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671 Background: Pancreatic cancer (PaC) is a highly fatal disease with a 5-year survival rate of 5-10%. Effective screening is not available, and most patients (50-55%) present with metastatic (50%-55%) disease at diagnosis. For patients with advanced PaC, the standard chemotherapy combinations include FOLFIRINOX and/or gemcitabine/nab-paclitaxel which has results in a overall survival of 7-11 months. The lack of effective therapies underscores the importance of exploring other agents. We propose that quantitating therapy-associated protein biomarkers including markers of antibody-drug conjugates (ADC) can improve treatment personalization for PaC. Methods: FFPE tumor tissues from 185 clinical PaC patients were microdissected and solubilized for mass spectrometry-based targeted proteomic analysis. We quantified biomarkers for ADCs simultaneously from 2-3 section of FFPE. These biomarkers include antibody targets such as EGFR, HER2, HER3, FRalpha, and Trop2 and payload biomarkers of sensitivity (TOPO1) and resistance (TUBB3). The multiplexed assay also quantified additional 65 clinically relevant protein biomarkers for chemotherapy, immunotherapy and targeted therapy. Results: Expression of EGFR was observed in majority of samples (88%) while only overexpressed ( > 1000 amol/µg) in 3% of samples. HER2 was expressed in half of patients (52%) and overexpressed ( > 750 amol/µg) in 5% of cases while the rest of HER2 protein expression ranged from 300 -750 amol/µg which corresponds to low Her2 expression. Trop2 was expressed in majority of patients (91%) with a 25x distribution between lowest and highest expressor. Other ADC biomarkers include HER3(55%, 5x), Axl (24%, 12x), Mesothelin (65%, 58x), Folate receptor alpha (10%, 17x). Expression of TUBB3 (77%, 8x) and TOPO1 (92%, 8x) in antibody target-positive subset may suggest resistance or response for several known payloads, such as taxanes and irinotecan/deruxtecan/govitecan, respectively. Conclusions: There is currently no approved ADC for pancreatic cancer, but several ADC clinical trials are underway. Quantitative proteomics identified antibody targets as well as markers of resistance or response to payloads for a variety of approved and investigational ADC therapies, which could aid in patient stratification in ADC clinical trials.

343 Background: Esophageal adenocarcinoma (EAC) continues to have extremely poor prognosis despite advances in surgical and oncological regimens. Targeted and immune therapy hold promise to improve outcomes in solid tumors including EAC. At present HER-2 and anti-PD-L1 therapies are approved by the FDA for EAC. Additionally, off-label use of EGFR directed therapy has been reported. The aim of this study is to measure expression of 80+ oncoproteins in EAC using multiplexed mass spectrometry to elucidate expression of currently approved-drug targets and explore promising new targets for drug development. Methods: After Institutional Review Board approval (Mayo Clinic and KUMC), EAC patients whose tumors were resected via esophagectomy before chemotherapy and/or radiation were identified. Normal esophageal mucosa samples with no history of EAC were also retrieved for analysis. Tissue sections were microdissected to exclude stroma from adenocarcinoma cells. Isolated EAC cells were solubilized for mass spectrometry-based quantitation of more than 80 clinically relevant tumor markers. Results: 55 EAC tissues were analyzed. HER2, PD-L1 and EGFR were overexpressed only in 16.3%, 0%, and 0% of patients, respectively. New oncoproteins #2 and #6 were noted to have high (7,634.0 and 27,222.6 attomoles/ug) and consistent (98.1% and 94.5%) overexpression in EAC samples. Expression of HER2, oncoproteins #2 and #6 were significantly higher in EAC compared to normal esophageal mucosa. There was no statistical difference in the expression of PD-L1 and EGFR between EAC and normal samples. Conclusions: Mass spectrometry revealed a very low prevalence of target oncoproteins in EAC for currently used drugs. Our study potentially reveals alternate markers which are near universally present at high levels and should be further explored for targeted treatment.[Table: see text]

249 Background: Since 1975, Esophageal adenocarcinoma (EAC) incidence has increased more than any other cancer (̃700%). Pre-malignant metaplastic tissue, or Barrett's Esophagus (BE), is the only known precursor for EAC. Periodic histological assessment of endoscopic biopsy along dysplasia-carcinoma sequence is used to assess progression risk. Current surveillance protocols have clinical limitations as 80% of patients still present with stage II-IV EAC. The aim of this study is to compare novel proteomic expression patterns to distinguish stable BE patients from those who developed EAC using the STLA101 assay. Methods: After Institutional Review Board approval patients with BE/GERD were identified. Two cohorts were selected: progressors (progressed to EAC at subsequent interval of 0.5-3 years) and non–progressors (̃10 yrs of f/u surveillance without progression along dysplasia/carcinoma path). 24 normal esophageal mucosa samples with no h/o of BE were also retrieved for baseline expression levels. Tissue sections were microdissected to isolate pure BE cells from surrounding stromal cells. BE cells underwent a multi-step process where tissue was heated and digested to produce a liquid biopsy for mass spec. Isotopically labeled control peptides representing the STLA101 panel were spiked in with digested tissue samples to quantify all of the markers simultaneously using a Triple-quad mass spectrometer. Results: A total of 95 tissues were analyzed. 29 BE biopsies progressed to EAC within a mean of 380 days while the remaining 28 BE and 14 GERD specimens did not progress over a 10-yr period. All 8 biomarkers were expressed at a higher level in BE tissue that progressed to cancer when compared to BE/GERD tissue that remained stable over 10 years. The assay had a 100% technical success rate as no samples yielded “non-detectable” data points. Conclusions: The differential overexpression of the STLA101 panel on progressive and non-progressive BE tissues demonstrates the clinical utility of STLA101 as a predictive biomarker panel for early detection of esophageal carcinogenesis. Non-progressive BE tissues were similar to normal mucosa, therefore, patients whose BE tissue samples resemble this profile may receive a reduced screening cadence. On the other hand, patients with overexpression of the STLA101 panel may be eligible to receive more frequent screening or therapeutic intervention.

206 Background: Human serum metabolome profiles have been analyzed to explore the molecular changes that occur with aging. We hypothesized that deep metabolic profiling of sera with different ages would allow the identification of distinct metabolic chronologic patterns as a normal biological baseline to study personal aging. We further hypothesized that metabolic assessment of this chronologic deviation, resulting from advanced precancerous lesion (APL) and stage I/II/III CRC, from the normal reference baseline, would be instrumental for prognosis of relapse revealing underlying pathophysiology. Methods: A cohort of normal (n=3,616, training; n=1,170, testing), 631 advanced adenoma, 1,019 stage I, 404 stage II and 417 stage III serum samples were assembled. Innovative global LCMS metabolomic production were applied to deep profile these subjects. Identification of the age-associated molecular patterns in normal subjects, modeled with an elastic net algorithm, established the reference baseline to mirror a metabolic clock. CRC associated deviation from the precise chronologically paced metabolic patterns was quantified to associate the clinical endpoints of relapse, OSF and PFS, and to identify the tightly associated metabolic pathways. Results: We observed that for those CRCs, the predicted metabolic age can differ from the chronological age with consistent variations, resulting “older” or “younger” metabolic age subgroup in reference to the chronological age. Significant disruptions from the normal baseline were observed in CRCs patients, and consistent stage specific patterns were observed. Outlier, “Older” or “younger” metabolic age subgroup, CRC patients were found with significant future relapse enrichment. Predictive models were derived to case find the patients at risk of future relapse at the CRC diagnosis timepoint. Conclusions: Deviations from the meticulously timed metabolic aging patterns may provide utility to allow prognosis of future clinical endpoints of relapse and overall survival. Close examination of the underlying metabolic pathways, associated with CRC stage specific metabolic patterns, disrupting the baseline ageotypes, not only may improve the sensitivity and specificity of prognostic tests of CRC relapse, but also shed new insights into CRC therapeutics.