Cervical cancer (CC) is the second most common cause of cancer deaths in women worldwide. The main objective of the RAIDs project is to use this model system which is accessible to repeat biopsies to learn how to stratify patients into targeted therapies. The patients’ tumors will be classified into molecular subtypes based on their molecular profile by use of high-throughput technologies (sequencing, RPPA) combined with integrative bioinformatics analysis. Stratification is a learning process which will need constant readaptation. It will rely on prognostic and predictive biomolecular information gained from patients who will receive either standard therapy (the cognitive cohort) or patients who receive (first generation) targeted therapy. Bioinformatics will be an essential tool to allow integrative genome/proteome analysis and provide functional interpretation of the results at each step. Machine learning techniques will be used to select the most reliable biomarkers. These tools will be used to predict response to cc treatment or progression. Moreover, systems biology approaches will be used to unravel the gene regulatory networks and signaling pathways involved in the tumor progression and should be helpful to select predictive biomarkers and putative drug targets.Tumor material will be sampled before and following standard therapy or therapeutic HPV vaccination and/or novel targeted drug treatments. Clinical annotation by imaging methods as well as through biomarkers will have quality control procedures and relevant statistical models will be applied.RAIDS is a multidisciplinary approach integrating genomic studies, protein arrays, viral genotyping and immunohistochemical investigations on CC cells and tissues between academic clinical centers and SME’s .The project is expected to provide new tools for early diagnosis and targeted treatments exploited by SMEs and to accelerate innovation of CC therapy and improve quality of life.