The Cromwell Hospital is a private sector hospital located in the South Kensington area of London. It is operated by international healthcare company Bupa.
3531 Background: Colorectal cancer (CRC) is a heterogeneous disease with distinct molecular and clinical differences between right- and left-sided tumors. This study analyzes these variations to understand their impact on tumor behavior and treatment strategies. Methods: A total of 445 colonic tumor samples (132 right-sided, 313 left-sided) were profiled to assess mutations, amplifications, and fusions in key cancer-related genes along with targeted transcriptome analysis of 20,802 genes in a subset using semiconductor based next-generation sequencing (NGS) platform at Datar Cancer Genetics. Immunotherapy biomarkers (TMB, MSI, and PD-L1 22C3 TPS) were analyzed in a subset. Results: Right-sided and left-sided colon cancers exhibit substantial molecular heterogeneity, driven by distinct genetic and epigenetic alterations (Table 1). Right-sided tumors were more frequently associated with MSI and had statistically significant higher incidence of BRAF mutations. KRAS mutations were frequently observed in both right-sided and left-sided tumors at equal rates. ERBB2 amplifications were exclusive to left side tumors, whereas oncogenic ERBB2 mutations were equally distributed. Located around ERBB2, PGAP3 gene co‐amplification too was exclusive to left sided tumors. TFE3 alterations were absent from left sided tumors and common on right side. TP53 mutations, though more common in left-sided tumors, the difference was not statistically significant. Gene expression profiling of a subset, including 103 left-sided and 41 right-sided colon tumors, revealed activation of the Wnt / β-catenin signalling pathway, RAS/MAPK pathway, TGF-β signalling pathway, and immune-related pathways, though these differences were not statistically significant, suggesting that while specific drivers may differ—such as the predominance of APC mutations in left-sided tumors (56.8% vs 37.9%) leading to WNT activation and the higher incidence of RSPO2/3 fusions (7.1% vs 1.7%) in right-sided tumors -eventually some pathways are commonly implicated in colorectal cancer biology. Conclusions: Existing therapies like ICIs, HER2 inhibitors, and emerging molecules such as RSPO2/RSPO3 inhibitors could have differing impact based on tumor sidedness. Integrating these distinctions into drug development and clinical trials holds potential to optimize treatment outcomes. Molecular profiles of right- and left-sided colon tumors. Gene Right (%) Left (%) p-Value(Chi-square test) TP53 64.5% 73.2% 0.075644 APC 37.9% 56.8% 0.001354 KRAS 50.0% 43.6% 0.220194 BRAF 18.8% 3.0% 0.00001 TFE3 9.5% 0% 0.109087 ERBB2 mutation 2.3% 2.0% 0.80941 ERBB2 amplification 0% 5.9% 0.023006 PGAP3 amplification 0% 7.1% 0.673427 RSPO2/3 fusion 7.1% 1.7% 0.827207 Immunotherapy Biomarkers TMB 10-14 24.7% 29.3% 0.458066 TMB ³15 15.6% 7.6% 0.059925 MSI-High 8.1% 3.4% 0.066213 PD-L1 Positive 15% 5.6% 0.012571
Introduction & Objectives: ORKA-001 is a novel half-life extended monoclonal antibody targeting IL-23p19 with similar potency and epitope binding to risankizumab. ORKA-001’s extended half-life has the potential to enable once-yearly dosing, increased efficacy, and extended off-treatment remission in psoriasis. Here, 24-week results of the First in Human (FIH) Phase 1 study of ORKA-001 in healthy volunteers are presented. Materials & Methods: This Phase 1, double-blinded, placebo-controlled, randomized FIH study evaluated safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of single ascending doses (SAD) of ORKA-001 in 24 healthy adult volunteers. Participants were randomized 6:2 to receive a single subcutaneous (SC) dose of ORKA-001 or placebo across three ascending dose-level cohorts: 300 mg, 600 mg, and 1200 mg. Participants were admitted to a Clinical Research Unit, where they remained until Day 4 and then returned to the clinic for follow-up safety and PK assessments over one year. Results: Eight participants were dosed in each of the 3 cohorts: 6 with ORKA-001 and 2 with placebo. Baseline characteristics were typical of a healthy volunteer population. Half-life of ORKA-001 was approximately 100 days. Individual PK profiles showed no indication of anti-drug antibodies (ADAs). In an ex vivo assay, serum from subjects dosed with ORKA-001 potently inhibited IL-23-mediated STAT3 signaling for 24 weeks (study duration to date). The study remains blinded; however, no serious or severe adverse events (AEs) were reported, and no discontinuations occurred. AEs reported in >2 participants were headache, upper respiratory tract infection, and transient erythema at the injection site. All of these events were mild. No dose-dependent trends in AEs were observed. Conclusion: PK and PD results in this Phase 1 study of ORKA-001 support the potential for once-yearly dosing while maintaining trough antibody concentrations above approved IL-23 targeting antibodies like risankizumab. In addition, the PK profile supports evaluation of higher antibody exposures that may allow ORKA-001 to achieve higher rates of skin clearance than that of the current standard of care and long-term off-treatment remission in some patients. ORKA-001 was well-tolerated across all dose levels, with a favorable safety profile consistent with the IL-23p19 inhibitor class. These attributes are being further explored in an ongoing Phase 2a study, EVERLAST-A, which is evaluating efficacy and safety of ORKA-001 in adults with moderate-to-severe psoriasis.
e15170 Background: Despite significant morphological overlap between sub-groups, significant clinical heterogeneity is the hallmark of NENs. We sought to examine the molecular profiles of NENs to demonstrate the molecular heterogeneity that underpins differences in tumor biology. Methods: Tissue samples from 76 patients with NENs were analyzed, encompassing six WHO categories and various anatomic sites of origin (ASO). Molecular profiling was conducted using a semiconductor-based NGS platform at Datar Cancer Genetics. Mutation frequencies and pathway alterations were evaluated across WHO categories. Results: The cohort was distributed across WHO categories: NET grade 1 (N = 1), NET grade 2 (N = 9), NET grade 3 (N = 8), small cell NEC (N = 25), large cell NEC (N = 18), and mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs) (N = 5). An additional 10 tumors were classified as poorly differentiated NEC. Lung was the most common ASO (37%), followed by the pancreas (16%), gastrointestinal tract accounted (15%), female genitourinary tract (8%) and other rare sites contributed the rest. PD-L1 22C3 analysis (N = 42) showed negative status in 90% of cases, and 10% of cases showed positivity, with positive cases being limited to NECs. MSI status was stable in all cases (N = 40). High TMB (>10 muts/mb) was observed in 27% of samples (N = 13/48).High TMB was restricted to NECs, except for two cases of pancreatic NETs showing high TMB. Molecular profiling of the entire cohort together showed TP53 muattions as most frequent alterations (49%), followed by RB1 mutations (17%), CTNND2 amplifications (16%), RICTOR (11%), and MYC (10%) amplifications, along with NF1 mutations (9%). The PI3K/AKT/mTOR pathway was altered in 25% of tumors, and MAPK/ERK alterations in 16% of cases. PIK3CA , KRAS , and RB1 alterations were restricted to NECs and absent in NETs. Except for TP53 acting as a sole driver in 5% of cases, no two tumors shared an identical molecular profile, highlighting the pronounced molecular heterogeneity even within the same grade and organ of origin. Conclusions: We highlight the significant molecular heterogeneity among NENs, even within the same grade and ASO. These findings emphasize the necessity of integrating molecular profiling into the clinical management of NENs to enable personalized therapeutic strategies. Further studies in larger cohorts may shed added light into further characterisation of NENs. Incidence of genomic alterations across neuroendocrine tumor grades/subtypes. Gene NETGrade 1/2 NETGrade 3 Small cell NEC Large cell NEC MiNEN SNVs /InDels TP53 10.0% 37.5% 56.0% 55.6% 60.0% RB1 -- -- 36.0% 11.1% -- NF1 -- 12.5% 4.0% 16.7% -- KRAS -- -- -- 16.7% 20.0% PIK3CA -- -- -- 11.1% 20.0% CDKN2A 10.0% 12.5% 4.0% -- -- RET 10.0% 12.5% -- -- -- BRAF -- -- -- -- 20.0% Amplifications MYC -- -- 4.0% 16.7% -- CTNND2 -- -- 16.0% 11.1% 20.0% RICTOR -- -- 12.0% 11.1% 20.0% PIK3CA -- -- 20.0% -- 20.0% FGFR1 -- -- -- 11.1% -- PDGFRA -- -- -- 5.6% 20.0% ERBB2 -- -- -- 5.6% --
3054 Background: Effusions in cancer patients pose several critical challenges for clinicians. In known cancer patients, an effusion may signal recurrence, whereas in newly diagnosed, seemingly localized cases, it indicates a more advanced stage. In many patients the effusion may be secondary to complications of treatment or comorbidities, rather than malignant. Diagnosing malignant involvement of body fluids remains a challenge due to the limitations of conventional cytology. This study explores the potential of molecular profiling of body fluids to identify actionable molecular alterations and its role in diagnosing malignant effusions. Methods: We analyzed cfDNA from body fluids—ascitic fluid (N=26), cerebrospinal fluid (N=7), pleural fluid (N=11), and pericardial fluid (N=1), collected from 45 patients with solid tumors, including lung (N=12), breast (N=9), ovarian (N=9), pancreas (N=4), gastrointestinal cancers (N=4), cervix (N=2), and one each of CNS, endometrial cancer, HCC, liposarcoma, and melanoma. In a subset, results from fluid samples were compared with tissue and plasma samples to assess concordance across different sample types. Results: Pathogenic alterations were identified in 89% (40/45) of fluid samples. The most frequently mutated genes were TP53 (53%), EGFR (20%), KRAS (18%), PIK3CA (9%), CTNNB1 (7%), FGFR3 (7%), GNAS (7%), MYC (7%), and ESR1 (4%). Simultaneous analysis of body fluid and tissue samples (n=11) revealed that 7 patients (64%) had at least one concordant pathogenic alteration. Similarly, analysis of body fluid and plasma samples (n=16) showed that 8 patients (50%) had at least one concordant pathogenic alteration. Body fluid analysis identified acquired resistance alterations, such as EGFR T790M and ALK C1156Y , which influenced therapy decisions. Among the alterations detected exclusively in fluid samples were ERBB2 amplification and ESR1 D538G mutation in two breast cancer patients. In evaluating molecular profiling against cytology for detecting malignant effusions, 17 of 25 samples were positive by both methods, while 4 of 5 cytology-negative samples were ctDNA-positive. Notably, 3 of 4 ctDNA-negative cases were cytology-positive. These results emphasize the potential role of molecular profiling for diagnosis when cytology is inconclusive. Conclusions: This study highlights the importance of body fluid ctDNA profiling (ascites, pleural, pericardial, CSF) in identifying actionable mutations, including unique druggable alterations not found tissue or liquid biopsies. The ability to detect ctDNA in cytology-negative samples underscores the potential of body fluid ctDNA as a valuable complement to fluid cytology for diagnosing malignant involvement. ESCAT classification of pathogenic variants identified in body fluids from 45 patients. Tier Level Incidence (%) Number of unique patients IA 28.9% 13 IIA 0% 0 IIIA 35.6% 16 IIIB 4.4% 2 IVA 55.6% 25 IVB 2.2% 1 X 22.2% 10
3061 Background: Direct circulating tumor cell (CTC) detection is a promising biomarker for early cancer detection and monitoring. Traditional fluorescence microscopy and AI-driven methods have limitations such as subjectivity and labor-intensiveness. We developed a deep-learning pipeline using a U-Net–type encoder–decoder architecture for precise pixel-level CTC discrimination in peripheral blood nucleated cells (PBNCs). This method preserves morphological and fluorescence details, overcoming convolutional neural network (CNN) limitations by maintaining fine features through skip connections for better discrimination. We present specificity and sensitivity data from a case-control study. Methods: We collected 5 ml of peripheral blood in EDTA tubes from 1383 asymptomatic healthy volunteers (744, 54% male; 639, 46% females with mean age of 49 [(20 to 93) yrs], 38 individuals diagnosed with non-malignant conditions including prostatitis, PCOD and acute pancreatitis, and 143 individuals recently diagnosed with surgically resectable early stage cancers (Stage 1/ 2) - Head and Neck (N=50, 35%), Breast (N=31, 22%), Colorectal (N=17, 12%), Pancreas (N=8, 6%), Prostate (N=8, 6%), Lung (N=5, 3%), Ovary (N=5, 3%) others (N=19, 13%). Nucleated cells were isolated from the samples after RBC lysis and centrifugation and stained with EPCAM and DAPI and set on imaging slides. 60X images were obtained and processed by AI utilizing U-Net–Based Encoder–Decoder Architecture and context discrimination to detect CTCs. The customized U-Net pipeline encodes spatial information through successive convolutional and pooling layers, generating a highly compressed representation of cells in the bottleneck. By employing transposed convolutions in the decoder stage—and incorporating skip connections from the encoder layers—the AI model reconstructs a pixel-wise segmentation mask to identify potential CTCs with cell diameter >10 microns. This approach aims to surpass existing methods that rely on bounding-box–based detection by offering enhanced sensitivity and specificity through end-to-end learned feature extraction. Ground truth annotations were established via expert cytopathology review, and training procedures involved cross-validation to ensure generalizable performance. Results: Analysis of total 1564 samples showed that our U-Net–based model achieved a sensitivity of 89% (95% CI: 88.81) and specificity of 97% (95% CI: 97.98) for detecting CTCs. Performance remained consistent across solid tumors, highlighting the flexibility and adaptability of the architecture in various fluorescence staining conditions. Conclusions: Our U-Net pipeline uses pixel-level segmentation and skip connections to enhance CTC detection accuracy. Integrating fluorescence and morphology, it can streamline cancer screening and disease monitoring.
