Background: Prospective survival prediction of patients with metastatic colorectal cancer is difficult. Prognosis estimation based on readily available clinicopathologic factors has the potential to inform clinical practice and improve risk stratification for clinical trials. We constructed prognostic nomograms for OS and PFS in mCRC using the multi-trial ARCAD database. Methods: Data from 19,678 mCRC pts accrued to 24 first line randomized phase III clinical trials since 1997 were used to construct and validate Cox models for PFS and OS, stratified by treatment arm within each study. Candidate variables included age, gender, BMI, performance status, colon vs. rectal cancer, prior chemotherapy, number of metastatic sites, sites of metastases (liver, lung, lymph nodes), and baseline bilirubin, albumin, white blood cell count, hemoglobin, platelets, absolute neutrophil count, and derived neutrophil:lymphocyte ratio (dNLR). Missing data (<11%) were imputed, continuous variables modeled with splines, and clinically relevant pairwise interactions considered if p<0.001. Final models were internally validated via bootstrapping to obtain optimism-corrected calibration and discrimination C-indices, and externally validated using a 10% holdout sample from each trial. Results: Nomograms for OS and PFS including remaining variables were well calibrated with C-indices of 0.66 and 0.60, respectively. Evaluation of external validity revealed good concordance; 71% and 67% respectively between predicted (> vs. <50% probability) and actual (yes/no) 1-year OS and 6-month PFS, and median 1-year OS and 6-month PFS predictions fell within the actual 95% Kaplan-Meier intervals. Gender, liver and lung metastases, and dNLR were not prognostic for OS; prior chemo, colon vs. rectum, dNLR, liver and lymph node metastases, and gender did not predict for PFS. No clinically relevant pairwise interactions were identified. Conclusions: The proposed nomograms are well calibrated and internally and externally valid. These tools have the potential to aid prognostication and patient/physician communication, and balance risk in randomized trials in mCRC.