A variable selection approach for highly correlated predictors in high-dimensional genomic data

In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings. We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also successfully illustrated on publicly available gene expression data in breast cancer. Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network

Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

In clinical trials, identification of prognostic and predictive biomarkers is essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso (Prognostic Predictive Lasso) integrating prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso approach on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic data from clinical trial RV144. Our method is implemented in the PPLasso R package which will be soon available from the Comprehensive R Archive Network (CRAN)

Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice

International audienceWith the genomic revolution and the era of targeted therapy, prognostic and predictive gene signatures are becoming increasingly important in clinical research. They are expected to assist prognosis assessment and therapeutic decision making. Notwithstanding, an evidence-based approach is needed to bring gene signatures from the laboratory to clinical practice. In early breast cancer, multiple prognostic gene signatures are commercially available without having formally reached the highest levels of evidence-based criteria. We discuss specific concepts for developing and validating a prognostic signature and illustrate them with contemporary examples in breast cancer. When a prognostic signature has not been developed for predicting the magnitude of relative treatment benefit through an interaction effect, it may be wishful thinking to test its predictive value. We propose that new gene signatures be built specifically for predicting treatment effects for future patients and outline an approach for this using a cross-validation scheme in a standard phase III trial. Replication in an independent trial remains essential

Data from: Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades-off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach to the standard lasso and ten other competitors: AIC, AICC, BIC, eBIC, HQIC, RIC, one-standard-error rule, adaptive lasso, stability selection and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC and the adaptive lasso which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted.<br

Sign Consistency of the Generalized Elastic Net Estimator

In this paper, we propose a novel variable selection approach in the framework of high-dimensional linear models where the columns of the design matrix are highly correlated. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the columns of the design matrix and in applying a generalized Elastic Net criterion since it can be seen as an extension of the generalized Lasso.The properties of our approach called gEN (generalized Elastic Net) are investigated both from a theoretical and a numerical point ofview. More precisely, we provide a new condition called GIC (Generalized Irrepresentable Condition) which generalizes the EIC (Elastic Net Irrepresentable Condition) of Jia and Yu (2010) under which we prove that our estimator can recover the positions of the null and non-null entries of the coefficients when the sample size tends to infinity.We also assess the performance of our methodology using synthetic data and compare it with alternative approaches. Our numerical experiments show that our approach improves the variable selection performance in many cases

AMEERA-3: Randomized Phase II Study of Amcenestrant (Oral Selective Estrogen Receptor Degrader) Versus Standard Endocrine Monotherapy in Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer.

PURPOSE: Amcenestrant (oral selective estrogen receptor degrader) demonstrated promising safety and efficacy in earlier clinical studies for endocrine-resistant, estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) advanced breast cancer (aBC). PATIENTS AND METHODS: In AMEERA-3 (ClinicalTrials.gov identifier: NCT04059484), an open-label, worldwide phase II trial, patients with ER+/HER2- aBC who progressed in the (neo)adjuvant or advanced settings after not more than two previous lines of endocrine therapy (ET) were randomly assigned 1:1 to amcenestrant or single-agent endocrine treatment of physician\u27s choice (TPC), stratified by the presence/absence of visceral metastases, previous/no treatment with cyclin-dependent kinase 4/6 inhibitor, and Eastern Cooperative Oncology Group performance status (0/1). The primary end point was progression-free survival (PFS) by independent central review, compared using a stratified log-rank test (one-sided type I error rate of 2.5%). RESULTS: Between October 22, 2019, and February 15, 2021, 290 patients were randomly assigned to amcenestrant (n = 143) or TPC (n = 147). PFS was numerically similar between amcenestrant and TPC (median PFS [mPFS], 3.6 CONCLUSION: AMEERA-3 did not meet its primary objective of improved PFS with amcenestrant versus TPC although a numerical improvement in PFS was observed in patients with baseline ESR1 mutation. Efficacy and safety with amcenestrant were consistent with the standard of care for second-/third-line ET for ER+/HER2- aBC

Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors

International audienceAssessment of the immune response to tumors is growing in importance as the prognostic implications of this response are increasingly recognized, and as immunotherapies are evaluated and implemented in different tumor types. However, many different approaches can be used to assess and describe the immune response, which limits efforts at implementation as a routine clinical biomarker. In part 1 of this review, we have proposed a standardized methodology to assess tumor-infiltrating lymphocytes (TILs) in solid tumors, based on the International Immuno-Oncology Biomarkers Working Group guidelines for invasive breast carcinoma. In part 2 of this review, we discuss the available evidence for the prognostic and predictive value of TILs in common solid tumors, including carcinomas of the lung, gastrointestinal tract, genitourinary system, gynecologic system, and head and neck, as well as primary brain tumors, mesothelioma and melanoma. The particularities and different emphases in TIL assessment in different tumor types are discussed. The standardized methodology we propose can be adapted to different tumor types and may be used as a standard against which other approaches can be compared. Standardization of TIL assessment will help clinicians, researchers and pathologists to conclusively evaluate the utility of this simple biomarker in the current era of immunotherapy