Comprehensive evaluation of the relationship between biomarker profiles and neoadjuvant chemotherapy outcomes for breast cancer patients

BACKGROUND

Accurately predicting the response to neoadjuvant chemotherapy (NAC) in breast cancer patients is crucial for guiding treatment strategies and enhancing clinical outcomes. Current studies have primarily focused on a limited set of biomarkers. More importantly, the results of many studies are in conflict. To address this, we conducted a comprehensive evaluation of the predictive value of a diverse range of clinically available molecular biomarkers in breast cancer, including HER2, ER, PR, TOPO II, EGFR, Ki67, CK5/6, AR, and p53. Additionally, we assessed changes in these biomarkers after NAC administration.

METHODS

Our study involved 189 patients with invasive breast cancer who underwent NAC at our institute. We examined biomarker profiles in core-needle biopsies taken before NAC and in surgical specimens obtained after NAC. We examined the association between these biomarkers and NAC outcomes, focusing on two main aspects: the rate of pathological complete response (pCR) and the reduction in tumor size. We used Chi-square and Mann-Whitney U tests to compare biomarker status changes between pCR and non-pCR patients. Linear regression analysis was employed to evaluate the relationship between biomarker status and tumor shrinkage rate. Additionally, we compared the expression status of these biomarkers before and after NAC using Chi-square and Wilcoxon signed-rank tests.

RESULTS AND CONCLUSIONS

Our results demonstrated significant differences in the expression levels of HER2, ER, PR, TOPO II, EGFR, and Ki67 between pCR and non-pCR patients, underscoring their potential as predictive markers for NAC outcomes. Importantly, our results have shed light on the contentious issue surrounding TOPO II in NAC outcome prediction. We have provided evidence that establishes a significantly positive association between TOPO II expression level and the pCR rate. Notably, tumor size was identified as a relevant predictive factor for achieving pCR. Regarding biomarker profiles, only Ki67 levels and TOPO II status exhibited changes following NAC, resolving previous controversies. While the ER and PR status remained unchanged, their expression values exhibited a slight but significant decrease post-NAC. Our results provide clarity and insights into the value and potential of using these biomarkers to predict NAC responses and prognosis in breast cancer patients.

Data augmentation based on waterfall plots to increase value of response data generated by small single arm Phase II trials

Waterfall plots have been increasingly used to visualize tumor response measurements in Phase II clinical trials. Despite the growing popularity of waterfall plots, quantitative summaries and distribution features of the data indicating antitumor activities are typically not reported. Statistical summaries from the raw and augmented data may provide valuable information for understanding such features. This issue has not been discussed adequately in the literature or fully recognized within the oncology community. In this article, we propose to augment the data using a statistical distribution system. Summary statistics of the data set corresponding to waterfall plot can be calculated using the original sample of the tumor changes or the augmentation sample, which may lead to additional insights into the treatment effect. We demonstrate the proposed method in numerical studies and in a Phase II clinical trial investigating the efficacy of a treatment for ovarian carcinoma. We recommend the proposed statistical analyses for making inferences in addition to the waterfall plot visualization.

Independent action models and prediction of combination treatment effects for response rate, duration of response and tumor size change in oncology drug development

An unprecedented number of new cancer targets are in development, and most are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage development. The most common early endpoints to be assessed in such decision-making include objective response rate, duration of response and tumor size change. In this paper, using independent-drug-action and Bliss-drug-independence concepts as a foundation, we introduce simple models to predict combination therapy efficacy for duration of response and tumor size change. These models complement previous publications using the independent action models (Palmer 2017, Schmidt 2020) to predict progression-free survival and objective response rate and serve as new predictive models to understand drug combinations for early endpoints. The models can be applied to predict the combination treatment effect for early endpoints given monotherapy data, or to estimate the possible effect of one monotherapy in the combination if data are available from the combination therapy and the other monotherapy. Such quantitative work facilitates strategic planning and decision making in early stage oncology drug development.