Biomarkers in Patients with Metastatic Breast Cancer and the PRAEGNANT Study Network

Abstract

Progress has been made in the treatment of metastatic breast cancer in recent decades, but very few therapies use patient or tumor-specific characteristics to tailor individualized treatment. More than ten years after the publication of the reference human genome sequence, analysis methods have improved enormously, fostering the hope that biomarkers can be used to individualize therapies and offer precise treatment based on tumor and patient characteristics. Biomarkers at every level of the system (genetics, epigenetics, gene expression, micro-RNA, proteomics and others) can be used for this. This has led to changes in clinical study designs, with drug developments often only focusing on small or very small subgroups of patients and tumors. The screening and registration of patients and their molecular tumor data has therefore become very important for the successful completion of clinical studies. This new form of medicine presents particular challenges for patients and physicians. Even in this new age of genome-wide analysis, the focus should still be on the patientsʼ quality of life. This review summarizes recent developments and describes how the PRAEGNANT study network manages the aforementioned medical challenges and changes to create a professional infrastructure for patients and physicians.

Zusammenfassung

In den letzten Jahrzehnten sind zwar Fortschritte in der Behandlung des metastasierten Mammakarzinoms erzielt worden, jedoch orientieren sich nur wenige Therapien an den individuellen Eigenschaften der Patientin oder der Tumorerkrankung. Mehr als 10 Jahre nach der Veröffentlichung der menschlichen genetischen Sequenz haben sich die Analysemethoden dahingehend verbessert, dass große Hoffnung besteht, die Therapie weiterzuentwickeln und im Sinne einer „Präzisionsmedizin“ individuell für die Patientin und die Tumorerkrankung zu gestalten. Hierbei werden Biomarker auf jeder Ebene der Systembiologie herangezogen (Genetik, Epigenetik, Genexpression, micro-RNA, Proteomics und weitere). Diese Individualisierung hat zu einer neuen Generation von Studien geführt, sodass manche Arzneimittelentwicklungen nur noch für sehr kleine Patientinnen-Subgruppen durchgeführt werden. Die Organisation der Erfassung von Patientinnen und molekularen Tumordaten ist deswegen von besonderer Bedeutung für die erfolgreiche Durchführung von klinischen Studien. Diese neue Form der Medizin stellt eine besondere Herausforderung für Patientinnen und Ärzte dar. Der Erhalt der Lebensqualität sollte auch in diesem neuen Zeitalter der Medizin als zentrales Therapieziel im Fokus stehen. In diesem Übersichtsartikel werden wir die Entwicklungen der letzten Jahre aufzeigen und das PRAEGNANT-Studiennetzwerk vorstellen, welches die oben genannten Probleme und Ziele angeht und eine professionalisierte Infrastruktur hierfür aufbaut.

Key words

breast cancer - metastasis - PRAEGNANT - mutation - biomarker - prognosis

Schlüsselwörter

Brustkrebs - Metastasen - PRAEGNANT - Mutation - Biomarker - Prognose

^* Shared lead authorship

• References

• 1 Lux MP, Maass N, Schutz F et al. Breast cancer 2013 – interpretation of new and known data. Geburtsh Frauenheilk 2013; 73: 584-598
• 2 Largillier R, Ferrero JM, Doyen J et al. Prognostic factors in 1,038 women with metastatic breast cancer. Ann Oncol 2008; 19: 2012-2019
• 3 Loehberg CR, Almstedt K, Jud SM et al. Prognostic relevance of Ki-67 in the primary tumor for survival after a diagnosis of distant metastasis. Breast Cancer Res Treat 2013; 138: 899-908
• 4 Bogina G, Bortesi L, Marconi M et al. Comparison of hormonal receptor and HER-2 status between breast primary tumours and relapsing tumours: clinical implications of progesterone receptor loss. Virchows Arch 2011; 459: 1-10
• 5 Duchnowska R, Dziadziuszko R, Trojanowski T et al. Conversion of epidermal growth factor receptor 2 and hormone receptor expression in breast cancer metastases to the brain. Breast Cancer Res 2012; 14: R119
• 6 Hoefnagel LD, van de Vijver MJ, van Slooten HJ et al. Receptor conversion in distant breast cancer metastases. Breast Cancer Res 2010; 12: R75
• 7 Hoefnagel LD, Moelans CB, Meijer SL et al. Prognostic value of estrogen receptor α and progesterone receptor conversion in distant breast cancer metastases. Cancer 2012; 118: 4929-4935
• 8 Gong Y, Han EY, Guo M et al. Stability of estrogen receptor status in breast carcinoma: a comparison between primary and metastatic tumors with regard to disease course and intervening systemic therapy. Cancer 2011; 117: 705-713
• 9 Fabi A, Di Benedetto A, Metro G et al. HER2 protein and gene variation between primary and metastatic breast cancer: significance and impact on patient care. Clin Cancer Res 2011; 17: 2055-2064
• 10 Wilking U, Karlsson E, Skoog L et al. HER2 status in a population-derived breast cancer cohort: discordances during tumor progression. Breast Cancer Res Treat 2011; 125: 553-561
• 11 Simon R, Nocito A, Hubscher T et al. Patterns of her-2/neu amplification and overexpression in primary and metastatic breast cancer. J Natl Cancer Inst 2001; 93: 1141-1146
• 12 Kommission Mamma der Arbeitsgemeinschaft für Gynäkologische Onkologie (AGO-Mamma). Diagnosis and treatment of patients with primary and metastatic breast cancer. Online: http://wwwago-onlinede/de/infothek-fuer-aerzte/leitlinienempfehlungen/mamma/maerz-2013/2014 Last update: 28.03.2014; last access: 05.01.2015
• 13 Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747-752
• 14 van de Vijver MJ, He YD, vanʼt Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347: 1999-2009
• 15 Schmidt M, Fasching PA, Beckmann MW et al. Biomarkers in breast cancer – an update. Geburtsh Frauenheilk 2012; 72: 819-832
• 16 Blows FM, Driver KE, Schmidt MK et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med 2010; 7: e1000279
• 17 Ali AM, Provenzano E, Bartlett JM et al. Prognosis of early breast cancer by immunohistochemistry defined intrinsic sub-types in patients treated with adjuvant chemotherapy in the NEAT/BR9601 trial. Int J Cancer 2013; 133: 1470-1478
• 18 Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790-800
• 19 Sotiriou C, Wirapati P, Loi S et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006; 98: 262-272
• 20 Cheang MC, Chia SK, Voduc D et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 2009; 101: 736-750
• 21 Fasching PA, Heusinger K, Haeberle L et al. Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC Cancer 2011; 11: 486
• 22 Keam B, Im SA, Lee KH et al. Ki-67 can be used for further classification of triple negative breast cancer into two subtypes with different response and prognosis. Breast Cancer Res 2011; 13: R22
• 23 Yerushalmi R, Woods R, Ravdin PM et al. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol 2010; 11: 174-183
• 24 Venter JC, Adams MD, Myers EW et al. The sequence of the human genome. Science 2001; 291: 1304-1351
• 25 Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860-921
• 26 1000 Genomes Project Consortium. Abecasis GR, Auton A et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56-65
• 27 Fasching PA, Ekici AB, Wachter DL et al. Breast cancer risk – from genetics to molecular understanding of pathogenesis. Geburtsh Frauenheilk 2013; 73: 1228-1235
• 28 Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490: 61-70
• 29 Curtis C, Shah SP, Chin SF et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486: 346-352
• 30 Loibl S, von Minckwitz G, Schneeweiss A et al. PIK3CA mutations are associated with lower rates of pathologic complete response to anti-human epidermal growth factor receptor 2 (her2) therapy in primary HER2-overexpressing breast cancer. J Clin Oncol 2014; 32: 3212-3220
• 31 Baselga J, Piccart M, Gnant M et al. Minimal molecular alteration in PI3KCA, FGFR1 and CCND1 is associated with increased benefit from everolimus in hormone receptor-positive, HER2-advanced breast cancer: Insights from the BOLERO-2 trial. Eur J Cancer 2013; 49: S404-S405
• 32 Ellis MJ, Ding L, Shen D et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 2012; 486: 353-360
• 33 Griffith OL, Griffith M, Luo J et al. Prognostic effects of gene mutation in estrogen receptor positive breast cancer. San Antonio Breast Cancer Symposium 2014; Abstract No. S1–02.
• 34 Shah SP, Roth A, Goya R et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012; 486: 395-399
• 35 Couch FJ, Hart SN, Sharma P et al. Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer. J Clin Oncol 2014;
• 36 Lakhani SR, Reis-Filho JS, Fulford L et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin Cancer Res 2005; 11: 5175-5180
• 37 Fasching PA, Weihbrecht S, Haeberle L et al. HER2 and TOP2A amplification in a hospital-based cohort of breast cancer patients: associations with patient and tumor characteristics. Breast Cancer Res Treat 2014; 145: 193-203
• 38 Broeks A, Schmidt MK, Sherman ME et al. Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum Mol Genet 2011; 20: 3289-3303
• 39 Purrington KS, Slager S, Eccles D et al. Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple negative breast cancer. Carcinogenesis 2014; 35: 1012-1019
• 40 Stevens KN, Fredericksen Z, Vachon CM et al. 19p13.1 is a triple negative-specific breast cancer susceptibility locus. Cancer Res 2012; 72: 1795-1803
• 41 Garcia-Closas M, Couch FJ, Lindstrom S et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 2013; 45: 392-398
• 42 Milne RL, Benitez J, Nevanlinna H et al. Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. J Natl Cancer Inst 2009; 101: 1012-1018
• 43 Figueroa JD, Garcia-Closas M, Humphreys M et al. Associations of common variants at 1p11.2 and 14q24.1 (RAD51L1) with breast cancer risk and heterogeneity by tumor subtype: findings from the Breast Cancer Association Consortium. Hum Mol Genet 2011; 20: 4693-4706
• 44 French JD, Ghoussaini M, Edwards SL et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am J Hum Genet 2013; 92: 489-503
• 45 Stevens KN, Vachon CM, Lee AM et al. Common breast cancer susceptibility loci are associated with triple-negative breast cancer. Cancer Res 2011; 71: 6240-6249
• 46 Fasching PA, Gayther S, Pearce L et al. Role of genetic polymorphisms and ovarian cancer susceptibility. Mol Oncol 2009; 3: 171-181
• 47 Bolton KL, Tyrer J, Song H et al. Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet 2010; 42: 880-884
• 48 Antoniou AC, Wang X, Fredericksen ZS et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 2010; 42: 885-892
• 49 Fasching PA, Loehberg CR, Strissel PL et al. Single nucleotide polymorphisms of the aromatase gene (CYP19A1), HER2/neu status, and prognosis in breast cancer patients. Breast Cancer Res Treat 2008; 112: 89-98
• 50 Azzato EM, Tyrer J, Fasching PA et al. Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival. J Natl Cancer Inst 2010; 102: 650-662
• 51 Fasching PA, Pharoah PD, Cox A et al. The role of genetic breast cancer susceptibility variants as prognostic factors. Hum Mol Genet 2012; 21: 3926-3939
• 52 Weischer M, Nordestgaard BG, Pharoah P et al. CHEK2*1100delC heterozygosity in women with breast cancer associated with early death, breast cancer-specific death, and increased risk of a second breast cancer. J Clin Oncol 2012; 30: 4308-4316
• 53 Vachon CM, Scott CG, Fasching PA et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev 2012; 21: 1156-1166
• 54 Lindstrom S, Thompson DJ, Paterson AD et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 2014; 5: 5303
• 55 Fasching PA, Heusinger K, Loehberg CR et al. Influence of mammographic density on the diagnostic accuracy of tumor size assessment and association with breast cancer tumor characteristics. Eur J Radiol 2006; 60: 398-404
• 56 Heusinger K, Jud SM, Haberle L et al. Association of mammographic density with hormone receptors in invasive breast cancers: results from a case-only study. Int J Cancer 2012; 131: 2643-2649
• 57 Heusinger K, Jud SM, Haberle L et al. Association of mammographic density with the proliferation marker Ki-67 in a cohort of patients with invasive breast cancer. Breast Cancer Res Treat 2012; 135: 885-892
• 58 Hack CC, Haberle L, Geisler K et al. Mammographic density and prediction of nodal status in breast cancer patients. Geburtsh Frauenheilk 2013; 73: 136-141
• 59 Murdter TE, Schroth W, Bacchus-Gerybadze L et al. Activity levels of tamoxifen metabolites at the estrogen receptor and the impact of genetic polymorphisms of phase I and II enzymes on their concentration levels in plasma. Clin Pharmacol Ther 2011; 89: 708-717
• 60 Schroth W, Hamann U, Fasching PA et al. CYP2D6 polymorphisms as predictors of outcome in breast cancer patients treated with tamoxifen: expanded polymorphism coverage improves risk stratification. Clin Cancer Res 2010; 16: 4468-4477
• 61 Schroth W, Goetz MP, Hamann U et al. Association between CYP2D6 polymorphisms and outcomes among women with early stage breast cancer treated with tamoxifen. JAMA 2009; 302: 1429-1436
• 62 Province MA, Goetz MP, Brauch H et al. CYP2D6 genotype and adjuvant tamoxifen: meta-analysis of heterogeneous study populations. Clin Pharmacol Ther 2014; 95: 216-227
• 63 Robson ME, Tutt A, Balmaña J et al. OlympiA, Neo-Olympia and OlympiAD: randomized phase III trials of olaparib in patients (pts) with breast cancer (BC) and a germline BRCA1/2 mutation (gBRCAm). San Antonio Breast Cancer Symposium 2014; Abstract No. OT1-1 – 04.
• 64 Turner NC, Balmana J, Domchek SM et al. A phase 2, 2-stage, 2-cohort study of the oral PARP inhibitor BMN 673 in patients with germline BRCA mutation and locally advanced and/or metastatic breast cancer (ABRAZO study). San Antonio Breast Cancer Symposium 2014; Abstract No. OT1-1 – 14.
• 65 Litton JK, Blum JL, Eiermann W et al. A phase 3, open-label, randomized, parallel, 2-arm multi-center study of the oral PARP inhibitor BMN 673 versus physicianʼs choice in germline BRCA mutation subjects with locally advanced and/or metastatic breast cancer (EMBRACA study). San Antonio Breast Cancer Symposium 2014; Abstract No. OT1-1 – 12.
• 66 Muller V, Riethdorf S, Rack B et al. Prognostic impact of circulating tumor cells assessed with the CellSearch System and AdnaTest Breast in metastatic breast cancer patients: the DETECT study. Breast Cancer Res 2012; 14: R118
• 67 Wallwiener M, Hartkopf AD, Baccelli I et al. The prognostic impact of circulating tumor cells in subtypes of metastatic breast cancer. Breast Cancer Res Treat 2013; 137: 503-510
• 68 Bidard FC, Peeters DJ, Fehm T et al. Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data. Lancet Oncol 2014; 15: 406-414
• 69 Cristofanilli M, Budd GT, Ellis MJ et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004; 351: 781-791
• 70 Rack B, Schindlbeck C, Juckstock J et al. Circulating tumor cells predict survival in early average-to-high risk breast cancer patients. J Natl Cancer Inst 2014;
• 71 Fehm T, Becker S, Duerr-Stoerzer S et al. Determination of HER2 status using both serum HER2 levels and circulating tumor cells in patients with recurrent breast cancer whose primary tumor was HER2 negative or of unknown HER2 status. Breast Cancer Res 2007; 9: R74
• 72 Fehm T, Muller V, Aktas B et al. HER2 status of circulating tumor cells in patients with metastatic breast cancer: a prospective, multicenter trial. Breast Cancer Res Treat 2010; 124: 403-412
• 73 Melcher CA, Janni W, Rack B et al. DETECT III – a multicenter, randomized, phase III study to compare standard therapy alone versus standard therapy plus lapatinib in patients with initially HER2-negative metastatic breast cancer and HER2-positive circulating tumor cells. Eur J Cancer 2012; 48: S95-S96
• 74 Hagenbeck C, Melcher CA, Janni JW et al. DETECT III: a multicenter, randomized, phase III study to compare standard therapy alone versus standard therapy plus lapatinib in patients (pts) with initially HER2-negative metastatic breast cancer but with HER2-positive circulating tumor cells (CTC). J Clin Oncol 2012; 30 (Suppl.) Abstr. TPS1146
• 75 Melcher C, Schochter F, Albrecht S et al. DETECT IV – a multicenter, single arm, phase II study evaluating the efficacy of Everolimus in combination with endocrine therapy in patients with HER2-negative, hormone-receptor positive metastatic breast cancer and exclusively HER2-negative circulating tumor cells (CTCs). Oncol Res Treat 2014; 37: 29-29
• 76 Albrecht S, Schochter F, Melcher C et al. DETECT III – a multicenter, randomized, phase III trial to assess efficacy of lapatinib in patients with HER2-negative metastatic breast cancer and HER2-positive circulating tumor cells (CTCs). Oncol Res Treat 2014; 37: 29-29
• 77 Mandel P, Metais P. Les acides nucleiques du plasma sanguin chez lʼhomme. C R Seances Soc Biol Fil 1948; 142: 241-243
• 78 Haber DA, Velculescu VE. Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA. Cancer Discov 2014; 4: 650-661
• 79 Dixon JM, Turnbull AK, Fan C et al. In-depth genomic analysis of ER+ breast cancers during development of endocrine resistance. San Antonio Breast Cancer Symposium 2014; Abstract No. S1-05.
• 80 Rothe F, Laes JF, Lambrechts D et al. Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analysis in breast cancer. Ann Oncol 2014; 25: 1959-1965
• 81 Krop I, Johnston S, Mayer IA et al. The FERGI phase II study of the PI3 K inhibitor pictilisib (GDC-0941) plus fulvestrant vs. fulvestrant plus placebo in patients with ER+, aromatase inhibitor (AI)- resistant advanced or metastatic breast cancer. San Antonio Breast Cancer Symposium 2014; Abstract No. S2–02.
• 82 Fasching PA, Decker T, Schneeweiss A et al. Breast cancer treatment with everolimus and exemestane for ER+ women – Results of the 2nd interim analysis of the non-interventional trial BRAWO. European Society of Medical Oncology Meeting (ESMO). 2014; Abstract No. LBA9.