Criteria for the use of omics-based predictors in clinical trials.

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

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

Abstract 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.http://deepblue.lib.umich.edu/bitstream/2027.42/134536/1/12916_2013_Article_1104.pd

Identifying the needs of brain tumor patients and their caregivers

The purpose of this study is to identify the needs of brain tumor patients and their caregivers to provide improved health services to these populations. Two different questionnaires were designed for patients and caregivers. Both questionnaires contained questions pertaining to three realms: disease symptoms/treatment, health care provider, daily living/finances. The caregivers’ questionnaires contained an additional domain on emotional needs. Each question was evaluated for the degree of importance and satisfaction. Exploratory analyses determined whether baseline characteristics affect responder importance or satisfaction. Also, areas of high agreement/disagreement in satisfaction between the participating patient-caregiver pairs were identified. Questions for which >50% of the patients and caregivers thought were “very important” but >30% were dissatisfied include: understanding the cause of brain tumors, dealing with patients’ lower energy, identifying healthful foods and activities for patients, telephone access to health care providers, information on medical insurance coverage, and support from their employer. In the emotional realm, caregivers identified 9 out of 10 items as important but need further improvement. Areas of high disagreement in satisfaction between participating patient-caregiver pairs include: getting help with household chores (P value = 0.006) and finding time for personal needs (P value < 0.001). This study provides insights into areas to improve services for brain tumor patients and their caregivers. The caregivers’ highest amount of burden is placed on their emotional needs, emphasizing the importance of providing appropriate medical and psychosocial support for caregivers to cope with emotional difficulties they face during the patients’ treatment process

Activation of PI3K/mTOR pathway occurs in most adult low-grade gliomas and predicts patient survival

Recent evidence suggests the Akt-mTOR pathway may play a role in development of low-grade gliomas (LGG). We sought to evaluate whether activation of this pathway correlates with survival in LGG by examining expression patterns of proteins within this pathway. Forty-five LGG tumor specimens from newly diagnosed patients were analyzed for methylation of the putative 5′-promoter region of PTEN using methylation-specific PCR as well as phosphorylation of S6 and PRAS40 and expression of PTEN protein using immunohistochemistry. Relationships between molecular markers and overall survival (OS) were assessed using Kaplan-Meier methods and exact log-rank test. Correlation between molecular markers was determined using the Mann-Whitney U and Spearman Rank Correlation tests. Eight of the 26 patients with methylated PTEN died, as compared to 1 of 19 without methylation. There was a trend towards statistical significance, with PTEN methylated patients having decreased survival (P = 0.128). Eight of 29 patients that expressed phospho-S6 died, whereas all 9 patients lacking p-S6 expression were alive at last follow-up. There was an inverse relationship between expression of phospho-S6 and survival (P = 0.029). There was a trend towards decreased survival in patients expressing phospho-PRAS40 (P = 0.077). Analyses of relationships between molecular markers demonstrated a statistically significant positive correlation between expression of p-S6(235) and p-PRAS40 (P = 0.04); expression of p-S6(240) correlated positively with PTEN methylation (P = 0.04) and negatively with PTEN expression (P = 0.03). Survival of LGG patients correlates with phosphorylation of S6 protein. This relationship supports the use of selective mTOR inhibitors in the treatment of low grade glioma

Effect of imaging and catheter characteristics on clinical outcome for patients in the PRECISE study

The PRECISE study used convection enhanced delivery (CED) to infuse IL13-PE38QQR in patients with recurrent glioblastoma multiforme (GBM) and compared survival to Gliadel Wafers (GW). The objectives of this retrospective evaluation were to assess: (1) catheter positioning in relation to imaging features and (2) to examine the potential impact of catheter positioning, overall catheter placement and imaging features on long term clinical outcome in the PRECISE study. Catheter positioning and overall catheter placement were scored and used as a surrogate of adequate placement. Imaging studies obtained on day 43 and day 71 after resection were each retrospectively reviewed. Catheter positioning scores, catheter overall placement scores, local tumor control and imaging change scores were reviewed and correlated using Generalized Linear Mixed Models. Cox PH regression analysis was used to examine whether these imaging based variables predicted overall survival (OS) and progression free survival (PFS) after adjusting for age and KPS. Of 180 patients in the CED group, 20 patients did not undergo gross total resection. Of the remaining 160 patients only 53% of patients had fully conforming catheters in respect to overall placement and 51% had adequate catheter positioning scores. Better catheter positioning scores were not correlated with local tumor control (P = 0.61) or imaging change score (P = 0.86). OS and PFS were not correlated with catheter positioning score (OS: P = 0.53; PFS: P = 0.72 respectively), overall placement score (OS: P = 0.55; PFS: P = 0.35) or imaging changes on day 43 MRI (P = 0.88). Catheter positioning scores and overall catheter placement scores were not associated with clinical outcome in this large prospective trial

NRG/RTOG 0837: Randomized, Phase II, Double-Blind, Placebo-Controlled Trial of Chemoradiation With or Without Cediranib in Newly Diagnosed Glioblastoma

BACKGROUND: A randomized, phase II, placebo-controlled, and blinded clinical trial (NCT01062425) was conducted to determine the efficacy of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, versus placebo in combination with radiation and temozolomide in newly diagnosed glioblastoma. METHODS: Patients with newly diagnosed glioblastoma were randomly assigned 2:1 to receive (1) cediranib (20 mg) in combination with radiation and temozolomide; (2) placebo in combination with radiation and temozolomide. The primary endpoint was 6-month progression-free survival (PFS) based on blinded, independent radiographic assessment of postcontrast T1-weighted and noncontrast T2-weighted MRI brain scans and was tested using a 1-sided RESULTS: One hundred and fifty-eight patients were randomized, out of which 9 were ineligible and 12 were not evaluable for the primary endpoint, leaving 137 eligible and evaluable. 6-month PFS was 46.6% in the cediranib arm versus 24.5% in the placebo arm ( CONCLUSIONS: This study met its primary endpoint of prolongation of 6-month PFS with cediranib in combination with radiation and temozolomide versus placebo in combination with radiation and temozolomide. There was no difference in overall survival between the 2 arms

Targeted Gene Expression Profiling Predicts Meningioma Outcomes and Radiotherapy Responses

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 \u3c 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses

Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration

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. © 2013 McShane et al.; licensee BioMed Central Ltd

A Simulation Based Evaluation of Sample Size Methods for Biomarker Studies

Cancer researchers are often interested in identifying biomarkers that are indicative of poor outcomes (prognostic biomarkers) or response to specific therapies (predictive biomarkers). In designing a biomarker study, the first statistical issue encountered is the sample size requirement for adequate detection of a biomarker effect. In biomarker studies, the desired effect size is typically larger than those targeted in therapeutic trials and the biomarker prevalence is rarely near the optimal 50%. In this article, we review sample size formulas that are routinely used in designing therapeutic trials. We then conduct simulation studies to evaluate the performances of these methods when applied to biomarker studies. In particular, we examine the impact that deviations from certain statistical assumptions (i.e., biomarker positive prevalence and effect size) have on statistical power and type I error. Our simulation results indicate that when the true biomarker prevalence is close to 50%, all methods perform well in terms of power regardless of the magnitude of the targeted biomarker effect. However, when the biomarker positive prevalence rate deviates from 50%, the empirical power based on some existing methods may be substantially different from the nominal power, and this discrepancy becomes more profound for large biomarker effects. The type I error is maintained close to the 5% nominal level in all scenarios we investigate, although there is a slight inflation as the targeted effect size increases. Based on these results, we delineate the range of parameters within which the use of some sample size methods may be sufficiently robust