Isatuximab Monotherapy for Desensitization in Highly Sensitized Patients Awaiting Kidney Transplant.

Significance Statement: There is no standardized desensitization regimen for kidney transplant candidates. CD38, expressed by plasma cells, could be targeted for desensitization to deplete plasma cells producing alloantibodies and donor-specific antibodies. Few studies and case reports are available regarding the use of CD38 antibodies for desensitization in patients awaiting kidney transplant.

The search for therapeutic targets in lung cancer: Preclinical and human studies of carcinoembryonic antigen-related cell adhesion molecule 5 expression and its associated molecular landscape.

Objectives: CEACAM5 is a cell-surface glycoprotein expressed on epithelial cells of some solid tumors. Tusamitamab ravtansine (SAR408701), a humanized antibody-drug conjugate targeting CEACAM5, is in clinical development for nonsquamous non-small cell lung cancer (NSQ-NSCLC) with CEACAM5 high expression (HE), defined as membranous CEACAM5 immunohistochemistry staining at ≥ 2+ intensity in ≥ 50% of tumor cells.

Materials And Methods: We investigated correlations between CEACAM5 expression by immunohistochemistry, CEACAM5 protein expression by ELISA, and CEACAM5 RNA expression by RNA-seq in NSQ-NSCLC patient-derived xenograft (PDX) models, and tumor responses to tusamitamab ravtansine in these models.

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's 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).

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

In clinical trials, identification of prognostic and predictive biomarkers has became 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.

AMEERA-1 phase 1/2 study of amcenestrant, SAR439859, in postmenopausal women with ER-positive/HER2-negative advanced breast cancer.

AMEERA-1 is a Phase 1/2 open-label single-arm study evaluating once-daily (QD) amcenestrant, an orally bioavailable selective estrogen receptor (ER) degrader, in postmenopausal women with ER+/HER2- advanced breast cancer (NCT03284957), who were mostly heavily pretreated (including targeted therapies and fulvestrant). In the dose escalation phase (Part A: n = 16), patients received amcenestrant 20-600 mg QD. Based on absence of dose-limiting toxicities, paired functional F-fluoroestradiol positron emission tomography, and pharmacokinetics, 400 mg QD was selected as recommended Phase 2 dose (RP2D) for the dose expansion phase (Part B: n = 49).

UGT1A1 genetic variants are associated with increases in bilirubin levels in rheumatoid arthritis patients treated with sarilumab.

Sarilumab is a human monoclonal antibody against interleukin (IL)-6Rα that has been approved for the treatment of adult patients with moderately to severely active rheumatoid arthritis (RA) and an inadequate response or intolerance to one or more disease-modifying antirheumatic drugs (DMARDs). Mild liver function test abnormalities have been observed in patients treated with sarilumab. We describe a genome-wide association study of bilirubin elevations in RA patients treated with sarilumab.

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

Motivation: 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.

Update on tumor-infiltrating lymphocytes (TILs) in breast cancer, including recommendations to assess TILs in residual disease after neoadjuvant therapy and in carcinoma in situ: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Morphological evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer is gaining momentum as evidence strengthens the clinical relevance of this immunological biomarker. TILs in the post-neoadjuvant residual disease setting are acquiring increasing importance as a stratifying marker in clinical trials, considering the raising interest on immunotherapeutic strategies after neoadjuvant chemotherapy. TILs in ductal carcinoma in situ, with or without invasive carcinoma, represent an emerging area of clinical breast cancer research.

biospear: an R package for biomarker selection in penalized Cox regression.

Summary: The R package biospear allows selecting the biomarkers with the strongest impact on survival and on the treatment effect in high-dimensional Cox models, and estimating expected survival probabilities. Most of the implemented approaches are based on penalized regression techniques.

Availability And Implementation: The package is available on the CRAN.

Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from the International Immuno-Oncology Biomarkers Working Group: Part 2: TILs in Melanoma, Gastrointestinal Tract Carcinomas, Non-Small Cell Lung Carcinoma and Mesothelioma, Endometrial and Ovarian Carcinomas, Squamous Cell Carcinoma of the Head and Neck, Genitourinary Carcinomas, and Primary Brain Tumors.

Assessment 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.

Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method From the International Immunooncology Biomarkers Working Group: Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma In Situ, Metastatic Tumor Deposits and Areas for Further Research.

Assessment of tumor-infiltrating lymphocytes (TILs) in histopathologic specimens can provide important prognostic information in diverse solid tumor types, and may also be of value in predicting response to treatments. However, implementation as a routine clinical biomarker has not yet been achieved. As successful use of immune checkpoint inhibitors and other forms of immunotherapy become a clinical reality, the need for widely applicable, accessible, and reliable immunooncology biomarkers is clear.

Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials.

Background: Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions.

Methods: Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso), we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation); estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap); and visualize them graphically (pointwise or smoothed with spline).

Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces.

Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lasso); dimensionality reduction of the main effect matrix via linear combinations (PCA+lasso (where PCA is principal components analysis) or PLS+lasso (where PLS is partial least squares)); penalization of modified covariates or of the arm-specific biomarker effects (two-I model); gradient boosting; and univariate approach with control of multiple testing. We compared these methods via simulations, evaluating their selection abilities in null and alternative scenarios.

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.

Contour plot assessment of existing meta-analyses confirms robust association of statin use and acute kidney injury risk.

Objectives: Robustness of an existing meta-analysis can justify decisions on whether to conduct an additional study addressing the same research question. We illustrate the graphical assessment of the potential impact of an additional study on an existing meta-analysis using published data on statin use and the risk of acute kidney injury.

Study Design And Setting: A previously proposed graphical augmentation approach is used to assess the sensitivity of the current test and heterogeneity statistics extracted from existing meta-analysis data.

Statistical methods applied to omics data: predicting response to neoadjuvant therapy in breast cancer.

Purpose Of Review: Omics technologies have become an essential part of clinical trials in oncology to provide a better understanding of molecular mechanisms and to unveil therapeutic targets. Standard statistical methods often fail in the high-dimensional setting. Therefore, an adequate modelling of the omics data is needed in order to identify 'target' genes of interest.

Spontaneous regression of primary abdominal wall desmoid tumors: more common than previously thought.

Purpose: The relevance of the initial observational approach for desmoid tumors (DTs) remains unclear. We investigated a new conservative management treatment for primary abdominal wall DTs.

Methods: Data were collected from 147 patients between 1993 and 2012.