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

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.

Characterizing the KRAS G12C mutation in metastatic colorectal cancer: a population-based cohort and assessment of expression differences in The Cancer Genome Atlas.

Introduction: In metastatic colorectal cancer (mCRC), RAS mutations impart inferior survival and resistance to anti-epidermal growth factor receptor (EGFR) antibodies. KRAS G12C inhibitors have been developed and we evaluated how KRAS G12C differs from other RAS mutations. Patients and Methods: This retrospective review evaluated patients in British Columbia, Canada with mCRC and RAS testing performed between 1 January 2016 and 31 December 2018. Sequencing information from The Cancer Genome Analysis (TCGA) was also obtained and analysed. Results: Age at diagnosis, sex, anatomic location and stage at diagnosis did not differ by RAS mutation type. Progression free survival on first chemotherapy for patients with metastatic KRAS G12C tumours was 11 months. Median overall survival did not differ by RAS mutation type but was worse for both KRAS G12C (27 months) and non-G12C alterations (29 months) than wildtype (43 months) (p = 0.01). Within the TCGA, there was no differential gene expression between KRAS G12C and other RAS mutations. However, eight genes with copy number differences between the G12C and non-G12C RAS mutant groups were identified after adjusting for multiple comparisons (FITM2, PDRG1, POFUT1, ERGIC3, EDEM2, PIGU, MANBAL and PXMP4). We also noted that other RAS mutant mCRCs had a higher tumour mutation burden than those with KRAS G12C mutations (median 3.05 vs 2.06 muts/Mb, p = 4.2e-3) and that KRAS G12C/other RAS had differing consensus molecular subtype distribution from wildtype colorectal cancer (CRC) (p < 0.0001) but not each other (p = 0.14). Conclusion: KRAS G12C tumours have similar clinical presentation to other RAS mutant tumours, however, are associated with differential copy number alterations.