The landscape and driver potential of site-specific hotspots across cancer genomes

In silico protein structural analysis of PRMT5 and RUVBL1 mutations arising in human cancers

DNA double strand breaks (DSBs) can be generated spontaneously during DNA replication and are repaired primarily by Homologous Recombination (HR). However, efficient repair requires chromatin remodeling to allow the recombination machinery access to the break. TIP60 is a complex conserved from yeast to humans that is required for histone acetylation and modulation of HR activity at DSBs. Two enzymatic activities within the TIP60 complex, KAT5 (a histone acetyltransferase) and RUVBL1 (an AAA+ ATPase) are required for efficient HR repair. Post-translational modification of RUVBL1 by the PRMT5 methyltransferase activates the complex acetyltransferase activity and facilitates error free HR repair. In S. pombe a direct interaction between PRMT5 and the acetyltransferase subunit of the TIP60 complex (KAT5) was also identified. The TIP60 complex has been partially solved experimentally in both humans and S. cerevisiae, but not S. pombe. Here, we used in silico protein structure analysis to investigate structural conservation between S. pombe and human PRMT5 and RUVBL1. We found that there is more similarity in structure conservation between S. pombe and human proteins than between S. cerevisiae and human. Next, we queried the COSMIC database to analyze how mutations occurring in human cancers affect the structure and function of these proteins. Artificial intelligence algorithms that predict how likely mutations are to promote cellular transformation and immortalization show that RUVBL1 mutations should have a more drastic effect than PRMT5. Indeed, in silico protein structural analysis shows that PRMT5 mutations are less likely to destabilize enzyme function. Conversely, most RUVBL1 mutations occur in a region required for interaction with its partner (RUVBL2). These data suggests that cancer mutations could destabilize the TIP60 complex. Sequence conservation analysis between S. pombe and humans shows that the residues identified in cancer cells are highly conserved, suggesting that this may be an essential process in eukaryotic DSB repair. These results shed light on mechanisms of DSB repair and also highlight how S. pombe remains a great model system for analyzing DSB repair processes that are tractable in human cells.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

The theory of massively repeated evolution and full identifications of cancer-driving nucleotides (CDNs)

Tumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are cancer-driving nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size n=300-1000), point mutations may recur in i out of n patients. This study explores a wide range of mutation characteristics to determine the limit of recurrences (i*) driven solely by neutral evolution. Since no neutral mutation can reach i*=3, all mutations recurring at i≥3 are CDNs. The theory shows the feasibility of identifying almost all CDNs if n increases to 100,000 for each cancer type. At present, only <10% of CDNs have been identified. When the full sets of CDNs are identified, the evolutionary mechanism of tumorigenesis in each case can be known and, importantly, gene targeted therapy will be far more effective in treatment and robust against drug resistance.

Identification and characterization of ADAR1 mutations and changes in gene expression in human cancers

ADAR1 (Adenosine deaminase action on RNA1) is involved in post-transcriptional RNA editing. ADAR1 mutations have been identified in many cancers but its role in tumor formation is still not well understood. Here we used available cancer genomes deposited on CSOMIC and cBioPortal to identify and characterize mutations and changes in ADAR1 expression in cancer cells. We identify several high frequency substitutions including one at R767 which is located in one of the dsRNA interacting domains. In silico protein structure analysis suggest the R767 mutations affect the protein stability and are likely to destabilize interaction with dsRNA. Gene expression analysis shows that in samples with under-expressed ADAR1, there is a statistically significant increase in expression of BLCAP (Bladder Cancer Associated Protein). Although BLCAP was initially identified in bladder cancers, more recent evidence shows that it is a tumor suppressor and BLCAP mutations have been detected in many cancer cells. Epistatic analysis using the cBioPortal mutual exclusivity calculator for the TCGA pan-cancer data shows that co-mutations between ADAR1 and other genes regulated by it are likely in cancer cells except for PTEN, AKT1 and BLCAP. This suggests that when ADAR1 function is impaired, PTEN, AKT1 and BLCAP become essential for survival of cancer cells. We also identified several samples with high mutation burden between ADAR1 and other genes regulated primarily in endometrial cancers. Finally, we show that the deaminase domain is highly conserved in metazoans and mutations within conserved residues do occur in human cancers suggesting that destabilization of the enzyme function is contributing to cancer development.

DriverDetector: An R package providing multiple statistical methods for cancer driver genes detection and tools for downstream analysis

Identifying driver genes in cancer is a difficult task because of the heterogeneity of cancer as well as the complex interactions among genes. As sequencing data become more readily available, there is a growing need for detecting cancer driver genes based on statistical and mathematical modeling methods. Currently, plenty of driver gene identification algorithms have been published, but they fail to achieve consistent results. In order to obtain gene sets with high confidence, we present DriverDetector, an R package providing a convenient workflow for cancer driver genes detection and downstream analysis. We develop the background mutation rate calculating module based on the distance between genes in covariate space and binomial test, followed by the driver gene selection module which integrates 11 methods, including two already recognized approaches, a de novo method, and five variants of Fisher's method which are applied to driver gene identification for the first time. Through verification on 12 TCGA datasets, each method is able to identify a set of confirmed driver genes while the number of resulting genes vary significantly across different methods. For robust driver genes detection, a voting strategy based on 10 of the statistical methods is further applied. Results show that the collective prediction based on the voting strategy demonstrates superiority in achieving the consistency of prediction while ensuring a reasonable number of predicted genes and confirmed drivers. By comparing the results of each cancer dataset, we also find that sample size has a huge impact on the number of predicted genes. For downstream analysis, DriverDetector automatically generates plenty of plots and tables to elaborate the results. We propose DriverDetector as a user-friendly tool promoting early diagnosis of cancer and the development of targeted drugs.

KDM4B mutations in human cancers

Homologous recombination (HR) is essential for repair of DNA double-strand breaks (DSBs) and restart of stalled or collapsed replication forks. Most cancers are characterized by mutations in components of the DSB repair pathways. Redundant DSB repair pathways exist in eukaryotes from yeast to humans and recent evidence has shown that complete loss of HR function appears to be lethal. Recent evidence has also shown that cancer cells with mutations in one DSB repair pathway can be killed by inhibiting one or more parallel pathways, a strategy that is currently aggressively explored as a cancer therapy. KDM4B is a histone demethylase with pleiotropic functions, which participates in preparing DSBs for repair by contributing to chromatin remodeling. In this report we carried out a pan-cancer analysis of KDM4B mutations with the goal of understanding their distribution and interaction with other DSB genes. We find that although KDM4B mutations co-occur with DSB repair genes, most KDM4B mutations are not drivers or pathogenic. A sequence conservation analysis from yeast to humans shows that highly conserved residues are resistant to mutation. Finally, all mutations occur in a heterozygous state. A single mutation, R986L, was predicted to significantly affect protein structure using computational modeling. This analysis suggests that KDM4B makes contributions to DSB repair but is not a key player.

In silico analysis of several frequent SLX4 mutations appearing in human cancers

SLX4 is an interactor and activator of structure-specific exonuclease that helps resolve tangled recombination intermediates arising at stalled replication forks. It is one of the many factors that assist with homologous recombination, the major mechanism for restarting replication. SLX4 mutations have been reported in many cancers but a pan cancer map of all the mutations has not been undertaken. Here, using data from the Catalogue of Somatic Mutations in Cancers (COSMIC), we show that mutations occur in almost every cancer and many of them truncate the protein which should severely alter the function of the enzyme. We identified a frequent R1779W point mutation that occurs in the SLX4 domain required for heterodimerization with its partner, SLX1. In silico protein structure analysis of this mutation shows that it significantly alters the protein structure and is likely to destabilize the interaction with SLX1. Although this brief communication is limited to only in silico analysis, it identifies certain high frequency SLX4 mutations in human cancers that would warrant further in vivo studies. Additionally, these mutations may be potentially actionable for drug therapies.

Whole-genome mapping of APOBEC mutagenesis in metastatic urothelial carcinoma identifies driver hotspot mutations and a novel mutational signature

Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) enzymes mutate specific DNA sequences and hairpin-loop structures, challenging the distinction between passenger and driver hotspot mutations. Here, we characterized 115 whole genomes of metastatic urothelial carcinoma (mUC) to identify APOBEC mutagenic hotspot drivers. APOBEC-associated mutations were detected in 92% of mUCs and were equally distributed across the genome, while APOBEC hotspot mutations (ApoHMs) were enriched in open chromatin. Hairpin loops were frequent targets of didymi (twins in Greek), two hotspot mutations characterized by the APOBEC SBS2 signature, in conjunction with an uncharacterized mutational context (Ap[C>T]). Next, we developed a statistical framework that identified ApoHMs as drivers in coding and non-coding genomic regions of mUCs. Our results and statistical framework were validated in independent cohorts of 23 non-metastatic UCs and 3,744 samples of 17 metastatic cancers, identifying cancer-type-specific drivers. Our study highlights the role of APOBEC in cancer development and may contribute to developing novel targeted therapy options for APOBEC-driven cancers.

The Role of the Transcriptional Coactivator BOB.1/OBF.1 in Adaptive Immunity

BOB.1/OBF.1 is a transcriptional coactivator involved in octamer-dependent transcription. Thereby, BOB.1/OBF.1 is involved in the transcriptional regulation of genes important for lymphocyte physiology. BOB.1/OBF.1-deficient mice reveal multiple B- and T-cell developmental defects. The most prominent defect of these mice is the complete absence of germinal centers (GCs) resulting in severely impaired T-cell-dependent immune responses. In humans, BOB.1/OBF.1 is associated with several autoimmune and inflammatory diseases but also linked to liquid and solid tumors. Although its role for B-cell development is relatively well understood, its exact role for the GC reaction and T-cell biology has long been unclear. Here, the contribution of BOB.1/OBF.1 for B-cell maturation is summarized, and recent findings regarding its function in GC B- as well as in various T-cell populations are discussed. Finally, a detailed perspective on how BOB.1/OBF.1 contributes to different pathologies is provided.

Hotspots of Somatic Genetic Variation in Pituitary Neuroendocrine Tumors

The most common genetic drivers of pituitary neuroendocrine tumors (PitNETs) lie within mutational hotspots, which are genomic regions where variants tend to cluster. Some of these hotspot defects are unique to PitNETs, while others are associated with additional neoplasms. Hotspot variants in GNAS and USP8 are the most common genetic causes of acromegaly and Cushing's disease, respectively. Although it has been proposed that these genetic defects could define specific clinical phenotypes, results are highly variable among studies. In contrast, DICER1 hotspot variants are associated with a familial syndrome of cancer predisposition, and only exceptionally occur as somatic changes. A small number of non-USP8-driven corticotropinomas are due to somatic hotspot variants in USP48 or BRAF; the latter is a well-known mutational hotspot in cancer. Finally, somatic variants affecting a hotspot in SF3B1 have been associated with multiple cancers and, more recently, with prolactinomas. Since the associations of BRAF, USP48, and SF3B1 hotspot variants with PitNETs are very recent, their effects on clinical phenotypes are still unknown. Further research is required to fully define the role of these genetic defects as disease biomarkers and therapeutic targets.

Sequence dependencies and mutation rates of localized mutational processes in cancer

BACKGROUND

Cancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show nucleotide sequence and functional region preferences. As a result, some sequence contexts mutate at much higher rates than others, with additional variation found between functional regions. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes.

METHODS

We count the 11-mer genomic sequences across the genome, and using the PCAWG set of 2583 pan-cancer whole genomes, we associate 11-mers with mutational signatures, hotspots of single nucleotide variants, and specific genomic regions. We evaluate the mutation rates of individual and combined sets of 11-mers and derive mutational sequence motifs.

RESULTS

We show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE deficiency), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate and sequence preference increase further when focusing on certain genomic regions, such as signature 62 in transcribed regions, where the mutation rate is increased up to 9-folds over cancer type and mutational signature average.

CONCLUSIONS

We summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.

Mutation Hotspots Found in Bladder Cancer Aid Prediction of Carcinogenic Risk in Normal Urothelium

More than 80,000 new cases of bladder cancer are estimated to be diagnosed in 2023. However, the 5-year survival rate for bladder cancer has not changed in decades, highlighting the need for prevention. Numerous cancer-causing mutations are present in the urothelium long before signs of cancer arise. Mutation hotspots in cancer-driving genes were identified in non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) tumor samples. Mutation burden within the hotspot regions was measured in normal urothelium with a low and high risk of cancer. A significant correlation was found between the mutation burden in normal urothelium and bladder cancer tissue within the hotspot regions. A combination of measured hotspot burden and personal risk factors was used to fit machine learning classification models. The efficacy of each model to differentiate between adjacent benign urothelium from bladder cancer patients and normal urothelium from healthy donors was measured. A random forest model using a combination of personal risk factors and mutations within MIBC hotspots yielded the highest AUC of 0.9286 for the prediction of high- vs. low-risk normal urothelium. Currently, there are no effective biomarkers to assess subclinical field disease and early carcinogenic progression in the bladder. Our findings demonstrate novel differences in mutation hotspots in NMIBC and MIBC and provide the first evidence for mutation hotspots to aid in the assessment of cancer risk in the normal urothelium. Early risk assessment and identification of patients at high risk of bladder cancer before the clinical presentation of the disease can pave the way for targeted personalized preventative therapy.

HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis

Mutations found in skin are acquired in specific patterns, clustering around mutation-prone genomic locations. The most mutation-prone genomic areas, mutation hotspots, first induce the growth of small cell clones in healthy skin. Mutations accumulate over time, and clones with driver mutations may give rise to skin cancer. Early mutation accumulation is a crucial first step in photocarcinogenesis. Therefore, a sufficient understanding of the process may help predict disease onset and identify avenues for skin cancer prevention. Early epidermal mutation profiles are typically established using high-depth targeted next-generation sequencing. However, there is currently a lack of tools for designing custom panels to capture mutation-enriched genomic regions efficiently. To address this issue, we created a computational algorithm that implements a pseudo-exhaustive approach to identify the best genomic areas to target. We benchmarked the current algorithm in three independent mutation datasets of human epidermal samples. Compared to the sequencing panel designs originally used in these publications, the mutation capture efficacy (number of mutations/base pairs sequenced) of our designed panel improved 9.6-12.1-fold. Mutation burden in the chronically sun-exposed and intermittently sun-exposed normal epidermis was measured within genomic regions identified by hotSPOT based on cutaneous squamous cell carcinoma (cSCC) mutation patterns. We found a significant increase in mutation capture efficacy and mutation burden in cSCC hotspots in chronically sun-exposed vs. intermittently sun-exposed epidermis (p < 0.0001). Our results show that our hotSPOT web application provides a publicly available resource for researchers to design custom panels, enabling efficient detection of somatic mutations in clinically normal tissues and other similar targeted sequencing studies. Moreover, hotSPOT also enables the comparison of mutation burden between normal tissues and cancer.