BackgroundImmunohistochemistry (IHC)-based surrogate assay is the prevailing method in daily clinical practice to determine the necessity of chemotherapy for Luminal-like breast cancer patients worldwide. It relies on Ki67 scores to separate Luminal A-like from Luminal B-like breast cancer subtypes. Yet, IHC-based Ki67 assessment is known to be plagued with subjectivity and inconsistency to undermine the performance of the surrogate assay. A novel method needs to be explored to improve the clinical utility of Ki67 in daily clinical practice.Materials and MethodsThe Ki67 protein levels in a cohort of 253 specimens were assessed with IHC and quantitative dot blot (QDB) methods, respectively, and used to assign these specimens into Luminal A-like and Luminal B-like subtypes accordingly. Their performances were compared with the Kaplan–Meier, univariate, and multivariate survival analyses of the overall survival (OS) of Luminal-like patients.ResultsThe surrogate assay based on absolutely quantitated Ki67 levels (cutoff at 2.31 nmol/g) subtyped the Luminal-like patients more effectively than that based on Ki67 scores (cutoff at 14%) (Log rank test, p = 0.00052 vs. p = 0.031). It is also correlated better with OS in multivariate survival analysis [hazard ratio (HR) at 6.89 (95% CI: 2.66–17.84, p = 0.0001) vs. 2.14 (95% CI: 0.89–5.11, p = 0.087)].ConclusionsOur study showed that the performance of the surrogate assay may be improved significantly by measuring Ki67 levels absolutely, quantitatively, and objectively using the QDB method.

BackgroundKi67 is a biomarker of proliferation to be used in immunohistochemistry (IHC)-based surrogate assay to determine the necessity of cytotoxic therapy for Luminal-like breast cancer patients. cyclinD1 is another frequently used biomarker of proliferation. A retrospective study was performed here to investigate if these two biomarkers may be combined to improve the prognosis of Luminal-like patients.MethodsBoth Ki67 and cyclinD1 protein levels were measured absolutely and quantitatively using Quantitative Dot Blot method in 143 Luminal-like specimens. Optimized cutoffs for these two biomarkers were developed to evaluate their prognostic roles using Kaplan–Meier overall survival (OS) analysis.ResultscyclinD1 was found as an independent prognostic factor from Ki67 in univariate and multivariate OS analyses. At optimized cutoffs (cyclinD1 at 0.44 μmol/g and Ki67 at 2.31 nmol/g), the subgroup with both biomarkers below the cutoffs (n = 65) had 10-year survival probability at 90% in comparison to those with both biomarkers above the cutoffs (n = 18) with 8-year survival probability at 26% (log-rank test, p <0.0001). This finding was used to modify the surrogate assay using IHC-based cyclinD1 scores, with p-value decreased from 0.031 to 0.00061 or from 0.1 to 0.02, when the Ki67 score of 14 or 20% was used as cutoff, respectively, in the surrogate assay.ConclusionThe current study supports the prospective investigation of cyclinD1 relevance in the clinic.

Aims To translate a clinical research finding into daily clinical practice requires well‐controlled clinical trials. We have demonstrated the usage of absolute quantitation of Ki67 and cyclinD1 protein levels to improve prognosis of Luminal‐like patients based on overall survival (OS) analysis of a cohort of 155 breast cancer specimens (cohort 1). However, this finding is considered the D level of evidence (LOE) to require subsequent validation before it may be used in daily clinical practice. To set the stage for future clinical trials, our findings were validated through OS analysis of an independent cohort (cohort 2) of 173 Luminal‐like patients. Methods Both Ki67 and cyclinD1 levels were measured absolutely and quantitatively using the Quantitative Dot Blot (QDB) method in cohort 2. The proposed cutoffs for both biomarkers from cohort 1 were re‐evaluated in cohort 2 and in the merged cohort of 1 and 2, respectively, through univariate, multivariate and Kaplan–Meier survival analysis. Results The proposed cutoffs of 2.31 nmol/g for Ki67 and 0.44 μmol/g for cyclinD1 were validated as effective cutoffs in cohort 2 and the merged cohort through OS analysis. The combined use of both biomarkers allowed us to identify patients with both biomarker levels below the cutoffs (59.3%) with10‐year survival probability (SP) of 89%, in comparison to those above the cutoffs (8.3%) with 8 year SP of 28% through OS analysis in the merged cohort. Conclusions This study validated our findings that absolute quantitation of Ki67 and cyclinD1 allows effective subtyping of luminal‐like patients. It sets the stage for prospective or prospective‐retrospective clinical studies.

Inherent issues of subjectivity and inconsistency have long plagued immunohistochemistry (IHC)-based Her2 assessment, leading to the repeated issuance of guidelines by the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) for its standardization for breast cancer patients. Yet, all these efforts may prove insufficient with the advent of Trastuzumab deruxtecan (T-Dxd), a drug with the promise to expand to tumors traditionally defined as Her2 negative (Her2−). In this study, we attempted to address these issues by exploring an ELISA-like quantitative dot blot (QDB) method as an alternative to IHC. The QDB method has been used to measure multiple protein biomarkers including ER, PR, Ki67, and cyclin D1 in breast cancer specimens. Using an independent cohort (cohort 2) of breast cancer formalin-fixed paraffin-embedded (FFPE) specimens, we validated cutoffs developed in cohort 1 (Yu et al., Scientific Reports 2020 10:10502) with overall 100% specificity (95% CI: 100–100) and 97.56% sensitivity (95% CI: 92.68–100) in cohort 2 against standard practice with the dichotomized absolutely quantitated values. Using the limit of detection (LOD) of the QDB method as the putative cutoff point, tumors with no Her2 expression were identified with the number comparable to those of IHC 0. Our results support further evaluation of the QDB method as an alternative to IHC to meet the emerging need of identifying tumors with low Her2 expression (Her2-low) in daily clinical practice.