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. 2024 Mar 6;16(5):1072.
doi: 10.3390/cancers16051072.

The Impact of Mutational Hotspots on Cancer Survival

Affiliations

The Impact of Mutational Hotspots on Cancer Survival

Melissa Gonzalez-Cárdenas et al. Cancers (Basel). .

Abstract

Background: Cofactors, biomarkers, and the mutational status of genes such as TP53, EGFR, IDH1/2, or PIK3CA have been used for patient stratification. However, many genes exhibit recurrent mutational positions known as hotspots, specifically linked to varying degrees of survival outcomes. Nevertheless, few hotspots have been analyzed (e.g., TP53 and EGFR). Thus, many other genes and hotspots remain unexplored.

Methods: We systematically screened over 1400 hotspots across 33 TCGA cancer types. We compared the patients carrying a hotspot against (i) all cases, (ii) gene-mutated cases, (iii) other mutated hotspots, or (iv) specific hotspots. Due to the limited number of samples in hotspots and the inherent group imbalance, besides Cox models and the log-rank test, we employed VALORATE to estimate their association with survival precisely.

Results: We screened 1469 hotspots in 6451 comparisons, where 314 were associated with survival. Many are discussed and linked to the current literature. Our findings demonstrate associations between known hotspots and survival while also revealing more potential hotspots. To enhance accessibility and promote further investigation, all the Kaplan-Meier curves, the log-rank tests, Cox statistics, and VALORATE-estimated null distributions are accessible on our website.

Conclusions: Our analysis revealed both known and putatively novel hotspots associated with survival, which can be used as biomarkers. Our web resource is a valuable tool for cancer research.

Keywords: TCGA; VALORATE; biomarkers; cox; log-rank; recurrent mutations.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The four tests were performed to identify hotspots associated with survival. Top: as an example, a hypothetical gene is schematized for 54 patients, where 34 patients show mutations in their tumor sample. Each colored sphere corresponds to a patient carrying a mutation. An empty sphere corresponds to a patient not mutated in the gene. For this figure, 3 mutations in the same amino acid position defined a hotspot. Thus, there are 4 hotspots at positions X, Y, Z, and W. Bottom: the four tests performed. A vertical dashed bar splits the set of patients being tested. The four tests differ in the set of patients being compared with X, a specific hotspot (in red).
Figure 2
Figure 2
Survival curves represent the top significant hotspot for each cancer type. The red curve represent patients carrying the specified hotspot. The blue curve represent the rest of patients. The gray curve include all patients (for comparison). The axis of all curves has been removed to enhance clarity. Complete figures are accessible on our website. The non-symmetrical null distributions are depicted on the right, showcasing a selected example per row. In each row, the hotspot drawn on the right is highlighted with a gray square. The bottom row displays an additional distribution for EEF1A1, a gene that has received limited study in liver cancer. The p-values obtained from VALORATE are estimated by calculating twice the shaded pink area, corresponding to a two-sided test aimed at evaluating the log-rank statistic’s difference from zero, utilizing an empirical null distribution. * refers to underestimated HR given no events.
Figure 3
Figure 3
Hotspots in TP53 associated with survival across cancers in the X vs. Y comparison. The top bars display the hotspots per position and cancer type. The bottom Kaplan–Meier curves show examples of the hotspots at amino acid position 248 across eight of the nine observed cancer types.
Figure 4
Figure 4
Preview (picture) of the web resource at http://bioinformatics.mx/SurvHotspots.

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Grants and funding

We thank the Mexican Science Council, CONAHCYT, for the master’s degree scholarship to M.G.