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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Updates on breast biomarkers.

Saleh Najjar1, Kimberly H Allison2

  • 1Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA.

Virchows Archiv : an International Journal of Pathology
|January 14, 2022
PubMed
Summary
This summary is machine-generated.

Precision medicine advances breast cancer treatment with new personalized therapies and biomarkers. Pathologists play a key role in assessing these markers, guiding treatment decisions for estrogen receptor (ER) and HER2-positive cancers.

Keywords:
Breast cancer; Predictive; Prognostic; Biomarker; HER2; ER; Gene expression panel

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Area of Science:

  • Oncology
  • Pathology
  • Genomics

Background:

  • Breast cancer treatment is increasingly personalized, driven by advancements in precision medicine and the identification of novel biomarkers.
  • The role of pathologists has become central in managing breast cancer, requiring expertise in assessing predictive and prognostic biomarkers.
  • Estrogen receptor (ER) and HER2 status are critical for categorizing breast cancers and guiding targeted therapy selection.

Purpose of the Study:

  • To review the evolution and latest updates of the CAP/ASCO guidelines for ER and HER2 biomarkers in breast cancer.
  • To discuss emerging concepts in HER2 testing, including heterogeneity, HER2-low, and HER2-mutated cancers.
  • To cover other relevant biomarkers in specific clinical scenarios for early-stage and metastatic breast cancer.

Main Methods:

  • Review of current literature and updated CAP/ASCO guidelines for breast cancer biomarkers.
  • Analysis of diagnostic modalities and algorithms for assessing predictive and prognostic biomarkers.
  • Discussion of multi-gene expression panels, response to neoadjuvant therapy, Ki67, germline BRCA mutations, PD-L1, PIK3CA, MSI/MMR, TMB, and NTRK.

Main Results:

  • Multi-gene expression panels are standard for chemotherapy decisions in early-stage ER-positive/HER2-negative disease.
  • Response to neoadjuvant therapy is a key biomarker in aggressive early-stage breast cancers (ER-negative/HER2-positive or triple-negative).
  • Ki67 aids in identifying high-risk ER-positive/HER2-negative cancers for cell cycle inhibitor therapy.
  • Germline BRCA testing, PD-L1, PIK3CA mutations, and comprehensive genomic profiling are crucial in the metastatic setting.

Conclusions:

  • Understanding and applying evolving biomarker data is essential for optimal breast cancer patient care.
  • New biomarkers and testing strategies, including those for HER2 heterogeneity and low expression, continue to refine treatment selection.
  • Personalized medicine in breast cancer relies heavily on accurate biomarker assessment by pathologists to guide targeted and immunotherapies.