journal article·2022-06-29Open access

Pan-Cancer Analysis for Immune Cell Infiltration and Mutational Signatures Using Non-Negative Canonical Correlation Analysis

Je-Keun Rhee

Applied Sciences· Volume 12· Issue 13

Abstract

Mutational signatures indicate the mutational processes and substitution patterns in cancer cell genomes. However, the functional consequences of mutational signatures remain unclear, and there have been no comprehensive systematic studies to examine the relationships between the mutational signatures and the immune cell infiltration. Here, the relationship between mutational signatures and immune cell infiltration using non-negative canonical correlation analysis based on 8927 patients across 25 tumor types was investigated. By inspecting mutational signatures with the maximal coefficients determined by the non-negative canonical correlation analysis, the study identified mutational signatures related to immune cell infiltration composed of tumor microenvironments. The analysis was validated by showing that the genes associated with the identified mutational signatures were linked to overall survival by a Kaplan–Meier curve and a log-rank test and were mainly related to immunity by gene set enrichment analysis. These results will help expand our knowledge of tumor biology and recognize the functional roles and associations of immune systems with mutational signatures.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cancer Genomics and Diagnostics · Ferroptosis and cancer prognosis · Epigenetics and DNA Methylation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Bionformatics & Life Science, Soongsil University, Seoul 06978, Korea

Funding

National Research Foundation of Korea (NRF) · Ministry of Education

References (62)

Show source-provided references
1. Mutation signatures of carcinogen exposure: Genome-wide detection and new opportunities for cancer prevention10.1186/gm541 ↗
2. 5-Methylcytosine as an endogenous mutagen in the human LDL receptor and p53 genes10.1126/science.1697983 ↗
3. The therapeutic significance of mutational signatures from DNA repair deficiency in cancer10.1038/s41467-018-05228-y ↗
4. APOBECs orchestrate genomic and epigenomic editing across health and disease10.1016/j.tig.2021.07.003 ↗
5. The repertoire of mutational signatures in human cancer10.1038/s41586-020-1943-3 ↗
6. Mutational signatures: From methods to mechanisms10.1146/annurev-biodatasci-122320-120920 ↗
7. Signatures of mutational processes in human cancer10.1038/nature12477 ↗
8. Van Hoeck, A., Tjoonk, N.H., van Boxtel, R., and Cuppen, E. (2019). Portrait of a cancer: Mutational signature analyses for cancer diagnostics. BMC Cancer, 19.10.1186/s12885-019-5677-2 ↗
9. Mutational signatures: Emerging concepts, caveats and clinical applications10.1038/s41568-021-00377-7 ↗
10. Therapeutic and prognostic insights from the analysis of cancer mutational signatures10.1016/j.tig.2021.08.007 ↗
11. The immune response-related mutational signatures and driver genes in non-small-cell lung cancer10.1111/cas.14113 ↗
12. APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer10.1038/s41388-018-0245-9 ↗
13. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers10.1038/ng.2702 ↗
14. Association of germline variants in the APOBEC3 region with cancer risk and enrichment with APOBEC-signature mutations in tumors10.1038/ng.3670 ↗
15. Härdle, W.K., and Simar, L. (2015). Canonical correlation analysis. Applied Multivariate Statistical Analysis, Springer.10.1007/978-3-662-45171-7 ↗
16. Tensor canonical correlation analysis for multi-view dimension reduction10.1109/TKDE.2015.2445757 ↗
17. A survey on canonical correlation analysis10.1109/TKDE.2019.2958342 ↗
18. Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis
19. Generalized canonical correlation analysis for classification10.1016/j.jmva.2014.05.011 ↗
20. Chaudhuri, K., Kakade, S.M., Livescu, K., and Sridharan, K. (2009, January 14–18). Multi-view clustering via canonical correlation analysis. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, BC, Canada.10.1145/1553374.1553391 ↗
21. Rhee, J.K., Joung, J.G., Chang, J.H., Fei, Z., and Zhang, B.T. (2009). Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis. BMC Genom. Biomed. Cent., 10.10.1186/1471-2164-10-S3-S29 ↗
22. Soneson, C., Lilljebjörn, H., Fioretos, T., and Fontes, M. (2010). Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. BMC Bioinform., 11.10.1186/1471-2105-11-191 ↗
23. Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: A comparison study10.1093/bioinformatics/btaa530 ↗
24. Nonnegative constrained graph based canonical correlation analysis for multi-view feature learning10.1007/s11063-018-9904-7 ↗
25. Sigg, C., Fischer, B., Ommer, B., Roth, V., and Buhmann, J. (2007, January 27–29). Nonnegative CCA for audiovisual source separation. Proceedings of the 2007 IEEE Workshop on Machine Learning for Signal Processing, Thessaloniki, Greece.10.1109/MLSP.2007.4414315 ↗
26. Visualizing and interpreting cancer genomics data via the Xena platform10.1038/s41587-020-0546-8 ↗
27. HTSeq—A Python framework to work with high-throughput sequencing data10.1093/bioinformatics/btu638 ↗
28. Pachter, L. (2011). Models for transcript quantification from RNA-Seq. arXiv.
29. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository10.1186/s12967-021-02936-w ↗
30. DeconstructSigs: Delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution10.1186/s13059-016-0893-4 ↗
31. (2015, March 31). Mutational Signatures v2. Available online: https://cancer.sanger.ac.uk/signatures/signatures_v2/.
32. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data10.7554/eLife.26476 ↗
33. Mackey, L. (2008, January 8–10). Deflation methods for sparse PCA. Proceedings of the 21st International Conference on Neural Information Processing Systems, Vancouver, BC, Canada.
34. A tutorial on canonical correlation methods10.1145/3136624 ↗
35. Vía, J., Santamaría, I., and Pérez, J. (2005, January 23). A robust RLS algorithm for adaptive canonical correlation analysis. Proceedings of the (ICASSP’05) IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA.
36. Fischer, B., Roth, V., and Buhmann, J.M. (2007). Time-series alignment by non-negative multiple generalized canonical correlation analysis. BMC Bioinform. Biomed. Cent., 8.10.1186/1471-2105-8-S10-S4 ↗
37. Sun, L., Ji, S., and Ye, J. (2008, January 5–9). A least squares formulation for canonical correlation analysis. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.10.1145/1390156.1390285 ↗
38. On the exact distribution of maximally selected rank statistics10.1016/S0167-9473(02)00225-6 ↗
39. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes10.1038/ng1180 ↗
40. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles10.1073/pnas.0506580102 ↗
41. The molecular signatures database hallmark gene set collection10.1016/j.cels.2015.12.004 ↗
42. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer10.1038/ng.3934 ↗
43. Correlation of homologous recombination deficiency induced mutational signatures with sensitivity to PARP inhibitors and cytotoxic agents
44. Mutational signatures associated with tobacco smoking in human cancer10.1126/science.aag0299 ↗
45. The COSMIC Cancer Gene Census: Describing genetic dysfunction across all human cancers10.1038/s41568-018-0060-1 ↗
46. Devarajan, K. (2008). Nonnegative matrix factorization: An analytical and interpretive tool in computational biology. PLoS Comput. Biol., 4.10.1371/journal.pcbi.1000029 ↗
47. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data10.1093/bioinformatics/btv544 ↗
48. Lin, X., and Boutros, P.C. (2020). Optimization and expansion of non-negative matrix factorization. BMC Bioinform., 21.10.1186/s12859-019-3312-5 ↗
49. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology10.1093/bioinformatics/btz363 ↗
50. APOBEC Mutagenesis Inhibits Breast Cancer Growth through Induction of T cell–Mediated Antitumor Immune Responses10.1158/2326-6066.CIR-21-0146 ↗
51. Perspective: APOBEC mutagenesis in drug resistance and immune escape in HIV and cancer evolution10.1093/annonc/mdy003 ↗
52. APOBEC mutagenesis is tightly linked to the immune landscape and immunotherapy biomarkers in head and neck squamous cell carcinoma10.1016/j.oraloncology.2019.07.020 ↗
53. Caspase-8 mutations in head and neck cancer confer resistance to death receptor-mediated apoptosis and enhance migration, invasion, and tumor growth10.1016/j.molonc.2014.03.018 ↗
54. In silico analysis reveals a shared immune signature in CASP8-mutated carcinomas with varying correlations to prognosis10.7717/peerj.6402 ↗
55. BPTF promotes tumor growth and predicts poor prognosis in lung adenocarcinomas10.18632/oncotarget.5302 ↗
56. BPTF biomarker correlates with poor survival in human NSCLC
57. BPTF Depletion Enhances T-cell–Mediated Antitumor Immunity10.1158/0008-5472.CAN-15-3125 ↗
58. Microtubule actin cross-linking factor 1, a novel potential target in cancer10.1111/cas.13344 ↗
59. Expression and Clinical Significance of Microtubule-Actin Cross-Linking Factor 1 in Serous Ovarian Cancer10.2174/1574892816666210211091543 ↗
60. Passenger mutations in more than 2,500 cancer genomes: Overall molecular functional impact and consequences10.1016/j.cell.2020.01.032 ↗
61. The effects of mutational processes and selection on driver mutations across cancer types10.1038/s41467-018-04208-6 ↗
62. Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions10.1038/s41467-021-27792-6 ↗