Comprehensive genomic profiling of infiltrative follicular variant of papillary thyroid carcinoma.

diagnosis genetic alterations infiltrative follicular variant of papillary thyroid carcinoma subclonal architecture whole‐exome sequencing

Journal

Cancer
ISSN: 1097-0142
Titre abrégé: Cancer
Pays: United States
ID NLM: 0374236

Informations de publication

Date de publication:
14 Aug 2024
Historique:
revised: 11 07 2024
received: 28 03 2024
accepted: 28 07 2024
medline: 14 8 2024
pubmed: 14 8 2024
entrez: 14 8 2024
Statut: aheadofprint

Résumé

Infiltrative follicular variant of papillary thyroid carcinoma (IFVPTC) exhibits nuclear characteristics typical of papillary thyroid carcinoma (PTC) but demonstrates a follicular growth pattern. The diagnosis of IFVPTC presenting with atypical nuclear features of PTC poses challenges for both preoperative cytopathology and postoperative histopathology. In such cases, molecular markers are needed to serve as diagnostic aids. Given the limited knowledge of IFVPTC's genomic features, this study aimed to characterize its genetic alterations and identify clinically relevant molecular markers. Whole-exome sequencing of 50 IFVPTC tumor-normal pairs identified single-nucleotide variants, somatic copy number alterations (sCNAs), and subclonal architecture. Key mutations were verified via polymerase chain reaction and Sanger sequencing, whereas valuable biomarkers were validated via immunohistochemistry (IHC). This study found that endogenous processes rather than exogenous mutagens dominated the shaping of the genome of IFVPTC during tumorigenesis. BRAF V600E was the only common trunk mutation and significantly mutated gene in IFVPTC. Subcloning analysis found that most IFVPTC samples harbored two or more coexisting clones. sCNA analysis revealed that human leukocyte antigen C (HLA-C) and HLA-A were significantly amplified. Subsequent IHC investigations indicated that HLA-C shows promise in averting the misclassification of challenging-to-interpret IFVPTC and invasive encapsulated follicular variant of PTC (I-EFVPTC) as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Although there were several similarities between classic PTC and IFVPTC, they differed significantly in their sCNA patterns. This study provides valuable insights into IFVPTC's genetic alterations and highlights the potential of HLA-C IHC to distinguish challenging-to-interpret IFVPTC and I-EFVPTC from NIFTP, which will enhance the understanding of its molecular features for improved diagnosis and management.

Sections du résumé

BACKGROUND BACKGROUND
Infiltrative follicular variant of papillary thyroid carcinoma (IFVPTC) exhibits nuclear characteristics typical of papillary thyroid carcinoma (PTC) but demonstrates a follicular growth pattern. The diagnosis of IFVPTC presenting with atypical nuclear features of PTC poses challenges for both preoperative cytopathology and postoperative histopathology. In such cases, molecular markers are needed to serve as diagnostic aids. Given the limited knowledge of IFVPTC's genomic features, this study aimed to characterize its genetic alterations and identify clinically relevant molecular markers.
METHODS METHODS
Whole-exome sequencing of 50 IFVPTC tumor-normal pairs identified single-nucleotide variants, somatic copy number alterations (sCNAs), and subclonal architecture. Key mutations were verified via polymerase chain reaction and Sanger sequencing, whereas valuable biomarkers were validated via immunohistochemistry (IHC).
RESULTS RESULTS
This study found that endogenous processes rather than exogenous mutagens dominated the shaping of the genome of IFVPTC during tumorigenesis. BRAF V600E was the only common trunk mutation and significantly mutated gene in IFVPTC. Subcloning analysis found that most IFVPTC samples harbored two or more coexisting clones. sCNA analysis revealed that human leukocyte antigen C (HLA-C) and HLA-A were significantly amplified. Subsequent IHC investigations indicated that HLA-C shows promise in averting the misclassification of challenging-to-interpret IFVPTC and invasive encapsulated follicular variant of PTC (I-EFVPTC) as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Although there were several similarities between classic PTC and IFVPTC, they differed significantly in their sCNA patterns.
CONCLUSIONS CONCLUSIONS
This study provides valuable insights into IFVPTC's genetic alterations and highlights the potential of HLA-C IHC to distinguish challenging-to-interpret IFVPTC and I-EFVPTC from NIFTP, which will enhance the understanding of its molecular features for improved diagnosis and management.

Identifiants

pubmed: 39141684
doi: 10.1002/cncr.35517
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Natural Science Foundation of China
ID : 81972804

Informations de copyright

© 2024 American Cancer Society.

Références

Boucai L, Zafereo M, Cabanillas ME. Thyroid cancer: a review. JAMA. 2024;331(5):425‐435. doi:10.1001/jama.2023.26348
Qu N, Chen D, Ma B, et al. Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks. Nat Commun. 2024;15(1):3175. doi:10.1038/s41467‐024‐47581‐1
Baloch ZW, Asa SL, Barletta JA, et al. Overview of the 2022 WHO classification of thyroid neoplasms. Endocr Pathol. 2022;33(1):27‐63. doi:10.1007/s12022‐022‐09707‐3
Englum BR, Pura J, Reed SD, Roman SA, Sosa JA, Scheri RP. A bedside risk calculator to preoperatively distinguish follicular thyroid carcinoma from follicular variant of papillary thyroid carcinoma. World J Surg. 2015;39(12):2928‐2934. doi:10.1007/s00268‐015‐3192‐4
Ganly I, Wang L, Tuttle RM, et al. Invasion rather than nuclear features correlates with outcome in encapsulated follicular tumors: further evidence for the reclassification of the encapsulated papillary thyroid carcinoma follicular variant. Hum Pathol. 2015;46(5):657‐664. doi:10.1016/j.humpath.2015.01.010
Stojanov IJ, Mete O, Asa SL. Obstacles to tumor capsule assessment in noninvasive follicular thyroid neoplasm with papillary‐like nuclear features (NIFTP). Endocr Pathol. 2023;34(4):484‐486. doi:10.1007/s12022‐023‐09791‐z
Hernandez‐Prera JC, Wenig BM. RAS‐mutant follicular thyroid tumors: a continuous challenge for pathologists. Endocr Pathol. Published online June 18, 2024. doi:10.1007/s12022‐024‐09812‐5
Macerola E, Poma AM, Vignali P, et al. Molecular genetics of follicular‐derived thyroid cancer. Cancers. 2021;13(5):1139. doi:10.3390/cancers13051139
Halada S, Baran JA, Bauer AJ, et al. Clinicopathologic characteristics of pediatric follicular variant of papillary thyroid carcinoma subtypes: a retrospective cohort study. Thyroid. 2022;32(11):1353‐1361. doi:10.1089/thy.2022.0239
Song YS, Won JK, Yoo SK, et al. Comprehensive transcriptomic and genomic profiling of subtypes of follicular variant of papillary thyroid carcinoma. Thyroid. 2018;28(11):1468‐1478. doi:10.1089/thy.2018.0198
Ioachim D. The Bethesda System for Reporting Thyroid Cytopathology. Acta Endocrinol. 2018;14(2):282‐283. doi:10.4183/aeb.2018.282
Haugen BR. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: what is new and what has changed? Cancer. 2017;123(3):372‐381. doi:10.1002/cncr.30360
Coca‐Pelaz A, Shah JP, Hernandez‐Prera JC, et al. Papillary thyroid cancer—aggressive variants and impact on management: a narrative review. Adv Ther. 2020;37(7):3112‐3128. doi:10.1007/s12325‐020‐01391‐1
Carter SL, Cibulskis K, Helman E, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012;30(5):413‐421. doi:10.1038/nbt.2203
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114‐2120. doi:10.1093/bioinformatics/btu170
Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754‐1760. doi:10.1093/bioinformatics/btp324
DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next‐generation DNA sequencing data. Nat Genet. 2011;43(5):491‐498. doi:10.1038/ng.806
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high‐throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. doi:10.1093/nar/gkq603
Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68‐74. doi:10.1038/nature15393
Smigielski EM, Sirotkin K, Ward M, Sherry ST. dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res. 2000;28(1):352‐355. doi:10.1093/nar/28.1.352
Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein‐coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285‐291. doi:10.1038/nature19057
Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2019;47(D1):D941‐D947. doi:10.1093/nar/gky1015
Liu X, Wu C, Li C, Boerwinkle E. dbNSFP v3.0: a one‐stop database of functional predictions and annotations for human nonsynonymous and splice‐site SNVs. Hum Mutat. 2016;37(3):235‐241. doi:10.1002/humu.22932
Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high‐performance genomics data visualization and exploration. Brief Bioinform. 2013;14(2):178‐192. doi:10.1093/bib/bbs017
Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer‐associated genes. Nature. 2013;499(7457):214‐218. doi:10.1038/nature12213
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747‐1756. doi:10.1101/gr.239244.118
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847‐2849. doi:10.1093/bioinformatics/btw313
Tan VY, Févotte C. Automatic relevance determination in nonnegative matrix factorization with the β‐divergence. IEEE Trans Pattern Anal Mach Intell. 2013;35(7):1592‐1605. doi:10.1109/tpami.2012.240
Gehring JS, Fischer B, Lawrence M, Huber W. SomaticSignatures: inferring mutational signatures from single‐nucleotide variants. Bioinformatics. 2015;31(22):3673‐3675. doi:10.1093/bioinformatics/btv408
McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next‐generation DNA sequencing data. Genome Res. 2010;20(9):1297‐1303. doi:10.1101/gr.107524.110
Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy‐number alteration in human cancers. Genome Biol. 2011;12(4):R41. doi:10.1186/gb‐2011‐12‐4‐r41
Favero F, Joshi T, Marquard AM, et al. Sequenza: allele‐specific copy number and mutation profiles from tumor sequencing data. Ann Oncol. 2015;26(1):64‐70. doi:10.1093/annonc/mdu479
Roth A, Khattra J, Yap D, et al. PyClone: statistical inference of clonal population structure in cancer. Nat Methods. 2014;11(4):396‐398. doi:10.1038/nmeth.2883
Andor N, Harness JV, Müller S, Mewes HW, Petritsch C. EXPANDS: expanding ploidy and allele frequency on nested subpopulations. Bioinformatics. 2014;30(1):50‐60. doi:10.1093/bioinformatics/btt622
Wu Q, Feng L, Wang Y, et al. Multi‐omics analysis reveals RNA splicing alterations and their biological and clinical implications in lung adenocarcinoma. Signal Transduct Target Ther. 2022;7(1):270. doi:10.1038/s41392‐022‐01098‐5
Agrawal N, Akbani R, Aksoy BA, et al. Integrated genomic characterization of papillary thyroid carcinoma. Cell. 2014;159(3):676‐690. doi:10.1016/j.cell.2014.09.050
Kauffmann RM, Hamner JB, Ituarte PHG, Yim JH. Age greater than 60 years portends a worse prognosis in patients with papillary thyroid cancer: should there be three age categories for staging? BMC Cancer. 2018;18(1):316. doi:10.1186/s12885‐018‐4181‐4
Nishino M, Jacob J. Invasion in thyroid cancer: controversies and best practices. Semin Diagn Pathol. 2020;37(5):219‐227. doi:10.1053/j.semdp.2020.02.003
Shyr C, Tarailo‐Graovac M, Gottlieb M, Lee JJ, van Karnebeek C, Wasserman WW. FLAGS, frequently mutated genes in public exomes. BMC Med Genomics. 2014;7(1):64. doi:10.1186/s12920‐014‐0064‐y
Chen X, Xiang H, Yu S, Lu Y, Wu T. Research progress in the role and mechanism of cadherin‐11 in different diseases. J Cancer. 2021;12(4):1190‐1199. doi:10.7150/jca.52720
Li R, Zhang H, Yu W, et al. ZIP: a novel transcription repressor, represses EGFR oncogene and suppresses breast carcinogenesis. EMBO J. 2009;28(18):2763‐2776. doi:10.1038/emboj.2009.211
Giles KA, Taberlay PC. Mutations in chromatin remodeling factors. In: Boffetta P, Hainaut P, eds. Encyclopedia of Cancer. 3rd ed. Academic Press; 2019:511‐527.
Yuan C, Yao X, Dai P, Zhao Y, Sun Y. Genomic alterations dissection revealed MUC4 mutation as a potential driver in lung adenocarcinoma local recurrence. Transl Lung Cancer Res. 2023;12(5):985‐998. doi:10.21037/tlcr‐22‐793
Bhatia R, Siddiqui JA, Ganguly K, et al. Muc4 loss mitigates epidermal growth factor receptor activity essential for PDAC tumorigenesis. Oncogene. 2023;42(10):759‐770. doi:10.1038/s41388‐022‐02587‐1
Li X, Wu Z, He J, et al. OGT regulated O‐GlcNAcylation promotes papillary thyroid cancer malignancy via activating YAP. Oncogene. 2021;40(30):4859‐4871. doi:10.1038/s41388‐021‐01901‐7
Uhlen M, Zhang C, Lee S, et al. A pathology atlas of the human cancer transcriptome. Science. 2017;357(6352):eaan2507. doi:10.1126/science.aan2507
Givechian KB, Garner C, Garban H, Rabizadeh S, Soon‐Shiong P. CAD/POLD2 gene expression is associated with poor overall survival and chemoresistance in bladder urothelial carcinoma. Oncotarget. 2018;9(51):29743‐29752. doi:10.18632/oncotarget.25701
Alexandrov LB, Kim J, Haradhvala NJ, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578(7793):94‐101. doi:10.1038/s41586‐020‐1943‐3
Roberts SA, Lawrence MS, Klimczak LJ, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat Genet. 2013;45(9):970‐976. doi:10.1038/ng.2702
Goyette MA, Lipsyc‐Sharf M, Polyak K. Clinical and translational relevance of intratumor heterogeneity. Trends Cancer. 2023;9:726‐737. doi:10.1016/j.trecan.2023.05.001
Manini C, Laruelle A, Rocha A, López JI. Convergent insights into intratumor heterogeneity. Trends Cancer. 2024;10(1):12‐14. doi:10.1016/j.trecan.2023.08.009
Andor N, Graham TA, Jansen M, et al. Pan‐cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016;22(1):105‐113. doi:10.1038/nm.3984
Virk RK, Van Dyke AL, Finkelstein A, et al. BRAFV600E mutation in papillary thyroid microcarcinoma: a genotype‐phenotype correlation. Mod Pathol. 2013;26(1):62‐70. doi:10.1038/modpathol.2012.152
American Association for Cancer Research Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 2017;7(8):818‐831.
Sebastian SO, Gonzalez JM, Paricio PP, et al. Papillary thyroid carcinoma: prognostic index for survival including the histological variety. Arch Surg. 2000;135(3):272‐277. doi:10.1001/archsurg.135.3.272
Hotomi M, Sugitani I, Toda K, Kawabata K, Fujimoto Y. A novel definition of extrathyroidal invasion for patients with papillary thyroid carcinoma for predicting prognosis. World J Surg. 2012;36(6):1231‐1240. doi:10.1007/s00268‐012‐1518‐z
Sauter JL, Grogg KL, Vrana JA, Law ME, Halvorson JL, Henry MR. Young investigator challenge: validation and optimization of immunohistochemistry protocols for use on Cellient cell block specimens. Cancer Cytopathol. 2016;124(2):89‐100. doi:10.1002/cncy.21660
Yoo SK, Lee S, Kim SJ, et al. Comprehensive analysis of the transcriptional and mutational landscape of follicular and papillary thyroid cancers. PLoS Genet. 2016;12(8):e1006239. doi:10.1371/journal.pgen.1006239
Sohn SY, Lee JJ, Lee JH. Molecular profile and clinicopathologic features of follicular variant papillary thyroid carcinoma. Pathol Oncol Res. 2020;26(2):927‐936. doi:10.1007/s12253‐019‐00639‐8
Muyas F, Sauer CM, Valle‐Inclán JE, et al. De novo detection of somatic mutations in high‐throughput single‐cell profiling data sets. Nat Biotechnol. 2024;42(5):758‐767. doi:10.1038/s41587‐023‐01863‐z
Tang G, Liu X, Cho M, Li Y, Tran DH, Wang X. Pan‐cancer discovery of somatic mutations from RNA sequencing data. Commun Biol. 2024;7(1):619. doi:10.1038/s42003‐024‐06326‐y
Marotta V, Bifulco M, Vitale M. Significance of RAS mutations in thyroid benign nodules and non‐medullary thyroid cancer. Cancers. 2021;13(15):3785. doi:10.3390/cancers13153785
Kim M, Jeon MJ, Oh HS, et al. BRAF and RAS mutational status in noninvasive follicular thyroid neoplasm with papillary‐like nuclear features and invasive subtype of encapsulated follicular variant of papillary thyroid carcinoma in Korea. Thyroid. 2018;28(4):504‐510. doi:10.1089/thy.2017.0382
Jang E, Kim K, Jung CK, Bae JS, Kim JS. Clinicopathological parameters for predicting non‐invasive follicular thyroid neoplasm with papillary features (NIFTP). Ther Adv Endocrinol Metab. 2021;12:20420188211000500. doi:10.1177/20420188211000500
Kim MJ, Won JK, Jung KC, et al. Clinical characteristics of subtypes of follicular variant papillary thyroid carcinoma. Thyroid. 2018;28(3):311‐318. doi:10.1089/thy.2016.0671
Senkin S, Moody S, Díaz‐Gay M, et al. Geographic variation of mutagenic exposures in kidney cancer genomes. Nature. 2024;629(8013):910‐918. doi:10.1038/s41586‐024‐07368‐2
Bai J, Ma K, Xia S, et al. Pan‐cancer mutational signature surveys correlated mutational signature with geospatial environmental exposures and viral infections. Comput Struct Biotechnol J. 2023;21:5413‐5422. doi:10.1016/j.csbj.2023.10.041
Pfeifer GP, Jin SG. Methods and applications of genome‐wide profiling of DNA damage and rare mutations. Nat Rev Genet. Published online June 25, 2024. doi:10.1038/s41576‐024‐00748‐4
Hill RJ, Bona N, Smink J, et al. p53 regulates diverse tissue‐specific outcomes to endogenous DNA damage in mice. Nat Commun. 2024;15(1):2518. doi:10.1038/s41467‐024‐46844‐1
Hynds RE, Huebner A, Pearce DR, et al. Representation of genomic intratumor heterogeneity in multi‐region non‐small cell lung cancer patient‐derived xenograft models. Nat Commun. 2024;15(1):4653. doi:10.1038/s41467‐024‐47547‐3
Edrisi M, Huang X, Ogilvie HA, Nakhleh L. Accurate integration of single‐cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA. Nat Commun. 2023;14(1):8262. doi:10.1038/s41467‐023‐44014‐3
LeBlanc VG, Trinh DL, Aslanpour S, et al. Single‐cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter‐ and intratumor heterogeneity. Cancer Cell. 2022;40(4):379‐392.e9. doi:10.1016/j.ccell.2022.02.016
Ding L, Ellis MJ, Li S, et al. Genome remodelling in a basal‐like breast cancer metastasis and xenograft. Nature. 2010;464(7291):999‐1005. doi:10.1038/nature08989
Nik‐Zainal S, Van Loo P, Wedge DC, et al. The life history of 21 breast cancers. Cell. 2012;149(5):994‐1007. doi:10.1016/j.cell.2012.04.023
Ríos A, Rodríguez JM, Moya MR, et al. Frequency of HLA‐C alleles in differentiated thyroid carcinoma in southeastern Spain. Cancer. 2004;100(2):264‐269. doi:10.1002/cncr.11914
Haghpanah V, Khalooghi K, Adabi K, et al. Associations between HLA‐C alleles and papillary thyroid carcinoma. Cancer Biomark. 2009;5(1):19‐22. doi:10.3233/cbm‐2009‐0564
Lee SR, Jung CK, Kim TE, et al. Molecular genotyping of follicular variant of papillary thyroid carcinoma correlates with diagnostic category of fine‐needle aspiration cytology: values of RAS mutation testing. Thyroid. 2013;23(11):1416‐1422. doi:10.1089/thy.2012.0640

Auteurs

Quanyou Wu (Q)

Division of Abdominal Cancer, Department of Medical Oncology, Cancer Center and Laboratory of Molecular Targeted Therapy in Oncology, West China Hospital, Sichuan University, Chengdu, China.
State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Chunfang Hu (C)

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Lin Feng (L)

State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Xin Yang (X)

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Ying Cui (Y)

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Huan Zhao (H)

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Ting Xiao (T)

State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Huiqin Guo (H)

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China.

Classifications MeSH