Virtual Library
Start Your Search
Yaxiong Zhang
Author of
-
+
P1.10 - Prevention and Tobacco Control (ID 175)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Prevention and Tobacco Control
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
-
+
P1.10-03 - Gut Microbiota and Lung Cancer: A Mendelian Randomisation Study (Now Available) (ID 386)
09:45 - 18:00 | Author(s): Yaxiong Zhang
- Abstract
Background
Several studies have highlighted the association between the gut microbiota and lung cancer. However, robust epidemiological evidence able to discern this causal relationship does not exist. We aimed to investigate whether gut microbiota is causally associated with lung cancer through a two-sample Mendelian randomisation (MR) approach.
Method
Genetic instrumental variables of 23 genera at genome-wide significance (P < 5×10-8) were obtained from five available genome-wide association study (GWAS) of the gut microbiota. We conducted a two-sample MR analysis to access the causality between 23 genera of gut microbiota and lung cancer, based on the publicly available GWAS summary data from the International Lung Cancer Consortium (ILCCO, 11 348 lung cancer cases and 15 861 controls) and other consortiums. We applied several different MR methods for deriving causal estimates: Wald ratio, inverse‐variance weighted, weighted median, and MR‐Egger. Additional sensitivity analyses were utilized to detect potential pleiotropy bias.
Result
Among 23 genera, a 1 allele increase in single nucleotide polymorphisms related to higher Oscillospira was associated with a 26.1% lower risk of lung cancer (odds ratio (OR) 0.739, 95% confidence interval (CI) 0.570 to 0.959, P = 0.023). We also identified genetic predisposition towards higher Weissella, based on 1 SNP, was associated with lower risk of lung cancer (OR 0.804, 95% CI 0.693 to 0.933, P = 0.004). No associations were found for the other 21 genera, namely Acidaminococcus, Acinetobacter, Aggregatibacter, Anaerostipes, Atopobium, Bacteroides, Bifidobacterium, Coprococcus, Desulfovibrio, Dorea, Eggerthella, Eubacterium, Faecalibacterium, Lachnospira, Lactobacillus, Leuconostoc, Megamonas, Mogibacterium, Pseudobutyrivibrio, Roseburia, and Slackia.
Table 1. Mendelian randomisation estimates of the associations between 23 gut microbiota and risk of lung cancer.
Microbiota
SNP
IVW / (Wald ratio, SNP<3) Weighted median MR-Egger OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Acidaminococcus 5 1.001 (0.999, 1.004) 0.355 1.001 (0.998, 1.004) 0.424 1.000 (0.996, 1.005) 0.894 Acinetobacter 1 1.193 (0.984, 1.447) 0.073 Aggregatibacter 1 0.960 (0.884, 1.043) 0.335 Anaerostipes 2 0.989 (0.933, 1.048) 0.701 Atopobium 1 0.940 (0.876, 1.008) 0.083 Bacteroides 5 0.999 (0.997, 1.000) 0.134 0.999 (0.997, 1.000) 0.152 0.999 (0.997, 1.000) 0.213 Bifidobacterium 2 1.008 (0.934, 1.087) 0.844 Coprococcus 1 1.138 (0.732, 1.769) 0.567 Desulfovibrio 2 0.992 (0.958, 1.028) 0.658 Dorea 1 1.058 (0.984, 1.137) 0.128 Eggerthella 1 1.001 (0.998, 1.005) 0.442 Eubacterium 1 0.999 (0.922, 1.083) 0.987 Faecalibacterium 3 1.004 (0.952, 1.058) 0.889 1.002 (0.947, 1.060) 0.949 0.990 (0.907, 1.080) 0.852 Lachnospira 1 1.028 (0.925, 1.141) 0.609 Lactobacillus 2 0.972 (0.897, 1.054) 0.497 Leuconostoc 1 0.964 (0.860, 1.081) 0.533 Megamonas 3 1.016 (0.990, 1.043) 0.231 1.016 (0.985, 1.048) 0.307 0.908 (0.725, 1.137) 0.556 Mogibacterium 0.998 (0.897, 1.110) 0.973 Oscillospira 1 0.739, (0.570, 0.959) 0.023* Pseudobutyrivibrio 1 0.961 (0.877, 1.053) 0.397 Roseburia 1 0.980 (0.843, 1.140) 0.796 Slackia 1 0.980 (0.935, 1.027) 0.401 Weissella 1 0.804 (0.693, 0.933) 0.004* *: P value < 0.05; IVW: inverse-variance weighted; OR: odds ratio; CI: confidence interval.
Conclusion
Our present mendelian randomisation study provided evidence of a causal effect of the gut microbiota on lung cancer, suggesting Oscillospira and Weissella might be the focus of future research. Further studies are needed to confirm these causality and elucidate the potential mechanisms.
-
+
P1.11 - Screening and Early Detection (ID 177)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
-
+
P1.11-09 - Risk of Second Primary Malignancy After Non-Small-Cell Lung Cancer: A Competing Risk Nomogram Based on the SEER Database (ID 975)
09:45 - 18:00 | Author(s): Yaxiong Zhang
- Abstract
Background
With the improvement of survival for non-small-cell lung cancer (NSCLC), research focused on second primary malignancy (SPM) in NSCLC survivors is becoming urgent. This study aimed to estimate the incidence and risk of SPM in NSCLC patients.
Method
We retrospectively analysed 78,175 NSCLC patients diagnosed between 2004 and 2010 in SEER database, with 3,161 (4.04%) SPM cases observed. We firstly evaluated the crude and cumulative incidence of SPM. SPM incidence in NSCLC survivors compared to that in the age-specific reference population was calculated as standardized incidence ratio (SIR). A competing risk nomogram was also built, to predict the risk of SPM.
Result
The crude and 10-year cumulative incidences of SPM were 4.04% and 5.05% (95% CI 4.87%-5.25%), respectively, while the SIR was 1.62 (95% CI 1.56-1.68). Initial primary cancer (IPC) diagnosed when aged 60-74 years old, male, black people, being married, IPC in the upper lobe and indicators of better prognosis of IPC were risk factors of SPM after the initial primary NSCLC. A competing risk nomogram was built for the prediction of SPM after the initial primary NSCLC. (Fig. 1) The nomogram was well calibrated and had good discriminative ability, with c-index of 0.80. It showed a significantly wide interval of SPM cumulative incidence between the first and tenth-decile according to the risk model (1.04% vs. 16.70%, p<0.05). The decision curve analysis indicated that the clinical net benefit of the risk model was larger than that in other scenarios (all-screening or no-screening) in a range of threshold probabilities (1% to 20%).
Conclusion
Our study firstly performed a systematic estimation of the incidence of SPM in NSCLC, which implied the necessity of a risk predicting model. We developed the first competing risk nomogram to predict the risk of SPM, which performed well in the evaluation and might be helpful for individualized SPM screening.
-
+
P2.04 - Immuno-oncology (ID 167)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Immuno-oncology
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
-
+
P2.04-13 - Interleukin-18 and Lung Cancer: A Mendelian Randomization Study (ID 336)
10:15 - 18:15 | Author(s): Yaxiong Zhang
- Abstract
Background
Previous studies have shown that Interleukin-18 (IL-18) suppresses the growth of lung cancer. IL-18 might restore natural killer cell-mediated immunosurveillance against MHC class I-deficient tumors and enhance the therapeutic effects of cancer immunotherapy. However, whether there is a causal influence of higher IL-18 protecting against lung cancer remains unknown. We aim to explore whether genetically predicted circulating level of IL-18 is associated with lung cancer through 2-sample Mendelian randomization (MR) analysis.
Method
We obtained the summary data for significant single-nucleotide polymorphisms (SNPs, P<5×10–8 ) associated with serum IL-18 from a genome-wide association study of 8293 healthy adults. Their associations with lung cancer and its histological subtypes were evaluated in the International Lung Cancer Consortium (ILCCO, 11348 lung cancer cases, and 15861 controls) applying Inverse variance–weighted (IVW) meta-analysis, Weighted-median analysis, Mendelian randomization–Egger regression, Simple mode method, and Weighted mode method. We also performed several sensitivity analyses to evaluate the potential violation of MR assumptions.
Result
Genetically predicted IL-18 level is associated with lower risk of lung cancer (Odds ratio [OR] per 1 standard deviation (SD) increase: 0.824, 95% confidence interval (CI) 0.762-0.890, p<0.001). Similar trends were shown in the histological subtypes of lung cancer: lung adenocarcinoma (OR per 1 SD increase: 0.816, 95%CI 0.708-0.941, p=0.005) and squamous cell lung cancer (OR per 1 SD increase: 0.883, 95%CI 0.787-0.990, p=0.034). Our sensitivity analyses also showed that there was no directional pleiotropy bias and horizontal pleiotropy bias.
Table 1. Mendelian randomization estimates of associations of genetically predicted circulating IL-18 and lung cancer and its histological subtypes using different analysis methods.
Variants
Outcome
Method
OR
95%CI
P value
Heterogeneity p†
MR-Egger intercept p‡
IL-18
Lung cancer
IVW
0.824
0.762-0.890
<0.001*
0.868
MR Egger
0.964
0.750-1.239
0.787
0.976
0.251
Weighted median
0.823
0.748-0.906
<0.001*
Simple mode
0.826
0.714-0.955
0.042*
Weighted mode
0.826
0.727-0.938
0.026*
IL-18
Lung adenocarcinoma
IVW
0.816
0.708-0.941
0.005*
0.216
MR Egger
1.137
0.752-1.720
0.568
0.372
0.159
Weighted median
0.840
0.713-0.989
0.036*
Simple mode
0.807
0.622-1.046
0.156
Weighted mode
0.827
0.663-1.032
0.144
IL-18
Squamous cell lung cancer
IVW
0.883
0.787-0.990
0.034*
0.809
MR Egger
0.791
0.557-1.123
0.247
0.765
0.545
Weighted median
0.927
0.803-1.070
0.301
Simple mode
0.919
0.736-1.146
0.480
Weighted mode
0.939
0.765-1.153
0.572
*: P value < 0.05; IVW: inversevariance weighted; OR: odds ratio; CI: confidence interval.
Conclusion
Genetically predicted higher IL-18 is causally associated with lower lung cancer risk, indicating that IL-18 might have the potential to be used clinically to protect against lung cancer. Additional work is warranted to confirm the causality and underline the potential mechanisms.