Background: Nearly 50% of pts with mPDAC never receive a 2nd line of therapy for metastatic disease following frontline FFX or GA. Genomic alterations in the DDR pathway (e.g. BRCA1/2) are associated with increased progression-free survival (PFS) on platinum-containing regimens (e.g. FFX), but other biomarker-treatment associations that predict benefit from GA and/or FFX in mPDAC remain unexplored. In this retrospective real-world evidence (RWE) study, we use a data-driven machine learning approach to gain new insights from the mutational landscape in mPDAC and validate the PDACai signature in predicting personalized benefit from both FFX and GA. Methods: We analyzed real-world outcomes from 711 pts with mPDAC who underwent genomic profiling via the Know Your Tumor program or were referred to Perthera by treating oncologists. Chart-abstracted PFS data on either 1st line FFX or GA were split (60:40) into independent training and validation cohorts for each regimen. All models integrate a shared set of clinical (age < 63, sex) and lab-agnostic molecular features derived from clinical NGS testing reports (DDR pathway alterations, specific KRAS variants, frequently mutated genes). Relative benefit scores predicted by FFX or GA models were evenly binned into three PDACai signature categories representing lower, middle, and upper tertiles for each independent cohort. Statistical differences in median PFS were evaluated using ordinal Cox regression. Results: Median PFS followed predicted trends as generated by PDACai for each therapy in training and validation cohorts. The predictive utility of PDACai was confirmed in the independent validation cohorts when comparing PFS on FFX (p = 0.03744; HR = 0.75 [95% CI: 0.58-0.98]) and GA (p = 0.00006861; HR = 0.65 [95% CI: 0.53-0.8]) across tertiles. Conclusions: Response to chemotherapy is heterogeneous and difficult to predict in pts with mPDAC. Using RWE, the PDACai signature successfully predicted relative differences in PFS on both FFX and GA in mPDAC. With prospective validation, personalized insights generated by PDACai from pts with similar biomarker profiles could be used to tailor treatment sequencing in pts with NGS testing results, particularly those without actionable genomic findings.