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Background: Understanding resistance mechanisms to chemotherapy is key to improving therapeutic outcomes. Despite the considerable importance of tumor drug resistance to cancer morbidity and mortality, our comprehension of the various molecular mechanisms involved in resistance is limited. The actual response of an individual patient remains a ‘black box’. Previous studies profiling pre-and post-treatment samples were based on population statistics and did not result in a personalized view of resistance. To dissect the individualized emergence of resistance in breast cancer patients we applied longitudinal transcriptomics combined with temporal dynamics analysis approach. Methods: Matched triplets of archived tumor biopsies from pre-treatment, post-treatment and adjacent normal epithelium were collected from 33 individual patients that underwent neo-adjuvant chemotherapy treatment. Full transcriptome analysis was performed by mRNA sequencing. Longitudinal pattern analysis algorithm was developed to follow dynamic expression fluctuations in individual patients. Data analysis incorporated long-term clinical and pathological follow-up information. Pathway enrichment was used to map the resistant pathways and create a “chemo-resistome” map in individual patients by following the rewiring of their molecular pathways through the course of therapy. Results: To identify genes associated with resistance we used longitudinal pattern classification. Each pattern represents a different scenario through tumor progression and treatment stages. We identified 253 genes that their pattern is significantly correlated with pathological response score. Enrichment analysis of these genes pinpointed pathways and functions associated with resistance. We found multiple pathways directly related to the mechanism of action of the administered chemotherapies, such as, pathways involved in microtubule polymerization and DNA repair. Other pathways that emerge involve multi-drug resistance pathways, such as, specific subsets of ABC transporters and pathways related to immune-modulation. Interestingly, we noticed that the mechanisms of resistance are patient-specific. We, therefore, calculated a chemo-resistome map for each patient using the most potent resistant pathway categories. The chemo-resistome maps illustrate the co-existence of several resistance categories in the same patient, whereas some categories exhibit patient- or subtype- specific occurrence. Conclusions: Mapping the complexity of the various resistance pathways in individual patients can provide important insights on the mechanisms underlying tumor cell survival. Depicting an individual road map to resistance by analyzing expression rewiring can offer personalized therapeutic strategies in the future. Citation Format: Maya Dadiani, Gilgi Friedlander, Gili Perry, Nora Balint-Lahat, Anya Pavlovsky, Anjana Shenoy, Iris Barshack, Tamar Geiger, Bella Kaufman, Einav N Gal-Yam. Mapping a personalized chemo-resistome in breast cancer patients by longitudinal transcriptomics [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-03-01.