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Weiping Tao
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P1.04 - Immuno-oncology (ID 164)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Immuno-oncology
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.04-21 - Cellular Landscape of Normal Adjacent to Tumor Microenvironment in Non-Small Cell Lung Cancer (ID 424)
09:45 - 18:00 | Author(s): Weiping Tao
- Abstract
Background
Cellular heterogeneity is the dominant ingredient in tumor microenvironment, which plays essential roles in cancer malignancy. Growing evidence have shed light on the important role of tumor-infiltrating immune and stromal cells in cancer progression.
Method
Here, we portrayed the cellular landscape of a total of 64 cell types in 313 normal lung tissues, 110 adjacent normal tissues and 992 non-small cell lung cancer (NSCLC) tissues using transcriptomic data by integrated bioinformatics analysis.
Result
In general, adjacent normal tissues presented an intermediate state between normal and tumor tissues, which was that the fraction of immune cells decreased while fraction of stromal cells increased from normal, adjacent to tumor tissues. Moreover, huge difference of tumor-infiltrating cells were detected between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Interestingly, in LUAD, rather than in LUSC, subtypes of CD4+ and CD8+ T cells were significant higher in tumor tissues compared with adjacent and normal tissues. Stromal cells, such as fibroblast and endothelial cells, also showed great diversity among the normal, adjacent and tumor tissues. Moreover, immune inhibitory receptors (PD1, CTLA4, LAG3 and TIM3) were more commonly co-expressed on certain subtypes of T cells in both LUAD and LUSC compared with adjacent normal tissues. Besides, significant clinical relevance between tumor-infiltrating cells and tumor stages were more prevalence in LUAD, compared with LUSC. Lastly, there were 45 cell types and 27 cell types were significantly correlated with patients overall survival in LUAD and LUSC, respectively. Surprisingly, certain subtypes of T cells were adverse prognosis factors for NSCLC. Taken together, we built powerful prognosis predictors for LUAD and LUSC patient using tumor-infiltrating cells.
Conclusion
In summary, our analysis provided extensive details of cellular landscape in normal adjacent to tumor tissues in NSCLC and how they were involved in tumor progression. Better understanding of the complex crosstalk between tumor cells and infiltrating cells might provide novel therapeutic targets and biomarker for NSCLC, especially in the immune therapies.
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P2.04 - Immuno-oncology (ID 167)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Immuno-oncology
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.04-40 - Tumor Mutation Score Is More Powerful Than Tumor Mutation Burden in Predicting Response to Immunotherapy in Non-Small Cell Lung Cancer (Now Available) (ID 566)
10:15 - 18:15 | Author(s): Weiping Tao
- Abstract
Background
Tumor mutation burden (TMB) and PD-L1 expression are the two important biomarkers for immune checkpoint inhibitors (ICIs) in lung cancer. However, growing evidences are showing that not all mutations, such as EGFR mutation, are favorable factors in predicting clinical outcome of ICIs and the power of TMB, which is unselective, might be attenuated. Therefore, we developed tumor mutation score (TMS) as better biomarker for response of ICIs in non-small cell lung cancer (NSCLC).
Method
TMS was defined as the number of genes with nonsynonymous somatic mutations. Mutations were detected by targeted next-generation sequencing (NGS) in 240 NSCLC patients treated with anti-PD-(L)1 monotherapy or in combination with anti-CTLA4. Durable clinical benefit (DCB) was defined as complete response (CR)/partial response (PR)/stable disease (SD) that lasted 6 months. TMS, TMB and PD-L1 expression were compared among DCB and no durable benefit (NDB) NSCLC patients.
Result
The total TMS was significantly correlated with TMB (R=0.98, P<0.001) and performed almost equally to TMB in the analysis. 12 genes and 11 genes (5 sharing genes) were significantly associated with longer progression-free survival (PFS) and response (DCB vs NDB), respectively. The number of mutated genes within these 18 genes were defined as TMS18. In the survival analysis of PFS, the HRs of the high group were TMS19 (HR=0.307, P<0.001), TMB (HR=0.455, P<0.001), and PD-L1 expression (HR= 0.403, P=0.02), separately. Moreover, patients with DCB had significantly higher TMS18 (P<0.001), TMB (P=0.006), and PD-L1 expression (P=0.032). High TMS18 group had highest proportion of CR/PR/SD patients, which was 74.1% (CR/PR/SD: 3/17/20), especially in distinguishing CR patients. Taken together, TMS18 was more powerful than TMB and PD-L1 in predicting response of ICIs in NSCLC.
Conclusion
Simple transformation from unselective TMB to selective TMS greatly enhanced the power of mutations-based biomarkers. TMS in combination with PD-L1 expression might yield better efficiency in predicting response of ICIs in NSCLC with future validation in larger cohorts.
