Yamamoto KN, Nakamura A, Liu LL, Stein S, Tramontano AC, Kartoun U, Shimizu T, Inoue Y, Asakuma M, Haeno H, Kong CY, Uchiyama K, Gonen M, Hur C, Michor F. 2019. Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies. PloS one. 14(4):e0215409. Pubmed: 31026288 DOI:10.1371/journal.pone.0215409


Pancreatic ductal adenocarcinoma (PDAC) exhibits a variety of phenotypes with regard to disease progression and treatment response. This variability complicates clinical decision-making despite the improvement of survival due to the recent introduction of FOLFIRINOX (FFX) and nab-paclitaxel. Questions remain as to the timing and sequence of therapies and the role of radiotherapy for unresectable PDAC. Here we developed a computational analysis platform to investigate the dynamics of growth, metastasis and treatment response to FFX, gemcitabine (GEM), and GEM+nab-paclitaxel. Our approach was informed using data of 1,089 patients treated at the Massachusetts General Hospital and validated using an independent cohort from Osaka Medical College. Our framework establishes a logistic growth pattern of PDAC and defines the Local Advancement Index (LAI), which determines the eventual primary tumor size and predicts the number of metastases. We found that a smaller LAI leads to a larger metastatic burden. Furthermore, our analyses ascertain that i) radiotherapy after induction chemotherapy improves survival in cases receiving induction FFX or with larger LAI, ii) neoadjuvant chemotherapy improves survival in cases with resectable PDAC, and iii) temporary cessations of chemotherapies do not impact overall survival, which supports the feasibility of treatment holidays for patients with FFX-associated adverse effects. Our findings inform clinical decision-making for PDAC patients and allow for the rational design of clinical strategies using FFX, GEM, GEM+nab-paclitaxel, neoadjuvant chemotherapy, and radiation.

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Franziska Michor uses the tools of theoretical evolutionary biology, applied mathematics, statistics, and computational biology to address important questions in cancer research.

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