Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis
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Evaluation Summary:
This paper provides a comprehensive co-mutation analysis of over 30 thousand cancer patients and 1700+ cancer cell lines to identify associations with prognosis and drug resistance that could have translational value for clinical practice. Once validated, it would provide a useful framework for precision oncology.
“(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript.The reviewers remained anonymous to the authors.”.)
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Abstract
Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.
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Evaluation Summary:
This paper provides a comprehensive co-mutation analysis of over 30 thousand cancer patients and 1700+ cancer cell lines to identify associations with prognosis and drug resistance that could have translational value for clinical practice. Once validated, it would provide a useful framework for precision oncology.
“(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript.The reviewers remained anonymous to the authors.”.)
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Reviewer #1 (Public Review):
In the manuscript, by Jiang et al. ("Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis") the authors showed how the investigation of co-mutations can shed some light on tumor progression and the different outcomes for several tumor types. While this manuscript contributes to the field providing results of approximately 30,000 subjects (and over 50 cancer types), they aim to cover a larger number of subjects and tumor types than previous works. The authors are using different sources of public data and a great amount of genetic data to find co-mutations and show their relevance to most cancer types. However, it was not very clear why they are using different sources that use different methodologies to find mutations.
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Reviewer #2 (Public Review):
Jiang et al. provide a repertoire of co-mutations in genes from 3 large cancer genomics datasets. The authors propose that some of the highly frequent co-mutations in some tumor types may be due to the high mutation burden associated with POLE and MSI signatures. Furthermore, some of the most frequently co-mutated pairs involve large genes. In addition to showing disparities in co-mutations according to age, and race, they also identify co-mutations associated with survival.
Although the repertoire is extensive, there is no statistical test to determine whether a pair of co-mutations is significant compared to expected results from the mutation rate model. In Figure 1, Jiang et al. describe multiple known confounders, such as mutation rate and gene length, that have been integrated in multiple cancer driver …
Reviewer #2 (Public Review):
Jiang et al. provide a repertoire of co-mutations in genes from 3 large cancer genomics datasets. The authors propose that some of the highly frequent co-mutations in some tumor types may be due to the high mutation burden associated with POLE and MSI signatures. Furthermore, some of the most frequently co-mutated pairs involve large genes. In addition to showing disparities in co-mutations according to age, and race, they also identify co-mutations associated with survival.
Although the repertoire is extensive, there is no statistical test to determine whether a pair of co-mutations is significant compared to expected results from the mutation rate model. In Figure 1, Jiang et al. describe multiple known confounders, such as mutation rate and gene length, that have been integrated in multiple cancer driver discovery methods since 2013 (e.g. Lawrence et al., Nature, 2013, and Rheinbay et al. (PCAWG), Nature, 2020). With respect to the co-mutation analysis, the Maftools R package has a somatic interaction function that calculates Fisher's exact test to determine if co-mutations occur more than expected. The developers of DISCOVER, based on pan-cancer datasets, argued that most co-occurrence of mutations in genes is by chance (PMID: 27986087).
The association with survival is noteworthy, showing that TP53-KRAS is a predictor of survival for pancreatic tumors. However >95% of pancreatic cancer patients have KRAS mutations, making it unclear how the authors find a similar number for WT compared to mutated patients. In addition, the results from the TCGA UCEC and ICGC UCEC-US show an excellent prognosis of tumors with co-mutations MUC16-KIAA2022 and PTEN-PCDHB13, however, ~100% overall survival is typical for POLE hypermutated endometrial cancers. It might be that co-mutations of MUC16-KIAA2022 and PTEN-PCDHB13 are biomarkers of POLE tumors. It will be important to test this, demonstrating again the importance of statistical tests to determine if the frequencies of these co-mutations are significantly enriched.
Jiang et al. provide a repertoire of co-mutations in genes from 3 large cancer genomics datasets. The authors propose that some of the highly frequent co-mutations in some tumor types may be due to the high mutation burden associated with POLE and MSI signatures. Furthermore, some of the most frequently co-mutated pairs involve large genes. In addition to showing disparities in co-mutations according to age, and race, they also identify co-mutations associated with survival.
Although the repertoire is extensive, there is no statistical test to determine whether a pair of co-mutations is significant compared to expected results from the mutation rate model. In Figure 1, Jiang et al. describe multiple known confounders, such as mutation rate and gene length, that have been integrated in multiple cancer driver discovery methods since 2013 (e.g. Lawrence et al., Nature, 2013, and Rheinbay et al. (PCAWG), Nature, 2020). With respect to the co-mutation analysis, the Maftools R package has a somatic interaction function that calculates Fisher's exact test to determine if co-mutations occur more than expected. The developers of DISCOVER, based on pan-cancer datasets, argued that most co-occurrence of mutations in genes is by chance (PMID: 27986087).
The association with survival is noteworthy, showing that TP53-KRAS is a predictor of survival for pancreatic tumors. However >95% of pancreatic cancer patients have KRAS mutations, making it unclear how the authors find a similar number for WT compared to mutated patients. In addition, the results from the TCGA UCEC and ICGC UCEC-US show an excellent prognosis of tumors with co-mutations MUC16-KIAA2022 and PTEN-PCDHB13, however, ~100% overall survival is typical for POLE hypermutated endometrial cancers. It might be that co-mutations of MUC16-KIAA2022 and PTEN-PCDHB13 are biomarkers of POLE tumors. It will be important to test this, demonstrating again the importance of statistical tests to determine if the frequencies of these co-mutations are significantly enriched.
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