Research Papers:

Building personalized treatment plans for early-stage colorectal cancer patients

Hung-Hsin Lin, Nien-Chih Wei, Teh-Ying Chou, Chun-Chi Lin, Yuan-Tsu Lan, Shin-Ching Chang, Huann-Sheng Wang, Shung-Haur Yang, Wei- Shone Chen, Tzu-Chen Lin, Jen-Kou Lin and Jeng-Kai Jiang _

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Oncotarget. 2017; 8:13805-13817. https://doi.org/10.18632/oncotarget.14638

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Hung-Hsin Lin1,2,*, Nien-Chih Wei3,*, Teh-Ying Chou4,5, Chun-Chi Lin1,2, Yuan-Tsu Lan1,2, Shin-Ching Chang1,2, Huann-Sheng Wang1,2, Shung-Haur Yang1,2, Wei-Shone Chen1,2, Tzu-Chen Lin1,2, Jen-Kou Lin1,2, Jeng-Kai Jiang1,2

1Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taiwan

2Department of Surgery, School of Medicine, National Yang-Ming University, Taiwan

3Auspex Diagnostics, Taiwan

4Division of Molecular Pathology, Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

5Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan

*These authors contributed equally to this work

Correspondence to:

Jeng-Kai Jiang, email: jkjiang@vghtpe.gov.tw

Keywords: recurrence, drug efficacy, microarray, colorectal cancer, personalized treatment

Received: July 06, 2016     Accepted: January 06, 2017     Published: January 13, 2017


We developed a series of models to predict the likelihood of recurrence and the response to chemotherapy for the personalized treatment of stage I and II colorectal cancer patients. A recurrence prediction model was developed from 235 stage I/II patients. The model successfully distinguished between high-risk and low-risk groups, with a hazard ratio of recurrence of 4.66 (p < 0.0001). More importantly, the model was accurate for both stage I (hazard ratio = 5.87, p = 0.0006) and stage II (hazard ratio = 4.30, p < 0.0001) disease. This model performed much better than the Oncotype and ColoPrint commercial services in identifying patients at high risk for stage II recurrence. And unlike the commercial services, the robust model included recurrence prediction for stage I patients. As stage I/II CRC patients usually do not receive chemotherapy, we generated chemotherapy efficacy prediction models with data from 358 stage III patients. The predictions were highly accurate: the hazard ratio of recurrence for responders vs. non-responders was 4.13 for those treated with FOLFOX (p < 0.0001), and 3.16 (p = 0.0012) for those treated with fluorouracil. We have thus created a prognostic model that accurately identifies patients at high risk for recurrence, and the first accurate chemotherapy efficacy prediction model for individual patients. In the future, complete personalized treatment plans for stage I/II patients may be developed if the drug prediction models generated from stage III patients are verified to be effective for stage I and II patients in prospective studies.

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