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Breast Cancer Molecular Testing for Prognosis and Prediction

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Management of Breast Diseases
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Abstract

Hormone receptors (ER, PgR) and HER2 are the only established biomarkers for early and advanced breast cancer. They are prognostic but mostly importantly also predictive for response to the respective targeted therapies. In luminal HER2-negative early breast cancer with up to three involved lymph nodes, evidence-based multigene assays (e.g., Oncotype DX, MammaPrint, EndoPredict, Prosigna, Breast Cancer Index) have become available for accurate assessment of relapse risk. All these tests were developed and validated in archival tissue cohorts, mostly from prospective clinical trials. First prospective data from validation trials for Oncotype DX and MammaPrint demonstrated excellent survival in genomically low-risk patients. Adjuvant chemotherapy with its toxicities may thus safely be omitted in such low-risk patients. So far, next to ER, PR, and HER2, no molecular factors have validated clinical utility for prediction of therapy response or resistance to a specific drug or therapy regimen. Considering the advent of several highly effective targeted agents in breast cancer, new molecular biomarkers, in particular for prediction of therapy response, are urgently needed in order to individualize therapy. Molecular analysis methods and modern high-throughput techniques provide great promise for identification of new biomarkers. Yet, as a bad biomarker can potentially be as dangerous for patients as a bad drug, thorough technical and clinical validations together with undisputed clinical utility are the perquisites for introducing new markers into the clinic.

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References

  1. Hayes DF. Considerations for implementation of cancer molecular diagnostics into clinical care. Am Soc Clin Oncol Educ Book. 2016;35:292–6.

    Article  PubMed  Google Scholar 

  2. Hayes DF, Bast RC, Desch CE, et al. Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst. 1996;88(20):1456–66.

    Article  CAS  PubMed  Google Scholar 

  3. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. 2009;101(21):1446–52.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.

    Article  CAS  PubMed  Google Scholar 

  5. Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98(19):10869–74.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Coates AS, Winer EP, Goldhirsch A, et al. Tailoring therapies–improving the management of early breast cancer: St Gallen International Expert Consensus on the primary therapy of early breast cancer 2015. Ann Oncol. 2015;26(8):1533–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Anders CK, Abramson V, Tan T, Dent R. The evolution of triple-negative breast cancer: from biology to novel therapeutics. Am Soc Clin Oncol Educ Book. 2016;35:34–42.

    Article  PubMed  Google Scholar 

  8. Harris LN, Ismaila N, McShane LM, et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol. 2016;34(10):1134–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. AGO recommendations 2016 for diagnosis and treatment of early and advanced breast cancer. Available from: www.ago-online.de.

  10. Cronin M, Sangli C, Liu ML, et al. Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem. 2007;53(6):1084–91.

    Article  CAS  PubMed  Google Scholar 

  11. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26.

    Article  CAS  PubMed  Google Scholar 

  12. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006;24(23):3726–34.

    Article  CAS  PubMed  Google Scholar 

  13. Albain KS, Barlow WE, Shak S, et al. Breast Cancer Intergroup of North America. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 2010;11(1):55–65.

    Article  CAS  PubMed  Google Scholar 

  14. Dowsett M, Cuzick J, Wale C, et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J Clin Oncol. 2010;28(11):1829–34.

    Article  PubMed  Google Scholar 

  15. Habel LA, Shak S, Jacobs MK, et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 2006;8(3):R25.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shak S, Petkov VI, Miller DP, et al. Breast cancer specific survival in 38,568 patients with node negative hormone receptor positive invasive breast cancer and oncotype DX recurrence score results in the SEER database. SABCS 2015: P5-15-01.

    Google Scholar 

  17. Stemmer SM, Steiner M, Rizel S, et al. Real-life analysis evaluating 1594 N0/Nmic breast cancer patients for whom treatment decisions incorporated the 21-gene recurrence score result: 5-year KM estimate for breast cancer specific survival with recurrence score results ≤30 is >98 %. SABCS 2015: P5-08-02.

    Google Scholar 

  18. Sparano JA, Gray RJ, Makower DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med. 2015;373(21):2005–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gluz O, Nitz U, Christgen M, et al. The WSG phase III PlanB trial: first prospective outcome data for the 21-gene recurrence score assay and concordance of prognostic markers by central and local pathology assessment. J Clin Oncol. 2016;34(20):2341–9.

    Google Scholar 

  20. Gluz O, Nitz U, Christgen M, et al. Prognostic impact of 21 gene recurrence score, IHC4, and central grade in high-risk HR+/HER2− early breast cancer (EBC): 5-year results of the prospective Phase III WSG PlanB trial. J Clin Oncol. 2016;34:(suppl; abstr 556).

    Google Scholar 

  21. van ‘t Veer LJ1, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.

    Google Scholar 

  22. van de Vijver MJ, He YD, van’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999–2009.

    Google Scholar 

  23. Glas AM, Floore A, Delahaye LJ, et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genom. 2006;30(7):278.

    Article  Google Scholar 

  24. Sapino A, Roepman P, Linn SC, et al. MammaPrint molecular diagnostics on formalin-fixed, paraffin-embedded tissue. J Mol Diagn. 2014;16(2):190–7.

    Article  CAS  PubMed  Google Scholar 

  25. Krijgsman O, Roepman P, Zwart W, et al. A diagnostic gene profile for molecular subtyping of breast cancer associated with treatment response. Breast Cancer Res Treat. 2012;133(1):37–47.

    Article  CAS  PubMed  Google Scholar 

  26. Buyse M, Loi S, van’t Veer L, et al. TRANSBIG consortium. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98(17):1183–92.

    Google Scholar 

  27. Bueno-de-Mesquita JM, van Harten WH, Retel VP, et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol. 2007;8(12):1079–87.

    Article  CAS  PubMed  Google Scholar 

  28. Knauer M, Mook S, Rutgers EJ, et al. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Res Treat. 2010;120(3):655–61.

    Article  CAS  PubMed  Google Scholar 

  29. Cardoso F, Van’t Veer L, Rutgers E, et al. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol. 2008;26(5):729–35.

    Article  PubMed  Google Scholar 

  30. Piccart M, Rutgers E, van’t Veer L, et al. On behalf of TRANSBIG consortium and MINDACT investigators. Primary analysis of the EORTC 10041/ BIG 3-04 MINDACT study: a prospective, randomized study evaluating the clinical utility of the 70-gene signature (MammaPrint) combined with common clinical-pathological criteria for selection of patients for adjuvant chemotherapy in breast cancer with 0–3 positive nodes. AACR 2016: CT039.

    Google Scholar 

  31. Dubsky P, Filipits M, Jakesz R, et al. Austrian Breast and Colorectal Cancer Study Group (ABCSG). EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol. 2013;24(3):640–7.

    Article  CAS  PubMed  Google Scholar 

  32. Filipits M, Rudas M, Jakesz R, et al. EP Investigators. 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(18):6012–20.

    Article  CAS  PubMed  Google Scholar 

  33. Dubsky P, Brase JC, Jakesz R, et al. Austrian Breast and Colorectal Cancer Study Group (ABCSG). The EndoPredict score provides prognostic information on late distant metastases in ER+/HER2− breast cancer patients. Br J Cancer. 2013;109(12):2959–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Martin M, Brase JC, Calvo L, et al. Clinical validation of the EndoPredict test in node-positive, chemotherapy-treated ER +/HER2- breast cancer patients: results from the GEICAM 9906 trial. Breast Cancer Res. 2014;16(2):R38.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Martin M, Brase JC, Ruiz A, et al. Prognostic ability of EndoPredict compared to research-based versions of the PAM50 risk of recurrence (ROR) scores in node-positive, estrogen receptor-positive, and HER2-negative breast cancer. A GEICAM/9906 sub-study. Breast Cancer Res Treat. 2016;156(1):81–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Denkert C, Kronenwett R, Schlake W, et al. Decentral gene expression analysis for ER+/Her2− breast cancer: results of a proficiency testing program for the EndoPredict assay. Virchows Arch. 2012;460(3):251–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Müller BM, Brase JC, Haufe F, et al. Comparison of the RNA-based EndoPredict multigene test between core biopsies and corresponding surgical breast cancer sections. J Clin Pathol. 2012;65(7):660–2.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wallden B, Storhoff J, Nielsen T, et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med Genomics. 2015;22(8):54.

    Article  Google Scholar 

  39. Nielsen T, Wallden B, Schaper C, et al. Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer. 2014;13(14):177.

    Article  Google Scholar 

  40. Gnant M, Filipits M, Greil R, et al. Austrian Breast and Colorectal Cancer Study Group. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann Oncol. 2014;25(2):339–45.

    Article  CAS  PubMed  Google Scholar 

  41. Filipits M, Nielsen TO, Rudas M, et al. Austrian Breast and Colorectal Cancer Study Group. The PAM50 risk-of-recurrence score predicts risk for late distant recurrence after endocrine therapy in postmenopausal women with endocrine-responsive early breast cancer. Clin Cancer Res. 2014;20(5):1298–305.

    Article  CAS  PubMed  Google Scholar 

  42. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, Ferree S, Storhoff J, Schaper C, Cuzick J. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol. 2013;31(22):2783–90.

    Article  PubMed  Google Scholar 

  43. Sestak I, Cuzick J, Dowsett M, et al. Prediction of late distant recurrence after 5 years of endocrine treatment: a combined analysis of patients from the Austrian breast and colorectal cancer study group 8 and arimidex, tamoxifen alone or in combination randomized trials using the PAM50 risk of recurrence score. J Clin Oncol. 2015;33(8):916–22.

    Article  CAS  PubMed  Google Scholar 

  44. Gnant M, Sestak I, Filipits M, et al. Identifying clinically relevant prognostic subgroups of postmenopausal women with node-positive hormone receptor-positive early-stage breast cancer treated with endocrine therapy: a combined analysis of ABCSG-8 and ATAC using the PAM50 risk of recurrence score and intrinsic subtype. Ann Oncol. 2015;26(8):1685–91.

    Article  CAS  PubMed  Google Scholar 

  45. Liu S, Chapman JA, Burnell MJ, et al. Prognostic and predictive investigation of PAM50 intrinsic subtypes in the NCIC CTG MA.21 phase III chemotherapy trial. Breast Cancer Res Treat. 2015;149(2):439–48.

    Article  CAS  PubMed  Google Scholar 

  46. Zhang Y, Schnabel CA, Schroeder BE, 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(15):4196–205.

    Article  CAS  PubMed  Google Scholar 

  47. Sestak I, Zhang Y, Schroeder BE, et al. Cross stratification and differential risk by breast cancer index and recurrence score in women with hormone receptor positive lymph-node negative early stage breast cancer. Clin Cancer Res. 2016.

    Google Scholar 

  48. Sgroi DC, Sestak I, Cuzick J, et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 2013;14(11):1067–76.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Habel LA, Sakoda LC, Achacoso N, Ma XJ, Erlander MG, Sgroi DC, Fehrenbacher L, Greenberg D, Quesenberry CP Jr. HOXB13:IL17BR and molecular grade index and risk of breast cancer death among patients with lymph node-negative invasive disease. Breast Cancer Res. 2013;15(2):R24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sgroi DC, Chapman JA, Badovinac-Crnjevic T, et al. Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study. Breast Cancer Res. 2016;18(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bartlett JM, Bayani J, Marshall A, et al. OPTIMA TMG. Comparing breast cancer multiparameter tests in the OPTIMA prelim trial: no test is more equal than the others. J Natl Cancer Inst. 2016;108(9).

    Google Scholar 

  52. Lum DW, Perel P, Hingorani AD, Holmes MV. CYP2D6 genotype and tamoxifen response for breast cancer: a systematic review and meta-analysis. PLoS ONE. 2013;8(10):e76648.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Di Leo A, Desmedt C, Bartlett JM, et al. HER2/TOP2A Meta-analysis Study Group. HER2 and TOP2A as predictive markers for anthracycline-containing chemotherapy regimens as adjuvant treatment of breast cancer: a meta-analysis of individual patient data. Lancet Oncol. 2011;12(12):1134–42.

    Article  PubMed  Google Scholar 

  54. Loibl S, Majewski I, Guarneri V, et al. PIK3CA mutations are associated with reduced pathological complete response rates in primary HER2-positive breast cancer: pooled analysis of 967 patients from five prospective trials investigating lapatinib and trastuzumab. Ann Oncol. 2016.

    Google Scholar 

  55. André F, Hurvitz S, Fasolo A, et al. Molecular alterations and everolimus efficacy in human epidermal growth Factor receptor 2-overexpressing metastatic breast cancers: combined exploratory biomarker analysis from BOLERO-1 and BOLERO-3. J Clin Oncol. 2016;34(18):2115–24.

    Article  PubMed  Google Scholar 

  56. Zhou Y, Wang C, Zhu H, et al. Diagnostic Accuracy of PIK3CA mutation detection by circulating free DNA in breast cancer: a meta-analysis of diagnostic test accuracy. PLoS ONE. 2016;11(6):e0158143.

    Article  PubMed  PubMed Central  Google Scholar 

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Harbeck, N. (2016). Breast Cancer Molecular Testing for Prognosis and Prediction. In: Jatoi, I., Rody, A. (eds) Management of Breast Diseases. Springer, Cham. https://doi.org/10.1007/978-3-319-46356-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-46356-8_11

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