Subtype-specific metagene-based prediction of outcome after neoadjuvant and adjuvant treatment in breast cancer

Clinical Cancer Research

Volumn 22, Issue 2, 2016, Pages 337-345

  Callari, Maurizio ^a   Cappelletti, Vera ^a   D'Aiuto, Francesca ^a   Musella, Valeria ^a   Lembo, Antonio ^b   Petel, Fabien ^c   Karn, Thomas ^d   Iwamoto, Takayuki ^e   Provero, Paolo ^b,f   Daidone, Maria Grazia ^a   Gianni, Luca ^f   Bianchini, Giampaolo ^f  

^a NATIONAL CANCER INSTITUTE   (Italy)

^b UNIVERSITY OF TURIN   (Italy)

^c Research Department   (France)

^d GOETHE UNIVERSITY   (Germany)

^e OKAYAMA UNIVERSITY HOSPITAL   (Japan)

^f SAN RAFFAELE HOSPITAL   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR 2; ESTROGEN RECEPTOR; TAMOXIFEN; TRASTUZUMAB; TRANSCRIPTOME; TUMOR MARKER;

ADJUVANT THERAPY; ARTICLE; BREAST CANCER; CANCER ADJUVANT THERAPY; CANCER HORMONE THERAPY; CANCER PROGNOSIS; CANCER RECURRENCE; DISTANT METASTASIS FREE SURVIVAL; GENE CLUSTER BASED METAGENE; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; HIGH RISK PATIENT; HUMAN; IMMUNE RESPONSE; MINIMAL RESIDUAL DISEASE; PRIORITY JOURNAL; ADJUVANT CHEMOTHERAPY; BREAST NEOPLASMS; CELL PROLIFERATION; DISEASE FREE SURVIVAL; DRUG EFFECTS; FEMALE; GENETICS; METAGENOME; NEOADJUVANT THERAPY; PROCEDURES; PROGNOSIS; TUMOR RECURRENCE;

BIOMARKERS, TUMOR; BREAST NEOPLASMS; CELL PROLIFERATION; CHEMOTHERAPY, ADJUVANT; DISEASE-FREE SURVIVAL; FEMALE; GENE EXPRESSION PROFILING; HUMANS; METAGENOME; NEOADJUVANT THERAPY; NEOPLASM RECURRENCE, LOCAL; PROGNOSIS; RECEPTOR, ERBB-2; RECEPTORS, ESTROGEN; TRANSCRIPTOME;

EID: 84958954416     PISSN: 10780432     EISSN: 15573265     Source Type: Journal    
DOI: 10.1158/1078-0432.CCR-15-0757     Document Type: Article

References (52)

1. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: Classification, prognostication, and prediction. Lancet 2011;378:1812-23.
2. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009;360:790-800.
3. Dawson S-J, Rueda OM, Aparicio S, Caldas C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J 2013;32:617-28.
4. Davies C, Godwin J,Gray R, ClarkeM, Cutter D, Darby S, et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: Patient-level meta-analysis of randomised trials. Lancet 2011;378:771-84.
5. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: Meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012;379:432-44.
6. EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005;365:1687-717.
7. Simon R. The use of genomics in clinical trial design. Clin Cancer Res 2008;14:5984-93.
8. Cardoso F, Piccart-Gebhart M, Van't Veer L, Rutgers E, Van't VL, Rutgers E. The MINDACT trial: The first prospective clinical validation of a genomic tool. Mol Oncol 2007;1:246-51.
9. Sparano JA. TAILORx: Trial assigning individualized options for treatment (Rx). Clin Breast Cancer 2006;7:347-50.
10. Zhang Y, Schnabel CA, Schroeder BE, Jerevall P-L, Jankowitz RC, Fornander T, et al. Breast cancer index identifies early-stage estrogen receptor-positive breast cancer patients at risk for early-and late-distant recurrence. Clin Cancer Res 2013;19:4196-205.
11. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. JClin Oncol 2009;27:1160-7.
12. Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res 2011;17:6012-20.
13. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol 2005;23:7332-41.
14. Weigelt B, Pusztai L, Ashworth A, Reis-Filho JS. Challenges translating breast cancer gene signatures into the clinic. Nat Rev Clin Oncol 2011; 9:58-64.
15. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognosticandpredictivebiomarkers. JNatlCancerInst2009;101:1446-52.
16. Williams PM, Li R, Johnson NA, WrightG, Heath JD, Gascoyne RD. A novel method of amplification of FFPET-derived RNA enables accurate disease classification with microarrays. J Mol Diagn 2010;12:680-6.
17. Prat A, Bianchini G, Thomas M, Belousov A, Cheang MCU, Koehler A, et al. Research-based PAM50 subtype predictor identifies higher responses and improved survival outcomes in HER2-positive breast cancer in the NOAH study. Clin Cancer Res 2014;20:511-21.
18. Musella V, Callari M, Di Buduo E, Scuro M, Dugo M, Miodini P, et al. Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients. PLoS One 2015;10:e0123194.
19. Callari M, Lembo A, Bianchini G, Musella V, Cappelletti V, Gianni L, et al. Accurate data processing improves the reliability of affymetrix gene expression profiles from FFPE samples. PLoS One 2014;9:e86511.
20. Bianchini G, Iwamoto T, Qi Y, Coutant C, Shiang CY, Wang B, et al. Prognostic and therapeutic implications of distinct kinase expression patterns in different subtypes of breast cancer. Cancer Res 2010;70: 8852-62.
21. Bianchini G, Qi Y, Alvarez RH, Iwamoto T, Coutant C, Ibrahim NK, et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and-negative cancers. J Clin Oncol 2010; 28:4316-23.
22. Callari M, Musella V, Di Buduo E, Sensi M, Miodini P, Dugo M, et al. Subtype-dependent prognostic relevance of an interferon-induced pathway metagene in node-negative breast cancer. Mol Oncol 2014; 8:1278-89.
23. Geyer FC, Rodrigues DN, Weigelt B, Reis-Filho JS. Molecular classification of estrogen receptor-positive/luminal breast cancers. Adv Anat Pathol 2012;19:39-53.
24. Ignatiadis M, Singhal SK, Desmedt C, Haibe-Kains B, Criscitiello C, Andre F, et al. Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: A pooled analysis. J Clin Oncol 2012;30:1996-2004.
25. Rody A, Holtrich U, Pusztai L, Liedtke C, Gaetje R, Ruckhaeberle E, et al. T-cell metagene predicts a favorable prognosis in estrogen receptornegative and HER2-positive breast cancers. Breast Cancer Res 2009;11: R15.
26. Schmidt M, Bohm D, Von TC, Steiner E, Puhl A, Pilch H, et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 2008;68:5405-13.
27. Bianchini G, Pusztai L, Karn T, Iwamoto T, Rody A, Kelly C, et al. Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers. Breast Cancer Res 2013;15:R86.
28. Nagalla S, Chou JW, Willingham MC, Ruiz J, Vaughn JP, Dubey P, et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol 2013;14:R34.
29. Gu-Trantien C, Loi S, Garaud S, Equeter C, Libin M, De WA, et al. CD4 follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest 2013;123:2873-92.
30. Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007;8:R157.
31. Staaf J, RingnerM, Vallon-Christersson J, Jonsson G, Bendahl PO, Holm K, et al. Identification of subtypes in human epidermal growth factor receptor 2-positive breast cancer reveals a gene signature prognostic of outcome. J Clin Oncol 2010;28:1813-20.
32. Kim C, Tang G, Pogue-Geile KL, Costantino JP, Baehner FL, Baker J, et al. Estrogen receptor (ESR1) mRNA expression and benefit from tamoxifen in the treatment and prevention of estrogen receptor-positive breast cancer. J Clin Oncol 2011;29:4160-7.
33. Sotiriou C,Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006;98:262-72.
34. Rooney MS, Shukla SA,Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 2015;160:48-61.
35. Buyse M, Loi S, van't Veer L, Viale G, DelorenziM,Glas AM, et al. Validation and clinical utility of a 70-gene prognostic signature for women with nodenegative breast cancer. J Natl Cancer Inst 2006;98:1183-92.
36. Stewart DJ, Kurzrock R. Cancer: The road to amiens. J Clin Oncol 2009; 27:328-33.
37. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 2004;10:6759-63.
38. Liedtke C, Hatzis C, Symmans WF, Desmedt C, Haibe-Kains B, Valero V, et al. Genomic grade index is associated with response to chemotherapy in patients with breast cancer. J Clin Oncol 2009;27:3185-91.
39. Gianni L, Zambetti M, Clark K, Baker J, Cronin M, Wu J, et al. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol 2005;23:7265-77.
40. Goldstein LJ, Gray R, Badve S, Childs BH, Yoshizawa C, Rowley S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 2008;26:4063-71.
41. Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicinbased chemotherapy: BIG 02-98. J Clin Oncol 2013;31:860-7.
42. Adams S, Gray RJ, Demaria S, Goldstein L, Perez EA, Shulman LN, et al. Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials:ECOG 2197 and ECOG 1199. J Clin Oncol 2014;32:2959-66.
43. Nanda R, Chow L, Dees E, Berger R, Gupta S, Geva R, et al. A phase Ib study of pembrolizumab (MK-3475) in patients with advanced triple-negative breast cancer. SABCS 2014. page Abstract S1-09.
44. Loi S, Michiels S, Salgado R, Sirtaine N, Jose V, Fumagalli D, et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: Results from the FinHER trial. Ann Oncol 2014;25:1544-50.
45. Johnson BJ, Costelloe EO, Fitzpatrick DR, Haanen JBAG, Schumacher TNM, Brown LE, et al. Single-cell perforin and granzyme expression reveals the anatomical localization of effector CD8 T cells in influenza virusinfected mice. Proc Natl Acad Sci U S A 2003;100:2657-62.
46. Tanaka N, Kawakami T, Taniguchi T. Recognition DNA sequences of interferon regulatory factor 1 (IRF-1) and IRF-2, regulators of cell growth and the interferon system. Mol Cell Biol 1993;13:4531-8.
47. Koebel CM, Vermi W, Swann JB, Zerafa N, Rodig SJ, Old LJ, et al. Adaptive immunity maintains occult cancer in an equilibrium state. Nature 2007;450:903-7.
48. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: Integrating immunity's roles in cancer suppression and promotion. Science 2011; 331:1565-70.
49. Smyth MJ, Dunn GP, Schreiber RD. Cancer immunosurveillance and immunoediting: The roles of immunity in suppressing tumor development and shaping tumor immunogenicity. Adv Immunol 2006;90: 1-50.
50. Galluzzi L, Kepp O, Vander Heiden MG, Kroemer G. Metabolic targets for cancer therapy. Nat Rev Drug Discov 2013;12:829-46.
51. Zitvogel L, Apetoh L, Ghiringhelli F, Kroemer G. Immunological aspects of cancer chemotherapy. Nat Rev Immunol 2008;8:59-73.
52. Zitvogel L, KeppO, Kroemer G.Immune parameters affecting the efficacy of chemotherapeutic regimens. Nat Rev Clin Oncol 2011;8:151-60.