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Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.

Cancer biomarker discoveries typically involve using patient specimens. In practice, there is often strong desire to preserve high quality biospecimens for studies that are most likely to yield useful information. Previously, we proposed a two-stage adaptive design for binary endpoints which terminates the biomarker study in a futility interim if the model performance is unsatisfactory. In this work, we extend the two-stage design framework to accommodate time-to-event endpoints. The first stage of the procedure involves testing whether the measure of discrimination for survival models (C-index) exceeds a prespecified threshold. We describe the computation of cross-validated C-index and evaluation of the statistical significance using resampling techniques. The second stage involves an independent model validation. Our simulation studies show that under the null hypothesis, the proposed design maintains Type I error at the nominal level and has high probabilities of terminating the study early. Under the alternative hypothesis, power of the design is a function of the true event proportion, the sample size, and the targeted improvement in the discriminant measure. We apply the method to design of a prognostic biomarker study in patients with triple-negative breast cancer. Some practical aspects of the proposed method are discussed. [ABSTRACT FROM AUTHOR]

Applications in early-phase cancer trials have motivated the development of many statistical designs since the late 1980s, including dose-finding methods, futility screening, treatment selection, and early stopping rules. These methods are often proposed to address the conventional cytotoxic therapeutics for neoplastic diseases and cancer. Recent advances in precision medicine have motivated novel trial designs, most notably the idea of master protocol (eg, platform trial, basket trial, umbrella trial, N-of-1 trial), for the evaluation of molecularly targeted cancer therapies. In this article, we review the concepts and methodology of early-phase cancer trial designs with a focus on dose finding and treatment screening and put these methods in the context of platform trials of molecularly targeted cancer therapies. Because most cancer trial designs have been developed for cytotoxic agents, we will discuss how these time-tested design principles hold relevance for targeted cancer therapies, and we will delineate how a master protocol may serve as an efficient platform for safety and efficacy evaluations of novel targeted therapies. [ABSTRACT FROM AUTHOR]

Recent advances in biotechnology and cancer genomics have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this article, we provide an updated review of commonly used biomarker-based randomized clinical trial designs, with a particular focus on design efficiency. When the efficacy of a new therapy may be limited to a biomarker-defined subgroup, the choice of an appropriate randomized clinical trial design should be guided by the strength of the biomarker's credentials. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design (eg, a subgroup-specific or Marker Sequential Test strategy) would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology. [ABSTRACT FROM AUTHOR]

Background: The clinical utility of a new biomarker should ideally be established in a prospective randomized clinical trial. However, such trials are not always practical. As such, it is common for investigators to identify promising biomarkers using archived specimens and clinical data collected from previously completed therapeutic trials. Simon et al. defined such biomarker studies as prospective-retrospective studies and proposed specific conditions to satisfy for such evaluations to be more than hypothesis generating. One condition they proposed is that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. Purpose: The purpose of this article is to provide a new perspective on how to estimate power for assessing the prognostic and predictive values of a single binary biomarker in prospective-retrospective biomarker studies. Computer programs are provided to facilitate the use of these methods in practice. Methods: The proposed methods utilize additional information that becomes available during the course of the treatment trial including sample size, accrual time, additional follow-up time, and the observed number of events at time of biomarker analysis. These methods involve solving for the exponential hazard rates that give rise to the event numbers that are consistent with those observed while satisfying other design parameter constraints. Conclusion: Simon et al. proposed a new paradigm for biomarker design, conduct, analysis, and evaluation in prospective-retrospective studies that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they suggest that samples from at least two-thirds of the patients be available for analysis. In this article, I expand on this issue and provide a methodological tool useful for estimating study power in prospective-retrospective biomarker studies. It is my hope that these incremental efforts to improve the quality and statistical rigor in biomarker studies will hasten the introduction of useful tumor biomarkers into clinical practice. [ABSTRACT FROM AUTHOR]

Cancer biomarkers are frequently evaluated using archived specimens collected from previously conducted therapeutic trials. Routine collection and banking of high quality specimens is an expensive and time-consuming process. Therefore, care should be taken to preserve these precious resources. Here, we propose a novel two-stage adaptive cutoff design that affords the possibility to stop the biomarker study early if an evaluation of the model performance is unsatisfactory at an early stage, thereby allowing one to preserve the remaining specimens for future research. In addition, our design integrates important elements necessary to meet statistical rigor and practical demands for developing and validating a prognostic biomarker signature, including maintaining strict separation between the datasets used to build and evaluate the model and producing a locked-down signature to facilitate future validation. We conduct simulation studies to evaluate the operating characteristics of the proposed design. We show that under the null hypothesis when the model performance is deemed undesirable, the proposed design maintains type I error at the nominal level, has high probabilities of terminating the study early, and results in substantial savings in specimens. Under the alternative hypothesis, power is generally high when the total sample size and the targeted degree of improvement in prediction accuracy are reasonably large. We illustrate the use of the procedure with a dataset in patients with diffuse large-B-cell lymphoma. The practical aspects of the proposed designs are discussed. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Background The intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies. Methods This study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods. Results We show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited. Conclusions Decision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language. [ABSTRACT FROM AUTHOR]

Summary Follicular lymphoma (FL) is an indolent B cell malignancy characterized by an extensive but poorly functional T cell infiltrate in the tumor microenvironment. Using mass cytometry, we identified at least 12 subsets of intratumoral CD4+ T cells, 3 of which were unique to FL biopsies versus control tissues. Of these subsets, the frequency of naive T cells correlated with improved patient survival. Although total PD-1+ T cell numbers were not associated with patient outcome, specific PD-1+ T cell subpopulations were associated with poor survival. Intratumoral T cells lacking CD27 and CD28 co-stimulatory receptor expression were enriched in FL and correlated with inferior patient outcomes. In vitro models revealed that CD70+ lymphoma cells played an important role in expanding this population. Taken together, our mass cytometry results identified CD4+ memory T cell populations that are poorly functional due to loss of co-stimulatory receptor expression and are associated with an inferior survival in FL. Graphical Abstract Highlights • T cell profiles in the FL tumor microenvironment are distinct from control tissues • Specific subsets of intratumoral T cells are associated with patient survival in FL • Reduced expression of CD27 and CD28 represents a specific immune signature in FL Yang et al. utilize mass cytometry (CyTOF) to characterize intratumoral T cells and explore the clinical relevance of T cell subsets in follicular lymphoma (FL). Clustering analysis reveals an immune signature with reduced expression of co-stimulatory molecules on intratumoral T cells that correlated with a poor prognosis in FL. [ABSTRACT FROM AUTHOR]

Advances in molecular profiling have led to improved understanding of glioma heterogeneity. Results have been used to inform diagnostic classification and targeted treatment strategies. Validation of these tests is necessary in the development of biomarkers that can aid in treatment decision, allowing for personalized medicine in neuro-oncologic diseases. Although not all populations have benefitted equally from awareness of and access to testing, opportunities arise regarding incorporating this testing into the standard of care for patients with glioma.

Purpose: Programmed death ligand 1 [PD-(L)1]-targeted therapies have shown modest survival benefit in triple-negative breast cancer (TNBC). PD-L1 + microenvironments in TNBC are not well characterized and may inform combinatorial immune therapies. Herein, we characterized clinicopathologic features, RNA-based immune signatures, and spatially defined protein-based tumor-immune microenvironments (TIME) in early-stage PD-L1 + and PD-L1 - TNBC., Experimental Design: From a large cohort of chemotherapy-naïve TNBC, clinicopathologic features, deconvoluted RNA immune signatures, and intraepithelial and stromal TIME (Nanostring GeoMX) were identified in subsets of PD-L1 + and PD-L1 - TNBC, as defined by FDA-approved PD-L1 companion assays., Results: 228 of 499 (46%) TNBC were PD-L1 + (SP142: ≥1% immune cells-positive). Using PD-L1 22C3, 46% had combined positive score (CPS) ≥ 1 and 16% had CPS ≥10. PD-L1 + TNBC were higher grade with higher tumor-infiltrating lymphocytes (TIL; P < 0.05). PD-L1 was not associated with improved survival following adjustment for TILs and other variables. RNA profiles of PD-L1 + TNBC had increased dendritic cell, macrophage, and T/B cell subset features; and decreased myeloid-derived suppressor cells. PD-L1+ stromal and intraepithelial TIMEs were highly enriched in IDO-1, HLA-DR, CD40, and CD163 compared with PD-L1-TIME, with spatially specific alterations in CTLA-4, Stimulator of Interferon Genes (STING), and fibronectin. Macrophage- and antigen presentation-related proteins correlated most strongly with PD-L1 protein., Conclusions: In this early-stage TNBC cohort, nearly 50% were PD-L1 + (SP142 companion assay) while 16% were PD-L1 + with the 22C3 companion assay. PD-L1 + TNBC had specific myeloid-derived and lymphoid features. Spatially defined PD-L1 + TIME were enriched in several clinically actionable immune proteins. These data may inform future studies on combinatorial immunotherapies for patients with PD-L1 + TNBC. See related commentary by Symmans, p. 5446 ., (©2021 The Authors; Published by the American Association for Cancer Research.)

As part of a randomized, prospective clinical trial in large cell lymphoma, we conducted serial fluorodeoxyglucose positron emission tomography (FDG-PET) at baseline, after 2 cycles of chemotherapy (interim PET [i-PET]), and at end of treatment (EoT) to identify biomarkers of response that are predictive of remission and survival. Scans were interpreted in a core laboratory by 2 imaging experts, using the visual Deauville 5-point scale (5-PS), and by calculating percent change in FDG uptake (change in standardized uptake value [ΔSUV]). Visual scores of 1 through 3 and ΔSUV ≥66% were prospectively defined as negative. Of 524 patients enrolled in the parent trial, 169 agreed to enroll in the PET substudy and 158 were eligible for final analysis. In this selected population, all had FDG-avid disease at baseline; by 5-PS, 55 (35%) remained positive on i-PET and 28 (18%) on EoT PET. Median ΔSUV on i-PET was 86.2%. With a median follow-up of 5 years, ΔSUV, as continuous variable, was associated with progression-free survival (PFS) (hazard ratio [HR] = 0.99; 95% confidence interval [CI], 0.97-1.00; P = .02) and overall survival (OS) (HR, 0.98; 95% CI, 0.97-0.99; P = .03). ΔSUV ≥66% was predictive of OS (HR, 0.31; 95% CI, 0.11-0.85; P = .02) but not PFS (HR, 0.47; 95% CI, 0.19-1.13; P = .09). Visual 5-PS on i-PET did not predict outcome. ΔSUV, but not visual analysis, on i-PET predicted OS in DLBCL, although the low number of events limited the statistical analysis. These data may help guide future clinical trials using PET response-adapted therapy. This trial was registered at www.clinicaltrials.gov as #NCT00118209.

Follicular lymphoma (FL) is an indolent B cell malignancy characterized by an extensive but poorly functional T cell infiltrate in the tumor microenvironment. Using mass cytometry, we identified at least 12 subsets of intratumoral CD4 + T cells, 3 of which were unique to FL biopsies versus control tissues. Of these subsets, the frequency of naive T cells correlated with improved patient survival. Although total PD-1 + T cell numbers were not associated with patient outcome, specific PD-1 + T cell subpopulations were associated with poor survival. Intratumoral T cells lacking CD27 and CD28 co-stimulatory receptor expression were enriched in FL and correlated with inferior patient outcomes. In vitro models revealed that CD70 + lymphoma cells played an important role in expanding this population. Taken together, our mass cytometry results identified CD4 + memory T cell populations that are poorly functional due to loss of co-stimulatory receptor expression and are associated with an inferior survival in FL., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Malignant astrocytomas are a deadly solid tumor in children. Limited understanding of their underlying genetic basis has contributed to modest progress in developing more effective therapies. In an effort to identify such alterations, we performed a genome-wide search for DNA copy number aberrations (CNA) in a panel of 33 tumors encompassing grade 1 through grade 4 tumors. Genomic amplifications of 10-fold or greater were restricted to grade 3 and 4 astrocytomas and included the MDM4 (1q32), PDGFRA (4q12), MET (7q21), CMYC (8q24), PVT1 (8q24), WNT5B (12p13), and IGF1R (15q26) genes. Homozygous deletions of CDKN2A (9p21), PTEN (10q26), and TP53 (17p3.1) were evident among grade 2 to 4 tumors. BRAF gene rearrangements that were indicated in three tumors prompted the discovery of KIAA1549-BRAF fusion transcripts expressed in 10 of 10 grade 1 astrocytomas and in none of the grade 2 to 4 tumors. In contrast, an oncogenic missense BRAF mutation (BRAF(V600E)) was detected in 7 of 31 grade 2 to 4 tumors but in none of the grade 1 tumors. BRAF(V600E) mutation seems to define a subset of malignant astrocytomas in children, in which there is frequent concomitant homozygous deletion of CDKN2A (five of seven cases). Taken together, these findings highlight BRAF as a frequent mutation target in pediatric astrocytomas, with distinct types of BRAF alteration occurring in grade 1 versus grade 2 to 4 tumors.