» Articles » PMID: 24132290

Mutational Landscape and Significance Across 12 Major Cancer Types

Abstract

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

Citing Articles

Mediates Immunosuppression of the Tumor Microenvironment in Non-Small Cell Lung Cancer.

Fan Y, Ji X, Yuan K, Wu Q, Lou M J Inflamm Res. 2025; 18:3333-3347.

PMID: 40078575 PMC: 11900795. DOI: 10.2147/JIR.S509316.


Identification of a conserved subset of cold tumors responsive to immune checkpoint blockade.

Moore J, Gkantalis J, Guix I, Chou W, Yuen K, Lazar A J Immunother Cancer. 2025; 13(3).

PMID: 40050047 PMC: 11887281. DOI: 10.1136/jitc-2024-010528.


Glioblastoma multiforme: insights into pathogenesis, key signaling pathways, and therapeutic strategies.

Pouyan A, Ghorbanlo M, Eslami M, Jahanshahi M, Ziaei E, Salami A Mol Cancer. 2025; 24(1):58.

PMID: 40011944 PMC: 11863469. DOI: 10.1186/s12943-025-02267-0.


AAV-based vectors for human diseases modeling in laboratory animals.

Aliev T, Yudkin D Front Med (Lausanne). 2025; 11:1499605.

PMID: 40007819 PMC: 11859266. DOI: 10.3389/fmed.2024.1499605.


A Perspective on Therapeutic Targeting Against Ubiquitin Ligases to Stabilize Tumor Suppressor Proteins.

Ganesan I, Kiyokawa H Cancers (Basel). 2025; 17(4).

PMID: 40002221 PMC: 11853300. DOI: 10.3390/cancers17040626.


References
1.
Ma Z, Leijon A . Bayesian estimation of beta mixture models with variational inference. IEEE Trans Pattern Anal Mach Intell. 2011; 33(11):2160-73. DOI: 10.1109/TPAMI.2011.63. View

2.
Cibulskis K, Lawrence M, Carter S, Sivachenko A, Jaffe D, Sougnez C . Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9. PMC: 3833702. DOI: 10.1038/nbt.2514. View

3.
Welch J, Ley T, Link D, Miller C, Larson D, Koboldt D . The origin and evolution of mutations in acute myeloid leukemia. Cell. 2012; 150(2):264-78. PMC: 3407563. DOI: 10.1016/j.cell.2012.06.023. View

4.
Vogelstein B, Papadopoulos N, Velculescu V, Zhou S, Diaz Jr L, Kinzler K . Cancer genome landscapes. Science. 2013; 339(6127):1546-58. PMC: 3749880. DOI: 10.1126/science.1235122. View

5.
Parsons D, Jones S, Zhang X, Cheng-Ho Lin J, Leary R, Angenendt P . An integrated genomic analysis of human glioblastoma multiforme. Science. 2008; 321(5897):1807-12. PMC: 2820389. DOI: 10.1126/science.1164382. View