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Yun Fan
Zhejiang cancer hospital
Corresponding Author
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Background
Prognosis of stage II/III non–small-cell lung cancer (NSCLC) is unsatisfactory even after complete tumor resection and adjuvant chemotherapy. Tumors with high immunogenicity were defined as “hot tumors” and associated with clinical benefits from immunotherapy. Here we assessed the prognostic and predictive value of immunogenomic signatures and gene mutation characters for stage II/III non-small cell lung cancer in Chinese patients.
Methods
Ninety-one paired resected stage II/III NSCLC and normal tissues and peripheral blood samples, including 47 squamous cell lung carcinomas (SCC) and 44 lung adenocarcinomas (ADC), were collected and analyzed using the whole exome sequencing (WES) to identify gene mutation and immunogenomic signatures for association with clinicopathological variables and disease-free survival (DFS).
Results
A high number of tumor mutation burden (TMB, > 4 mutations/Mb) was associated with better DFS of NSCLC patients, although there was no such an association in SCC and ADC subgroups. Moreover, higher neoantigen load (NAL, > 2 neoantigens/Mb) exhibited better DFS and survival benefit after adjuvant chemotherapy in a low NAL subgroup of SCC patients but not in ADC subgroup. A high DNA damage repair (DDR) index (gene mutations occurred in at least three different DNA repair pathways) was associated with high NAL numbers and favorable DFS of SCC, but not in ADC patients. However, mutations of individual gene, oncogene pathways, and antigen presentation machinery genes, and human leukocyte antigen (HLA)-I number and HLA-I loss of heterozygosity (LOH) had no prognostic or predictive value for DFS of SCC or ADC patients.
Conclusion
Given the present information, NAL was a useful biomarker for lung SCC prognosis and prediction of chemotherapy responses in Chinese patients. Further study with a larger sample size from multiple institutions is needed to confirm these data.
Keywords
NSCLC, whole exome sequencing, neoantigen load, biomarker, prognosis
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Supp.Figs.docx
Supplemental Fig. 1. Gene mutation spectra and significantly mutated genes in ADC stratified by tobacco smoking status and EGFR mutations. A. Gene mutation spectra in ADC with or without tobacco smoking history. *P ≤ 0.05 and ***P ≤ 0.001 using Student’s t-test. B, The top 30 significantly mutated genes in ADC stratified by tobacco smoking history. The samples were made in order according to their somatic non-synonymous mutation burden (in the top panel) and genes were ranked by mutation frequencies. C, Gene mutation spectra in ADC stratified by EGFR mutations. *P ≤ 0.05 using Student’s t-test. D, The top 30 significantly mutated genes in ADC stratified by EGFR mutations. The samples were ordered according to their somatic non-synonymous tumor mutation burden (in the top panel) and genes were ranked by mutation frequencies. Supplemental Fig. 2. Association of mutated individual genes with DFS of SCC and ADC patients. A, SCC. B, ADC. Supplemental Fig. 3. Illustration of the frequent gene mutations enriched in the oncogenic pathways in ACC stratified by tobacco smoking status and EGFR mutations. A, The gene mutation status in the top ten oncogenic pathways in ADC with or without tobacco smoking history. B, The gene mutation status of the top ten oncogenic pathways in ADC with or without EGFR mutations. Supplemental Fig. 4. Kaplan-Meier curves of DFS stratified by the ten oncogenic pathways in SCC and ADC. A, SCC. B, ADC. Supplemental Fig. 5. Illustration of HLA-I number and LOH and EGFR mutations in NSCLC. A, The distribution of HLA-I number in ADC stratified by tobacco smoking status and EGFR mutations. B, The distribution of HLA-I LOH in ADC stratified by tobacco smoking status and EGFR mutations. C, Kaplan-Meier curves. The data showed a prognostic significance of HLA number and LOH in prediction of DFS in SCC. D, Kaplan-Meier curves. The data showed a prognostic significance of HLA number and LOH in prediction of DFS in ADC. Supplemental Fig. 6. Immunogenomic profiling of ADC stratified by smoking history and EGFR mutations. A, smoking history. B, EGFR mutation Supplemental Fig. 7. Kaplan-Meier plots. The data showed the prognostic significance of the DDR and APM pathways in prediction of DFS in SCC (A and C) and ADC (B and D). Supplemental Fig. 8. Association of the DDR index with other immunogenic variants in NSCLC. A, Association of the DDR index with TMB and NAL in SCC patients. B, Association of the DDR index with HLA number and LOH in SCC patients. C, Association of the DDR index with TMB and NAL in ADC patients. D, Kaplan-Meier plots. The data showed a prognostic significance of the DDR index in prediction of DFS in ADC. E, Association of the DDR index and HLA number and LOH in ADC patients. Supplemental Fig. 9. Association of different clinicopathological data with DFS of NSCLC patients. A, Comparison of TMB with NAL between ADC patients with or without tobacco smoking history. B, Comparison of TMB with NAL between ADC patients with or without EGFR mutations. C, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in all NSCLC patients. D, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in ADC. E, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in SCC. F, Kaplan-Meier plots. The data showed a prognostic significance of NAL in prediction of DFS in ADC. Supplemental Fig. 10. Association of different genomic and immunogenic features stratified by high vs. low NAL. A, Gene mutation spectra in ADC stratified by NAL. *P ≤ 0.05 and ***P ≤ 0.001, using Student’s t-test. B, the top 30 significantly mutated genes in ADC stratified by NAL. The samples were made in order according to their somatic non-synonymous tumor mutation burden (in the top panel) and genes were ranked by mutation frequencies. C, Gene mutation status of the top ten oncogenic pathways in SCC stratified by NAL. D, HLA number and LOH stratified by NAL. The similar data showed between high and low NAL. E, Gene mutation status of the DDR pathways in SCC stratified by NAL. F, Gene mutation status of the APM pathways in SCC stratified by NAL.
- supplementTable.docx
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Yun Fan
Zhejiang cancer hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 21 Dec, 2020
No community comments so far
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PDF Downloads: ...
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Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Prognosis of stage II/III non–small-cell lung cancer (NSCLC) is unsatisfactory even after complete tumor resection and adjuvant chemotherapy. Tumors with high immunogenicity were defined as “hot tumors” and associated with clinical benefits from immunotherapy. Here we assessed the prognostic and predictive value of immunogenomic signatures and gene mutation characters for stage II/III non-small cell lung cancer in Chinese patients.
Methods
Ninety-one paired resected stage II/III NSCLC and normal tissues and peripheral blood samples, including 47 squamous cell lung carcinomas (SCC) and 44 lung adenocarcinomas (ADC), were collected and analyzed using the whole exome sequencing (WES) to identify gene mutation and immunogenomic signatures for association with clinicopathological variables and disease-free survival (DFS).
Results
A high number of tumor mutation burden (TMB, > 4 mutations/Mb) was associated with better DFS of NSCLC patients, although there was no such an association in SCC and ADC subgroups. Moreover, higher neoantigen load (NAL, > 2 neoantigens/Mb) exhibited better DFS and survival benefit after adjuvant chemotherapy in a low NAL subgroup of SCC patients but not in ADC subgroup. A high DNA damage repair (DDR) index (gene mutations occurred in at least three different DNA repair pathways) was associated with high NAL numbers and favorable DFS of SCC, but not in ADC patients. However, mutations of individual gene, oncogene pathways, and antigen presentation machinery genes, and human leukocyte antigen (HLA)-I number and HLA-I loss of heterozygosity (LOH) had no prognostic or predictive value for DFS of SCC or ADC patients.
Conclusion
Given the present information, NAL was a useful biomarker for lung SCC prognosis and prediction of chemotherapy responses in Chinese patients. Further study with a larger sample size from multiple institutions is needed to confirm these data.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Supp.Figs.docx
Supplemental Fig. 1. Gene mutation spectra and significantly mutated genes in ADC stratified by tobacco smoking status and EGFR mutations. A. Gene mutation spectra in ADC with or without tobacco smoking history. *P ≤ 0.05 and ***P ≤ 0.001 using Student’s t-test. B, The top 30 significantly mutated genes in ADC stratified by tobacco smoking history. The samples were made in order according to their somatic non-synonymous mutation burden (in the top panel) and genes were ranked by mutation frequencies. C, Gene mutation spectra in ADC stratified by EGFR mutations. *P ≤ 0.05 using Student’s t-test. D, The top 30 significantly mutated genes in ADC stratified by EGFR mutations. The samples were ordered according to their somatic non-synonymous tumor mutation burden (in the top panel) and genes were ranked by mutation frequencies. Supplemental Fig. 2. Association of mutated individual genes with DFS of SCC and ADC patients. A, SCC. B, ADC. Supplemental Fig. 3. Illustration of the frequent gene mutations enriched in the oncogenic pathways in ACC stratified by tobacco smoking status and EGFR mutations. A, The gene mutation status in the top ten oncogenic pathways in ADC with or without tobacco smoking history. B, The gene mutation status of the top ten oncogenic pathways in ADC with or without EGFR mutations. Supplemental Fig. 4. Kaplan-Meier curves of DFS stratified by the ten oncogenic pathways in SCC and ADC. A, SCC. B, ADC. Supplemental Fig. 5. Illustration of HLA-I number and LOH and EGFR mutations in NSCLC. A, The distribution of HLA-I number in ADC stratified by tobacco smoking status and EGFR mutations. B, The distribution of HLA-I LOH in ADC stratified by tobacco smoking status and EGFR mutations. C, Kaplan-Meier curves. The data showed a prognostic significance of HLA number and LOH in prediction of DFS in SCC. D, Kaplan-Meier curves. The data showed a prognostic significance of HLA number and LOH in prediction of DFS in ADC. Supplemental Fig. 6. Immunogenomic profiling of ADC stratified by smoking history and EGFR mutations. A, smoking history. B, EGFR mutation Supplemental Fig. 7. Kaplan-Meier plots. The data showed the prognostic significance of the DDR and APM pathways in prediction of DFS in SCC (A and C) and ADC (B and D). Supplemental Fig. 8. Association of the DDR index with other immunogenic variants in NSCLC. A, Association of the DDR index with TMB and NAL in SCC patients. B, Association of the DDR index with HLA number and LOH in SCC patients. C, Association of the DDR index with TMB and NAL in ADC patients. D, Kaplan-Meier plots. The data showed a prognostic significance of the DDR index in prediction of DFS in ADC. E, Association of the DDR index and HLA number and LOH in ADC patients. Supplemental Fig. 9. Association of different clinicopathological data with DFS of NSCLC patients. A, Comparison of TMB with NAL between ADC patients with or without tobacco smoking history. B, Comparison of TMB with NAL between ADC patients with or without EGFR mutations. C, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in all NSCLC patients. D, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in ADC. E, Kaplan-Meier plots. The data showed a prognostic significance of TMB in prediction of DFS in SCC. F, Kaplan-Meier plots. The data showed a prognostic significance of NAL in prediction of DFS in ADC. Supplemental Fig. 10. Association of different genomic and immunogenic features stratified by high vs. low NAL. A, Gene mutation spectra in ADC stratified by NAL. *P ≤ 0.05 and ***P ≤ 0.001, using Student’s t-test. B, the top 30 significantly mutated genes in ADC stratified by NAL. The samples were made in order according to their somatic non-synonymous tumor mutation burden (in the top panel) and genes were ranked by mutation frequencies. C, Gene mutation status of the top ten oncogenic pathways in SCC stratified by NAL. D, HLA number and LOH stratified by NAL. The similar data showed between high and low NAL. E, Gene mutation status of the DDR pathways in SCC stratified by NAL. F, Gene mutation status of the APM pathways in SCC stratified by NAL.
- supplementTable.docx
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