Oncology Clinical Trials Research Articles

Novel clinical trial designs emerging from the molecular reclassification of cancer.

Next-generation sequencing has revealed the disruptive reality that advanced/metastatic cancers have complex and individually distinct genomic landscapes, necessitating a rethinking of treatment strategies and clinical trial designs. Indeed, the molecular reclassification of cancer suggests that it is the molecular underpinnings of the disease, rather than the tissue of origin, that mostly drives outcomes. Consequently, oncology clinical trials have evolved from standard phase 1, 2, and 3 tissue-specific studies; to tissue-specific, biomarker-driven trials; to tissue-agnostic trials untethered from histology (all drug-centered designs); and, ultimately, to patient-centered, N-of-1 precision medicine studies in which each patient receives a personalized, biomarker-matched therapy/combination of drugs. Innovative technologies beyond genomics, including those that address transcriptomics, immunomics, proteomics, functional impact, epigenetic changes, and metabolomics, are enabling further refinement and customization of therapy. Decentralized studies have the potential to improve access to trials and precision medicine approaches for underserved minorities. Evaluation of real-world data, assessment of patient-reported outcomes, use of registry protocols, interrogation of exceptional responders, and exploitation of synthetic arms have all contributed to personalized therapeutic approaches. With greater than 1×1012 potential patterns of genomic alterations and greater than 4.5 million possible three-drug combinations, the deployment of artificial intelligence/machine learning may be necessary for the optimization of individual therapy and, in the near future, also may permit the discovery of new treatments in real time.

The ELECTRA Trial: Approach to Contemporary Challenges in the Development and Implementation of Double-Blinded, Randomised, Controlled Clinical Trials in Low-Volume High-Complexity Surgical Oncology

The ELECTRA Trial: Approach to Contemporary Challenges in the Development and Implementation of Double-Blinded, Randomised, Controlled Clinical Trials in Low-Volume High-Complexity Surgical Oncology

Embracing the Future of Clinical Trials in Radiotherapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

This white paper examines the potential of pioneering technologies and artificial intelligence (AI)-driven solutions in advancing clinical trials involving radiotherapy. As the field of radiotherapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiotherapy, image-guided radiation therapy (IGRT), and AI promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect (LET/RBE), and the combination of radiotherapy and immunotherapy create new avenues for innovation in clinical trials. The paper aims to provide an overview of these emerging technologies and discuss their challenges and opportunities in shaping the future of radiation oncology clinical trials. By synthesizing knowledge from experts across various disciplines, this white paper aims to present a foundation for the successful integration of these innovations into radiotherapy research and practice, ultimately enhancing patient outcomes and revolutionizing cancer care.

Missing data in the eligibility criteria of synthetic controls from real-world data

ABSTRACT Randomized clinical trials (RCTs) can benefit from using Real-World Data (RWD) as a supplementary data source to enhance their analysis. An Augmented RCT combines randomized treatment and control groups with synthetic controls derived from RWD. This way, the trial can achieve less prospective enrollment, higher statistical power, and lower costs. However, to ensure scientific validity, the synthetic controls must satisfy the same eligibility criteria as the trial participants. A major challenge is that RWD often have missing data that hinder the eligibility assessment. This problem has been overlooked in the literature and this paper offers statistical solutions to address it. We use multiple imputations to handle missing data in the variables involved in the eligibility criteria. We also propose a generalized propensity score weighting procedure to adjust for the life expectancy requirement, a common eligibility criterion in oncology clinical trials but usually unavailable in RWD. Since the life expectancy is an unmeasured confounder, we discuss the statistical assumptions required to correct its bias. We validate the proposed solutions through simulation studies and the analysis of an Augmented RCT in oncology.

Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review.

There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different aspects of oncology clinical trials. Pertinent keywords were used to find relevant articles published in PubMed, Scopus, and Google Scholar databases, which described the clinical application of AI approaches. A quality evaluation utilizing a customized checklist specifically adapted was conducted. This study is registered with PROSPERO (CRD42024537153). Out of the identified 2833 studies, 72 studies satisfied the inclusion criteria. Clinical Trial Enrollment & Eligibility were among the most commonly studied clinical trial aspects with 30 papers. The prediction of outcomes was covered in 25 studies of which 15 addressed the prediction of patients' survival and 10 addressed the prediction of drug outcomes. The trial design was studied in 10 articles. Three studies addressed each of the personalized treatments and decision-making, while one addressed data management. The results demonstrate using AI in cancer clinical trials has the potential to increase clinical trial enrollment, predict clinical outcomes, improve trial design, enhance personalized treatments, and increase concordance in decision-making. Additionally, automating some areas and tasks, clinical trials were made more efficient, and human error was minimized. Nevertheless, concerns and restrictions related to the application of AI in clinical studies are also noted. AI tools have the potential to revolutionize the design, enrollment rate, and outcome prediction of oncology clinical trials.

Incomplete Toxicity Reporting and Use of Toxicity-Minimizing Language in Phase III Oncology Trials.

This study aimed to determine complete toxicity reporting (CTR), and the use of subjective toxicity-minimizing language (TML) among phase III oncology trials. Two-arm superiority-design phase III oncology trials published from 2002 to 2020 were reviewed for toxicity data. CTR was defined as reporting total adverse events (TAEs), total serious adverse events (SAEs), total deaths, and study therapy discontinuations because of toxicity. Guideline concordance was defined according to guidelines published in the BMJ (defined as reporting total SAEs, total deaths, and study therapy discontinuations because of toxicity). TML was defined as a set of terms that subjectively downplay the harm of therapies. A total of 407 trials enrolling 322,645 patients were included. Most (51%, n = 207) reported SAEs, 88% (n = 358) reported total deaths, and 84% (n = 340) reported study therapy discontinuation because of toxicity. Although 55% of trials (n = 223) reported TAEs, only 32% (n = 131; 95% credible interval, 28 to 37) fit the criteria for CTR. CTR was more common in trials with industry sponsorship (37%) than with cooperative group sponsorship (4%). All 131 trials where CTR was observed were industry-sponsored, and only 3% (4/131) were cooperative group-sponsored trials. TML was used in 46% of trials (n = 186; 95% credible interval, 41 to 51), with no trial-related factors (including sponsorship source) associated with the odds of TML use. Toxicity in phase III oncology clinical trials is often incompletely reported and is frequently minimized in its interpretation. Industry-sponsored trials more comprehensively report toxicity than do cooperative group-sponsored trials. CTR may improve patients' and oncologists' understanding of new treatments; thus, a more standardized approach to reporting toxicity data is needed.

POP-REFINE: A Comprehensive Framework for Evaluating and Optimizing Representativeness in Clinical Trials.

Clinical research has historically failed to include representative levels of historically underrepresented populations and these inequities continue to persist. Ensuring representativeness in clinical trials is crucial for patients to receive clinically appropriate treatment and have equitable access to novel therapies; enhancing the generalizability of study results; and reducing the need for post-marketing commitments focused on underrepresented groups. As demonstrated by recent legislation and guidance documents, regulatory agencies have shown an increased interest in understanding how novel therapies will impact the patient population that will receive them. Despite these efforts, a systematic approach to measure and optimize representativeness remains underdeveloped. Here, we introduce the novel Population Optimization, Representativeness Evaluation, and Fine-tuning Framework, designed to quantify and enhance representativeness. Our framework includes methods for evaluating overall and subgroup representativeness, identifying drivers of non-representativeness, and optimizing eligibility criteria to achieve representative populations. We demonstrate our framework by selecting patients who met the eligibility criteria for nine oncology clinical trials from a nationwide electronic health record-derived de-identified database and quantifying the representativeness of each trial's eligible population. This framework addresses gaps in current literature by providing a comprehensive, data-driven approach to enhance the representativeness of clinical trials, thereby supporting regulatory and internal decision-making processes. This framework is adaptable to various disease indications and can be extended to evaluate enrolled study samples, ensuring that clinical trials are representative.

Has the UK lost its position as a destination for world-leading clinical research? A comparative analysis of haematological cancer clinical trials performance before Brexit

ObjectivesTo understand the competitive position of the UK in comparison to Europe and the USA for haematological cancer clinical research.DesignUsing commercially available databases, clinical trial numbers, their effectiveness and publication...

Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction.

Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc. Particularly, treatments relying on the (re)activation of the immune system have become increasingly present in the clinical trial pipeline. However, the complexities of the immune response and cancer-immune interactions pose a challenge to the development of these therapies. Quantitative systems pharmacology (QSP), as a computational approach to predict tumor response to treatments of interest, can be used to conduct in silico clinical trials with virtual patients (and emergent use of digital twins) in place of real patients, thus lowering the time and cost of clinical trials. In line with improved mechanistic understanding of the human immune system and promising results from recent cancer immunotherapy, QSP models can play critical roles in model-informed drug development in immuno-oncology. In this chapter, we discuss how QSP models were designed to serve different study objectives, including hypothesis testing, dose optimization, and efficacy prediction, via case studies in immuno-oncology.

Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2-Stained Clinical Specimens in Breast Cancer.

Precise determination of biomarker status is necessary for clinical trial enrollment and endpoint analyses, as well as for optimal treatment determination in real-world practice. However, variabilities may be introduced into this process due to the processing of clinical specimens by different laboratories and assessment by distinct pathologists. Machine learning tools have the potential to minimize inconsistencies, although their use is not presently widespread. To assess the applicability of machine learning to the quality control process for biomarker scoring in oncology, we developed and validated an automated machine learning model to be applied as a quality control tool for monitoring the assessment of human epidermal growth factor-2 (HER2). The model was trained using whole slide images from multiple sources to quantify HER2 expression and measure immunohistochemistry stain intensity, tumor area, and the presence of artifacts or ductal carcinoma in situ across breast cancer phenotypes. The quality control tool was deployed in a real-world cohort of HER2-stained breast cancer sample images collected from routine diagnostic practice to evaluate trends in HER2 testing quality indicators and between pathology laboratories. Automated image analysis for HER2 scoring is consistent and reliable using this algorithm. Deployment of the HER2 quality control tool across 3 clinical laboratories revealed interlaboratory variability in HER2 scoring and inconsistencies in data reporting. These results support the future incorporation of quality control algorithms for real-time monitoring of clinical laboratories contributing to clinical trials in oncology and in the real-world setting of HER2 immunohistochemistry testing in local clinical laboratories and hospitals.

A snapshot of cancer in Chile II: an update on research, strategies and analytical frameworks for equity, innovation and national development

IntroductionChile has achieved developed nation status and boasts a life expectancy of 81 + years; however, the healthcare and research systems are unprepared for the social and economic burden of cancer. One decade ago, the authors put forward a comprehensive analysis of cancer infrastructure, together with a series of suggestions on research orientated political policy.ObjectivesProvide an update and comment on policy, infrastructure, gender equality, stakeholder participation and new challenges in national oncology. Assess the funding and distribution of cancer investigation. Present actions for the development of oncology research, innovation and patient care.MethodsTriangulating objective system metrics of economic, epidemiological, private and public sector resources together with policy analysis, we assessed cancer burden, infrastructure, and investigation. We analyzed governmental and private-sector cancer databases, complemented by interviews with cancer stakeholders.ResultsGovernmental policy and patient advocacy have led to the recognition of cancer burden, a cancer law, and a national cancer plan. Cancer has become the leading cause of death in Chile (59,876 cases and 31,440 cancer deaths in 2022), yet only 0.36% gross domestic product (GDP) is directed to research and development. Inequalities in treatment regimens persist. Prevention policy has lowered tobacco consumption, sugar intake via soft drinks and offered a high coverage of HPV vaccines. A high-quality cancer research community is expanding, and internationally sponsored clinical oncology trials are increasing.ConclusionsThe cancer law has facilitated advancement in policy. Prevention policies have impacted tobacco and sugar intake, while gender equality and care inequality have entered the public forum. Cancer research is stagnated by the lack of investment. Implementation of a cancer registry and biobanking, reinforcement of prevention strategies, development of human resources, promotion of clinical trial infrastructure and investment in new technologies must be placed as a priority to permit advancements in innovation and equitable cancer care.

Oncology Clinical Trial Design Planning Based on a Multistate Model That Jointly Models Progression-Free and Overall Survival Endpoints.

When planning an oncology clinical trial, the usual approach is to assume proportional hazards and even an exponential distribution for time-to-event endpoints. Often, besides the gold-standard endpoint overall survival (OS), progression-free survival (PFS) is considered as a second confirmatory endpoint. We use a survival multistate model to jointly model these two endpoints and find that neither exponential distribution nor proportional hazards will typically hold for both endpoints simultaneously. The multistate model provides a stochastic process approach to model the dependency of such endpoints neither requiring latent failure times nor explicit dependency modeling such as copulae. We use the multistate model framework to simulate clinical trials with endpoints OS and PFS and show how design planning questions can be answered using this approach. In particular, nonproportional hazards for at least one of the endpoints are a consequence of OS and PFS being dependent and are naturally modeled to improve planning. We then illustrate how clinical trial design can be based on simulations from a multistate model. Key applications are coprimary endpoints and group-sequential designs. Simulations for these applications show that the standard simplifying approach may very well lead to underpowered or overpowered clinical trials. Our approach is quite general and can be extended to more complex trial designs, further endpoints, and other therapeutic areas. An R package is available onCRAN.

Cyclin Dependent Kinase 2 (CDK2) Inhibitors in Oncology Clinical Trials: A Review.

Cyclin dependent kinase 2 (CDK2) is responsible for enforcing progression through the G1-S phase transition. Mutations and alterations in the CDK2 signaling pathway are associated with various cancers, most commonly breast, ovarian, prostate, leukemia, and lymphoma. CDK2 inhibitors have shown promising preclinical and early clinical results, and this class of agents may be most effective against cancers with cyclin E overactivity. Common side effects observed include nausea, vomiting, diarrhea, anemia, and fatigue. This clinical review summarizes past and current CDK2 inhibitors in clinical trials.

Early termination and nonpublication of phase III/IV melanoma clinical trials: a cross-sectional study

Introduction Melanoma is a common cancer worldwide. Introduction of new treatments through clinical trials is essential to reduce the global burden from melanoma; however, it is estimated that 22% of oncological clinical trials are terminated early. We conducted the first cross-sectional study to assess melanoma clinical trial termination and nonpublication with an aim to guide scientists conducting such trials. Methods We identified all phase III/IV clinical trials evaluating melanoma therapies in the ClinicalTrials.gov database between 2010 and 2024. For each trial, we extracted data on the trial’s status, melanoma stage, melanoma subtype, included age, funding sources, trial locations, publication status, and reasons for termination. A descriptive and frequency analysis was performed in JASP 0.19 software. Results A total of 108 trials were analyzed; the majority of trials included stage III/IV melanoma (n = 95), and cutaneous melanoma was the most common subtype. Only 15 trials included pediatric patients. Industrial funding accounted for 74% (n = 80) of trials’ financing. Most of the trials were conducted internationally in North America, Europe, Australia, and New Zealand, with a few trials conducted in South Africa (n = 1), South America (n = 1), or China (n = 5). Early termination was observed in 21% (n = 23) of trials, with no association between early termination and melanoma stage, subtype, age, funding source, or trial locations. Notably, the most common reason for early termination was publication of interim efficacy and safety results (n = 14/23). Conclusion Our study confirms that early termination of phase III and IV melanoma trials doesn’t raise a significant concern; however, diversified funding and broader geographic representation are needed to create more equitable and inclusive trials. We also suggest conducting further cross-sectional studies on phase I/II melanoma trials.

Oncology Clinical Trials Targeting Members of the Cadherin Superfamily: A Review.

The cadherin superfamily of proteins is critical for cell-cell interactions and demonstrates tissue-specific expression profiles. In cancers, disruption of cell-cell adhesion is frequently associated with oncogenesis and metastasis. As such, these proteins have been the targets of multiple attempts to develop novel therapeutics in malignancy. This review article discusses prior and current clinical trials targeting the cadherin proteins.

Cardiovascular Safety in Oncology Clinical Trials

Cardiovascular Safety in Oncology Clinical Trials

Setting "cold" tumors on fire: Cancer therapy with live tumor-targeting bacteria.

Immunotherapy with checkpoint blockade has shown remarkable efficacy in many patients with a variety of different types of cancer. However, the majority of patients with cancer have yet to benefit from this revolutionary therapy. Studies have shown that checkpoint blockade works best against immune-inflamed tumors characterized by the presence of tumor-infiltrating lymphocytes (TILs). In this review, we summarize studies using live tumor-targeting bacteria to treat cancer and describe various strategies to engineer the tumor-targeting bacteria for maximized immunoregulatory effects. We propose that tumor-localized infections by such engineered bacteria can create an immune microenvironment in favor of a more effective antitumor immunity with or without other therapies, such as immune checkpoint blockade (ICB). Finally, we will briefly outline some exemplary oncology clinical trials involving ICB plus live therapeutic bacteria, with a focus on their ability to modulate antitumor immune responses.

Telehealth in Gynecologic Oncology Clinical Trials

OBJECTIVE: To describe patient, research staff, and clinician perspectives regarding the effects of telehealth and remote clinical trial operations on safety, quality, and experience in gynecologic oncology clinical trials. METHODS: This qualitative study used semistructured interviews conducted from May to June 2022 with purposively sampled clinical trial participants, research staff, and clinicians involved in gynecologic oncology clinical trials with telehealth utilization. Participants described telehealth in clinical trial experiences, including benefits and barriers to receipt and provision of care, satisfaction, and quality. Transcripts were coded and analyzed with a modified content analysis approach based on research objectives and emergent themes. An adapted version of the validated Telehealth Usability Questionnaire was administered to all invited participants. RESULTS: Five patients, seven clinicians, and five research staff were interviewed. Patients and clinicians reported that telehealth, remote testing, and medication delivery positively affected quality of life by reducing financial burden, wait times, and transportation needs. Interviewees did not report telehealth-related changes in treatment-related adverse effects, referrals for urgent evaluation, or compromise of privacy but expressed concerns about the lack of physical examinations. Patients reported that telehealth increased scheduling burden without negative effects on care quality, counseling comprehension, relationships with trial teams, or satisfaction. Clinicians and research staff reported improved workflows regarding remote consent, sponsor interactions, and documentation but challenges with virtual patient education and off-site testing. Clinicians highlighted disparities for patients with limited technology access and reported institutional and insurance-based telehealth policies as barriers. Survey responses supported qualitative findings. CONCLUSION: Despite notable limitations, patients, research staff, and clinicians recommended continued utilization of telehealth and remote clinical trial operations in clinical trials. Future clinical trial designs should consider telehealth inclusion.

Racial/Ethnic Disparities in Oncology Clinical Trial Participation: Investigator Attitudes, Needs, and Motivation to Improve.

The perspective of all stakeholders involved in clinical trials is critical to addressing disparities in racial/ethnic minority (REM) clinical trial participation. Little is known about clinical trial investigator perspectives. To our knowledge, there are no published studies assessing differences in investigator perspectives on the basis of their primary role in clinical trials (ie, principal investigator [PI] or subinvestigator [sub-I]). Differences likely exist in investigator perspectives on the basis of having a more design-oriented (PI) or recruitment-oriented (sub-I) role. We conducted a cross-sectional, anonymous, pilot survey of 107 oncology clinical trial investigators at an academic center. The survey assessed five domains about disparities in REM clinical trial participation. We performed a subgroup analysis of mid- and late-career investigators (≥10 years of experience) comparing PIs (opened ≥one clinical trial in the past year) with sub-Is (did not). Among 60 respondents, a majority (83%) strongly agreed disparities exist in REM clinical trial participation and that this is problematic (75%). Notably, 45% agreed they cannot directly address this problem. In the subgroup analysis, PIs were more likely to have received training about barriers/facilitators to REM clinical trial participation and strategies to increase participation. They were also more likely to endorse wanting help in this area. Our results suggest clinical trial investigators are aware of disparities in REM clinical trial participation but do not feel they can address them. PIs are more likely to have received previous training on this topic and to want help to improve their ability to address disparities. More studies are needed to inform targeted interventions and structural improvements to enhance investigators' self-efficacy for improving REM participation in oncology clinical trials.

Ancestral differences in anti-cancer treatment efficacy and their underlying genomic and molecular alterations.

Systematic multi-omics analysis revealed ancestry-dependent molecular alterations, but their impact on the efficacy of anti-cancer treatment is yet largely unknown. Here, we analyzed clinical trials from ClinicalTrials.gov and found that only 8,779/102,721 (8.5%) oncology clinical trials posted information on enrollment by race/ethnicity. The underrepresentation of non-White populations suggests that it remains challenging to determine differences in the efficacy of anti-tumor treatments among different racial groups. Through a comprehensive analysis of clinically actionable genes, imputed drug responses, and immune features, we identified potential differences in treatment response to targeted, chemo and immunotherapies between different ancestral populations. Further analysis of multiple independent cohorts confirmed some of our key findings. Such potential ancestral effects are also identified in response to emerging new treatments like CAR-T therapy and PROTACs. These findings are made publicly available in a comprehensive web portal, Ancestral Differences of Efficacy in Cancers (ADEC; https://hanlaboratory.com/ADEC), to facilitate their further investigation.