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<p>Male breast cancer (MBC) is a rare disease accounting for less than 1% of all breast cancers. MBC is almost always of the estrogen receptor (ER) positive luminal subtype. Although differences have been described between male and female breast cancers, therapies for MBC are still largely extrapolated from female breast cancer (FBC). Treatment planning requires accurate prediction models. Several FBC prediction models have also been validated for MBC. Here, we validate the IHC4 + C algorithm in MBC, known to perform well for ER positive FBC. IHC4 + C combines immunohistochemical scores of ER, progesterone receptor, HER2, and Ki67, supplemented by four clinicopathologic features (age, grade, T- and Nstatus). IHC4 + C was validated in a retrospective MBC cohort of 143 patients. The algorithm performed best in MBC for the endpoint of 5-year overall survival, with C-indices between 0.72 and 0.75. Also for MBC, contributing the clinical score to IHC4 improved predictive power. A clear distinction between groups of MBC patients with good, moderate, and poor prognosis was demonstrated using Kaplan-Meier analyses and was highly statistically significant according to the Log-rank test (<i>p</i> = 0.001). The IHC4 + C algorithm that was originally developed for FBC patients performed well in an MBC cohort. It accurately separated patients with good, intermediate, and poor performance at high statistical significance. Development of a MBC-specific prediction tool remains desirable.</p>

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Validation of IHC4 + C for male breast cancer

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Carmen van der Pol,
  • Anne de Hond,
  • Dieuwke Mink van der Molen,
  • Quirine Manson,
  • Inne Borel-Rinkes,
  • Arjen Witkamp,
  • Elsken van der Wall,
  • Helena Verkooijen,
  • Paul van Diest

摘要

Male breast cancer (MBC) is a rare disease accounting for less than 1% of all breast cancers. MBC is almost always of the estrogen receptor (ER) positive luminal subtype. Although differences have been described between male and female breast cancers, therapies for MBC are still largely extrapolated from female breast cancer (FBC). Treatment planning requires accurate prediction models. Several FBC prediction models have also been validated for MBC. Here, we validate the IHC4 + C algorithm in MBC, known to perform well for ER positive FBC. IHC4 + C combines immunohistochemical scores of ER, progesterone receptor, HER2, and Ki67, supplemented by four clinicopathologic features (age, grade, T- and Nstatus). IHC4 + C was validated in a retrospective MBC cohort of 143 patients. The algorithm performed best in MBC for the endpoint of 5-year overall survival, with C-indices between 0.72 and 0.75. Also for MBC, contributing the clinical score to IHC4 improved predictive power. A clear distinction between groups of MBC patients with good, moderate, and poor prognosis was demonstrated using Kaplan-Meier analyses and was highly statistically significant according to the Log-rank test (p = 0.001). The IHC4 + C algorithm that was originally developed for FBC patients performed well in an MBC cohort. It accurately separated patients with good, intermediate, and poor performance at high statistical significance. Development of a MBC-specific prediction tool remains desirable.