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.

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.

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.

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.

A clinical calculator to predict disease outcomes in women with hormone receptor-positive advanced breast cancer treated with first-line endocrine therapy.

PURPOSE: Endocrine therapy (ET) is an effective strategy to treat hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC) but nearly all patients eventually progress. Our goal was to develop and validate a web-based clinical calculator for predicting disease outcomes in women with HR+ABC who are candidates for receiving first-line single-agent ET. METHODS: The meta-database comprises 891 patient-level data from the control arms of five contemporary clinical trials where patients received first-line single-agent ET (either aromatase inhibitor or fulvestrant) for ABC. Risk models were constructed for predicting 24-months progression-free survival (PFS-24) and 24-months overall survival (OS-24). Final models were internally validated for calibration and discrimination using ten-fold cross-validation. RESULTS: Higher number of sites of metastases, measurable disease, younger age, lower body mass index, negative PR status, and prior endocrine therapy were associated with worse PFS. Final PFS and OS models were well-calibrated and associated with cross-validated time-dependent area under the curve (AUC) of 0.61 and 0.62, respectively. CONCLUSIONS: The proposed ABC calculator is internally valid and can accurately predict disease outcomes. It may be used to predict patient prognosis, aid planning of first-line treatment strategies, and facilitate risk stratification for future clinical trials in patients with HR+ABC. Future validation of the proposed models in independent patient cohorts is warranted.

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.

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.

A clinical calculator to predict disease outcomes in women with triple-negative breast cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by substantial risks of early disease recurrence and mortality. We constructed and validated clinical calculators for predicting recurrence-free survival (RFS) and overall survival (OS) for TNBC. METHODS: Data from 605 women with centrally confirmed TNBC who underwent primary breast cancer surgery at Mayo Clinic during 1985-2012 were used to train risk models. Variables included age, menopausal status, tumor size, nodal status, Nottingham grade, surgery type, adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor-infiltrating lymphocytes (sTIL) score, and neutrophil-to-lymphocyte ratio (NLR). Final models were internally validated for calibration and discrimination using ten-fold cross-validation and compared with their base-model counterparts which include only tumor size and nodal status. Independent external validation was performed using data from 478 patients diagnosed with stage II/III invasive TNBC during 1986-1992 in the British Columbia Breast Cancer Outcomes Unit database. RESULTS: Final RFS and OS models were well calibrated and associated with C-indices of 0.72 and 0.73, as compared with 0.64 and 0.62 of the base models (p < 0.001). In external validation, the discriminant ability of the final models was comparable to the base models (C-index: 0.59-0.61). The RFS model demonstrated greater accuracy than the base model both overall and within patient subgroups, but the advantages of the OS model were less profound. CONCLUSIONS: This TNBC clinical calculator can be used to predict patient outcomes and may aid physician's communication with TNBC patients regarding their long-term disease outlook and planning treatment strategies.

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.

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.

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.

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.

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.