Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils

BACKGROUND

Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials.

METHODS

In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data.

RESULTS

Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate.

CONCLUSIONS

While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.

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 < 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.

Molecular Profiling in Neuro-Oncology: Where We Are, Where We're Heading, and How We Ensure Everyone Can Come Along

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.

Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

Joint Final Report of EORTC 26951 and RTOG 9402: Phase III Trials With Procarbazine, Lomustine, and Vincristine Chemotherapy for Anaplastic Oligodendroglial Tumors

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the basis of the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Anaplastic oligodendroglial tumors (AOTs) are chemotherapy-sensitive brain tumors. We report the final very long-term survival results from European Organization for the Research and Treatment of Cancer 26951 and Radiation Therapy Oncology Group 9402 phase III trials initiated in 1990s, which both studied radiotherapy with/without neo/adjuvant procarbazine, lomustine, and vincristine (PCV) for newly diagnosed anaplastic oligodendroglial tumors. The median follow-up duration in both was 18-19 years. For European Organization for the Research and Treatment of Cancer 26951, median, 14-year, and probable 20-year overall survival rates without versus with PCV were 2.6 years, 13.4%, and 10.1% versus 3.5 years, 25.1%, and 16.8% (N = 368 overall; hazard ratio [HR] 0.78; 95% CI, 0.63 to 0.98; P = .033), with 1p19q codeletion 9.3 years, 26.2%, and 13.6% versus 14.2 years, 51.0%, and 37.1% (n = 80; HR 0.60; 95% CI, 0.35 to 1.03; P = .063), respectively. For Radiation Therapy Oncology Group 9402, analogous results were 4.8 years, 16.5%, and 11.2% versus 4.8 years, 29.1%, and 24.6% (N = 289 overall; HR 0.79; 95% CI, 0.61 to 1.03; P = .08), with codeletion 7.3 years, 25.0%, and 14.9% versus 13.2 years, 46.1%, and 37% (n = 125; HR 0.61; 95% CI, 0.40 to 0.94; P = .02), respectively. With that, the studies show similar long-term survival even without tumor recurrence in a significant proportion of patients after first-line treatment with radiotherapy/PCV.

CALGB 40603 (Alliance): Long-Term Outcomes and Genomic Correlates of Response and Survival After Neoadjuvant Chemotherapy With or Without Carboplatin and Bevacizumab in Triple-Negative Breast Cancer

PURPOSE

CALGB 40603 (NCT00861705), a 2 × 2 randomized phase II trial, demonstrated that adding carboplatin or bevacizumab to weekly paclitaxel (wP) followed by doxorubicin and cyclophosphamide significantly increased the pathologic complete response (pCR) rate in stage II-III triple-negative breast cancer. We now report long-term outcomes (LTOs) and correlative science end points.

PATIENTS AND METHODS

The Kaplan-Meier method was used to estimate LTOs in 443 patients who initiated study treatment. Log-rank tests and Cox proportional hazards models evaluated the impact of clinical characteristics, pathologic response, calculated residual cancer burden (RCB) in patients with residual disease (RD), treatment assignment, and dose delivery during wP on LTOs, including event-free survival (EFS). Genomic predictors of treatment response and outcomes were assessed on pretreatment tumor samples by mRNA sequencing.

RESULTS

Among baseline characteristics, only the clinical stage was associated with LTOs. At a median follow-up of 7.9 years, LTOs were not significantly improved with either carboplatin or bevacizumab, overall or in patients with basal-like subtype cancers by genomic analysis. Patients with pCR (n = 205, 46.3%) had significantly higher 5-year EFS (85.5% v 56.6%, log-rank P < .0001) and overall survival (87.9% v 63.4%, P < .0001) rates compared with patients with RD, even those with RCB class I. Among clinical and genomic features, evidence of immune activation, including tumor-infiltrating lymphocytes and low B-cell receptor evenness, was associated with pCR and improved EFS.

CONCLUSION

Despite higher pCR rates, neither carboplatin nor bevacizumab appeared to improve LTOs although the study was not powered to assess these secondary end points. pCR was associated with superior LTOs even when compared with minimal RD. Markers of immune activation in pretreatment tumor biopsies were independently associated with higher pCR rates and improved survival.

Two-Stage Adaptive Design for Prognostic Biomarker Signatures with a Survival Endpoint

Cancer biomarker discoveries typically involve utilizing 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 pre-specified threshold. We describe the computation of cross-validated C-index and evaluation of the statistical significance using re-sampling 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.

Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1-positive Triple-negative Breast Cancer

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.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Statistical Considerations in the Evaluation of Continuous Biomarkers

Discovery of biomarkers has been steadily increasing over the past decade. Although a plethora of biomarkers has been reported in the biomedical literature, few have been sufficiently validated for broader clinical applications. One particular challenge that may have hindered the adoption of biomarkers into practice is the lack of reproducible biomarker cut points. In this article, we attempt to identify some common statistical issues related to biomarker cut point identification and provide guidance on proper evaluation, interpretation, and validation of such cut points. First, we illustrate how discretization of a continuous biomarker using sample percentiles results in significant information loss and should be avoided. Second, we review the popular "minimal-P-value" approach for cut point identification and show that this method results in highly unstable P values and unduly increases the chance of significant findings when the biomarker is not associated with outcome. Third, we critically review a common analysis strategy by which the selected biomarker cut point is used to categorize patients into different risk categories and then the difference in survival curves among these risk groups in the same dataset is claimed as the evidence supporting the biomarker's prognostic strength. We show that this method yields an exaggerated P value and overestimates the prognostic impact of the biomarker. We illustrate that the degree of the optimistic bias increases with the number of variables being considered in a risk model. Finally, we discuss methods to appropriately ascertain the additional prognostic contribution of the new biomarker in disease settings where standard prognostic factors already exist. Throughout the article, we use real examples in oncology to highlight relevant methodologic issues, and when appropriate, we use simulations to illustrate more abstract statistical concepts.

Abstract PS10-02: A good prognosis of endocrine-dependent tumors among residual invasive cancer after anti-HER2 therapy: CALGB 40601 (Alliance) and validation studies

Background: Patients with HER2+ breast cancer who have residual disease after neoadjuvant anti-HER2 plus chemotherapy have a high risk of recurrence and benefit from adjuvant trastuzumab emtansine (T-DM1). We hypothesize that endocrine-responsive residual tumors after neoadjuvant treatments may have good outcomes among patients receiving only adjuvant endocrine therapy plus trastuzumab. Using paired pre- and post-treatment samples from CALGB 40601 and other neoadjuvant cohorts that did not include adjuvant T-DM1, we investigated survival by pretreatment and residual disease ESR1 and PgR gene expression in CALGB 40601, and by estrogen receptor (ER) and progesterone receptor (PR) immunohistochemistry (IHC) in the other cohorts. We considered endocrine-responsive tumors those with ER and/or PR expression by gene expression or IHC.

Methods: CALGB 40601 is a randomized neoadjuvant trial of single vs. dual HER2-targeting (trastuzumab and/or lapatinib added to paclitaxel). We only included those patients who had not suffered from disease progression or death during their preoperative treatments. In total, 77 patients with paired pretreatment and residual disease tumors were profiled by mRNA sequencing and studied. Cutoffs for ESR1 and PgR mRNA expression mimicking clinical positivity were obtained using 265 pretreatment CALGB 40601 tumors (and 1,045 TCGA samples for ESR1). We also examined ER and PR IHC in paired tumors from 202 patients treated at 4 different collaborating institutions; all had residual disease after neoadjuvant HER2 targeting plus chemotherapy. We considered ER- or PR-positive as ≥10% positively staining cells. The primary endpoint was EFS, defined as the time from randomization to event in CALGB 40601 and from the first systemic therapy to event in the 4-institution validation cohort.

Results: In 77 patients from CALGB 40601 with paired (pretreatment/residual disease) specimens, 38 (49.3%) had ESR1+/ESR1+ tumors. The EFS was superior in the ESR1+/ESR1+ (n=38) group than in the remaining others (the log-rank test, p=0.011). The 5- and 7-year EFS rates for the ESR1+/ESR1+ (n=38) were 92.1% and 89.2%, whereas the rates were uniformly &lt; 70% in the others. In particular, the 5-year EFS rate among 11 patients with ESR1+/ESR1- tumors was 61.4%. This remained significant in multivariable analysis with clinical stage and treatment arm; the hazard ratio (HR) for EFS in ESR1+/ESR1+ versus all others was 0.29 (95% CI, 0.09-0.90). In ESR1+/ESR1+ tumors, 5-year EFS rates were high for those whose residual disease and also being PgR+ (n=32) or PAM50 LumA or Normal-like (n=34) (93.8% and 97.1%, respectively). In the institutional validation cohort, pretreatment /residual disease ER(+)/ER(+) tumors (n=113) had superior 3-year EFS versus all others (p=0.010). At a median follow-up of 35.9 months, the 3-year EFS rates for ER(+)/ER(+) and all other groups were 96.6% and 82.6%, respectively. This remained strongly significant in multivariable analysis with clinical stage; the HR for EFS in ER(+)/ER(+) versus all other groups was 0.28 (95% CI, 0.09-0.88). Among 46 who also had PR+ in the residual disease, the 3-year EFS was 100.0%.

Conclusions: HER2+ patients with ER+ pretreatment and ER+ residual disease after neoadjuvant chemotherapy + HER2-targeting have a very good survival outcome despite not receiving additional anti-HER2 targeting with T-DM1. This may provide a simple mechanism to better tailor therapy within residual disease patients using serial ER measurements.

Citation Format: Sung Gwe Ahn, Aranzazu Fernandez-Martinez, Mei-Yin C. Polley, Soong June Bae, Seho Park, Núria Chic, Fara Brasó-Maristany, Benedetta Conte, Valentina Guarneri, Maria Vittoria Dieci, Gaia Griguolo, PierFranco Conte, Joon Jeong, Aleix Prat, Lisa A. Carey, Charles M Perou. A good prognosis of endocrine-dependent tumors among residual invasive cancer after anti-HER2 therapy: CALGB 40601 (Alliance) and validation studies [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS10-02.

Prognostic value of interim FDG-PET in diffuse large cell lymphoma: results from the CALGB 50303 Clinical Trial

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.

Mass Cytometry Analysis Reveals that Specific Intratumoral CD4+ T Cell Subsets Correlate with Patient Survival in Follicular Lymphoma

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.

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.

Folate receptor alpha expression associates with improved disease-free survival in triple negative breast cancer patients

Triple negative breast cancer (TNBC) comprises 15-20% of all invasive breast cancer and is associated with a poor prognosis. As therapy options are limited for this subtype, there is a significant need to identify new targeted approaches for TNBC patient management. The expression of the folate receptor alpha (FRα) is significantly increased in patients with TNBC and is therefore a potential biomarker and therapeutic target. We optimized and validated a FRα immunohistochemistry method, specific to TNBC, to measure FRα expression in a centrally confirmed cohort of 384 patients with TNBC in order to determine if expression of the protein is associated with invasive disease-free survival (IDFS) and overall survival (OS). The FRα IHC demonstrated exceptional performance characteristics with low intra- and interassay variability as well as minimal lot-to-lot variation. FRα expression, which varied widely from sample to sample, was detected in 274 (71%) of the TNBC lesions. In a multivariable model adjusted for baseline characteristics, FRα expression was associated with improved IDFS (HR = 0.63, p = 0.01) but not with OS. The results demonstrate the potential of targeting the FRα in the majority of TNBC patients and suggest that variable expression may point to a need to stratify on FRα expression in clinical studies.

Phase III Precision Medicine Clinical Trial Designs That Integrate Treatment and Biomarker Evaluation

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.

Early-Phase Platform Trials: A New Paradigm for Dose Finding and Treatment Screening in the Era of Precision Oncology

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.

Randomized Trial of Standard Adjuvant Chemotherapy Regimens Versus Capecitabine in Older Women With Early Breast Cancer: 10-Year Update of the CALGB 49907 Trial

PURPOSE

Older women with breast cancer remain under-represented in clinical trials. The Cancer and Leukemia Group B 49907 trial focused on women age 65 years and older. We previously reported the primary analysis after a median follow-up of 2.4 years. Standard adjuvant chemotherapy showed significant improvements in recurrence-free survival (RFS) and overall survival compared with capecitabine. We now update results at a median follow-up of 11.4 years.

PATIENTS AND METHODS

Patients age 65 years or older with early breast cancer were randomly assigned to either standard adjuvant chemotherapy (physician's choice of either cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide and doxorubicin) or capecitabine. An adaptive Bayesian design was used to determine sample size and test noninferiority of capecitabine. The primary end point was RFS.

RESULTS

The design stopped accrual with 633 patients at its first sample size assessment. RFS remains significantly longer for patients treated with standard chemotherapy. At 10 years, in patients treated with standard chemotherapy versus capecitabine, the RFS rates were 56% and 50%, respectively (hazard ratio [HR], 0.80; P = .03); breast cancer-specific survival rates were 88% and 82%, respectively (HR, 0.62; P = .03); and overall survival rates were 62% and 56%, respectively (HR, 0.84; P = .16). With longer follow-up, standard chemotherapy remains superior to capecitabine among hormone receptor-negative patients (HR, 0.66; P = .02), but not among hormone receptor-positive patients (HR, 0.89; P = .43). Overall, 43.9% of patients have died (13.1% from breast cancer, 16.4% from causes other than breast cancer, and 14.1% from unknown causes). Second nonbreast cancers occurred in 14.1% of patients.

CONCLUSION

With longer follow-up, RFS remains superior for standard adjuvant chemotherapy versus capecitabine, especially in patients with hormone receptor-negative disease. Competing risks in this older population dilute overall survival benefits.

Dose-Adjusted EPOCH-R Compared With R-CHOP as Frontline Therapy for Diffuse Large B-Cell Lymphoma: Clinical Outcomes of the Phase III Intergroup Trial Alliance/CALGB 50303

PURPOSE

Alliance/CALGB 50303 (NCT00118209), an intergroup, phase III study, compared dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (DA-EPOCH-R) with standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as frontline therapy for diffuse large B-cell lymphoma.

PATIENTS AND METHODS

Patients received six cycles of DA-EPOCH-R or R-CHOP. The primary objective was progression-free survival (PFS); secondary clinical objectives included response rate, overall survival (OS), and safety.

RESULTS

Between 2005 and 2013, 524 patients were registered; 491 eligible patients were included in the final analysis. Most patients (74%) had stage III or IV disease; International Prognostic Index (IPI) risk groups included 26% IPI 0 to 1, 37% IPI 2, 25% IPI 3, and 12% IPI 4 to 5. At a median follow-up of 5 years, PFS was not statistically different between the arms (hazard ratio, 0.93; 95% CI, 0.68 to 1.27; P = .65), with a 2-year PFS rate of 78.9% (95% CI, 73.8% to 84.2%) for DA-EPOCH-R and 75.5% (95% CI, 70.2% to 81.1%) for R-CHOP. OS was not different (hazard ratio, 1.09; 95% CI, 0.75 to 1.59; P = .64), with a 2-year OS rate of 86.5% (95% CI, 82.3% to 91%) for DA-EPOCH-R and 85.7% (95% CI, 81.4% to 90.2%) for R-CHOP. Grade 3 and 4 adverse events were more common (P < .001) in the DA-EPOCH-R arm than the R-CHOP arm, including infection (16.9% v 10.7%, respectively), febrile neutropenia (35.0% v 17.7%, respectively), mucositis (8.4% v 2.1%, respectively), and neuropathy (18.6% v 3.3%, respectively). Five treatment-related deaths (2.1%) occurred in each arm.

CONCLUSION

In the 50303 study population, the more intensive, infusional DA-EPOCH-R was more toxic and did not improve PFS or OS compared with R-CHOP. The more favorable results with R-CHOP compared with historical controls suggest a potential patient selection bias and may preclude generalizability of results to specific risk subgroups.

Power estimation in biomarker studies where events are already observed

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.

Comparison of confidence interval methods for an intra-class correlation coefficient (ICC)

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.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2288-14-121) contains supplementary material, which is available to authorized users.

Two-stage adaptive cutoff design for building and validating a prognostic biomarker signature

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.

An international Ki67 reproducibility study

BACKGROUND

In breast cancer, immunohistochemical assessment of proliferation using the marker Ki67 has potential use in both research and clinical management. However, lack of consistency across laboratories has limited Ki67's value. A working group was assembled to devise a strategy to harmonize Ki67 analysis and increase scoring concordance. Toward that goal, we conducted a Ki67 reproducibility study.

METHODS

Eight laboratories received 100 breast cancer cases arranged into 1-mm core tissue microarrays-one set stained by the participating laboratory and one set stained by the central laboratory, both using antibody MIB-1. Each laboratory scored Ki67 as percentage of positively stained invasive tumor cells using its own method. Six laboratories repeated scoring of 50 locally stained cases on 3 different days. Sources of variation were analyzed using random effects models with log2-transformed measurements. Reproducibility was quantified by intraclass correlation coefficient (ICC), and the approximate two-sided 95% confidence intervals (CIs) for the true intraclass correlation coefficients in these experiments were provided.

RESULTS

Intralaboratory reproducibility was high (ICC = 0.94; 95% CI = 0.93 to 0.97). Interlaboratory reproducibility was only moderate (central staining: ICC = 0.71, 95% CI = 0.47 to 0.78; local staining: ICC = 0.59, 95% CI = 0.37 to 0.68). Geometric mean of Ki67 values for each laboratory across the 100 cases ranged 7.1% to 23.9% with central staining and 6.1% to 30.1% with local staining. Factors contributing to interlaboratory discordance included tumor region selection, counting method, and subjective assessment of staining positivity. Formal counting methods gave more consistent results than visual estimation.

CONCLUSIONS

Substantial variability in Ki67 scoring was observed among some of the world's most experienced laboratories. Ki67 values and cutoffs for clinical decision-making cannot be transferred between laboratories without standardizing scoring methodology because analytical validity is limited.

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.

Statistical and practical considerations for clinical evaluation of predictive biomarkers

Predictive biomarkers to guide therapy for cancer patients are a cornerstone of precision medicine. Discussed herein are considerations regarding the design and interpretation of such predictive biomarker studies. These considerations are important for both planning and interpreting prospective studies and for using specimens collected from completed randomized clinical trials. Specific issues addressed are differentiation between qualitative and quantitative predictive effects, challenges due to sample size requirements for predictive biomarker assessment, and consideration of additional factors relevant to clinical utility assessment, such as toxicity and cost of new therapies as well as costs and potential morbidities associated with routine use of biomarker-based tests.

Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical robustness and clinical utility

BACKGROUND

Many articles have been published in biomedical journals reporting on the development of prognostic and therapy-guiding biomarkers or predictors developed from high-dimensional data generated by omics technologies. Few of these tests have advanced to routine clinical use.

PURPOSE

We discuss statistical issues in the development and evaluation of prognostic and therapy-guiding biomarkers and omics-based tests.

METHODS

Concepts relevant to the development and evaluation of prognostic and therapy-guiding clinical tests are illustrated through discussion and examples. Some differences between statistical approaches for test evaluation and therapy evaluation are explained. The additional complexities introduced in the evaluation of omics-based tests are highlighted.

RESULTS

Distinctions are made between clinical validity of a test and clinical utility. To establish clinical utility for prognostic tests, it is explained why absolute risk should be evaluated in addition to relative risk measures. The critical role of an appropriate control group is emphasized for evaluation of therapy-guiding tests. Common pitfalls in the development and evaluation of tests generated from high-dimensional omics data such as model overfitting and inappropriate methods for test performance evaluation are explained, and proper approaches are suggested.

LIMITATIONS

The cited references do not comprise an exhaustive list of useful references on this topic, and a systematic review of the literature was not performed. Instead, a few key points were highlighted and illustrated with examples drawn from the oncology literature.

CONCLUSIONS

Approaches for the development and statistical evaluation of clinical tests useful for predicting prognosis and selecting therapy differ from standard approaches for therapy evaluation. Proper evaluation requires an understanding of the clinical setting and what information is likely to influence clinical decisions. Specialized expertise relevant to building mathematical predictor models from high-dimensional data is helpful to avoid common pitfalls in the development and evaluation of omics-based tests.

Criteria for use of omics-based predictors in NCI-sponsored clinical trials

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Background: High-throughput omics technologies (e.g., genomics, epigenomics, proteomics, metabolomics) offer exciting opportunities for new biological insights into cancer. The IOM report on translational omics defined omics as the study of related sets of biological molecules in a comprehensive fashion. (IOM (Institute of Medicine) 2012. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington, DC: The National Academic Press.) The promise of omics technologies has proven problematic to translate into clinically useful tests. Difficulty obtaining biospecimens, unrecognized preanalytical influences, and suboptimal assay analytical performance can lead to unreliable results and conflicting reports. Poor reporting of study details and limited access to data and computer code can thwart efforts to replicate published results or to detect flaws in study design and analysis methods. Methods: NCI held an interactive workshop for a wide variety of stakeholders to explore better approaches to omics-based test development and validation. This workshop heavily informed the ideas presented here. Recommendations are stated concisely, then explained. Results: A checklist of items to consider when evaluating the evidence for clinical use of an omics-based predictor, including in a trial where it will guide therapy, is presented. It covers specimen and assay requirements, the predictor model development process, clinical study design and conduct, and regulatory, ethical, and legal issues. The list applies to any trial involving investigational use of an omics test that will alter the clinical management of patients. The criteria also largely apply to situations in which the test will be evaluated retrospectively on specimens collected from patients who were prospectively enrolled on clinical studies. Conclusions: The proposed checklist should serve as a useful guide to investigators planning to submit proposals for NCI-funded studies involving use of an omics-based test. Ideally, this checklist will be consulted in the assay planning and development phases so that the necessary evidence will have been collected in a well-documented fashion by the time definitive evaluation of the test is desired.

Randomized phase II trial designs with biomarkers

Efficient development of targeted therapies that may only benefit a fraction of patients requires clinical trial designs that use biomarkers to identify sensitive subpopulations. Various randomized phase III trial designs have been proposed for definitive evaluation of new targeted treatments and their associated biomarkers (eg, enrichment designs and biomarker-stratified designs). Before proceeding to phase III, randomized phase II trials are often used to decide whether the new therapy warrants phase III testing. In the presence of a putative biomarker, the phase II trial should also provide information as to what type of biomarker phase III trial is appropriate. A randomized phase II biomarker trial design is proposed, which, after completion, recommends the type of phase III trial to be used for the definitive testing of the therapy and the biomarker. The recommendations include the possibility of proceeding to a randomized phase III of the new therapy with or without using the biomarker and also the possibility of not testing the new therapy further. Evaluations of the proposed trial design using simulations and published data demonstrate that it works well in providing recommendations for phase III trial design.

Conditional probability of survival in patients with newly diagnosed glioblastoma

PURPOSE

The disease outcome for patients with cancer is typically described in terms of estimated survival from diagnosis. Conditional probability offers more relevant information regarding survival for patients once they have survived for some time. We report conditional survival probabilities on the basis of 498 patients with glioblastoma multiforme receiving radiation and chemotherapy. For 1-year survivors, we evaluated variables that may inform subsequent survival. Motivated by the trend in data, we also evaluated the assumption of constant hazard.

PATIENTS AND METHODS

Patients enrolled onto seven phase II protocols between 1975 and 2007 were included. Conditional survival probabilities and 95% CIs were calculated. The Cox proportional hazards model was used to evaluate prognostic values of age, Karnofsky performance score (KPS), and prior progression 1-year post diagnosis. To assess the constant hazard assumption, we used a likelihood-ratio test to compare the Weibull and exponential distributions.

RESULTS

The probabilities of surviving an additional year given survival to 1, 2, 3, and 4 years were 35%, 49%, 69%, and 93%, respectively. For patients who survived for 1 year, lower KPS and progression were significantly predictive of shorter survival (both P < .001), but age was not (hazard ratio, 1.22 for a 10-year increase; P = .25). The Weibull distribution fits the data significantly better than exponential (P = .02), suggesting nonconstant hazard.

CONCLUSION

Conditional probabilities provide encouraging information regarding life expectancy to survivors of glioblastoma multiforme. Our data also showed that the constant hazard assumption may be violated in modern brain tumor trials. For single-arm trials, we advise using individual patient data from historical data sets for efficacy comparisons.

Impact of bevacizumab chemotherapy on craniotomy wound healing

Object

The FDA approval of bevacizumab for recurrent glioblastoma has resulted in its increased use in this patient population. Phase II trials reported 4%–6% impaired wound healing for bevacizumab initiated postoperatively. The effect of preoperative bevacizumab on subsequent craniotomy healing has not been addressed.

Methods

The authors retrospectively reviewed the cases of patients who underwent craniotomy for recurrent glioblastoma between 2005 and 2009, evaluating bevacizumab therapy/duration and healing complications (dehiscence, pseudomeningocele, CSF leak, and wound/bone infection). The Wilcoxon rank-sum test and Kruskal-Wallis test were used to compare continuous variables between groups. The Fisher exact test was used to assess for an association between categorical variables, including the comparison of wound-healing complication rates. Logistic regression models were used to estimate odds ratios of wound-healing complications while adjusting for baseline variables.

Results

Two hundred nine patients underwent a second craniotomy (161 patients) or third craniotomy (48 patients) for recurrent glioblastoma. Twenty-six individuals (12%) developed wound-healing complications. One hundred sixty-eight patients received no bevacizumab, 23 received preoperative bevacizumab, and 18 received postoperative bevacizumab. Significantly more patients receiving preoperative bevacizumab developed healing complications (35%) than non–bevacizumab-treated patients (10.0%, p = 0.004). Postoperative bevacizumab was associated with 6% impaired healing, not significantly different from non–bevacizumab-treated controls (p = 1.0). Preoperative bevacizumab treatment duration (weeks) did not influence healing (OR 0.98, p = 0.55). More healing complications occurred in patients receiving preoperative bevacizumab than in non–bevacizumab-treated controls before the third craniotomy (44% vs 9%, p = 0.03).

Conclusions

Although subject to the limitations of a retrospective study, we demonstrate that preoperative bevacizumab treatment resulted in impaired healing after a second and third craniotomy, compared with minimal effect of postoperative bevacizumab. This effect is more striking for the third craniotomy and for a shorter delay between bevacizumab and surgery. These complications should be acknowledged as increased bevacizumab use results in more post–bevacizumab-treated patients in whom surgery for recurrent glioblastoma is considered. Based on these results, the authors recommend performing repeated craniotomy more than 28 days after last administered dose of bevacizumab whenever possible.

Practical modifications to the time-to-event continual reassessment method for phase I cancer trials with fast patient accrual and late-onset toxicities

The goal of phase I cancer trials is to determine the highest dose of a treatment regimen with an acceptable toxicity rate. Traditional designs for phase I trials, such as the Continual Reassessment Method (CRM) and the 3 + 3 design, require each patient or a cohort of patients to be fully evaluated for the dose-limiting toxicity (DLT) before new patients can be enrolled. As such, the trial duration may be prohibitively long. The Time-to-Event Continual Reassessment Method (TITE-CRM, Cheung and Chappell, 2000) circumvents this limitation by allowing staggered patient accrual without the need for complete DLT follow-up of previously treated patients. However, in the setting of fast patient accrual and late-onset toxicities, the TITE-CRM results in overly aggressive dose escalation and exposes a considerable number of patients to toxic doses. We examine a modification to the TITE-CRM proposed by the original TITE-CRM creator and propose an alternative approach useful in this setting by incorporating an accrual suspension rule. A simulation study designed based on a neuro-oncology trial indicates that the modified methods provide a much improved degree of safety than the TITE-CRM while maintaining desirable design accuracy. The practical aspects of the proposed designs are discussed. The modifications presented are useful when planning phase I trials involving chemoradiation therapy.

Ex vivo MR spectroscopic measure differentiates tumor from treatment effects in GBM

The motivation of this study was to address the urgent clinical problem related to the inability of magnetic resonance (MR) imaging measures to differentiate tumor progression from treatment effects in patients with glioblastoma multiforme (GBM). While contrast enhancement on MR imaging (MRI) is routinely used for assessment of tumor burden, therapy response, and progression-free survival in GBM, it is well known that changes in enhancement following treatment are nonspecific to tumor. To address this issue, the objective of this study was to investigate whether MR spectroscopy can provide improved biomarker surrogates for tumor following treatment. High-resolution metabolic profiles of tissue samples obtained from patients with GBM were directly correlated with their pathological assessment to determine metabolic markers that correspond to pathological indications of tumor or treatment effects. Acquisition of tissue samples with image guidance enabled the association of ex vivo biochemical and pathological properties of the tissue samples with in vivo MR anatomical and structural properties derived from presurgical MR images. Using this approach, we found that metabolic concentration levels of [Myo-inositol/total choline (MCI)] in tissue samples are able to differentiate tumor from nontumor and treatment-induced reactive astrocytosis with high significance (P < .001) in newly diagnosed and recurrent GBM. The MCI index has a sensitivity of 93% to tumor in recurrent GBM and delineates the contribution of cellularity that originates from tumor and astrocytic proliferation following treatment. Low levels of MCI for tumor were associated with a reduced apparent diffusion coefficient and elevated choline-N-acetyl-aspartate index derived from in vivo MR images.

Evaluation of diffusion parameters as early biomarkers of disease progression in glioblastoma multiforme

The purpose of this study was to evaluate diffusion parameters at pre-, mid-, and post-radiation therapy (RT) in contrast-enhancing and nonenhancing lesions of postsurgical glioblastoma multiforme patients treated with the standard of care RT concurrently with temozolomide (TMZ) followed by adjuvant TMZ and an antiangiogenic drug. The diffusion parameters explored include baseline and short-term changes in apparent diffusion coefficient, fractional anisotropy, and eigenvalues. These diffusion parameters were examined as early markers for disease progression by relating them to clinical outcome of 6-month progression-free survival. The results indicated that changes from mid- to post-RT were significantly different between patients who progressed within 6 months vs those who were free of progression for 6 months after initiation of therapy. The study also showed that the changes in diffusion parameters from the mid- to post-RT scan may be more significant than those from pre- to mid-RT and pre- to post-RT. This is important because the mid-RT scan is currently not performed as part of the standard clinical care.

Oncogenic BRAF mutation with CDKN2A inactivation is characteristic of a subset of pediatric malignant astrocytomas

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.

Six-month progression-free survival as an alternative primary efficacy endpoint to overall survival in newly diagnosed glioblastoma patients receiving temozolomide

We assessed six-month progression-free survival (PFS) as an alternative primary efficacy endpoint to overall survival in newly diagnosed glioblastoma multiforme (GBM) patients receiving temozolomide (TMZ). A total of 183 patients with newly diagnosed GBM enrolled in 3 phase II protocols at the University of California-San Francisco were included. Patients were treated with interventions based on the Stupp regimen, each with the added component of a second oral agent given concurrently with radiotherapy and TMZ, followed by its coadministration with adjuvant TMZ. We examined whether progression status at 2, 4, and 6 months predicted subsequent survival using the landmark analysis. The hazard ratios of death as a function of progression status were estimated based on the Cox proportional hazards model after adjustment for putative prognostic factors. Progression status at 2, 4, and 6 months were all consistently found to be strong predictors of subsequent survival in all studies. The study-specific hazard ratios associated with progression status at 6 months ranged from 2.03 to 3.39. The hazard ratios associated with the earlier time points (2- and 4-month progression) all exceeded 2 in magnitude, ranging from 2.29 to 4.73. P-values were statistically significant for all time points. In this report, we demonstrated a strong association between the endpoints of PFS at 2, 4, and 6 months and survival. Patients who showed the signs of early progression were at significantly higher risk of earlier death. Our analysis suggests that 6-month PFS may be an appropriate primary endpoint in the context of phase II upfront GBM trials in the TMZ era.