A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation

Association of ESR1 germline variants with TP53 somatic variants in breast tumors in a genome-wide study

Background

In breast tumors, somatic mutation frequencies in TP53 and PIK3CA vary by tumor subtype and ancestry. HER2 positive and triple negative breast cancers (TNBC) have a higher frequency of TP53 somatic mutations than other subtypes. PIK3CA mutations are more frequently observed in hormone receptor positive tumors. Emerging data suggest tumor mutation status is associated with germline variants and genetic ancestry. We aimed to identify germline variants that are associated with somatic TP53 or PIK3CA mutation status in breast tumors.

Methods

A genome-wide association study was conducted using breast cancer mutation status of TP53 and PIK3CA and functional mutation categories including TP53 gain of function (GOF) and loss of function mutations and PIK3CA activating/hotspot mutations. The discovery analysis consisted of 2850 European ancestry women from three datasets. Germline variants showing evidence of association with somatic mutations were selected for validation analyses based on predicted function, allele frequency, and proximity to known cancer genes or risk loci. Candidate variants were assessed for association with mutation status in a multi-ancestry validation study, a Malaysian study, and a study of African American/Black women with TNBC.

Results

The discovery Germline x Mutation (GxM) association study found five variants associated with one or more TP53 phenotypes with P values <1×10-6, 33 variants associated with one or more TP53 phenotypes with P values <1×10-5, and 44 variants associated with one or more PIK3CA phenotypes with P values <1×10-5. In the multi-ancestry and Malaysian validation studies, germline ESR1 locus variant, rs9383938, was associated with the presence of TP53 mutations overall (P values 6.8×10-5 and 9.8×10-8, respectively) and TP53 GOF mutations (P value 8.4×10-6). Multiple variants showed suggestive evidence of association with PIK3CA mutation status in the validation studies, but none were significant after correction for multiple comparisons.

Conclusions

We found evidence that germline variants were associated with TP53 and PIK3CA mutation status in breast cancers. Variants near the estrogen receptor alpha gene, ESR1, were significantly associated with overall TP53 mutations and GOF mutations. Larger multi-ancestry studies are needed to confirm these findings and determine if these variants contribute to ancestry-specific differences in mutation frequency.

Characterization on the oncogenic effect of the missense mutations of p53 via machine learning

Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Emerging insights into ethnic-specific TP53 germline variants

The recent expansion of human genomics repositories has facilitated the discovery of novel TP53 variants in populations of different ethnic origins. Interpreting TP53 variants is a major clinical challenge because they are functionally diverse, confer highly variable predisposition to cancer (including elusive low-penetrance alleles), and interact with genetic modifiers that alter tumor susceptibility. Here, we discuss how a cancer risk continuum may relate to germline TP53 mutations on the basis of our current review of genotype-phenotype studies and an integrative analysis combining functional and sequencing datasets. Our study reveals that each ancestry contains a distinct TP53 variant landscape defined by enriched ethnic-specific alleles. In particular, the discovery and characterization of suspected low-penetrance ethnic-specific variants with unique functional consequences, including P47S (African), G334R (Ashkenazi Jewish), and rs78378222 (Icelandic), may provide new insights in terms of managing cancer risk and the efficacy of therapy. Additionally, our analysis highlights infrequent variants linked to milder cancer phenotypes in various published reports that may be underdiagnosed and require further investigation, including D49H in East Asians and R181H in Europeans. Overall, the sequencing and projected functions of TP53 variants arising within ethnic populations and their interplay with modifiers, as well as the emergence of CRISPR screens and AI tools, are now rapidly improving our understanding of the cancer susceptibility spectrum, leading toward more accurate and personalized cancer risk assessments.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

TP53 R249S mutation in hepatic organoids captures the predisposing cancer risk

BACKGROUND AND AIMS

Major genomic drivers of hepatocellular carcinoma (HCC) are nowadays well recognized, although models to establish their roles in human HCC initiation remain scarce. Here, we used human liver organoids in experimental systems to mimic the early stages of human liver carcinogenesis from the genetic lesions of TP53 loss and L3 loop R249S mutation. In addition, chromatin immunoprecipitation sequencing (ChIP-seq) of HCC cell lines shed important functional insights into the initiation of HCC consequential to the loss of tumor-suppressive function from TP53 deficiency and gain-of-function activities from mutant p53.

APPROACH AND RESULTS

Human liver organoids were generated from surgical nontumor liver tissues. CRISPR knockout of TP53 in liver organoids consistently demonstrated tumor-like morphological changes, increased in stemness and unrestricted in vitro propagation. To recapitulate TP53 status in human HCC, we overexpressed mutant R249S in TP53 knockout organoids. A spontaneous increase in tumorigenic potentials and bona fide HCC histology in xenotransplantations were observed. ChIP-seq analysis of HCC cell lines underscored gain-of-function properties from L3 loop p53 mutants in chromatin remodeling and overcoming extrinsic stress. More importantly, direct transcriptional activation of PSMF1 by mutant R249S could increase organoid resistance to endoplasmic reticulum stress, which was readily abrogated by PSMF1 knockdown in rescue experiments. In a patient cohort of primary HCC tumors and genome-edited liver organoids, quantitative polymerase chain reaction corroborated ChIP-seq findings and verified preferential genes modulated by L3 mutants, especially those enriched by R249S.

CONCLUSIONS

We showed differential tumorigenic effects from TP53 loss and L3 mutations, which together confer normal hepatocytes with early clonal advantages and prosurvival functions.

Updated benchmarking of variant effect predictors using deep mutational scanning

The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.

Mis-splicing Drives Loss of Function of p53E224D Point Mutation

Background

Tp53 is the most commonly mutated gene in cancer. Canonical Tp53 DNA damage response pathways are well characterized and classically thought to underlie the tumor suppressive effect of Tp53. Challenging this dogma, mouse models have revealed that p53 driven apoptosis and cell cycle arrest are dispensable for tumor suppression. Here, we investigated the inverse context of a p53 mutation predicted to drive expression of canonical targets, but is detected in human cancer.

Methods

We established a novel mouse model with a single base pair mutation (GAG>GAC, p53E221D) in the DNA-Binding domain that has wild-type function in screening assays, but is paradoxically found in human cancer in Li-Fraumeni syndrome. Using mouse p53E221D and the analogous human p53E224D mutant, we evaluated expression, transcriptional activation, and tumor suppression in vitro and in vivo.

Results

Expression of human p53E224D from cDNA translated to a fully functional p53 protein. However, p53E221D/E221D RNA transcribed from the endogenous locus is mis-spliced resulting in nonsense mediated decay. Moreover, fibroblasts derived from p53E221D/E221D mice do not express a detectable protein product. Mice homozygous for p53E221D exhibited increased tumor penetrance and decreased life expectancy compared to p53 WT animals.

Conclusions

Mouse p53E221D and human p53E224D mutations lead to splice variation and a biologically relevant p53 loss of function in vitro and in vivo.

The potential role of miRNAs in the pathogenesis of testicular germ cell tumors - A Focus on signaling pathways interplay

Testicular germ cell tumors (TGCTs) are the most common testicular neoplasms in adolescents and young males. Understanding the genetic basis of TGCTs represents a growing need to cope with the increased incidence of these neoplasms. Although the cure rates have been comparatively increased, investigation of mechanisms underlying the incidence, progression, metastasis, recurrence, and therapy resistance is still necessary. Early diagnosis and non-compulsory clinical therapeutic agents without long-term side effects are now required to reduce the cancer burden, especially in the younger age groups. MicroRNAs (miRNAs) control an extensive range of cellular functions and exhibit a pivotal action in the development and spreading of TGCTs. Because of their dysregulation and disruption in function, miRNAs have been linked to the malignant pathophysiology of TGCTs by influencing many cellular functions involved in the disease. These biological processes include increased invasive and proliferative perspective, cell cycle dysregulation, apoptosis disruption, stimulation of angiogenesis, epithelial-mesenchymal transition (EMT) and metastasis, and resistance to certain treatments. Herein, we present an up-to-date review of the biogenesis of miRNAs, miRNA regulatory mechanisms, clinical challenges, and therapeutic interventions of TGCTs, and role of nanoparticles in the treatment of TGCTs.

The Spectrum of Germline Nucleotide Variants in Gastric Cancer Patients in the Kyrgyz Republic

Gastric cancer is a major challenge in modern oncology due to its high detection rate and prevalence. While sporadic cases make up the majority of gastric cancer, hereditary gastric cancer is caused by germline mutations in several genes linked to different syndromes. Thus, identifying hereditary forms of gastric cancer is considered crucial globally. A survey study using NGS-based analysis was conducted to determine the frequency of different types of hereditary gastric cancer in the yet-unstudied Kyrgyz population. The study cohort included 113 patients with diagnosed gastric cancer from Kyrgyzstan. The age of patients was 57.6 ± 8.9. Next-generation sequencing analysis of genomic DNA was performed using a custom Roche NimbleGen enrichment panel. The results showed that 6.2% (7/113) of the patients had pathogenic or likely pathogenic genetic variants. Additionally, 3.5% (4/113) of the patients carried heterozygous pathogenic/likely pathogenic variants in high penetrance genes, such as TP53, POLD1, RET, and BRCA2. Moreover, 2.7% (3/113) of the patients carried heterozygous mutations in genes linked to autosomal recessive conditions, specifically PALB2, FANCA, and FANCD2. We have not identified any genetic variants in hereditary GC-associated genes: CDH1, STK11, SMAD4, BMPRIA, APC, MLH1, and others. Our study included patients with sporadic features of GC. The use of recognized criteria (NCCN, Gastric Cancer, Version 2.2022) would increase the number of identified genetic variants in hereditary GC-associated genes. Further research is required to determine the clinical relevance of the genetic variants identified in the current study.

Genetic mutations in HER2-positive breast cancer: possible association with response to trastuzumab therapy

BACKGROUND

HER2-positive breast cancer occurs in 15-20% of breast cancer patients and is characterized by poor prognosis. Trastuzumab is considered the key drug for treatment of HER2-positive breast cancer patients. It improves patient survival; however, resistance to trastuzumab remains a challenge in HER2-positive breast cancer patients. Therefore, the prediction of response to trastuzumab is crucial to choose optimal treatment regimens. The aim of the study was to identify genetic variants that could predict response to anti-HER2-targeted therapy (trastuzumab) using next-generation sequencing.

METHOD

Genetic variants in the hotspot regions of 17 genes were studied in 24 Formalin-Fixed Paraffin-Embedded (FFPE) samples using Ion S5 next-generation sequencing system. FFPE samples were collected from HER2‑positive breast cancer patients previously treated with anti‑HER2‑targeted treatment (Trastuzumab). Patients were divided into two groups; trastuzumab-sensitive group and trastuzumab-resistant group based on their response to targeted therapy.

RESULTS

We identified 29 genetic variants in nine genes that only occurred in trastuzumab-resistant patients and could be associated with resistance to targeted therapy including TP53, ATM, RB1, MLH1, SMARCB1, SMO, GNAS, CDH1, and VHL. Four variants out of these 29 variants were repeated in more than one patient; two variants in TP53, one variant in ATM gene, and the last variant in RB1 gene. In addition, three genes were found to be mutated only in resistant patients; MLH1, SMARCB1 and SMO genes. Moreover, one novel allele (c.407A > G, p. Gln136Arg) was detected within exon 4 of TP53 gene in one resistant patient.

CONCLUSION

NGS sequencing is a useful tool to detect genetic variants that could predict response to trastuzumab therapy.

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features

Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.0, ACMG SF v2.0). DeMAG improves performance over existing VEPs by reaching balanced specificity (82%) and sensitivity (94%) on clinical data, and includes a novel epistatic feature, the 'partners score', which leverages evolutionary and structural partnerships of residues. The 'partners score' provides a general framework for modeling epistatic interactions, integrating both clinical and functional information. We provide our tool and predictions for all missense variants in 316 clinically actionable disease genes (demag.org) to facilitate the interpretation of variants and improve clinical decision-making.

Homogenous TP53mut-associated tumor biology across mutation and cancer types revealed by transcriptome analysis

TP53 is the most frequently mutated gene in human cancer. While no TP53-targeting drugs have been approved in the USA or Europe so far, preclinical and clinical studies are underway to investigate targeting of specific or all TP53 mutations, for example, by restoration of the functionality of mutated TP53 (TP53mut) or protecting wildtype TP53 (TP53wt) from negative regulation. We performed a comprehensive mRNA expression analysis in 24 cancer types of TCGA to extract (i) a consensus expression signature shared across TP53 mutation types and cancer types, (ii) differential gene expression patterns between tumors harboring different TP53 mutation types such as loss of function, gain of function or dominant-negative mutations, and (iii) cancer-type-specific patterns of gene expression and immune infiltration. Analysis of mutational hotspots revealed both similarities across cancer types and cancer type-specific hotspots. Underlying ubiquitous and cancer type-specific mutational processes with the associated mutational signatures contributed to explaining this observation. Virtually no genes were differentially expressed between tumors harboring different TP53 mutation types, while hundreds of genes were over- and underexpressed in TP53mut compared to TP53wt tumors. A consensus list included 178 genes that were overexpressed and 32 genes that were underexpressed in the TP53mut tumors of at least 16 of the investigated 24 cancer types. In an association analysis of immune infiltration with TP53 mutations in 32 cancer subtypes, decreased immune infiltration was observed in six subtypes, increased infiltration in two subtypes, a mixed pattern of decreased and increased immune cell populations in four subtypes, while immune infiltration was not associated with TP53 status in 20 subtypes. The analysis of a large cohort of human tumors complements results from experimental studies and supports the view that TP53 mutations should be further evaluated as predictive markers for immunotherapy and targeted therapies.

Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities

Pediatric solid and central nervous system tumors are the leading cause of cancer-related death among children. Identifying new targeted therapies necessitates the use of pediatric cancer models that faithfully recapitulate the patient's disease. However, the generation and characterization of pediatric cancer models has significantly lagged behind adult cancers, underscoring the urgent need to develop pediatric-focused cell line resources. Herein, we establish a single-site collection of 261 cell lines, including 224 pediatric cell lines representing 18 distinct extracranial and brain childhood tumor types. We subjected 182 cell lines to multi-omics analyses (DNA sequencing, RNA sequencing, DNA methylation), and in parallel performed pharmacological and genetic CRISPR-Cas9 loss-of-function screens to identify pediatric-specific treatment opportunities and biomarkers. Our work provides insight into specific pathway vulnerabilities in molecularly defined pediatric tumor classes and uncovers biomarker-linked therapeutic opportunities of clinical relevance. Cell line data and resources are provided in an open access portal.

Diverse rescue potencies of p53 mutations to ATO are predetermined by intrinsic mutational properties

Tumor suppressor p53 is inactivated by thousands of heterogeneous mutations in cancer, but their individual druggability remains largely elusive. Here, we evaluated 800 common p53 mutants for their rescue potencies by the representative generic rescue compound arsenic trioxide (ATO) in terms of transactivation activity, cell growth inhibition, and mouse tumor-suppressive activities. The rescue potencies were mainly determined by the solvent accessibility of the mutated residue, a key factor determining whether a mutation is a structural one, and the temperature sensitivity, the ability to reassemble the wild-type DNA binding surface at a low temperature, of the mutant protein. A total of 390 p53 mutants were rescued to varying degrees and thus were termed as type 1, type 2a, and type 2b mutations, depending on the degree to which they were rescued. The 33 type 1 mutations were rescued to amounts comparable to the wild type. In PDX mouse trials, ATO preferentially inhibited growth of tumors harboring type 1 and type 2a mutants. In an ATO clinical trial, we report the first-in-human mutant p53 reactivation in a patient harboring the type 1 V272M mutant. In 47 cell lines derived from 10 cancer types, ATO preferentially and effectively rescued type 1 and type 2a mutants, supporting the broad applicability of ATO in rescuing mutant p53. Our study provides the scientific and clinical communities with a resource of the druggabilities of numerous p53 mutations (www.rescuep53.net) and proposes a conceptual p53-targeting strategy based on individual mutant alleles rather than mutation type.

Genomic profile of two Brazilian choroid plexus tumors by whole-exome sequencing

Choroid plexus tumors (CPTs) are rare intracranial neoplasms, representing <1% of all brain tumors, yet they represent 20% of first-year pediatric brain tumors. Although these tumors have been linked to TP53 germline mutations in the context of Li-Fraumeni syndrome, their somatic driver alterations remain poorly understood. In this study, we report two cases of lateral ventricle tumors: 3-yr-old male diagnosed with an atypical choroid plexus papilloma (aCPP), and a 6-mo-old female diagnosed with a choroid plexus carcinoma (CPC). We performed whole-exome sequencing of paired blood and tumor tissue in both patients, categorized somatic variants, and determined copy-number alterations. Our analysis revealed a tier II variant (Association for Molecular Pathology [AMP] criteria) in BRD1, a H3 and TP53 acetylation agent, in the aCPP. In addition, we detected copy-number gains on Chromosomes 12, 18, and 20 and copy-number losses on Chromosomes 13q and 22q (BRD1 locus) in this tumor. The CPC tumor had only a pathogenic germline TP53 variant, based on American College of Medical Genetics (ACMG) criteria, with a clinical and familiar history of Li-Fraumeni syndrome. The CPC patient presented loss of heterozygosity (LoH) of TP53 loci and hyperdiploid genome. Both tumors were microsatellite-stable. This is the first study performing whole-exome sequencing in Brazilian choroid plexus tumors, and in line with the literature, we corroborate the absence of recurrent somatic mutations in these tumors. Further studies with larger sample sizes are necessary to confirm our findings and better understand the underlying biology of these tumors.

An evolution-based machine learning to identify cancer type-specific driver mutations

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants

Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations.

SIGNIFICANCE

A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

The mutational spectrum in whole exon of p53 in oral squamous cell carcinoma and its clinical implications

Mutations in p53 are common in human oral squamous cell carcinoma (OSCC). However, in previous analyses, only detection of mutant p53 protein using immunohistochemistry or mutations in some exons have been examined. Full length mutant p53 protein in many cases shows a loss of tumor suppressor function, but in some cases possibly shows a gain of oncogenic function. In this study, we investigate relationships of outcomes with the mutational spectrum of p53 (missense and truncation mutations) in whole exon in OSCC. Specimens from biopsy or surgery (67 cases) were evaluated using next-generation sequencing for p53, and other oncogenic driver genes. The data were compared with overall survival (OS) and disease-free survival (DFS) using univariate and multivariate analyses. p53 mutations were detected in 54 patients (80.6%), 33 missense mutations and 24 truncation mutations. p53 mutations were common in the DNA-binding domain (43/52) and many were missense mutations (31/43). Mutations in other regions were mostly p53 truncation mutations. We detected some mutations in 6 oncogenic driver genes on 67 OSCC, 25 in NOTCH1, 14 in CDKN2A, 5 in PIK3CA, 3 in FBXW7, 3 in HRAS, and 1 in BRAF. However, there was no associations of the p53 mutational spectrum with mutations of oncogenic driver genes in OSCC. A comparison of cases with p53 mutations (missense or truncation) with wild-type p53 cases showed a significant difference in lymph node metastasis. DFS was significantly poorer in cases with p53 truncation mutations. Cases with p53 truncation mutations increased malignancy. In contrast, significant differences were not found between cases with p53 missense mutations and other mutations. The p53 missense mutation cases might include cases with mostly similar function to that of the wild-type, cases with loss of function, and cases with various degrees of gain of oncogenic function.

Complete Models of p53 Better Inform the Impact of Hotspot Mutations

Mutations in tumor suppressor genes often lead to cancerous phenotypes. Current treatments leverage signaling pathways that are often compromised by disease-derived deficiencies in tumor suppressors. P53 falls into this category as genetic mutations lead to physical changes in the protein that impact multiple cellular pathways. Here, we show the first complete structural models of mutated p53 to reveal how hotspot mutations physically deviate from the wild-type protein. We employed a recently determined structure for the p53 monomer to map seven frequent clinical mutations using computational modeling approaches. Results showed that missense mutations often changed the conformational structure of p53 in the DNA-binding site along with its electrostatic surface charges. We posit these changes may amplify the toxic effects of these hotspot mutations by destabilizing an important zinc ion coordination region in p53 to impede proper DNA interactions. These results highlight the imperative need for new studies on patient-derived proteins that may assist in redesigning structure-informed targeted therapies.

Predicting functional effect of missense variants using graph attention neural networks

Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

Current insights into the regulation of programmed cell death by TP53 mutation in cancer

Gene mutation is a complicated process that influences the onset and progression of cancer, and the most prevalent mutation involves the TP53 gene. One of the ways in which the body maintains homeostasis is programmed cell death, which includes apoptosis, autophagic cell death, pyroptosis, ferroptosis, NETosis, and the more recently identified process of cuprotosis. Evasion of these cell deaths is a hallmark of cancer cells, and our elucidation of the way these cells die helps us better understands the mechanisms by which cancer arises and provides us with more ways to treat it.Studies have shown that programmed cell death requires wild-type p53 protein and that mutations of TP53 can affect these modes of programmed cell death. For example, mutant p53 promotes iron-dependent cell death in ferroptosis and inhibits apoptotic and autophagic cell death. It is clear that TP53 mutations act on more than one pathway to death, and these pathways to death do not operate in isolation. They interact with each other and together determine cell death. This review focuses on the mechanisms via which TP53 mutation affects programmed cell death. Clinical investigations of TP53 mutation and the potential for targeted pharmacological agents that can be used to treat cancer are discussed.

A novel germline mutation of TP53 with breast cancer diagnosed as Li-Fraumeni syndrome

TP53 is a tumor suppressor gene and, when dysfunctional, it is known to be involved in the development of cancers. Li-Fraumeni syndrome (LFS) is a hereditary tumor with autosomal dominant inheritance that develops in people with germline pathogenic variants of TP53. LFS frequently develops in parallel to tumors, including breast cancer. We describe a novel germline mutation in TP53 identified by performing a multi-gene panel assay in a breast cancer patient with bilateral breast cancer.

A common pathway to cancer: Oncogenic mutations abolish p53 oscillations

The tumor suppressor p53 oscillates in response to DNA double-strand breaks, a behavior that has been suggested to be essential to its anti-cancer function. Nearly all human cancers have genetic alterations in the p53 pathway; a number of these alterations have been shown to be oncogenic by experiment. These alterations include somatic mutations and copy number variations as well as germline polymorphisms. Intriguingly, they exhibit a mixed pattern of interactions in tumors, such as co-occurrence, mutual exclusivity, and paradoxically, mutual antagonism. Using a differential equation model of p53-Mdm2 dynamics, we employ Hopf bifurcation analysis to show that these alterations have a common mode of action, to abolish the oscillatory competence of p53, thereby, we suggest, impairing its tumor suppressive function. In this analysis, diverse genetic alterations, widely associated with human cancers clinically, have a unified mechanistic explanation of their role in oncogenesis.

Identification of key somatic oncogenic mutation based on a confounder-free causal inference model

Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations.

Identifying key mutations of cancers is helpful to better understand the mechanisms of cancer cell transformation and is critical for therapeutic approaches. Besides sequence and structure-based computational approaches, some functional impact-based methods which consider the association between mutation events and the activity of cancer-related biological processes have also been developed to detect key mutations. However, these methods mainly consider the correlation but ignore that the correlation is far from causality due to the existence of observed and unobserved confounding factors. We develop a confounder-free machine learning-based causal inference framework to estimate the causal effect of mutations on abnormal cell proliferation and epithelial-mesenchymal transition (EMT). It fills a gap in the use of causal mechanisms to discover potential driver mutations in cancer biological systems. Applying our method to 10 cancer types, the identified key mutations are highly consistent with public well-verified ones. Additionally, some new key mutations have also been discovered.

Comprehensive analysis of TP53 and SPOP mutations and their impact on survival in metastatic prostate cancer

Background

Although TP53 and SPOP are frequently mutated in metastatic prostate cancer (PCa), their prognostic value is ambiguous, and large sample studies are lacking, especially when they co-occur with other genetic alterations.

Methods

Genomic data and patients’ clinical characteristics in PCa were downloaded from the cBioPortal database. We extensively analyzed other gene alterations in different mutation status of TP53 and SPOP. We further subdivided TP53 and SPOP mutation into subgroups based on different mutation status, and then evaluated the prognostic value. Two classification systems for TP53 survival analysis were used.

Results

A total of 2,172 patients with PCa were analyzed in our study, of which 1,799 were metastatic PCa patients. The mutual exclusivity analysis showed that TP53 and SPOP mutation has a strong mutual exclusion (p&lt;0.001). In multivariable analysis, truncating TP53 mutations (HR=1.773, 95%CI:1.403-2.239, p&lt;0.001) and other TP53 mutations(HR=1.555, 95%CI:1.267-1.908, p&lt;0.001) were independent negative prognostic markers in metastatic PCa, whereas SPOP mutations(HR=0.592, 95%CI:0.427-0.819, p&lt;0.001) were an independent prognostic factor for better prognosis. Mutations in TP53 were significantly associated with wild-type status for SPOP and CDK12, structural variants/fusions for TMPRSS2 and ERG, AR amplification and PTEN deletion (p&lt;0.001). And truncating TP53 mutations have higher AR amplification rates than other TP53 mutations (p=0.022). Consistently, truncating TP53 mutations had a worse prognosis than other TP53 mutations (p&lt;0.05). Then Kaplan-Meier survival curve showed that Co-occurring TP53 mutations in AR amplification or PTEN deletion tumors significantly reduced survival (p&lt;0.05). Furthermore, those with SPOP-mutant tumors with co-occurring TP53 truncating mutations had shorter overall survival than those with SPOP-mutant tumors with wild-type or other TP53 mutations.

Conclusions

This study found that TP53 and SPOP mutations were mutually exclusive and both were independent prognostic markers for metastatic PCa. Genomic alteration and survival analysis revealed that TP53 and SPOP mutations represented distinct molecular subtypes. Our data suggest that molecular stratification on the basis of TP53 and SPOP mutation status should be implemented for metastatic PCa to optimize and modify clinical decision-making.

Germline Testing in a Cohort of Patients at High Risk of Hereditary Cancer Predisposition Syndromes: First Two-Year Results from South Italy

Germline pathogenic variants (PVs) in oncogenes and tumour suppressor genes are responsible for 5 to 10% of all diagnosed cancers, which are commonly known as hereditary cancer predisposition syndromes (HCPS). A total of 104 individuals at high risk of HCPS were selected by genetic counselling for genetic testing in the past 2 years. Most of them were subjects having a personal and family history of breast cancer (BC) selected according to current established criteria. Genes analysis involved in HCPS was assessed by next-generation sequencing (NGS) using a custom cancer panel with high- and moderate-risk susceptibility genes. Germline PVs were identified in 17 of 104 individuals (16.3%) analysed, while variants of uncertain significance (VUS) were identified in 21/104 (20.2%) cases. Concerning the germline PVs distribution among the 13 BC individuals with positive findings, 8/13 (61.5%) were in the BRCA1/2 genes, whereas 5/13 (38.4%) were in other high- or moderate-risk genes including PALB2, TP53, ATM and CHEK2. NGS genetic testing showed that 6/13 (46.1%) of the PVs observed in BC patients were detected in triple-negative BC. Interestingly, the likelihood of carrying the PVs in the moderate-to-high-risk genes calculated by the cancer risk model BOADICEA was significantly higher in pathogenic variant carriers than in negative subjects. Collectively, this study shows that multigene panel testing can offer an effective diagnostic approach for patients at high risk of hereditary cancers.

TP53 copy number and protein expression inform mutation status across risk categories in acute myeloid leukemia

Mutant TP53 is an adverse risk factor in acute myeloid leukemia (AML), but large-scale integrated genomic-proteomic analyses of TP53 alterations in patients with AML remain limited. We analyzed TP53 mutational status, copy number (CN), and protein expression data in AML (N = 528) and provide a compilation of mutation sites and types across disease subgroups among treated and untreated patients. Our analysis shows differential hotspots in subsets of AML and uncovers novel pathogenic variants involving TP53 splice sites. In addition, we identified TP53 CN loss in 70.2% of TP53-mutated AML cases, which have more deleterious TP53 mutations, as well as copy neutral loss of heterozygosity in 5/32 (15.6%) AML patients who had intact TP53 CN. Importantly, we demonstrate that mutant p53 protein expression patterns by immunohistochemistry evaluated using digital image-assisted analysis provide a robust readout that integrates TP53 mutation and allelic states in patients with AML. Expression of p53 by immunohistochemistry informed mutation status irrespective of TP53 CN status. Genomic analysis of comutations in TP53-mutant AML shows a muted landscape encompassing primarily mutations in genes involved in epigenetic regulation (DNMT3A and TET2), RAS/MAPK signaling (NF1, KRAS/NRAS, PTPN11), and RNA splicing (SRSF2). In summary, our data provide a rationale to refine risk stratification of patients with AML on the basis of integrated molecular and protein-level TP53 analyses.

Identification of Spliceogenic Variants beyond Canonical GT-AG Splice Sites in Hereditary Cancer Genes

Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in APC, ATM, FH, LZTR1, MSH6, PALB2, RAD51C, and TP53 genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members.

GigaAssay - An adaptable high-throughput saturation mutagenesis assay platform

High-throughput assay systems have had a large impact on understanding the mechanisms of basic cell functions. However, high-throughput assays that directly assess molecular functions are limited. Herein, we describe the "GigaAssay", a modular high-throughput one-pot assay system for measuring molecular functions of thousands of genetic variants at once. In this system, each cell was infected with one virus from a library encoding thousands of Tat mutant proteins, with each viral particle encoding a random unique molecular identifier (UMI). We demonstrate proof of concept by measuring transcription of a GFP reporter in an engineered reporter cell line driven by binding of the HIV Tat transcription factor to the HIV long terminal repeat. Infected cells were flow-sorted into 3 bins based on their GFP fluorescence readout. The transcriptional activity of each Tat mutant was calculated from the ratio of signals from each bin. The use of UMIs in the GigaAssay produced a high average accuracy (95%) and positive predictive value (98%) determined by comparison to literature benchmark data, known C-terminal truncations, and blinded independent mutant tests. Including the substitution tolerance with structure/function analysis shows restricted substitution types spatially concentrated in the Cys-rich region. Tat has abundant intragenic epistasis (10%) when single and double mutants are compared.

High-throughput functional evaluation of human cancer-associated mutations using base editors

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

Fundamental immune-oncogenicity trade-offs define driver mutation fitness

Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3-6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical 'free fitness' framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.

Different hotspot p53 mutants exert distinct phenotypes and predict outcome of colorectal cancer patients

The TP53 gene is mutated in approximately 60% of all colorectal cancer (CRC) cases. Over 20% of all TP53-mutated CRC tumors carry missense mutations at position R175 or R273. Here we report that CRC tumors harboring R273 mutations are more prone to progress to metastatic disease, with decreased survival, than those with R175 mutations. We identify a distinct transcriptional signature orchestrated by p53R273H, implicating activation of oncogenic signaling pathways and predicting worse outcome. These features are shared also with the hotspot mutants p53R248Q and p53R248W. p53R273H selectively promotes rapid CRC cell spreading, migration, invasion and metastasis. The transcriptional output of p53R273H is associated with preferential binding to regulatory elements of R273 signature genes. Thus, different TP53 missense mutations contribute differently to cancer progression. Elucidation of the differential impact of distinct TP53 mutations on disease features may make TP53 mutational information more actionable, holding potential for better precision-based medicine.

Are We Moving the Needle for Patients with TP53-Mutated Acute Myeloid Leukemia?

The currently available therapeutic options for patients with TP53-mutated acute myeloid leukemia (AML) are insufficient, as they translate to a median overall of only 6-9 months, and less than 10% of patients undergoing the most aggressive treatments, such as intensive induction therapy and allogeneic hematopoietic stem cell transplantation, will be cured. The lack of clear differences in outcomes with different treatments precludes the designation of a standard of care. Recently, there has been growing attention on this critical area of need by way of better understanding the biology of TP53 alterations and the disparities in outcomes among patients in this molecular subgroup, reflected in the development and testing of agents with novel mechanisms of action. Promising preclinical and efficacy data exist for therapies that are directed at the p53 protein rendered dysfunctional via mutation or that inhibit the CD47/SIRPα axis or other immune checkpoints such as TIM-3. In this review, we discuss recently attractive and emerging therapeutic agents, their preclinical rationale and the available clinical data as a monotherapy or in combination with the currently accepted backbones in frontline and relapsed/refractory settings for patients with TP53-mutated AML.

Mutant p53: it's not all one and the same

Mutation of the TP53 tumor suppressor gene is the most common genetic alteration in cancer, and almost 1000 alleles have been identified in human tumors. While virtually all TP53 mutations are thought to compromise wild type p53 activity, the prevalence and recurrence of missense TP53 alleles has motivated countless research studies aimed at understanding the function of the resulting mutant p53 protein. The data from these studies support three distinct, but perhaps not necessarily mutually exclusive, mechanisms for how different p53 mutants impact cancer: first, they lose the ability to execute wild type p53 functions to varying degrees; second, they act as a dominant negative (DN) inhibitor of wild type p53 tumor-suppressive programs; and third, they may gain oncogenic functions that go beyond mere p53 inactivation. Of these possibilities, the gain of function (GOF) hypothesis is the most controversial, in part due to the dizzying array of biological functions that have been attributed to different mutant p53 proteins. Herein we discuss the current state of understanding of TP53 allele variation in cancer and recent reports that both support and challenge the p53 GOF model. In these studies and others, researchers are turning to more systematic approaches to profile TP53 mutations, which may ultimately determine once and for all how different TP53 mutations act as cancer drivers and whether tumors harboring distinct mutations are phenotypically unique. From a clinical perspective, such information could lead to new therapeutic approaches targeting the effects of different TP53 alleles and/or better sub-stratification of patients harboring TP53 mutant cancers.

Cross-talk between mutant p53 and p62/SQSTM1 augments cancer cell migration by promoting the degradation of cell adhesion proteins

Missense mutations in the TP53 gene, encoding the p53 tumor suppressor, are very frequent in human cancer. Some of those mutations, particularly the more common (“hotspot”) ones, not only abrogate p53’s tumor suppressor activities but also endow the mutant protein with oncogenic gain of function (GOF). We report that p53^R273H, the most common p53 mutant in pancreatic cancer, interacts with the SQSTM1/p62 protein to accelerate the degradation of cell adhesion proteins. This enables pancreatic cancer cells to detach from the epithelial sheet and engage in individualized cell migration, probably augmenting metastatic spread. By providing insights into mechanisms that underpin mutant p53 GOF, this study may suggest ways to interfere with the progression of cancers bearing particular p53 mutants.

Missense mutations in the p53 tumor suppressor abound in human cancer. Common (“hotspot”) mutations endow mutant p53 (mutp53) proteins with oncogenic gain of function (GOF), including enhanced cell migration and invasiveness, favoring cancer progression. GOF is usually attributed to transcriptional effects of mutp53. To elucidate transcription-independent effects of mutp53, we characterized the protein interactome of the p53^R273H mutant in cells derived from pancreatic ductal adenocarcinoma (PDAC), where p53^R273H is the most frequent p53 mutant. We now report that p53^R273H, but not the p53^R175H hotspot mutant, interacts with SQSTM1/p62 and promotes cancer cell migration and invasion in a p62-dependent manner. Mechanistically, the p53^R273H-p62 axis drives the proteasomal degradation of several cell junction–associated proteins, including the gap junction protein Connexin 43, facilitating scattered cell migration. Concordantly, down-regulation of Connexin 43 augments PDAC cell migration, while its forced overexpression blunts the promigratory effect of the p53^R273H-p62 axis. These findings define a mechanism of mutp53 GOF.

Inhibitory Response to CK II Inhibitor Silmitasertib and CDKs Inhibitor Dinaciclib Is Related to Genetic Differences in Pancreatic Ductal Adenocarcinoma Cell Lines

Casein kinase II (CK2) and cyclin-dependent kinases (CDKs) frequently interact within multiple pathways in pancreatic ductal adenocarcinoma (PDAC). Application of CK2- and CDK-inhibitors have been considered as a therapeutic option, but are currently not part of routine chemotherapy regimens. We investigated ten PDAC cell lines exposed to increasing concentrations of silmitasertib and dinaciclib. Cell proliferation, metabolic activity, biomass, and apoptosis/necrosis were evaluated, and bioinformatic clustering was used to classify cell lines into sensitive groups based on their response to inhibitors. Furthermore, whole exome sequencing (WES) and RNA sequencing (RNA-Seq) was conducted to assess recurrent mutations and the expression profile of inhibitor targets and genes frequently mutated in PDAC, respectively. Dinaciclib and silmitasertib demonstrated pronounced and limited cell line specific effects in cell death induction, respectively. WES revealed no genomic variants causing changes in the primary structure of the corresponding inhibitor target proteins. RNA-Seq demonstrated that the expression of all inhibitor target genes was higher in the PDAC cell lines compared to non-neoplastic pancreatic tissue. The observed differences in PDAC cell line sensitivity to silmitasertib or dinaciclib did not depend on target gene expression or the identified gene variants. For the PDAC hotspot genes kirsten rat sarcoma virus (KRAS) and tumor protein p53 (TP53), three and eight variants were identified, respectively. In conclusion, both inhibitors demonstrated in vitro efficacy on the PDAC cell lines. However, aberrations and expression of inhibitor target genes did not appear to affect the efficacy of the corresponding inhibitors. In addition, specific aberrations in TP53 and KRAS affected the efficacy of both inhibitors.

Repurposing antiparasitic antimonials to noncovalently rescue temperature-sensitive p53 mutations

The tumor suppressor p53 is inactivated by over hundreds of heterogenous mutations in cancer. Here, we purposefully selected phenotypically reversible temperature-sensitive (TS) p53 mutations for pharmacological rescue with thermostability as the compound-screening readout. This rational screening identified antiparasitic drug potassium antimony tartrate (PAT) as an agent that can thermostabilize the representative TS mutant p53-V272M via noncovalent binding. PAT met the three basic criteria for a targeted drug: availability of a co-crystal structure, compatible structure-activity relationship, and intracellular target specificity, consequently exhibiting antitumor activity in a xenograft mouse model. At the antimony dose in clinical antiparasitic therapy, PAT effectively and specifically rescued p53-V272M in patient-derived primary leukemia cells in single-cell RNA sequencing. Further scanning of 815 frequent p53-missense mutations identified 65 potential PAT-treatable mutations, most of which were temperature sensitive. These results lay the groundwork for repurposing noncovalent antiparasitic antimonials for precisely treating cancers with the 65 p53 mutations.

Cancer mutation profiles predict ICIs efficacy in patients with non-small cell lung cancer

Although immune checkpoint inhibitors (ICIs) have produced remarkable responses in non-small cell lung cancer (NSCLC) patients, receivers still have a relatively low response rate. Initial response assessment by conventional imaging and evaluation criteria is often unable to identify whether patients can achieve durable clinical benefit from ICIs. Overall, there are sparse effective biomarkers identified to screen NSCLC patients responding to this therapy. A lot of studies have reported that patients with specific gene mutations may benefit from or resist to immunotherapy. However, the single gene mutation may be not effective enough to predict the benefit from immunotherapy for patients. With the advancement in sequencing technology, further studies indicate that many mutations often co-occur and suggest a drastic transformation of tumour microenvironment phenotype. Moreover, co-mutation events have been reported to synergise to activate or suppress signalling pathways of anti-tumour immune response, which also indicates a potential target for combining intervention. Thus, the different mutation profile (especially co-mutation) of patients may be an important concern for predicting or promoting the efficacy of ICIs. However, there is a lack of comprehensive knowledge of this field until now. Therefore, in this study, we reviewed and elaborated the value of cancer mutation profile in predicting the efficacy of immunotherapy and analysed the underlying mechanisms, to provide an alternative way for screening dominant groups, and thereby, optimising individualised therapy for NSCLC patients.

Landscape of TP53 Alterations in Chronic Lymphocytic Leukemia via Data Mining Mutation Databases

Locus-specific databases are invaluable tools for both basic and clinical research. The extensive information they contain is gathered from the literature and manually curated by experts. Cancer genome sequencing projects generate an immense amount of data, which are stored directly in large repositories (cancer genome databases). The presence of a TP53 defect (17p deletion and/or TP53 mutations) is an independent prognostic factor in chronic lymphocytic leukemia (CLL) and TP53 status analysis has been adopted in routine clinical practice. For that reason, TP53 mutation databases have become essential for the validation of the plethora of TP53 variants detected in tumor samples. TP53 profiles in CLL are characterized by a great number of subclonal TP53 mutations with low variant allelic frequencies and the presence of multiple minor subclones harboring different TP53 mutations. In this review, we describe the various characteristics of the multiple levels of heterogeneity of TP53 variants in CLL through the analysis of TP53 mutation databases and the utility of their diagnosis in the clinic.

p53 partial loss-of-function mutations sensitize to chemotherapy

The tumor suppressive transcription factor p53 is frequently inactivated in cancer cells by missense mutations that cluster in the DNA binding domain. 30% hit mutational hotspot residues, resulting in a complete loss of transcriptional activity and mutant p53-driven chemotherapy resistance. Of the remaining 70% of non-hotspot mutants, many are partial loss-of-function (partial-LOF) mutants with residual transcriptional activity. The therapeutic consequences of a partial-LOF have remained largely elusive. Using a p53 mutation engineered to reduce DNA binding, we demonstrate that partial-LOF is sufficient to enhance oncogene-driven tumorigenesis in mouse models of lung and pancreatic ductal adenocarcinoma and acute myeloid leukemia. Interestingly, mouse and human tumors with partial-LOF mutations showed mutant p53 protein accumulation similar as known for hotspot mutants. Different from the chemotherapy resistance caused by p53-loss, the partial-LOF mutant sensitized to an apoptotic chemotherapy response and led to a survival benefit. Mechanistically, the pro-apoptotic transcriptional activity of mouse and human partial-LOF mutants was rescued at high mutant protein levels, suggesting that accumulation of partial-LOF mutants enables the observed apoptotic chemotherapy response. p53 non-hotspot mutants with partial-LOF, therefore, represent tumorigenic p53 mutations that need to be distinguished from other mutations because of their beneficial impact on survival in a therapy context.

Cutting-edge Nanotechnological Approaches for Lung Cancer Therapy

Lung cancer is the second leading cancer with a high rate of mortality. It can be treated using different intervention techniques such as chemotherapy, radiation therapy, surgical removal, and photodynamic therapy. All of these interventions lack specificity, implying that it harms the normal cells adjacent to the infected ones. Nanotechnology provides a promising solution that increases the bioavailability of anticancer drugs at the tumor site with reduced toxicity and improved therapeutic efficacy. Nanotechnology also improves the way lung cancer is diagnosed and treated. Various nanocarriers like liposomes, polymeric nanoparticles, magnetic nanoparticles, and different theranostic approaches are already approved for medical use, while various are under clinical and preclinical stages. This review article covers the details about lung cancer, types of overexpressed receptors, and cutting-edge nanocarriers used for treating lung cancer at its specific target.

Somatic Mutations in TP53 Gene in Colombian Patients With Non-melanoma Skin Cancer

BACKGROUND/AIM

Non-melanoma skin cancer is the most common cancer in the world. Somatic mutations in the TP53 gene are associated with the development of this cancer. To describe mutations in exons 5-8 of the TP53 gene in a sample of Colombian patients with non-melanoma skin cancer.

MATERIALS AND METHODS

One hundred and fifteen patients with non-melanoma skin cancer were included. Exons 5-8 were amplified and analyzed by PCR-High Resolution Melting and Sanger sequencing.

RESULTS

Fifty-seven patients with basal cell carcinomas and 58 with squamous cell carcinomas were studied. 16% of patients with basal cell carcinoma and 26% of patients with squamous cell carcinoma had mutations in the TP53 gene. The most frequent mutations were substitutions, while three patients had deletions. The most frequent mutation was p.R158G.

CONCLUSION

The analysis showed that Colombian individuals with non-melanoma skin cancer have genetic TP53 variants different from those reported as recurrent for this disease.

A Novel Germline TP53 Mutation in a Patient With Li-Fraumeni Syndrome: Resolving a Variant of Uncertain Significance

Increasing availability of genomic testing poses new challenges to clinicians, particularly where variant interpretation from commercial sources may be equivocal. The authors report a patient with recurrent rhabdomyosarcoma and subsequent bilateral breast cancer who was found to harbor a previously undescribed germline TP53 sequence alteration annotated by the commercial laboratory as a variant of uncertain significance. By investigating publicly available databases of aggregated normal germline and malignant somatic genomic sequences, the authors conclude that this missense variant, c.476C>T (p.A159V), is a novel, pathogenic Li-Fraumeni syndrome mutation and demonstrate the utility of these resources in clinical pediatric hematology and oncology practice.

Clinical and Functional Significance of TP53 Exon 4-Intron 4 Splice Junction Variants

Germline TP53 splicing variants are uncommon, and their clinical relevance is unknown. However, splice-altering variants at exon 4-intron 4 junctions are relatively enriched in pediatric adrenocortical tumors (ACT). Nevertheless, family histories of cancer compatible with classic Li-Fraumeni syndrome are rarely seen in these patients. We used conventional and in silico assays to determine protein stability, splicing, and transcriptional activity of 10 TP53 variants at exon 4-intron 4 junctions and analyzed their clinical correlates. We reviewed public databases that report the impact of TP53 variants in human cancer and examined individual reports, focusing on family history of cancer. TP53 exon 4-intron 4 junction germline variants were identified in 9 of 75 pediatric ACTs enrolled in the International Pediatric Adrenocortical Tumor Registry and Children's Oncology Group ARAR0332 study. An additional eight independent TP53 variants involving exon 4 splicing were identified in the Pediatric Cancer Genome Project (n = 5,213). These variants resulted in improper expression due to ineffective splicing, protein instability, altered subcellular localization, and loss of function. Clinical case review of carriers of TP53 exon 4-intron 4 junction variants revealed a high incidence of pediatric ACTs and atypical tumor types not consistent with classic Li-Fraumeni syndrome. Germline variants involving TP53 exon 4-intron 4 junctions are frequent in ACT and rare in other pediatric tumors. The collective impact of these germline TP53 variants on the fidelity of splicing, protein structure, and function must be considered in evaluating cancer susceptibility. IMPLICATIONS: Taken together, the data indicate that splice variants at TP53 codon 125 and surrounding bases differentially impacted p53 gene expression and function.