Traditional molecular markers and response to adjuvant endocrine or trastuzumab-based therapies

Higher ER load is not associated with better outcome in stage 1-3 breast cancer: a descriptive overview of quantitative HR analysis in operable breast cancer

Purpose

In breast cancer, hormone receptor (HR) status is generally a qualitative measure; positive or negative. Quantitatively measured oestrogen and progesterone receptors (ER and PR) are frequently proposed prognostic and predictive markers, some guidelines even provide different treatment options for patients with strong versus weak expression.

Aim

To evaluate quantitative HR load assessed by immunohistochemistry as a prognostic and predictive measure in stage 1–3 breast cancer.

Methods

We reviewed all the available literature on quantitatively measured HRs using immunohistochemistry.

Results

All included studies (n = 19) comprised a cohort of 30,754 patients. Only 2 out of 17 studies found a clear correlation between higher quantitative ER and better disease outcome. Only one trial examined quantitative ER both as prognostic and predictive marker and found no association between ER% and survival. Ten studies examined quantitative PR load, only two of those found a significant correlation between higher PR load and better disease outcome. Two trials examined quantitative PR both as prognostic and predictive marker, neither found any association between PR% and disease outcome.

Conclusions

There is no clear evidence for using quantitatively assessed ER and PR as prognostic nor predictive marker in patients with stage 1–3 breast cancer. We recommend only using a qualitative HR status in future guidelines and treatment considerations.

Electronic supplementary material

The online version of this article (10.1007/s10549-019-05233-9) contains supplementary material, which is available to authorized users.

Comparison of anti-estrogen receptor antibodies SP1, 6F11, and 1D5 in breast cancer: lower 1D5 sensitivity but questionable clinical implications

We compared the anti-estrogen receptors (ER) SP1, 6F11, and 1D5 antibodies in breast carcinoma cases with different ranges of positive cells to evaluate whether this could generate different therapies for patients. We selected 66 cases of breast cancer, each of which was immunostained with the 3 antibodies. 1D5 was less sensitive than SP1 and 6F11, as seen in 26, 20, and 21 negative cases, respectively. Nine cases showed differences in endocrine-therapy indications, of which 8 1D5-negative cases showed low positivity for SP1 and/or 6F11. However, these cases were prevalently G3, progesterone receptor-negative or low-positive, with high Ki-67 and positive HER-2 findings, all biological features associated with endocrine resistance. Finally ER values obtained with these 3 antibodies had no implications for chemotherapy.

A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.

Identifying responders to trastuzumab therapy in breast cancer

In breast cancer, HER2-targeted therapy with trastuzumab has gained significant attention, owing to the dramatic response observed in a subset of HER2-positive patients. The mechanisms of action are complex and not fully understood, and much effort has been spent in order to identify responders. Good patient management, side effects of the humanized monoclonal antibody and socioeconomics all demand that the drug should be administered only to the patients who will benefit from it. This has been a difficult task and contributions to solve it have been proposed from a variety of research. In this article we describe some of these contributions based on the literature and provide our viewpoint as to which identifiers will emerge in the following decade.