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. We assessed prevalence of CEACAM5 HE, clinicopathologic characteristics and molecular markers in patients with NSQ-NSCLC in clinical cohorts.

RESULTS

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).

CONCLUSIONS

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.

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). 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 v 3.7 months; stratified hazard ratio [HR], 1.051 [95% CI, 0.789 to 1.4]; one-sided P = .643). Among patients with baseline mutated ESR1; (n = 120 of 280), amcenestrant numerically prolonged PFS versus TPC (mPFS, 3.7 v 2.0 months; stratified HR, 0.9 [95% CI, 0.565 to 1.435]). Overall survival data were immature but numerically similar between groups (HR, 0.913; 95% CI, 0.595 to 1.403). In amcenestrant versus TPC groups, treatment-emergent adverse events (any grade) occurred in 82.5% versus 76.2% of patients and grade ≥3 events occurred in 21.7% versus 15.6%.

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.

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. 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 P1-17-11: Updated data from AMEERA-1: Phase 1/2 study of amcenestrant (SAR439859), an oral selective estrogen receptor (ER) degrader (SERD), combined with palbociclib in postmenopausal women with ER+/HER2- advanced breast cancer

Background: In Arm 2 of the ongoing AMEERA-1 trial (NCT03284957), amcenestrant, an optimized oral SERD combined with the CDK4/6 inhibitor (CDK4/6i) palbociclib demonstrated favorable safety and encouraging antitumor activity among patients with endocrine-resistant ER+/HER2− advanced breast cancer in dose escalation (Part C) and dose expansion (Part D) (Chandarlapaty et al., ASCO 2021; abstract 1058). Here we report an update of safety, antitumor activity data, and progression-free survival (PFS), of amcenestrant 200 mg in combination with palbociclib. Analysis of genomic data, including modulation over time and correlation with clinical outcome, will also be presented. Methods: The trial enrolled postmenopausal women with ER+/HER2- locally-advanced or metastatic breast cancer with disease progression while on ≥ 6 months of prior endocrine therapy (ET) in the advanced setting, or who relapsed on adjuvant ET after the first 2 years of treatment or within 12 months of completing adjuvant ET. Prior chemotherapy (≤ 1) was allowed as well as prior CDK4/6i-based therapy (≤ 1, in Part C only). In this pooled analysis (N = 39), patients in Parts C + D received amcenestrant 200 mg once daily + palbociclib 125 mg (21 days on/7 days off), administered in 28-day cycles. Safety in the pooled analysis was reported using methods previously described (Chandarlapaty et al., ASCO 2021; abstract 1058). Data from investigator-assessed, response-evaluable patients in the pooled analysis without prior exposure to targeted therapies (N = 34) were used to evaluate antitumor activity per RECIST v1.1, including the objective response rate (ORR), clinical benefit rate (CBR), and PFS. Results: At a data cutoff of May 30, 2021, in the pooled analysis (N = 39), the median (range) duration of treatment exposure was 44.3 weeks (1-80). Of 39 patients, 24 (61.5%) had initiated at least 10 cycles (40 weeks) of treatment, with 20/39 (51.3%) still receiving ongoing treatment. Among the 34/39 (87.2%) patients in the response-evaluable population, median follow-up was 48.3 weeks with a PFS probability of being event free at 24 weeks of 78.2% (95% CI: 59.6%; 89.0%). Median PFS is not yet mature, with 14/34 (41.2%) patients having had a PFS event (all were progression events and no deaths occurred). The ORR was 11/34 (32.4%; all partial responses). Clinical benefit at 24 weeks was seen in 25/34 (CBR = 73.5%) patients. Median (range) time to first response was 16.3 weeks (8-32). Amcenestrant treatment-related adverse events (TRAEs) and palbociclib TRAEs, respectively, occurred in 27/39 (69.2%) and 35/39 (89.7%) patients for all grade events and in 5/39 (12.8%) and 18/39 (46.2%) patients for Grade ≥ 3 events. Non-hematological amcenestrant and palbociclib TRAEs are reported in Table 1. Neutrophil count decrease based on hematological laboratory abnormalities was observed in the majority of patients (94.9%; with Grade ≥ 3 in 56.4%).

Conclusions: Among postmenopausal women with endocrine-resistant ER+/HER2- advanced breast cancer, amcenestrant 200 mg in combination with the approved dose of palbociclib continues to demonstrate encouraging long-term antitumor activity, sustained clinical benefit, and a favorable safety profile consistent with previous results. Funding: Sanofi.

Table 1.Non-hematological amcenestrant and palbociclib TRAEs occurring in > 10% of patientsPooled Analysis. Amcenestrant 200 mg + Palbociclib. (Parts C + D; N = 39)Amcenestrant Non-hematological TRAEs, n (%)All GradesGrade ≥ 3–Fatigue7 (17.9)0–Nausea7 (17.9)0–Arthralgia4 (10.3)0–Asthenia4 (10.3)0–Hot flush4 (10.3)0Palbociclib Non-hematological TRAEs, n (%)All GradesGrade ≥ 3–Fatigue12 (30.8)0–Nausea10 (25.6)0–Asthenia4 (10.3)0–Dysgeusia4 (10.3)0–Stomatitis4 (10.3)0

Citation Format: Sarat Chandarlapaty, Hannah M Linden, Patrick Neven, Katarina Petrakova, Aditya Bardia, Peter Kabos, Sofia Braga, Valentina Boni, Alice Gosselin, Marina Celanovic, Patrick Cohen, Gautier Paux, Vasiliki Pelekanou, Nils Ternès, Joon Sang Lee, Mario Campone. Updated data from AMEERA-1: Phase 1/2 study of amcenestrant (SAR439859), an oral selective estrogen receptor (ER) degrader (SERD), combined with palbociclib in postmenopausal women with ER+/HER2- advanced breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-17-11.

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.

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.

AVAILABILITY

Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network (CRAN).

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Predictors of responses to immune checkpoint blockade in advanced melanoma

Immune checkpoint blockers (ICB) have become pivotal therapies in the clinical armamentarium against metastatic melanoma (MMel). Given the frequency of immune related adverse events and increasing use of ICB, predictors of response to CTLA-4 and/or PD-1 blockade represent unmet clinical needs. Using a systems biology-based approach to an assessment of 779 paired blood and tumor markers in 37 stage III MMel patients, we analyzed association between blood immune parameters and the functional immune reactivity of tumor-infiltrating cells after ex vivo exposure to ICB. Based on this assay, we retrospectively observed, in eight cohorts enrolling 190 MMel patients treated with ipilimumab, that PD-L1 expression on peripheral T cells was prognostic on overall and progression-free survival. Moreover, detectable CD137 on circulating CD8⁺ T cells was associated with the disease-free status of resected stage III MMel patients after adjuvant ipilimumab + nivolumab (but not nivolumab alone). These biomarkers should be validated in prospective trials in MMel.The clinical management of metastatic melanoma requires predictors of the response to checkpoint blockade. Here, the authors use immunological assays to identify potential prognostic/predictive biomarkers in circulating blood cells and in tumor-infiltrating lymphocytes from patients with resected stage III melanoma.

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). 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.

Results

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.

Conclusion

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0354-0) contains supplementary material, which is available to authorized users.

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. 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.

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

With 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.

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 with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (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. Copyright © 2016 John Wiley & Sons, Ltd.

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. 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.