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Xiangxue Wang
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MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Pathology
- Presentations: 1
- Now Available
- Moderators:John Le Quesne, Noriko Motoi
- Coordinates: 9/09/2019, 15:45 - 17:15, Tokyo (1982)
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MA15.05 - Computerized Measurements of Cellular Diversity on H&E Tissue Are Prognostic of OS and Associated with Mutational Status in NSCLC (Now Available) (ID 1975)
15:45 - 17:15 | Author(s): Xiangxue Wang
- Abstract
- Presentation
Background
Tumor heterogeneity is known to be implicated in chemotherapeutic resistance and poor prognosis for non-small cell lung cancer (NSCLC). In this study we sought to evaluate the role of computer extracted features reflecting the intrinsic cellular morphological diversity (ICMD) of tumors from digitized H&E stained images of early-stage NSCLC patients. Additionally, we sought to evaluate the association of these ICMD features in adenocarcinomas with the ALK and EGFR mutational status.
Method
Two cohorts, D1 and D2, of digitized H&E stained tissue microarray images (TMA) of NSCLC, n=395 and n=91, respectively, were used for modeling the ICMD predictor. A pretrained deep learning model was used for segmentation of nuclei, and clusters of proximally located nuclei were identified. The ICMD features were then extracted as the variations in shape, size, and texture measurements of nuclei within the clusters. A Cox proportional hazard model using the ICMD features was then trained for lung adenocarcinomas (LUAD, n=270), and squamous cell carcinomas (LUSC, n=216), separately, and was validated on independent cohort from (D3) The Cancer Genome Atlas (TCGA) (n=473) to predict Overall Survival (OS). Univariate and multivariate analyses were performed on (D3).
Result
In (D3), high risk patients predicted by the ICMD features had significantly poorer survival (HR (95% CI) = 1.48 (1.06-2.06), p=0.021 for LUSC, HR (95% CI) = 1.59 (1.11-2.29), p=0.006 for LUAD) in univariate analysis. In multivariate analysis, controlling for major clinical variables, ICMD was independently associated with 5-year OS (p<0.016). (See Table 1) We also found that ICMD features were associated with driver mutations ALK (p=0.0204) and EGFR (p=0.0017) in LUAD.
Table 1| Multivariate analysis for overall survival on the validation set D3.
ConclusionMultivariate Cox Proportional Hazard Model Analysis Controlling for Other Variables
TCGA-LUSC
TCGA-LUAD
Variable
HR (95% CI)
p value
HR (95% CI)
p value
Age (>65 vs <=65)
1.14(0.81-1.61)
0.451
0.89(0.63-1.28)
0.540
Smoking status
1.36(0.83-2.23)
0.221
1.14(0.64-2.01)
0.661
Overall Stage (Stage II vs I)
1.13(0.66-1.94)
0.651
1.86(1.04-3.32)
0.037
T-Stage (T2,3 vs T1)
1.26(0.85-1.87)
0.244
1.25(0.85-1.85)
0.263
N-Stage (N1 vs N0)
1.36(0.77-2.41)
0.292
3.11(1.55-6.23)
0.001
Developed Model
High risk vs. Low risk
1.52(1.08-2.13)
0.016
1.55(1.09-2.22)
0.015
CI = 95% confidence interval; HR = Mantel-Haenszel Hazard ratio. Values in bold are statistically significant, p<=0.05.
Computer extracted image features of cellular diversity were able to predict OS in NSCLC and were also associated with the ALK and EGFR mutational status. Future work will entail evaluating ICMD features in predicting added benefit of adjuvant therapy in early stage NSCLCs as well as correlating with gene expression data.
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MA25 - Precision Medicine in Advanced NSCLC (ID 352)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Advanced NSCLC
- Presentations: 1
- Now Available
- Moderators:Sebastian Defranchi, Karen Kelly
- Coordinates: 9/10/2019, 14:30 - 16:00, Dublin (1997)
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MA25.02 - Arrangement and Architecture of Tumor-Infiltrating Lymphocyte on H&E Slides Predict OS in Nivolumab Treated Non-Small Cell Lung Cancer (Now Available) (ID 2911)
14:30 - 16:00 | Author(s): Xiangxue Wang
- Abstract
- Presentation
Background
Immune checkpoint inhibitors (ICI) are a promising and novel approach to treating chemotherapy refractory advanced NSCLC as well as first-line combination therapy in certain NSCLC. Nivolumab, a PD-L1 inhibitor is a promising ICI showing durable benefit with low toxicity in these patients. While PD-L1 positivity is an established tissue based biomarker for response to Nivolumab, studies have shown response rates ranging from 20-50%. Recent research has shown that TILs have been implicated in cancer aggressiveness as well as immune response. In this work, we go beyond simply counting TILs, and apply novel computer-extracted features characterizing the interaction and spatial co-localization of TILs and cancer nuclei (SpaTIL) in stratifying patients based on OS following nivolumab therapy.
Method
H&E tissue slides obtained from pre-treatment biopsies of 96 NSCLC patients treated with nivolumab were digitized and included for this study from 3 different institutions with the tumor region annotated by pathologists. Then 85 SpaTIL features related to TIL density, architecture and co-localization with tumor cells have been extracted to represent each patient. The most discriminative and uncorrelated features were selected by Elastic-Net regularized Cox-regression model to predict OS. The model was trained on D1 (n=25) and independently validated in D2 (n=32) and D3 (n=64). Multivariate analysis with clinico-pathologic factors was also performed.
Result
The top features consisted of the abundance of TILs around tumor cells and the distribution of the TILs. On the validation set, SpaTIL classifier yielded a HR=3.03 (95%CI=1.1 -8.35; p=0.042) on D2 and HR=4.12 (95%CI=1.87-9.09; p=0.02) on D3 by a log-rank test. On multivariate analysis with stage, smoking, histologic type, total lymphocyte count (See Table 1) SpaTIL was independently prognostic of OS (HR=7.88; 95%CI=1.66 – 37.216; p=0.009).
Table 1. Multivariate analysis for overall survival on the validation sets D2 and D3 Variables
HR(95% CI)
p value
Age (>65 vs <=65 yrs)
0.99(0.97-1.03)
0.67
Gender (Male vs Female)
1.05(0.75-2.79)
0.88
Smoking Status
(Former vs Never smoker)
3.19(0.92-11.061)
0.07
Histological Subtypes (Adeno vs Squamous)1
1.06(0.13-8.54)
0.95
EGFR status
1.32(0.49-3.52)
0.58
ALK status
0.63(0.36-1.10)
0.10
Total lymphocyte count
0.99(0.99-1.00)
0.33
SpaTIL Classifier
7.88(1.66-37.216)
0.009
CI = confidence interval; HR = Mantel-Haenszel Hazard ratio. Values in bold are statistically significant, p<=0.05.
Conclusion
Spatial interaction of TILs and cancer are independently prognostic of OS in nivolumab treated NSCLC. Further validation needs to be done to evaluate its utility.
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P2.17 - Treatment of Early Stage/Localized Disease (ID 189)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Treatment of Early Stage/Localized Disease
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.17-35 - Integrating CT Radiomic & Quantitative Histomorphometric Whole Slide Image Features Predicts Disease Free Survival in ES-NSCLC (ID 2910)
10:15 - 18:15 | Author(s): Xiangxue Wang
- Abstract
Background
Early-Stage non-small cell lung cancer (ES-NSCLC) accounts for approximately 40% of NSCLC cases, with 5-year survival rates varying between 31-49%. Radiomic textural features from pre-treatment CT scans and QH features from H&E stained WSIs have been shown to be independently prognostic of outcome. With diagnostic CT scans and surgical resection, the standard of care in ES-NSCLC, in this work we seek to take a multimodality approach using routine imaging to improve the predictive performance in determining DFS following resection.
Method
A retrospective chart review of Stage I and II (ES-NSCLC) pts undergoing surgical resection between 2005-14 with available CT and resected tissue yielded 70 pts. A total of 248 radiomic CT textural features from inside the tumor (Intratumoral –IT) and outside the tumor (Peritumoral – PT) and 242 QH features related to the nuclear shape, texture and spatial orientation and architecture from H&E WSI were extracted. We developed two risk models, Radiomic and QH using the most stable, discriminative and uncorrelated features from CT and WSI respectively determined by Lasso-regularized Cox regression to predict Disease free survival (DFS). Model performances were analyzed using Hazard Ratios (HR), Concordance Index (C-index) and Decision curve analysis. We built a nomogram to calculate the DFS based around the individual models as well as an integration of the QH and Radiomic models.
Result
Top 6 Radiomic features included 2 IT and 4 PT features from the Haralick and Collage families. The QH model comprised 6 nuclear shape and graph features. In predicting DFS, While the Radiomic model had a HR of 2.4 (p <0.01) with C-index – 0.67, the QH model had HR – 3.1 (p <0.01) with C-index – 0.74. Integration of the Radiomic and QH model yielded a C-index of 0.78 (p< 0.01). After addition of prognostic clinical factors (LVI, AJCC stage) to the model, the C-index was 0.80, almost doubling either modalities alone. The constructed nomogram visualized the apparent benefits of the three models while a decision curve clearly demonstrated the increased benefit of combined integrated model.
Conclusion
Integration of CT-derived radiomic and tissue-derived QH features was found to show improved performance in predicting RFS when compared to either radiomics or QH alone.
