AstraZeneca (India)

Background: Despite extensive evidence of strict blood pressure (BP) control benefits in preventing hypertension (HTN)-related morbidity and mortality, HTN control rates (defined as SBP < 130 mmHg and DBP < 80 mmHg), remain low. Thus, understanding the clinical burden of uncontrolled HTN is crucial. The iCaReMe Global Registry is an opportunity to gain contemporary insights into the socio-demographic, clinical characteristics, management patterns and determinants of uncontrolled HTN in routine care. Research Questions: To describe the real-world clinical burden of uncontrolled HTN and management patterns in patients with cardiovascular-renal-metabolic comorbidities. Methods: The iCaReMe Global Registry (NCT03549754) is a multinational, prospective, observational study assessing the management of HTN, T2DM, CKD, and/or HF. Baseline data of participants with HTN and office BP measures enrolled in 29 countries across six WHO regions between 2018 and 2024 were used for a cross-sectional descriptive analysis of demographic, clinical characteristics and medication. Data were grouped into: Controlled HTN (SBP and DBP<130/80mHg) and uncontrolled HTN (SBP or DBP≥130/80mmHg). Results: We examined 21,837 subjects with HTN and BP measures (mean age: 60.3 years, 52.0% female). Comorbidities included: T2DM in 71.3%, dyslipidemia in 51.3%, CKD in 37.8%, and HF in 15.0%. Overall, 24.6% of patients had SBP and DBP<130/80 mmHg (mean SBP/DBP: 114/67 mmHg), and 75.4% had SBP or DBP ≥130/80 mmHg (mean SBP/DBP: 141/84 mmHg) including 61% with SBP or DBP ≥140/90 mmHg. Patients with uncontrolled HTN, compared to controlled HTN group, were younger (59.8 vs 61.8 years), had higher obesity rates (42.5% vs 34.6%) and had more comorbid CKD (39.3% vs 33.1%). Among those with uncontrolled HTN, 60.2% were prescribed antihypertensive drugs including: 69.0% ACEi/ARB, 38.6% CCB, 17% diuretics, and only 46.2% received two or more antihypertensive drugs. Conclusion: Our findings reveal a significant burden of uncontrolled HTN, affecting 3 out of 4 of the study population, characterized by younger age, obesity, and higher comorbid CKD. Despite established guidelines and readily available medications, adherence to guideline directed medical therapy remains low in this high-risk HTN population. These data highlight the urgent need for improved treatment strategies to better control BP and prevent complications.

Series of Novel isatinderivatives were investigated forinhibitionof HIVIntegrase/ Lens epithelium derived growth factor (LEDGF)protein-protein interaction by using ALPHA screen technique. Hypothetical binding modes of the selected compound in HIV integrase were generated using GLIDE docking tool.Isatin derivatives (SP III-5H and SPIII-NA) inhibits HIV IN/LEDGF interaction and SPIII-5H more potent compound (15.1 µM) in this series. Molecular modeling studies indicate that the SPIII-5H can bind within the active site of HIV integrase (DDE) and thus interrupt the binding of HIV integrase with LEDGF.

LBA4 Background: ESR1 mutations ( ESR1 m) constitutively activate the estrogen receptor (ER) and are the most common mechanism of acquired resistance to aromatase inhibitor (AI) + CDK4/6i. Molecular monitoring by ctDNA analysis can detect the emergence of ESR1 m during 1L AI + CDK4/6i. Camizestrant, the next-generation selective ER degrader (SERD) and complete ER antagonist, has shown anti-tumor activity in pts with and without detectable ESR1 m. SERENA-6 is the first global registrational Phase 3 trial assessing a ctDNA-guided approach to detect the emergence of ESR1 m during 1L AI + CDK4/6i to inform a switch in therapy ahead of disease progression. Methods: Pts with HR+/HER2– ABC who had received ≥6 months of 1L AI (anastrozole/letrozole) + CDK4/6i (abemaciclib/palbociclib/ribociclib) were enrolled and had ctDNA tested for ESR1 m every 2–3 months, coinciding with routine imaging. At ESR1 m detection, pts without evidence of disease progression were randomized 1:1 to switch to camizestrant (75 mg) with continued CDK4/6i (type and dose maintained) + placebo for AI vs continuing AI + CDK4/6i + placebo for camizestrant. The primary endpoint was investigator-assessed PFS (per RECIST v1.1). Prespecified interim analysis data cutoff was Nov 28, 2024. Results: 3,256 eligible pts were surveilled for ESR1 m using ctDNA until 315 eligible pts were randomized to switch to camizestrant (n=157) or continue with AI (n=158). All pts remained on the same CDK4/6i. ~50% of randomized pts had ESR1 m detected at the first ctDNA test. Baseline characteristics were well balanced between treatments. After 171 PFS events, hazard ratio for PFS was 0.44 (95% CI 0.31–0.60, p<0.00001; median PFS 16.0 vs 9.2 months). PFS benefit was consistent across subgroups. PFS rate at 12 months was 60.7% (95% CI 51.1–69.0) vs 33.4% (95% CI 24.9–42.2) and at 24 months was 29.7% (95% CI 19.0–41.2) vs 5.4% (95% CI 0.7–18.2). PFS2 hazard ratio was 0.52 (95% CI 0.33–0.81; 27% maturity). OS is immature (12%). Camizestrant + CDK4/6i was well tolerated with safety consistent with the known profiles of camizestrant, and of each CDK4/6i. Rates of treatment discontinuation due to adverse events were 1.3% for camizestrant and 1.9% for AI. Conclusions: Camizestrant + CDK4/6i guided by emergence of ESR1 m during 1L AI + CDK4/6i in pts with HR+/HER2– ABC resulted in a statistically significant and clinically meaningful improvement in PFS. SERENA-6 is the first global Phase 3 trial to demonstrate clinical utility of using ctDNA to detect and treat emerging resistance, ahead of disease progression. These findings represent a potential new treatment strategy to optimize and improve 1L patient outcomes. Clinical trial information: NCT04964934 .

e13088 Background: CDK4/6i combined with ET is 1L standard of care treatment for HR+/HER2- for mBC patients, however duration of response varies with some patients experiencing disease progression within 12-months. Limited predictive factors related to ET+CDK4/6i treatment response exist. This pilot aims to assess the feasibility of developing a ML derived risk score for CDK4/6i+ET across 5 different data modalities to inform escalation and de-escalation therapeutic strategies. Methods: A pilot study on a retrospective cohort of 131 patients with mBC from the Memorial Sloan Kettering Cancer Center (MSK) treated with CDK4/6i and ET in the 1L setting was carried out to develop a ML based algorithm for predicting rwPFS at an individual patient level. Baseline multimodal data (including clinical, demographic, histopathological, genomic and radiomic) alongside clinical outcomes were collected. For radiomic analysis, up to 5 lesions were segmented in 3D on baseline PET/CT scans per patient using the SOPHiA DDMTM Radiomics platform. Radiomic features, extracted per IBSI standards, were combined with other data modalities. Baseline tumor genomic analysis was performed using the MSK-IMPACT assay, including genes with an alteration frequency > 5% in the cohort. A filter-based variable selection method was applied prior to training multiple ML algorithms, with the optimization criteria being the Brier score for rwPFS prediction. Due to the limited cohort size, a nested cross-validation approach was employed to ensure robust performance estimation. Results: A Random Forest survival model, combined with k-Nearest Neighbor (k-NN) imputation techniques for handling missing data, achieved an AUC of 0.787 (95% CI, 0.716–0.859) at 12 months for predicting rwPFS. Presence of liver metastases, SUVmax, total tumor volume, CEA, CA 15-3, TP53 mutation and PET uptake heterogeneity were among the top weighted features. These clinically plausible features, combined into a multimodal algorithm, allowed to stratify patients into two risk groups based on their predicted rwPFS with a median rwPFS of 45.2 (95% CI, 27.1-n/a) in low-risk vs 11.3m (95% CI, 9.1-16.1m) in high-risk group (hazard ratio: 4.10, 95% CI, 2.64–6.37). Conclusions: This pilot study demonstrates the feasibility of a ML, multimodal approach to predict rwPFS for patients with HR+/HER2- mBC treated with 1L CDK4/6i + ET. This approach could be implemented in the clinical setting to guide treatment choice and to inform clinical trial design by identifying high-risk individuals. Further training and validation of the model is planned in a larger, multicentric cohort.

8541 Background: Biomarker testing is essential to optimize lung cancer (LC) care, yet uptake of testing is suboptimal due to lack of access, cost, and long turnaround times (TAT). Recent advances now require biomarker testing in early-stage LC. In 2024, the International Association for the Study of Lung Cancer (IASLC) launched a 2 nd global survey to measure improvements and barriers to implementation of testing. We compared results from North America (NA) with global results by high income (HIC) and low or middle income countries (LMIC). Methods: A multi-disciplinary committee of oncologists, pathologists, pulmonologists, epidemiologists, and advocacy partners created the survey. We used mixed methods, with focus groups and in-depth interviews informing the quantitative survey with IRB oversite. Chi-square tests were utilized to compare frequencies between NA v Other HIC (OHIC) and HIC v LMIC. Results: Of the 1677 responses globally, 1501 were from HIC and 176 from LMIC. HIC included 337 responses from NA (287 United States and 50 Canada). Nearly all NA respondents (99%) believe biomarker testing significantly impacts patient outcomes and 94% report a clear understanding of who should be tested (v 91% OHIC, p=0.09). In NA, 66% and 40% ranked biomarker testing as highly important in late- and early- stage LC, respectively (64% and 28% OHIC, p=0.68 and p<0.01). Only 45% of NA respondents were satisfied with biomarker testing conditions (v 52% OHIC, p=0.03), and 69% estimate at least half of LC patients receive biomarker testing (71% OHIC), an increase from 45% in the 2018 survey (p<0.01). We found 40% of respondents from NA sometimes or often began treatment prior to obtaining biomarker results (41% OHIC). Key barriers identified were cost (23%), time (22%), and sample quality (20%), consistent with global and OHIC trends. Mean TAT in NA was 17.1 days (SD 7.8) v 16.1 days (SD 9.0) in HIC. Insufficient tumor was the primary cause for re-biopsy in late and early-stage patients for NA (58%) and HIC (48%). Lastly, 14% of NA reported no additional training in next-generation sequencing beyond medical education (16% OHIC). Globally, conditions were worse in LMIC v HIC including those who sometimes or often begin treatment prior to obtaining biomarker results (73% v 41%, p<0.01) and those who are confident or extremely confident in the adequacy of testing at their institution (48% v 68%, p<0.01). Conclusions: Respondents from NA believe they understand the value of biomarker testing for LC and who should be tested. Testing practices have reportedly improved since 2018, yet less than half of NA respondents are satisfied with biomarker testing practices and many patients are still treated without biomarker information. Responses from NA were similar to OHIC, with some exceptions, but significant disparities were evident in LMIC. We identified key barriers that should be addressed to optimize testing practices and patient outcomes.

e17623 Background: EC is the sixth most common cancer in women. Pts with high-risk EC are more susceptible to recurrence. Per ESMO guidelines, high-risk subtypes without macroscopic residual disease following surgical resection are defined as: International Federation of Gynecology and Obstetrics (FIGO) Stages I–IVA with p53 abnormalities or serous/undifferentiated carcinoma with myometrial invasion and Stages III–IVA. Defects in MMR occur in ~30% of pts with EC; however, there is conflicting evidence on the effect of MMR status on clinical outcomes and its utility to guide adjuvant therapy decisions. We report real-world disease-free survival (rwDFS) and overall survival (rwOS) by MMR status in pts with high-risk EC. Methods: This retrospective observational study included pts with treatment-naïve, high-risk EC without residual disease following surgical resection from the nationwide Flatiron Health electronic health record-derived, de-identified database. Pts were diagnosed Jan 2018–Dec 2023, underwent a hysterectomy (index date), and initiated adjuvant chemotherapy within 4 months. As data on p53 abnormalities were limited, human epidermal growth factor receptor 2 expression (positive/equivocal) was used as a proxy. The primary and secondary endpoints were rwDFS at 36 months and rwOS by MMR status, respectively. Results: Overall, 599 pts were identified; 70.5% were aged >60 years and 96.5% had Stage III disease at diagnosis. Most pts had endometrioid (62.8%) or serous (20.0%) carcinoma; 29.7% had MMR-deficient (dMMR) and 70.3% had MMR-proficient (pMMR) EC. 416 (69.4%) pts received radiotherapy (prior to or post index date). At 36 months from the index date, rwDFS (95% CI) was 58.2% (53.6, 63.1) in all pts and 71.6% (64.5, 79.4) and 51.7% (46.1, 58.0) in those with dMMR and pMMR EC, respectively. Landmark rwDFS and rwOS at 12, 24, and 36 months are shown in the Table. Conclusions: Poor rwDFS and rwOS was observed in pts with high-risk EC, particularly in those with pMMR vs dMMR EC. Although low numbers of pts with early-stage disease limit broader population insights, these findings highlight the potential need to further evaluate existing treatments and explore novel therapeutic strategies in the adjuvant setting. All dMMR pMMR n 599 178 421 Median rwDFS, months (IQR) 48.2 (44.4, not estimable [NE]) 66.0 (55.8, NE) 39.5 (30.2, 54.4) rwDFS at 12 months, % (95% CI) 82.9 (79.8, 86.1) 86.7 (81.6, 92.0) 81.3 (77.5, 85.3) rwDFS at 24 months, % (95% CI) 66.0 (61.9, 70.4) 74.4 (67.7, 81.7) 62.3 (57.2, 67.8) rwDFS at 36 months, % (95% CI) 58.2 (53.6, 63.1) 71.6 (64.5, 79.4) 51.7 (46.1, 58.0) Median rwOS, months (IQR) Not reached (NR) NR 62.7 (54.2, NE) rwOS at 12 months, % (95% CI) 95.8 (94.1, 97.5) 97.6 (95.3, 100.0) 95.0 (92.8, 97.2) rwOS at 24 months, % (95% CI) 86.2 (83.1, 89.4) 94.0 (90.3, 97.9) 82.7 (78.6, 87.0) rwOS at 36 months, % (95% CI) 76.7 (72.5, 81.1) 90.4 (85.4, 95.6) 70.0 (64.5, 76.0)

BackgroundPerioperative FLOT (fluorouracil, leucovorin, oxaliplatin, and docetaxel) is a standard therapy for resectable gastric and gastroesophageal junction adenocarcinomas, but recurrence rates remain high. Immunotherapy plus chemotherapy may improve outcomes.MethodsIn a phase 3, multinational, double-blind, randomized trial, we assigned participants with resectable gastric or gastroesophageal junction adenocarcinoma, in a 1:1 ratio, to receive durvalumab at a dose of 1500 mg or placebo every 4 weeks plus FLOT for 4 cycles (2 cycles each of neoadjuvant and adjuvant therapy), followed by durvalumab or placebo every 4 weeks for 10 cycles. The primary end point was event-free survival; secondary end points included overall survival and pathological complete response.ResultsA total of 474 participants were randomly assigned to the durvalumab group, and 474 to the placebo group (median follow-up, 31.5 months; interquartile range, 26.7 to 36.6). Two-year event-free survival (Kaplan–Meier estimate) was 67.4% among the participants in the durvalumab group and 58.5% among those in the placebo group (hazard ratio for event or death, 0.71; 95% confidence interval [CI], 0.58 to 0.86; P<0.001). Two-year overall survival was 75.7% in the durvalumab group and 70.4% in the placebo group (piecewise hazard ratio for death during months 0 to 12, 0.99 [95% CI, 0.70 to 1.39], and during the period from month 12 onward, 0.67 [95% CI, 0.50 to 0.90]; P=0.03 by a stratified log-rank test [exceeding the significance threshold of P<0.0001]). The percentage of participants with a pathological complete response was 19.2% in the durvalumab group and 7.2% in the placebo group (relative risk, 2.69 [95% CI, 1.86 to 3.90]). Adverse events with a maximum grade of 3 or 4 were reported in 340 participants (71.6%) in the durvalumab group and in 334 (71.2%) in the placebo group. The percentage of participants with delayed surgery was 10.1% and 10.8%, respectively, and the percentage with delayed initiation of adjuvant treatment was 2.3% and 4.6%.ConclusionsPerioperative durvalumab plus FLOT led to significantly better event-free survival outcomes than FLOT alone among participants with resectable gastric or gastroesophageal junction adenocarcinoma. (Funded by AstraZeneca; MATTERHORN ClinicalTrials.gov number, NCT04592913.)

In the global CAPItello-291 randomized phase 3 study (NCT04305496) in patients with hormone receptor-positive/HER2-negative advanced breast cancer and progression during/after aromatase inhibitor treatment, capivasertib–fulvestrant significantly improved progression-free survival (PFS) in the overall population and patients with PIK3CA/AKT1/PTEN-altered tumors versus placebo–fulvestrant. We assessed efficacy and safety of capivasertib–fulvestrant in a prespecified exploratory analysis of a Chinese cohort (n = 24) and extended study with the same protocol (n = 110). Clinically meaningful PFS benefit for capivasertib–fulvestrant was observed in the overall population (median PFS: 6.9 [capivasertib–fulvestrant] versus 2.8 [placebo–fulvestrant] months; hazard ratio 0.51, 95% CI 0.34–0.76), patients with PIK3CA/AKT1/PTEN-altered tumors (n = 46; 5.7 versus 1.9 months; hazard ratio 0.41, 95% CI 0.19–0.85) and PIK3CA/AKT1/PTEN-non-altered tumors (patients with confirmed next-generation sequencing results [n = 68]; 9.2 versus 2.7 months; hazard ratio 0.38; 95% CI 0.21–0.68). The most frequent adverse events (AEs) with capivasertib–fulvestrant were diarrhea (60.6% versus 11.3% with placebo–fulvestrant) and hyperglycemia (57.7% versus 17.7%). AEs leading to capivasertib–fulvestrant discontinuation were reported in 11.3% of patients versus 3.2% for placebo–fulvestrant. The benefit-risk profile of capivasertib–fulvestrant in the Chinese cohort was favorable; further exploration in patients with PIK3CA/AKT1/PTEN-non-altered tumors is warranted.

Abstract Drug-tolerant persister cells (DTPs) are cancer cells that evade the effects of therapies through a reversible, non-genetic mechanism, leading to therapeutic resistance and cancer recurrence. Single cell RNA sequencing data is a promising modality to study the mechanism behind DTPs’ survival. The first step of such studies is to classify persister vs. non-persister cells. Today, bioinformaticians identify persister cells manually from RNA sequencing data; this is a tedious and time consuming task. This paper introduces the first end-to-end, fully automatic pipeline for DTPs classification using an adapted Transformer-based scGPT model. Our contributions are as follows: Persister cells are transient and reversible in nature. In our training data, even if a cell is labeled as a persister cell, it is better to inform the model that this cell has certain probability of being a persister cell and some probability of being a non-persister cell. The default one-hot cross-entropy loss of the scGPT model assumes label certainty and will lead to overfitting. To overcome this, we fine-tuned a pre-trained scGPT model using a label smoothing cross-entropy loss that encourages the model to give probabilities to both classes. Single cell data is sparse; many genes are not expressed. Non-expressed genes bring noise to the model. We leveraged statistical analysis to understand significant genes across cell lines, resulting in a threshold that our pipeline uses to only keep highly variable genes as inputs to the model. When assigning data points to different cross validation folds, the original scGPT pipeline stratifies them by only constraining on cell types. This will result in imbalanced distribution of data points from different batches in different folds, preventing the model from generalizing to unseen batches. We stratified data using both cell type and batch, mitigating the batch effect. We evaluated our pipeline on a proprietary data processed through the Persist-seq baseline scRNA-seq pipeline, with 41071 data points, coming from four non-small cell lung cancer cell lines. We performed 4-fold cross validation with each fold using data from 3 cell lines for training and data from the fourth cell line for testing. Averaging over testing folds, our model achieved 90% accuracy, with a 0.89 F1 score. Our finding suggests that transformer-based deep learning pipelines are effective alternatives to traditional manual approaches for persister cell classification. Future research will focus on developing a multi-step approach, where the initial step is to classify cancer cells vs non-cancer cells, followed by persister cell classification. Citation Format: Balaji Selvaraj, Abinaya Vairam, Pablo Andres Moreno Cortez, Ramya Tanikanti, Anca M. Farcas, Yi Wei, Martin L. Miller, Viia E. Valge-archer, Ultan McDermott. AI-Driven Identification of Drug-Tolerant Persister Cells in Lung Cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6312.