Exploration of the role of tumor mutation burden in clinical significance, immunotherapy response predictor and immune cell infiltration in colon cancer

doi:10.21203/rs.3.rs-31934/v1

Download PDF

Research article

Exploration of the role of tumor mutation burden in clinical significance, immunotherapy response predictor and immune cell infiltration in colon cancer

https://doi.org/10.21203/rs.3.rs-31934/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Backgroud:

Tumor mutation burden has become a powerful bio-marker to predict prognosis and immunotherapy responsiveness to patients in various cancers, but the role of TMB in colon cancer is still unclear.

Methods:

The transcriptome profiling data of colon patients and the simple nucleotide variation data of colon cases were downloaded from the Cancer Genome Atlas (TCGA) database. The groups were divided into high TMB and low TMB group according to the median of TMB. Then we explored the relationship between immune checkpoints, immune cells and TMB, respectively.

Results:

Mutation profiles of 399 colon cancer samples were analyzed in TCGA database. The senior (age＞65) had a strong relationship with higher-TMB level(p=0.001). Low-TMB group correlated with advanced N stage (P＜0.001), M stage (P＜0.001), and pathologic stage(P＜0.001). High-TMB group had significantly higher mRNA level of PD-L1, TIGIT, HAVCR2, and LAG3 than low-TMB group, which indicated high-TMB referred to better immunotherapy responsiveness in colon cancer. And high-TMB level correlated with higher fractions of CD8T cells (p=0.021), higher CD4 memory T cells(p=0.039), follicular helper T cells (p=0.002)and M1 macrophages (p＜0.001), while the low-TMB groups correlated with higher regulator T cells (p=0.002). So high-TMB correlated with stronger immune cell infiltration

Conclusions:

The high TMB referred to better clinical pathologic features, better immunotherapy responsiveness and stronger immune cells infiltration in colon cancer. Hence TMB may be a very promising bio-marker to predict prognosis and immunotherapy responsiveness to patients in colon cancer.

Cancer Biology

Oncology

Colorectal cancer (CRC) is the second prevalent cancer and the fourth leading cause of cancer-related lethality globally.¹ Traditional treatment, such as surgical resection, chemotherapy, had obvious limitations in recurrence and distant metastasis. Recently

immunotherapy seems to be the potential and effective treatment in various cancers^2-10, including colon cancer⁷. However, only about 20% of patients with cancer have good response to immunotherapy¹¹. Because of the scarcity of bio-mark applied to predict the efficacy of immunotherapy in colon cancer, there is a compelling need to find out who could be the candidates to receive immunotherapy.

Researchers find the phenomenon CRC tumor of microsatellite instability (MSI) phenotype express more neo-antigen and are enriched with more immune cells, which activate antitumor responses. Hence CRC patients with microsatellite stability (MSS) significantly correlate with poorer immune cell infiltration and worse prognosis than CRC patients with MSI¹². MSI in cancers is characterized by a tumor mutation burden (TMB), defined as the number of DNA damages, including somatic single variant (SNV), insertions, deletions and frameshift mutations^12,13. Previous studies demonstrated TMB was strong relative to immune checkpoint inhibitors (ICI) and immune cell infiltration in various cancers^14-18, which are heavily associated with response to immunotherapy and tumor behavior^19-21.

Given the importance of TMB and less effective immunotherapy in CRC, it is urgent to explore the role of tumor mutation burden in clinical significance, immunotherapy response predictor, and immune cell infiltration in colon cancer.

Getting transcriptome profiling data, simple nucleotide variation and clinical data

Our data was based on the Cancer Genome Atlas (TCGA) database via the GDC data portal (https://portal.gdc.cancer.gov/). We downloaded the transcriptome profiling, simple nucleotide variation and clinical data of colon cancer in the Data Category as the data source, and choose “VarScan2” software from the “Masked Somatic Mutation”. Then, we got 471 tumor samples from the transcriptome profiling and the analysis result of simple nucleotide variation which was Genecloud, interaction, summary and warterfall maps by the “maftools” package. Besides, the clinical data was related to the case of colon cancer, comparing with age, gender, TNM grouping and stage.

Analysis of TMB

We got the data of simple nucleotide variation from TCGA database and use “VarScan2” software to get each gene exact value of TMB (total number of mutations per megabase in colon cancer). We divided TMB into high and low expression group by median. Then using “limma” package, we explored the relationship between TMB and clinical basal information, such as age, gender, TNM grouping and stage.

Immune checkpoints and TMB

Immune checkpoints are the predictor of the efficacy of immunotherapy. In this study, we tied to explore the association between TMB and the mRNA level of immune checkpoints (PDL-1, TIGIT, LAG3, HAVCR2).

Gene Set Enrichment Analysis (GSEA) and TMB

Via “org.Hs.eg.db” package, we can know the symbol ID to analyze. Then GSEA analysis was performed between high- TMB group and low-TMB group by Molecular Signatures Database v7.1 (http://software.broadinstitute.org/gsea/msigdb/).

Immune cells and TMB

By removing extreme low expression genes, we used CIBERSORT to get the immune cells via "limma" and "preprocessCore" packages. Obviously, we get Histogram of immune cell content (p=0.05), after that we also compared different immune cells in high- TMB group and low-TMB group by “vioplot” and “limma” packages.

Statistical analysis

We also used the “Limma” package, which is data analysis, linear models and differential expression for microarray data. We conducted the association between TMB and immune checkpoints via Student Test in Graphpad 8.0. Wilcoxon test was applied to compare data between different groups. All P-values were two tailed, and P＜0.05 was considered statistically significant. Last, all statistical analysis was run on R x64(Version 3.9.6).

Landscape of mutation profiles in colon cancer

We loaded the results of whole-exome sequencing of 471 patients with colon cancer and the simple nucleotide variation data of 433 colon cases from the Cancer Genome Atlas (TCGA) database, and mutation profiles of 399 colon cancer samples were analyzed in TCGA database. Then we used the “maftools” package to visualize and classify the mutation profiles. In summary, missense mutation was the most fraction, SNP occurred most frequently, and C＞T tranversion accounted for the most common type of SNV in colon cancer. We also showed the number of altered bases per sample and the mutation type (Figure 1A). Furthermore, we found out the top 10 mutated genes in colon cancer, including APC (75%), TP53 (55%), TTN (49%), KRAS(43%), SYNE1(29%), PIK3CA(28%), MUC16(28%), FAT(23%), ZFHX4(21%), RYR2(21%). Meanwhile, the frequencies of all mutated genes were presented by genecloud in Figure S1 and the main interactions across them were shown in Figure S2. At last we exhibited mutation information of the top 30 mutated genes in each sample (Figure 1B).

TMB correlated with clinical relevance and immune checkpoint inhibitor

After calculating the frequencies of mutations per million bases as the TMB for 399 patients with colon cancer, we divided the patients into high-TMB group and low-TMB group by the cutoff value, which is the median number of TMB. Interestingly, the senior (age＞65) had a strong relationship with higher-TMB level(p=0.001) (Figure 2A), which made it hard to analysis the overall survival between high-TMB group and low-TMB group with Kaplan-Meier plotter. Obviously, low-TMB group correlated with advanced N stage (P＜0.001), M stage (P＜0.001), and pathologic stage(P＜0.001), which indicated low-TMB group had a poorer survival for patients with colon cancer (Figure 2D-F). There were no significant differences in association of TMB with gender and T stage (Figure 2B-C).

High-TMB group had significantly higher CD274 (P＜0.001), HAVCR2 (P＜0.001), and LAG3 (P＜0.001), TIGIT (P=0.001) than low-TMB group in Figure 3. Hence high-TMB group might be more sensitive to immune checkpoint inhibitor.

Association of TMB and immune cell infiltration

We conducted GSEA analysis between high-TMB group and low-TMB group, and we found the top items(|NES|＞2, FDR＜0.05, p＜0.01), including antigen processing and presentation(Figure 4A), natural killer cell mediated cytotoxicity (Figure 4B), autoimmune thyroid disease(Figure 4C), graft versus host disease(Figure 4D). Because the results of GSEA had a correlation with immune signature, we performed immune cells analysis. We presented 22 specific immune fractions in each colon cancer sample in Figure 5A. And CIBERSORT algorithm was performed to calculate the 22 specific immune fractions between high-TMB group and low-TMB group in Figure 5B. We found high-TMB level correlated with higher fractions of CD8T cells (p=0.021), higher CD4 memory T cells(p=0.039), follicular helper T cells (p=0.002)and M1 macrophages (p＜0.001), while the low-TMB groups correlated with higher regulator T cells (p=0.002). So compared with the low-TMB group, the high TMB referred to stronger immune cells infiltration in colon cancer

Tumor genesis and development follows a complex and multistep process, involving somatic mutations in genome^22-24. Somatic mutations lead to a change in amino aicds which may increase the number of neo-antigens theoretically, which could activate the immune system and strengthen the immune cell infiltration. The change in immune microenvironment could affect immunotherapy response and have an impact of prognosis in patients with different cancers^25-28. Based on these, TMB is becoming a hot biomarker for various cancers^29-36. However, the role of TMB in colon cancer is still undefined^14,34,36,37. In this study, we firstly and comprehensively explored the role of tumor mutation burden in clinical significance, immunotherapy response predictor, and immune cell infiltration in colon cancer using TCGA database.

We demonstrated that APC, TP53, PIK3CA and KRAS mutated high frequently in colon cancer, which were similar to previous studies³⁸. And APC, P53 and KRAS mutations play a vital role in the colon tumorigenesis³⁸. We firstly found the older were more likely to be with high TMB. Furthermore, we demonstrated low-TMB group correlated with advanced N stage, M stage, and pathologic stage, which indicated low-TMB group had a poorer survival than high-TMB group for patients with colon cancer. Lee et al also reported that TMB-high was an independent positive prognostic factor for patients with colorectal cancer who treated with curative surgery and adjuvant chemotherapy³⁹. Similar results were drawn in breast cancer, non-small lung cancer and melanoma^40,41.

Based on the relationship between TMB and colon oncology clinic, shown above, we conducted GSEA analysis between high-TMB group and low-TMB group, and we found the items, including antigen processing and presentation, natural killer cell mediated cytotoxicity, suspended on the top of list, which consolidated that TMB were strongly relative to the tumor immune microenvironment. Immune cells infiltration can affect tumor biological behavior, such its growth, invasion and migration^15,16,27. In our study, we found high-TMB level correlated with higher fractions of CD8T cells, CD4 memory T cells, follicular helper T cells and M1 macrophages, which are immunoreactive cells⁴². And numerous previous studies linked high fractions of CD8T cells, CD4 memory T cells, and follicular helper T cells with better prognosis in colon cancer^43,44. M1 macrophages can secrete pro-infammatory cytokines to prevent tumor growth, metastasis and angiogenesis⁴⁵. And Xiong Y reported M1 macrophages correlated with a favorable clinical outcome in CRC⁴⁶. Likewise, we found high TMB had a lower fraction of regulator T cells (Tregs) in colon cancer. Several studies demonstrated that Tregs negatively correlated with prognosis in CRC^47,48. Interestingly, Tregs may suppress the proliferation of immunoreactive T cells, such as CD8T cell ⁴⁹.

Nowadays immunotherapy is becoming a potential treatment for cancers^50-53. However, only about 20% of patients with cancer have good response to immunotherapy¹¹. Researchers have been making great efforts to ICBs for cancers, and CD274 (PD-L1), PD-1, TIGIT, HAVCR2, VISTA and LAG3 et al. have become the popular ICBs to predict immunotherapy responsiveness. In our study we demonstrated high-TMB group had significantly higher PD-L1, TIGIT, HAVCR2, and LAG3 than low-TMB group. And many studies prove TMB can be applied to predict the efficacy to immunotherapy in various cancers^{10,19,43,50,54,55}, including colorectal cancer³⁴. Hence TMB may also be a reliable bio-predictor to immunotherapy responsiveness in patients with colon cancer.

There are still some limitations in this study. Firstly, TMB does not reflect the actual number of neo-antigens that can activate the immune system. Secondly, because of the significant difference of clinical and basic information, for instance the age, between high TMB and low TMB group, the relationship between TMB and overall survival is still unclear. Thirdly, present silicon analysis may be limited by the quality and quantity of samples.

Totally, the older were more likely to be with high TMB. Compared with the low-TMB group, the high TMB referred to better clinical pathologic features, better immunotherapy responsiveness and stronger immune cells infiltration in colon cancer. Hence TMB may be a very promising bio-marker to predict prognosis and immunotherapy responsiveness to patients in colon cancer.

CONFLICT OF INTEREST DISCLOSURES

The authors made no disclosures.

Data and Materials Availability Statement

The data and materials used to support the findings of this study are included within the article.

Funding

This work was funded by a grant from the National Natural Science Foundation of China (Grant Serial Numbers: 81972720, 81172362)

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69(1): 7-34.
Liu C, Zheng S, Jin R, et al. The superior efficacy of anti-PD-1/PD-L1 immunotherapy in KRAS-mutant non-small cell lung cancer that correlates with an inflammatory phenotype and increased immunogenicity. Cancer Lett 2020; 470: 95-105.
Dummer R, Brase JC, Garrett J, et al. Adjuvant dabrafenib plus trametinib versus placebo in patients with resected, BRAF(V600)-mutant, stage III melanoma (COMBI-AD): exploratory biomarker analyses from a randomised, phase 3 trial. Lancet Oncol 2020; 21(3): 358-72.
Gainor JF, Rizvi H, Jimenez Aguilar E, et al. Clinical activity of programmed cell death 1 (PD-1) blockade in never, light, and heavy smokers with non-small-cell lung cancer and PD-L1 expression >/=50. Ann Oncol 2020; 31(3): 404-11.
Lepletier A, Madore J, O'Donnell JS, et al. Tumor CD155 expression is associated with resistance to anti-PD1 immunotherapy in metastatic melanoma. Clin Cancer Res 2020.
Eguren-Santamaria I, Sanmamed MF, Goldberg SB, et al. PD-1/PD-L1 blockers in NSCLC brain metastases: challenging paradigms and clinical practice. Clin Cancer Res 2020.
Ludford K, Cohen R, Svrcek M, et al. Pathological tumor response following immune checkpoint blockade for deficient mismatch repair advanced colorectal cancer. J Natl Cancer Inst 2020.
Ho WJ, Jaffee EM. Disrupting a converging metabolic target turns up the immunologic-heat in pancreatic tumors. J Clin Invest 2020; 130(1): 71-3.
Daassi D, Mahoney KM, Freeman GJ. The importance of exosomal PDL1 in tumour immune evasion. Nat Rev Immunol 2020; 20(4): 209-15.
Liu D, Schilling B, Liu D, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med 2019; 25(12): 1916-27.
Braun DA, Burke KP, Van Allen EM. Genomic Approaches to Understanding Response and Resistance to Immunotherapy. Clinical cancer research : an official journal of the American Association for Cancer Research 2016; 22(23): 5642-50.
Ciardiello D, Vitiello PP, Cardone C, et al. Immunotherapy of colorectal cancer: Challenges for therapeutic efficacy. Cancer treatment reviews 2019; 76: 22-32.
Mankor JM, Paats MS, Groenendijk FH, et al. Impact of panel design and cut-off on tumour mutational burden assessment in metastatic solid tumour samples. Br J Cancer 2020; 122(7): 953-6.
Picard E, Verschoor CP, Ma GW, Pawelec G. Relationships Between Immune Landscapes, Genetic Subtypes and Responses to Immunotherapy in Colorectal Cancer. Front Immunol 2020; 11: 369.
Petitprez F, de Reynies A, Keung EZ, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 2020; 577(7791): 556-60.
Wu TD, Madireddi S, de Almeida PE, et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 2020; 579(7798): 274-8.
Zhang D, Zheng Y, Lin Z, et al. Equipping Natural Killer Cells with Specific Targeting and Checkpoint Blocking for Enhanced Adoptive Immunotherapy in Solid Tumors. Angew Chem Int Ed Engl 2020.
Hollern DP, Xu N, Thennavan A, et al. B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer. Cell 2019; 179(5): 1191-206.e21.
Wang S, He Z, Wang X, Li H, Liu XS. Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. Elife 2019; 8.
Wang Z, Zhao J, Wang G, et al. Comutations in DNA Damage Response Pathways Serve as Potential Biomarkers for Immune Checkpoint Blockade. Cancer Res 2018; 78(22): 6486-96.
Lv J, Zhu Y, Ji A, Zhang Q, Liao G. Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer. Biosci Rep 2020.
Liu L, Bai X, Wang J, et al. Combination of TMB and CNA Stratifies Prognostic and Predictive Responses to Immunotherapy Across Metastatic Cancer. Clin Cancer Res 2019; 25(24): 7413-23.
Ladanyi A, Timar J. Immunologic and immunogenomic aspects of tumor progression. Semin Cancer Biol 2020; 60: 249-61.
Jia Q, Wu W, Wang Y, et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun 2018; 9(1): 5361.
Jia M, Yao L, Yang Q, Chi T. Association of MSH2 Expression With Tumor Mutational Burden and the Immune Microenvironment in Lung Adenocarcinoma. Front Oncol 2020; 10: 168.
Isomoto K, Haratani K, Hayashi H, et al. Impact of EGFR-TKI Treatment on the Tumor Immune Microenvironment in EGFR Mutation-Positive Non-Small Cell Lung Cancer. Clin Cancer Res 2020; 26(8): 2037-46.
Garris CS, Luke JJ. Dendritic cells, the T cell-inflamed tumor microenvironment and immunotherapy treatment response. Clinical cancer research : an official journal of the American Association for Cancer Research 2020.
Schoonderwoerd MJ, Koops MF, Angela RA, et al. Targeting endoglin expressing regulatory T cells in the tumor microenvironment enhances the effect of PD1 checkpoint inhibitor immunotherapy. Clinical cancer research : an official journal of the American Association for Cancer Research 2020.
Chen H, Chong W, Wu Q, Yao Y, Mao M, Wang X. Association of LRP1B Mutation With Tumor Mutation Burden and Outcomes in Melanoma and Non-small Cell Lung Cancer Patients Treated With Immune Check-Point Blockades. Front Immunol 2019; 10: 1113.
Gandara DR, Paul SM, Kowanetz M, et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med 2018; 24(9): 1441-8.
Cyriac G, Gandhi L. Emerging biomarkers for immune checkpoint inhibition in lung cancer. Semin Cancer Biol 2018; 52(Pt 2): 269-77.
Peng H, Zhang Y, Zhou Z, et al. Intergrated analysis of ELMO1, serves as a link between tumour mutation burden and epithelial-mesenchymal transition in hepatocellular carcinoma. EBioMedicine 2019; 46: 105-18.
Wang X, Li M. Correlate tumor mutation burden with immune signatures in human cancers. BMC Immunol 2019; 20(1): 4.
Schrock AB, Ouyang C, Sandhu J, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann Oncol 2019; 30(7): 1096-103.
Park SE, Park K, Lee E, et al. Clinical implication of tumor mutational burden in patients with HER2-positive refractory metastatic breast cancer. Oncoimmunology 2018; 7(8): e1466768.
Pai SG, Carneiro BA, Chae YK, et al. Correlation of tumor mutational burden and treatment outcomes in patients with colorectal cancer. J Gastrointest Oncol 2017; 8(5): 858-66.
Stein MK, Williard FW, Xiu J, et al. Comprehensive tumor profiling reveals unique molecular differences between peritoneal metastases and primary colorectal adenocarcinoma. J Surg Oncol 2020.
Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012; 487(7407): 330-7.
Lee DW, Han SW, Bae JM, et al. Tumor Mutation Burden and Prognosis in Patients with Colorectal Cancer Treated with Adjuvant Fluoropyrimidine and Oxaliplatin. Clin Cancer Res 2019; 25(20): 6141-7.
Park SE, Park K, Lee E, et al. Clinical implication of tumor mutational burden in patients with HER2-positive refractory metastatic breast cancer. Oncoimmunology 2018; 7(8): e1466768.
Goodman AM, Kato S, Bazhenova L, et al. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Molecular cancer therapeutics 2017; 16(11): 2598-608.
Long J, Wang A, Bai Y, et al. Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine 2019; 42: 363-74.
Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science (New York, NY) 2006; 313(5795): 1960-4.
Tosolini M, Kirilovsky A, Mlecnik B, et al. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer research 2011; 71(4): 1263-71.
Mantovani A, Marchesi F, Malesci A, Laghi L, Allavena P. Tumour-associated macrophages as treatment targets in oncology. Nature reviews Clinical oncology 2017; 14(7): 399-416.
Xiong Y, Wang K, Zhou H, Peng L, You W, Fu Z. Profiles of immune infiltration in colorectal cancer and their clinical significant: A gene expression-based study. Cancer medicine 2018; 7(9): 4496-508.
De Simone M, Arrigoni A, Rossetti G, et al. Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells. Immunity 2016; 45(5): 1135-47.
Waniczek D, Lorenc Z, Śnietura M, Wesecki M, Kopec A, Muc-Wierzgoń M. Tumor-Associated Macrophages and Regulatory T Cells Infiltration and the Clinical Outcome in Colorectal Cancer. Arch Immunol Ther Exp (Warsz) 2017; 65(5): 445-54.
Strasser K, Birnleitner H, Beer A, et al. Immunological differences between colorectal cancer and normal mucosa uncover a prognostically relevant immune cell profile. Oncoimmunology 2019; 8(2): e1537693.
Rizvi H, Sanchez-Vega F, La K, et al. Molecular Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-Ligand 1 (PD-L1) Blockade in Patients With Non-Small-Cell Lung Cancer Profiled With Targeted Next-Generation Sequencing. J Clin Oncol 2018; 36(7): 633-41.
Ready N, Hellmann MD, Awad MM, et al. First-Line Nivolumab Plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer (CheckMate 568): Outcomes by Programmed Death Ligand 1 and Tumor Mutational Burden as Biomarkers. J Clin Oncol 2019; 37(12): 992-1000.
Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncol 2019.
Lamberti G, Spurr LF, Li Y, et al. Clinicopathological and genomic correlates of Programmed Cell Death Ligand 1 (PD-L1) expression in nonsquamous non-small cell lung cancer. Ann Oncol 2020.
Romero D. TMB is linked with prognosis. Nat Rev Clin Oncol 2019; 16(6): 336.
Willis C, Fiander M, Tran D, et al. Tumor mutational burden in lung cancer: a systematic literature review. Oncotarget 2019; 10(61): 6604-22.

Download PDF

Version 1

posted

You are reading this latest preprint version

Exploration of the role of tumor mutation burden in clinical significance, immunotherapy response predictor and immune cell infiltration in colon cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Declarations

References

Supplementary Files

Status:

Version 1