Core Clinical Data Elements for Cancer Genomic Repositories: A Multi-stakeholder Consensus.

The Center for Medical Technology Policy and the Molecular Evidence Development Consortium gathered a diverse group of more than 50 stakeholders to develop consensus on a core set of data elements and values essential to understanding the clinical utility of molecularly targeted therapies in oncology.

Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trials.

BACKGROUND: Our objective was to determine the correlation between preclinical toxicity found in animal models (mouse, rat, dog and monkey) and clinical toxicity reported in patients participating in Phase 1 oncology clinical trials. METHODS: We obtained from two major early-Phase clinical trial centres, preclinical toxicities from investigational brochures and clinical toxicities from published Phase 1 trials for 108 drugs, including small molecules, biologics and conjugates. Toxicities were categorised according to Common Terminology Criteria for Adverse Events version 4.0. Human toxicities were also categorised based on their reported clinical grade (severity). Positive predictive values (PPV) and negative predictive values (NPV) were calculated to determine the probability that clinical studies would/would not show a particular toxicity category given that it was seen in preclinical toxicology analysis. Statistical analyses also included kappa statistics, and Matthews (MCC) and Spearman correlation coefficients. RESULTS: Overall, animal toxicity did not show strong correlation with human toxicity, with a median PPV of 0.65 and NPV of 0.50. Similar results were obtained based on kappa statistics and MCC. CONCLUSIONS: There is an urgent need to assess more novel approaches to the type and conduct of preclinical toxicity studies in an effort to provide better predictive value for human investigation.

Real-world progression, treatment, and survival outcomes during rapid adoption of immunotherapy for advanced non-small cell lung cancer.

BACKGROUND: Despite the rapid adoption of immunotherapies in advanced non-small cell lung cancer (advNSCLC), knowledge gaps remain about their real-world (rw) performance. METHODS: This retrospective, observational, multicenter analysis used the Flatiron Health deidentified electronic health record-derived database of rw patients with advNSCLC who received treatment with PD-1 and/or PD-L1 (PD-[L]1) inhibitors before July 1, 2017 (N = 5257) and had ≥6 months of follow-up. The authors investigated PD-(L)1 line of treatment and PD-L1 testing rates and the relationship between overall survival (OS) and rw intermediate endpoints: progression-free survival (rwPFS), rw time to progression (rwTTP), rw time to next treatment (rwTTNT), and rw time to discontinuation (rwTTD). RESULTS: First-line PD-(L)1 inhibitor use increased from 0% (in the third quarter of 2014 [Q3 2014]) to 42% (Q2 2017) over the study period. PD-L1 testing also increased (from 3% in Q3 2015 to 70% in Q2 2017). The estimated median OS was 9.3 months (95% CI, 8.9-9.8 months), and the estimated rwPFS was 3.2 months (95% CI, 3.1-3.3 months). Longer OS and rwPFS were associated with ≥50% PD-L1 percentage staining results. Correlations (⍴) between OS and intermediate endpoints were ⍴ = 0.75 (95% CI, 0.73-0.76) for rwPFS and ⍴ = 0.60 (95% CI, 0.57-0.63) for rwTTP, and, for treatment-based intermediate endpoints, correlations were ⍴ = 0.60 (95% CI, 0.56-0.64) for rwTTNT (N = 856) and ⍴ = 0.81 (95% CI, 0.80-0.82) for rwTTD. CONCLUSIONS: The use of first-line PD-(L)1 inhibitors and PD-L1 testing has substantially increased, with better outcomes for patients who have ≥50% PD-L1 percentage staining. Intermediate rw tumor-dynamics estimates were moderately correlated with OS in patients with advNSCLC who received immunotherapy, highlighting the need for optimizing and standardizing rw endpoints to enhance the understanding of patient outcomes outside clinical trials.

Real-World Outcomes of Patients with Metastatic Non-Small Cell Lung Cancer Treated with Programmed Cell Death Protein 1 Inhibitors in the Year Following U.S. Regulatory Approval.

BACKGROUND: Evidence from cancer clinical trials has strong internal validity but can be difficult to generalize to real-world patient populations. Here we analyzed real-world outcomes of patients with metastatic non-small cell lung cancer (mNSCLC) treated with programmed cell death protein 1 (PD-1) inhibitors in the first year following U.S. regulatory approval. MATERIALS AND METHODS: This retrospective study leveraged electronic health record (EHR) data collected during routine patient care in community cancer care clinics. The cohort included patients with mNSCLC who had received nivolumab or pembrolizumab for metastatic disease (n = 1,344) with >1 EHR-documented visit from January 1, 2011, to March 31, 2016. Patients with a > 90-day gap between advanced disease diagnosis and first EHR structured data entry were excluded. RESULTS: Estimated median overall survival (OS) was 8.0 months (95% confidence interval 7.4-9.0 months). Estimated median OS was 4.7 months (3.4-6.6) for patients with anaplastic lymphoma kinase rearrangement- and epidermal growth factor receptor mutation-positive tumors, and 8.6 months (7.7-10.6) for patients without such mutations. Age at PD-1 inhibitor initiation or line of therapy did not impact OS. CONCLUSION: This analysis suggests OS in real-world patients may be shorter than in conventional clinical trial patient cohorts, potentially due to narrow trial eligibility criteria. The lack of difference in OS by line of therapy or age at immunotherapy initiation suggests sustained benefit of PD-1 inhibitors in multitreated patients with mNSCLC and that age is not a predictor of outcome. Further studies are underway in patients with comorbidities, organ dysfunction, and multiple prior therapies. IMPLICATIONS FOR PRACTICE: This study evaluated data derived from electronic health records of patients with metastatic non-small cell lung cancer treated with programmed cell death protein 1 (PD-1) inhibitors in the year following regulatory approval. This real-world cohort had shorter overall survival (OS) indexed to PD-1 inhibitor initiation than reported in clinical trials. Late-line treatment did not influence OS, and patients aged >75 at immunotherapy initiation did not have worse outcomes than younger patients. As new therapies enter clinical practice, real-world data can complement clinical trial evidence providing information on generalizability and helping inform clinical treatment decisions.

Characteristics of Real-World Metastatic Non-Small Cell Lung Cancer Patients Treated with Nivolumab and Pembrolizumab During the Year Following Approval.

BACKGROUND: Evidence from cancer clinical trials can be difficult to generalize to real-world patient populations, but can be complemented by real-world evidence to optimize personalization of care. Further, real-world usage patterns of programmed cell death protein 1 (PD-1) inhibitors following approval can inform future studies of subpopulations underrepresented in clinical trials. MATERIALS AND METHODS: We performed a multicenter analysis using electronic health record data collected during routine care of patients treated in community cancer care clinics in the Flatiron Health network. Real-world metastatic non-small cell lung cancer (NSCLC) patients who received nivolumab or pembrolizumab in the metastatic setting (n = 1,344) were selected from a starting random sample of 55,969 NSCLC patients with two or more documented visits from January 1, 2011, through March 31, 2016. The primary study outcome measurement was demographic and treatment characteristics of the cohort. RESULTS: Median age at PD-1 inhibitor initiation was 69 years (interquartile range 61-75). Patients were 56% male, 88% smokers, 65% nonsquamous histology, and 64% diagnosed at stage IV. Of 1,344 patients, 112 (8%) were tested for programmed death-ligand 1 expression. Overall, 50% received nivolumab or pembrolizumab in the second line, with a substantial proportion of third and later line use that began to decline in Q4 2015. CONCLUSION: During the year following U.S. regulatory approval of PD-1 inhibitors for treatment of NSCLC, real-world patients receiving nivolumab or pembrolizumab were older at treatment initiation and more had smoking history relative to clinical trial cohorts. Studies of outcomes in underrepresented subgroups are needed to inform real-world treatment decisions. IMPLICATIONS FOR PRACTICE: Evidence gathered in conventional clinical trials used to assess safety and efficacy of new therapies is not necessarily generalizable to real-world patients receiving these drugs following regulatory approval. Real-world evidence derived from electronic health record data can yield complementary evidence to enable optimal clinical decisions. Examined here is a cohort of programmed cell death protein 1 inhibitor-treated metastatic non-small cell lung cancer patients in the first year following regulatory approval of these therapies in this indication. The analysis revealed how the real-world cohort differed from the clinical trial cohorts, which will inform which patients are underrepresented and warrant additional studies.

Cixutumumab for patients with recurrent or refractory advanced thymic epithelial tumours: a multicentre, open-label, phase 2 trial.

BACKGROUND: No standard treatment exists for refractory or relapsed advanced thymic epithelial tumours. We investigated the efficacy of cixutumumab, a fully human IgG1 monoclonal antibody targeting the insulin-like growth factor 1 receptor in thymic epithelial tumours after failure of previous chemotherapy. METHODS: Between Aug 25, 2009, and March 27, 2012, we did a multicentre, open-label, phase 2 trial in patients aged 18 years or older with histologically confirmed recurrent or refractory thymic epithelial tumours. We enrolled individuals who had progressed after at least one previous regimen of platinum-containing chemotherapy, had an Eastern Cooperative Oncology Group performance status of 0 or 1, and had measurable disease and adequate organ function. Eligible patients received intravenous cixutumumab (20 mg/kg) every 3 weeks until disease progression or development of intolerable toxic effects. The primary endpoint was the frequency of response, analysed on an intention-to-treat basis. We also did pharmacodynamic studies. This trial is registered with ClinicalTrials.gov, number NCT00965250. FINDINGS: 49 patients were enrolled (37 with thymomas and 12 with thymic carcinomas) who received a median of eight cycles of cixutumumab (range 1-46). At the final actuarial analysis when follow-up data were updated (Nov 30, 2012), median potential follow-up (from on-study date to most current follow-up date) was 24·0 months (IQR 17·3-36·9). In the thymoma cohort, five (14%) of 37 patients (95% CI 5-29) achieved a partial response, 28 had stable disease, and four had progressive disease. In the thymic carcinoma cohort, none of 12 patients (95% CI 0-26) had a partial response, five had stable disease, and seven had progressive disease. The most common grade 3-4 adverse events in both cohorts combined were hyperglycaemia (five [10%]), lipase elevation (three [6%]), and weight loss, tumour pain, and hyperuricaemia (two each [4%]). Nine (24%) of 37 patients with thymoma developed autoimmune conditions during treatment (five were new-onset disorders), the most common of which was pure red-cell aplasia. Two (4%) patients died; one was attributed to disease progression and the other to disease-related complications (respiratory failure, myositis, and an acute coronary event), which could have been precipitated by treatment with cixutumumab. INTERPRETATION: Cixutumumab monotherapy is well-tolerated and active in relapsed thymoma. Development of autoimmunity during treatment needs further investigation. FUNDING: Division of Cancer Treatment and Diagnosis at the National Cancer Institute (National Institutes of Health), ImClone Systems.

U.S. Food and Drug Administration approval summary: Erlotinib for the first-line treatment of metastatic non-small cell lung cancer with epidermal growth factor receptor exon 19 deletions or exon 21 (L858R) substitution mutations.

On May 14, 2013, the U.S. Food and Drug Administration approved erlotinib (Tarceva, Astellas Pharma Inc., Northbrook, IL, http://www.us.astellas.com/) for the first-line treatment of patients with metastatic non-small cell lung cancer (NSCLC) whose tumors have epidermal growth factor receptor (EGFR) exon 19 deletions or exon 21 (L858R) substitution mutations. This indication for erlotinib was approved concurrently with the cobas EGFR Mutation Test (Roche Molecular Systems, Inc., Basel, Switzerland, http://www.molecular.roche.com), a companion diagnostic test for patient selection. The approval was based on clinically important improvements in progression-free survival (PFS) and objective response rate (ORR) and an acceptable toxicity profile demonstrated in a multicenter, open label trial enrolling 174 patients with metastatic NSCLC whose tumors had EGFR mutations as determined by a laboratory-developed test. Patients were randomized (1:1) to receive erlotinib (150 mg/day) or platinum-based doublet chemotherapy. The primary endpoint was investigator-assessed PFS. Secondary endpoints included overall survival (OS) and ORR. Superior PFS (hazard ratio [HR] 0.34; 95% confidence interval [CI]: 0.23, 0.49; p < .001) and ORR (65% vs. 16%) were observed in the erlotinib arm. Median PFS was 10.4 months and 5.2 months in the erlotinib and chemotherapy arms, respectively. There was no difference in OS (HR 0.93; 95% CI: 0.64, 1.35) with median OS of 22.9 months and 19.5 months in the erlotinib and chemotherapy arms, respectively. The most frequent (≥30%) adverse reactions in the erlotinib-treated patients were rash, diarrhea, asthenia, cough, dyspnea, and decreased appetite. The most frequent (≥5%) grade 3 and 4 adverse reactions were rash and diarrhea.

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.

Real-world evidence for regulatory decision-making: updated guidance from around the world.

Leveraging the value of real-world evidence (RWE) to make informed regulatory decisions in the field of health care continues to gain momentum. Improving clinical evidence generation by evaluating the outcomes and patient experiences at the point-of-care would help achieve the ultimate aim of ensuring that effective and safe treatments are rapidly approved for patient use. In our previous publication, we assessed the global regulatory landscape with respect to RWE and provided a review of the regional availability of frameworks and guidance through May 2021 on the basis of 3 key regulatory elements: regulatory RWE frameworks, data quality guidance, and study methods guidance. In the current review, we have updated and elaborated upon recent developments in the regulatory RWE environment from a regional perspective under the same 3 regulatory elements stated above. In addition, we have also included a new category on procedural guidance. The review also discusses the perceived gaps and potential opportunities for future development and harmonization in this field to support framework establishment in regions without pre-existing RWE policies. Additionally, the article reviews current developments of health technology assessment (HTA) bodies pertaining to RWE and discusses the status of evidentiary alignment among regulators and HTA agencies.

A novel approach to reducing disparities in health outcomes by enhancing interpretation of cancer clinical trials for underrepresented patient groups.

There has been a growing realization, based on emerging evidence from the point of care, that real-world outcomes of patients with cancer are often inferior to those reported in conventional clinical trials. This phenomenon can be attributed in part to deficits in external validity that are present in many studies. Several factors contribute to external validity deficits, including: narrow eligibility criteria; differences between protocol-specified procedures and routine care; and inadequate access to clinical trial participation among underrepresented and socioeconomically disadvantaged groups. As a result, the current body of clinical evidence derived from conventional clinical trials can be inadequate to inform patient-specific treatment decisions at the point of care. Furthermore, lack of practical guidance on how to evaluate the impact of external validity deficits can impede both the design of more generalizable clinical trials and efforts to personalize treatment decisions for individual patients. In this methodological review, we suggest an approach to aid clinicians in such evaluations, providing visual and quantitative methods for assessing the magnitude of, and adjusting for, the impact of external validity deficits in conventional clinical trials. Our methods and visualizations have broad applicability across important areas of real-world medical decision-making and research, providing opportunities to design clinical studies that are more reflective of the diverse needs of patients with cancer, including those excluded from traditional clinical trials due to narrow eligibility criteria, socioeconomic disadvantages, and other systemic barriers to equitable access to healthcare resources.

The design and evaluation of hybrid controlled trials that leverage external data and randomization.

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

A digital highway for data fluidity and data equity in precision medicine.

Recent technological advances continue to expand the universe of big data in biomedicine along the four axes of variety, veracity, volume, and velocity, fueling innovations in research and discovery while transforming care delivery. These advances allow quantitative capture of multimodal health, behavioral, social, and environmental data from n-of-all in near real-time to support the development of new therapies and personalization of treatment decisions for the n-of-one. Application of advanced analytical methods, including artificial intelligence and machine learning, to these modern data assets can greatly propel our understanding of health and disease, accelerating the development of safer and more effective anticancer therapies. In this perspective, we rationalize the creation of a universally accessible digital highway system as a foundational infrastructure to enable data fluidity in an equitable manner. An interoperable and integrated digital inter-state highway can facilitate efficient derivation of insights from biomedical big data to improve health outcomes and ensure that the U.S. remains at the leading-edge innovations in technology, advanced analytics, and precision medicine.

Immune-related adverse events associated with immune checkpoint inhibitors: a call to action for collecting and sharing clinical trial and real-world data.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer, improving outcomes in patients with advanced malignancies. The use of ICIs in clinical practice, and the number of ICI clinical trials, are rapidly increasing. The use of ICIs in combination with other forms of cancer therapy, such as chemotherapy, radiotherapy, or targeted therapy, is also expanding. However, immune-related adverse events (irAEs) can be serious in up to a third of patients. Critical questions remain surrounding the characteristics and outcomes of irAEs, and how they may affect the overall risk-benefit relationship for combination therapies. This article proposes a framework for irAE classification and reporting, and identifies limitations in the capture and sharing of data on irAEs from current clinical trial and real-world data. We outline key gaps and suggestions for clinicians, clinical investigators, drug sponsors, patients, and other stakeholders to make these critical data more available to researchers for pooled analysis, to advance contemporary understanding of irAEs, and ultimately improve the efficacy of ICIs.

Impact of Broadening Trial Eligibility Criteria for Patients with Advanced Non-Small Cell Lung Cancer: Real-World Analysis of Select ASCO-Friends Recommendations.

PURPOSE: Cancer clinical trials often accrue slowly or miss enrollment targets. Strict eligibility criteria are a major reason. Restrictive criteria also limit opportunities for patient participation while compromising external validity of trial results. We examined the impact of broadening select eligibility criteria on characteristics and number of patients eligible for trials, using recommendations of the American Society of Clinical Oncology (ASCO) and Friends of Cancer Research. EXPERIMENTAL DESIGN: A retrospective, observational analysis used electronic health record data from ASCO's CancerLinQ Discovery database. Study cohort included patients with advanced non-small cell lung cancer treated from 2011 to 2018. Patients were grouped by traditional criteria [no brain metastases, no other malignancies, and creatinine clearance (CrCl) ≥ 60 mL/minute] and broadened criteria (including brain metastases, other malignancies, and CrCl ≥ 30 mL/minute). RESULTS: The analysis cohort included 10,500 patients. Median age was 68 years, and 73% of patients were White. Most patients had stage IV disease (65%). A total of 5,005 patients (48%) would be excluded from trial participation using the traditional criteria. The broadened criteria, however, would allow 98% of patients (10,346) to be potential participants. Examination of patients included by traditional criteria (5,495) versus those added (4,851) by broadened criteria showed that the number of women, patients aged 75+ years, and those with stage IV cancer was significantly greater using broadened criteria. CONCLUSIONS: This analysis of real-world data demonstrated that broadening three common eligibility criteria has the potential to double the eligible patient population and include trial participants who are more representative of those encountered in practice.See related commentary by Giantonio, p. 2369.

Patient-Centered Approach to Benefit-Risk Characterization Using Number Needed to Benefit and Number Needed to Harm: Advanced Non-Small-Cell Lung Cancer.

This work summarizes the benefit and risk of the results of clinical trials submitted to the US Food and Drug Administration of therapies for the treatment of non-small cell lung cancer (NSCLC) using number needed to benefit (NNB) and number needed to harm (NNH) metrics. NNB and NNH metrics have been reported as potentially being more patient centric and more intuitive to medical practitioners than more common metrics, such as the hazard ratio, and valuable to medical practitioners in complementing other metrics, such as the median time to event. This approach involved the characterization of efficacy and safety results in terms of NNB and NNH of 30 clinical trials in advanced NSCLC supporting US Food and Drug Administration approval decisions from 2003 to 2017. We assessed trends of NNB over time of treatment (eg, for programmed death 1 inhibitors) and variation of NNB across subpopulations (eg, characterized by epidermal growth factor receptor mutation, programmed death ligand 1 expression, Eastern Cooperative Oncology Group performance status, age, and extent of disease progression). Furthermore, the evolution of NNB of treatments for advanced NSCLC was charted from 2003 to 2017. Across subpopulations, NNB, on average, was 4 patients for approved targeted therapies in molecularly enriched populations, 11 patients for approved therapies in nonmolecularly enriched populations, and 23 patients for withdrawn or unapproved therapies. Furthermore, the NNB analysis showed variation for attributes of epidermal growth factor receptor mutations, level of programmed death 1 expression, Eastern Cooperative Oncology Group performance status, etc. When considering the best-case subpopulations and available drugs, the NNB frontier reduced from an estimated value of 7.7 in 2003 to an estimated value of 2.5 in 2017 at the estimated median overall survival-equal to 6 months-of an untreated patient.

Status Update on Data Required to Build a Learning Health System.

Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed "real-world" data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.