Research Article: Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies

Date Published: April 26, 2019

Publisher: Public Library of Science

Author(s): Kimiyo N. Yamamoto, Akira Nakamura, Lin L. Liu, Shayna Stein, Angela C. Tramontano, Uri Kartoun, Tetsunosuke Shimizu, Yoshihiro Inoue, Mitsuhiro Asakuma, Hiroshi Haeno, Chung Yin Kong, Kazuhisa Uchiyama, Mithat Gonen, Chin Hur, Franziska Michor, Aamir Ahmad.


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.

Partial Text

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most devastating malignancies with a 5-year survival rate of 8% and is predicted to become the 2nd leading cause of cancer-related death around 2020 [1, 2]. PDAC is a complex disorder composed of distinct progression patterns of local invasion and metastasis [3, 4]. A subset of patients die of complications caused by locally advanced pancreatic cancer (LAPC), including biliary sepsis and gastrointestinal obstruction (Fig 1A), while others succumb to widespread metastatic disease without presenting with intensive local invasion (Fig 1B) [3–5]. Whether a patient will develop widespread metastatic disease or local invasion is important for clinical decision-making; however, the course of disease remains difficult to predict in clinical practice [3, 4]. Hence, the development of a novel platform that fully depicts the divergence of PDAC progression phenotypes is needed.

We have developed a novel computational modeling approach that was parameterized using the largest-to-date clinical cohort of PDAC patients. Our model captures the logistic tumor growth patterns observed in patients and can be used to estimate the eventual size a primary tumor will reach in a patient, termed the local advancement index (LAI) (Fig 1). Using our model, we found that patients with a small LAI are likely to develop widely metastatic disease, while patients with a large LAI tend to exhibit complications due to local tumor or progression with a small metastatic burden (Fig 2). The predictions from our computational modeling platform were then confirmed using clinical cohorts (Fig 3). These findings may provide new insights into clinical decision-making, suggesting that adjuvant systemic therapies could be necessary for patients with a small LAI who eventually develop widespread metastatic PDAC, while intensive local control as well as systemic therapies are necessary for patients with a large LAI.