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Future Role of Molecular Profiling in Small Breast Samples and Personalised Medicine

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A Comprehensive Guide to Core Needle Biopsies of the Breast

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Abstract

Molecular technologies for analysing clinical samples are developing at a fast pace, with clinical trials now being designed around molecular tests, such as gene expression assays and targeted gene sequencing. Given this drive to implement clinical trials of precision medicine based on the molecular determinants of a patient’s tumour, it is imperative that these molecular assays are applicable to small biopsy samples. Here, we review the developments in molecular profiling that have occurred in recent years and describe how they may contribute to the future management of breast cancer patients in the core biopsy setting.

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References

  1. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Holderfield M, Deuker MM, McCormick F, McMahon M. Targeting RAF kinases for cancer therapy: BRAF-mutated melanoma and beyond. Nat Rev Cancer. 2014;14(7):455–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Hicks J, Krasnitz A, Lakshmi B, Navin NE, Riggs M, Leibu E, et al. Novel patterns of genome rearrangement and their association with survival in breast cancer. Genome Res. 2006;16(12):1465–79.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Russnes HG, Vollan HKM, Lingjaerde OC, Krasnitz A, Lundin P, Naume B, et al. Genomic architecture characterizes tumor progression paths and fate in breast cancer patients. Sci Transl Med. 2010;2(38):38ra47.

    PubMed  PubMed Central  Google Scholar 

  5. Vollan HK, Rueda OM, Chin SF, Curtis C, Turashvili G, Shah S, et al. A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer. Mol Oncol. 2015;9(1):115–27.

    CAS  PubMed  Google Scholar 

  6. Telli ML, Timms KM, Reid J, Hennessy B, Mills GB, Jensen KC, et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin Cancer Res. 2016;22(15):3764–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Tutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, et al. Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial. Nat Med. 2018;24(5):628–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Timms KM, Mills GB, Perry M, Gutin A, Lanchbury J, Brown R. Comparison of genomic instability test scores used for predicting PARP activity in ovarian cancer. J Clin Oncol. 2020;38(15_Suppl):1586.

    Google Scholar 

  9. Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578(7793):94–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Alexandrov LB, Stratton MR. Mutational signatures: the patterns of somatic mutations hidden in cancer genomes. Curr Opin Genet Dev. 2014;24:52–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Zamborszky J, Szikriszt B, Gervai JZ, Pipek O, Poti A, Krzystanek M, et al. Loss of BRCA1 or BRCA2 markedly increases the rate of base substitution mutagenesis and has distinct effects on genomic deletions. Oncogene. 2017;36(6):746–55.

    CAS  PubMed  Google Scholar 

  13. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med. 2017;23(4):517–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Nik-Zainal S, Davies H, Staaf J, Ramakrishna M, Glodzik D, Zou X, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature. 2016;534(7605):47–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Nones K, Johnson J, Newell F, Patch AM, Thorne H, Kazakoff SH, et al. Whole-genome sequencing reveals clinically relevant insights into the aetiology of familial breast cancers. Ann Oncol. 2019;30(7):1071–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lord CJ, Tutt AN, Ashworth A. Synthetic lethality and cancer therapy: lessons learned from the development of PARP inhibitors. Annu Rev Med. 2015;66:455–70.

    CAS  PubMed  Google Scholar 

  17. Chopra N, Tovey H, Pearson A, Cutts R, Toms C, Proszek P, et al. Homologous recombination DNA repair deficiency and PARP inhibition activity in primary triple negative breast cancer. Nat Commun. 2020;11(1):2662.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Denkert C, Liedtke C, Tutt A, von Minckwitz G. Molecular alterations in triple-negative breast cancer-the road to new treatment strategies. Lancet. 2017;389(10087):2430–42.

    CAS  PubMed  Google Scholar 

  19. Waks AG, Winer EP. Breast cancer treatment: a review. JAMA. 2019;321(3):288–300.

    CAS  PubMed  Google Scholar 

  20. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Jeselsohn R, Yelensky R, Buchwalter G, Frampton G, Meric-Bernstam F, Gonzalez-Angulo AM, et al. Emergence of constitutively active estrogen receptor-alpha mutations in pretreated advanced estrogen receptor-positive breast cancer. Clin Cancer Res. 2014;20(7):1757–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Vasan N, Yelensky R, Wang K, Moulder S, Dzimitrowicz H, Avritscher R, et al. A targeted next-generation sequencing assay detects a high frequency of therapeutically targetable alterations in primary and metastatic breast cancers: implications for clinical practice. Oncologist. 2014;19(5):453–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Young G, Wang K, He J, Otto G, Hawryluk M, Zwirco Z, et al. Clinical next-generation sequencing successfully applied to fine-needle aspirations of pulmonary and pancreatic neoplasms. Cancer Cytopathol. 2013;121(12):688–94.

    CAS  PubMed  Google Scholar 

  24. van Geelen CT, Savas P, Teo ZL, Luen SJ, Weng CF, Ko YA, et al. Clinical implications of prospective genomic profiling of metastatic breast cancer patients. Breast Cancer Res. 2020;22(1):91.

    PubMed  PubMed Central  Google Scholar 

  25. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Razavi P, Chang MT, Xu G, Bandlamudi C, Ross DS, Vasan N, et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell. 2018;34(3):427–38.e6.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Schwaederle M, Parker BA, Schwab RB, Daniels GA, Piccioni DE, Kesari S, et al. Precision oncology: the UC San Diego Moores Cancer Center PREDICT experience. Mol Cancer Ther. 2016;15(4):743–52.

    CAS  PubMed  Google Scholar 

  28. Garrido-Castro AC, Spurr LF, Hughes ME, Li YY, Cherniack AD, Kumari P, et al. Genomic characterization of de novo metastatic breast cancer. Clin Cancer Res. 2020;27(4):1105–18.

    PubMed  PubMed Central  Google Scholar 

  29. Andre F, Bachelot T, Commo F, Campone M, Arnedos M, Dieras V, et al. Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER). Lancet Oncol. 2014;15(3):267–74.

    CAS  PubMed  Google Scholar 

  30. Swanton C. SAFIR01: steps towards precision treatment in breast cancer. Lancet Oncol. 2014;15(3):242–3.

    PubMed  Google Scholar 

  31. Massard C, Michiels S, Ferte C, Le Deley MC, Lacroix L, Hollebecque A, et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 2017;7(6):586–95.

    CAS  PubMed  Google Scholar 

  32. Bertucci F, Ng CKY, Patsouris A, Droin N, Piscuoglio S, Carbuccia N, et al. Genomic characterization of metastatic breast cancers. Nature. 2019;569(7757):560–4.

    CAS  PubMed  Google Scholar 

  33. Koike Folgueira MA, Brentani H, Carraro DM, De Camargo Barros Filho M, Hirata Katayama ML, Santana de Abreu AP, et al. Gene expression profile of residual breast cancer after doxorubicin and cyclophosphamide neoadjuvant chemotherapy. Oncol Rep. 2009;22(4):805–13.

    PubMed  Google Scholar 

  34. Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh IT, et al. 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.

    CAS  PubMed  Google Scholar 

  35. Dowsett M, Cuzick J, Wale C, Forbes J, Mallon EA, Salter J, 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.

    PubMed  Google Scholar 

  36. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.

    CAS  PubMed  Google Scholar 

  37. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009;101(10):736–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res. 2008;14(16):5158–65.

    CAS  PubMed  Google Scholar 

  41. Guedj M, Marisa L, de Reynies A, Orsetti B, Schiappa R, Bibeau F, et al. A refined molecular taxonomy of breast cancer. Oncogene. 2012;31(9):1196–206.

    CAS  PubMed  Google Scholar 

  42. Fulford LG, Reis-Filho JS, Ryder K, Jones C, Gillett CE, Hanby A, et al. Basal-like grade III invasive ductal carcinoma of the breast: patterns of metastasis and long-term survival. Breast Cancer Res. 2007;9(1):R4.

    PubMed  PubMed Central  Google Scholar 

  43. Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, et al. Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res. 2007;9(5):R65.

    PubMed  PubMed Central  Google Scholar 

  44. Putti TC, El-Rehim DM, Rakha EA, Paish CE, Lee AH, Pinder SE, et al. Estrogen receptor-negative breast carcinomas: a review of morphology and immunophenotypical analysis. Mod Pathol. 2005;18(1):26–35.

    CAS  PubMed  Google Scholar 

  45. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Masuda H, Baggerly KA, Wang Y, Zhang Y, Gonzalez-Angulo AM, Meric-Bernstam F, et al. Differential response to neoadjuvant chemotherapy among 7 triple-negative breast cancer molecular subtypes. Clin Cancer Res. 2013;19(19):5533–40.

    CAS  PubMed  Google Scholar 

  47. Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010;12(5):R68.

    PubMed  PubMed Central  Google Scholar 

  48. Fougner C, Bergholtz H, Norum JH, Sorlie T. Re-definition of claudin-low as a breast cancer phenotype. Nat Commun. 2020;11(1):1787.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Farmer P, Bonnefoi H, Becette V, Tubiana-Hulin M, Fumoleau P, Larsimont D, et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene. 2005;24(29):4660–71.

    CAS  PubMed  Google Scholar 

  50. Lehmann-Che J, Hamy AS, Porcher R, Barritault M, Bouhidel F, Habuellelah H, et al. Molecular apocrine breast cancers are aggressive estrogen receptor negative tumors overexpressing either HER2 or GCDFP15. Breast Cancer Res. 2013;15(3):R37.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. van de Vijver MJ. Molecular tests as prognostic factors in breast cancer. Virchows Archiv. 2014;464(3):283–91.

    PubMed  Google Scholar 

  52. Zelnak AB, O’Regan RM. Genomic subtypes in choosing adjuvant therapy for breast cancer. Oncology (Williston Park). 2013;27(3):204–10.

    PubMed  Google Scholar 

  53. Giuliano AE, Edge SB, Hortobagyi GN. Eighth Edition of the AJCC cancer staging manual: breast cancer. Ann Surg Oncol. 2018;25(7):1783–5.

    PubMed  Google Scholar 

  54. https://cancerstaging.org/references-tools/deskreferences/Pages/Breast-Cancer-Staging.aspx.

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

    CAS  PubMed  Google Scholar 

  56. Chang JC, Makris A, Gutierrez MC, Hilsenbeck SG, Hackett JR, Jeong J, et al. Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients. Breast Cancer Res Treat. 2008;108(2):233–40.

    CAS  PubMed  Google Scholar 

  57. 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(29):7265–77.

    CAS  PubMed  Google Scholar 

  58. Andre F, Ismaila N, Henry NL, Somerfield MR, Bast RC, Barlow W, et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: ASCO clinical practice guideline update-integration of results from TAILORx. J Clin Oncol. 2019;37(22):1956–64.

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  60. Buyse M, Loi S, van’t Veer L, Viale G, Delorenzi M, Glas AM, et al. 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.

    CAS  PubMed  Google Scholar 

  61. Knauer M, Mook S, Rutgers EJ, Bender RA, Hauptmann M, van de Vijver MJ, 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.

    CAS  PubMed  Google Scholar 

  62. Cardoso F, van’t Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-Gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med. 2016;375(8):717–29.

    CAS  PubMed  Google Scholar 

  63. Tsai M, Lo S, Audeh W, Qamar R, Budway R, Levine E, et al. Association of 70-gene signature assay findings with physicians’ treatment guidance for patients with early breast cancer classified as intermediate risk by the 21-gene assay. JAMA Oncol. 2018;4(1):e173470.

    PubMed  Google Scholar 

  64. Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 2004;5(6):607–16.

    CAS  PubMed  Google Scholar 

  65. Sgroi DC, Carney E, Zarrella E, Steffel L, Binns SN, Finkelstein DM, et al. Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker. J Natl Cancer Inst. 2013;105(14):1036–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, Durbecq V, et al. A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clin Cancer Res. 2008;14(9):2601–8.

    CAS  PubMed  Google Scholar 

  67. Desmedt C, Sotiriou C. Proliferation: the most prominent predictor of clinical outcome in breast cancer. Cell Cycle. 2006;5(19):2198–202.

    CAS  PubMed  Google Scholar 

  68. 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(4):262–72.

    CAS  PubMed  Google Scholar 

  69. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, et al. 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.

    PubMed  Google Scholar 

  70. Nielsen T, Wallden B, Schaper C, Ferree S, Liu S, Gao D, 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;14:177.

    PubMed  PubMed Central  Google Scholar 

  71. Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. 2010;16(21):5222–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Chia SK, Bramwell VH, Tu D, Shepherd LE, Jiang S, Vickery T, et al. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin Cancer Res. 2012;18(16):4465–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Sestak I, Cuzick J, Dowsett M, Lopez-Knowles E, Filipits M, Dubsky P, 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.

    CAS  PubMed  Google Scholar 

  74. Dunbier AK, Anderson H, Ghazoui Z, Salter J, Parker JS, Perou CM, et al. Association between breast cancer subtypes and response to neoadjuvant anastrozole. Steroids. 2011;76(8):736–40.

    CAS  PubMed  Google Scholar 

  75. Ohnstad HO, Borgen E, Falk RS, Lien TG, Aaserud M, Sveli MAT, et al. Prognostic value of PAM50 and risk of recurrence score in patients with early-stage breast cancer with long-term follow-up. Breast Cancer Res. 2017;19(1):120.

    PubMed  PubMed Central  Google Scholar 

  76. Paquet ER, Hallett MT. Absolute assignment of breast cancer intrinsic molecular subtype. J Natl Cancer Inst. 2015;107(1):357.

    PubMed  Google Scholar 

  77. Staaf J, Ringner M. Making breast cancer molecular subtypes robust? J Natl Cancer Inst. 2015;107(1):386.

    PubMed  Google Scholar 

  78. 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(18):6012–20.

    CAS  PubMed  Google Scholar 

  79. Loibl S, Weber K, Huober J, Krappmann K, Marme F, Schem C, et al. Risk assessment after neoadjuvant chemotherapy in luminal breast cancer using a clinicomolecular predictor. Clin Cancer Res. 2018;24(14):3358–65.

    CAS  PubMed  Google Scholar 

  80. Sestak I, Buus R, Cuzick J, Dubsky P, Kronenwett R, Denkert C, et al. Comparison of the performance of 6 prognostic signatures for estrogen receptor-positive breast cancer: a secondary analysis of a randomized clinical trial. JAMA Oncol. 2018;4(4):545–53.

    PubMed  PubMed Central  Google Scholar 

  81. Stein RC, Dunn JA, Bartlett JM, Campbell AF, Marshall A, Hall P, et al. OPTIMA prelim: a randomised feasibility study of personalised care in the treatment of women with early breast cancer. Health Technol Assess. 2016;20(10):xxiii–xxix, 1–201.

    PubMed  Google Scholar 

  82. Rakha EA, Reis-Filho JS, Ellis IO. Combinatorial biomarker expression in breast cancer. Breast Cancer Res Treat. 2010;120(2):293–308.

    CAS  PubMed  Google Scholar 

  83. Cuzick J, Dowsett M, Pineda S, Wale C, Salter J, Quinn E, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J Clin Oncol. 2011;29(32):4273–8.

    PubMed  Google Scholar 

  84. Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, 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(5):e1000279.

    PubMed  PubMed Central  Google Scholar 

  85. Cheang MC, Voduc D, Bajdik C, Leung S, McKinney S, Chia SK, et al. Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin Cancer Res. 2008;14(5):1368–76.

    CAS  PubMed  Google Scholar 

  86. Barton S, Zabaglo L, A’Hern R, Turner N, Ferguson T, O’Neill S, et al. Assessment of the contribution of the IHC4+C score to decision making in clinical practice in early breast cancer. Br J Cancer. 2012;106(11):1760–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Jin L, Chen K, Tan C, Li J, Luo J, Yang Y, et al. Prognostic value of modified IHC4 score in patients with estrogen receptor-positive metastatic breast cancer. Oncologist. 2020;25(8):e1170–e80.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Lakhanpal R, Sestak I, Shadbolt B, Bennett GM, Brown M, Phillips T, et al. IHC4 score plus clinical treatment score predicts locoregional recurrence in early breast cancer. Breast (Edinburgh, Scotland). 2016;29:147–52.

    PubMed  Google Scholar 

  89. Sheri A, Smith IE, Hills M, Jones RL, Johnston SR, Dowsett M. Relationship between IHC4 score and response to neo-adjuvant chemotherapy in estrogen receptor-positive breast cancer. Breast Cancer Res Treat. 2017;164(2):395–400.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Tan W, Luo W, Jia W, Liang G, Xie X, Zheng W, et al. A combination of Nottingham prognostic index and IHC4 score predicts pathological complete response of neoadjuvant chemotherapy in estrogen receptor positive breast cancer. Oncotarget. 2016;7(52):87312–22.

    PubMed  PubMed Central  Google Scholar 

  91. Harbeck N, Sotlar K, Wuerstlein R, Doisneau-Sixou S. Molecular and protein markers for clinical decision making in breast cancer: today and tomorrow. Cancer Treat Rev. 2014;40(3):434–44.

    CAS  PubMed  Google Scholar 

  92. Liu M, Tang SX, Tsang JYS, Shi YJ, Ni YB, Law BKB, et al. Core needle biopsy as an alternative to whole section in IHC4 score assessment for breast cancer prognostication. J Clin Pathol. 2018;71(12):1084–9.

    CAS  PubMed  Google Scholar 

  93. Ellis MJ, Suman VJ, Hoog J, Goncalves R, Sanati S, Creighton CJ, et al. Ki67 proliferation index as a tool for chemotherapy decisions during and after neoadjuvant aromatase inhibitor treatment of breast cancer: results from the American College of Surgeons Oncology Group Z1031 Trial (Alliance). J Clin Oncol. 2017;35(10):1061–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Johnston S, Puhalla S, Wheatley D, Ring A, Barry P, Holcombe C, et al. Randomized phase II study evaluating palbociclib in addition to letrozole as neoadjuvant therapy in estrogen receptor-positive early breast cancer: PALLET trial. J Clin Oncol. 2019;37(3):178–89.

    CAS  PubMed  Google Scholar 

  95. Smith I, Robertson J, Kilburn L, Wilcox M, Evans A, Holcombe C, et al. Long-term outcome and prognostic value of Ki67 after perioperative endocrine therapy in postmenopausal women with hormone-sensitive early breast cancer (POETIC): an open-label, multicentre, parallel-group, randomised, phase 3 trial. Lancet Oncol. 2020;21(11):1443–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012;481(7381):389–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Cummings MC, Simpson PT, Reid LE, Jayanthan J, Skerman J, Song S, et al. Metastatic progression of breast cancer: insights from 50 years of autopsies. J Pathol. 2014;232(1):23–31.

    CAS  PubMed  Google Scholar 

  98. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.

    Google Scholar 

  99. Shi Y, Huang W, Tan Y, Jin X, Dua R, Penuel E, et al. A novel proximity assay for the detection of proteins and protein complexes: quantitation of HER1 and HER2 total protein expression and homodimerization in formalin-fixed, paraffin-embedded cell lines and breast cancer tissue. Diagn Mol Pathol. 2009;18(1):11–21.

    PubMed  Google Scholar 

  100. Lipton A, Kostler WJ, Leitzel K, Ali SM, Sperinde J, Weidler J, et al. Quantitative HER2 protein levels predict outcome in fluorescence in situ hybridization-positive patients with metastatic breast cancer treated with trastuzumab. Cancer. 2010;116(22):5168–78.

    CAS  PubMed  Google Scholar 

  101. Scaltriti M, Nuciforo P, Bradbury I, Sperinde J, Agbor-Tarh D, Campbell C, et al. High HER2 expression correlates with response to the combination of lapatinib and trastuzumab. Clin Cancer Res. 2015;21(3):569–76.

    CAS  PubMed  Google Scholar 

  102. Duchnowska R, Sperinde J, Czartoryska-Arlukowicz B, Mysliwiec P, Winslow J, Radecka B, et al. Predictive value of quantitative HER2, HER3 and p95HER2 levels in HER2-positive advanced breast cancer patients treated with lapatinib following progression on trastuzumab. Oncotarget. 2017;8(61):104149–59.

    PubMed  PubMed Central  Google Scholar 

  103. Nishimura R, Toh U, Tanaka M, Saimura M, Okumura Y, Saito T, et al. Role of HER2-related biomarkers (HER2, p95HER2, HER3, PTEN, and PIK3CA) in the efficacy of lapatinib plus capecitabine in HER2-positive advanced breast cancer refractory to trastuzumab. Oncology. 2017;93(1):51–61.

    CAS  PubMed  Google Scholar 

  104. Gromov P, Moreira JM, Gromova I. Proteomic analysis of tissue samples in translational breast cancer research. Expert Rev Proteomics. 2014;11(3):285–302.

    CAS  PubMed  Google Scholar 

  105. Wulfkuhle JD, Berg D, Wolff C, Langer R, Tran K, Illi J, et al. Molecular analysis of HER2 signaling in human breast cancer by functional protein pathway activation mapping. Clin Cancer Res. 2012;18(23):6426–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Sonntag J, Schluter K, Bernhardt S, Korf U. Subtyping of breast cancer using reverse phase protein arrays. Expert Rev Proteomics. 2014;11(6):757–70.

    CAS  PubMed  Google Scholar 

  107. Gundisch S, Annaratone L, Beese C, Drecol E, Marchio C, Quaglino E, et al. Critical roles of specimen type and temperature before and during fixation in the detection of phosphoproteins in breast cancer tissues. Lab Invest. 2015;95:561–71.

    PubMed  PubMed Central  Google Scholar 

  108. Meric-Bernstam F, Akcakanat A, Chen H, Sahin A, Tarco E, Carkaci S, et al. Influence of biospecimen variables on proteomic biomarkers in breast cancer. Clin Cancer Res. 2014;20(14):3870–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F, Sahin A, Liu W, Ju Z, et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteomics. 2011;8(1):11.

    PubMed  PubMed Central  Google Scholar 

  110. Berger AC, Korkut A, Kanchi RS, Hegde AM, Lenoir W, Liu W, et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell. 2018;33(4):690–705.e9.

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Al-Ejeh F, Miranda M, Shi W, Simpson PT, Song S, Vargas AC, et al. Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer. Oncotarget. 2014;5(10):3145–58.

    PubMed  PubMed Central  Google Scholar 

  112. Kolch W, Pitt A. Functional proteomics to dissect tyrosine kinase signalling pathways in cancer. Nat Rev Cancer. 2010;10(9):618–29.

    CAS  PubMed  Google Scholar 

  113. Casadonte R, Caprioli RM. Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry. Nat Protoc. 2011;6(11):1695–709.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature. 2016;534(7605):55–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Satpathy S, Jaehnig EJ, Krug K, Kim BJ, Saltzman AB, Chan DW, et al. Microscaled proteogenomic methods for precision oncology. Nat Commun. 2020;11(1):532.

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Tyanova S, Albrechtsen R, Kronqvist P, Cox J, Mann M, Geiger T. Proteomic maps of breast cancer subtypes. Nat Commun. 2016;7:10259.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Francis A, Thomas J, Fallowfield L, Wallis M, Bartlett JM, Brookes C, et al. Addressing overtreatment of screen detected DCIS; the LORIS trial. Eur J Cancer. 2015;51(16):2296–303.

    PubMed  Google Scholar 

  118. Elshof LE, Tryfonidis K, Slaets L, van Leeuwen-Stok AE, Skinner VP, Dif N, et al. Feasibility of a prospective, randomised, open-label, international multicentre, phase III, non-inferiority trial to assess the safety of active surveillance for low risk ductal carcinoma in situ - The LORD study. Eur J Cancer. 2015;51(12):1497–510.

    PubMed  Google Scholar 

  119. Hwang ES, Hyslop T, Lynch T, Frank E, Pinto D, Basila D, et al. The COMET (Comparison of Operative versus Monitoring and Endocrine Therapy) trial: a phase III randomised controlled clinical trial for low-risk ductal carcinoma in situ (DCIS). BMJ Open. 2019;9(3):e026797.

    PubMed  PubMed Central  Google Scholar 

  120. Rea D, Francis A, Wallis M, Thomas J, Bartlett J, Bowden S, et al. Confusion over differences in registration and randomization criteria for the LORIS (Low-Risk DCIS) trial. Ann Surg Oncol. 2017;24(Suppl 3):566–7.

    PubMed  Google Scholar 

  121. Van Bockstal MR, Agahozo MC, Koppert LB, van Deurzen CHM. A retrospective alternative for active surveillance trials for ductal carcinoma in situ of the breast. Int J Cancer. 2020;146(5):1189–97.

    PubMed  Google Scholar 

  122. Sneige N, Lagios MD, Schwarting R, Colburn W, Atkinson E, Weber D, et al. Interobserver reproducibility of the Lagios nuclear grading system for ductal carcinoma in situ. Hum Pathol. 1999;30(3):257–62.

    CAS  PubMed  Google Scholar 

  123. van Dooijeweert C, van Diest PJ, Willems SM, Kuijpers C, Overbeek LIH, Deckers IAG. Significant inter- and intra-laboratory variation in grading of ductal carcinoma in situ of the breast: a nationwide study of 4901 patients in the Netherlands. Breast Cancer Res Treat. 2019;174(2):479–88.

    PubMed  Google Scholar 

  124. Schnitt SJ. Diagnosis of ductal carcinoma in situ in an era of de-escalation of therapy. Mod Pathol. 2021;34(Suppl 1):1–7.

    PubMed  Google Scholar 

  125. Shee K, Muller KE, Marotti J, Miller TW, Wells WA, Tsongalis GJ. Ductal carcinoma in situ biomarkers in a precision medicine era: current and future molecular-based testing. Am J Pathol. 2019;189(5):956–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Rane SU, Mirza H, Grigoriadis A, Pinder SE. Selection and evolution in the genomic landscape of copy number alterations in ductal carcinoma in situ (DCIS) and its progression to invasive carcinoma of ductal/no special type: a meta-analysis. Breast Cancer Res Treat. 2015;153(1):101–21.

    PubMed  Google Scholar 

  127. Pareja F, Brown DN, Lee JY, Da Cruz Paula A, Selenica P, Bi R, et al. Whole-exome sequencing analysis of the progression from non-low-grade ductal carcinoma in situ to invasive ductal carcinoma. Clin Cancer Res. 2020;26(14):3682–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Prat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012;135(1):301–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Lesurf R, Aure MR, Mork HH, Vitelli V, Oslo Breast Cancer Research Consortium, Lundgren S, et al. Molecular features of subtype-specific progression from ductal carcinoma in situ to invasive breast cancer. Cell Rep. 2016;16(4):1166–79.

    CAS  PubMed  Google Scholar 

  130. Gil Del Alcazar CR, Huh SJ, Ekram MB, Trinh A, Liu LL, Beca F, et al. Immune escape in breast cancer during in situ to invasive carcinoma transition. Cancer Discov. 2017;7(10):1098–115.

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Marshall E. Breast cancer. Dare to do less. Science. 2014;343(6178):1454–6.

    PubMed  Google Scholar 

  132. Printz C. New method to predict DCIS recurrence. Cancer. 2013;119(22):3899.

    PubMed  Google Scholar 

  133. Solin LJ, Gray R, Baehner FL, Butler SM, Hughes LL, Yoshizawa C, et al. A multigene expression assay to predict local recurrence risk for ductal carcinoma in situ of the breast. J Natl Cancer Inst. 2013;105(10):701–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Wood WC, Alvarado M, Buchholz DJ, Hyams D, Hwang S, Manders J, et al. The current clinical value of the DCIS Score. Oncology (Williston Park). 2014;28(Suppl 2):C2, 1–8, C3.

    PubMed  Google Scholar 

  135. Hughes LL, Wang M, Page DL, Gray R, Solin LJ, Davidson NE, et al. Local excision alone without irradiation for ductal carcinoma in situ of the breast: a trial of the Eastern Cooperative Oncology Group. J Clin Oncol. 2009;27(32):5319–24.

    PubMed  PubMed Central  Google Scholar 

  136. Bremer T, Whitworth PW, Patel R, Savala J, Barry T, Lyle S, et al. A biological signature for breast ductal carcinoma in situ to predict radiotherapy benefit and assess recurrence risk. Clin Cancer Res. 2018;24(23):5895–901.

    PubMed  Google Scholar 

  137. Weinmann S, Leo MC, Francisco M, Jenkins CL, Barry T, Leesman G, et al. Validation of a ductal carcinoma in situ biomarker profile for risk of recurrence after breast-conserving surgery with and without radiotherapy. Clin Cancer Res. 2020;26(15):4054–63.

    PubMed  Google Scholar 

  138. Bettington M, Lakhani SR, Ung OA. Is the one-step nucleic acid amplification assay better for intra-operative assessment of breast sentinel nodes? ANZ J Surg. 2014;84(10):725–9.

    PubMed  Google Scholar 

  139. Pathmanathan N, Renthawa J, French JR, Edstrom-Elder E, Hall G, Mahajan H, et al. Intraoperative sentinel lymph node assessment in breast cancer: a comparison of rapid diagnostic method based on CK19 mRNA expression and imprint cytology. ANZ J Surg. 2014;84(10):730–4.

    PubMed  Google Scholar 

  140. Tamaki Y. One-step nucleic acid amplification assay (OSNA) for sentinel lymph node biopsy. Breast Cancer (Tokyo, Japan). 2015;22(3):230–4.

    PubMed  Google Scholar 

  141. Shi F, Liang Z, Zhang Q, Wang C, Liu X. The performance of one-step nucleic acid amplification assay for intraoperative detection of sentinel lymph node macrometastasis in breast cancer: an updated meta-analysis. Breast (Edinburgh, Scotland). 2018;39:39–45.

    PubMed  Google Scholar 

  142. Fougo JL, Amendoeira I, Brito MJ, Correia AP, Goncalves A, Honavar M, et al. Sentinel node total tumour load as a predictive factor for non-sentinel node status in early breast cancer patients - the porttle study. Surg Oncol. 2020;32:108–14.

    PubMed  Google Scholar 

  143. Pena KB, Kepa A, Cochs A, Riu F, Parada D, Guma J. Total tumor load of mRNA cytokeratin 19 in the sentinel lymph node as a predictive value of axillary lymphadenectomy in patients with neoadjuvant breast cancer. Genes (Basel). 2021;12(1):77.

    CAS  PubMed  Google Scholar 

  144. Hintzen KFH, de Rooij L, Schouten N, van Bastelaar J, Corvers SAJ, Janssen A, et al. Molecular analysis of sentinel lymph nodes in patients with breast cancer using one-step nucleic acid amplification (OSNA): does not lead to overtreatment in the current era of de-escalating axillary management. Surg Oncol. 2020;35:224–8.

    CAS  PubMed  Google Scholar 

  145. Sansano I, Vieites B, Sancho de Salas M, Garcia C, Amendoeira I, Bernet L, et al. Axillary staging based on molecular analysis: results of the B-CLOSER-II study. Pathol Res Pract. 2020;216(11):153197.

    CAS  PubMed  Google Scholar 

  146. Denley H, Pinder SE, Elston CW, Lee AH, Ellis IO. Preoperative assessment of prognostic factors in breast cancer. J Clin Pathol. 2001;54(1):20–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  147. Verkooijen HM, Peeters PH, Buskens E, Koot VC, Borel Rinkes IH, Mali WP, et al. Diagnostic accuracy of large-core needle biopsy for nonpalpable breast disease: a meta-analysis. Br J Cancer. 2000;82(5):1017–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Fox CH, Johnson FB, Whiting J, Roller PP. Formaldehyde fixation. J Histochem Cytochem. 1985;33(8):845–53.

    CAS  PubMed  Google Scholar 

  149. Cronin M, Pho M, Dutta D, Stephans JC, Shak S, Kiefer MC, et al. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol. 2004;164(1):35–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Hewitt SM, Lewis FA, Cao Y, Conrad RC, Cronin M, Danenberg KD, et al. Tissue handling and specimen preparation in surgical pathology: issues concerning the recovery of nucleic acids from formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med. 2008;132(12):1929–35.

    PubMed  Google Scholar 

  151. Belluoccio D, Rowley L, Little CB, Bateman JF. Maintaining mRNA integrity during decalcification of mineralized tissues. PLoS One. 2013;8(3):e58154.

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Singh VM, Salunga RC, Huang VJ, Tran Y, Erlander M, Plumlee P, et al. Analysis of the effect of various decalcification agents on the quantity and quality of nucleic acid (DNA and RNA) recovered from bone biopsies. Ann Diagn Pathol. 2013;17(4):322–6.

    PubMed  Google Scholar 

  153. Yates LR, Campbell PJ. Evolution of the cancer genome. Nat Rev Genet. 2012;13(11):795–806.

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Casasent AK, Schalck A, Gao R, Sei E, Long A, Pangburn W, et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell. 2018;172(1–2):205–17.e12.

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Desmedt C, Fumagalli D, Pietri E, Zoppoli G, Brown D, Nik-Zainal S, et al. Uncovering the genomic heterogeneity of multifocal breast cancer. J Pathol. 2015;236(4):457–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Weng Z, Spies N, Zhu SX, Newburger DE, Kashef-Haghighi D, Batzoglou S, et al. Cell-lineage heterogeneity and driver mutation recurrence in pre-invasive breast neoplasia. Genome Med. 2015;7(1):28.

    PubMed  PubMed Central  Google Scholar 

  157. Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21(7):751–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Yates LR, Knappskog S, Wedge D, Farmery JHR, Gonzalez S, Martincorena I, et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell. 2017;32(2):169–84.e7.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512(7513):155–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Pearson A, Proszek P, Pascual J, Fribbens C, Shamsher MK, Kingston B, et al. Inactivating NF1 mutations are enriched in advanced breast cancer and contribute to endocrine therapy resistance. Clin Cancer Res. 2020;26(3):608–22.

    CAS  PubMed  Google Scholar 

  162. National Comprehensive Cancer Network. Clinical practice guidelines in oncology: breast cancer Version 6; 2020.

    Google Scholar 

  163. Andre F, Vicier C, Delaloge S. The horizon of precision medicine in breast cancer: fragmentation, alliance, or reunification? American Society of Clinical Oncology educational book. ASCO American Society of Clinical Oncology Meeting. 2014;e5–10.

    Google Scholar 

  164. Ignatiadis M, Dawson SJ. Circulating tumor cells and circulating tumor DNA for precision medicine: dream or reality? Ann Oncol. 2014;25(12):2304–13.

    CAS  PubMed  Google Scholar 

  165. Pantel K, Alix-Panabieres C. Bone marrow as a reservoir for disseminated tumor cells: a special source for liquid biopsy in cancer patients. Bonekey Rep. 2014;3:584.

    PubMed  PubMed Central  Google Scholar 

  166. Alix-Panabieres C, Schwarzenbach H, Pantel K. Circulating tumor cells and circulating tumor DNA. Annu Rev Med. 2012;63:199–215.

    CAS  PubMed  Google Scholar 

  167. Pantel K, Alix-Panabières C. Detection methods of circulating tumor cells. J Thorac Dis. 2012;4(5):446–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Bidard FC, Michiels S, Riethdorf S, Mueller V, Esserman LJ, Lucci A, et al. Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: a meta-analysis. J Natl Cancer Inst. 2018;110(6):560–7.

    PubMed  Google Scholar 

  169. Bidard FC, Peeters DJ, Fehm T, Nole F, Gisbert-Criado R, Mavroudis D, 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(4):406–14.

    PubMed  Google Scholar 

  170. Ye Z, Wang C, Wan S, Mu Z, Zhang Z, Abu-Khalaf MM, et al. Association of clinical outcomes in metastatic breast cancer patients with circulating tumour cell and circulating cell-free DNA. Eur J Cancer. 2019;106:133–43.

    CAS  PubMed  Google Scholar 

  171. Alix-Panabieres C. EPISPOT assay: detection of viable DTCs/CTCs in solid tumor patients. Recent Results Cancer Res. 2012;195:69–76.

    PubMed  Google Scholar 

  172. Saucedo-Zeni N, Mewes S, Niestroj R, Gasiorowski L, Murawa D, Nowaczyk P, et al. A novel method for the in vivo isolation of circulating tumor cells from peripheral blood of cancer patients using a functionalized and structured medical wire. Int J Oncol. 2012;41(4):1241–50.

    PubMed  PubMed Central  Google Scholar 

  173. Magbanua MJM, Swigart LB, Wu HT, Hirst GL, Yau C, Wolf DM, et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann Oncol. 2020;32(2):229–39.

    PubMed  Google Scholar 

  174. Rothe F, Silva MJ, Venet D, Campbell C, Bradburry I, Rouas G, et al. Circulating tumor DNA in HER2-amplified breast cancer: a translational research substudy of the NeoALTTO phase III trial. Clin Cancer Res. 2019;25(12):3581–8.

    CAS  PubMed  Google Scholar 

  175. Rossi G, Ignatiadis M. Promises and pitfalls of using liquid biopsy for precision medicine. Cancer Res. 2019;79(11):2798–804.

    CAS  PubMed  Google Scholar 

  176. Jaeger BAS, Neugebauer J, Andergassen U, Melcher C, Schochter F, Mouarrawy D, et al. The HER2 phenotype of circulating tumor cells in HER2-positive early breast cancer: a translational research project of a prospective randomized phase III trial. PLoS One. 2017;12(6):e0173593.

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Mazel M, Jacot W, Pantel K, Bartkowiak K, Topart D, Cayrefourcq L, et al. Frequent expression of PD-L1 on circulating breast cancer cells. Mol Oncol. 2015;9(9):1773–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  178. Schmid P, Rugo HS, Adams S, Schneeweiss A, Barrios CH, Iwata H, et al. Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2020;21(1):44–59.

    CAS  PubMed  Google Scholar 

  179. Papadaki MA, Kallergi G, Zafeiriou Z, Manouras L, Theodoropoulos PA, Mavroudis D, et al. Co-expression of putative stemness and epithelial-to-mesenchymal transition markers on single circulating tumour cells from patients with early and metastatic breast cancer. BMC Cancer. 2014;14:651.

    PubMed  PubMed Central  Google Scholar 

  180. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6(224):224ra24.

    PubMed  PubMed Central  Google Scholar 

  181. Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science (New York, NY). 2018;359(6378):926–30.

    CAS  Google Scholar 

  182. Dawson SJ, Tsui DW, Murtaza M, Biggs H, Rueda OM, Chin SF, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013;368(13):1199–209.

    CAS  PubMed  Google Scholar 

  183. Leary RJ, Kinde I, Diehl F, Schmidt K, Clouser C, Duncan C, et al. Development of personalized tumor biomarkers using massively parallel sequencing. Sci Transl Med. 2010;2(20):20ra14.

    PubMed  PubMed Central  Google Scholar 

  184. Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci Transl Med. 2017;9(403):eaan2415.

    PubMed  PubMed Central  Google Scholar 

  185. Sefrioui D, Perdrix A, Sarafan-Vasseur N, Dolfus C, Dujon A, Picquenot JM, et al. Short report: Monitoring ESR1 mutations by circulating tumor DNA in aromatase inhibitor resistant metastatic breast cancer. Int J Cancer. 2015;137(10):2513–9.

    CAS  PubMed  Google Scholar 

  186. Gevensleben H, Garcia-Murillas I, Graeser MK, Schiavon G, Osin P, Parton M, et al. Noninvasive detection of HER2 amplification with plasma DNA digital PCR. Clin Cancer Res. 2013;19(12):3276–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  187. Ma CX, Bose R, Gao F, Freedman RA, Telli ML, Kimmick G, et al. Neratinib efficacy and circulating tumor DNA detection of HER2 mutations in HER2 nonamplified metastatic breast cancer. Clin Cancer Res. 2017;23(19):5687–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  188. Romero A, Acosta-Eyzaguirre D, Sanz J, Moreno F, Serrano G, Diaz-Rubio E, et al. Identification of E545k mutation in plasma from a PIK3CA wild-type metastatic breast cancer patient by array-based digital polymerase chain reaction: circulating-free DNA a powerful tool for biomarker testing in advance disease. Transl Res. 2015;166(6):783–7.

    CAS  PubMed  Google Scholar 

  189. Chandarlapaty S, Chen D, He W, Sung P, Samoila A, You D, et al. Prevalence of ESR1 mutations in cell-free DNA and outcomes in metastatic breast cancer: a secondary analysis of the BOLERO-2 clinical trial. JAMA Oncol. 2016;2(10):1310–5.

    PubMed  PubMed Central  Google Scholar 

  190. O’Leary B, Cutts RJ, Liu Y, Hrebien S, Huang X, Fenwick K, et al. The genetic landscape and clonal evolution of breast cancer resistance to palbociclib plus fulvestrant in the PALOMA-3 trial. Cancer Discov. 2018;8(11):1390–403.

    PubMed  PubMed Central  Google Scholar 

  191. Turner NC, Swift C, Kilburn L, Fribbens C, Beaney M, Garcia-Murillas I, et al. ESR1 mutations and overall survival on fulvestrant versus exemestane in advanced hormone receptor-positive breast cancer: a combined analysis of the phase III SoFEA and EFECT trials. Clin Cancer Res. 2020;26(19):5172–7.

    CAS  PubMed  Google Scholar 

  192. Juric D, Andre F, Singer C, Sohn J, Campone M, Loibl S, et al. Abstract P4-10-04: Clinical outcomes of alpelisib in hormone receptor-positive, human epidermal growth factor receptor-2-negative advanced breast cancer by next-generation sequencing-detected PIK3CA alteration status and phosphatase and tensin homolog loss: biomarker analysis from the SOLAR-1 study. 2020;P4-10 p.

    Google Scholar 

  193. Juric D, Ciruelos E, Rubovszky G, Campone M, Loibl S, Rugo HS, et al. Abstract GS3-08: Alpelisib + fulvestrant for advanced breast cancer: subgroup analyses from the phase III SOLAR-1 trial. Cancer Res. 2019;79:GS3–08.

    Google Scholar 

  194. Rugo HS, Mayer I, Conte P, Loibl S, Campone M, Juric D, et al. Abstract CT142: Prevalence of PIK3CA mutations in patients with hormone receptor-positive, human epidermal growth factor-2-negative advanced breast cancer from the SOLAR-1 trial. Cancer Res. 2019;79(13 Suppl):CT142. https://doi.org/10.1158/1538-7445.SABCS18-CT142.

    Article  Google Scholar 

  195. Cardoso F, Paluch-Shimon S, Senkus E, Curigliano G, Aapro MS, Andre F, et al. 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5). Ann Oncol. 2020;31(12):1623–49.

    CAS  PubMed  Google Scholar 

  196. Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. Arch Pathol Lab Med. 2018;142(10):1242–53.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Fares Al Ejeh, Nic Waddell, Sriganesh Srihari and Rachael Murray for provision of images used in Fig. 23.1.

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Authors and Affiliations

  1. UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia

    Samuel Y. F. Foong, Peter T. Simpson, Margaret C. Cummings & Sunil R. Lakhani

  2. Pathology Queensland, The Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia

    Samuel Y. F. Foong, Margaret C. Cummings & Sunil R. Lakhani

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  1. Department of Pathology and Laboratory Medicine, Albany Medical College, Albany, NY, USA

    Sandra J. Shin

  2. Department of Pathology, University of California San Francisco, San Francisco, CA, USA

    Yunn-Yi Chen

  3. Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA

    Paula S. Ginter

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Foong, S.Y.F., Simpson, P.T., Cummings, M.C., Lakhani, S.R. (2022). Future Role of Molecular Profiling in Small Breast Samples and Personalised Medicine. In: Shin, S.J., Chen, YY., Ginter, P.S. (eds) A Comprehensive Guide to Core Needle Biopsies of the Breast . Springer, Cham. https://doi.org/10.1007/978-3-031-05532-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05531-7

  • Online ISBN: 978-3-031-05532-4

  • eBook Packages: MedicineMedicine (R0)

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