Citations

11533 Background: After initial response to first line therapy with imatinib, GISTs commonly progress due to secondary resistance mutations in KIT. As the KIT mutation targeting profiles of bezuclastinib (type I TKI) and sunitinib are distinct, when combined they inhibit a broad spectrum of secondary KIT mutations. Herein we report extended experience of patients treated with 2nd line combination bezuclastinib and sunitinib, including response assessment and safety. Methods: Peak (NCT05208047), a global randomized Phase 3, open-label study, aims to evaluate efficacy and safety of bezuclastinib + sunitinib vs sunitinib in GIST pts with imatinib intolerance or resistance. In Part 1a of the 3-part study, the dose of an optimized formulation of bezuclastinib was escalated in serial cohorts to achieve a target exposure comparable to that achieved at the RP2D established in the prior Phase 1b/2a study. Part 1b evaluated the interaction between bezuclastinib + sunitinib. Key inclusion: adult with locally advanced, metastatic and/or unresectable GIST, ≥1 measurable lesion according to modified RECIST v1.1, and ECOG PS 0 to 2. Part 1 completed enrollment in Apr 2023. Based upon PK and safety, a dose of bezuclastinib 600 mg QD + sunitinib 37.5 mg QD was selected for Part 2. All Part 1 data reported herein are as of Aug 2023; updated safety and efficacy will be presented. Results: Part 1 has completed enrollment with 19 pts in Part 1a and 23 in 1b. In Part 1a, Cohort 1 included 5 pts starting at bezuclastinib 300 mg QD + sunitinib 37.5 mg QD; Cohort 2 included 14 pts at bezuclastinib 600 mg QD + sunitinib 37.5 mg QD. Pt characteristics in Part 1a+1b: median age - 60 yrs (range: 33-77); 81% men; 98% ECOG PS 0-1; 98% metastatic and 2% locally advanced. The majority of TEAEs were low grade and reversible with low rate (38%) of Grade 3+ events. There were limited (24%) dose reductions and infrequent (n=2) discontinuations due to TEAEs. Three pts experienced serious AEs possibly associated with study medications. In pts evaluable for response (received ≥1 cycle of study treatment and had assessments at baseline and post-baseline [n=40]), the objective response rate (ORR) was 20% (6 confirmed PRs, 2 unconfirmed PRs). Data remain immature to estimate median PFS for Part 1 pts. In a subset of pts with prior imatinib only (n=6), the closest approximation for pts enrolling in Part 2, the ORR was 33% (2 confirmed PRs). The majority of 2nd-line pts remain on treatment past 12 months. Conclusions: Data from Peak Part 1 show an encouraging safety and tolerability profile generally consistent with published sunitinib monotherapy experience. ORR in evaluable pts from Part 1 was 20%; ORR in 2nd line pts was 33%. Part 2 of the Peak study is actively enrolling pts globally at the selected dose of bezuclastinib 600 mg QD + sunitinib 37.5 mg QD versus sunitinib 37.5 mg QD. Clinical trial information: NCT05208047 .

11504 Background: Surgery & radiation therapy (RT) yield high local control rates for soft tissue sarcoma (STS) of the extremity and limb girdle. However, patients (pts) with high-grade stage III STS are at significant risk for developing metastasis. Median survival for pts with metastatic STS is < 2 years. SARC028 (NCT02301039) evaluated the efficacy of pembrolizumab (pembro) for metastatic STS, showing 20% and 8.7% response rates in undifferentiated pleomorphic sarcoma (UPS) and pleomorphic/dedifferentiated liposarcoma (LPS), respectively. We hypothesized that neoadjuvant pembro with concurrent RT followed by surgery and adjuvant pembro for stage III UPS, including myxofibrosarcoma, or LPS would stimulate an anti-tumor immune response to eliminate micrometastatic disease & improve disease-free survival (DFS). SU2C-SARC032 (NCT03092323) is a multi-institutional, international, randomized phase 2 trial evaluating the safety and efficacy of adding pembro to standard of care (SOC) RT & surgery for pts with stage III UPS or LPS. Methods: Pts aged > 12 yo with stage III (FNCLCC grade 2 or 3) UPS or LPS of the extremity and limb girdle were enrolled. Pts were randomized (1:1, stratified by grade) to neoadjuvant RT (50 Gy/25 fx) then surgery (SOC arm) or neoadjuvant pembro and RT then surgery & adjuvant pembro (EXP arm). Pembro was given 200 mg IV Q3 wk for 3 doses (before, during & after RT) & up to 14 adjuvant cycles. The primary endpoint was 2-yr DFS. Secondary endpoints included local recurrence-free survival (LRFS), distant disease-free survival (DDFS), & overall survival (OS). Target enrollment of 126 evaluable patients (max 144 total) provided 80% power (1-sided α = 0.05) to distinguish between a null hypothesis of 50% 2-yr DFS rate & alternative hypothesis of 75% 2-yr DFS rate by log-rank test, with initial analysis at 45 DFS events. Cox models were stratified by grade; primary analysis was a one-sided stratified log-rank test. Results: Between July 2017-November 2023, 143 patients were enrolled, predominantly with UPS (85%) & grade 3 (64%) histology. Median follow-up for alive patients is 24.1 mo. DFS in the EXP arm is significantly higher than the SOC arm (p = 0.023; HR 0.57, 90% CI: 0.35, 0.91). Estimated 2-yr DFS is 53% (90% CI: 43, 66%) for SOC vs 70% (90% CI: 61, 81%) for EXP arm. Currently, there is no statistically significant difference in LRFS (HR 0.55, 95% CI: 0.21, 1.42), DDFS (HR 0.57, 95% CI: 0.32, 1.01), or OS (HR 0.39, 95% CI: 0.14, 1.12). Pts with grade 3 sarcomas had improved DFS with pembro (HR 0.47, 95% CI: 0.25, 0.89), but no difference in DFS was observed in grade 2 tumors (HR 1.21, 95% CI: 0.35, 4.18). The proportion of patients with grade 3+ adverse events was significantly higher in EXP (52%) vs SOC arm (26%) (p = 0.002). Conclusions: Theaddition of neoadjuvant & adjuvant pembro to RT and surgery significantly improves DFS for pts with stage III UPS and LPS of the extremity and limb girdle. Clinical trial information: NCT03092323 .

TPS11588 Background: Imatinib is the worldwide standard for first-line therapy of advanced KIT-mutant GIST. However, secondary resistance mutations in the KIT ATP-binding domain (exons 13, 14), activation loop (exons 17, 18), or both develop and result in loss of imatinib-sensitivity. Bezuclastinib has a highly selective inhibition profile, leading to minimal off-target toxicities and allowing for combination with sunitinib. While no single tyrosine kinase inhibitor (TKI) inhibits all KIT mutations, the combination of bezuclastinib + sunitinib targets commonly occurring primary (exons 9, 11) and secondary (exons 13, 14, 17, and 18) KIT mutations and may provide more durable responses. The early signs of clinical activity, tolerability, and safety observed with the combination thus far in Peak Part 1 (Somaiah et al. [presentation] CTOS 2023. Paper 62) are consistent with the completed Phase 1/2 study (PLX121-01). Methods: Peak (NCT05208047) is a global, randomized, open-label, multi-part Phase 3 study evaluating the efficacy and safety of bezuclastinib + sunitinib versus sunitinib as second-line treatment in adult pts who were intolerant to imatinib or whose tumors had imatinib-resistance. Current Peak study sites are located in North America (3 countries), South America (3 countries), Europe (12 countries), and Asia-Pacific (4 countries). The lead-in portion to test a new formulation of bezuclastinib, (Part 1) completed enrollment in April 2023. Based upon PK and safety, a dose of bezuclastinib 600 mg QD + sunitinib 37.5 mg QD was determined for Part 2 of the Peak study. Part 2 will enroll ~388 pts to be randomized (1:1) to bezuclastinib 600 mg QD + sunitinib 37.5 mg QD or sunitinib 37.5 mg QD alone. Key inclusion: >1 measurable lesion per modified Response Evaluation Criteria in Solid Tumors (mRECIST) v1.1, Eastern Cooperative Oncology Group Performance Status 0-2, adequate organ function, and prior imatinib therapy (no other prior therapy). Key exclusion: known PDGFR mutations or succinate dehydrogenase deficiency, clinically significant cardiac disease, and use of strong CYP3A4 inhibitors or inducers. The primary endpoint is progression-free survival (PFS) confirmed by blinded independent central review per mRECIST v1.1. Additional efficacy (including overall survival and objective response rate) and safety endpoints will be evaluated and circulating tumor DNA (ctDNA) will be collected and assessed. Clinical trial information: NCT05208047 .

PURPOSE This multicenter, single-arm, open-label, phase Ib study was designed to determine the recommended phase II dose (RP2D) and to evaluate the safety and preliminary efficacy of unesbulin plus dacarbazine (DTIC) in patients with advanced leiomyosarcoma (LMS). PATIENTS AND METHODS Adult subjects with locally advanced, unresectable or metastatic, relapsed or refractory LMS were treated with escalating doses of unesbulin orally twice per week in combination with DTIC 1,000 mg/m2 intravenously (IV) once every 21 days. The time-to-event continual reassessment method was used to determine the RP2D on the basis of dose-limiting toxicities (DLTs) assessed during the first two 21-day treatment cycles. All explored doses of unesbulin (200 mg up to 400 mg) were in combination with DTIC. An expansion cohort was enrolled to evaluate the safety and efficacy of unesbulin at the RP2D. RESULTS Unesbulin 300 mg administered orally twice per week in combination with DTIC 1,000 mg/m2 IV once every 21 days was identified as the RP2D. On the basis of data from 27 subjects who were deemed DLT-evaluable, toxicity was higher in the unesbulin 400 mg group, with three of four subjects (75%) experiencing DLTs versus one of four subjects (25%) in the 200 mg group and three of 19 subjects (15.8%) in the 300 mg group. The most commonly reported DLTs and treatment-related grade 3 and 4 adverse events were thrombocytopenia and neutropenia. At the RP2D, seven subjects who were efficacy evaluable achieved partial response for an objective response rate of 24.1%. CONCLUSION Unesbulin 300 mg twice per week plus DTIC 1,000 mg/m2 once every 21 days was identified as the RP2D, demonstrating a favorable benefit-risk profile in a heavily pretreated population of adults with advanced LMS.

Introduction: Graves’ disease (GD) is known to cause menstrual changes and has been linked to infertility in women. This can be due to the increased production of androstenedione and testosterone induced by the excess thyroid hormone. However, screening for sex hormone levels in all women diagnosed with GD to identify patients more likely to have infertility is not cost-effective and not clinically indicated. AI-augmented ECG can identify the signature of female with an AUC of 0.97. In this model, AI- ECG gives a probability number between 0-100% of being female, with higher numbers indicating high probability of being female. Hypothesis: We hypothesized that this inexpensive AI ECG tool is reflective of hormonal levels and the resulting changes in menstrual cycle and fertility associated with GD in women. Methods: Patients diagnosed with GD (2009-2019) at our institution were included. Three intervals were identified: 1) Before GD: >2 months before GD diagnosis 2) During GD: within 2 months before GD and before attaining euthyroidism 3) After GD: after attaining euthyroidism. The matched-pairs t-test was used to compare probability of being a female; patients acted as their own controls. A subset analysis was done on females <45 years at GD diagnosis to study the association between female probability during GD and menstrual changes (defined as shorter, lighter, or newly irregular menstrual cycles). Results: 154 females with ECGs both before and during GD were identified. AI ECG female probability decreased with GD diagnosis (85% vs. 77%, P<.001). On the other hand, 137 females with GD had ECGs during and after GD. Female probability increased after attaining euthyroidism (76% vs 83%, P=.005). Moreover, 72 females <45 years were identified; 30 (42%) developed menstrual changes and had significantly lower AI-ECG female probability [77 (58-94)% vs 98 (92-100)%, P<.001] Conclusions: AI-enabled ECG signature of female gender in women was altered by GD and was associated with menstrual changes, likely reflecting changes in sex hormone levels. Future studies to confirm its role in screening for infertility in women with GD are needed.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

Abstract Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.