Breast cancer genomics: From portraits to landscapes

Ulrich Pfeffer, Valentina Mirisola, Alessia Isabella Esposito, Adriana Amaro, Giovanna Angelini

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Breast cancer is the most frequent female cancer and still one of the major causes of death although early diagnosis and improved therapies have had a great impact on survival after breast cancer diagnosis. However, there are still many unresolved problems in breast cancer such as the fraction of breast cancers that do not respond to current therapies and considerable overtreatment due to imperfect prognostication. The application of genomics to breast cancer has led to the identification of clinically relevant molecular subtypes, especially the distinction of luminal A and luminal B subtypes within the class of hormone receptor positive cancers. Many prognostic signatures have been developed and two of them are being applied in oncologic decision making yet their utility most likely does not go beyond the distinction of luminal A and B subtypes that show a highly different proliferative potential. Integration of copy number variation has identified even more subclasses with distinct clinical characteristics. Genome wide association studies have identified many single nucleotide polymorphisms that are associated with breast cancer risk and several of them resist in validation studies. Their application for the design of risk based preventive strategies has been proposed. Next generation sequencing shows a wide variation of driver mutations in breast cancer, most of them within interrelated signaling pathways. Several genes such as TP53 or PIK3CA show frequent mutations but many mutations are almost private. Sequencing also identified several actionable mutations, among which those that occur in genes more frequently involved in other cancers that could indicate specific treatments. Better prognostication and response prediction by means of genomic analyses and mutation screening will almost certainly contribute to the improvement of therapy and to the reduction of unnecessary toxicity. Breast cancer genomics has also led to a conceptual shift in our understanding of the process of metastasis that seems to be determined from very early stages of the disease although additional mutations occur at later stages.

Original languageEnglish
Title of host publicationCancer Genomics: Molecular Classification, Prognosis and Response Prediction
PublisherSpringer Netherlands
Pages255-294
Number of pages40
Volume9789400758421
ISBN (Print)9789400758421, 9400758413, 9789400758414
DOIs
Publication statusPublished - Sep 1 2013

Fingerprint

Genomics
Breast Neoplasms
Mutation
Neoplasms
Validation Studies
Genome-Wide Association Study
Genes
Single Nucleotide Polymorphism
Early Diagnosis
Cause of Death
Decision Making
Therapeutics
Hormones
Neoplasm Metastasis

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Pfeffer, U., Mirisola, V., Esposito, A. I., Amaro, A., & Angelini, G. (2013). Breast cancer genomics: From portraits to landscapes. In Cancer Genomics: Molecular Classification, Prognosis and Response Prediction (Vol. 9789400758421, pp. 255-294). Springer Netherlands. https://doi.org/10.1007/978-94-007-5842-1_9

Breast cancer genomics : From portraits to landscapes. / Pfeffer, Ulrich; Mirisola, Valentina; Esposito, Alessia Isabella; Amaro, Adriana; Angelini, Giovanna.

Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Vol. 9789400758421 Springer Netherlands, 2013. p. 255-294.

Research output: Chapter in Book/Report/Conference proceedingChapter

Pfeffer, U, Mirisola, V, Esposito, AI, Amaro, A & Angelini, G 2013, Breast cancer genomics: From portraits to landscapes. in Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. vol. 9789400758421, Springer Netherlands, pp. 255-294. https://doi.org/10.1007/978-94-007-5842-1_9
Pfeffer U, Mirisola V, Esposito AI, Amaro A, Angelini G. Breast cancer genomics: From portraits to landscapes. In Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Vol. 9789400758421. Springer Netherlands. 2013. p. 255-294 https://doi.org/10.1007/978-94-007-5842-1_9
Pfeffer, Ulrich ; Mirisola, Valentina ; Esposito, Alessia Isabella ; Amaro, Adriana ; Angelini, Giovanna. / Breast cancer genomics : From portraits to landscapes. Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Vol. 9789400758421 Springer Netherlands, 2013. pp. 255-294
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