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

Understanding large scale sequencing datasets through changes to protein folding

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis

Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and other conditions. Although the most frequently mutated CH driver genes have been identified, a systematic landscape of the mutations capable of initiating this phenomenon is still lacking. In this study, we trained machine learning models for 12 of the most recurrent CH genes to identify their driver mutations. These models outperform expert-curated rules based on prior knowledge of the function of these genes. Moreover, their application to identify CH driver mutations across almost half a million donors of the UK Biobank reproduces known associations between CH driver mutations and age, and the prevalence of several diseases and conditions. We thus propose that these models support the accurate identification of CH across healthy individuals. Significance: We developed and validated gene-specific machine learning models to identify CH driver mutations, showing their advantage with respect to expert-curated rules. These models can support the identification and clinical interpretation of CH mutations in newly sequenced individuals. See related commentary by Arends and Jaiswal, p. 1581.

Breast Carcinoma With Tubulopapillary Features Has a Distinct Immunophenotypic and Molecular Signature: A Report of Two Tumors and Literature Review

Breast carcinoma with tubulopapillary features is a newly described entity associated with poor prognosis with only 14 tumors reported in the literature. We report 2 additional tumors and identify novel immunohistochemical and molecular features of the tumor. The first tumor was from a 72-year-old woman with nonmetastatic breast carcinoma and the second was from a 32-year-old woman with metastatic breast carcinoma who received neoadjuvant therapy. Both tumors had high-grade nuclear features with a distinctive morphology characterized by infiltrating open glands with intratubular papillary and micropapillary projections in >90% of the invasive carcinoma. In addition to the usual predictors of aggressive behavior, both tumors showed a high expression of p16 and SOX10, which has not been previously described. Targeted tumor sequencing revealed pathogenic variants of TP53 in both tumors, in agreement with previous reports. Prior studies have shown a correlation between p16 and SOX10 expression with high-grade features and worse prognosis; typically seen in triple-negative carcinomas as demonstrated in both of our tumors. However, not all reported tumors of breast carcinoma with tubulopapillary features have demonstrated a triple-negative profile as there are a few reports of tumors with estrogen receptor and/or human epidermal growth factor 2 expression. Due to their distinct morphologic and molecular characteristics, breast carcinoma with tubulopapillary features may represent a new breast cancer histologic subtype.

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

Deciphering the significance of p53 mutant proteins

Mutations in the p53 gene compromise its role as guardian of genomic integrity, yielding predominantly missense p53 mutant proteins. The gain-of-function hypothesis has long suggested that these mutant proteins acquire new oncogenic properties; however, recent studies challenge this notion, indicating that targeting these mutants may not impact the fitness of cancer cells. Mounting evidence indicates that tumorigenesis involves a cooperative interplay between driver mutations and cellular state, influenced by developmental stage, external insults, and tissue damage. Consistently, the behavior and properties of p53 mutants are altered by the context. This article aims to provide a balanced summary of the evolving evidence regarding the contribution of p53 mutants in the biology of cancer while contemplating alternative frameworks to decipher the complexity of p53 mutants within their physiological contexts.

Germline NGS targeted analysis in adult patients with sporadic adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare cancer that arises sporadically or due to hereditary syndromes. Data on germline variants (GVs) in sporadic ACC are limited. Our aim was to characterize GVs of genes potentially related to adrenal diseases in 150 adult patients with sporadic ACC.

METHODS

This was a retrospective analysis of stage I-IV ACC patients with sporadic ACC from two reference centers for ACC in Italy. Patients were included in the analysis if they had confirmed diagnosis of ACC, a frozen peripheral blood sample and complete clinical and follow-up data. Next generation sequencing technology was used to analyze the prevalence of GVs in a custom panel of 17 genes belonging to either cancer-predisposition genes or adrenocortical-differentiation genes categories.

RESULTS

We identified 18 GVs based on their frequency, enrichment and predicted functional characteristics. We found six pathogenic (P) or likely pathogenic (LP) variants in ARMC5, CTNNB1, MSH2, PDE11A and TP53 genes; and twelve variants lacking evidence of pathogenicity. New unique P/LP variants were identified in TP53 (p.G105D) and, for the first time, in ARMC5 (p.P731R). The presence of P/LP GVs was associated with reduced survival outcomes and had a significant and independent impact on both progression-free survival and overall survival.

CONCLUSIONS

GVs were present in 6.7 % of patients with sporadic ACC, and we identified novel variants of ARMC5 and TP53. These findings may improve understanding of ACC pathogenesis and enable genetic counseling of patients and their families.

Bioinformatic Identification of TP53 Gene Mutation Hotspots in Colorectal Cancer

Mutations and inactivation of the TP53 gene are frequently observed in various types of malignancies. Precise knowledge of the genetic structure and detection of mutation hotspots are crucial, as these indicate a high probability of developing cancer. The aim of our study was to perform the bioinformatic detection of mutation hotspots in the TP53 gene in patients diagnosed with malignant colon neoplasms using self-developed software (version 1). We compared TP53 gene sequences from 50 healthy individuals with those from 50 patients diagnosed with colorectal carcinoma. Of the 50 samples from cancer patients, the most frequent mutations were observed in exons 5 and 8 (12 mutations per exon) and gene sequences of 12 samples, which differed from those of the 50 samples from healthy individuals. Based on our results, the distribution of mutations in the TP53 gene structure was not even across different exons. By comparing the gene sequences of healthy individuals with those of colon cancer samples, we conclude that structural changes occurring in similar gene regions are not associated with increases in susceptibility to malignancies in every case, namely, that the pathological mechanism is multifactorial.

Canonical and non-canonical functions of p53 isoforms: potentiating the complexity of tumor development and therapy resistance

Full-length p53 (p53α) plays a pivotal role in maintaining genomic integrity and preventing tumor development. Over the years, p53 was found to exist in various isoforms, which are generated through alternative splicing, alternative initiation of translation, and internal ribosome entry site. p53 isoforms, either C-terminally altered or N-terminally truncated, exhibit distinct biological roles compared to p53α, and have significant implications for tumor development and therapy resistance. Due to a lack of part and/or complete C- or N-terminal domains, ectopic expression of some p53 isoforms failed to induce expression of canonical transcriptional targets of p53α like CDKN1A or MDM2, even though they may bind their promoters. Yet, p53 isoforms like Δ40p53α still activate subsets of targets including MDM2 and BAX. Furthermore, certain p53 isoforms transactivate even novel targets compared to p53α. More recently, non-canonical functions of p53α in DNA repair and of different isoforms in DNA replication unrelated to transcriptional activities were discovered, amplifying the potential of p53 as a master regulator of physiological and tumor suppressor functions in human cells. Both regarding canonical and non-canonical functions, alternative p53 isoforms frequently exert dominant negative effects on p53α and its partners, which is modified by the relative isoform levels. Underlying mechanisms include hetero-oligomerization, changes in subcellular localization, and aggregation. These processes ultimately influence the net activities of p53α and give rise to diverse cellular outcomes. Biological roles of p53 isoforms have implications for tumor development and cancer therapy resistance. Dysregulated expression of isoforms has been observed in various cancer types and is associated with different clinical outcomes. In conclusion, p53 isoforms have expanded our understanding of the complex regulatory network involving p53 in tumors. Unraveling the mechanisms underlying the biological roles of p53 isoforms provides new avenues for studies aiming at a better understanding of tumor development and developing therapeutic interventions to overcome resistance.

BRCA1 Intragenic Duplication Combined with a Likely Pathogenic TP53 Variant in a Patient with Triple-Negative Breast Cancer: Clinical Risk and Management

For patients with hereditary breast and ovarian cancer, the probability of carrying two pathogenic variants (PVs) in dominant cancer-predisposing genes is rare. Using targeted next-generation sequencing (NGS), we investigated a 49-year-old Caucasian woman who developed a highly aggressive breast tumor. Our analyses identified an intragenic germline heterozygous duplication in BRCA1 with an additional likely PV in the TP53 gene. The BRCA1 variant was confirmed by multiplex ligation probe amplification (MLPA), and genomic breakpoints were characterized at the nucleotide level (c.135-2578_442-1104dup). mRNA extracted from lymphocytes was amplified by RT-PCR and then Sanger sequenced, revealing a tandem duplication r.135_441dup; p.(Gln148Ilefs*20). This duplication results in the synthesis of a truncated and, most likely, nonfunctional protein. Following functional studies, the TP53 exon 5 c.472C > T; p.(Arg158Cys) missense variant was classified as likely pathogenic by the Li-Fraumeni Syndrome (LFS) working group. This type of unexpected association will be increasingly identified in the future, with the switch from targeted BRCA sequencing to hereditary breast and ovarian cancer (HBOC) panel sequencing, raising the question of how these patients should be managed. It is therefore important to record and investigate these rare double-heterozygous genotypes.

Clinical and genetic characteristics of carriers of the TP53 c.541C > T, p.Arg181Cys pathogenic variant causing hereditary cancer in patients of Arab-Muslim descent

TP53 pathogenic variants cause Li-Fraumeni syndrome (LFS), with some variants causing an attenuated phenotype. Herein, we describe the clinical phenotype and genetic characteristics of carriers of NM_000546.6 (TP53): c.541C > T, (p.Arg181Cys) treated at Hadassah Medical Center. We retrospectively examined our genetic databases to identify all carriers of TP53 p.Arg181Cys. We reached out to carriers and their relatives and collected clinical and demographic data, lifestyle factors, carcinogenic exposures as well as additional blood samples for genetic testing and whole exome sequencing. Between 2005 and 2022 a total of 2875 cancer patients underwent genetic testing using genetic panels, whole exome sequencing or targeted TP53 assays. A total of 30 cancer patients, all of Arab-Muslim descent, were found to be carriers of TP53 p.Arg181Cys, the majority from Jerusalem and Hebron, two of which were homozygous for the variant. Carriers were from 24 distinct families of them, 15 families (62.5%) met updated Chompret criteria for LFS. Median age of diagnosis was 35 years-old (range 1-69) with cancers characteristic of LFS (16 Breast cancer; 6 primary CNS tumors; 3 sarcomas) including 4 children with choroid plexus carcinoma, medulloblastoma, or glioblastoma. A total of 21 healthy carriers of TP53 p.Arg181Cys were identified at a median age of 39 years-old (range 2-54)-19 relatives and 2 additional pediatric non-cancer patients, in which the finding was incidental. We report a shared haplotype of 350kb among carriers, limited co-morbidities and low BMI in both cancer patients and healthy carriers. There were no demographic factors or carcinogenic exposures unique to carriers who developed malignancy. Upon exome analysis no other known pathogenic variants in cancer predisposing genes were identified. TP53 p.Arg181Cys is a founder pathogenic variant predominant to the Arab-Muslim population in Jerusalem and Hebron, causing attenuated-LFS. We suggest strict surveillance in established carriers and encourage referral to genetic testing for all cancer patients of Arab-Muslim descent in this region with LFS-associated malignancies as well as family members of established carriers.

P53 Gene as a Promising Biomarker and Potential Target for the Early Diagnosis of Reproductive Cancers

One of the crucial aspects of cancer research is diagnosis with specificity and accuracy. Early cancer detection mostly helps make appropriate decisions regarding treatment and metastasis. The well-studied transcription factor tumor suppressor protein p53 is essential for maintaining genetic integrity. p53 is a key tumor suppressor that recognizes the carcinogenic biological pathways and eradicates them by apoptosis. A wide range of carcinomas, especially gynecological such as ovarian, cervical, and endometrial cancers, frequently undergo TP53 gene mutations. This study evaluates the potential of the p53 gene as a biological marker for the diagnosis of reproductive system neoplasms. Immunohistochemistry of p53 is rapid, easy to accomplish, cost-effective, and preferred by pathologists as a surrogate for the analysis of TP53 mutation. Thus, this review lays a groundwork for future efforts to develop techniques using p53 for the early diagnosis of cancer.

TP53-mutated acute myeloid leukemia and myelodysplastic syndrome: biology, treatment challenges, and upcoming approaches

Improved understanding of TP53 biology and the clinicopathological features of TP53-mutated myeloid neoplasms has led to the recognition of TP53-mutated acute myeloid leukemia/myelodysplastic syndrome (TP53m AML/MDS) as a unique entity, characterized by dismal outcomes following conventional therapies. Several clinical trials have investigated combinations of emerging therapies for these patients with the poorest molecular prognosis among myeloid neoplasms. Although some emerging therapies have shown improvement in overall response rates, this has not translated into better overall survival, hence the notion that p53 remains an elusive target. New therapeutic strategies, including novel targeted therapies, immune checkpoint inhibitors, and monoclonal antibodies, represent a shift away from cytotoxic and hypomethylating-based therapies, towards approaches combining non-immune and novel immune therapeutic strategies. The triple combination of azacitidine and venetoclax with either magrolimab or eprenetapopt have demonstrated safety in early trials, with phase III trials currently underway, and promising interim clinical results. This review compiles background on TP53 biology, available and emerging therapies along with their mechanisms of action for the TP53m disease entity, current treatment challenges, and recently published data and status of ongoing clinical trials for TP53m AML/MDS.

A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening

Prime editing installs precise edits into the genome with minimal unwanted byproducts, but low and variable editing efficiencies have complicated application of the approach to high-throughput functional genomics. Leveraging several recent advances, we assembled a prime editing platform capable of high-efficiency substitution editing across a set of engineered prime editing guide RNAs (epegRNAs) and corresponding target sequences (80% median intended editing). Then, using a custom library of 240,000 epegRNAs targeting >17,000 codons with 175 different substitution types, we benchmarked our platform for functional interrogation of small substitution variants (1-3 nucleotides) targeted to essential genes. Resulting data identified negative growth phenotypes for nonsense mutations targeted to ~8,000 codons, and comparing those phenotypes to results from controls demonstrated high specificity. We also observed phenotypes for synonymous mutations that disrupted splice site motifs at 3' exon boundaries. Altogether, we establish and benchmark a high-throughput prime editing approach for functional characterization of genetic variants with simple readouts from multiplexed experiments.

Phase I Study of ORIC-101, a Glucocorticoid Receptor Antagonist, in Combination with Enzalutamide in Patients with Metastatic Castration-resistant Prostate Cancer Progressing on Enzalutamide

PURPOSE

Increased glucocorticoid receptor (GR) signaling is a proposed compensatory mechanism of resistance to androgen receptor (AR) inhibition in metastatic castration-resistant prostate cancer (mCRPC). ORIC-101 is a potent and selective orally-bioavailable GR antagonist.

PATIENTS AND METHODS

Safety, pharmacokinetic/pharmacodynamic, and antitumor activity of ORIC-101 in combination with enzalutamide were studied in patients with mCRPC progressing on enzalutamide. ORIC-101 doses ranging from 80 to 240 mg once daily were tested in combination with enzalutamide 160 mg once daily. Pharmacokinetics/pharmacodynamics was assessed after a single dose and at steady state. Disease control rate (DCR) at 12 weeks was evaluated at the recommended phase 2 dose (RP2D).

RESULTS

A total of 41 patients were enrolled. There were no dose-limiting toxicities and the RP2D was selected as 240 mg of ORIC-101 and 160 mg of enzalutamide daily. At the RP2D, the most common treatment-related adverse events were fatigue (38.7%), nausea (29.0%), decreased appetite (19.4%), and constipation (12.9%). Pharmacokinetic/pharmacodynamic data confirmed ORIC-101 achieved exposures necessary for GR target engagement. Overall, for 31 patients treated at the RP2D, there was insufficient clinical benefit based on DCR (25.8%; 80% confidence interval: 15.65-38.52) which did not meet the prespecified target rate, leading to termination of the study. Exploratory subgroup analyses based on baseline GR expression, presence of AR resistance variants, and molecular features of aggressive variant prostate cancer suggested possible benefit in patients with high GR expression and no other resistance markers, although this would require confirmation.

CONCLUSIONS

Although the combination of ORIC-101 and enzalutamide demonstrated an acceptable tolerability profile, GR target inhibition with ORIC-101 did not produce clinical benefit in men with metastatic prostate cancer resistant to enzalutamide.

ERK pathway agonism for cancer therapy: evidence, insights, and a target discovery framework

At least 40% of human cancers are associated with aberrant ERK pathway activity (ERKp). Inhibitors targeting various effectors within the ERKp have been developed and explored for over two decades. Conversely, a substantial body of evidence suggests that both normal human cells and, notably to a greater extent, cancer cells exhibit susceptibility to hyperactivation of ERKp. However, this vulnerability of cancer cells remains relatively unexplored. In this review, we reexamine the evidence on the selective lethality of highly elevated ERKp activity in human cancer cells of varying backgrounds. We synthesize the insights proposed for harnessing this vulnerability of ERK-associated cancers for therapeutical approaches and contextualize these insights within established pharmacological cancer-targeting models. Moreover, we compile the intriguing preclinical findings of ERK pathway agonism in diverse cancer models. Lastly, we present a conceptual framework for target discovery regarding ERKp agonism, emphasizing the utilization of mutual exclusivity among oncogenes to develop novel targeted therapies for precision oncology.

A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer

BACKGROUND

There is currently a limited number of studies on transglutaminase type 1 (TGM1) in tumors. The objective of this study is to perform a comprehensive analysis across various types of cancer to determine the prognostic significance of TGM1 in tumors and investigate its role in the immune environment.

METHOD

Pan-cancer and mutational data were retrieved from the TCGA database and analyzed using R (version 3.6.4) and its associated software package. The expression difference and prognosis of TGM1 were examined, along with its correlation with tumor heterogeneity, stemness, mutation landscape, and RNA modification. Additionally, the relationship between TGM1 expression and tumor immunity was investigated using the TIMER method.

RESULTS

TGM1 is expressed differently in various tumors and normal samples and is associated with the overall survival and progression-free time of KIRC, ACC, SKCM, LIHC, and STES. In LICH, we found a negative correlation between TGM1 expression and 6 indicators of tumor stemness. The mutation frequencies of BLCA, LIHC, and KIRC were 1.7%, 0.3%, and 0.3% respectively. In BLCA and BRCA, there was a significant correlation between TGM1 expression and the infiltration of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells.

CONCLUSION

TGM1 has the potential to serve as both a prognostic marker and a drug target.

High-throughput evaluation of genetic variants with prime editing sensor libraries

Tumor genomes often harbor a complex spectrum of single nucleotide alterations and chromosomal rearrangements that can perturb protein function. Prime editing has been applied to install and evaluate genetic variants, but previous approaches have been limited by the variable efficiency of prime editing guide RNAs. Here we present a high-throughput prime editing sensor strategy that couples prime editing guide RNAs with synthetic versions of their cognate target sites to quantitatively assess the functional impact of endogenous genetic variants. We screen over 1,000 endogenous cancer-associated variants of TP53-the most frequently mutated gene in cancer-to identify alleles that impact p53 function in mechanistically diverse ways. We find that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. Our results emphasize the physiological importance of gene dosage in shaping native protein stoichiometry and protein-protein interactions, and establish a framework for studying genetic variants in their endogenous sequence context at scale.

Primary vulvar adenocarcinoma of intestinal type: Report of two cases showing molecular similarity with colorectal adenocarcinoma

Primary vulvar adenocarcinoma is a particularly rare tumor with poorly understood histogenesis and unclear clinical characteristics and prognosis. Vulvar adenocarcinoma of intestinal type (VAIt) is a very uncommon subtype of primary vulvar adenocarcinoma and only 27 cases have been described in the literature in the past. Of these cases, two have been described as human papillomavirus (HPV)-associated VAIt. The current report presents two additional cases of primary VAIt showing variants in the KRAS, TP53, and DPYD genes and no evidence of HPV DNA by real-time polymerase chain reaction (RT-PCR). Next-generation sequencing (NGS) revealed TP53 pathogenic variants in both cases, but only one case had aberrant p53 protein immunohistochemical characteristics. KRAS and DPYD mutations were identified separately in the two cases. Due to their capacity to imitate the spread of more prevalent gastrointestinal carcinomas, these tumors may present diagnostic issues. Additional cases can contribute to a better understanding of the pathophysiology and prognosis of VAIt.

Understanding the prion-like behavior of mutant p53 proteins in triple-negative breast cancer pathogenesis: The current therapeutic strategies and future directions

Breast cancer (BC) is viewed as a significant public health issue and is the primary cause of cancer-related deaths among women worldwide. Triple-negative breast cancer (TNBC) is a particularly aggressive subtype that predominantly affects young premenopausal women. The tumor suppressor p53 playsa vital role in the cellular response to DNA damage, and its loss or mutations are commonly present in many cancers, including BC. Recent evidence suggests that mutant p53 proteins can aggregate and form prion-like structures, which may contribute to the pathogenesis of different types of malignancies, such as BC. This review provides an overview of BC molecular subtypes, the epidemiology of TNBC, and the role of p53 in BC development. We also discuss the potential implications of prion-like aggregation in BC and highlight future research directions. Moreover, a comprehensive analysis of the current therapeutic approaches targeting p53 aggregates in BC treatment is presented. Strategies including small molecules, chaperone inhibitors, immunotherapy, CRISPR-Cas9, and siRNA are discussed, along with their potential benefits and drawbacks. The use of these approaches to inhibit p53 aggregation and degradation represents a promising target for cancer therapy. Future investigations into the efficacy of these approaches against various p53 mutations or binding to non-p53 proteins should be conducted to develop more effective and personalized therapies for BC treatment.

BRCA1, BRCA2, and TP53 germline and somatic variants and clinicopathological characteristics of Brazilian patients with epithelial ovarian cancer

BACKGROUND

Approximately 3/4 of ovarian cancers are diagnosed in advanced stages, with the high-grade epithelial ovarian carcinoma (EOC) accounting for 90% of the cases. EOC present high genomic instability and somatic loss-of-function variants in genes associated with homologous recombination mutational repair pathway (HR), such as BRCA1 and BRCA2, and in TP53. The identification of germline variants in HR genes in EOC is relevant for treatment of platinum resistant tumors and relapsed tumors with therapies based in synthetic lethality such as PARP inhibitors. Patients with somatic variants in HR genes may also benefit from these therapies. In this work was analyzed the frequency of somatic variants in BRCA1, BRCA2, and TP53 in an EOC cohort of Brazilian patients, estimating the proportion of variants in tumoral tissue and their association with progression-free survival and overall survival.

METHODS

The study was conducted with paired blood/tumor samples from 56 patients. Germline and tumoral sequences of BRCA1, BRCA2, and TP53 were obtained by massive parallel sequencing. The HaplotypeCaller method was used for calling germline variants, and somatic variants were called with Mutect2.

RESULTS

A total of 26 germline variants were found, and seven patients presented germline pathogenic or likely pathogenic variants in BRCA1 or BRCA2. The analysis of tumoral tissue identified 52 somatic variants in 41 patients, being 43 somatic variants affecting or likely affecting protein functionality. Survival analyses showed that tumor staging was associated with overall survival (OS), while the presence of somatic mutation in TP53 was not associated with OS or progression-free survival.

CONCLUSION

Frequency of pathogenic or likely pathogenic germline variants in BRCA1 and BRCA2 (12.5%) was lower in comparison with other studies. TP53 was the most altered gene in tumors, with 62.5% presenting likely non-functional or non-functional somatic variants, while eight 14.2% presented likely non-functional or non-functional somatic variants in BRCA1 or BRCA2.

Pharmacological reactivation of p53 in the era of precision anticancer medicine

p53, which is encoded by the most frequently mutated gene in cancer, TP53, is an attractive target for novel cancer therapies. Despite major challenges associated with this approach, several compounds that either augment the activity of wild-type p53 or restore all, or some, of the wild-type functions to p53 mutants are currently being explored. In wild-type TP53 cancer cells, p53 function is often abrogated by overexpression of the negative regulator MDM2, and agents that disrupt p53-MDM2 binding can trigger a robust p53 response, albeit potentially with induction of p53 activity in non-malignant cells. In TP53-mutant cancer cells, compounds that promote the refolding of missense mutant p53 or the translational readthrough of nonsense mutant TP53 might elicit potent cell death. Some of these compounds have been, or are being, tested in clinical trials involving patients with various types of cancer. Nonetheless, no p53-targeting drug has so far been approved for clinical use. Advances in our understanding of p53 biology provide some clues as to the underlying reasons for the variable clinical activity of p53-restoring therapies seen thus far. In this Review, we discuss the intricate interactions between p53 and its cellular and microenvironmental contexts and factors that can influence p53's activity. We also propose several strategies for improving the clinical efficacy of these agents through the complex perspective of p53 functionality.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

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.