A p53 transcriptional signature in primary and metastatic cancers derived using machine learning

Abstract

The tumor suppressor gene, TP53, has the highest rate of mutation among all genes in human cancer. This transcription factor plays an essential role in the regulation of many cellular processes. Mutations in TP53 result in loss of wild-type p53 function in a dominant negative manner. Although TP53 is a well-studied gene, the transcriptome modifications caused by the mutations in this gene have not yet been explored in a pan-cancer study using both primary and metastatic samples. In this work, we used a random forest model to stratify tumor samples based on TP53 mutational status and detected a p53 transcriptional signature. We hypothesize that the existence of this transcriptional signature is due to the loss of wild-type p53 function and is universal across primary and metastatic tumors as well as different tumor types. Additionally, we showed that the algorithm successfully detected this signature in samples with apparent silent mutations that affect correct mRNA splicing. Furthermore, we observed that most of the highly ranked genes contributing to the classification extracted from the random forest have known associations with p53 within the literature. We suggest that other genes found in this list including GPSM2, OR4N2, CTSL2, SPERT, and RPE65 protein coding genes have yet undiscovered linkages to p53 function. Our analysis of time on different therapies also revealed that this signature is more effective than the recorded TP53 status in detecting patients who can benefit from platinum therapies and taxanes. Our findings delineate a p53 transcriptional signature, expand the knowledge of p53 biology and further identify genes important in p53 related pathways.

pyRforest: A comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R

“…In addition to this, integration with SHAP values provides new insights into the effects of each feature on outcomes. Our rank-based permutation approach was also applied in a study by Keshavarz-Ragahi et al (includes Kolisnik) [27] to identify multiple key features associated with p53 activity, enhancing the understanding of TP53-related transcriptional signatures across various cancer types.…”

Gain of Function (GOF) Mutant p53 in Cancer—Current Therapeutic Approaches

“…The development of machine learning technology may come with an easier answer to the future of cancer treatment. Recently a study using such technology reported new genes ( GPSM2, OR4N2, CTSL2, SPERT, RPE65 ) that may be associated with p53 functions, which seem to be a better fit for the platinum-based therapies for patients than their TP53 status [ 104 ]. The ongoing discussion between researchers on whether personal therapy, which considers the investigation of molecules targeting the exact type of mutation of TP53 , should be pursued or not has yet to be unraveled [ 105 , 106 , 107 , 108 , 109 ].…”

Complexity of the Genetic Background of Oncogenesis in Ovarian Cancer—Genetic Instability and Clinical Implications

“…In conclusion, with its acquired oncogenic capabilities, mutated p53 can be regarded as a renegade p53, and therefore it is essential to comprehend individual mutations in the gene before seeking associations between TP53 mutations and patient outcomes. After a long debate, it was postulated that each TP53 mutation has context-specific effects in response to different cancer therapeutics used [14,15].…”