Criteria for assessing evidence for biomarker-targeted therapies in rare cancers-an extrapolation framework.

Background: Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making. Objectives: To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker. Design: A series of questions articulating essential criteria for extrapolation. Methods: The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia's Medical Services Advisory Committee were incorporated. Results: We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies. Conclusion: This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers.

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials.

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

Next-generation sequencing, should I use anti-HER2 therapy for HER2-amplified tumors off-label? Illustrating an extrapolation framework.

Background: Next-generation sequencing is used to increase targeted treatment opportunities, particularly for patients who have exhausted standard options. Where randomized controlled trial evidence for a targeted therapy is available for molecular alterations in one tumor type, the dilemma for the clinician is whether 'matching' targeted agents should be recommended off-label for the same molecular alterations detected in other tumor types, for which no trial data are available to guide practice. To judge the likely benefits, it may be possible to extrapolate evidence from cancers where treatment benefits have been established. Methods: We present a framework for assessing the appropriateness of extrapolation using trastuzumab, an anti-HER2 antibody, for HER2-amplified tumors where trastuzumab use would be off-label as an illustrative example. Results: The following should be considered for the tumor type where trastuzumab would be off-label: (a) reliability of the NGS assay for detecting HER2 amplification; (b) criteria for defining HER2 positivity; (c) strength of evidence supporting the actionability of HER2 amplification and trastuzumab; (d) whether better clinical outcomes with trastuzumab are due to a more favorable natural history rather than trastuzumab effect; (e) signals of trastuzumab activity and whether it translates to clinically meaningful benefit; (f) whether the safety profile of trastuzumab differs from established indications; and (g) discussion points for shared decision making (SDM) to facilitate informed consent. Conclusion: We present a systematic approach for appraising evidence to support extrapolating trastuzumab benefits from established indications to off-label applications. Extrapolation criteria and areas of uncertainty to inform SDM are outlined. This framework is potentially generalizable to other tumor-agnostic biomarker-targeted therapy scenarios. It is a practical approach for clinicians to apply in routine practice and should be considered by molecular tumor boards who make off-label recommendations.

Extrapolating evidence for molecularly targeted therapies from common to rare cancers: a scoping review of methodological guidance.

OBJECTIVES: Cancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may be infeasible. Extrapolating existing evidence of targeted therapy from common cancers to rare cancers sharing the same biomarker may reduce evidence requirements for regulatory approval in rare cancers. It is unclear whether guidelines exist for extrapolation. We sought to identify methodological guidance for extrapolating evidence from targeted therapies used for common cancers to rare biomarker-defined cancers. DESIGN: Scoping review. DATA SOURCES: Websites of health technology assessment agencies, regulatory bodies, research groups, scientific societies and industry. EBM Reviews-Cochrane Methodology Register and Health Technology Assessment, Embase and MEDLINE databases (1946 to 11 May 2022). ELIGIBILITY CRITERIA: Papers proposing a framework or recommendations for extrapolating evidence for rare cancers, small populations and biomarker-defined cancers. DATA EXTRACTION AND SYNTHESIS: We extracted framework details where available and guidance for components of extrapolation. We used these components to structure and summarise recommendations. RESULTS: We identified 23 papers. One paper provided an extrapolation framework but was not cancer specific. Extrapolation recommendations addressed six distinct components: strategies for grouping cancers as the same biomarker-defined disease; analytical validation requirements of a biomarker test to use across cancer types; strategies to generate control data when a randomised concurrent control arm is infeasible; sources to inform biomarker clinical utility assessment in the absence of prospective clinical evidence; requirements for surrogate endpoints chosen for the rare cancer; and assessing and augmenting safety data in the rare cancer. CONCLUSIONS: In the absence of an established framework, our recommendations for components of extrapolation can be used to guide discussions about interpreting evidence to support extrapolation. The review can inform the development of an extrapolation framework for biomarker-targeted therapies in rare cancers.

Validation of Progression-Free Survival Rate at 6 Months and Objective Response for Estimating Overall Survival in Immune Checkpoint Inhibitor Trials: A Systematic Review and Meta-analysis.

Importance: Progression-free survival (PFS) rate at 6 months has been proposed as a potential surrogate for overall survival (OS) rate at 12 months for immune checkpoint inhibitor (ICI) trials but requires further assessment for validation. Objective: To validate 6-month PFS and objective response rate (ORR) as estimators of 12-month OS in the ICI arms of randomized clinical trials (RCTs). Data Sources: Electronic databases (Medline, EMBASE, and the Cochrane Central Register of Controlled Trials) were searched for ICI RCTs published between January 2000 and June 2019. Study Selection: Eligible studies were phase 2 and phase 3 ICI RCTs in advanced solid cancers that reported ORR, PFS, and OS. A total of 99 articles (from 60 studies) of 2502 articles were selected by consensus. Data Extraction and Synthesis: Data were screened and extracted independently. Estimation models for 12-month OS and to assess correlation coefficient between end points were developed using linear regression. Data were extracted in July 2019, and analyses were conducted in September 2019. This study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: Validation of previously reported 6-month PFS and ORR estimation models for 12-month OS using contemporary RCTs. Calibration of 6-month PFS and ORR model-estimated vs observed 12-month OS in ICI arms were assessed by correlation coefficient (r) and weighted Brier scores. Secondary analyses were performed for subgroups (ie, ICI-only, ICI-combination, line of therapy, programmed cell death 1 ligand 1 selected, and unselected). Results: Data from 60 RCTs with 74 experimental ICI arms were used. The development data set included 25 arms from studies published January 2000 to January 2017. The estimation model for 12-month OS using 6-month PFS was: (1.06 × PFS6) + 0.16 + (0.04 × melanoma) - (0.03 × NSCLC) + (0 × other tumors), in which PFS6 indicates 6-month PFS and NSCLC indicates non-small cell lung cancer. The estimation model for 12-month OS using ORR was (0.15 × ORR) + 0.52 + (0 × melanoma) - (0.02 × NSCLC) - (0.01 × other tumors). A total of 49 arms from studies published after January 2017 to June 2019 formed the validation data set. When the models were applied on the validation data set, calibration between the 6-month PFS model estimated vs observed 12-month OS was good (r = 0.89; Brier score, 0.008), but poor for the ORR model (r = 0.47; Brier score, 0.03). Findings were similar across all subgroups. Conclusions and Relevance: The findings of this study suggest that the estimation model using 6-month PFS could reliably estimate 12-month OS in ICI trials. This study could assist in better selection and prioritization of ICI agents for testing in RCTs based on phase 2 single-arm RCT results.

Benchmarking single-arm studies against historical controls from non-small cell lung cancer trials - an empirical analysis of bias.

Background: Recent trials of novel agents in 'rare' molecular subtypes of non-small cell lung cancer (NSCLC) have used single-arm trial designs and benchmarked outcomes against historical controls. We assessed the consistency of historical control outcomes using docetaxel data from published NSCLC randomized controlled trials (RCTs).Material and methods: Advanced NSCLC RCTs including a docetaxel monotherapy arm were included. Heterogeneity in tumor objective response rates (ORRs), progression-free survival (PFS) and overall survival (OS), and correlations between outcomes and year of trial commencement were assessed.Results: Among 63 trials (N = 10,633) conducted between 2000 and 2017, ORR ranged from 0% to 26% (I2 = 76.1%, pheterogeneity < .0001). Mean of the median PFS was 3.0 months (range: 1.4-6.4), 3-month PFS ranged from 25% to 85% (I2 = 86.0%, pheterogeneity < .0001). Mean of the median OS was 9.1 months (range: 4.7-22.9), 9-month OS ranged from 23% to 79% (I2 = 83.0%, pheterogeneity < .0001). Each later year of trial commencement was associated with 0.3% (p = .046), 0.5% (p = .11) and 0.9% (p = .001) improvement in ORR, 3-month PFS and 9-month OS rates, respectively.Conclusions: There was significant heterogeneity and an improving trend in docetaxel outcomes across trials conducted over 20 years. Benchmarking biomarker-targeted agents against historical controls may not be a valid approach to replace RCTs. Innovative study designs involving a concurrent control arm should be considered.

Efficacy of late line pertuzumab with trastuzumab and chemotherapy in HER2-positive metastatic breast cancer: An Australian case series.

BACKGROUND: Pertuzumab, when combined with trastuzumab and chemotherapy, is a highly active human epidermal growth factor receptor 2 (HER2), targeting agent in the neoadjuvant, adjuvant and first-line metastatic HER2-positive breast cancer setting. The efficacy of late-line (after first/second-line) pertuzumab in combination with trastuzumab and chemotherapy is unknown. AIMS: To establish pertuzumab efficacy by performing an audit of patients who received pertuzumab after first-line HER2 directed therapy. We sought to establish whether efficacy differed by clinicopathological factors. METHODS: The primary endpoint was progression-free survival (PFS) and the secondary endpoint, overall survival (OS). Clinicopathological factors, PFS and OS data were collated and clinicopathological factors associated with PFS were evaluated using Cox regression models. RESULTS: Fourteen women were identified. Six (43%) had hormone receptor (HR) negative and eight (57%) had HR-positive, metastatic HER2-positive breast cancer. Median follow up was 22.8 months, median prior lines of therapy were 5 (range: 1-9). Median time from diagnosis of metastatic disease to receiving pertuzumab was 4.5 years (range: 4.2-5.8). All patients received initial chemotherapy with pertuzumab and trastuzumab (taxane-based 71%). Median PFS was 9 months (95% confidence interval [CI]: 7-not estimable [NE]) and median OS was not reached (95% CI, 16 months-NE). Univariable analysis demonstrated that HR-negative patients had a significantly longer PFS than HR-positive patients (hazard ratio = 0.11; 95% CI, 0.01-0.88; P = 0.04). CONCLUSION: This small cases series reports a favorable PFS and OS for pertuzumab with trastuzumab and chemotherapy in the later line metastatic setting. This finding warrants further study.

Clinical Equipoise for Trials of Novel Biologic Therapies, Therapeutic Success Rates, and Predictors of Success: A Meta-Analysis.

PURPOSE: The demand for more rapid access to novel biologic therapies than randomized controlled trials can deliver is a topic of ongoing study and debate. We aimed to inform this debate by estimating therapeutic success from phase III trials comparing novel biologic therapies with standard of care and identifying predictors of success. METHODS: This was a meta-analysis of phase III trials evaluating novel biologic therapies in advanced breast, colorectal, lung, and prostate cancers. Therapeutic success was defined as statistically significant results for the primary end point favoring novel biologic therapies. RESULTS: Of 119 included phase III trials (76,726 patients), therapeutic success was 41%, with a statistically significant relative reduction in disease progression and death for novel biologic therapies over standard of care of 20% and 8%. Therapeutic success did not improve over time (pre-2010, 33%; 2010 to 2014, 44%; P = .2). Predictors of success were a biomarker-selected population (odds ratio, 4.74; 95% CI, 2.05 to 10.95) and progression-free survival end point compared with overall survival (odds ratio, 5.22; 95% CI, 2.41 to 11.39). Phase III trials with a biomarker-selected population showed a larger 28% progression-free survival benefit than phase III trials overall (hazard ratio, 0.72; 95% CI, 0.70 to 0.75) but similar 8% overall survival benefit (hazard ratio, 0.92; 95% CI, 0.90 to 0.94). Therapeutic success of phase III trials with and without a preceding phase II trial were 43% and 30%, respectively. CONCLUSION: Therapeutic success of novel biologic therapies in phase III trials, including therapies with a matching predictive biomarker, was modest and has not significantly improved over time. Equipoise remains and supports the ongoing ethical and scientific requirement for phase III randomized controlled trials to estimate treatment efficacy and assess the value of potential biomarkers.