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. This study shows that isatuximab, a CD38-targeting therapy, was well tolerated in kidney transplant candidates, with a durable decrease in anti-HLA antibodies and partial desensitization activity. The short treatment period and long follow-up of this study allowed for the understanding of the mechanism and timing for any antibody rebound. Isatuximab could be further investigated as an option for adjunct therapy to existing desensitization for patients on the kidney transplant waitlist. BACKGROUND: Patients with calculated panel reactive antibody (cPRA) ≥80.00%, particularly those with cPRA ≥99.90%, are considered highly sensitized and underserved by the Kidney Allocation System. Desensitization removes circulating reactive antibodies and/or suppresses antibody production to increase the chances of a negative crossmatch. CD38 is expressed highly on plasma cells, thus is a potential target for desensitization. METHODS: This was an open-label single-arm phase 1/2 study investigating the safety, pharmacokinetics, and preliminary efficacy of isatuximab in patients awaiting kidney transplantation. There were two cohorts, cohorts A and B, which enrolled cPRA ≥99.90% and 80.00% to <99.90%, respectively. RESULTS: Twenty-three patients (12 cohort A, 11 cohort B) received isatuximab 10 mg/kg weekly for 4 weeks then every 2 weeks for 8 weeks. Isatuximab was well tolerated with pharmacokinetic and pharmacodynamic profiles that indicated similar exposure to multiple myeloma trials. It resulted in decreases in CD38 + plasmablasts, plasma cells, and NK cells and significant reductions in HLA-specific IgG-producing memory B cells. Overall response rate, on the basis of a predefined composite desensitization end point, was 83.3% and 81.8% in cohorts A and B. Most responders had decreases in anti-HLA antibodies that were maintained for 26 weeks after the last dose. Overall, cPRA values were minimally affected, however, with only 9/23 patients (39%) having cPRA decreases to target levels. By study cutoff (median follow-up of 68 weeks), six patients received transplant offers, of which four were accepted. CONCLUSIONS: In this open-label trial, isatuximab was well tolerated and resulted in a durable decrease in anti-HLA antibodies with partial desensitization activity. CLINICAL TRIAL REGISTRATION NUMBER: NCT04294459 .

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.

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 18F-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). No Grade ≥3 treatment-related adverse events or clinically significant cardiac/eye toxicities were reported. The Part B primary endpoint, confirmed objective response rate (ORR) was 3/45 at the interim analysis and 5/46 (10.9%) at the final analysis. The overall clinical benefit rate (CBR) was 13/46 (28.3%). CBRs among patients with baseline wild-type and mutated ESR1 were 9/26 (34.6%) and 4/19 (21.1%), respectively. Paired tumor biopsy and cell-free DNA analyses revealed ER inhibition and degradation, and a reduction in detectable ESR1 mutations, including Y537S. In conclusion, amcenestrant at RP2D of 400 mg QD for monotherapy is well-tolerated with no dose-limiting toxicities, and demonstrates preliminary antitumor activity irrespective of baseline ESR1 mutation status.

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. Array genotyping and exome sequencing were performed on DNA samples from 1075 patients. Variants in the UGT1A1 gene were strongly associated with maximum bilirubin elevations in sarilumab-treated patients (rs4148325; p = 2.88 × 10-41) but were not associated with aminotransferase elevations. No other independent loci showed evidence of association with bilirubin elevations after sarilumab treatment. These findings suggest that most bilirubin increases during sarilumab treatment are related to genetic variation in UGT1A1 rather than underlying liver injury.

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. 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.

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. (https://CRAN.R-project.org/package=biospear). Contact: stefan.michiels@gustaveroussy.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

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. 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.

Are we reproducible in measurement of NET liver metastasis?

Accurate measurement of well-differentiated neuroendocrine tumours (NET) liver metastases is critical to determine tumour slope and to assess treatment efficacy. Our objectives were to determine which CT or MRI sequence is the most reproducible to measure NET liver metastases and to assess the percentage of variability of measurements. Intra and inter-observer variability were studied on triphasic abdominal CT or liver MRI in 22 and 32 NET patients respectively. Patients were treatment-naïve or under somatostatin analogues. A maximum of 5 liver target lesions per patient was defined and three radiologists measured them on each sequence. Reproducibility were analysed by calculating the relative variation (RV) as defined by RECIST criteria. We analysed 1656 target measurements for CT and 3384 for MRI. Intra-observers RV were better than inter-observers. T2 for MRI and portal-phase for CT were associated with the lowest measurement variability. The MRI sequence offering the best intra and inter-observer reproducibility is the T2W-sequence. MRI allows more reproducible measurement than CT (inter-observer RV <20% in 96.8% for MRI and 81% for CT). Our study demonstrates intermediate to high imaging reproducibility of liver metastases measurements in NET patients. Non-enhanced MRI should be preferred to triphasic-CT for follow-up, assessment of treatment and trials.

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. In part 1 of this review we briefly discuss the host immune response to tumors and different approaches to TIL assessment. We propose a standardized methodology to assess TILs in solid tumors on hematoxylin and eosin sections, in both primary and metastatic settings, based on the International Immuno-Oncology Biomarker Working Group guidelines for TIL assessment in invasive breast carcinoma. A review of the literature regarding the value of TIL assessment in different solid tumor types follows in part 2. The method we propose is reproducible, affordable, easily applied, and has demonstrated prognostic and predictive significance in invasive breast carcinoma. This standardized methodology may be used as a reference against which other methods are compared, and should be evaluated for clinical validity and utility. Standardization of TIL assessment will help to improve consistency and reproducibility in this field, enrich both the quality and quantity of comparable evidence, and help to thoroughly evaluate the utility of TILs assessment in this era of immunotherapy.

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. 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.

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.

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.

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.

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. RECENT FINDINGS: Several genes or gene signatures have been identified to predict the response to neoadjuvant therapies in breast cancer trials. We first reviewed statistical methods used to identify genes in 13 recent publications. Most of these studies had a small sample size (median: 42 patients) and were nonrandomized. We then focused on some popular methods - especially the so-called penalized methods used by three of the reviewed articles - and on the more recent methods proposed to predict causal estimates from observational data. We finally illustrated these methods in a nonrandomized neoadjuvant phase II trial of letrozole in estrogen receptor-positive breast cancer patients. SUMMARY: The review highlighted small sample sizes, few randomized trials and a large panel of statistical methods used in this setting. In our illustrated neoadjuvant example, causal inference methods did not outperform the penalized methods.

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. The initial therapeutic approaches were categorized as front-line surgery [surgery group (SG), n = 41, 28 %] and initial observation or medical treatment [nonsurgery group (NSG), n = 106, 72 %]. The cumulative incidence of the last strategy modification was estimated using competing risk methods with variable censoring times. RESULTS: Of the 147 patients, 143 were female (97 %). In the SG, 27 patients (66 %) required full-thickness abdominal wall mesh repair. In the NSG, 102 patients (96 %) underwent initial observation and four received medical treatment. In the NSG, the 1- and 3-year incidences of changing to medical treatment (no further changes during the follow-up) were 19 % [95 % confidence interval (CI) 11-28] and 25 % (95 % CI 17-35), respectively, and the 1- and 3-year incidences of a final switch to surgery were 14 % (95 % CI 8-22) and 16 % (95 % CI 9-24), respectively. An initial tumor size of >7 cm was associated with a higher strategy modification risk (p = 0.004). Of the 102 patients initially observed, 29 experienced spontaneous regression over a median follow-up period of 32 months. All second-intent resections were macroscopically completed, with R0 resections achieved in 82 % of patients. CONCLUSIONS: This study supports an initial nonsurgical approach to abdominal wall DTs ≤7 cm, followed by surgery based on tumor growth in select cases.