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. The aim of this report is to update pathologists, clinicians and researchers on TIL assessment in both the post-neoadjuvant residual disease and the ductal carcinoma in situ settings. The International Immuno-Oncology Working Group proposes a method for assessing TILs in these settings, based on the previously published International Guidelines on TIL Assessment in Breast Cancer. In this regard, these recommendations represent a consensus guidance for pathologists, aimed to achieve the highest possible consistency among future studies.
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. 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, that integrates 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 and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.
Abstract
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. (https://CRAN.R-project.org/package=biospear)
Supplementary information
Supplementary data are available at Bioinformatics online.
Abstract
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.
Results
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 illustrated on publicly available gene expression data in breast cancer.
Availabilityand implementation
Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network (CRAN).
Supplementary information
Supplementary data are available at Bioinformatics online.
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. We varied the number of biomarkers, of nonnull main effects and true biomarker‐by‐treatment interactions. We also proposed a novel measure evaluating the interaction strength of the developed gene signatures. In the null scenarios, the group‐lasso, two‐I model, and gradient boosting performed poorly in the presence of nonnull main effects, and performed well in alternative scenarios with also high interaction strength. The adaptive lasso with grouped weights was too conservative. The modified covariates, PCA+lasso, PLS+lasso, and ridge+lasso performed moderately. The full‐lasso and adaptive lassos performed well, with the exception of the full‐lasso in the presence of only nonnull main effects. The univariate approach performed poorly in alternative scenarios. We also illustrate the methods using gene expression data from 614 breast cancer patients treated with adjuvant chemotherapy.
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.
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). We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured.
In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4 clinical covariates, the main effects of 98 biomarkers and 24 biomarker-by-treatment interactions, but there was high variability of the expected survival probabilities, with very large confidence intervals.
Based on our simulations, we propose a unified framework for: developing a prediction model with biomarker-by-treatment interactions in a high-dimensional setting and validating it in absence of external data; accurately estimating the expected survival probability of future patients with associated confidence intervals; and graphically visualizing the developed prediction model. All the methods are implemented in the R package biospear, publicly available on the CRAN.
•Treatments are limited in progressive advanced non-small cell lung cancer (NSCLC).•Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) could be targeted.•High expression (HE) of ...CEACAM5 occurs in 25% of patients with nonsquamous NSCLC.•CEACAM5 HE is more prevalent with altered KRAS (35%; G12C 41%) vs wild-type (20%).•These findings support the development of CEACAM5-targeted antibody-drug conjugates.
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.
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. We assessed prevalence of CEACAM5 HE, clinicopathologic characteristics and molecular markers in patients with NSQ-NSCLC in clinical cohorts.
In a lung PDX set of 10 NSQ-NSCLC specimens, correlations between CEACAM5 by IHC, ELISA and RNA-seq ranged from 0.72 to 0.88. In a larger lung PDX set, higher H-scores were present in NSQ- (n = 93) vs SQ-NSCLC (n = 128) models, and in 12 of these NSQ-NSCLC models, more tumor responses to tusamitamab ravtansine occurred in CEACAM5 HE (5/8; 62.5%) versus moderate or negative expression (1/4; 25%), including 3 with KRAS mutations among the 6 responders. In clinical NSQ-NSCLC samples, CEACAM5 HE prevalence was (52/214; 24.3%) in primary tumors and (6/17; 35.3%) in metastases. In NSQ-NSCLC primary tumors, CEACAM5 HE prevalence was significantly higher in KRAS-altered versus wild-type (35.0% vs 19.5%; P = 0.028) and in programmed cell death ligand 1 (PD-L1) negative (tumor cells 0%)/low (1–49%) versus high (≥50%) (33.3%, 26.1%, 5.0%; P = 0.031), but not significantly different in EGFR-mutated versus wild-type (20.0% vs 25.7%, P = 0.626).
In NSQ-NSCLC tumors, CEACAM5 HE prevalence was 24.3% overall and was higher with KRAS altered and with PD-L1 negative/low tumors but similar regardless of EGFR mutation status. These findings support targeting CEACAM5 and the clinical development of tusamitamab ravtansine for patients with NSQ-NSCLC with CEACAM5 HE.
Abstract 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. In addition, we extended the graphical augmentation approach to assess potential changes in the pooled effect estimate after updating a current meta-analysis and applied the three graphical contour definitions to data from meta-analyses on statin use and acute kidney injury risk. Results In the considered example data, the pooled effect estimates and heterogeneity indices demonstrated to be considerably robust to the addition of a future study. Supportingly, for some previously inconclusive meta-analyses, a study update might yield statistically significant kidney injury risk increase associated with higher statin exposure. Conclusions The illustrated contour approach should become a standard tool for the assessment of the robustness of meta-analyses. It can guide decisions on whether to conduct additional studies addressing a relevant research question.
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