Hormonothérapie du cancer du sein

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine

BACKGROUND

The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint.

RESULTS

To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect.

CONCLUSIONS

Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

Immunomodulatory effects of anti-estrogenic drugs

There are substantial experimental, epidemiological and clinical evidences that show that breast cancer pathology is influenced by endogenous estrogens. This knowledge is the foundation upon which endocrine deprivation therapy has been developed as a major modality for the management of breast cancer. Tamoxifen, which functions as a competitive partial agonist-inhibitor of estrogen at its receptor, has been widely used for more than three decades for adjuvant endocrine treatment in breast cancer. Currently, other effective drugs for endocrine therapy include raloxifene, different aromatase inhibitors (particularly third-generation agents) and luteinizing hormone-releasing hormone agonists. In recent years, a growing body of evidence suggests that these drugs can also act as immune modulators by altering the function of various leukocytes and the release of different cytokines. Moreover, there is evidence that anti-estrogens may prove to be beneficial in the treatment or prevention of some autoimmune diseases due to their effects on immune function. However, their immunopharmacological aspects in the present state of knowledge are not precisely comprehensible. Only a clear pathophysiological understanding could lead to an efficient strategy for breast cancer prevention and decrease in the mortality due to this disease.