Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry

Tim Mandelkow, Elena Bady, Magalie C.J. Lurati, Jonas B. Raedler, Jan H. Müller, Zhihao Huang, Eik Vettorazzi, Maximilian Lennartz, Till S. Clauditz, Patrick Lebok, Lisa Steinhilper, Linn Woelber, Guido Sauter, Enikö Berkes, Simon Bühler, Peter Paluchowski, Uwe Heilenkötter, Volkmar Müller, Barbara Schmalfeldt, Albert von der AssenFrank Jacobsen, Till Krech, Rainer H. Krech, Ronald Simon, Christian Bernreuther, Stefan Steurer, Eike Burandt, Niclas C. Blessin

Research output: Contribution to journalArticlepeer-review

Abstract

Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.

Original languageEnglish
Article number3175
JournalBiomedicines
Volume11
Issue number12
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • artificial intelligence
  • breast cancer
  • multiplex fluorescence immunohistochemistry
  • prognosis markers

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