David Venet

A statistical approach to central monitoring of data quality in clinical trials

Background Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings. Purpose This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. Methods A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p-values, which can be analyzed by statistical methods and bioinformatic tools to identify extreme centers. Results Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring. Limitations Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies. Conclusion Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.

InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor

Genomics datasets are increasingly useful for gaining biomedical insights, with adoption in the clinic underway. However, multiple hurdles related to data management stand in the way of their efficient large-scale utilization. The solution proposed is a web-based data storage hub. Having clear focus, flexibility and adaptability, InSilico DB seamlessly connects genomics dataset repositories to state-of-the-art and free GUI and command-line data analysis tools. The InSilico DB platform is a powerful collaborative environment, with advanced capabilities for biocuration, dataset sharing, and dataset subsetting and combination. InSilico DB is available from https://insilicodb.org.

Gene expression in thyroid autonomous adenomas provides insight into their physiopathology

The purpose of this study was to use the microarray technology to define expression profiles characteristic of thyroid autonomous adenomas and relate these findings to physiological mechanisms. Experiments were performed on a series of separated adenomas and their normal counterparts on Micromax cDNA microarrays covering 2400 genes (analysis I), and on a pool of adenomatous tissues and their corresponding normal counterparts using microarrays of 18 000 spots (analysis II). Results for genes present on the two arrays corroborated and several gene regulations previously determined by Northern blotting or microarrays in similar lesions were confirmed. Five overexpressed and 24 underexpressed genes were also confirmed by real-time RT–PCR in some of the samples used for microarray analysis, and in additional tumor specimens. Our results show: (1) a change in the cell populations of the tumor, with a marked decrease in lymphocytes and blood cells and an increase in endothelial cells. The latter increase would correspond to the establishment of a close relation between thyrocytes and endothelial cells and is related to increased N-cadherin expression. It explains the increased blood flow in the tumor; (2) a homogeneity of tumor samples correlating with their common physiopathological mechanism: the constitutive activation of the thyrotropin (TSH)/cAMP cascade; (3) a low proportion of regulated genes consistent with the concept of a minimal deviation tumor; (4) a higher expression of genes coding for specific functional proteins, consistent with the functional hyperactivity of the tumors; (5) an increase of phosphodiesterase gene expression which explains the relatively low cyclic AMP levels measured in these tumors; (6) an overexpression of antiapoptotic genes and underexpression of proapoptotic genes compatible with their low apoptosis rate; (7) an overexpression of N-cadherin and downregulation of caveolins, which casts doubt about the use of these expressions as markers for malignancy.

RNA Sequencing to Predict Response to Neoadjuvant Anti-HER2 Therapy: A Secondary Analysis of the NeoALTTO Randomized Clinical Trial

Importance In neoadjuvant trials, treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancers with dual HER2 blockade resulted in increased pathologic complete response (pCR) rates compared with each targeted agent alone. Amplification and/or overexpression of HER2 currently remains the only biomarker for therapeutic decisions, but it is insufficient to explain the heterogeneous response to anti-HER2 agents. Objective To investigate the ability of clinically and biologically relevant genes and gene signatures (GSs) measured by RNA sequencing to predict the efficacy of anti-HER2 agents. Design, Setting, and Participants The neoadjuvant NeoALTTO trial randomized 455 women with HER2-positive early-stage breast cancer to trastuzumab, lapatinib, or the combination for 6 weeks followed by the addition of weekly paclitaxel for 12 weeks, followed by 3 cycles of fluorouracil, epirubicin, and cyclophosphamide after surgery. The present substudy, which was planned in the NeoALTTO main protocol, evaluated the association of pretreatment gene expression levels defined using RNA sequencing with pCR and event-free survival (EFS). Main Outcomes and Measures Gene expression–based biomarkers using RNA sequencing were examined for their association with response to anti-HER2 therapy and long-term outcome. Results Sequencing data were available for 254 (56%) of the NeoALTTO participants (mean [SD] age of substudy participants, 48.8 [11.2] years). The expression of ERBB2/HER2 was the most significant predictor of pCR, followed by HER2-enriched subtype, ESR1, treatment arm, ER immunohistochemical analysis scores, Genomic Grade Index, immune, proliferation, and AKT/mTOR GSs. Adjusting for clinicopathological variables and treatment arms, ERBB2/HER2, HER2-enriched subtype, ESR1, and Genomic Grade Index remained significant. Immune GSs were associated with higher pCR only in the combination arm (odds ratio, 2.1; 95% CI, 1.2-4.0; interaction test P = .01), while the stroma GSs were significantly associated with higher pCR in the single arms and with lower pCR in the combination arm (odds ratio, 0.46; 95% CI, 0.25-0.84; P = .009). None of the evaluated variables was associated with EFS after correction for multiple testing, but this analysis was underpowered. Conclusions and Relevance High levels of ERBB2/HER2 and low levels of ESR1 were associated with pCR in all treatment arms. In the combination arm, high expression of immune and stroma GSs were significantly associated with higher and lower pCR rates, respectively, and should be further explored as candidate predictive markers. Trial Registration clinicaltrials.gov Identifier: NCT00553358

Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2-positive breast cancer

BackgroundThe likelihood of recurrence in patients with breast cancer who have HER2-positive tumors is relatively high, although trastuzumab is a remarkably effective drug in this setting. Signal transducer and activator of transcription 3 protein (STAT3), a transcription factor that is persistently tyrosine-705 phosphorylated (pSTAT3) in response to numerous oncogenic signaling pathways, activates downstream proliferative and anti-apoptotic pathways. We hypothesized that pSTAT3 expression in HER2-positive breast cancer will confer trastuzumab resistance.MethodsWe integrated reverse phase protein array (RPPA) and gene expression data from patients with HER2-positive breast cancer treated with trastuzumab in the adjuvant setting.ResultsWe show that a pSTAT3-associated gene signature (pSTAT3-GS) is able to predict pSTAT3 status in an independent dataset (TCGA; AUC = 0.77, P = 0.02). This suggests that STAT3 induces a characteristic set of gene expression changes in HER2-positive cancers. Tumors characterized as high pSTAT3-GS were associated with trastuzumab resistance (log rank P = 0.049). These results were confirmed using data from the prospective, randomized controlled FinHer study, where the effect was especially prominent in HER2-positive estrogen receptor (ER)-negative tumors (interaction test P = 0.02). Of interest, constitutively activated pSTAT3 tumors were associated with loss of PTEN, elevated IL6, and stromal reactivation.ConclusionsThis study provides compelling evidence for a link between pSTAT3 and trastuzumab resistance in HER2-positive primary breast cancers. Our results suggest that it may be valuable to add agents targeting the STAT3 pathway to trastuzumab for treatment of HER2-positive breast cancer.

Linear mixed-effects models for central statistical monitoring of multicenter clinical trials

Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed-effects model to detect location differences between each center and all other centers. We describe the performance of the procedure as a function of contamination rate and signal-to-noise ratio. We investigate the effect of center size and variance structure and illustrate the use of the procedure using data from two multicenter clinical trials.

HER2-overexpressing breast cancers amplify FGFR signaling upon acquisition of resistance to dual therapeutic blockade of HER2

Purpose: Dual blockade of HER2 with trastuzumab and lapatinib or pertuzumab has been shown to be superior to single-agent trastuzumab. However, a significant fraction of HER2-overexpressing (HER2+) breast cancers escape from these drug combinations. In this study, we sought to discover the mechanisms of acquired resistance to the combination of lapatinib + trastuzumab. Experimental Design: HER2+ BT474 xenografts were treated with lapatinib + trastuzumab long-term until resistance developed. Potential mechanisms of acquired resistance were evaluated in lapatinib + trastuzumab-resistant (LTR) tumors by targeted capture next-generation sequencing. In vitro experiments were performed to corroborate these findings, and a novel drug combination was tested against LTR xenografts. Gene expression and copy-number analyses were performed to corroborate our findings in clinical samples. Results: LTR tumors exhibited an increase in FGF3/4/19 copy number, together with an increase in FGFR phosphorylation, marked stromal changes in the tumor microenvironment, and reduced tumor uptake of lapatinib. Stimulation of BT474 cells with FGF4 promoted resistance to lapatinib + trastuzumab in vitro. Treatment with FGFR tyrosine kinase inhibitors reversed these changes and overcame resistance to lapatinib + trastuzumab. High expression of FGFR1 correlated with a statistically shorter progression-free survival in patients with HER2+ early breast cancer treated with adjuvant trastuzumab. Finally, FGFR1 and/or FGF3 gene amplification correlated with a lower pathologic complete response in patients with HER2+ early breast cancer treated with neoadjuvant anti-HER2 therapy. Conclusions: Amplification of FGFR signaling promotes resistance to HER2 inhibition, which can be diminished by the combination of HER2 and FGFR inhibitors. Clin Cancer Res; 23(15); 4323–34. ©2017 AACR.

A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations

Background The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies. Results As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the “Signal-to-Noise Applied to Gene Expression Experiments”, or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results. Conclusions We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor.

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial

IntroductionData quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials.MethodsCSM with SMART™ uses advanced statistical tools that help identify centers with atypical data patterns which might be the sign of an underlying quality issue. This approach was used to assess the quality and consistency of the data collected in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, involving 1495 patients across 232 centers in Japan.ResultsIn the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, very few atypical data patterns were found among the participating centers, and none of these patterns were deemed to be related to a quality issue that could significantly affect the outcome of the trial.DiscussionCSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.

Unravelling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis.

Abstract Background Recent efforts of genome-wide gene expression profiling analyses have improved our understanding of the biological complexity and diversity of triple-negative breast cancers (TNBCs) reporting, at least six different molecular subtypes of TNBC namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). However, little is known regarding the potential driving molecular events within each subtype, their difference in survival and response to therapy. Further insight into the underlying genomic alterations is therefore needed. Patients and methods This study was carried out using copy-number aberrations, somatic mutations and gene expression data derived from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas. TNBC samples (n = 550) were classified according to Lehmann’s molecular subtypes using the TNBCtype online subtyping tool (http://cbc.mc.vanderbilt.edu/tnbc/). Results Each subtype showed significant clinic-pathological characteristic differences. Using a multivariate model, IM subtype showed to be associated with a better prognosis (HR = 0.68; CI = 0.46–0.99; P = 0.043) whereas LAR subtype was associated with a worst prognosis (HR = 1.47; CI = 1.0–2.14; P = 0.046). BL1 subtype was found to be most genomically instable subtype with high TP53 mutation (92%) and copy-number deletion in genes involved in DNA repair mechanism (BRCA2, MDM2, PTEN, RB1 and TP53). LAR tumours were associated with higher mutational burden with significantly enriched mutations in PI3KCA (55%), AKT1 (13%) and CDH1 (13%) genes. M and MSL subtypes were associated with higher signature score for angiogenesis. Finally, IM showed high expression levels of immune signatures and check-point inhibitor genes such as PD1, PDL1 and CTLA4. Conclusion Our findings highlight for the first time the substantial genomic heterogeneity that characterize TNBC molecular subtypes, allowing for a better understanding of the disease biology as well as the identification of several candidate targets paving novel approaches for the development of anticancer therapeutics for TNBC.