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209 Background: NEPC includes both pure small cell carcinoma and mixed tumors with varying degrees of adenocarcinoma and neuroendocrine histology. It arises de novo or is treatment associated (TA) post androgen deprivation therapy. Clinical outcome data and prognostic biomarkers are limited and were thus explored. Methods: Patients with high grade prostate cancer and morphologic and/or immunohistochemical (IHC) NEPC features were included in this retrospective multicentre study. Clinical stage, Gleason score, and serum biomarkers were recorded. Kaplan-Meier method and log-rank test calculated and compared overall survival (OS) from time of NEPC diagnosis.Cox proportional hazards regression assessed prognostic impact of serum biomarkers at diagnosis and de novo vs TA status, adjusting for clinical stage and castration resistance. Results: 135 NEPC cases were identified. 124 (92%) were mixed tumors. 56 (41%) arose de novo. 79 (59%) were TA. 77% of those with a Gleason score (N=85/110) were grade group 5. Median PSA pre-NEPC biopsy was 11.6 ng/mL. At NEPC diagnosis, 19 (14%) had localized disease (median OS 123.0 mo); 33 (24%) non-metastatic castrate-sensitive disease (median OS 42.3 mo); 6 (4%) non-metastatic castrate-resistant disease (median OS 14.3 mo); 35 (26%) metastatic castrate-sensitive disease (median OS 17.6 mo); and 42 (31%) metastatic castrate-resistant disease (median OS 9.6 mo). Median OS for those with visceral metastases was 8.6 mo (95% CI 6.0 – 14.6), compared to patients with non-visceral metastases (11.1 mo; 95% CI 13.7 – 21.5) and no metastases (42.3 mo; 95% CI 47 – 89). Anemia (adjusted HR 1.66; 95% CI 1.05 - 2.16, p = 0.031) and NLR >3 (adjusted HR 1.51; 95% CI 1.01 - 2.52, p = 0.045) were associated with increased risk of death. De novo disease, elevated LDH, serum PSA, and Gleason score were not prognostic. Conclusions: This study identifies NEPC clinical outcomes by stage, with survival poorer than expected in pure prostate adenocarcinoma. Anemia and elevated NLR >3 are prognostic biomarkers that may help risk stratify and guide treatment intensification, including platinum-based chemotherapy. Further biomarker characterization of NEPC through IHC-staining pattern and genomic analysis is currently underway by this group.

e18043 Background: Head and neck cancer is the 6th most common cancer worldwide. Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck cancer that is characterized by aggressive local invasion and metastasis. Despite the leading edge (invasive front) of the tumor being a driver of OSCC pathophysiology, its biology and clinical relevance have not been fully characterized. We used spatial transcriptomics to explore signaling patterns within the leading edge and tumor core. Methods: Fresh-frozen, surgically resected OSCC samples from three HPV-negative OSCC patients were profiled using the 10x Genomics Visium Spatial Gene Expression platform. Leading edge and tumor core regions were defined by pathologist annotations and expression of previously identified edge and core gene signatures from the literature. Spatial differential gene expression (DGE) analysis and pathway analysis was performed using the Seurat package and Ingenuity Pathway Analysis (IPA), respectively. Cell-cell interaction networks were reconstructed using the CellChat package. Results: The leading edge and tumor core displayed unique transcriptional and signaling profiles that were conserved across all three OSCC patient samples. DEG analysis revealed 31 genes enriched in the leading edge and 62 genes enriched in the tumor core with a log2FC > 0.58 and adjusted p-value < 0.01. The top genes upregulated in the leading edge were FN1, COL1A1, COL1A2, IFITM3, and SPARC. Top tumor-core genes included CRCT1, LCE3D, DEFB4A, SPRR2A, and CNFN. IPA analysis of upregulated DEGs in the leading edge and tumor core predicted the activation of wound healing and GP6 signaling pathways, and activation of intrinsic prothrombin activation and MSP-RON signaling pathways, respectively. Cell communication analysis revealed that the leading edge had higher intercellular signaling than the tumor core. Upregulated leading edge cell signaling modules included collagen, CD99, CSPG4, and non-canonical WNT pathways, which have been linked to tumor invasion, metastasis, and adhesion. COL1A1 and COL1A2 ligands and CD44 and SDC1 receptors were upregulated in leading edge signaling. The tumor core was enriched for ANGPTL and PERIOSTIN cell signaling modules. Conclusions: This is the first study to characterize the tumor core and leading edge of OSCC tumors using spatial transcriptomics. Further investigation of the therapeutic potential of identified signaling pathways may improve OSCC outcomes.

10035 Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite the development of risk stratification tools to improve prognostication, prediction of patient survival outcomes in NB remains poor. In this study we used an unbiased machine-learning algorithm to develop and validate a transcriptomic signature capable of predicting 5-year overall (OS) and event-free survival (EFS) in these patients. Methods: The TARGET-Neuroblastoma dataset (n = 243) was used as the training set. Two independent NB cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 266) were used as validation sets. Elastic net regression was employed to identify transcripts associated with EFS. Association of the developed signature with EFS and OS was evaluated using univariate Cox proportional hazards (CoxPH), Kaplan-Meier, and 5-year receiver-operator characteristic curves in validation cohorts. Further, the independent prognostic value of the signature was assessed using multivariate CoxPH models with relevant clinicopathologic variables including age, INSS stage, and N-Myc amplification status in both validation sets. Finally, a nomogram was developed to integrate the signature with prognostic clinicopathologic variables to evaluate their combined efficacy for prediction of 5-year EFS and OS. Results: We identified a 21-gene signature that demonstrates significant association with EFS and OS in both E-MTAB-178 and GSE49710 validation cohorts. Moreover, the signature is independent of clinicopathological variables and can be effectively incorporated into a risk model, improving the prognostic performance. Several genes within the signature have been previously implicated in NB, including ECEL1, HOXC9 and ARAF1. Conclusions: To the best of our knowledge, we are the first to use an unbiased machine learning approach to generate a transcriptomic gene signature for neuroblastoma prognosis externally validated in multiple cohorts across platforms. This 21-gene transcriptomic signature significantly associated with EFS and OS in this disease. Combining this signature with current prognostic clinicopathologic variables will improve risk stratification of affected patients and may inform effective clinical decision-making.[Table: see text]

e21139 Background: Hypoxia confers relative resistance to radiotherapy (RT) in squamous cell cancers (SCCs). However, endogenous markers of hypoxia (EMH) have not reproducibly predicted clinical ou...

e21146 Background: Oral squamous cell cancer (OSCC) is characterized by poor prognosis, high morbidity and mortality. The epidermal growth factor receptor (EGFR) is overexpressed in most OSCCs; how...