BCL-2 expression aids in the immunohistochemical prediction of the Oncotype DX breast cancer recurrence score

Attempt to Substitute the Oncotype DX Breast Recurrence Score® Test by Histopathological Factors and MUC1 Protein Expression

Background/Aim

Oncotype DX Breast Recurrence Score® test (ODx) is a gene profiling assay predicting the benefit of adjuvant chemotherapy for early-stage hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Meanwhile, to avoid unnecessary financial burden on the patient, many studies have attempted to establish alternatives to ODx using conventional clinicopathological factors, but these have not yet been successful. Thus, we retrospectively investigated clinicopathological factors to establish alternatives to ODx.

Patients and Methods

Data from 114 Japanese women who underwent ODx were retrospectively examined to investigate the relationship between ODx recurrence score (RS) and clinicopathological features, including MUC1 staining patterns on immunohistochemical assessment. An RS of 0-25 was defined as low, and 26-100 as high.

Results

Ninety patients (79%) had low RS and 24 patients (21%) had high RS. Univariate analysis revealed that low tumor grade, high progesterone receptor (PgR) expression, and low Ki67 labeling index (LI) were significantly associated with low RS (p=0.025, p<0.001, and p<0.001, respectively). Tumors with an apical pattern of MUC1 staining also frequently had a low RS (p=0.024). In multivariate analysis, PgR expression and Ki67 LI were independent factors associated with RS (p<0.001, for both). When the ODx results were categorized with a combination of these two factors, only 2% of the PgR-high and Ki67-low group (one in 51 cases) had a high RS.

Conclusion

PgR expression and Ki67 LI were independent factors correlated with RS. MUC1 staining pattern also has the potential to be a useful marker. We believe that it is crucial to continue attempts to identify patients who are unlikely to benefit from ODx.

STIM1/SOX2 proteins are co-expressed in the tumor and microenvironmental stromal cells of pancreatic ductal adenocarcinoma and ampullary carcinoma

Pancreatic ductal adenocarcinoma (PDAC) and ampullary carcinoma (AAC) are lethal malignancies with modest benefits from surgery. SOX2 and STIM1 have been linked to anticancer activity in several human malignancies. This study included 94 tumor cases: 48 primary PDAC, 25 metastatic PDAC, and 21 primary AAC with corresponding non-tumor tissue. All cases were immunohistochemically stained for STIM1 and SOX2 and results were correlated with clinicopathologic data, patient survival, and BCL2 immunostaining results. Results revealed that STIM1 and SOX2 epithelial/stromal expressions were significantly higher in PDAC and AAC in comparison to the control groups. STIM1 and SOX2 expressions were positively correlated in the primary and metastatic PDAC (P = 0.016 and, P = 0.001, respectively). However, their expressions were not significantly associated with BCL2 expression. SOX2 epithelial/stromal expressions were positively correlated with the large tumor size in the primary AAC group (P = 0.052, P = 0.044, respectively). STIM1 stromal and SOX2 epithelial over-expressions had a bad prognostic impact on the overall survival of AAC (P = 0.002 and P = 0.001, respectively). Therefore, STIM1 and SOX2 co-expression in tumor cells and intra-tumoral stroma could contribute to the development of PDAC and AAC. STIM1/SOX2 expression is linked to a bad prognosis in AAC.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

A CTC-Cluster-Specific Signature Derived from OMICS Analysis of Patient-Derived Xenograft Tumors Predicts Outcomes in Basal-Like Breast Cancer

Circulating tumor cell clusters (CTCcl) have a higher metastatic potential compared to single CTCs and predict long-term outcomes in breast cancer (BC) patients. Because of the rarity of CTCcls, molecular characterization of primary tumors that give rise to CTCcl hold significant promise for better diagnosis and target discovery to combat metastatic BC. In our study, we utilized the reverse-phase protein array (RPPA) and transcriptomic (RNA-Seq) data of 10 triple-negative BC patient-derived xenograft (TNBC PDX) transplantable models with CTCs and evaluated expression of upregulated candidate protein Bcl2 (B-cell lymphoma 2) by immunohistochemistry (IHC). The sample-set consisted of six CTCcl-negative (CTCcl-) and four CTCcl-positive (CTCcl+) models. We analyzed the RPPA and transcriptomic profiles of CTCcl- and CTCcl+ TNBC PDX models. In addition, we derived a CTCcl-specific gene signature for testing if it predicted outcomes using a publicly available dataset from 360 patients with basal-like BC. The RPPA analysis of CTCcl+ vs. CTCcl- TNBC PDX tumors revealed elevated expression of Bcl2 (false discovery rate (FDR) < 0.0001, fold change (FC) = 3.5) and reduced acetyl coenzyme A carboxylase-1 (ACC1) (FDR = 0.0005, FC = 0.3) in CTCcl+ compared to CTCcl- tumors. Genome-wide transcriptomic analysis of CTCcl+ vs. CTCcl- tumors revealed 549 differentially expressed genes associated with the presence of CTCcls. Apoptosis was one of the significantly downregulated pathways (normalized enrichment score (NES) = -1.69; FDR < 0.05) in TNBC PDX tumors associated with CTCcl positivity. Two out of four CTCcl+ TNBC PDX primary tumors had high Bcl2 expression by IHC (H-score > 34); whereas, only one of six CTCcl- TNBC PDX primary tumors met this criterion. Evaluation of epithelial-mesenchymal transition (EMT)-specific signature did not show significant differences between CTCcl+ and CTCcl- tumors. However, a gene signature associated with the presence of CTCcls in TNBC PDX models was associated with worse relapse-free survival in the publicly available dataset from 360 patients with basal-like BC. In summary, we identified the multigene signature of primary PDX tumors associated with the presence of CTCcls. Evaluation of additional TNBC PDX models and patients can further illuminate cellular and molecular pathways facilitating CTCcl formation.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.