Virtual Library
Start Your Search
L. Jiang
Author of
-
+
MINI 02 - Immunotherapy (ID 92)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:P. Forde, S.J. Antonia
- Coordinates: 9/07/2015, 10:45 - 12:15, Four Seasons Ballroom F3+F4
-
+
MINI02.11 - Immunological Markers Predict the Prognosis of Patients with Squamous Non-Small Cell Lung Cancer (ID 605)
11:45 - 11:50 | Author(s): L. Jiang
- Abstract
- Presentation
Background:
As one of the novel therapy strategies, PD-L1 has been shown the function of down-regulating T-cell activation through receptor PD-1. Moreover, prognosis of cancer patients are based not only on tumor-related factors but also on host-related factors, particularly systemic inflammatory response. As significant indicators of patients’ inflammation status, circulating monocyte count, neutrophil ratio and lymphocyte ratio were proved as predictors of prognosis in various cancers. Squamous non-small cell lung cancer (NSCLC) revealed to be divergent clinical and molecular phenotypes compared with non-squamous NSCLC. Significantly, combining application of appropriate biomarkers in prognosis prediction is emerging its high importance in cancer research.
Methods:
Chart review was performed on 1286 consecutive patients, 156 of these patients were enrolled in the final analysis. Patients with squamous NCSLC were randomly assigned (2:1) centrally by computer into training group and validation group. Monocyte ratio, Neutrophils to Lymphocytes Ratio, PD-L1 immunostaining score and PD-1-positive stained tumor-infiltrating lymphocytes counts were assessed by Fisher’s linear discriminant analysis to discriminate if OS would exceeding 5 years. The final model was used to calculate the discriminant score in each study participant. And this prediction model was validated in a second set of squamous NCSLC patients. We internally validated the model using a cross-validation procedure.
Results:
4 independent predictors of OS were identified by using FLDA with stepwise variant-selection. The clinical classifying model was described by the following equation: Y = −1.212 + 0.211 × NLR ratio + 0.437 × monocyte ratio - 0.390 × PD-L1 + 0.035 × PD-1 (eigenvalue 0.673, canonical correlation 0.634, P < 0.001). In this equation, PD-L1 represented PD-L1 immunostaining score; and PD-1 represented PD-1 positive TILs counts. For the training set of 104 leave-one-out-cross-validated cases, 27 of 29 OS > 5 years (93.1% sensitivity) and 61 of 75 OS <= 5 years (81.3% specificity) were correctly classified with an overall accuracy of 84.6% (88 of 104) and an AUC of 0.938 [P < 0.001, 95% confidence interval (CI) 0.864–1] Next, the predicting model consisting of the 4 predictors (NLR ratio, monocyte ratio, PD-L1 and PD-1) were applied to the validation set of 52 patients (14 OS > 5 years and 38 OS <= 5 years). A survival prediction for 38 of the 52 patients (73.1%) with an AUC of 0.908 (P < 0.001, 95% CI 0.806–1) was achieved. Also, 12 of 14 OS > 5 years (85.7% sensitivity) and 26 of 38 OS <= 5 years (68.4% specificity) were correctly identified.
Conclusion:
The analysis of a set of immunological markers could effectively and reproducibly classify patients with squamous NCSLC according to their overall survival. Further prospective validation in larger independent cohorts of patients with similar or different regimens is warranted to fully assess its predictive power. The 4-immunological-marker model offers a novel tool for survival prediction and could have important clinical implications for the consideration of differential treatment strategies in patients with squamous NCSLC, thus providing a framework for future individualized therapy.
Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
-
+
P3.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 235)
- Event: WCLC 2015
- Type: Poster
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
-
+
P3.04-080 - Combination of BMI and OLR1 in Prognosis Prediction of Patients with Squamous Non-Small Cell Lung Cancer (ID 604)
09:30 - 09:30 | Author(s): L. Jiang
- Abstract
Background:
Lung cancer, especially non-small cell lung cancer (NSCLC), represents enormous challenges in continuously achieving treatment improvements. Besides cancer, obesity is becoming more and more prevalent. Obesity is increasingly recognized as a major risk factor for several types of common cancers. Significant mechanisms overlap in the pathobiology of obesity and tumorigenesis. One of these mechanisms involves oxidized low density lipoprotein receptor 1 (OLR1), as link between obesity and cancer. Additionally, body mass index (BMI) has been widely used in exploiting the role of obesity on a series of diseases, including cancer. Significantly, squamous NSCLC revealed to be divergent clinical and molecular phenotypes compared with non-squamous NSCLC.
Methods:
Chart review was performed on 1286 consecutive patients who suffered from squamous NSCLC with between November 2004 and March 2008. 131 of the 1286 patients were enrolled in the final analysis. These 131 patients were randomly assigned (2:1) centrally by computer into training group (n=87) and validation group (n=44). BMI was calculated as follow: BMI (kg/m2) = weight (kg)/height (m2). Surgically resected or biopsied specimens were fixed in formalin and embedded in paraffin for routine histopathological diagnosis and immunohistochemical analysis. Then, PFS was defined as the time from the first documentation to the time of tumor progression or death. The total OLR1 immunostaining score was calculated as the sum of the positively stained tumor cells and staining intensity. OLR1 immunostaining score and BMI were assessed by Fisher’s linear discriminant analysis to discriminate if progression-free survival (PFS) would exceeding 2 years.
Results:
The mean follow-up for survivors as of December 2014 was 47.23 months. Mean PFS was 724 days and the overall 1-, 2- and 3-year PFS rates were 87.8%, 47.3% and 39.7%, respectively. OLR1 expressed on tumor cells. There was no significant difference between the training (n=87) and validation (n=44) cohorts (P > 0.1). The clinical classifying model was described by the following equation: Y = -5.811 + 1.285 ×OLR1 immunostaining score + 0.152 ×BMI (eigenvalue 1.272, canonical correlation 0.748, P < 0.001). Group centroids for PFS <= 2 years and PFS > 2 years were 0.914 and - 1.359, respectively. Next, a cut score halfway between the two centroids was determined: cut score= (−1.359 + 0.914)/2 = -0.2225. For the training set of 87 leave-one-out-cross-validated cases, 49 of 52 PFS > 2 years (94.2% sensitivity) and 30 of 35 PFS <= 2 years (85.7% specificity) were correctly classified with an overall accuracy of 90.8% (79 of 87) and an area under the curve (AUC) of 0.938. In the validation set, survival prediction for 40 of the 44 patients (90.9%) with an AUC of 0.979was achieved.
Conclusion:
The analysis of combination of BMI and OLR1 could effectively and reproducibly classify patients with squamous NCSLC according to their PFS. Further prospective validation in larger independent cohorts of patients with similar or different regimens is warranted to fully assess its predictive power. However, the combinational model offers a novel tool for survival prediction and could provide a framework for future individualized therapy in patients with squamous NCSLC.
