Association of human endogenous retrovirus (hERV) expression with clinical efficacy of PD-1 blockade in metastatic clear cell renal cell carcinoma (mccRCC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4568-4568 ◽  
Author(s):  
Jean-Christophe Pignon ◽  
Opeyemi Jegede ◽  
Sachet A Shukla ◽  
David A. Braun ◽  
Christine Horak ◽  
...  
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Endogenous Retrovirus ◽  
Phase Iii ◽  
Human Endogenous Retrovirus ◽  
Rank Test ◽  
Type I ◽  
Limit Of Quantification ◽  
Cell Renal Cell Carcinoma ◽  
Exact Test

4568 Background: hERV levels positively correlate with tumor immune infiltrate and were recently shown to be associated with clinical benefit to PD-1/PD-L1 blockade in two small cohorts of patients (pts) with mccRCC (Smith C.C. et al and Panda A. et al; 2018). We tested whether hERV levels correlate with efficacy of nivolumab in a prospective phase II study of pts with mccRCC (Checkmate 010). Methods: Reverse transcribed RNA extracted from 99 FFPE pretreatment tumors were analyzed by RT-qPCR to assess levels of pan- ERVE4, pan- ERV3.2, hERV4700 GAG or ENV, and the reference genes 18S and HPRT1. Normalized hERV levels were transformed as categorical value (high or low) using population quartiles as cutoffs. For each cutoff, samples with non-quantifiable hERV levels for which the limit of quantification was above the tested cutoff could not be categorized and were excluded from analysis. Log rank test was used to test the association of hERV levels with PFS/irPFS (RECISTv1.1/irRECIST) at each cutoff using Holm-Bonferroni correction for Type I error control; adjusted P-values are reported. Fisher’s exact test was then used to explore the association with ORR/irORR (RECISTv1.1/irRECIST). Results: Among the hERV studied, only hERV4700 ENV was significantly associated with PFS/irPFS. At the 25th percentile cutoff, 45 pts had high levels of hERV4700 ENV and 24 pts had low levels of hERV4700 ENV. Median PFS and irPFS were significantly longer in the high- hERV4700 ENV group [7.0 (95% CI: 2.2 - 10.2) and 8.5 (95% CI: 4.2 - 14.1) months, respectively] versus the low- hERV4700 ENV group [2.6 (95% CI: 1.4 - 5.4) and 2.9 (95% CI: 1.4 - 5.7) months, respectively] ( P = 0.010 for PFS and P = 0.028 for irPFS). At the same cutoff, ORR and irORR rates were significantly higher in the high- hERV4700 ENV group [35.6 (95% CI: 21.9 - 51.2) % for both ORR/irORR] versus the low- hERV4700 ENV group [12.5 (95% CI: 2.7 - 32.4) and 8.3 (95% CI: 1.0 - 27.0) %, respectively] ( P = 0.036 for ORR and P = 0.012 for irORR). Conclusions: hERV4700 ENV levels may predict outcome on nivolumab in mccRCC. Validation of our results and correlation of hERV levels with immune markers in a controlled phase III trial (CheckMate 025) is ongoing.

Comparison of Means: An F Test Followed by a Modified Multiple Range Procedure

1979 ◽  
Vol 4 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juliet Popper Shaffer
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Critical Value ◽  
The Other ◽  
Type I ◽  
F Test ◽  
Range Test

If used only when a preliminary F test yields significance, the usual multiple range procedures can be modified to increase the probability of detecting differences without changing the control of Type I error. The modification consists of a reduction in the critical value when comparing the largest and smallest means. Equivalence of modified and unmodified procedures in error control is demonstrated. The modified procedure is also compared with the alternative of using the unmodified range test without a preliminary F test, and it is shown that each has advantages over the other under some circumstances.

scholarly journals Efficient Noninferiority Testing Procedures for Simultaneously Assessing Sensitivity and Specificity of Two Diagnostic Tests

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guogen Shan ◽  
Amei Amei ◽  
Daniel Young
Keyword(s):  
Sensitivity And Specificity ◽  
Diagnostic Tests ◽  
Error Control ◽  
Type I Error ◽  
Binary Outcomes ◽  
Type I ◽  
Test Statistics ◽  
Asymptotic Approach ◽  
Testing Procedures ◽  
Assessing Sensitivity

Sensitivity and specificity are often used to assess the performance of a diagnostic test with binary outcomes. Wald-type test statistics have been proposed for testing sensitivity and specificity individually. In the presence of a gold standard, simultaneous comparison between two diagnostic tests for noninferiority of sensitivity and specificity based on an asymptotic approach has been studied by Chen et al. (2003). However, the asymptotic approach may suffer from unsatisfactory type I error control as observed from many studies, especially in small to medium sample settings. In this paper, we compare three unconditional approaches for simultaneously testing sensitivity and specificity. They are approaches based on estimation, maximization, and a combination of estimation and maximization. Although the estimation approach does not guarantee type I error, it has satisfactory performance with regard to type I error control. The other two unconditional approaches are exact. The approach based on estimation and maximization is generally more powerful than the approach based on maximization.

scholarly journals Determinants of anti-PD1 response and resistance in clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Lewis Au ◽  
Emine Hatipoglu ◽  
Marc Robert de Massy ◽  
Kevin Litchfield ◽  
Andrew Rowan ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Cd8 T Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Endogenous Retrovirus ◽  
Human Endogenous Retrovirus ◽  
Cell Renal Cell Carcinoma ◽  
Pre Treatment

Antigen recognition and T-cell mediated cytotoxicity in clear-cell renal cell carcinoma (ccRCC) remains incompletely understood. To address this knowledge gap, we analysed 115 multiregion tumour samples collected from 15 treatment-naive patients pre- and post-nivolumab therapy, and at autopsy in three patients. We performed whole-exome sequencing, RNAseq, TCRseq, multiplex immunofluorescence and flow cytometry analyses and correlated with clinical response. We observed pre-treatment intratumoural TCR clonal expansions suggesting pre-existing immunity. Nivolumab maintained pre-treatment expanded, clustered TCR clones in responders, suggesting ongoing antigen-driven stimulation of T-cells. T-cells in responders were enriched for expanded TCF7+CD8+ T-cells and upregulated GZMK/B upon nivolumab-binding. By contrast, nivolumab promoted accumulation of new TCR clones in non-responders, replacing pre-treatment expanded clonotypes. In this dataset, mutational features did not correlate with response to nivolumab and human endogenous retrovirus expression correlated indirectly. Our data suggests that nivolumab potentiates clinical responses in ccRCC by binding pre-existing expanded CD8+ T-cells to enhance cytotoxicity.

scholarly journals Statistical inference of genetic pathway analysis in high dimensions

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 651-651
Author(s):  
Yang Liu ◽  
Wei Sun ◽  
Alexander P Reiner ◽  
Charles Kooperberg ◽  
Qianchuan He
Keyword(s):  
Statistical Inference ◽  
Pathway Analysis ◽  
Genetic Variants ◽  
Error Control ◽  
Genome Wide Association Study ◽  
Type I Error ◽  
High Density Lipoproteins ◽  
Type I ◽  
Genetic Pathway ◽  
A Genome

Summary Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size $n$. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension $p$ could be greater than $n$. Our method can be used to detect pathways that have nonsparse weak signals, as well as pathways that have sparse but stronger signals. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has correct Type I error control and is more powerful than existing approaches. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.

scholarly journals Type I error control in biomarker-stratified clinical trials

Trials ◽  
2015 ◽  
Vol 16 (S2) ◽  
Author(s):  
Deepak Parashar ◽  
Jack Bowden ◽  
Colin Starr ◽  
Lorenz Wernisch ◽  
Adrian Mander
Keyword(s):  
Clinical Trials ◽  
Error Control ◽  
Type I Error ◽  
Type I

No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Aaron T. L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
Software Package ◽  
Error Control ◽  
Degrees Of Freedom ◽  
Linear Models ◽  
Type I Error ◽  
Real Data ◽  
Type I ◽  
Rna Seq ◽  
Study Gene Expression ◽  
Complex Models

AbstractRNA sequencing (RNA-seq) is widely used to study gene expression changes associated with treatments or biological conditions. Many popular methods for detecting differential expression (DE) from RNA-seq data use generalized linear models (GLMs) fitted to the read counts across independent replicate samples for each gene. This article shows that the standard formula for the residual degrees of freedom (d.f.) in a linear model is overstated when the model contains fitted values that are exactly zero. Such fitted values occur whenever all the counts in a treatment group are zero as well as in more complex models such as those involving paired comparisons. This misspecification results in underestimation of the genewise variances and loss of type I error control. This article proposes a formula for the reduced residual d.f. that restores error control in simulated RNA-seq data and improves detection of DE genes in a real data analysis. The new approach is implemented in the quasi-likelihood framework of the edgeR software package. The results of this article also apply to RNA-seq analyses that apply linear models to log-transformed counts, such as those in the limma software package, and more generally to any count-based GLM where exactly zero fitted values are possible.

A phase II trial assessing pazopanib (paz) as third-line therapy for metastatic renal cell carcinoma (mRCC): Clinical outcome and temporal analysis of molecular profile.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4571-4571
Author(s):  
Sumanta Kumar Pal ◽  
Dewan Md Sakib Hossain ◽  
Qifang Zhang ◽  
Chan Gao ◽  
Paul Henry Frankel ◽  
...  
Keyword(s):  
Type I Error ◽  
Temporal Analysis ◽  
Phase Iii ◽  
Molecular Profile ◽  
Vegf Receptor ◽  
Type I ◽  
Prior Therapy ◽  
Best Response ◽  
Treatment Naïve ◽  
Line Setting

4571 Background: Paz, an oral vascular endothelial growth factor (VEGF) receptor inhibitor, was assessed in a phase III study conducted in patients (pts) with mRCC who were cytokine-refractory or treatment-naïve (Sternberg et al J Clin Oncol 2010). Clinical outcomes with paz have been associated with a molecular profile (Tran et al Lancet Oncol 2012). The activity of paz in the 3rd-line setting and temporal changes in molecular profile during paz therapy are poorly understood. Methods: Eligibility was limited to pts with 2 prior lines of therapy (including at least 1 VEGF-directed therapy), ECOG PS 0-2, and clear cell histology. Pts received paz 800 mg/daily on a 28d cycle, and were assessed for response by RECIST 1.1 every 2 cycles. A Simon MinMax 2-stage design was employed, with 80% power of declaring an encouraging overall response rate (ORR) of 23% (type I error=10%). Molecular profiles were assessed on a Luminex platform using the Human Cytokine 30-plex Cytokine Immunoassay (Invitrogen) at baseline, 6 mos and 12 mos. Results: 28 pts (20M, 8F) were enrolled, with a median age of 63 (range, 45-86). All patients received at least 2 lines of prior therapy, and 6 pts (21%) had received 2 prior lines of VEGF-directed therapy. In the pre-specified intent-to-treat analysis, 12/28 pts (43%) had a confirmed response (1 CR, 11 PR), with 1 additional unconfirmed PR. 8 pts (29%) had SD as a best response. Median PFS for the cohort was 17.4 mos (95% CI 14.7-NR). No grade 4 treatment-related toxicities were observed. The most common grade 3 toxicities were hypertension (46%) and proteinuria (14%). At baseline, IL-6 was marginally lower in patients who achieved PR/CR (responders) as compared to those who did not (non-responders; P=0.06). Amongst patients still on therapy at 6 months and 12 months, responders had lower levels of HGF, IL-2R, IL-6, IL-8, and VEGF (P<0.05 for each) at both time intervals. Conclusions: Paz demonstrated an ORR of 43%, representing the highest ORR observed to date in a 3rd-line trial in mRCC. At 6 and 12 months, differences in molecular profile emerged between responders and non-responders, potentially underscoring mechanisms of drug resistance. Clinical trial information: NCT01157091.

scholarly journals Rational Clinical Experiment: Assessing Prior Probability and Its Impact on the Success of Phase II Clinical Trials

2015 ◽  
Vol 33 (26) ◽  
pp. 2914-2919 ◽  
Author(s):  
Daniel M. Halperin ◽  
J. Jack Lee ◽  
Cecile Gonzales Dagohoy ◽  
James C. Yao
Keyword(s):  
Phase Ii ◽  
Prior Probability ◽  
Type I Error ◽  
Drug Approval ◽  
Phase Iii ◽  
Type I ◽  
Phase Ii Trials ◽  
Predictive Values ◽  
Phase Ii Studies ◽  
Therapeutic Decisions

Purpose Despite a robust clinical trial enterprise and encouraging phase II results, the vast minority of oncologic drugs in development receive regulatory approval. In addition, clinicians occasionally make therapeutic decisions based on phase II data. Therefore, clinicians, investigators, and regulatory agencies require improved understanding of the implications of positive phase II studies. We hypothesized that prior probability of eventual drug approval was significantly different across GI cancers, with substantial ramifications for the predictive value of phase II studies. Methods We conducted a systematic search of phase II studies conducted between 1999 and 2004 and compared studies against US Food and Drug Administration and National Cancer Institute databases of approved indications for drugs tested in those studies. Results In all, 317 phase II trials were identified and followed for a median of 12.5 years. Following completion of phase III studies, eventual new drug application approval rates varied from 0% (zero of 45) in pancreatic adenocarcinoma to 34.8% (24 of 69) for colon adenocarcinoma. The proportion of drugs eventually approved was correlated with the disease under study (P < .001). The median type I error for all published trials was 0.05, and the median type II error was 0.1, with minimal variation. By using the observed median type I error for each disease, phase II studies have positive predictive values ranging from less than 1% to 90%, depending on primary site of the cancer. Conclusion Phase II trials in different GI malignancies have distinct prior probabilities of drug approval, yielding quantitatively and qualitatively different predictive values with similar statistical designs. Incorporation of prior probability into trial design may allow for more effective design and interpretation of phase II studies.

scholarly journals Comparing Means under Heteroscedasticity and Nonnormality: Further Exploring Robust Means Modeling

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Alyssa Counsell ◽  
Robert Philip Chalmers ◽  
Robert A. Cribbie
Keyword(s):  
Monte Carlo ◽  
Error Control ◽  
Type I Error ◽  
Monte Carlo Study ◽  
Type I ◽  
Variance Heterogeneity ◽  
Variance Homogeneity

Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM.

scholarly journals A permutation test for assessing the presence of individual differences in treatment effects

2021 ◽  
pp. 096228022110336
Author(s):  
Chi Chang ◽  
Thomas Jaki ◽  
Muhammad Saad Sadiq ◽  
Alena Kuhlemeier ◽  
Daniel Feaster ◽  
...  
Keyword(s):  
Random Forests ◽  
Error Control ◽  
Treatment Effects ◽  
Type I Error ◽  
Permutation Test ◽  
Individual Treatment ◽  
Significant Heterogeneity ◽  
Type I ◽  
Data Set ◽  
Show Evidence

An important goal of personalized medicine is to identify heterogeneity in treatment effects and then use that heterogeneity to target the intervention to those most likely to benefit. Heterogeneity is assessed using the predicted individual treatment effects framework, and a permutation test is proposed to establish if significant heterogeneity is present given the covariates and predictive model or algorithm used for predicted individual treatment effects. We first show evidence for heterogeneity in the effects of treatment across an illustrative example data set. We then use simulations with two different predictive methods (linear regression model and Random Forests) to show that the permutation test has adequate type-I error control. Next, we use an example dataset as the basis for simulations to demonstrate the ability of the permutation test to find heterogeneity in treatment effects for a predicted individual treatment effects estimate as a function of both effect size and sample size. We find that the proposed test has good power for detecting heterogeneity in treatment effects when the heterogeneity was due primarily to a single predictor, or when it was spread across the predictors. Power was found to be greater for predictions from a linear model than from random forests. This non-parametric permutation test can be used to test for significant differences across individuals in predicted individual treatment effects obtained with a given set of covariates using any predictive method with no additional assumptions.

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