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Target-specific therapy for actionable and approved tumor-agnostic biomarkers: Influence on survival in rare cancers.
3123 Background: Rare cancers - defined as an incidence rate of less than 6 per 100,000 diagnoses per year - collectively account for close to 25% of cancer burden and represent a critical unmet clinical need. The emergence of regulatory approvals for tumor-agnostic therapies based on genomic biomarkers has expanded treatment options for these patients (pts) but clinical sequencing is not yet broadly adopted for rare cancers. Here, we assess the incidence and clinical utility of FDA-approved tumor-agnostic biomarkers in a large real-world rare cancers cohort. Methods: The Tempus Lens Platform used to query the de-identified Tempus multimodal database to analyze a cohort of 45,085 real-world pts with 615 rare cancer diagnoses. All pts had DNA (Tempus xT) and/or RNA sequencing (Tempus xR). Pts were screened for FDA-approved tumor-agnostic biomarkers, including high tumor mutational burden (TMB-H, ≥ 10 mut/Mb), high microsatellite instability (MSI-H), NTRK and/or RET fusions, BRAFV600E mutations and HER2 overexpression (IHC-3+). Pts were classified based on their reported biomarker status and treatment start of the approved targeted therapy relative to the FDA-approval date. Real-world overall survival (rwOS) was defined as the time from sample collection to death or loss to follow-up. Hazard ratios (HR) from Cox-proportional hazards models were adjusted for age, sex, race, ethnicity, and stage at p<0.05 (Wald test). Results: The median age of pts was 64 years old, 59% were female, 50% of those with documented race were white and 36% had metastatic disease. The most common diagnoses were within the genital system (25%), digestive system (23%), soft tissue (12%), brain (11%) and lung (6.5%). Of the total cohort, 13% (5,881) were positive for at least one tumor-agnostic biomarker. TMB-H was the most prevalent (10.3%), followed by HER2+ (5.5%), BRAFV600E (2.6%), MSI-H (1.9%), NTRK (0.5%) and RET (0.2%) fusions. Of 59% (3,480) of pts with recorded treatments, 33% (1,138) received the approved treatment following the approval date (tumor-agnostic biomarker-positive matched) while 64% (2,227) received other treatments (tumor-agnostic biomarker-positive unmatched). Pts with tumor-agnostic biomarker-positive matched treatments had significantly improved rwOS compared to tumor-agnostic biomarker-positive unmatched pts (HR=0.8, p=0.024) and to tumor-agnostic biomarker-negative individuals (HR=0.68, p<0.001). Conclusions: A large percent (13%) of pts with rare cancer diagnoses were found to harbor an actionable biomarker associated with an FDA-approved tumor-agnostic therapy. Pts who received therapies for approved tumor-agnostic biomarkers versus those pts with the biomarker who did not receive the matched treatment demonstrated a significant survival benefit, underscoring the importance of NGS testing to optimize treatment selection for rare cancer pts.
Sustaining oncology clinical trials under wartime conditions: Strategic lessons from Ukraine.
1512 Background: On February 24, 2022, when Russia launched its full-scale invasion of Ukraine, our center had already treated more than 5,000 patients across 410 oncology clinical trials (CTs) since 2002. At the onset of the war, 125 active CT contracts across 18 cancer types (64 sponsors) were ongoing, with 413 patients enrolled or under follow-up, including 210 in active treatment (57 CTs), 203 in follow-up (53 CTs), and 15 CTs pending initiation. The sudden transition to wartime conditions posed unprecedented challenges to patient safety, continuity of care, trial operations, regulatory compliance, and data integrity, requiring rapid adaptation of clinical, logistical, and digital infrastructures. Methods: We conducted statistical analyses to evaluate the feasibility of sustaining CTs over 35 consecutive months of martial law. Key domains included personnel retention, trial activity, protocol adherence, supply chain continuity for a research center located 80 miles from the frontline, and digital infrastructure performance, including utilization of the Medical Control Records (MCR) management system. Our goal was to identify factors applicable to clinical research in armed conflict, natural disasters, and other protracted crises. Results: Despite ongoing air raids, missile attacks, power outages, and mass displacement, 99% of scheduled patient visits and data transfers were completed. Of the 413 patients at the start of the war, 150 (36.3%) remain in active treatment (n = 42) or follow-up (n = 108) across 55 trials, representing 44% of the pre-war portfolio. Since February 2022, 7,891 patient visits, 16,155 blood tests, 1,762 tumor response assessments, 3,525 intravenous infusions, and 3,272 oral drug distributions have been completed, with 176 deaths recorded as protocol endpoints. Rapid restoration of supply chains, communications, patient logistics, and secure data management enabled uninterrupted trial activity. A key facilitator was the adaptive MCR digital platform, developed during the COVID-19 pandemic, enabling real-time monitoring, centralized, high-quality electronic medical records, timely e-CRF entry, and remote oversight of CT processes under extreme conditions. The system’s AI-assisted planning and multilingual tools are now being tested as a new paradigm for global CT continuity and effective performance. Conclusions: Armed conflict disrupts clinical research at every level, threatening patient safety, data integrity, and national research infrastructure. Our experience shows that rapid digital adaptation, operational flexibility, and collaborative governance can sustain oncology trials under extreme geopolitical conditions. These lessons offer a scalable framework for research continuity, regulatory alignment, and patient protection in conflict zones and disaster settings, reinforcing global oncology’s role in health system resilience and recovery.
Prognostic value of serial liquid biopsy EGFR mutation monitoring in Hispanic patients with advanced NSCLC treated with osimertinib.
e20761 Background: The prognostic role of serial circulating tumor DNA (ctDNA) dynamics during osimertinib treatment has been described mainly in Asian and European populations; however, it remains poorly defined in Hispanic patients with advanced EGFR-mutant non–small cell lung cancer (NSCLC). Our aim was to determine whether serial plasma EGFR testing identifies patients at high risk of early mortality. Methods: This multicenter, retrospective study included 64 Hispanic patients with stage IIIB–IV EGFR-mutant NSCLC treated with first-line osimertinib between 2018 and 2023. Tumor tissue genotyping was performed using hybrid-capture next-generation sequencing. Plasma EGFR mutations were assessed at baseline, week 8, and week 24. Overall survival (OS) and progression-free survival (PFS) were analyzed using Cox models adjusted for p53 status, RAF-pathway alterations, and metastatic burden. Results: The cohort had a median age of 61 years, with 65.6% of patients being female and 56.2% being never-smokers. Sensitizing EGFR mutations consisted mainly of exon 19 deletion (59.4%) and L858R (39.1%). Relevant co-mutations were identified in TP53 (29.7%) and RAF-pathway genes (6.2%). Plasma ctDNA remained positive in 42.2% of patients at week 8 and 22.0% at week 24. Week-8 ctDNA persistence was strongly associated with an inferior OS (HR 9.36, 95% CI 3.80–23.03, p < 0.001) after multivariable adjustment. Week-24 positivity also predicted worse OS (HR 3.34, 95% CI 1.13–9.89, p = 0.02). ctDNA status at week 8 or 24 was not significantly associated with PFS. Patients with TP53 or RAF pathway mutations demonstrated higher rates of molecular persistence. Conclusions: In Hispanic patients receiving Osimertinib, ctDNA positivity at 8-24 weeks is able to identify patients with poor survival outcomes. ctDNA monitoring may provide an early, non-invasive biomarker to guide treatment intensification and trial enrollment in EGFR-mutant NSCLC.
Insurance and cost-related access barriers to specialty oral anticancer medications for blood cancers in a nationwide sample of Medicare and commercially insured patients.
11031 Background: Specialty oral anticancer medications improve survival across blood cancers but may have access challenges. This study examined approval and fill rates of specialty oral anticancer medication prescriptions indicated for blood cancers in a nationwide sample of Medicare and commercially insured patients. Methods: Using nationwide claims (Symphony Health Solutions Integrated Dataverse), we identified patients with ≥1 new specialty oral anticancer medication prescription during 2022. Outcomes were measured for the initial prescription as well as any prescription for the same drug within 90 days and were classified as: (i) rejected by insurer, (ii) approved by insurer but not filled by patient, or (iii) approved by insurer and filled by patient. Among rejected prescriptions, we examined rejection reasons, eventual approval rates, and time to approval. Among approved prescriptions, we compared fill rates across out-of-pocket cost levels. Adjusted outcomes were estimated using multivariable logistic regressions. Results: The study included 12,134 patients (61.0% Medicare, 39.0% commercial) with a specialty oral anticancer medication prescription. Initially, very few prescriptions were both approved and filled (9.6%, Medicare; 4.0%, commercial). Insurers initially rejected most prescriptions (64.9% for Medicare; 84.0% for commercial), frequently due to prior authorization or formulary restrictions. By 90 days, approval rates rose to 85.0% (Medicare) and 62.9% (commercial), but many approved prescriptions were never filled; ultimately, only 54.5% of Medicare patients and 45.5% of commercial patients both received insurer approval and filled their prescription. Fill rates dropped sharply when out-of-pocket costs exceeded $175 per SOAM prescription. When out-of-pocket costs exceeded $2,000 per prescription, only 31.9% of Medicare patients and 26.9% of commercial patients filled their prescriptions. Adjusted rates were similar to unadjusted rates. Conclusions: This study found substantial access barriers to specialty oral anticancer medications for blood cancer, particularly among commercially insured patients. Even after insurer approval, many patients never filled their prescription. This highlights the importance of addressing both payer-level restrictions and patient-level affordability challenges to ensure treatment access.
Conversational AI-assisted exploratory data analysis (CA-EDA): A comparative evaluation of commercial large language models for clinical trial analysis.
1636 Background: Conventional analysis of clinical trial data requires statistical expertise, time and effort. Previous AI approaches have used structured, pre-processed datasets on specialized platforms. We hypothesized that widely available commercial large language models (LLMs) would perform rapid analysis of raw data. We evaluated three commercial LLM platforms fed identical data, and subsequently validated the outcomes with a formal statistical analysis. We name this approach "Conversational AI-assisted Exploratory Data Analysis" (CA-EDA). Methods: We used data from a completed phase 2 trial (NCT03323489; Lancet Oncol 2024; 25:1070-9); n=125; 2165 variables) that evaluated the efficacy of celiac plexus radiosurgery in controlling retroperitoneal pain syndrome amongst pancreatic cancer patients. Raw individual patient data was uploaded to three commercial LLMs: Claude Opus 4.1 (Anthropic), ChatGPT 5.2 (OpenAI) in deep research mode, and Gemini 3 Pro (Google). Each LLM received identical inputs: the raw Excel dataset, the codebook, the protocol, and the published manuscript. The prompt requested identification of baseline predictors of pain response and development of a clinical score. Outputs were compared for statistical analyses performed, predictors identified, visualizations generated, and clinical utility. Findings were formally verified using Stata IC/16.1. Results: Analysis completion time ranged from 10 to 30 minutes across all three platforms. Claude performed a comprehensive statistical analysis, generated a 9-panel visualization dashboard, identified prior exposure to neurotoxic chemotherapy as a novel predictor of response (OR 0.20, p<0.001), and created a 4-variable clinical score predictive of response (AUC 0.71). ChatGPT performed a statistical analysis but missed neurotoxic chemotherapy, creating a 3-variable score. Gemini produced an 8-page narrative with biological insights, but did not perform any statistical analysis. Analysis using Stata confirmed the association between prior exposure to neurotoxic chemotherapy and response (35% with prior exposure vs 75% without, p<0.001). Conclusions: Claude Opus 4.1 identified a clinically significant predictor that had not previously been recognized, and developed a helpful clinical score. CA-EDA always requires formal biostatistical verification due to concerns about LLMs’ tendency to hallucinate, may analyse only sampled data and potentially implement incorrect Python code. Despite these concerns, here we found CA-EDA of clinical trial data to be rapid, accurate and cost-effective. Financial support for clinical trial: Gateway for Cancer Research, The Israel Cancer Association.
Characterization of the immune infiltrate and the chromatin 3D structure of gastric tumors from Mexican patients.
e16145 Background: Identifying cellular populations within tumors and theirmicroenvironment, along with genome structure characterization in human samples,is crucial for understanding the extensive heterogeneity observed in clinical settingsand for gaining mechanistic insights into cellular transformation. These analyses aremade possible by technologies such as single-cell RNA sequencing (scRNA-seq)and genome conformation capture (Hi-C). We characterized the cellular compositionand the three-dimensional genome organization of gastric cancer samples andadjacent tissues from Mexican patients. Methods: Gastric and esophagogastric junction biopsies were obtained from twohistological subtypes: diffuse and intestinal. After quality control, we analyzedapproximately 80,000 cells using scRNA-seq and Hi-C. By applying canonicalmarkers and automated cell classifiers, we identified a diverse set of cellpopulations, including T cells, B cells, myeloid cells, epithelial cells, endothelial cells,mesenchymal cells, and fibroblasts. Results: Tumor samples showed a higher proportion of immune cells compared toadjacent tissues. Notably, a subset of NKT/T cells expressed FOXP3, a markercharacteristic of regulatory T cells. Other immune subsets exhibited increasedexpression of exhaustion-associated markers such as CTLA4, ICOS, LAIR2, andTIGIT, suggesting an exhausted phenotype and supporting the potential use ofimmunotherapeutic strategies. Hi-C revealed a total of 791 structural variants, whichwere visually confirmed in interaction matrices. Regarding their distribution, 83% ofthe variants identified in diffuse-type gastric tumors corresponded to deletions,whereas 50% of the variants detected in intestinal-type gastric tumors wereduplications. In samples from the esophagogastric junction, 42% and 62% of thevariants corresponded to translocations in the diffuse and intestinal subtypes,respectively. Hi-C identified a series of neo-topologically associating domains(neoTADs), with 234 out of 423 including at least one gene with differentialexpression, indicating potential functional implications of these genomicrearrangements. Among structural variants, translocations were the maincontributors to neoTAD formation, generating 121 neoTADs, corresponding to approximately 60% of the total, and exhibited the highest proportion of neoTADsassociated with differential gene expression. Conclusions: The integrative characterization of tumor samples using scRNA-seqand Hi-C provides valuable insights into the molecular mechanisms underlyinggastric cancer in an underrepresented population, such as individuals of Mexicandescent, and may contribute to the identification of novel biomarkers.
Evaluation of drug-drug interactions and progression-free survival in patients with prostate cancer treated with androgen receptor pathway inhibitors using the Drug-PIN software: The DDI-AID, a real-world observational study from the Meet-URO Network.
e17019 Background: Androgen Receptor Pathway Inhibitors (ARPIs) are commonly used in metastatic prostate cancer (mPC). Given the advanced age and comorbidity burden typical of this population, polypharmacy is frequent in mPC patients, increasing the risk of clinically relevant drug–drug interactions (DDIs), which are rarely assessed in routine practice. The Drug-PIN software, an intelligent system analyzing phenotypic data and DDIs, has been shown to identify patients at risk of severe toxicity or reduced survival. The DDI-AID study investigates the association of DDIs with survival in patients with mPC treated with ARPI in a real-world setting. Methods: DDI-AID is an Italian, multicenter, retro-prospective observational study. The study included 245 consecutive patients with mHSPC or mCRPC treated with ARPI, and receiving at least one concomitant medication. Clinically relevant DDIs were assessed at ARPI initiation and patients were classified into no / low- or high-risk-DDIs groups. Progression-free Survival (PFS) was defined as time from ARPI start to disease progression or death. Survival analyses were performed using Kaplan-Meier and Cox proportional hazards models were adjusted for relevant clinical covariates. The study was approved by Ethical Committee Lazio Area 1 (Ref. n. 7364, 2023/11/08). Results: At the data cut-off (15 January 2026), a total of 245 patients were included. The median age was 74 years (range 52–94), and the median Charlson Comorbidity Index (CCI) was 3 (range 1-8). Polypharmacy was observed in 116 patients (47%), and high-risk DDIs were identified in 80 patients (33%). Regarding treatments, 68 (28%) patients received Abiraterone, 97 (39%) Enzalutamide, 71 (29%) Apalutamide and 9 (4%) Darolutamide with Docetaxel. A total of 138 (56%) patients received ARPI in the mHSPC setting and no significant differences were observed in the ADT received (p = 0,093). The median PFS was 35.9 months (95% CI 27.8-NR) in the low-risk-DDI group compared to 19.8 months (95% CI 15.1-NR) in the high-risk DDI group. In the multivariable analysis, adjusted for age (≤74 vs ≥75 years), performance status (0 vs 1–3), Charlson Comorbidity Index (0–1 vs ≥2), and presence of visceral metastases, the presence of low-risk DDIs remained significantly associated with longer PFS (HR 0.59, 95% CI 0.38–0.90, p = 0.016). Moreover, treatment with ARPIs in the mHSPC setting (HR 0.49, 95% CI 0.31–0.78, p = 0.003) and achieving a PSA nadir ≤2 ng/mL (HR 0.16, 95% CI 0.09–0.26, p < 0.001) were independently associated with longer PFS. Conclusions: In this real-world cohort, DDIs are common in patients treated with ARPIs and the presence of high-risk DDIs was associated with reduced PFS. These findings highlight the importance of medication review in routine clinical practice.
Real-world evaluation of the decentralized MSK-ACCESS powered with SOPHiA DDM liquid biopsy assay for comprehensive tumor profiling.
e15084 Background: MSK-ACCESS powered with SOPHiA DDM is a decentralized next-generation sequencing (NGS) assay for circulating cell-free DNA (cfDNA) molecular profiling in patients diagnosed with solid tumors. It enables detection of biologically relevant alterations, including SNVs, INDELs, CNVs, and structural variants, with demonstrated analytical sensitivity down to 0.5% variant allele frequency (VAF). This study evaluated MSK-ACCESS powered with SOPHiA DDM performance and variant detection patterns across 2455 samples from diverse cancer types in a decentralized setting. Methods: 493 cfDNA samples from patients (n = 440) with colorectal, prostate, lung, breast, gynecological, and pancreatic cancers were analyzed in a retrospective, multicenter study (5 sites). Additional real-world data (n = 1962) submitted to the SOPHiA GENETICS DDM Platform submitted by the user base were reviewed for benchmarking. Materials were processed with the MSK-ACCESS powered with SOPHiA DDM solution including CUMIN molecular barcodes and variant detection was performed with the SOPHiA DDM Platform. The solution’s cfDNA/normal workflow incorporating matched white blood cell (WBC) genomic DNA (gDNA) sequencing was utilized to distinguish betweem somatic variants and clonal hematopoiesis (CH) calls based on the observed VAF ratio. Somatic variants were clustered within samples by their VAF to detect potential tumor subclones. Results: Per-sample QC criteria exceeded the minimum requirements in 94% of the study cohort and 93% of the real-world samples. Thus, both datasets were combined for further analyses. Somatic variants were detected in 85% of all samples, with high cancer-type specificity (e.g. AR in prostate, ESR1 and PIK3CA in breast, EGFR and ALK in lung cancer cases). SNVs and Indels were detected down to 0.1% VAF. Samples without detected alterations were not biased toward low quality metrics, suggesting that biological not technical factors are determinants of sensitivity. cfDNA/WBC gDNA VAF ratio filtering removed 40.1% of variant calls (6180/15449) as of CH origin. This highlights the advantage of this approach, since CH mutations in genes such as TP53, ATM and CHEK2 can be enriched for pathogenic variants, so a tumor-only approach can lead to an increased risk of false-positive calls. Subclonal variants included known resistance drivers (e.g. EGFR , ESR1 mutations in lung and breast cancer respectively). Follow-up analysis of subclonal variants to uncover additional resistance mechanisms and to distinguish primary resistance drivers from secondary events is ongoing. Conclusions: MSK-ACCESS powered with SOPHiA DDM demonstrated robust analytical performance and broad detection of biologically relevant alterations across multiple tumor types in a decentralized setting, supporting its role in comprehensive cfDNA-based genomic profiling.
Predicting chemotherapy benefit in premenopausal women with intermediate genomic scores using deep learning.
602 Background: The TAILORx trial demonstrated that adjuvant chemotherapy can be safely omitted in postmenopausal women with node-negative HR+/HER2- breast cancer and a 21-gene Recurrence Score (RS) of 11–25. However, chemotherapy benefit could not be excluded for pre/peri-menopausal women with RS 16–25, creating a clinical dilemma that may lead to overtreatment. Using TAILORx data, we have shown that deep learning (DL) applied to hematoxylin and eosin (H&E) can accurately identify women with low or high RS (Shamai et al., Lancet Oncol, 2026). We hypothesized that DL applied to H&E could be used to predict chemotherapy benefit in pre/peri-menopausal women with RS 16–25, thereby facilitating precise treatment de-escalation. Methods: We trained a DL model using H&E slides of both post- and pre/peri-menopausal women from TAILORx, with low (RS<16) and high (RS>25) genomic scores (N=5811), to predict distant recurrence-free interval (DRFI), while excluding patients with RS 16–25 from the training process. The test cohort consisted of women from TAILORx with RS 16–25. Within TAILORx, this test group was originally randomized to either endocrine therapy alone or chemo-endocrine therapy, enabling a direct evaluation of the model’s ability to predict chemotherapy benefit. For comparison, RSCLIN was also computed. Results: When testing on pre/peri-menopausal women with RS 16–25 (N=1234), the model classified 942 (76%) and 292 (24%) as low and high risk. For patients receiving no chemotherapy, the model demonstrated high prognostic performance for DRFI (C-index=0.75; 95% CI: 0.67–0.82), and strong risk stratification (HR=5.43; 95% CI: 2.87–10.29, p<0.001). In the low-risk group, no benefit from chemotherapy was observed (HR=1.07, 95% CI: 0.54–2.15, p=0.84). In the high-risk group, chemotherapy was associated with a significant improvement in DRFI (HR=3.57, 95% CI:1.54–8.30, p=0.003). A significant interaction between treatment and risk group was observed (p=0.024). The model outperformed RSCLIN (p-interaction=0.314). When testing on postmenopausal patients with RS 16–25 (N=2249), the model could not predict chemotherapy benefit (p-interaction=0.926). Conclusions: This digital pathology signature provides prognostic information for distant recurrence and predictive information for chemotherapy benefit in pre/peri-menopausal women with HR+/HER2-, node-negative early breast cancer with RS 16–25. These findings support the potential use of histopathology-based DL to guide chemotherapy de-escalation in this clinically challenging subgroup. The inability of the model to predict chemotherapy benefit in postmenopausal women suggests that the deep learning signature may be identifying tumors sensitive to chemotherapy-induced ovarian suppression, highlighting a subset who could benefit from OFS as an alternative to chemotherapy.
Preliminary results of a randomized phase II trial of chidamide plus fulvestrant with or without sintilimab in HR+/HER2- advanced breast cancer after progression on CDK4/6 inhibitors (CISFORT).
e13051 Background: Chidamide is a subtype-selective histone deacetylase (HDAC) inhibitor that has demonstrated clinical activity in hormone receptor–positive (HR+)/HER2-negative (HER2−) advanced breast cancer (aBC). Previous studies suggest that chidamide enhances tumor immunogenicity and synergizes with PD-1 blockade. This study evaluated the efficacy and safety of chidamide plus fulvestrant with or without sintilimab (a PD-1 inhibitor) in patients with HR+/HER2− aBC. Methods: This was a randomized, multi-center, phase II study enrolling patients with HR+/HER2− aBC who experienced disease progression after CDK4/6 inhibitor–based endocrine therapy and had received no more than one prior line of chemotherapy in the advanced setting. Patients were randomized 1:1 to receive either chidamide plus fulvestrant (Arm A) or chidamide plus fulvestrant in combination with sintilimab (Arm B). Treatment consisted of chidamide 30 mg orally twice weekly, fulvestrant 500 mg intramuscularly every 4 weeks (after loading doses), and sintilimab 200 mg intravenously every 3 weeks (Arm B only). The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. (www.chictr.org.cn, #ChiCTR2200058570). Results: By July 2025, 17 patients had been enrolled (Arm A, n = 9; Arm B, n = 8). The median age was 52 years; 59% had visceral metastases; 82% had received prior chemotherapy; and the median duration of prior CDK4/6 inhibitor therapy was 12.99 months. At a median follow-up of 40.2 months, 15 patients experienced disease progression (Arm A, n = 8; Arm B, n = 7), and 9 patients died (Arm A, n = 4; Arm B, n = 5). Median PFS was 3.5 months in Arm A and 1.3 months in Arm B (Arm A vs. Arm B, hazard ratio [HR], 0.26; 95% CI, 0.08–0.81; p = 0.014). Median OS was 19.04 months in both arms (Arm A vs. Arm B, HR, 0.59; 95% CI, 0.16–2.12; P = 0.415). No objective responses were observed. DCR was 57.1% in Arm A and 25.0% in Arm B ( p = 0.559). Grade ≥3 adverse events occurred in 33.3% and 37.5% of patients in Arms A and B, respectively. One case of immune-related pneumonitis was reported in Arm B. Conclusions: In this preliminary analysis of patients with HR+/HER2− aBC progressing after CDK4/6 inhibitor therapy, the addition of sintilimab to chidamide plus fulvestrant did not improve progression-free survival. Safety was manageable with no new toxicity signals identified. Further follow-up and enrollment are ongoing. Clinical trial information: ChiCTR2200058570. Clinical trial information: ChiCTR2200058570 .
Erythropoietin receptor (EPOR) expression as an independent prognostic biomarker in clear cell renal cell carcinoma, beyond angiogenesis, hypoxia, and erythropoietin ligand.
e16520 Background: Clear cell renal cell carcinoma (ccRCC) is characterized by dysregulated hypoxia and angiogenesis signaling. While erythropoietin receptor (EPOR) expression has been implicated in tumor biology, its prognostic relevance in ccRCC and independence from erythropoietin (EPO) ligand expression remain incompletely defined. Methods: We analyzed EPOR mRNA expression and overall survival (OS) in patients with ccRCC from The Cancer Genome Atlas (TCGA-KIRC; n≈533, events≈175). EPOR expression was evaluated as a continuous z-scaled variable and dichotomized by median. Cox proportional hazards models were constructed with adjustment for age, stage, and grade. Associations between EPOR and angiogenesis, hypoxia, and erythroid differentiation signatures were assessed using correlation analyses and joint survival models. Independence from EPO ligand expression was tested using multivariable Cox models including both EPOR and EPO. External validation was performed using the GEO GSE29609 cohort (n = 39). Results: Higher EPOR expression was significantly associated with worse OS in univariable analysis (HR 1.31, 95% CI 1.15–1.48; p < 0.001) and remained independently prognostic after adjustment for age, stage, and grade (HR 1.29, 95% CI 1.14–1.47; p < 0.001; C-index≈0.79). Patients with high EPOR expression demonstrated inferior survival compared with low EPOR expression (HR 1.58, p = 0.012). EPOR expression correlated with angiogenesis and hypoxia signatures, yet remained independently associated with OS in joint models. Importantly, EPOR retained prognostic significance after adjustment for EPO ligand expression (EPOR HR 1.29, p < 0.001), while EPO itself was not significantly associated with survival. External validation demonstrated directionally consistent results, though statistical significance was limited by sample size. Conclusions: EPOR is a robust, independent prognostic biomarker in ccRCC, with effects that extend beyond angiogenesis, hypoxia, erythroid differentiation, and EPO ligand expression. These findings support a potential non-erythropoietic role for EPOR signaling in ccRCC tumor biology.
Ultra-lightweight deep learning for glioma detection: MobileNet-based MRI classification to enable real-time neuro-oncology deployment.
e14085 Background: Gliomas, the most common CNS tumors, are associated with substantial morbidity and mortality. Magnetic resonance imaging (MRI) is central to diagnosis, treatment planning, and longitudinal surveillance; however, radiologic interpretation is time intensive and subject to interobserver variability, particularly in high-volume and resource-limited settings. Although AI has demonstrated strong diagnostic performance for brain tumor imaging, many AI models are computationally intensive and difficult to deploy at scale. MobileNet is a lightweight model designed to maximize efficiency through depth-wise separable convolutions. We evaluated whether a MobileNet-based framework could deliver meaningful glioma detection on MRI while enabling scalable, real-time deployment. Methods: We analyzed a curated dataset of brain MRI studies comprising gliomas and non-glioma intracranial lesions, with ground truth established by expert neuroradiologist consensus incorporating histopathology and longitudinal clinical follow-up. Images were standardized and partitioned at the patient level into training, validation, and held-out testing cohorts to minimize information leakage. A MobileNet architecture pretrained on ImageNet was fine-tuned for binary glioma classification. Depthwise separable convolutions decoupled spatial and channel-wise feature learning, substantially reducing parameter count and computational complexity while preserving discriminative capacity. Model performance was assessed using accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Inference latency and hardware requirements were explicitly evaluated to assess clinical deployability. Results: The MobileNet model achieved robust diagnostic performance, with overall accuracy exceeding 93% and AUROC greater than 0.94 for glioma detection. Sensitivity for glioma identification remained high (95%) while maintaining specificity (97%) against non-glioma lesions. Performance was stable across MRI sequences and imaging conditions. Mean inference time was under 0.1 seconds per study on standard CPU hardware, enabling real-time operation without graphical processing units or specialized infrastructure. Conclusions: MobileNet-based deep learning enables accurate, rapid, and computationally efficient glioma detection on MRI, addressing a key barrier to clinical translation of AI in neuro-oncology. By prioritizing deployability alongside diagnostic performance, this approach supports scalable AI-assisted imaging in high-throughput and resource-constrained settings. Prospective, workflow-integrated studies are warranted to evaluate impact on diagnostic turnaround time, triage efficiency, and access to timely neuro-oncologic care.
Diagnostic yield of percutaneous versus open surgical biopsy for tibial bone lesions: A retrospective review.
e23505 Background: Accurate histologic diagnosis is critical for tibial bone tumors, which are among the most common sites for primary bone sarcomas. While percutaneous biopsy (PB) offers minimally invasive sampling, diagnostic accuracy varies by anatomic location. Site-specific data comparing PB to open surgical biopsy (OSB) for tibial lesions is limited. This study compared the diagnostic yield of PB versus OSB for tibial bone lesions and identified factors associated with diagnostic success. Methods: Retrospective cohort study at a tertiary institution, including all tibial bone biopsies from 2015-2025. Primary outcome was diagnostic yield, defined as adequate tissue for definitive histopathologic diagnosis. Diagnostic rates were compared using chi-square tests. Anatomic location, pathologic fracture presence, and technical factors were analyzed for association with diagnostic success. Results: Of 104 tibial biopsies (60 OSB, 44 PB), OSB demonstrated significantly higher diagnostic rate (85% vs 52.3%, p < 0.001). OSB was 1.63 times more likely to yield a diagnosis (95% CI:1.24-2.13) with a number-needed-to-treat of 3.1. Among OSB, diaphyseal lesions had a higher rate of achieving a diagnosis than epiphyseal/metaphyseal (93.9% vs 74.1%, p = 0.032). Pathologic fractures showed a dramatic difference, with OSB achieving 100% diagnostic rate (8/8) versus PB achieving only 20% (1/5, p = 0.013). For PB, mid-diaphyseal (22.2%) and metaphyseal (33.3%) locations had the lowest diagnostic rates. Image guidance modality, needle gauge, and instrument type did not significantly affect outcomes within each biopsy type. Among non-diagnostic cases, 33.3% of OSB and 14.3% of PB ultimately proved malignant. Conclusions: OSB provides significantly superior diagnostic yield compared to PB for tibial lesions, particularly for pathologic fractures and metaphyseal/epiphyseal locations. The substantial difference in diagnostic rates supports preferential use of OSB for tibial bone lesions when feasible, especially given that one-third of non-diagnostic OSB cases ultimately represent malignancy. To our knowledge, this is the first study to examine the impact of tibial anatomic location on biopsy diagnostic yield. Diagnostic outcomes by biopsy method. Outcome OSB (n=60) PB (n=44) p-value Diagnostic rate 85.0% 52.3% <0.001 Relative risk (95% CI) 1.63 (1.24-2.13) - - Repeat biopsy required 16.7% 27.3% 0.287 Pathologic Fracture Diagnostic Rate 100% (8/8) 20% (1/5) 0.013 Diaphysis Diagnostic Rate 93.9% 53.1% <0.001 Epiphysis/Metaphysis Diagnosis Rate 74.1% 50.0% 0.164 OSB = open surgical biopsy; PB = percutaneous biopsy; CI = confidence interval.
Efficacy and safety of continuous intra-arterial cisplatin and recombinant human endostatin combined with systemic chemotherapy in osteosarcoma: A phase II study.
11523 Background: Previous studies have demonstrated that preoperative intra-arterial cisplatin infusion elicits a favorable histologic response—a key prognostic factor for survival in osteosarcoma. Recombinant human endostatin (rh-endostatin; Endostar), an anti-angiogenic agent, has been shown to significantly improve survival when administered intravenously in combination with chemotherapy for osteosarcoma. Herein, we report preliminary results on the efficacy and safety of continuous intra-arterial cisplatin and rh-endostatin combined with systemic chemotherapy in osteosarcoma (NCT06562673). Methods: This open-label, single-arm, single-center, phase II clinical trial enrolled patients with histologically confirmed primary localized extremity conventional osteosarcoma. Patients received 2 cycles of high-dose methotrexate and anthracyclines intravenously. Intra-arterial infusion was performed concurrently with anthracyclines. Rh-endostatin was administered intra-arterially (150 mg over 6 hours); then, cisplatin was administered intra-arterially (100-120 mg/m² over 6 hours). Efficacy and safety were evaluated after surgery. Results: Ten patients were enrolled from December 2024, to April 2025, including 7 males and 3 females with a mean age of 17.8 years (range, 12-42 years). Tumor locations included the femur (n=6), tibia (n=3), and humerus (n=1). Preoperative arterial infusions were administered 3 times (n=1), 2 times (n=8), and 1 time (n=1). All patients underwent limb-sparing surgery. The tumor necrosis rate was >90% in 5 cases (50%) and ≤90% in 5 cases (50%). Adverse events included grade 3 vomiting and nausea (n=1), grade 1 renal impairment (n=2), fever (n=1), and skin induration (n=1). Due to grade 3 gastrointestinal toxicity observed in the first enrolled female patient, the cisplatin dose was uniformly reduced to 100 mg/m² for all subsequent patients in the trial. Conclusions: Intra-arterial administration of high-dose rh-endostatin and cisplatin combined with systemic chemotherapy in osteosarcoma appears safe and achieves a good histologic response rate of 50%, compared with a historical rate of 30% with intravenous chemotherapy alone. Clinical trial information: NCT06562673 .
The weekend effect in spontaneous tumor lysis syndrome in patients with hematological malignancy: National trends in dialysis utilization, mortality, and resource use in the United States.
e18632 Background: Spontaneous tumor lysis syndrome (sTLS) is a rare but highly lethal oncologic emergency. Whether outcomes differ by admission timing and the unavailability of oncological and nephrological consultation remains unknown. We evaluated the association between weekend admission and clinical outcomes among patients hospitalized with sTLS. Methods: We conducted a retrospective analysis of the all-payer inpatient database from the Healthcare Cost and Utilization Project (HCUP) from 2018 to 2021. We included adult, non-elective hospitalizations with TLS and hematologic malignancy and excluded admissions involving recent cancer-directed therapy. Weekend versus weekday admission was the primary exposure. Primary outcomes were in-hospital mortality and dialysis utilization. Secondary outcomes included acute kidney injury (AKI), markers of critical illness, length of stay, and total hospital charges. Survey-weighted multivariable regression models were used to report odds ratios while adjusting for demographic, socioeconomic, comorbidity, and illness-severity factors. Sensitivity analyses were performed excluding AKI. Results: The final cohort included 6,950 hospitalizations, of which 21.7% occurred on weekends. Overall, in-hospital mortality was high 29.9%, and 10.7% of patients required dialysis. Weekend admission was independently associated with higher in-hospital mortality compared with weekday admission (adjusted OR 1.16, 95% CI 1.01–1.34, p=0.047), but not with dialysis utilization (adjusted OR 0.89, 95% CI 0.74–1.05). Dialysis use, mortality, length of stay, and hospital charges were primarily driven by illness severity, including AKI, mechanical ventilation, vasopressor use, and comorbidity burden. Conclusions: Among patients hospitalized with spontaneous TLS, weekend admission is associated with increased mortality but not increased dialysis use. These findings suggest a clinically meaningful weekend effect in this high-risk population and highlight the need for consistent, timely management of sTLS across admission days.
Detecting resistant subpopulations by profiling drug response heterogeneity in patient-derived cancer organoids (PDCOs) following first-line treatment.
e15603 Background: Colorectal cancer (CRC) frequently progresses due to resistance to standard first-line chemotherapy, such as irinotecan. Patient-derived cancer organoids (PDCOs) enable ex vivo drug sensitivity testing, but clinical impact requires detecting resistance linked to treatment failure in vivo . Conventional whole-well viability assays may fail to identify resistant tumor subpopulations. We report findings from Metsystem’s feasibility study (H-24038460) evaluating PDCO establishment from metastatic CRC (mCRC) and their ex vivo response to SN-38 (irinotecan’s active metabolite) using a whole-well readout and our proprietary methods. Methods: Adults with biopsy-confirmed mCRC and resectable hepatic metastases were enrolled in the study following informed consent. All patients had progressed after first-line chemotherapy (no regimen-specific eligibility criteria). PDCOs were established and expanded before drug screening. Ex v ivo dose-response testing was performed using SN-38, at increasing concentrations up to 5 uM. Drug response was assessed using a conventional whole-well viability assay (PrestoBlue, 7-day endpoint) and our proprietary methods. Results: PDCOs were successfully established and expanded from 7/8 patients. Drug-screening was performed for all 7, with 4 of them having paired evaluations using both methodological approaches. All evaluated drug screens showed viability changes when exposed to SN-38 regardless of the methodology used. Despite documented clinical resistance to irinotecan in some of the analyzed patients, whole-well viability measurements suggested near-complete sensitivity at higher SN-38 concentrations. In contrast, our method identified viable, treatment-resistant subpopulations persisting after high-dose SN-38 exposure. These resistant subpopulations were detected in all four samples and could comprise less than 15%, rendering them undetectable by whole well viability-based assays. Conclusions: In this feasibility study, PDCO establishment and multi-readout SN-38 testing were feasible in a clinical workflow. The study also indicates that irinotecan resistance in mCRC may be driven by small tumor subpopulations that can be undetectable by conventional whole-well ex vivo screening. Therefore, novel drug screening assays that can detect drug-resistant PDCOs may help in treatment or re-treatment decisions. Such findings underscore the need for integrating functional testing ex vivo into precision oncology workflows to improve therapeutic selection.
Decision support for treatment of patients with glioblastoma: A systems biology approach enabled by explainable AI.
2081 Background: Glioblastoma (GBM) patients (pts) have median survival of 15 months and 5-year survival <7% with standard therapy. Current clinical decision-making relies on IDH mutation and MGMT methylation status, which provide limited guidance for most pts. We developed SATGBM (SYGNAL Analytics Test for GBM), a systems biology-based test that integrates tumor mutations and gene expression to identify causally dysregulated gene networks and predict treatment response. Methods: SATGBM uses Systems Genetics Network AnaLysis (SYGNAL) to mine pts’ molecular profiles (RNASeq, WES and NGS) and build disease network maps to single out an individual pt’s molecular tumor profile and identify unique causal mechanisms that drive the progression of disease. Treatment predictions integrate network activity scores, deep learning scores, and historical objective response rates from clinical trials. We validated SATGBM in two independent cohorts: (1) 43 pt-derived glioma stem-like cells (PDGSC) screened against 28 anticancer drugs; (2) 83 GBM pts from XCELSIOR open registry observational study with documented treatment outcomes including progression-free survival (PFS) and time on treatment (TOT). Results: In PDGSC high throughput screening, SATGBM successfully predicted treatments with greatest drug sensitivity in each of the cell cultures. SATGBM predictions separated responders vs non-responders: median IC50 2.70×10⁻⁷ vs 1.00×10⁻³, Mann–Whitney p=2.1×10⁻⁴¹, Pearson r = -0.38, AUC = 0.79. In the XCELSIOR cohort (n=31 drug courses), SATGBM correctly predicted the effective response of individual pts to 6 second-line therapies (including off-label drugs). Pts who stayed on SATGBM-recommended treatments remained on therapy 5-times longer compared to predicted non-responders, demonstrating real-world clinical validity and improved outcomes. SATGBM predictions yielded median TOT of 260 vs 50.5 days, p=0.0021; sensitivity/specificity were 60.0% / 95.2% and AUC = 0.79 (benefit = ToT ≥120 days). Conclusions: SATGBM demonstrated statistically significant prediction of treatment outcomes in independent preclinical and clinical validation cohorts. Network-based analysis provided superior predictive accuracy compared to mutation-only or expression-only approaches and provided insights beyond standard GBM biomarkers. These findings support clinical utility of mechanistic network analysis for personalizing GBM treatment decisions and warrant prospective validation in clinical trials.
Targeting tumor–stroma crosstalk with ICAM1 antibody–drug conjugates to enable dual-compartment therapy in ovarian cancer.
e15024 Background: Antibody–drug conjugates (ADCs) have reshaped the treatment of solid tumors, yet their efficacy in ovarian cancer remains limited by stromal barriers and microenvironment-driven resistance, which are not addressed by epithelial-restricted targeting strategies. This study purposed to explore a new treatment strategy to remodel extracellular matrix of ovarian tumor in preclinical models via ICAM1 antibody drug conjugates (ADCs). Methods: The expression difference of ICAM1 was analyzed using RNAseq data from TCGA and the Genotype-Tissue Expression (GTEx) database. The prognostic analysis of ICAM1 was assessed by immunohistochemistry of tissue chip. Unbiased transcriptome-wide single-cell RNA sequencing analysis was performed using the GSE130000 dataset containing single-cell RNA expression profiling of 8 ovarian cancer samples. Two ICAM1-ADCs (ICAM1-MMAE and ICAM1-DXd) were rationally designed. The cytotoxic effect was investigated in vitro. The targeting and antitumor activity were assessed in xenograft model. The expression of ICAM1 was detected by western blot (WB) after co-cultured of ovarian cancer cells and cancer associated fibroblasts (CAFs). Sirius red staining and immunohistochemical was performed to evaluated the extracellular collagen after ICAM1-ADC treatment. Results: We identify intercellular adhesion molecule-1 (ICAM1) as a shared and dynamically regulated target across malignant epithelial cells and cancer-associated fibroblasts (CAFs), and develop ICAM1-ADCs to enable dual-compartment targeting in epithelial ovarian cancer. Integrative multi-omic and histological analyses reveal that ICAM1 is upregulated in ovarian cancer, associated with poor prognosis, enriched in CAF subsets linked to extracellular matrix remodeling, and inducible by tumor-derived soluble factors. ICAM1-ADCs conjugated to MMAE or DXd exhibit potent cytotoxicity against both ovarian cancer cells and ICAM1-positive CAFs while sparing normal ovarian epithelium. In vivo, these ADCs selectively accumulate in ovarian tumors and induce robust tumor regression. In tumor–CAF co-engrafted and intraperitoneal models, ICAM1-ADCs achieve superior tumor control accompanied by stromal remodeling marked by depletion of α-SMA⁺ fibroblasts and reduced collagen deposition. Increased ICAM1 expression in platinum-resistant clinical samples further supports its relevance in treatment-refractory disease. Conclusions: Collectively, this study establishes ICAM1 as a dual-compartment therapeutic target and provides a preclinical framework for ADCs designed to overcome stromal barriers through coordinated targeting of tumor cells and the supportive microenvironment.
Combined safety and efficacy results from three clinical studies evaluating alpha radiotherapy for advanced pancreatic cancer.
4215 Background: Pancreatic cancer (PDAC) remains associated with poor prognosis and limited treatment options, particularly in patients with advanced disease. Diffusing Alpha-Emitter Radiation Therapy (Alpha DaRT), a novel alpha-emitting radiation source, has shown initial clinical benefit in superficial epithelial solid tumors. The pooled studies presented here evaluated the safety and efficacy outcomes for endoscopic ultrasound (EUS)-guided alpha radiotherapy using Alpha DaRT in patients with PDAC. Methods: This analysis pooled data from three prospective clinical studies at 3 medical centers in Canada and Israel evaluating Alpha DaRT in patients with locally advanced or metastatic PDAC, regardless of previous lines of therapy. Primary outcome was safety, as assessed by CTCAE V5. Secondary outcomes were best overall response (BOR) of the primary tumor assessed per RECIST V1.1 and overall survival (OS), defined from Alpha DaRT treatment. Results: A total of 58 subjects (31 male, 27 female), median age 72 (range 41-91), were recruited between 2023 and 2025 across the three trials. Treatment-associated AEs of any grade occurred in 21 patients (36%). Grade ≥3 AEs occurred in 5 patients (9%) (biliary obstruction, abdominal pain, fever, liver enzyme imbalance and bacteremia). There were no treatment-related deaths and all grade ≥3 AEs resolved. Across all patients evaluable for response, the BOR showed a disease control rate of 87% and an objective response rate of 27%, with 3 complete and 9 partial responses. Median OS for the cohort was 7.9 months (95% CI: [6.1–11.6]): 10.4mo (95% CI: [7.3–13.4]) when given in 2nd line (N = 24) and 7.5 months (95% CI: [4.2–11.8]) when given in 3 rd line (N = 21). Conclusions: In this pooled analysis of three clinical studies, EUS-guided Alpha DaRT demonstrated a reassuring safety profile and promising early efficacy signals in patients with advanced PDAC. The observed survival outcomes and radiographic responses support further clinical investigation, which is underway. Clinical trial information: NCT04002479 ; NCT05781555 ; NCT05657743 .
Real-world outcomes of early unfractionated heparin use in NSTEMI patients with acute leukemia and severe thrombocytopenia.
6539 Background: Current AHA/ACC guidelines provide limited guidance for acute non-ST-segment elevation myocardial infarction (NSTEMI) management in patients with active hematologic malignancy and concurrent severe thrombocytopenia, as these individuals have been systematically excluded from randomized trials. Consequently, decisions regarding anticoagulation rely on expert opinion and limited observational data. We evaluated real-world outcomes in patients with acute leukemia, severe thrombocytopenia, and NSTEMI, comparing early unfractionated heparin use with no parenteral anticoagulation. Methods: We conducted a multicenter retrospective cohort study using the TriNetX Research Network to identify adults (≥18 years) with acute leukemia who experienced a first NSTEMI following leukemia diagnosis. Severe thrombocytopenia was defined as a platelet count < 50×10³/µL measured within 24 hours of NSTEMI. Patients with prior intracranial hemorrhage or on chronic anticoagulation were excluded. The primary exposure was unfractionated heparin administered within 24 hours of NSTEMI diagnosis versus no parenteral anticoagulation. Propensity score matching (1:1) was performed for demographics, cardiovascular risk factors and chronic co-morbidities. Primary efficacy outcomes were all-cause mortality at 30 days, 90 days, and 1 year. The primary safety outcome was major bleeding, defined as a composite of intracranial and gastrointestinal hemorrhage at 7 days, 30 days, and 90 days. Kaplan-Meier analysis, hazard ratios (HR), risk ratios (RR), and 95% confidence intervals (CI) were used to assess outcomes. Results: Among 1,734 patients who met the inclusion criteria, 639 received unfractionated heparin and 1095 did not. Following, PSM 528 patients remained in each group. Kaplan–Meier overall survival at 30 days, 90 days, and 1 year was significantly improved in patients receiving early unfractionated heparin (30-day HR = 0.78 [0.64-0.96]; 90-day HR = 0.77 [0.64-0.92]; 1-year HR = 0.75 [0.64-0.87]). Composite major bleeding events at 7 days, 30 days, and 90 days were similar between groups (7-day RR = 1.56 [0.95-2.56]; 30-day RR = 1.15 [0.81-1.77]; 90-day RR = 1.24 [0.88-1.83]). Conclusions: In this large real-world study of patients with acute leukemia, severe thrombocytopenia, and NSTEMI, early unfractionated heparin administration within 24 hours was associated with improved survival without an increased risk of major bleeding, addressing a critical evidence gap in this high-risk population. Overall Survival Timepoint Heparin (%)[n=446] No Heparin (%)[n=446] HR (95% CI) 30-day 66.9% 59.3% 0.78 [0.64-0.96] 90-day 55.3% 45.6% 0.77 [0.64-0.92] 1-year 36.9% 23.8% 0.75 [0.64-0.87] Major Bleeding Timepoint Heparin (%)[n=446] No Heparin (%)[n=446] RR (95% CI) 7-days 7.6% 5% 1.56 [0.95-2.56] 30-day 10.9% 9.8% 1.19 [0. 81-1.77] 90-day 14.1% 11.6% 1.27 [0.88-1.83]