskin lesions. (D) All lesions, including the sternal lesion, disappeared after the fifth cycle.
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mailto:[email protected]
SUPPORTSupported in part by the Center of Innovation Program of the JapanScience and Technology Agency and Ministry of Education, Culture,Sports, Science and Technology Innovative Area 15H05912.
AUTHOR CONTRIBUTIONSConception and design: Kazuaki Yokoyama, Arinobu TojoFinancial support: Kenzaburo Tani, Satoru MiyanoAdministrative support: Hiroshi YotsuyanagiProvision of study material or patients: Makoto Nakashima, Yasunori Ota,Kaoru UchimaruCollection and assembly of data: Yasuki Hijikata, Kazuaki Yokoyama,Makoto Yamagishi, Lay Ahyoung Lim, Kaoru UchimaruData analysis and interpretation: Yasuki Hijikata, Kazuaki Yokoyama,Nozomi Yokoyama, Yasuo Matsubara, Eigo Shimizu, Makoto Nakashima,Yasunori Ota, Rui Yamaguchi, Mika Ito, Yukihisa Tanaka, TamamiDenda, Kenzaburo Tani, Hiroshi Yotsuyanagi, Seiya Imoto, SatoruMiyanoManuscript writing: All authorsFinal approval of manuscript: All authorsAccountable for all aspects of the work: All authors
DATA AVAILABILITY STATEMENTBoth sequence data and materials described in this article are availableupon request to the corresponding author K.Y. ([email protected]), but the request must include a description of the researchproposal.
AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OFINTERESTThe following represents disclosure information provided by authors ofthis manuscript. All relationships are considered compensated unlessotherwise noted. Relationships are self-held unless noted. I = Immediate
Family Member, Inst = My Institution. Relationships may not relate to thesubject matter of this manuscript. For more information about ASCO’sconflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.Open Payments is a public database containing information reported bycompanies about payments made to US-licensed physicians (OpenPayments).
Rui YamaguchiHonoraria: Janssen Pharmaceuticals, Chugai Pharma
Kenzaburo TaniStock and Other Ownership Interests: Shinnihonseiyaku Co Ltd, PrecisionTherapeutics, Oncolys BioPharmaConsulting or Advisory Role: Precision TherapeuticsResearch Funding: Shinnihonseiyaku Co Ltd, Neopharma Japan,Takara Bio
Kaoru UchimaruHonoraria: CelgeneResearch Funding: Daiichi Sankyo, Celgene, NEC
Arinobu TojoHonoraria: Otsuka PharmaceuticalConsulting or Advisory Role: SysmexSpeakers’ Bureau: Novartis, Otsuka Pharmaceutical, Bristol-MyersSquibbResearch Funding: Chugai Pharma, Torii Pharmaceutical, KM Biologics
No other potential conflicts of interest were reported.
ACKNOWLEDGMENTWe thank all members of the study team as well as the patients and theirfamilies.
REFERENCES1. Agar NS, Wedgeworth E, Crichton S, et al: Survival outcomes and prognostic factors in mycosis fungoides/Sézary syndrome: Validation of the revised In-
ternational Society for Cutaneous Lymphomas/European Organisation for Research and Treatment of Cancer staging proposal. J Clin Oncol 28:4730-4739,2010
2. Wilcox RA: Cutaneous T-cell lymphoma: 2017 update on diagnosis, risk-stratification, and management. Am J Hematol 92:1085-1102, 2017
3. Wang L, Ni X, Covington KR, et al: Genomic profiling of Sézary syndrome identifies alterations of key T cell signaling and differentiation genes. Nat Genet47:1426-1434, 2015
4. Wakai T, Prasoon P, Hirose Y, et al: Next-generation sequencing-based clinical sequencing: Toward precision medicine in solid tumors. Int J Clin Oncol24:115-122, 2019
5. Patel NM, Michelini VV, Snell JM, et al: Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist 23:179-185, 2018
6. KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.kegg.jp or http://www.genome.jp/kegg
7. Kyoto Encyclopedia of Genes and Genomes: KEGG DRUG: Lenalidomide. https://www.kegg.jp/dbget-bin/www_bget?D04687+-us
8. Innovative Drug Research and Bioinformatics Group: Therapeutic Target Database. http://db.idrblab.net/ttd/data/drug/details/d0q5nx
9. Querfeld C, Rosen ST, Guitart J, et al: Results of an open-label multicenter phase 2 trial of lenalidomidemonotherapy in refractory mycosis fungoides and Sézarysyndrome. Blood 123:1159-1166, 2014
10. Bagot M, Hasan B, Whittaker S, et al: A phase III study of lenalidomide maintenance after debulking therapy in patients with advanced cutaneous T-celllymphoma - EORTC 21081 (NCT01098656): Results and lessons learned for future trial designs. Eur J Dermatol 27:286-294, 2017
11. Damodaran S, Berger MF, Roychowdhury S: Clinical tumor sequencing: Opportunities and challenges for precision cancer medicine. Am Soc Clin Oncol EducBook 35:e175-e182, 2015
12. Kritharis A, Coyle M, Sharma J, et al: Lenalidomide in non-Hodgkin lymphoma: Biological perspectives and therapeutic opportunities. Blood 125:2471-2476,2015
13. Kotla V, Goel S, Nischal S, et al: Mechanism of action of lenalidomide in hematological malignancies. J Hematol Oncol 2:36, 2009
14. Lenartić M, Jelenčić V, Zafirova B, et al: NKG2D promotes B1a cell development and protection against bacterial infection. J Immunol 198:1531-1542, 2017
15. Franssen LE, Nijhof IS, Couto S, et al: Cereblon loss and up-regulation of c-Myc are associated with lenalidomide resistance in multiple myeloma patients.Haematologica 103:e368-e371, 2018
16. Sriraman SK, Aryasomayajula B, Torchilin VP: Barriers to drug delivery in solid tumors. Tissue Barriers 2:e29528, 2014
17. Aue G, Sun C, Liu D, et al: Activation of Th1 immunity within the tumor microenvironment is associated with clinical response to lenalidomide in chroniclymphocytic leukemia. J Immunol 201:1967-1974, 2018
Hijikata et al
538 © 2020 by American Society of Clinical Oncology
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http://www.asco.org/rwchttps://ascopubs.org/po/author-centerhttps://ascopubs.org/po/author-centerhttps://openpaymentsdata.cms.gov/https://openpaymentsdata.cms.gov/http://www.kegg.jp or http://www.genome.jp/kegghttps://www.kegg.jp/dbget-bin/www_bget?D04687+-ushttp://db.idrblab.net/ttd/data/drug/details/d0q5nx
18. Bhutani M, Zhang Q, Friend R, et al: Investigation of a gene signature to predict a gene signature to predict response to immunomodulatory derivatives forpatients with multiple myeloma: An exploratory, retrospective study using microarray datasets from prospective clinical trials. Lancet Haematol 4:e443-e451,2017
19. Neri P, Gratton K, Ren L, et al: RNA-sequencing of paired pre-treatment and relapse samples reveals differentially expressed genes associated withlenalidomide resistance in multiple myeloma. Blood 122:530, 2013
n n n
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APPENDIX
Materials and Methods
Patients and samples. Our study protocol was approved by ourinstitutional research ethics committee and was in accordance with theDeclaration of Helsinki. Three patients were recruited for our study, allof whom provided written informed consent. The characteristics of tworeference patients with adult T-cell leukemia/lymphoma (ATL) arelisted in Table A2. The following samples were collected: (1) the tumor(cutaneous T-cell lymphoma [CTCL]), patient’s skin tumor samplebefore lenalidomide therapy, and control (CTCL), patient’s tumor-freenormal skin lesion, which had been histologically confirmed, and (2)the tumor (ATL), tumor-rich peripheral blood mononuclear cells(PBMNCs) before lenalidomide therapy in a patient with ATL(uniquepatient number 1 [UPN1]) for whom clinical partial response wasobserved under lenalidomide therapy, and control (ATL), tumor-richPBMNCs before lenalidomide therapy in another patient with ATL(UPN2) for whom no objective response had been observed withlenalidomide therapy.
Extraction of DNA and RNA. DNA was extracted using the GentraPuregene Blood Kit and the QIAamp Circulating Nucleic Acid Kit(QIAGEN, Hilden, Germany). DNA was quantified using the QubitdsDNA HS Assay Kit (Life Technologies, Carlsbad, CA). Total RNA wasextracted using the RNeasy Mini Kit (QIAGEN).
Whole-exome sequencing. To identify driver mutations, we per-formed whole-exome sequencing (WES). Libraries were prepared from200 ng of paired DNA (tumor/control) using SureSelectXT Human AllExon V5 (Agilent Technologies, Santa Clara, CA). WES was performedon the basis of 2× 100-base pair (bp) paired-end reads on the NextSeq500 platform with NextSeq 500/550 Kit v2 chemistry (Illumina, SanDiego, CA). All procedures were performed according to the manu-facturers’ protocols.
Somatic variant calling and subsequent primary filtering ofWES data. We used human genome build 19 as the referencesequence for our analysis. Primary processing of sequencing data wasperformed using our in-house software. Single nucleotide variantswere identified using Genomon2 (http://genomon.hgc.jp/exome/en/)with default parameters. All variant calls were annotated withANNOVAR (using an in-house annotation database resource). TheIntegrative Genomics Viewer (IGV) version 2.3.57 (https://software.broadinstitute.org/software/igv/download) was used to visualize andinspect the read alignments and variant calls. For our analysis, thedatabases used to annotate variants included the following: RefSeq(http://www.ncbi.nlm.nih.gov/RefSeq/), the 1000 Genomes Projectas of August 2015 (http://www.internationalgenome.org/data),dbSNP131 (http://www.ncbi.nlm.nih.gov/projects/SNP/), ToMMoversion 1 (https://ijgvd.megabank.tohoku.ac.jp/), the Human GeneticVariation Database as of October 2013 (http://www.hgvd.genome.med.kyoto-u.ac.jp/), ClinVar as of June 2015 (https://www.ncbi.nlm.nih.gov/clinvar/), the Human Gene Mutation Database Professional asof March 2015 (http://www.hgmd.cf.ac.uk/ac/index.php), the cBio-Portal for Cancer Genomics as of September 2015 (http://cbioportal.org), the TumorPortal as of September 2015 (http://www.tumorportal.org/), the Catalogue of Somatic Mutations in Cancer (COSMIC) version70 (http://cancer.sanger.ac.uk/cosmic), and the International CancerGenome Consortium Data Portal (https://dcc.icgc.org/). The compu-tational algorithms Sorting Intolerant From Tolerant (http://sift-dna.org)and PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) were used topredict whether mutations were damaging. Synonymous or noncodingvariants were excluded, and single nucleotide polymorphisms (SNPs;minor allele frequency . 1%) reported to be present in either of theaforementioned SNP databases were automatically excluded, unlessthey were recurrently (three or more times) found in the COSMICdatabase. Copy number variation analysis was performed using CNVkitversion 0.8.2 software (E. Talevich, University of Southern California,San Francisco, San Francisco, CA), and the data were filtered on thebasis of the following parameters: log2 ≥ 0.6 or log2 ≤ −1 and read
depth . 10. Upon somatic mutation calling followed by a primaryfiltering step, the list of variants was stored in a variant calling file format(.vcf). Gene-level copy number alterations, as a log2 ratio of tumor tonormal, were also stored in text format (.log2).
Identification of putative driver mutations fromWES data. Weclassified variants into three tiers. We regarded tier 1 mutations asdefinitive driver mutations. Briefly, tier 1 included definitive drivermutations with abundant evidence in the literature and/or cancermutation databases with a variant frequency (VAF) of ≥ 5%. Tier2 included mutations with a little or no evidence in the literature and/ordatabases, namely, uncertain/unclassified significance, with VAF≥ 5%. Tier 3 included all remaining variants. The selection of mutationcriteria for tier 1 was as follows: all changes in the amino acid codingregions of annotated exons that were frequently (at least three times)reported in hematopoietic tumor samples in the COSMIC database(version 71) or all predictive, highly deleterious variants (in consensussplice site regions, frameshift mutations, any premature predictedtruncation mutation, or large in-frame insertions/deletions) in non-Hodgkin lymphoma or CTCL-associated genes, according to previousstudies (Ungewickell A, et al: Nat Genet 47:1056-1060, 2015; Choi J,et al: Nat Genet 47:1011-1019, 2015; Lohr JG, et al: Proc Natl AcadSci U S A 109:3879-3884, 2012). Curated tier 1-2 somatic mutationsare listed in Table A1.
RNA SequencingWe used the following RNA samples: the tumor (CTCL), patient’s skintumor sample before lenalidomide therapy; control (CTCL), and pa-tient’s tumor-free normal skin lesion; the tumor (ATL) and tumor-richPBMNCs in lenalidomide-responsive ATL (UPN1); and the control(ATL) and tumor-rich PBMNCs in lenalidomide-primary refractory ATL(UPN2). cDNA libraries for next-generation sequencing were con-structed from 100 ng of total RNA using TruSeq RNA Access LibraryPrep Kit (Illumina). Each paired end–indexed library was sequenced toa length of 75 nucleotides per pair (2 × 75 bp) using a NextSeq in-strument. Sequence reads were processed by our in-house Genomon-RNA/Genomon-expression pipeline (Shiraishi Y, et al: PLoS One 9:e114263, 2014; http://genomon.hgc.jp/rna/). IGV version 2.3.57 wasused to visualize the mapped sequence. For gene expression, frag-ments per kilobase of gene model per million mapped read (FPKM)values were calculated and used.
To investigate any changes in gene expression in a certain enrichedpathway involved in the clinical features of the current patient withCTCL, as well as the reference patients with ATL, we used the onlineDAVID tool version 6.8 (https://david.ncifcrf.gov/) and gene set en-richment analysis (GSEA) with FPKMmetrics at the gene level (tumor vcontrol). For DAVID-based pathway analysis, upregulated genes rel-ative to expression in the control (fold-change ≥ 2 and with a cutoffvalue of FPKM ≥ 10) were stored in comma-separated value format(.csv) and inputted into the web-based interface of DAVID. Theseoutputs were reviewed, and significantly enriched Kyoto Encyclopediaof Genes and Genomes (KEGG) pathways are listed in Table A3. Thefour significant lenalidomide-target pathways were also visualizedusing Pathview (https://pathview.uncc.edu/) and displayed in FiguresA7-A10. For GSEA, a stand-alone application was used (Broad In-stitute, Cambridge, MA; http://broadinstitute.org/gsea/index.jsp) withthe MSIGDB library version 7.1 as well as in-house–created gene setsthat were based on a false discovery rate threshold of q , 0.05.
ImmunohistochemistryFormalin-fixed paraffin embedded tissue sections of 3-μm thick-ness were deparaffinized in xylene and rehydrated in a series ofgraded alcohols and heated in an antigen retrieval solution at pH9.0 (Vector Laboratories, Burlingame, CA) for 10 minutes at 120°C.Endogenous peroxidase was blocked by incubation with 3% hy-drogen peroxidase for 5 minutes at room temperature. Next,sections were incubated with the following primary antibodies for30 minutes at 37°C: CD4 (clone EPR6855, dilution 1:250; Abcam,Cambridge, United Kingdom), CD8 (clone SP16, dilution 1:100;
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http://genomon.hgc.jp/exome/en/https://software.broadinstitute.org/software/igv/downloadhttps://software.broadinstitute.org/software/igv/downloadhttp://www.ncbi.nlm.nih.gov/RefSeq/http://www.internationalgenome.org/datahttp://www.ncbi.nlm.nih.gov/projects/SNP/https://ijgvd.megabank.tohoku.ac.jp/http://www.hgvd.genome.med.kyoto-u.ac.jp/http://www.hgvd.genome.med.kyoto-u.ac.jp/https://www.ncbi.nlm.nih.gov/clinvar/https://www.ncbi.nlm.nih.gov/clinvar/http://www.hgmd.cf.ac.uk/ac/index.phphttp://cbioportal.orghttp://cbioportal.orghttp://www.tumorportal.org/http://www.tumorportal.org/http://cancer.sanger.ac.uk/cosmichttps://dcc.icgc.org/http://sift-dna.orghttp://genetics.bwh.harvard.edu/pph2/http://genomon.hgc.jp/rna/https://david.ncifcrf.gov/https://pathview.uncc.edu/http://broadinstitute.org/gsea/index.jsp
Abcam), CD25 (clone 305, dilution 1:80; Leica Biosystems,Newcastle, United Kingdom), and Foxp3 (clone SP97, dilution 1:80; Abcam). After washing, sections were treated with secondaryantibody (EnVision+ Dual Link System-HRP; Dako, Glostrup,Denmark) for 30 minutes at 37°C. To visualize antigen-antibody
complex, EnVision+ DAB kit (Dako) was used, and then sectionswere counterstained in hematoxylin. Normal lymph node wasplaced on the same slide glass as a positive control, and negativecontrols were used in phosphate-buffered saline instead of theprimary antibodies.
Tumor DNAskin tumor
Control DNAnormal tumor
Somatic mutation call byGenomon pipeline
Copy number alterationcall by CNVkit pipeline
1. Variants
.vcf file
2. Copy number alterations
.log2 file
Annotation by ANNOVARwith in-house medicalinformatics pipeline
Curation of candidategenes
MTBActionable gene alteration
WES on NextSeq(Illumina)
FIG A1. Flowchart of whole-exome sequencing. MTB,molecular tumor board; vcf, variant calling file; WES, wholeexome sequencing.
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Curation of candidate genes
Identification of lenalidomidetarget signature
Prediction of lenalidomideResponse in current patient
with CTCL
MTBActionable gene alteration
Tumor RNA-seqlenalidomide-responsive ATL (UPN1) PBMNC
Control RNA-seqlenalidomide-refractory ATL (UPN2) PBMNC
Tumor RNAskin tumor
Control RNAnormal tumor
Present CTCL case
RNA-seq on NextSeq(Illumina)
Transcript assembly/quantification (FPKM)by Genomon RNA
Differential gene expression Fold change ≥ 2 Tumor (or control) FPKM ≥10 .dge file
Exploring overlapping lenalidomide targetSignature in in-house RNA-seq data:A lenalidomide-responsive patient with ATL (Fig A3)
Functional–annotation/pathway analysis
GSEA DAVID
FIG A2. Flow of overall RNA sequencing. ATL, adult T-cell leukemia/lymphoma; CTCL, cutaneous T-cell lymphoma;.dge, digital gene expression; FPKM, fragments per kilobase of gene model per million mapped read; GSEA, gene setenrichment analysis; Len, lenalidomide; MTB, molecular tumor board; PBMNC, peripheral blood mononuclear cell;RNA-seq, RNA sequencing; UPN, unique patient number.
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RNA-seq: tumor v control
Differential gene expression,upregulated in tumor (v control):
1,250 genes (patient with CTCL) 1,197 genes (patient with ATL)
Pathway analysis by DAVID (KEGG pathway)
Curation of overlapping KEGG pathways
1. Lenalidomide target pathway in KEGG-DRUG/pathway
KEGG_NF-kappa_B_signaling_pathway
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY
KEGG_APOPTOSIS
2. HTLV-1 signature
KEGG_HTLV-1 infection
Identification of HTLV-1 + lenalidomide target pathway Confirmation of lenalidomide target signature by GSEA analysis
Current patient with CTCL
73 52 88
Patient with ATL
FIG A3. Flow chart of overlapping lenalidomide (Len) target signaturediscovery. ATL, adult T-cell leukemia/lymphoma; CTCL, cutaneousT-cell lymphoma; GSEA, gene set enrichment analysis; KEGG, KyotoEncyclopedia of Genes and Genomes; RNA-seq, RNA sequencing.
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Rank
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ist M
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(Sig
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Noi
se)
Enric
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Enrichment plot: GEORG ETALJOURNALOFIMMUNOLOGYTABLES2_LENALIDOMIDE-CLL_ALL_DAY8TODAY0
Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Enrichment plot: KEGG-NF-KAPPAB SIGNALING PATHWAY
Rank
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Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Enrichment plot:HINATA_NFKB_IMMU_INF
Rank
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(Sig
nal2
Noi
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Enric
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–5.0–2.50.02.55.0
0.00.10.20.30.40.5
0.70.6
0.8
0 5,000
Zero cross at 7660
‘TUMOR’ positively correlated)
‘NORMAL’ (negatively correlated)
10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tsc
ore
–5.0–2.50.02.55.0
0.450.400.350.300.250.200.150.100.050.00
–0.05–0.10
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Enrichment plot: KEGG_APOPTOSIS
Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
ore
–5.0–2.50.02.55.0
–0.1–0.2
0.00.10.20.3
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Enrichment plot:LANCETHEMATOLOGYTABLES2_ALL
Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
ore
–5.0–2.50.02.55.0
0.00.10.20.30.4
0.60.5
0.7
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Enrichment plot:TIAN_TNF_SIGNALING_VIA_NFKB
Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Zero cross at 7660
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
ore
–5.0–2.50.02.55.0
0.0–0.1–0.2
0.10.20.30.40.5
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Enrichment plot:KEGG_CYTOKINE_CYTOKINE_
RECEPTOR_INTERACTION
Zero cross at 7660
‘TUMOR’ positively correlated)
Enrichment plot:KEGG_NATURAL_KILLER_
CELL_MEDIATED_CYTOTOXICITY
‘NORMAL’ (negatively correlated)
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
ore
–5.0–2.50.02.55.0
0.0–0.1
0.10.20.30.40.5
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data SetEnrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
FIG A4. Lenalidomide-target signature enriched in cutaneous T-cell lymphoma (CTCL) on the basis of gene set enrichment analysis (GSEA; related toTable 1). Enrichment plots and heat maps from GSEA in the patient. GSEA of the tumor disclosed significant enrichment of nuclear factor-κB (NF-κB)pathways, including that of a custom gene set, GEORG ETALJOURNALOFIMMUNOLOGYTABLES2_LENALIDOMIDE-CLL_ALL_DAY8TODAY0, on the basisof the report and Supplementary Table 2 by Aue et al17 as follows: NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY from the MSIGDB library,CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION from the MSIGDB library, TIAN_TNF_SIGNALING_VIA_NFKB and APOPTOSIS from the MSIGDBlibrary, and a custom gene set, LANCETHEMATOLOGYTABLES2_ALL, on the basis of the report and Supplementary Table 2 by Bhutani et al18. Red indicatesthat gene expression increased compared with that in control. Blue indicates that gene expression decreased compared with that in control. KEGG, KyotoEncyclopedia of Genes and Genomes.
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Enrichment plot:KEGG_CYTOKINE_CYTOKINE_
RECEPTOR_INTERACTION
Rank
ed L
ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
ore
–5.0–2.50.02.55.0
0.00.10.20.30.4
0.60.5
0 5,000 10,000 15,000 20,000 25,000
Rank in Ordered Data Set
Enrichment plot: GEORG ETALJOURNALOFIMMUNOLOGYTABLES2_LENALIDO MIDE-CLL_ALL_DAY8TODAY0
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Enrichment plot:LANCETHEMATOLOGYTABLES2_ALL
Rank
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ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
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–5.0–2.50.02.55.0
0.0–0.1
0.10.20.30.40.5
0 5,000 10,000 15,000 20,000 25,000
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Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Rank
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ist M
etric
(Sig
nal2
Noi
se)
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hmen
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–5.0–2.50.02.55.0
0.0–0.1
0.10.20.30.40.5
0 5,000
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
10,000 15,000 20,000 25,000
Rank in Ordered Data SetEnrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
Rank
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ist M
etric
(Sig
nal2
Noi
se)
Enric
hmen
tSc
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–5.0–2.50.02.55.0
0.0–0.1
0.10.20.30.4
0.60.5
0 5,000 10,000 15,000 20,000 25,000
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Enrichment plot:TIAN_TNF_SIGNALING_VIA_NFKB
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Rank
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etric
(Sig
nal2
Noi
se)
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hmen
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–5.0–2.50.02.55.0
0.00–0.05–0.10
0.100.05
0.150.200.250.300.350.400.45
0 5,000 10,000 15,000 20,000 25,000
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Enrichment plot:KEGG_APOPTOSIS
Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
Rank
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etric
(Sig
nal2
Noi
se)
Enric
hmen
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–5.0–2.50.02.55.0
0.00.10.20.30.4
0.60.5
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Enrichment plot:HINATA_NFKB_IMMU_INF
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Zero cross at 8016
‘TUMOR’ (positively correlated)
‘NORMAL’ (negatively correlated)
Rank
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(Sig
nal2
Noi
se)
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Enrichment plot:KEGG_NATURAL_KILLER_CELL_
MEDIATED_CYTOTOXICITY
Zero cross at 8016
‘TUMOR’ (positively correlated)
Enrichment plot:KEGG-NF-KAPPA B SIGNALING PATHWAY
‘NORMAL’ (negatively correlated)
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Rank in Ordered Data SetEnrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores Enrichment profile Hits Ranking metric scores
FIG A5. Lenalidomide-target signature enriched in adult T-cell leukemia/lymphoma (ATL) on the basis of gene set enrichment analysis (GSEA; related toTable 1). Enrichment plots and heat maps from GSEA of ATL. In relation to Fig A4, GSEA of the tumor disclosed significant enrichment of nuclear factor-κB(NF-κB) pathways, including the gene sets of NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION,TIAN_TNF_SIGNALING_VIA_NFKB, APOPTOSIS and custom gene sets on the basis of the report and Appendix Table A2 by Aue et al17 and Bhutani et al18.Red indicates that gene expression increased compared with that in the control. Blue indicates that gene expression decreased compared with that in thecontrol. KEGG, Kyoto Encyclopedia of Genes and Genomes.
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B C
E F
A
D
IHG
FIG A6. Histologic and immunohistochemical analysis of tumor specimens before and after the lenalidomidetreatment. Hematoxylin and eosin staining and anti-CD8 and anti-Foxp3 antibody staining of tumor cells andlymphocytes (A, D, and G) before the lenalidomide treatment, (B, E, and H) in a tumor showing good response afterthe first course, and (C, F, and I) in a tumor showing a poor response after three courses of lenalidomide. (D and E) Aremarkable number of CD8+ cells are shown, whereas (G, H, and I) few Foxp3+ cells were observed in each tumor.The number of CD8+ cells after the first course of lenalidomide was increased compared with that (E) beforetreatment but was decreased in the poorly responding lesion (F) after the third course of lenalidomide.
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TCRLck
Zap
SykBLNK
LAT PLC1
PLC2Btk
LynBCR
IL-1R
TNF-R1TNF
IL-1
Antigen
MHC/antigen
EDA-A1
EDA-A2
LBPLPS(G-)
MD-2
CD40L
RANKL
LTALTB
LIGHT
BAFF
EDA-A2 XEDAR
BAFF-R
LT-R
TRAF2
TRAF2TRAF3
TRAF6cIAF1/2
+u
TIRAP
TRAF6
TRAF6
TRAF6
TRAF2/5TRADD
CYLD
TAK1TAB
Btk
IB
(Y42)
IB
p50
p50
p65
p50
IB Degradation
NEMO
SUMOylation
DNA
Shuttling
DNA damage
PARP1
–1 0 1
–1 0 1
ATMNEMO
NEMOPIDDRIP1
c-IAP1/2
Bcl-XL
TRAF1/2
p100
IL8
IL-1 TNF-
IB
MIP-2
uPA
Survival
Survival
Activation ofnoncanonical pathway
Survival
Positive feedback
Negative feedback
Inflammation
Inflammation
Survival
Lymphoid tissuehoming
B-cell development,B-cell survival
Myelpoiesis andB-cell lymphopoiesis
Lymphocyte adhesion,T-cell costimulation
(H/R)
Bcl-XL(NGF)
Bcl-XL
A1/Bf1-1
(UV)
MIP-1
(TNF-)VCAM-1
A20
COX2
(TNF-, IL-1, LPS)
(Antigen, TNF-)
c-FLIP
Bcl-2 gadd45
A1/Bf1-1
XIAP
PIASyUbs9
(S32, S36)+p
p65
p65
CK2UV, HER2
Atypical pathways
Pervanadate,H/R,H2O2, EGF, CNTF, NGF
+p
+p
+p +p
NF-B
NF-B
NF-B
NF-B
p100RelB
p52DNA
DNA
DNA
Degradation
Degradation
BLC ELC
SLC
SDF-1
BAFF
ICAM
(LT/LIGHT)
(S/T (PEST))
IKKIKK
NEMO
CARMA
PKC
+p+pCa2+
Ca2+
DAG
DAG
IP3
IP3 PKC8
Bcl10ELKS
MALT1
Canonical pathway
RIG-I–like receptorsignaling pathway
Ubiquitin-mediatedproteolysis
Proteasome
BCRsignaling pathway
TCRsignaling pathway
Genotoxic agent
Calcium signalingpathway
cIAP1/2
Apoptosis
Auto-ubiquitination
Auto-ubiquitination
Toll-like receptorsignaling pathway
Osteoclastdifferentiation
Cytokine-cytokinereceptor interaction
Data on KEGG graphrendered by Pathview
+u
-u
+u
+p
+u
+u
+p
+p
RIP1
TRAF3
RIG-I
TRAF6
TRIM25
TRAF2/6
IRAK1/4
IRAK1/4
TRAF6TRAM
MyD88
MyD88
TRIFRIP1
TRAF6TRAF2
NIK
NIK
cIAP1/2
(S866, S870)
Partialdegradation
IKK
Noncanonical pathway
TRAF5
TRAF2
TRAF2TRAF3
TRAF3TRAF6
TRAF3
RANK
TLR4
CD14
CD40
Virus
XEDAR
EDAR EDARADD
TCRLck
Zap
SykBLNK
LAT PLC1
PLC2BtkLyn
BCR
IL-1R
TNF-R1TNF
IL-1
Antigen
MHC/antigen
EDA-A1
EDA-A2
LBPLPS(G-)
MD-2
CD40L
RANKL
LTALTB
LIGHT
BAFF
EDA-A2 XEDAR
BAFF-R
LT-R
TRAF2
TRAF2TRAF3
TRAF6cIAP1/2
+u
TIRAP
TRAF6
TRAF6
TRAF6
TRAF2/5TRADD
CYLD
TAK1TAB
Btk
IBa
(Y42)
IkB
p50
p50
p65
p50
IB Degradation
NEMO
SUMOylation
DNA
Shuttling
DNA damage
PARP1ATM
NEMO
NEMOPIDDRIP1
c-IAP1/2
Bcl-XL
TRAF1/2
p100
IL8
IL-1 TNF-
IB
MIP-2
uPASurvival
Survival
Activation ofnoncanonical pathway
Survival
Positive feedback
Negative feedback
Inflammation
Inflammation
Survival
Lymphoid tissuehoming
B-cell development,B-cell survival
Myelpoiesis andB-cell lymphopoiesis
Lymphocyte adhesion,T-cell costimulation
(H/R)
Bcl-XL(NGF)
Bcl-XL
A1/Bf1-1
(UV)
MIP-1
(TNF-)VCAM-1
A20
COX2
(TNF-, IL-1, LPS)
(Antigen, TNF-)
c-FLIP
Bcl-2 gadd45
A1/Bf1-1
XIAP
PIASyUbs9
(S32, S36)+p
p65
p65
CK2UV, HER2
Atypical pathways
Pervanadate,H/R,H2O2, EGF, CNTF, NGF
+p
+p
+p +p
NF-B
NF-B
NF-B
p100RelB
p52 DNA
DNA
DNA
Degradation
Degradation
BLC ELC
SLC
SDF-1
BAFF
ICAM
(LT/LIGHT)
(S/T (PEST))
IKKIKKNEMO
CARMA
PKC
+p+pCa2+
Ca2+
DAG
DAG
IP3
IP3 PKC8
Bcl10ELKS
MALT1
Canonical pathway
RIG-I–like receptorsignaling pathway
Ubiquitin-mediatedproteolysis
Proteasome
BCRsignaling pathway
TCRsignaling pathway
Genotoxic agent
Calcium signalingpathway
cIAP1/2
Apoptosis
Auto-ubiquitination
Auto-ubiquitination
Toll-like receptorsignaling pathway
Osteoclastdifferentiation
Cytokine-cytokinereceptor interaction
Data on KEGG graphrendered by Pathview
-u
+u
+u
+p
+u
+u
+p
+p
RIP1
TRAF3
RIG-I
TRAF6
TRIM25
TRAF2/6
IRAK1/4
IRAK1/4
TRAF6TRAM
MyD88
MyD88
TRIFRIP1
TRAF6TRAF2
NIK
NIK
cIAP1/2
(S866, S870)
Partialdegradation
IKK
Noncanonical pathway
TRAF5
TRAF2
TRAF2TRAF3
TRAF3TRAF6
TRAF3
RANK
TLR4
CD14
CD40
Virus
XEDAR
EDAR EDARADD
NF-B Signaling Pathway
NF-B Signaling Pathway
A
B
FIG A7. Lenalidomide target pathway: nuclear factor-κB (NF-κB) signaling pathway. (A) Cutaneous T-cell lym-phoma. (B) Adult T-cell leukemia/lymphoma. Green gradient colors indicate downregulation of the gene, and redcolors indicate upregulation of the gene relative to the control sample. BCR, B-cell receptor; H/R, hypoxia/re-oxygenation; IL, interleukin; KEGG, Kyoto Encyclopedia of Genes and Genomes; LPS, lipopolysaccharide; MHC,major histocompatibility complex; TCR, T-cell receptor; TNF-α, tumor necrosis factor-α; UV, ultraviolet.
Clinical Sequencing in a Patient With Sézary Syndrome
JCO Precision Oncology 547
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HumanTarget cell
(MHCclass I)
Normal cell
Stressed ortumor cell
Apoptosis
Infectedcell
?
DNA
CASP3 Perforin
TRAILR
CD48
IFNyR
FAS FASL
FASL
Cytokine-cytokinereceptor interaction
JAK-STATsignaling pathway
Cytotoxic granulesmovement and exocytosis
Perforin
Granzyme
TRAIL
IFNsR
2B4
NKG2D DAP-10
SAP
EAT-2
LAT
+p
+p
+p
+p
+p
+p
Ca2+
DAG
NK-cell activation
IP3 -p
-p
-p
+p
+p
+p
Fyn
Syk
ZAP70
Lck
DAP-12
ITGAL
CD94NKG2A/B
KIR2DL
KIR3DL2
KIR3DL1HLA-B
HLA-A3
HLA-G1
HLA-B46
HLA-C
HLA-E
ICAM1/2
MICA
MICB
ULPP1-3
HA
?
?
?
SHP-2
SHP-1
Activating receptors
ITGB2
KIR2DS
NKG2C/E
NKp44
NKp46
FcR1
CD3FcRIII
Antigen
AntibodyIgG
NKp30
CD94 SLP-76
LATPLC
PI3K
Shc Grb2
PLC
Fyn
Sos Ras Raf
ERK1/2
MULT1
Rae-1
H-60
?
m157Antigen
IgGAntibody
NKG2DL
Cytokine release
TNF-
GM-CSF
IFN-DNA
DNA
NFATCaN
PKC
Calcium signalingpathway
NKR-P1C
NKp46
FcRIII
Ly49H
CD94NKG2C
Ly49D
ITGAL
NKG2A/BCD94
Activating receptors
NK cellInhibitory receptors
Ly49I
Ly49G2
Ly49C
Ly49AH-2Db
(MHCclass I)
Target cell
H-2Dd
H-2Dk
H-2Kb
H-2Kd
Qa-1
ICAM1/2
Inhibitory receptors
ITGB2
(MCMV)
?
MEK1/2MAPK signalingpathway
PAK1
Vav
Pyk-2
Rac
3BP2
IFNs
Bid
Granzyme
Data on KEGG graphrendered by Pathview
HumanTarget cell
(MHCclass I)
Normal cell
Stressed ortumor cell
Apoptosis
Infectedcell
?
DNA
CASP3 Perforin
Perforin
TRAILR
CD48
IFNR
FAS FASL
FASL
Cytokine-cytokinereceptor interaction
JAK-STATsignaling pathway
Cytotoxic granulesmovement and exocytosis
Perforin
Granzyme
TRAIL
IFNsR
2B4
NKG2D DAP-10
SAP
EAT-2
LAT
+p
+p
+p
+p
+p
+p
Ca2+
DAG
NK-cell activation
IP3 -p
-p
-p
+p
+p
+p
Fyn
Syk
ZAP70
Lck
DAP-12
ITGAL
CD94NKG2A/B
KIR2DL
KIR3DL2
KIR3DL1HLA-B
HLA-A3
HLA-G1
HLA-B46
HLA-C
HLA-E
ICAM1/2
MICA
MICB
ULPP1-3
HA
?
?
?
SHP-2
SHP-1
Activating receptors
ITGB2
KIR2DS
NKG2C/E
NKp44
NKp46
FcR1
CD3FcRIII
Antigen
Antibody
IgG
NKp30
CD94 SLP-76
LATPLC
PI3K
Shc Grb2
PLC
Fyn
Sos Ras Raf
ERK1/2
MULT1
Rae-1
H-60
?
m157Antigen
IgGAntibody
NKG2DL
Cytokine release
TNF-
GM-CSF
IFN-
TRAIL
TRAIL
DNA
DNA
NFATCaN
PKC
Calcium signalingpathway
NKR-P1C
NKp46
FcRIII
Ly49H
CD94NKG2C
Ly49D
ITGAL
NKG2A/BCD94
Activating receptors
NK cellInhibitory receptors
Ly49I
Ly49G2
Ly49C
Ly49AH-2Db
(MHCclass I)
Target cell
H-2Dd
H-2Dk
H-2Kb
H-2Kd
Qa-1
Inhibitory receptors
ITGB2
(MCMV)
?
MEK1/2MAPK signalingpathway
PAK1
Vav
Pyk-2
Rac
3BP2
IFNs
Bid
Granzyme
Data on KEGG graphrendered by Pathview
ICAM1/2
PI(3,4,5)P3
Perforin
PI(3,4,5)P3
NK-Cell–Mediated Cytotoxicity
NK-Cell–Mediated Cytotoxicity
–1 0 1
–1 0 1
A
B
FIG A8. Lenalidomide target pathway: natural killer (NK)-cell–mediated cytotoxicity. (A) Cutaneous T-cell lymphoma. (B)Adult T-cell leukemia/lymphoma. Green gradient colors indicate downregulation of the gene, and red colors indicateupregulation of the gene relative to the control sample. JAK-STAT, Janus kinase–signal transducers and activators oftranscription; KEGG, Kyoto Encyclopedia of genes and genomes; MAPK, mitogen-activated protein kinase; MCMV, murinecytomegalovirus; MHC, major histocompatibility complex.
Hijikata et al
548 © 2020 by American Society of Clinical Oncology
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CC subfamily
Chemokines
Cytokine-Cytokine Receptor Interaction
CXC subfamily
The class I helical cytokines
-Chain utilizing IL6/12-like Interferon family
IL1-like cytokines
IL17-like cytokinesThe class
II helical cytokinesIL10/28-like
Human/murine
Human
Prolactin family
IL4-like
C subfamily
CX3C subfamily
Nonclassified
NO signaltransduction
TNF family TGF- family
CCL1
CCL25
CCL19
CCL21
CCL3L1
CCL4CCL4L1
CCL4L2
CCL17
CCL22
CCL5
CCL3
CCL8
CCL9
CCL14
CCL16
CCL23
CCL15
CCL13
CCL6
CCL7
CCL2
CCL12
CCL11
CCL24
CCL27
CCL28
CCL18
CCL20
Data on KEGG graphrendered by Pathview
Data on KEGG graphrendered by Pathview
CCR6
CCR10
CCR3
CCR2
CCR11
CCR3
CCR1
CCR2
CCR11
CCR3
CCR5
CCR4
CCR7
CCR11
CCR9
CCR8 CXCL1
CXCL5
CXCL6
CXCL8
CXCL2
CXCL3
CXCL7
CXCL4
CXCL9
CXCL10
CXCL11
CXCL13
CXCL12
CXCL16
CXCL14
CXCR6
CXCR7
CXCR4
CXCR5
CXCR3
CXCR2
CXCR1
IL2RA
IL2
IL4
IL7
IL9
IL15
IL21
IL3
IL5
IL4
IL13
EPO
GH1
GH2
PRL
TPO
CSF3
LEPIL29
IL28BIL28A
IL26
IL22
IL24
IL20
IL19
IL10
OSM
OSM
LIF
CTF1
CNTF
CLCF1
IL31
IL27A
IL23A
IL12
IL11
IL6IL6R
IL6ST
IL11RA
IL12RB1
IL23RIL12RB1
IL27RA
IL31RA
IL6ST
OSMR
CNTFRLIFR
IL6ST
?LIFR
IL6ST
LIFRIL6ST
OSMRIL6ST
IL12RB2
IL6ST
IL10RAIL10RB
IL20RAIL20RB
IL22RA1IL20RB
IL22RA1IL10RB
IL20RAIL10RB
IL28RAIL10RB
CSF1
IL34
IL32
IL16
IL17E
IL17D
IL17C
IL17B
IL17F
IL17A
IL18
IL1F7
IL1F9
IL1F10
IL1F8
IL1F6
IL1F5
IL1RN
IL1R1
IL1R2
IL1RL2IL1RAP
IL18RAP
IL1RAP
IL17RC
IL17RB
IL17RE
IL17RAIL17RB
CD4
CSF1R
?
?
IL17RA
ST2
IL18R1
IL1RAPIL1B
IL1A
IFNG
IFNT1
IFNK
IFNW1
IFNB1
IFNATNF
LTA
LTB
LIGHT
FASLG
VEGI
TRAIL
?
EDA-A1
EDA-A2EDA
EDA
NGF
RANKL
TWEAK
CD70
CD30L
CD40LG
41BBL
OX-40L
GITRL
APRIL
BAFF
?
?
DCR3
FAS
HVEM
LTBR
TNFR2
TNFR1TGFB1
TGFB2
TGFB3
GDF15
GDF2
BMP10
BMP3
GDF10
GDF11
MSTN
INHBA
INHBBGDF1
GDF3
NODAL
GDF9
INHBE
AMH
AMHR2
AMHR2ACVR1
ACVR1B
ACVR2B
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVRL1BMPR2
ACVRL1
ACVRL1
TGFBR2
BMP2
BMP4
GDF5
GDF6
GDF7
BMP15
BMP5
BMP6
BMP7
BMP8
TGFBR2
TGFBR2TGFBR1
ACVR2A
?
TGFBR1
TGFBR1
ACVR1B
ACVR1B
ACVR1C
ACVR1CACVR2A
BMPR2
BMPR2BMPR1B
BMPR1B
BMPR1B
BMPR1A
BMPR1A
BMPR1A
BMPR2
BMPR2BMPR1B
BMPR1B
BMPR1B
BMPR1A
BMPR1A
BMPR1A
BMPR2
BMPR2
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVR1
ACVR1
ACVR1
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVR2A
ACVR2B
?
?
BMPR1A
INHBC
INHA
DR3
DR4
DR5
DCR1
DCR2
DR6
EDAR
XEDAR
NGFR
RANK
FN14
CD27
CD30
CD40
41BB
Ox40
GITR
BCMA
TACI
BAFFR
TROY
RELT
OPG
IFNAR1IFNAR2
IFNGR1IFNGR2
IL33
TSLP
CSF2
IL2RBIL2RG
IL2RBIL2RG
IL4R
IL7R
IL9R
IL15RA
IL2RG
IL2RG
IL2RG
IL2RB
IL21RIL2RG
IL7RTSLPR
CSF2RBIL3RA
CSF2RB
CSF2RB
IL4R
IL4R
IL4R
IL13RA1
IL13RA2
IL13RA1
IL13RA2
EPOR
GHR
PRLR
MPL
CSF3R
LEPR
CSF2RA
IL5RA
IL2RG
CXCL15
CXCL17
XCL1XCR1
?
?
?
CX3CR1
XCL2
CX3CL1
CXCL4L1
?
CCL26
CC subfamilyChemokines
Cytokine-Cytokine Receptor Interaction
CXC subfamilyThe class I helical cytokines
-Chain utilizing IL6/12-like Interferon family
IL1-like cytokines
IL17-like cytokines
The class II helical cytokinesIL10/28-like
Human/murine
Human
Prolactin family
IL4-like
C subfamily
CX3C subfamily
Nonclassified
NO signaltransduction
TNF family TGF- family–1 0 1
CCL1
CCL25
CCL19
CCL21
CCL3L1
CCL4CCL4L1
CCL4L2
CCL17
CCL22
CCL5
CCL3
CCL8
CCL9
CCL14
CCL16
CCL123
CCL15
CCL13
CCL6
CCL7
CCL2
CCL12
CCL11
CCL24
CCL27
CCL28
CCL18
CCL20 CCR6
CCR10
CCR3
CCR2
CCR11
CCR3
CCR1
CCR2
CCR11
CCR3
CCR5
CCR4
CCR7
CCR11
CCR9
CCR8 CXCL1
CXCL5CXCL6
CXCL8
CXCL2
CXCL3
CXCL7
CXCL4
CXCL9
CXCL10
CXCL11
CXCL13
CXCL12
CXCL16
CXCL14
CXCR6
CXCR7
CXCR4
CXCR5
CXCR3
CXCR2
CXCR1
IL2RA
IL2
IL4
IL7
IL9
IL15
IL21
IL3
IL5
IL4
IL13
EPO
GH1
GH2
PRL
TPO
CSF3
LEPIL29
IL28BIL28A
IL26
IL22
IL24
IL20
IL19
IL10
OSM
OSM
LIF
CTF1
CNTF
CLCF1
IL31
IL27A
IL23A
IL12
IL11
IL6 IL6RIL6ST
IL11RA
IL12RB1
IL23RIL12RB1
IL27RA
IL31RA
IL6ST
OSMR
CNTFRLIFR
IL6ST
?LIFR
IL6ST
LIFRIL6ST
OSMRIL6ST
IL12RB2
IL6ST
IL10RAIL10RB
IL20RAIL20RB
IL22RA1IL20RB
IL22RA1IL10RB
IL20RAIL10RB
IL28RAIL10RB
CSF1
IL34
IL32
IL16
IL17E
IL17D
IL17C
IL17B
IL17F
IL17A
IL18
IL1F7
IL1F9IL1F10
IL1F8
IL1F6
IL1F5
IL1RN
IL1R1
IL1R2
IL1RL2IL1RAP
IL18RAP
IL1RAP
IL17RC
IL17RB
IL17RE
IL17RAIL17RB
CD4
CSF1R
?
?
IL17RA
ST2
IL18R1
IL1RAPIL1B
IL1A
IFNG
IFNT1
IFNK
IFNW1
IFNB1
IFNATNF
LTA
LTB
LIGHT
FASLG
VEGI
TRAIL
?
EDA-A1
EDA-A2EDA
EDA
NGF
RANKL
TWEAK
CD70
CD30L
CD40LG
41BBL
OX-40L
GITRL
APRIL
BAFF
?
?
DCR3
FAS
HVEM
LTBR
TNFR2
TNFR1TGFB1
TGFB2
TGFB3
GDF15
GDF2
BMP10
BMP3
GDF10
GDF11
MSTN
INHBA
INHBBGDF1
GDF3
NODAL
GDF9
INHBE
AMH
AMHR2
AMHR2ACVR1
ACVR1B
ACVR2B
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVRL1BMPR2
ACVRL1
ACVRL1
TGFBR2
BMP2
BMP4
GDF5
GDF6
GDF7
BMP15
BMP5
BMP6
BMP7
BMP8
TGFBR2
TGFBR2TGFBR1
ACVR2A
?
TGFBR1
TGFBR1
ACVR1B
ACVR1B
ACVR1C
ACVR1CACVR2A
BMPR2
BMPR2BMPR1B
BMPR1B
BMPR1B
BMPR1A
BMPR1A
BMPR1A
BMPR2
BMPR2BMPR1B
BMPR1B
BMPR1B
BMPR1A
BMPR1A
BMPR1A
BMPR2
BMPR2
ACVR2B
ACVR2A
ACVR2A
ACVR2A
ACVR1
ACVR1
ACVR1
ACVR2B
ACVR2A
ACVR2B
ACVR2A
ACVR2A
ACVR2B
?
?
BMPR1A
INHBC
INHA
DR3
DR4
DR5
DCR1
DCR2
DR6
EDAR
XEDAR
NGFR
RANK
FN14
CD27
CD30
CD40
41BB
Ox40
GITR
BCMA
TACI
BAFFR
TROY
RELT
OPG
IFNAR1IFNAR2
IFNGR1IFNGR2
IL33
TSLP
CSF2
IL2RBIL2RG
IL2RBIL2RG
IL4R
IL7R
IL9R
IL15RA
IL2RG
IL2RG
IL2RG
IL2RB
IL21RIL2RG
IL7RTSLPR
CSF2RBIL3RA
CSF2RB
CSF2RB
IL4R
IL4R
IL4R
IL13RA1
IL13RA2
IL13RA1
IL13RA2
EPOR
GHR
PRLR
MPL
CSF3R
LEPR
CSF2RA
IL5RA
IL2RG
CXCL15
CXCL17
XCL1XCR1
?
?
?
CX3CR1
XCL2
CX3CL1
CXCL4L1
?
CCL26
–1 0 1
A
B
FIG A9. Lenalidomide target pathway: cytokine-cytokine receptor interaction. (A) Cutaneous T-cell lymphoma. (B) Adult T-cellleukemia/lymphoma. Green gradient colors indicate downregulation of the gene, and red colors indicate upregulation of thegene relative to the control sample. IFN, interferon; IL, interleukin; KEGG, Kyoto Encyclopedia of genes and genomes; TGF-β,transforming growth factor-β; TNF, tumor necrosis factor.
Clinical Sequencing in a Patient With Sézary Syndrome
JCO Precision Oncology 549
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TNF Signaling Pathway
Data on KEGG graphrendered by Pathview
cIAP1/2
ASK1
TAB1/2/3TAK1
NIKNF-B
Tpl2
IKK
IKK NF-B
PGAM5LMLKL
PI3K
TRAF1
TNF/LTA
TNF
TRAF2TRAF3
NIK
cIAP1/2
AIP1
RIP
JNK
MKK3
IKKs
NF-B
IB
p38
c-Jun
IRF1
Apoptosis
DNAIFN
Cell survival
Degradation
Necroptosis
DNA
DNA
Akt+p
+p
+p
+p
-p
+p
+p
+p
+p
+p
+p
+p
+p
+u
+p +p
+p
+p
RIP3RIP1
Deubiquitination
Necrosome
(Limited expression)
PGAM5SDrp1
NF-Bsignaling pathway
IB ubiquitinationand degradation
Ubiquitin-mediatedproteolysis
PI3K-Aktsignaling pathway
MEK1
MKK3/6
MSK1/2
AP-1
c/EBP
CREB
ERK1/2
p38
JNK1/2
ITCH c-FLIP
CASP3Apoptosis
Apoptosis
–1 0 1
–1 0 1
Ccl20Ccl5Ccl2
Cxcl1
Cxcl5
Csf1
Fas
IL1b
Bcl3
Fos
Mmp14Mmp9Mmp3
Vegfc
Nod2
Icam1 Sele Vcam1
Edn1
PRRs
Cell adhesion
Ptgs2
Synthesis of inflammatory mediators
Vascular effects
Jun JunB
Ifi47
Transcription factors
Remodeling of extracellular matrix
Intracellular signaling (negative)
Intracellular signaling (positive)
Nfkbia Socs3
IL6 IL15 Lif Tnf
Traf1Tnfaip3
Csf2
IL18R1
Leukocyte activation
Surface receptors
Inflammatory cytokines
Cxcl2
Cxcl10 Cx3cl1
Cxcl3
Leukocyte recruitment
CASP7CASP10
CASP8
FADD
MKK4/7
MAPKsignaling pathway
cIAP1/2 degradation
IBIKK
TRAF2/5
RIP1TRADD
SODD
Jag1
TNF Signaling Pathway
Data on KEGG graphrendered by Pathview
cIAP1/2
ASK1
TAB1/2/3TAK1
NIKNF-B
Tpl2
IKK
IKK NF-B
PGAM5LDrp1
PI3K
TRAF1
TNF/LTA
TNF
TRAF2
TRAF3
NIK
cIAP1/2
AIP1
RIP
JNK
MKK3
IKKs
NF-B
IB
p38
c-Jun
IRF1
Apoptosis
DNAIFN
Cell survival
Degradation
Necroptosis
DNA
DNA
Akt+p
+p
+p
+p
-p
+p
+p
+p
+p
+p
+p
+p
+p
+u
+p +p
+p
+p
RIP3 MLKLRIP1
Deubiquitination
Necrosome
(Limited expression)
PGAM5S
NF-Bsignaling pathway
IB ubiquitinationand degradation
Ubiquitin-mediatedproteolysis
PI3K-Aktsignaling pathway
MEK1
MKK3/6
MSK1/2
AP-1
c/EBP
CREB
ERK1/2
p38
JNK1/2
ITCH c-FLIP
CASP3Apoptosis
ApoptosisCcl20Ccl5Ccl2
Cxcl1
Cxcl5
Csf1
Fas
IL1b
Bcl3
Fos
Mmp14Mmp9Mmp3
Vegfc
Nod2
Icam1 Sele Vcam1
Edn1
PRRs
Cell adhesion
Ptgs2
Synthesis of inflammatory mediators
Vascular effects
Jun JunB
Ifi47
Transcription factors
Remodeling of extracellular matrix
Intracellular signaling (negative)
Intracellular signaling (positive)
Nfkbia Socs3
IL6 IL15 Lif Tnf
Traf1Tnfaip3
Csf2
IL18R1
Leukocyte activation
Surface receptors
Inflammatory cytokines
Cxcl2
Cxcl10 Cx3cl1
Cxcl3
Leukocyte recruitment
CASP7CASP10
CASP8
FADD
MKK4/7
MAPKsignaling pathway
cIAP1/2 degradation
IBIKK
TRAF2/5
RIP1TRADD
SODD
Jag1
TNFR1
TNFR2
TNFR1
TNFR2
A
B
FIG A10. Lenalidomide target pathway: tumor necrosis factor (TNF) signaling pathway. (A) Cutaneous T-cell lymphoma. (B)Adult T-cell leukemia/lymphoma. Green gradient colors indicate downregulation of the gene, and red colors indicateupregulation of the gene relative to the control sample. KEGG, Kyoto Encyclopedia of genes and genomes; MAPK, mitogen-activated protein kinase; NF-κB, nuclear factor-κB; PRR, pattern recognition receptor.
Hijikata et al
550 © 2020 by American Society of Clinical Oncology
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B
7 Nov 2017 12 Jan 2018 7 Feb 2018
Perc
ent
CD3–CD16+CD56+/lym
CD3+CD8+
CD3+CD4+CD45RA–Foxp3+high
0
10
20
30
40
50
60
70
16 Oct 2017
A
SSC-W, FSC-W subset102 103 104 105
CD235a/PI
0
0
102 103 104 1050 102 103 104 1050
102 103 104 1050 102 103 104 1050
50K
100K
150K
200K
250KFS
C56.6
PI–CD235a–250K
SSC
0
0
105
104
103
102
0
105
104
103
102
0
105
104
103
102
0
105
104
103
102
0
105
104
103
102
CD14
73.3
CD3–CD19– cells CD16
CD56
90.6
CD14– cells CD3
CD19
44.3
50.9
4.75
CD3+ T cells CD8
CD4
30.4
63
CD4+ T cells (Foxp3)
CD45
RA0.746 0.0403
6.0780.4 12.7
Doublet free
CD3+ T cells
CD235a–PI– cells CD14– cells CD3–CD19– cells
CD4+ T cells
50K 100K 150K 200K
FIG A11. Immunophenotypes of peripheral blood mononuclear cells (PBMNCs) from the patient during lenalidomide treatment. PBMNCs were stainedfor CD3, CD4, CD8, CD14, CD16, CD19, CD45RA, CD235a/PI, and Foxp3. (A) Lymphocytes (lym) were identified based on light scatter characteristics(low forward and low side scatter) and negative for CD14 and CD235/PI. Natural killer (NK) cells were defined as CD3−CD16+CD56+ after gating for CD19−
lymphocytes. Effector regulatory T (Treg) cells were defined as CD4+CD45RA−Foxp3+high. (B) The frequencies of NK cells and ratio of CD3+CD8+ T cellsincreased after lenalidomide treatment, which was not accompanied by an increase in effector Treg cells. FSC-W, forward scatter pulse width; SSC-W,side scatter pulse width.
Clinical Sequencing in a Patient With Sézary Syndrome
JCO Precision Oncology 551
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TABLEA1
.Cod
ingVa
riantsIden
tified
From
Who
le-ExomeSe
quen
cing
Tier
Grad
ing
(driv
er)
Chrom
Positio
nRe
fALT
Depth
(tumor)
Depth
(normal)
VAF
−Log
10
Pa
Gene
AminoAc
idCh
ange
COSM
IC70
IDSIFT
PolyPh
en-2
MutationTaster
117
7578
290
CT
9014
80.56
725
.1TP
53splicing
COSM
O41
3986
9—
—D
2(VUS)
1740
3121
26TC
TGGCAC
T14
813
40.07
43.0
KCNH4
p.P99
3fs
——
——
2(VUS)
1753
0074
53G
T89
630.07
91.4
TOM1L
1p.R24
7L—
DD
D
2(VUS)
418
7539
038
GT
164
133
0.10
44.4
FAT1
p.T2
901K
—T
PD
2(VUS)
1870
8042
1C
T12
398
0.12
23.8
LAMA1
p.A33
T—
TD
D
2(VUS)
312
6194
576
CG
4352
0.14
02.2
ZXDC
p.G45
R—
DB
N
2(VUS)
1772
8690
18G
A43
530.14
02.2
FDXR
p.R18
W—
DD
N
2(VUS)
1832
1506
5C
T57
520.14
02.2
MYO
M1
p.A53
T—
TD
D
2(VUS)
119
9519
97C
G49
840.14
33.2
MINOS1
p.Q11
2E—
——
—
2(VUS)
296
8011
04C
A97
790.14
43.5
ASTL
p.D77
Y—
DD
N
2(VUS)
1328
6113
93G
T12
511
30.14
45.2
FLT3
p.P41
3Q—
TD
D
2(VUS)
216
6854
673
GA
102
117
0.14
74.1
SCN1A
p.P14
51S
—D
DD
2(VUS)
1395
3638
07CGCGGCGGC
GGCGGCGGCGGCA
C60
670.15
03.1
SOX2
1p.15
9_16
6del
——
——
2(VUS)
412
1957
868
GA
9981
0.15
23.0
NDNF
p.R42
0X—
T—
D
2(VUS)
316
4732
921
CA
149
163
0.15
47.8
SIp.C13
30F
—T
PN
2(VUS)
1779
6619
73G
A17
316
10.16
28.5
HGS
p.R33
2QCOSM
3388
305
TD
D
2(VUS)
913
1252
77TT
T11
510
10.16
55.5
MPD
Zp.K15
82fs
——
——
2(VUS)
1739
5374
60G
T15
712
30.16
66.7
KRT3
4p.Q18
8K—
DB
N
2(VUS)
336
8734
72A
T12
113
40.17
47.2
TRAN
K1p.C24
90X
—T
—A
2(VUS)
995
2678
18AAATA
GA
134
137
0.17
97.8
ECM2
p.S4
86fs
——
——
2(VUS)
X15
5915
54GG
G53
430.18
92.7
ACE2
p.P49
2fs
——
——
2(VUS)
119
9520
61C
T63
740.19
04.3
MINOS1
p.S1
33F
——
——
2(VUS)
1689
6574
21C
T18
416
10.19
68.3
CPNE7
p.H45
0Y—
DD
D
2(VUS)
1870
5260
84G
A85
760.21
24.3
NET
O1
p.S1
49L
—T
DD
2(VUS)
353
5356
80A
T61
550.21
33.8
CACN
A1D
p.N13
9I—
DD
D
2(VUS)
178
4308
24C
A12
114
90.21
57.4
FUBP1
p.E1
89X
—T
—A
2(VUS)
123
7802
393
GT
7312
70.21
97.5
RYR
2p.R23
36I
—D
DD
2(VUS)
832
6568
5C
T18
213
80.22
09.1
CSMD1
p.E6
03K
—T
DD
2(VUS)
1193
5539
92CACAGCAGCG
C93
960.22
67.0
VSTM
5p.15
6_15
9del
——
——
2(VUS)
111
5840
44C
T48
610.22
94.2
PTCH
D2
p.T8
03M
—D
DD
2(VUS)
146
6509
54G
T55
480.25
54.2
TSPA
N1
p.V2
18L
—T
DD
2(VUS)
1286
7284
3AGATA
A12
013
20.25
811
.0CL
EC4D
p.D13
6fs
——
——
(Con
tinue
don
followingpa
ge)
Hijikata et al
552 © 2020 by American Society of Clinical Oncology
Downloaded from ascopubs.org by Tokyo Daigaku Ikagaku Kenkyujo on June 1, 2020 from 157.082.096.062Copyright © 2020 American Society of Clinical Oncology. All rights reserved.
TABLEA1
.Cod
ingVa
riantsIden
tified
From
Who
le-ExomeSe
quen
cing
(Con
tinue
d)Tier
Grad
ing
(driv
er)
Chrom
Positio
nRe
fALT
Depth
(tumor)
Depth
(normal)
VAF
−Log
10
Pa
Gene
AminoAc
idCh
ange
COSM
IC70
IDSIFT
PolyPh
en-2
MutationTaster
2(VUS)
2064
4532
CG
2534
0.28
02.9
SCRT2
p.G23
6A—
DD
D
2(VUS)
1915
8068
29G
A15
113
60.28
512
.0CY
P4F1
2p.R40
0Q—
DD
N
2(VUS)
1739
2217
48G
T12
212
80.28
712
.2KR
TAP2
-4p.T1
17N
—T
—N
2(VUS)
146
6509
55T
A52
420.28
84.1
TSPA
N1
p.V2
18E
—D
DD
2(VUS)
1720
1568
52G
T51
740.29
45.4
SPEC
C1p.S8
78I
COSM
1609
946
TD
D
2(VUS)
810
3287
953
CT
155
113
0.29
712
.4UBR5
p.G22
05R
—D
DD
2(VUS)
1480
2714
80C
A12
190
0.29
89.8
NRXN
3p.S9
44Y
——
DD
2(VUS)
2043
3535
25A
G41
320.31
73.5
WISP2
p.S1
42G
—T
PN
2(VUS)
X13
8633
26A
T11
286
0.32
110
.0F9
p.K18
8X—
T—
A
2(VUS)
729
6395
4G
T10
988
0.33
010
.5CA
RD11
p.S6
18Y
—D
DD
2(VUS)
313
5871
427
AG
9710
70.34
012
.1MSL
2p.L9
9P—
DB
D
2(VUS)
1953
5181
31A
G21
121
30.34
123
.4ER
VV-1
p.N26
3S—
——
—
2(VUS)
218
6609
095
CG
7010
10.34
310
.5FS
IP2
p.T2
34S
—D
—N
2(VUS)
2147
6274
32C
A42
720.35
76.2
LSS
p.E3
79D
—D
DD
2(VUS)
542
7970
23G
A53
630.37
77.8
CCDC1
52p.E1
75K
—T
PD
2(VUS)
773
0119
83G
T46
480.39
16.5
MLX
IPL
p.L3
78I
—T
DN
2(VUS)
1944
0966
99G
A51
290.39
24.6
IRGQ
p.P45
1S—
TD
N
2(VUS)
729
6395
5A
G11
488
0.40
413
.3CA
RD11
p.S6
18P
—D
DD
2(VUS)
1548
0552
95A
T71
116
0.40
812
.8SE
MA6
Dp.L2
47F
—D
DD
2(VUS)
1946
9156
85C
T15
615
60.41
019
.6CC
DC8
p.R12
8H—
DD
D
2(VUS)
1168
3694
16C
CC
9867
0.42
98.0
PPP6
R3
p.A76
0fs
——
——
2(VUS)
1256
4818
67C
A77
780.45
512
.8ER
BB3
p.Y2
65X
—T
—D
2(VUS)
1876
7552
35G
A83
830.45
813
.5SA
LL3
p.A10
82T
COSM
3388
512
TP
N
2(VUS)
739
0464
68G
A91
800.46
214
.0PO
U6F
2splicing
——
—N
2(VUS)
1232
1375
70GTG
TG
5147
0.47
18.3
KIAA
1551
p.12
28_
1228
del
——
——
2(VUS)
642
9930
08G
A72