|
1
|
Vannata B, Conconi A, Winkler J, Cascione
L, Casaluci GM, Nassi L, Moia R, Pirosa MC, Moccia AA, Stathis A,
et al: Late relapse in patients with diffuse large B-cell lymphoma:
Impact of rituximab on their incidence and outcome. Br J Haematol.
187:478–487. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Coiffier B and Sarkozy C: Diffuse large
B-cell lymphoma: R-CHOP failure-what to do? Hematology Am Soc
Hematol Educ Program. 2016:366–378. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
A predictive model for aggressive
non-Hodgkin's lymphoma. N Engl J Med. 329:987–994. 1993. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Alizadeh AA, Eisen MB, Davis RE, Ma C,
Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, et al:
Distinct types of diffuse large B-cell lymphoma identified by gene
expression profiling. Nature. 403:503–511. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Rosenwald A, Wright G, Chan WC, Connors
JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland
EB, Giltnane JM, et al: The use of molecular profiling to predict
survival after chemotherapy for diffuse large-B-cell lymphoma. N
Engl J Med. 346:1937–1947. 2002. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Hans CP, Weisenburger DD, Greiner TC,
Gascoyne RD, Delabie J, Ott G, Müller-Hermelink HK, Campo E,
Braziel RM, Jaffe ES, et al: Confirmation of the molecular
classification of diffuse large B-cell lymphoma by
immunohistochemistry using a tissue microarray. Blood. 103:275–282.
2004. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Monti S, Savage KJ, Kutok JL, Feuerhake F,
Kurtin P, Mihm M, Wu B, Pasqualucci L, Neuberg D, Aguiar RC, et al:
Molecular profiling of diffuse large B-cell lymphoma identifies
robust subtypes including one characterized by host inflammatory
response. Blood. 105:1851–1861. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Lenz G, Wright G, Dave SS, Xiao W, Powell
J, Zhao H, Xu W, Tan B, Goldschmidt N, Iqbal J, et al: Stromal gene
signatures in large-B-cell lymphomas. N Engl J Med. 359:2313–2323.
2008. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Schmitz R, Wright GW, Huang DW, Johnson
CA, Phelan JD, Wang JQ, Roulland S, Kasbekar M, Young RM, Shaffer
AL, et al: Genetics and pathogenesis of diffuse large B-cell
lymphoma. N Engl J Med. 378:1396–1407. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Chapuy B, Stewart C, Dunford AJ, Kim J,
Kamburov A, Redd RA, Lawrence MS, Roemer MGM, Li AJ, Ziepert M, et
al: Molecular subtypes of diffuse large B cell lymphoma are
associated with distinct pathogenic mechanisms and outcomes. Nat
Med. 24:679–690. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Wright GW, Huang DW, Phelan JD, Coulibaly
ZA, Roulland S, Young RM, Wang JQ, Schmitz R, Morin RD, Tang J, et
al: A probabilistic classification tool for genetic subtypes of
diffuse large B cell lymphoma with therapeutic implications. Cancer
Cell. 37:551–568.e514. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Reddy A, Zhang J, Davis NS, Moffitt AB,
Love CL, Waldrop A, Leppa S, Pasanen A, Meriranta L,
Karjalainen-Lindsberg ML, et al: Genetic and functional drivers of
diffuse large B cell lymphoma. Cell. 171:481–494.e415. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Swerdlow SH, Campo E, Pileri SA, Harris
NL, Stein H, Siebert R, Advani R, Ghielmini M, Salles GA, Zelenetz
AD and Jaffe ES: The 2016 revision of the World Health Organization
classification of lymphoid neoplasms. Blood. 127:2375–2390. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Kotlov N, Bagaev A, Revuelta MV, Phillip
JM, Cacciapuoti MT, Antysheva Z, Svekolkin V, Tikhonova E,
Miheecheva N, Kuzkina N, et al: Clinical and biological subtypes of
B-cell lymphoma revealed by microenvironmental signatures. Cancer
Discov. 11:1468–1489. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Li X, Singhal K, Deng Q, Chihara D,
Russler-Germain D, Harkins RA, Henderson J, Arita K, Kizhakeyil A,
Sun R, et al: Large B cell lymphoma microenvironment archetype
profiles. Cancer Cell. 43:1347–1364.e1313. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Challa-Malladi M, Lieu YK, Califano O,
Holmes AB, Bhagat G, Murty VV, Dominguez-Sola D, Pasqualucci L and
Dalla-Favera R: Combined genetic inactivation of β2-Microglobulin
and CD58 reveals frequent escape from immune recognition in diffuse
large B cell lymphoma. Cancer Cell. 20:728–740. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Schmitz R, Wright GW, Huang DW, Johnson
CA, Phelan JD, Wang JQ, Roulland S, Kasbekar M, Young RM, Shaffer
AL, et al: Genetics and pathogenesis of diffuse large B-cell
lymphoma. N Engl J Med. 378:1396–1407. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Visco C, Li Y, Xu-Monette ZY, Miranda RN,
Green TM, Li Y, Tzankov A, Wen W, Liu WM, Kahl BS, et al:
Comprehensive gene expression profiling and immunohistochemical
studies support application of immunophenotypic algorithm for
molecular subtype classification in diffuse large B-cell lymphoma:
A report from the International DLBCL Rituximab-CHOP Consortium
Program Study. Leukemia. 26:2103–2113. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Barrans SL, Crouch S, Care MA, Worrillow
L, Smith A, Patmore R, Westhead DR, Tooze R, Roman E and Jack AS:
Whole genome expression profiling based on paraffin embedded tissue
can be used to classify diffuse large B-cell lymphoma and predict
clinical outcome. Br J Haematol. 159:441–453. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Dubois S, Tesson B, Mareschal S, Viailly
PJ, Bohers E, Ruminy P, Etancelin P, Peyrouze P, Copie-Bergman C,
Fabiani B, et al: Refining diffuse large B-cell lymphoma subgroups
using integrated analysis of molecular profiles. EBioMedicine.
48:58–69. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Subramanian A, Tamayo P, Mootha VK,
Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub
TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A
knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Hänzelmann S, Castelo R and Guinney J:
GSVA: Gene set variation analysis for microarray and RNA-seq data.
BMC Bioinformatics. 14:72013. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Liberzon A, Birger C, Thorvaldsdóttir H,
Ghandi M, Mesirov JP and Tamayo P: The Molecular Signatures
Database (MSigDB) hallmark gene set collection. Cell Syst.
1:417–425. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Johnson WE, Li C and Rabinovic A:
Adjusting batch effects in microarray expression data using
empirical Bayes methods. Biostatistics. 8:118–127. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Carbone PP, Kaplan HS, Musshoff K,
Smithers DW and Tubiana M: Report of the committee on Hodgkin's
disease staging classification. Cancer Res. 31:1860–1861.
1971.PubMed/NCBI
|
|
27
|
Oken MM, Creech RH, Tormey DC, Horton J,
Davis TE, McFadden ET and Carbone PP: Toxicity and response
criteria of the Eastern cooperative oncology group. Am J Clin
Oncol. 5:649–655. 1982. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Yoshihara K, Shahmoradgoli M, Martínez E,
Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW,
Levine DA, et al: Inferring tumour purity and stromal and immune
cell admixture from expression data. Nat Commun. 4:26122013.
View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Heagerty PJ, Lumley T and Pepe MS:
Time-dependent ROC curves for censored survival data and a
diagnostic marker. Biometrics. 56:337–344. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Collins GS, Reitsma JB, Altman DG and
Moons KG: Transparent reporting of a multivariable prediction model
for individual prognosis or diagnosis (TRIPOD): The TRIPOD
statement. Ann Intern Med. 162:55–63. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Neefjes J, Jongsma ML, Paul P and Bakke O:
Towards a systems understanding of MHC class I and MHC class II
antigen presentation. Nat Rev Immunol. 11:823–836. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Davis RE, Ngo VN, Lenz G, Tolar P, Young
RM, Romesser PB, Kohlhammer H, Lamy L, Zhao H, Yang Y, et al:
Chronic active B-cell-receptor signalling in diffuse large B-cell
lymphoma. Nature. 463:88–92. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Ngo VN, Young RM, Schmitz R, Jhavar S,
Xiao W, Lim KH, Kohlhammer H, Xu W, Yang Y, Zhao H, et al:
Oncogenically active MYD88 mutations in human lymphoma. Nature.
470:115–119. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Wilson WH, Young RM, Schmitz R, Yang Y,
Pittaluga S, Wright G, Lih CJ, Williams PM, Shaffer AL, Gerecitano
J, et al: Targeting B cell receptor signaling with ibrutinib in
diffuse large B cell lymphoma. Nat Med. 21:922–926. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Wherry EJ: T cell exhaustion. Nat Immunol.
12:492–499. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Rooney MS, Shukla SA, Wu CJ, Getz G and
Hacohen N: Molecular and genetic properties of tumors associated
with local immune cytolytic activity. Cell. 160:48–61. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Newman AM, Liu CL, Green MR, Gentles AJ,
Feng W, Xu Y, Hoang CD, Diehn M and Alizadeh AA: Robust enumeration
of cell subsets from tissue expression profiles. Nat Methods.
12:453–457. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Mariathasan S, Turley SJ, Nickles D,
Castiglioni A, Yuen K, Wang Y, Kadel EE III, Koeppen H, Astarita
JL, Cubas R, et al: TGFβ attenuates tumour response to PD-L1
blockade by contributing to exclusion of T cells. Nature.
554:544–548. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Chen DS and Mellman I: Oncology meets
immunology: The cancer-immunity cycle. Immunity. 39:1–10. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
40
|
DerSimonian R and Laird N: Meta-analysis
in clinical trials. Control Clin Trials. 7:177–188. 1986.
View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Higgins JP, Thompson SG, Deeks JJ and
Altman DG: Measuring inconsistency in meta-analyses. BMJ.
327:557–560. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Harrell FE Jr, Lee KL and Mark DB:
Multivariable prognostic models: Issues in developing models,
evaluating assumptions and adequacy, and measuring and reducing
errors. Stat Med. 15:361–387. 1996. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Hanahan D and Weinberg RA: Hallmarks of
cancer: the next generation. Cell. 144:646–674. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Topalian SL, Drake CG and Pardoll DM:
Immune checkpoint blockade: A common denominator approach to cancer
therapy. Cancer Cell. 27:450–461. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Gao L and Gross DS: Using genomics and
proteomics to investigate mechanisms of transcriptional silencing
in Saccharomyces cerevisiae. Brief Funct Genomic Proteomic.
5:280–288a. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Pardoll DM: The blockade of immune
checkpoints in cancer immunotherapy. Nat Rev Cancer. 12:252–264.
2012. View Article : Google Scholar : PubMed/NCBI
|