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Jian Su
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FP01 - Early Stage/Localized Disease (ID 111)
- Event: WCLC 2020
- Type: Posters (Featured)
- Track: Early Stage/Localized Disease
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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FP01.03 - Genetic Predisposition for Pre-Invasive Lung Adenocarcinoma Manifesting as Ground-Glass Nodules with Family History of Lung Cancer (ID 1512)
00:00 - 00:00 | Author(s): Jian Su
- Abstract
Introduction
Lung cancer with family history have been increasing gradually of late years in East Asian, especially those presenting as pulmonary ground-glass nodules (GGNs). The predisposition of GGN with lung cancer family history remains baffling.
Methods
This prospective study (NCT04220268) enrolled patients with pulmonary pre-invasive or invasive adenocarcinoma, which presenting as GGN in computer tomography (CT) scans. We used extreme phenotype approach to select 50 GGN patients with a family history of lung cancer (FHLC) in one or more first-degree relatives. Blood samples were collected and sequenced by whole exome sequencing (WES) to investigate rare but potential pathogenic germline mutations with a stepwise filtering strategy including: variant quality and classification, minor allele frequency (MAF) < 0.01 in public and local database, functional prediction and family segregation.
Results
In total, 2325 single nucleotide variants (SNVs) and 238 frameshift mutations with MAF <0.01 were finally identified through the filter. The number of these rare, damaging germline mutations in non-smoking patients were significantly higher than those in smoking patients (Spearman’s ρ= -0.33, p=0.02). Fifty-nine SNVs and 10 frameshifts were not only rare and deleterious but also presented in more than two families. Importantly, twenty of them had been reported to be associated with higher risk or carcinogenesis of lung cancer. Two of them were validated in 126 nonoverlapping susceptibility loci for lung carcinogenesis identified by genome-wide association studies (GWAS).
Conclusion
Patients with GGNs and FHLC may have inheritable carcinogenesis mutations. These variants may potentially contribute to the risk of pulmonary pre-invasive adenocarcinoma susceptibility in Chinese population. Non-smoking patients with GGN probably had higher genetic predisposition than the smoking patients.
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MA02 - Technological Advances in Diagnostics, Imaging and Therapeutics for Lung Cancer (ID 103)
- Event: WCLC 2020
- Type: Mini Oral
- Track: Diagnostics and Interventional Pulmonology
- Presentations: 1
- Moderators:
- Coordinates: 1/29/2021, 14:15 - 15:15, Scientific Program Auditorium
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MA02.08 - Computed Tomography Attenuation Value as Considerable Predictor for Malignancy in Clinical T1 Lung Adenocarcinoma (ID 1611)
14:15 - 15:15 | Author(s): Jian Su
- Abstract
- Presentation
Introduction
To explore the quantitative variables of thoracic computed tomography for predicting the pathologic malignancy of cT1 lung adenocarcinoma.
Methods
We retrospectively collected data from 96 consecutive patients with clinical T1 lung adenocarcinoma. -160 Hu was used as the cutoff of solid and ground glass opacity portion. AAH, AIS, MIA and LPA were considered as less malignant (LM), while other subtypes of IACs were included into more malignant (MM) group.
Results
The area under receiver operating characteristic curves of m-CT value, D_solid, D_whole, Area_solid, Area_whole, 1D_CTR and 2D_CTR were respectively 0.89 (95%CI, 0.81 ~ 0.97; Se=83%, Sp=93%), 0.895 (95%CI, 0.832 ~ 0.958; Se=88%, Sp=79%), 0.736 (95%CI, 0.634 ~ 0.839; Se=87%, Sp=60%), 0.89(95%CI, 0.82 ~ 0.96; Se=87%, Sp=81%), 0.738 (95%CI, 0.634 ~ 0.841; Se=83%, Sp=63%), 0.861 (95%CI, 0.780 ~ 0.942; Se=90%, Sp=74%), 0.869 (95%CI, 0.788 ~ 0.949; Se=85%, Sp=84%). Multiple logistic regression revealed that mean CT value was the independent risk predictor of more pathologically malignancy of clinically T1 lung adenocarcinoma (p=0.003).
Table1: Clinicopathological comparison between the less malignant and more malignant groups Less malignant(n=43) More malignant(n=53) p value Age, years, mean 56.12 65.38 <0.001 Gender >0.05 Male 13 22 Female 30 31 Loaction >0.05 RUL 21 19 RML 3 3 RLL 7 12 LUL 7 10 LLL 5 9 D_solid(mm) 2.18 13.32 <0.001 D_whole(mm) 16.84 23.13 <0.001 Area_solid(mm²) 8.73 122.32 <0.001 Area_whole(mm²) 187.44 322.56 <0.001 1D_CTR 0.13 0.56 <0.001 2D_CTR 0.07 0.35 <0.001 2D m-CT Value(Hu) -629.40 -348.55 <0.001 EGFR Mutation >0.05 Mutation 19 25 Wild type 18 25 ALK Mutation >0.05 Mutation 0 2 Wild type 30 43 D_solid: the longest diameter of the solid portion in the greatest horizontal section of nodule; D_whole: the longest diameter of the greatest horizontal section of nodule; Area_solid: the area of the solid portion in the greatest horizontal section of nodule; Area_whole: the area of the greatest horizontal section of nodule; 1D_CTR: D_solid/D_whole; 2D_CTR: Area_solid/Area_whole; m-CT value: mean CT attenuation value of the greatest horizontal section of nodule. mm: millimeter; CTR: consolidation tumor ratio. Hu: Hounsfield unit. Table2: Univariate and multivariate analysis for predicting the more pathologically malignant cT1 lung adenocarcinoma. Univariate analyses Multivariate analyses Category odd ratio 95% CI p value odd ratio 95% CI p value Age continuity 1.075 1.032~1.119 <0.001 1.019 0.955~1.088 0.56 Gender Male vs Female 0.611 0.261~1.428 0.255 D_solid(mm) ≤3.473 vs > 3.473 29.593 9.624~90.996 <0.001 6.086 0.079~469.082 0.415 D_whole(mm) ≤14.807 vs >14.807 10.05 3.686~27.402 <0.001 14.991 0.635~353.753 0.093 Area_solid(mm²) ≤6.513 vs >6.513 28.75 9.517~86.850 <0.001 0.258 0.10~6.642 0.413 Area_whole(mm²) ≤156.641 vs >156.641 8.25 3.201~21.265 <0.001 0.336 0.017~6.748 0.476 1D_CTR ≤0.124 vs >0.124 27.927 8.862~88.011 <0.001 0.546 0.015~19.903 0.741 2D_CTR ≤0.040 vs >0.040 25.143 8.526~74.148 <0.001 4.232 0.454~39.442 0.205 m-CT Value(Hu) ≤-494.927 vs >494.927 65.185 16.481~257.815 <0.001 19.723 2.783~139.780 0.003 95%CI: 95% confidential index; mm: millimeter; CTR: consolidation tumor ratio. Hu: Hounsfield unit.
Conclusion
Mean CT attenuation value is useful for predicting the higher pathologically malignant degree of clinical T1 lung adenocarcinoma. M-CT value is a potential reference factor for the formulation of surgical procedure for cT1 lung adenocarcinoma.
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MA13 - Tumor Biology: Focus on EGFR Mutation, DNA Repair and Tumor Microenvironment (ID 214)
- Event: WCLC 2020
- Type: Mini Oral
- Track: Tumor Biology and Systems Biology - Basic and Translational Science
- Presentations: 1
- Moderators:
- Coordinates: 1/31/2021, 16:45 - 17:45, Scientific Program Auditorium
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MA13.09 - Heterogeneous Genomic Evolution and Immune Microenvironments in Metastatic Lung Cancer (ID 1155)
16:45 - 17:45 | Author(s): Jian Su
- Abstract
Introduction
The comprehensive insights into the genomic evolution and immune microenvironments of lung cancer metastasis remain unknown. Furthermore, whether non-stochastic patterns of lung cancer metastases to different sites exist is elusive.
Methods
We investigated the genomic evolution and immune microenvironments of paired primary-metastatic (P-M) tumors by employing multi-region whole-exome sequencing and immunohistochemistry in 179 samples from 51 lung cancer patients with metastases to the pleura, bone, adrenal gland, brain and additional lymph nodes.
Results
Our data revealed differences in genomic landscapes, molecular determinants, seeding patterns, and lymphocyte infiltration among different metastatic sites. Metastatic lymph nodes showed the highest P-M genomic divergence, followed by pleura, brain, bone, and adrenal gland. We identified MYC amplification as a selected event driving metastasis and associated with worse overall survival (P = 0.013). Interestingly, EGFR amplification and TP53 mutations were preferably selected in distant metastases whereas RICTOR amplification was selected in regional metastases (pleura and lymph nodes). Based on spatial tumor growth model, we demonstrated commonly late arising of metastatic seeding (61.1%) of lung cancer with quantitative evidence. However, mutation rate and timing of dissemination varied among different metastatic sites. Metastases at regional tissues were more frequently infiltrated with CD8+ tumor-infiltrating lymphocytes (TILs) than those at distant organs, among which bone metastases were merely infiltrated with CD8+ TILs. Furthermore, monoclonal and polyclonal seeding were associated with rapid and attenuated progression (P = 0.013), respectively, which supports the potential value as a prognostic predictor. Immune-heterogeneity and -homogeneity were primarily driven by arm-level and focal copy number events in primary tumors, respectively, indicating distinct mechanisms of tumor immune escape during metastasis.
Conclusion
These findings implied the combinatorial role of multiple factors in shaping patterns of dissemination and advanced the clinical evaluation and intervention of lung cancer metastasis.
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P72 - Tumor Biology and Systems Biology - Basic and Translational Science - Tumor Microenvironment (ID 211)
- Event: WCLC 2020
- Type: Posters
- Track: Tumor Biology and Systems Biology - Basic and Translational Science
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P72.02 - Cellular Landscape of Tumor Immune Microenvironment and Genetic Signatures Identify Prognostic of LUAD (ID 736)
00:00 - 00:00 | Author(s): Jian Su
- Abstract
Introduction
Tumour microenvironment (TME) has been recognized to support the initiation and progression of lung adenocarcinoma (LUAD). The innate and adaptive immune cells in the lung TME harbour both tumour-promoting and tumour-suppressing activities, which may also predict clinical outcome. Therefore we carried out a systematic analysis of cellular interactions in tumor immune microenvironment. And identify cell-intrinsic and cell-extrinsic pathways cell types and activation states that may serve as biomarkers of overall survival (OS).
Methods
Public gene-expression data and relevant clinical annotation were obtained from Gene-Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Three TME infiltration patterns were comprehensively analyzed in 442 LUAD patients using CIBERSORT algorithm and the LM22 gene signature. Based on the TME patterns, we build a model to calculate TMEscore based on gene set variation analysis via ssGSEA algorithm. Functional enrichment analysis were performed by GO and KEGG.
Results
Four datasets with available outcome data and clinical information in GEO and TCGA-LUAD were enrolled in our study. GSE72094 was used as the training cohort, while GSE11969, GSE26939, GSE31210 and TCGA-LUAD was used as validation cohorts. TME cell network established based on GSE72094 depicted a comprehensive landscape of tumor-immune cell interactions, cell lineages, and their correlation with OS (Fig. 1A, 1B). Three subgroups with distinct TME signature gene sets were obtained/identified based on unsupervised hierarchical clustering in 442 LUAD cases. OS in TME gene subgroup B was significantly longer than which in TME gene subgroup A and subgroup C. TME gene group B was associated immune activation (Fig. 1C). TMEscore was further constructed using principal component analysis algorithms. Lower TMEscore is significantly associated with better prognosis. Functional annotation analysis showed TMEscore had a positive correlation with cell cycle, DNA replication, homologous recombination, mismatch repair, nucleotide excision repair and DNA damage repair (Fig. 1D). The enriched pathways in subtype with lowest/low TMEscore involved bile_acid_metabolism, fatty_acid_metabolism and myogenesis. While high TMEscore subtype was characterized by significant enrichment of interferon_alpha_response, myc_targets and unfolded_protein_response pathway (Fig. 1E). TMEscore model was then validated on 525 patients from GEO datasets and 585 patients from TCGA-LUAD project and proved to be a valuable method for prognostic stratification of LUAD except for TNM stage(Fig. 1F).
Conclusion
Variability in the composition of the tumor immune microenvironment contributes to heterogeneity in OS. Deeper validation is in need to define the positive association between lower TMEscore and longer OS.
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P85 - Targeted Therapy - Clinically Focused - MET (ID 262)
- Event: WCLC 2020
- Type: Posters
- Track: Targeted Therapy - Clinically Focused
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P85.02 - NGS could not Replace FISH Regarding to MET Amplification as an Optimal Biomarker (ID 1581)
00:00 - 00:00 | Author(s): Jian Su
- Abstract
Introduction
MET amplification (MET amp) is known as an important mechanism of resistance to EGFR-TKIs in NSCLC. We investigated the association between survival benefits and MET status identified by different methods, to explore the appropriate biomarker to select patient for MET-TKIs treatment in advanced NSCLC.
Methods
Method: FISH (fluorescence in situ hybridization), IHC (immunohistochemistry) and NGS (next generation sequences) were performed prospectively from FFPE/liquid samples with NSCLC. MET amplification by FISH was defined as MET/CEP7 ratio＞2 or CN(copy number )＞6 and served as the standard reference for over-express by IHC and copy number gain (CNG) by NGS. Objective response (OR) and PFS were used to confirm optimal biomarker for MET inhibitor.
Results
We identified MET dysregulation of 37 NSCLC patients by FISH, IHC and NGS before MET-TKIs administration and assessing the survival benefits of 33 cases treated MET inhibitor. The consistence of FISH, IHC and NGS was only 54%. They are the different population. MET amplification identified by FISH proved the best predictive efficiency for survival benefits. The PR rate was 82% (18/22) and median PFS was 4.8 months in MET amp, compared to 1.0 months for cases with non-MET amp (P= 0.004). Both MET dysregulations identified by NGS or IHC failed to distinguish the significant survival difference in patients with MET-TKIs. Comparing with MET amplification by FISH, effective cases were more seen in patients with CNG > 4.0 or IHC score ＞290 . Based on these two cut-off values：CNG > 4.0 or IHC score ＞290 still did not predict efficacy of MET inhibitors, suggesting CNG by NGS had no significant associations with efficacy benefits.
Conclusion
Compared to MET amplification identified by FISH, CNG dysregulation by NGS or MET protein over-express by IHC could not serves as the predictive biomarker for MET inhibitors.
