Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection.

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

Building personalized treatment plans for early-stage colorectal cancer patients.

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.

Comparison of genomic signatures of non-small cell lung cancer recurrence between two microarray platforms.

BACKGROUND: Cancer genomic signatures may vary using different platforms. We compared the differential gene expression in non-small cell lung cancer (NSCLC) between two platforms in order to find the most relevant genomic signatures of tumor recurrence. MATERIALS AND METHODS: We analyzed gene expression in frozen lung cancer tissue from 59 selected patients who had undergone surgical resection of NSCLC. These patients were divided into two groups: group R, patients who had a tumor recurrence within four years, n=37; group NR, patients who remained disease-free four years following initial surgery, n=22. Each RNA sample was assayed twice using both Affymetrix and Illumina GeneChip. Data were analyzed by principal component analysis and leave-one-out cross-validation. RESULTS: Using the same filtering criteria, 13 genes that were differentially expressed between R and NR were identified by Affymetrix, while 21 genes were identified by Illumina GeneChip. In common, a total of six genes were detected by both systems. Using univariate analysis, four (lipocalin 2, LCN2; parathyroid hormone-like hormone, PTHLH; ras-related protein Rab-38, RAB38; and four jointed box 1, FJX1) of these six genes were associated with survival. A risk score of survival was calculated according to the four-gene expression. There was a significant difference in overall survival between low- and high-risk groups. CONCLUSION: A four-gene signature is associated with survival among patients with early-stage NSCLC. Further validation of these findings is warranted.