Mandrekar, Sumithra J.

Although structured reporting of adverse events related to medical therapies is a critical aspect of clinical trials, the current practice fails to capture postoperative complications accurately or systematically, which precludes our ability to fully assess the impact of the risks of surgical interventions on oncologic outcomes. The incorporation of standardized, procedure‐specific definitions and severity classifications for postoperative complications into adverse event reporting in trials conducted through the National Clinical Trials Network is proposed.

PURPOSE Incorporation of structured data elements within the electronic health record generates real-time data for many potential uses. However, this requires a change in documentation practices, which may increase documentation effort. The best strategies to support structured documentation interventions have not been thoroughly investigated. Herein, we report the results of extending a data capture tool across multiple disease sites and practice locations. METHODS A note-based data capture tool was developed to collect five data elements using minimum common oncology data elements language. Soft-stop close visit validation (CVV), offloading of data entry tasks to allied health staff, group education, individualized e-mails, and one-on-one assistance were used to support utilization. Baseline and follow-up surveys were collected. For each provider, we compared data capture in the 6 months before and after their CVV go-live. RESULTS Forty-five providers participated (31 physicians, 14 nurse pracationers and physician assistants [NPPAs]), resulting in 23,274 encounters pre-CVV go-live and 23,146 post-CVV go-live visits. Median percentage of all data elements captured increased from 69.5% to 82.6% across all providers. The effect of CVV was immediate and sustained. When surveyed, most respondents agreed with the value propositions of understanding practice population, outcomes, selecting trials to open, and identifying patients for trial enrollment. Most respondents (15/20, 75%) felt the soft stop was a helpful reminder. CONCLUSION Structured data can be successfully collected in provider notes with >80% data capture. Personalized assistance incorporating the structured data elements into users' preexisting templates and soft-stop CVV, along with monitoring the results of implementation and personalized follow-up, was used to support the structured data capture intervention.

Importance Preoperative and perioperative nivolumab improve event-free survival in resectable non–small cell lung cancer. The role of adjuvant nivolumab after upfront surgery is unknown. Objective To determine whether adjuvant nivolumab improves disease-free survival and overall survival in patients with resected non–small cell lung cancer with any tumor programmed death-ligand 1 (PD-L1) expression and in those with at least 50% PD-L1 expression. Design, Setting, and Participants This open-label, randomized phase 3 study enrolled participants from May 2016 through September 2019, with median follow-up of 72.6 months at the data cutoff in December 2025. The study was conducted at 378 centers in the US National Clinical Trials Network. Patients were identified through a screening trial. Those with resected tumors at least 4 cm and/or who were lymph node positive (N1/N2) were eligible for inclusion after completion of planned standard adjuvant therapy if the tumor was adenocarcinoma without sensitizing sequence variants in EGFR and ALK or squamous cell carcinoma. Intervention Patients were randomized in a 1:1 ratio to receive nivolumab 480 mg intravenously every 4 weeks for up to 1 year or standard care observation. Main Outcomes and Measures Co–primary end points were disease-free survival in the intention-to-treat population and in those with tumoral PD-L1 expression at least 50%. Overall survival was examined if the corresponding test of disease-free survival was statistically significant. Results A total of 466 patients (median age, 66 years; 241 [52%] male) were assigned to receive nivolumab and 469 (median age, 67 years; 245 [52%] male) to undergo standard care observation. The median duration of follow-up was 72.6 months. The trial was stopped for futility at 75% information. In the intention-to-treat population, median disease-free survival was 71.3 months with nivolumab and 68.8 months with observation (hazard ratio for progression or death, 0.97 [97% CI, 0.79-1.20]; [95% CI, 0.81-1.17]; 1-sided P = .39). In the subset of participants with PD-L1 of at least 50%, median disease-free survival was 89.8 months with nivolumab and 78.5 months with observation (hazard ratio for progression or death, 0.86 [98% CI, 0.55-1.34]; [95% CI, 0.59-1.25]; 1-sided P = .22). Conclusions and Relevance Adjuvant nivolumab was not associated with improved disease-free survival in patients with resected non–small cell lung cancer without sensitizing EGFR and ALK alterations when given after planned adjuvant chemotherapy and/or radiotherapy. Trial Registration Clinicaltrials.gov Identifier: NCT02595944

e13660 Background: We previously showed success with collection of structured data elements within the electronic health record using the minimum common oncology data elements (henceforth, mCODE tool) supported by soft-stop close visit validation (CVV) and staff support interventions in a limited cohort. Since the additional interventions are resource-intensive and difficult to apply across an entire department, we therefore implemented a department-wide roll-out supported by the least resource-intensive interventions while monitoring uptake. Methods: The mCODE tool collects five data elements: stage, disease status, performance status, intent of treatment, and intent to change therapy. The Department of Oncology providers received education on the mCODE tool and were asked to implement use at a faculty meeting; soft-stop CVV was enabled at that time. The percentage of data captured for each data element was collected from the notes of providers who did not participate in prior pilots for the 4 months post-implementation. mCODE tool usage was defined as entry of at least one data element except stage (as stage has long been a structured data element in Epic, can be entered independently of the mCODE tool, and many providers were entering stage prior to mCODE tool introduction). Results: 86 MDs and 20 APPs for a total of 106 providers were included, resulting in 14,973 visits and 8,745 unique patients. 73.6% of providers (n =78) utilized the mCODE tool. Of those who utilized the tool, median overall data capture across the four data elements was 24.0%. 22.6% of providers (n =24) achieved > 80% overall data capture and 32.1% (n =34) had over 50% overall data capture despite minimal intervention. 75.6% of MDs (n = 65) utilized the tool with a median data capture of 16.3% compared to 65% of APPs (n = 13) with a median data capture of 90.3%. Rates of data capture for individual data elements were similar. Conclusions: Use of soft-stop CVV appears to drive utilization of the mCODE tool but is insufficient to promote > 80% data capture for most providers without more intervention. Supportive interventions such as individualized assistance with incorporation into existing templates and directed communication from leadership remain necessary to promote use of structured data elements and will be implemented in sequence to understand how we achieve the target data capture ( > 80%) at scale.

8057 Background: The NCI Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trials (ALCHEMIST) study tested resected stage II-IIIB(N2) NSCLC for targetable EGFR and ALK alterations and PD-L1 expression to screen patients for biomarker-driven adjuvant therapy trials (NCT02194738). Local lab results were requested when available for comparison to central lab results. Methods: We identified patients with both central and local PD-L1 results in the ALCHEMIST adjuvant immunotherapy study screening population. Discrepancies in tumor proportion score (TPS) between central and local PD-L1 were defined using prespecified cut-points (granular: <1%, 1-<5%, 5-<10%, 10-<20%, 20-<30%, 30-<40%, 40-<50%, 50-60%, 60-<70%, 70-<80%, 80-<90%, 90-<100%; clinical: <1%, 1-<50%, 50-100%). Large discrepancies were defined as ≥ 50 percentage point differences; for these cases, local PD-L1 and hematoxylin & eosin-stained slides were requested. These and all central slides were digitally scanned at 40X. Three expert pathologists reviewed the scanned slides in a blinded fashion. Differences between patient or tumor characteristics were assessed by Chi-square and Wilcoxon rank sum tests. Percent agreement was computed within and between the reviewers. Results: Central and local PD-L1 results were available for 497 patients. 177 cases (36%) had discrepancies that placed patients in different clinical TPS categories (<1%, 1-<50%, 50-100%). Among these, 43 cases had large TPS discrepancies; 14 had available local testing slides. 46 of the 320 cases without discrepancy were randomly selected as controls. No patient or tumor characteristics were associated with discrepancy, including tumor location, histology, grade or cancer stage (p > 0.05). Pairwise comparisons of TPS according to three pathologists re-reading the same scanned PD-L1 slides showed similar levels of disagreement in clinical TPS category whether there was initial discrepancy between central and local results (24%, 34%, and 23%; mean 27%) or not (33%, 30%, and 20%; mean 28%). Among the 14 cases with large initial discrepancies and local slides available, there was agreement in clinical TPS category when the anonymized central and local scanned slides were re-read by the pathologists in 54%, 38%, and 43% of cases, despite 100% non-agreement in initial reads. There were higher rates of agreement when the tested slides both originated from the same (100%) versus different (35%) blocks, when the same (48%) versus different (40%) PD-L1 antibodies were used, and when the cases were of non-squamous (52%) versus squamous (14%) cell carcinoma. Conclusions: There are substantial differences in PD-L1 TPS read by pathologists that can result in placement of patients in different categories commonly used for clinical management. These differences have clear implications in clinical trials and routine patient care.

8001 Background: The ALCHEMIST clinical trial platform, launched by the National Clinical Trials Network (NCTN) in 2014, screened patients with resected non-small cell lung cancer (NSCLC) for molecular markers and tailored post-operative adjuvant therapy trials. A081105 evaluated whether adjuvant erlotinib improved overall survival (OS) following complete resection in patients with early stage EGFR mutated lung adenocarcinoma. Methods: A081105 was a randomized phase III trial enrolling patients with completely resected stage IB (≥4 cm), II, or IIIA non-squamous NSCLC (per the 7 th TNM staging classification) harboring EGFR exon 19 deletion or L858R mutations. Patients were randomized 1:1 to erlotinib 150 mg daily for up to 2 years or placebo/observation, stratified by stage, prior chemotherapy, EGFR mutation subtype, and ECOG performance status. The primary endpoint was OS in the per-protocol (PP) population with centrally confirmed EGFR mutations, designed to detect a hazard ratio (HR) of 0.67 in favor of erlotinib with a power of 86% and one sided type I error rate of 5%. Secondary endpoints included OS and disease free survival (DFS) in a modified intent-to-treat (mITT) population with local or centrally confirmed EGFR mutations, and safety. A prespecified backstop analysis was conducted after a minimum of 5 years of follow-up on all patients. Results: Trial enrollment was discontinued when 390 of the intended 450 patients had been enrolled due to the proven benefit of adjuvant osimertinib. Among 390 enrolled patients, the arms were balanced for age (median: 67.0 years), sex (70% female), race (70% white), stage (II: 51%, IIIA: 37%), prior chemotherapy (82%), ECOG PS 0 (60%), and exon 19 deletion (57%). Adjuvant erlotinib did not significantly improve OS compared with placebo/observation in the overall PP population (364 patients, 108 events, HR 0.89; 95% CI, 0.59–1.34; 1-sided P = 0.29) or within exon 19 deletion (HR: 0.94) or L858R mutation (HR: 0.82) subgroups. Five-year OS rates were 78.6% versus 77.9%, respectively. Results were consistent in the mITT analysis (379 patients, 113 events, HR 0.86; 95% CI, 0.58–1.28). Erlotinib improved DFS (mITT: 181 events, median: 68 vs 50 months, HR 0.74; 95% CI, 0.55–1.01). Across both arms, 11% of patients started non-protocol treatment (Osimertinib: 9%) prior to disease progression, and 9% withdrew consent for all follow-up prior to recurrence. In the erlotinib arm, 3 deaths on study were reported; grade 3 or higher adverse events were reported on 39% of patients. Median duration of treatment on Erlotinib arm was 16 months; only 37% of patients completed the planned treatment. Conclusions: In resected EGFR -mutant NSCLC, adjuvant erlotinib did not improve overall survival but improved DFS. Additional studies are exploring efficacy in selected subsets of patients using genomics and proteomic analyses. Clinical trial information: NCT02193282 .

8000 Background: The NCI’s ALCHEMIST program has screened thousands of patients with resected lung adenocarcinoma for mutations in EGFR and ALK. EA5142 studied the efficacy of adjuvant nivolumab after standard of care adjuvant therapy in patients with resected lung adenocarcinoma without sensitizing EGFR and ALK alterations and squamous cell carcinoma. Methods: Patients enrolled in the ALCHEMIST screening trial (A151216) with resected NSCLC tumors at least 4 cm and/or lymph node positive (N1/2) (for lung adenocarcinoma, without sensitizing EGFR and ALK alterations), were centrally tested for tumor cell PD-L1 expression using the DAKO 28-8 clone. After completion of all planned adjuvant therapy (chemotherapy and/or post-operative radiation) patients were randomized 1:1 to adjuvant nivolumab 480 mg IV every 4 weeks for up to 1 year or best supportive care. Patients were stratified by tumor histology, stage (AJCC 7 th edition IB/IIA vs IIB/IIIA), prior adjuvant treatment, and PD-L1 status (<1% vs ≥1%). The primary study endpoints were disease free survival (DFS) in all patients and in patients with high tumor PD-L1 expression (≥50%). Overall survival was powered to be tested hierarchically, if DFS was positive. Results: Between July 2016 and October 2019, 935 patients were randomized across 378 sites. The treatment arms were balanced for age (median 66 years), sex (52% male), smoking status (10% never smoked), histology (29% squamous), stage and PD-L1 expression (29% PD-L1 high). With a median follow-up of 72.6 months, nivolumab did not improve DFS in the intention to treat population (median DFS 71.3 months vs. 68.8 months in observation arm), hazard ratio (HR) 0.97 (95% CI 0.81-1.17, p=0.78) or the PD-L1 ≥50% subset (median DFS 89.8 months vs. 78.5 months in observation arm), HR 0.86 (95% CI 0.59-1.25, p=0.43). In multivariate analysis, adjusting for age, gender, and smoking history, the DFS HR was 0.99 (95% CI 0.82-1.19, p=0.89) in all randomized patients and 0.82 (95% CI 0.56-1.2, p=0.31) in the PD-L1 ≥50% subset. As the primary recurrence endpoint included any lung cancer, including new primaries, a sensitivity analysis was performed excluding new primary lung cancers, DFS HR 0.97 (95% CI 0.80-1.18, p=0.76) in all patients and HR 0.88 (95% CI 0.60-1.30, p=0.52 in the ≥50% subset. The median duration of treatment with adjuvant nivolumab was 9.4 months (range 0-12.7 months). The most common reasons for nivolumab discontinuation were adverse event (29%), patient withdrawal (11%), and disease progression while on treatment (10%). Treatment related grade 3-5 toxicities were reported in 25% of patients: Grade 3: 23%, Grade 4: 2%, Grade 5: <1%. Conclusions: In patients with resected NSCLC (≥4cm or LN+, EGFR/ALK -) adjuvant nivolumab did not improve DFS, irrespective of tumor PD-L1 expression. Clinical trial information: NCT02595944 .

Advances in oncology drug development are driving the emergence of novel therapies, challenging traditional dose-efficacy assumptions in dose-finding oncology trials. Traditional trial designs aim to identify a maximum tolerated dose (MTD) by assessing patients’ dose-limiting toxicities (DLTs) – adopting traditional dose-efficacy paradigms that efficacy increases with treatment dose. With these new therapies in mind, emphasis should shift toward methodological advancements in trial designs aimed at identifying optimal doses, rather than solely determining MTDs. Incorporating patient-reported outcomes (PROs) within dose-finding oncology trials is increasingly recommended to better understand treatments’ tolerability profiles, especially given the extended tolerability assessment windows for novel immunotherapies and targeted therapies. This article introduces PRO-ADD ( P atient- R eported O utcomes A ided D ose-optimisation D esign), a modular trial design framework for dose-optimisation. We leverage this framework to optimise dosage with respect to three key outcomes – clinician-assessed DLTs, PROs and preliminary efficacy. PRO-ADD performs well at identifying the optimal dose (both efficacious and tolerable) – successfully identifying the most tolerable effective dose and avoiding escalation to larger, safe doses offering no additional efficacy benefit. As the field evolves, patient-centric dose-finding approaches incorporating PROs are crucial in advancing our understanding of treatment tolerability, and in turn, shaping the future landscape of dose-finding oncology trials.

Abstract Introduction: Underlying tumor biology in metastatic castration-resistant prostate cancer (mCRPC) is highly complex and heterogeneous. To date, mCRPC state biomarker-enriched clinical trial designs incorporating either prognostic or predictive biomarkers are limited to alterations in homologous recombination repair pathway genes. In this study, we use an integrated clinico-molecular prognostic score in mCRPC that combines plasma prognostic proteins with a plasma-based copy number alteration (CNA) risk algorithm (PMID: 40036789; PMID: 36230636) to simulate a biomarker-enriched clinical trial design. Methods: A plasma cell-free DNA (cfDNA) prognostic risk algorithm from eight CNAs (gains in AR, MYC, PIK3CA, PIK3CB; deletions in NKX3-1, TMPRSS2, TP53, ZBTB16) is combined with clinical prognostic proteins (alkaline phosphatase, lactate dehydrogenase) to generate a prognostic risk-score (RS) in three independent treatment-naïve mCRPC cohorts. Using the RS classifier, we estimate the number of high-RS patients (pts) required to be screened from an unselected mCRPC population and the number needed for enrollment to achieve adequate statistical power in a randomized trial for a predefined effect size (Hazard Ratio- 0.70; 80% power). The trial design aims to enrich High-RS pts for targeting lethal mCRPC. Trade-offs are evaluated between number of pts screened and enrolled under varying RS enrichment thresholds and degrees of enrichment. Results: The range of RS distribution in 561 mCRPC pts from three pooled datasets was 0.362-11.737. The median overall survival (OS) (months; 95% Confidence Intervals) based on risk stratification for Low-RS (≤0.8) is 46.2 (38.8-55.4); “Intermediate-risk” (RS: 0.8-2.6) is 27.1(24.3-31.5); High-Risk (RS: &gt;2.6) is 14.9 (11.9-17.6). For a 2-year OS mCRPC endpoint, a biomarker-enriched trial restricted to pts with RS ≥2, which corresponds to the 65th percentile of the RS distribution, would require enrollment of 398 pts, after screening 1,101 mCRPC pts to achieve 80% power to detect a 0.7 treatment vs. control hazard ratio (HR). In contrast, a traditional non-enrichment clinical trial (no molecular classifier) would require 574 pts to achieve to achieve 80% power and still not achieve adequate power to detect strong treatment by risk interactions. As a pragmatic alternative, we evaluated a hybrid enrichment strategy enrolling 50% of pts from the general mCRPC population and 50% of pts with RS ≥1 (41st percentile). This biomarker enriched design which takes 50% pts with a RS of 1.0 (or more) in a trial with 24-month follow-up will require a sample size 525 patients for which 676 pts will be screened to achieve an 80% power to detect a 0.7 treatment vs. control hazard ratio. Conclusion: A plasma-based molecular classifier can be used to design an enrichment trial in biologically defined lethal mCRPC, incorporating 50% of pts with High-RS and yet balance the required sample size and number of pts to be screened enabling an adequately powered molecular classifier-based biomarker-enriched clinical trial. Citation Format: Manish Kohli, Liang Wang, Aik C. Tan, Muhammad Zaki Fadlullah, Anushka Shankar, Sumithra Mandrekar, Benjamin Haaland. Clinico-Genomic Risk Score-Based Biomarker Enrichment Clinical Trial Designs in Poor Prognosis Metastatic Castrate Resistant Prostate Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B039.