Chordoid glioma (ChG) is a characteristic, slow growing, and well-circumscribed diencephalic tumor, whose mutational landscape is unknown. Here we report the analysis of 16 ChG by whole-exome and RNA-sequencing. We found that 15 ChG harbor the same PRKCA ^D463H mutation. PRKCA encodes the Protein kinase C (PKC) isozyme alpha (PKCα) and is mutated in a wide range of human cancers. However the hot spot PRKCA ^D463H mutation was not described in other tumors. PRKCA ^D463H is strongly associated with the activation of protein translation initiation (EIF2) pathway. PKCα^D463H mRNA levels are more abundant than wild-type PKCα transcripts, while PKCα^D463H is less stable than the PCKα^WT protein. Compared to PCKα^WT, the PKCα^D463H protein is depleted from the cell membrane. The PKCα^D463H mutant enhances proliferation of astrocytes and tanycytes, the cells of origin of ChG. In conclusion, our study identifies the hallmark mutation for chordoid gliomas and provides mechanistic insights on ChG oncogenesis.

Cancer somatic mutations are the product of multiple mutational and repair processes, both of which are tightly associated with DNA replication. Distinctive patterns of somatic mutation accumulation, termed mutational signatures, are indicative of processes sustained within tumors. However, the association of various mutational processes with replication timing (RT) remains an open question. In this study, we systematically analyzed the mutational landscape of 2,787 tumors from 32 tumor types separately for early and late replicating regions using sequence context normalization and chromatin data to account for sequence and chromatin accessibility differences. To account for sequence differences between various genomic regions, an artificial genome–based approach was developed to expand the signature analyses to doublet base substitutions and small insertions and deletions. The association of mutational processes and RT was signature specific: Some signatures were associated with early or late replication (such as SBS7b and SBS7a, respectively), and others had no association. Most associations existed even after normalizing for genome accessibility. A focused mutational signature identification approach was also developed that uses RT information to improve signature identification; this approach found that SBS16, which is biased toward early replication, is strongly associated with better survival rates in liver cancer. Overall, this novel and comprehensive approach provides a better understanding of the etiology of mutational signatures, which may lead to improved cancer prevention, diagnosis, and treatment. Significance: Many mutational processes associate with early or late replication timing regions independently of chromatin accessibility, enabling development of a focused identification approach to improve mutational signature detection.

Correctly identifying the true driver mutations in a patient’s tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model’s predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3^S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Interrogating the genomics of circulating tumor DNA (ctDNA; the liquid biopsy) has advantages in patients in whom tissue biopsy is difficult. However, the reported concordance between genomic analysis of tissue DNA and ctDNA is variable among studies. Herein, we characterized the clinical implications of the relationship between mutations in TP53 genes in tissue DNA versus ctDNA. The molecular profiles of both liquid (Guardant Health) and tissue (Foundation Medicine) biopsies from 433 patients were analyzed (pan-cancer setting). In 71/433 (16%) cases, all same TP53 mutations were detected in both tissue DNA and ctDNA; in 18/433 (4%), same mutation plus additional mutation/mutations; and in 27/433 (6%), different TP53 mutations were detected. In 99/433 (23%) cases, TP53 mutations were detected only in tissue DNA; in 43/433 (10%), only in ctDNA; and in 175/433 (40%), no TP53 mutations were detected in either test. When TP53 mutations were identical in tissue and ctDNA, the alterations were enriched for nonsense mutations, and survival was significantly shorter in multivariate analysis (as compared with different mutations in ctDNA vs. tissue or no mutations); this finding was independent of tumor type, time interval between tests, and the %ctDNA for TP53 mutations. In summary, in 16% of 433 patients with diverse cancers, TP53 mutations were identical in tissue DNA and ctDNA. In these individuals, the alterations were enriched for stop–gain (nonsense) mutations (results in a premature termination codon). Though unknown confounders cannot be ruled out, these patients fared significantly worse than those whose ctDNA and tissue DNA harbored different TP53 mutation portfolios or no TP53 mutations.