Alveolar soft part sarcoma (ASPS) is an extremely rare tumor that frequently occurs in adolescent and young adults (AYA). Survival is poor for patients with metastatic and/or relapsed disease not amenable to local control, and limited therapeutic options are available. A major barrier to cancer care in the United States AYA population is lack of access to coordinated care and appropriate therapies for those who lack insurance or who are underinsured. We report a 25-year-old unemployed, uninsured, single mother who presented with a 12.8 • 21 cm soft tissue thigh mass with heterogeneous avidity, max standardized uptake value of 9, with metastatic disease to the ipsilateral inguinal lymph nodes and to the bilateral lungs. After local control of the primary mass was obtained, a recently developed, comprehensive drug replacement program (DRP) was used to gain access to nivolumab, and after frank progression was noted, ipilimumab was added every 6 weeks. No biomarkers associated with response to immunotherapy were identified. After four cycles, a complete response was observed and patient remains disease free 36 months after beginning dual immunotherapy treatment. We obtained immunotherapy agents through a DRP and describe the development and the utility of this program in the community setting. Our report highlights both first documented sustained complete response to sequenced immunotherapy in an AYA with ASPS as well as a comprehensive DRP, which enabled access to therapy for our patient.

672 Background: Pancreatic cancer is one of the deadliest cancers, with approximately 15-20% of patients who present at diagnosis with a resectable disease. The major barrier to better outcomes is the lack of early-detection molecular tools to enable timely intervention. We have developed a test that enables the detection of pancreatic cancer from a simple blood draw. The test incorporates a novel, genome-wide sequencing-based epigenomics detection method that enriches for DNA loci that undergo active de-methylation. The measurement of 5-hydroxymethylcytosine (5hmC) provides a unique and stable biomarker for the early detection of cancer including pancreatic cancer. Methods: Whole-blood was obtained from a training cohort of 660 individuals (consisting of 132 pancreatic cancers (PaCa) and 528 non-cancers) and a validation cohort of 2,150 individuals (consisting of 102 PaCa and 2,048 non-cancers). Cell-free DNA (cfDNA) was isolated from plasma from which 5hmC and whole-genome libraries were generated and sequenced. Logistic regression algorithms were employed using 5hmC feature sets combined with physical characteristics of DNA fragments to optimally partition cancer from non-cancer samples. Results: Cross validation of the training model yielded an overall sensitivity of 65.9%,(95% CI, 57.2%–73.9%), early-stage (stage I-II) sensitivity of 57.1% (95% CI, 44%–69.5%) and a specificity of 98%. The model was further validated in a separate, non-overlapping set of blinded and independently processed samples and yielded an early-stage sensitivity of 68.3% (95% CI, 51.9%–81.9%) and a specificity of 96.9% (95% CI, 96.0%–97.6%). Conclusions: Our results demonstrate that plasma-derived cfDNA 5hmC profiles enable the accurate detection of early-stage PaCa, providing a valuable non-invasive tool especially for those individuals at high risk for the disease, including individuals with genetic predisposition and newly diagnosed type 2 diabetes. A larger clinical study (NODMED - NCT05188586) is ongoing and will provide clinical validation for the detection in individuals at high risk for this deadly disease.

BACKGROUND Pancreatic cancer (PaC) has poor (10%) 5–year overall survival, largely due to predominant late-stage diagnosis. Patients with new-onset diabetes (NOD) are at a six– to eightfold increased risk for PaC. We developed a pancreatic cancer detection test for the use in a clinical setting that employs a logistic regression model based on 5–hydroxymethylcytosine (5hmC) profiling of cell-free DNA (cfDNA). METHODS: cfDNA was isolated from plasma from 89 subjects with PaC and 596 case–control non–cancer subjects, and 5hmC libraries were generated and sequenced. These data coupled with machine–learning, were used to generate a predictive model for PaC detection, which was independently validated on 79 subjects with PaC, 163 non–cancer subjects, and 506 patients with non–PaC cancers. RESULTS: The area under the receiver operating characteristic curve for PaC classification was 0.93 across the training data. Training sensitivity was 58.4% (95% confidence interval [CI]: 47.5–68.6) after setting a classification probability threshold that resulted in 98% (95% CI: 96.5–99) specificity. The independent validation dataset sensitivity and specificity were 51.9% (95% CI: 40.4–63.3) and 100.0% (95% CI: 97.8–100.0), respectively. Early–stage (stage I and II) PaC detection was 47.6% (95% CI: 23%–58%) and 39.4% (95% CI: 32%–64%) in the training and independent validation datasets, respectively. Sensitivity and specificity in NOD patients were 55.2% [95% CI: 35.7–73.6] and 98.4% [95% CI: 91.3–100.0], respectively. The PaC signal was identified in intraductal papillary mucinous neoplasm (64%), pancreatitis (56%), and non-PaC cancers (17%). CONCLUSIONS: The pancreatic cancer detection assay showed robust performance in the tested cohorts and carries the promise of becoming an essential clinical tool to enable early detection in high–risk NOD patients.

Background: Pancreatic cancer outcomes are poor, mostly due to the detection of cancer at late stages. Early-detection enables prompt initiation of treatment and has been reported to significantly improve outcomes. We have developed a novel sequencing-based epigenomics technology for cancer early-detection that enriches 5-hydroxymethylcytosine (5hmC) loci in cell-free DNA (cfDNA), and via machine learning algorithms, enables the detection of pancreatic cancer. Method: Whole-blood was obtained from a training cohort of 660 individuals (consisting of 132 pancreatic cancers (PaCa) and 528 non-cancers) and a validation cohort of 593 individuals (consisting of 86 PaCa and 507 non-cancers). cfDNA was isolated from plasma and 5hmC and whole-genome libraries were sequenced. Logistic regression algorithms were employed using 5hmC feature sets combined with physical characteristics to optimally partition cancers from non-cancers. Results: We trained and cross-validated a binomial logistic-regression model on PaCa and non-cancer samples. Cross validation of the training model yielded an overall sensitivity of 65.9%, early-stage (stage I-II) sensitivity of 57.1% and a specificity of 98%. The model was further validated on an independent set and yielded an overall sensitivity of 64%, early-stage sensitivity of 64.7% and a specificity of 98%. Conclusion: Our results demonstrate that plasma-derived cfDNA 5hmC profiles enable the detection of PaCa in early-stage disease providing a valuable tool especially for those individuals at high risk. A larger clinical study (NODMED: NCT05188586) is underway to validate the test in a high-risk population that would most benefit from early-detection. Citation Format: Anna Bergamaschi, David Haan, Micah Collins, Gulfem Guler, Melissa Peters, Lauren Gigliotti, Shimul Chowdhury, Wayne Volkmuth, Samuel Levy. Validation of an early-stage pancreatic cancer classification model using 5-Hydroxymethylation profiles in plasma-derived cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A027.

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