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Introduction Registrational trials are clinical trials which are used to justify regulatory approval of a new medication by the FDA. However, clinical trials may not be designed around patient-centred outcomes, like overall survival (OS) or quality of life. This disconnect raises concerns about whether current trial designs are truly aligned with the long-term needs and priorities of patients. A comprehensive analysis of trial designs and endpoints is necessary to ensure that future oncological therapies deliver meaningful clinical improvements. Our objective was to describe types of clinical trial design and primary endpoints used for registrational trials, and their trends over time. Research design and methods In this cohort study, the HemOnc database was used to identify registrational trials supporting cancer drug approval from 2004 through 2023. Clinical trial design (escalation, de-escalation, in-class switch, out-of-class switch) and primary endpoint (OS; time-to-event including progression-free survival; response-based endpoint) were characterized. Results Escalation trial designs were more common than in-class switches or out-of-class switches. De-escalation and mixed designs were exceedingly rare. The most common primary endpoint was a composite time-to-event endpoint. Conclusion Most clinical trials leading to FDA approval for cancer-directed therapies employ escalation designs with composite time-to-event endpoints. Designs reflecting paradigm shifts in therapy (in-class switches and out-of-class switches) are sparsely used. This approach raises the concern that trials may increase therapeutic toxicity without delivering meaningful improvements in OS or quality of life.

Abstract Objective Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process. Materials and Methods A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n = 5) and held-out test sets (n = 17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the 2 LLMs were considered concordant if they were the same for a given variable. The discordant responses from each LLM were provided to the other LLM for cross-critique. Accuracy, ie, the total number of correct responses divided by the total number of responses, was computed to assess performance. Results In the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, increasing accuracy to 0.76. Discussion Concordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy. Conclusion Large language models, when simulated in a collaborative, 2-reviewer workflow, can extract data with reasonable performance, enabling truly “living” systematic reviews.

PURPOSE The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

11079 Background: Phase 3 randomized clinical trials (RCTs) inform the standard of care in cancer treatment yet have a mixed track record of success. In this study, we sought to 1) determine the overall success rate of phase 3 RCTs; 2) assess whether authorship patterns differ between successful (positive) and unsuccessful (negative) trials; and 3) explore if individual authors have distinct track records of success. Methods: The HemOnc knowledgebase (KB) was queried for initial publications of phase 3 RCTs of systemic anticancer therapy; those without primary outcome described were excluded. RCTs were labeled positive if the primary endpoint was met per HemOnc standard criteria (P value ≤ 0.05 or hazard ratio upper bound ≤ 1 for any experimental arm[s]) and negative if they did not meet these criteria or if the experimental arm was statistically inferior. Trial metadata and author information were extracted from the HemOnc KB. Author positive publication rate (PPR) was defined as % of publications reporting a positive result divided by total number of eligible publications. Prolific authors were defined as those with 6+ eligible publications. Author count for manuscripts reporting positive vs negative results was evaluated with the Mann-Whitney U test; other statistics were descriptive. Results: 3328 studies met criteria as of February 5, 2024; these were associated with 3305 initial publications between 1960-2023, involving 25,412 unique authors. 1474 (44.3%) of the studies were positive. Publications reporting positive trial results had, on average, 3 more authors, median (interquartile range) of 18 (12-23) vs 15 (11-20), p<0.0001. The mean (±SD) PPR for n=1967 prolific authors was 47.7% (±22.3%). The PPR was not related to individual author output; mean PPR for non-prolific authors was 48.0% (SD not reported). Differences in PPR were apparent by the disease under study and the years of publication (Table). Among the 46 prolific authors who had a PPR 2+ SD above the mean, 26 (55%) were from China followed by Italy with 3 (6%). Conclusions: Despite known positive publication bias, the success rate of published phase 3 RCTs remains <50%. Publications reporting positive results had significantly more authors; this finding requires further study including adjustment for trial sample size. Significant variation in PPR amongst fields could be due to watershed therapeutic advances, differential selection of primary endpoints, and/or selective reporting of results. Finally, a signal for geographic association of outlier PPRs suggests the need for further investigation. [Table: see text]

11030 Background: Drug regulators operate on their own timelines to assess the safety and efficacy of new drugs and/or extensions to existing indications, such that there are delays between regulators. During such a gap, patients in the “early” domain have access to the approved drug, while those in the “late” domain do not, outside of compassionate use. Our study aimed to quantify the “opportunity lost” for new therapies approved by FDA and EMA, assuming patients in the late domain receive standard-of-care (SOC) therapy – which is presumably inferior to the new drug – during the gap. Methods: Anticancer drugs approved by both FDA and EMA were identified through the HemOnc knowledgebase. Inclusion criteria included: 1) drugs approved in both domains based on the same randomized clinical trial (RCTs) for the same indication; 2) a primary time-to-event endpoint; and 3) a quantitated median time-to-event for the primary endpoint. For each included trial, the approval gap between FDA and EMA in days and the ratio of approval gap to median control arm time-to-event duration was calculated. A ratio ≥2 implies that, on average, no late domain patients starting treatment in the first half of the gap would be expected to have access to the new drug prior to an event. Results: Of 60 eligible RCTs, 59 had calculable median event durations. 25 (42%) had a primary overall survival endpoint; the most common surrogate endpoint was progression-free survival (PFS; 29/59; 49%). The median (interquartile range [IQR]) approval gap was 186 (124-271) days, with FDA being the first approver in 56/59 (95%) cases. The median (IQR) ratio of approval gap to median control arm event duration was 0.63 (0.35-1.11). Drugs with ratio ≥2 (Table) were targeted or immunotherapies; 7/8 (88%) were approved with PFS endpoint; 5/8 (62.5%) were approved for second line or later. Conclusions: Our study shows that FDA approves most drugs prior to EMA and a nontrivial number of patients may experience progression or death events while awaiting access to anticancer therapies already approved in another domain. The analysis does not take into consideration scenarios in which the new therapy is inferior to SOC or delays in patient access (due to slow uptake; regulatory barriers [in Europe/UK]; or patient affordability [in US]). Further regulatory collaboration (e.g., Project Orbis) between FDA and EMA could help to reduce and potentially ameliorate this differential in access to new cancer drugs in the future. [Table: see text]

11023 Background: Randomized clinical trials (RCTs) in cancer typically compare experimental to control arm regimens. The choice of an appropriate control (ctrl) arm can have a major impact on the expected success of an RCT. The frequency with which cancer RCTs use control arms based on established dosing and scheduling protocols has not been described. Methods: The HemOnc knowledgebase (KB) was used to identify systemic anti-cancer therapy (SACT) regimen variants, defined as regimens with identical components that differ in dosing, scheduling, and/or route. Non-cancer, nonrandomized, and non-SACT studies were excluded. Study publication year was used to define standard variants as those evaluated in the experimental arm of a positive phase 3 RCT >1 year prior to publication as a control arm, regardless of cancer type or context of treatment. Study-variant dyads were evaluated to determine whether the variant was standard. Success rates of RCTs with standard vs non-standard control arms were evaluated with Fisher’s exact test. Results: 5221 studies were associated with ≥1 named variant in the HemOnc KB, as of 2024-02-06. After exclusions, there were 3511 study-variant dyads (2386 studies; 1714 variants). The 9 most common regimens are shown in the Table. The median (IQR) number of variants per regimen was 2 (1-3); carboplatin & paclitaxel (CP) had the most variants (n=33). Across all control arm study-variant dyads, 2228/3492 (64%) utilized non-standard variants. For example, 60/97 studies (62%) of docetaxel as control used the 75 mg/m2 q3wk variant after it was established in 2000, whereas n=27 others used 19 non-standard variants. Trials that used a standard control arm had a numerically higher success rate, 45% vs 42% (OR 1.10, 95% CI 0.92-1.32). Conclusions: Non-standard regimen variants are frequently used in cancer RCTs. Reasons for this could include toxicity, patient convenience, or emerging data from smaller studies that establish comparable efficacy or improved toxicity to standard variants. However, the a priori efficacy of non-standard control arms is less rigorously established, and trials with standard control arms might be more successful. Future directions include quantifying the granular differences between standard and non-standard variants and investigating whether certain types of non-standard variation (e.g. fewer cycles, lower doses) associate with trial success rates. [Table: see text]

Abstract Telehealth is a broad concept that refers to any delivery of health care in real time using technologies to connect people or information that are not in the same physical location. Until fairly recently, telehealth was more aspiration than reality. This situation changed radically due in part to the COVID-19 pandemic, which led to a near-overnight inability for patients to be seen for routine management of chronic health conditions, including those with cancer. The purpose of this brief narrative review is to outline some areas where emerging and future technology may allow for innovations with specific implications for people with a current or past diagnosis of cancer, including underserved and/or historically excluded populations. Specific topics of telehealth are broadly covered in other areas of the special issue.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32–1.67]); Black patients (aOR 1.74; 95 CI 1.24–2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70–6.79) and Other (aOR 2.97; 95 CI 1.71–5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83–12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63–3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20–2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66–3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89–22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701 .

ImportanceSystematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking.ObjectiveTo assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022.ExposureTreatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19.Main Outcomes and MeasuresMain outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up.ResultsOf 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13).Conclusions and RelevanceIn this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19–related thromboembolism in patients with cancer.