Building personalized treatment plans for early-stage colorectal cancer patients
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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
Jeng-Kai Jiang, email: email@example.com
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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