Multiple mutations within individual oncogenes

Protein stability models fail to capture epistatic interactions of double point mutations

There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single-point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We find that additive models of protein stability perform surprisingly well on this task, achieving similar performance to comparable non-additive predictors according to most metrics. Accordingly, we find that neither artificial intelligence-based nor physics-based protein stability models consistently capture epistatic interactions between single mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions than additive models on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling, as well as a novel data augmentation scheme, which mitigates some of the limitations in currently available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity.

Case Report: Concurrent de novo pathogenic variants in the LMNA gene as a cause of sporadic partial lipodystrophy

Introduction

Inherited lipodystrophies are a group of rare diseases defined by severe reduction in adipose tissue mass and classified as generalized or partial. We report a non-familial (sporadic) case of partial lipodystrophy caused by a novel genetic mechanism involving closely linked de novo pathogenic variants in the LMNA gene.

Methods

A female adult with partial lipodystrophy and her parents were evaluated for gene variants across the exome under different mendelian inheritance models (autosomal dominant, recessive, compound heterozygous, and X-linked) to find pathogenic variants. Body composition was assessed via dual-energy X-ray absorptiometry (DXA).

Results

The patient showed absence of adipose tissue in the limbs; preservation of adiposity in the face, neck, and trunk; muscular hypertrophy, hypertriglyceridemia and insulin resistance. DXA revealed a fat mass of 15.4%, with android-to-gynoid ratio, trunk/limb, and trunk/leg ratios exceeding the published upper limits of 90% reference intervals. Two heterozygous missense de novo pathogenic variants in cis within the LMNA gene were found in the proband: p.Y481H and p.K486N (NP_733821.1). These variants have functional effects and were reported in inherited Emery-Dreifuss muscular dystrophy 2 (p.Y481H) and familial partial lipodystrophy type 2 (p.K486N). Molecular modeling analyses provided additional insights into the protein instability conferred by these variants in the lamin A/C Ig-like domain.

Conclusion

In a case of sporadic partial lipodystrophy, we describe two concurrent de novo pathogenic variants within the same gene (LMNA) as a novel pathogenic mechanism. This finding expands the genetic and phenotypic spectrum of partial lipodystrophy and laminopathy syndromes.

Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

Protein stability models fail to capture epistatic interactions of double point mutations

There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations, despite the significance of mutation clusters for disease pathways and protein design studies. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We identify a blind spot in how predictors are typically evaluated on multiple mutations, finding that, contrary to assumptions in the field, current stability models are unable to consistently capture epistatic interactions between double mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling as well as a novel data augmentation scheme which mitigates some of the limitations in available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity.

Cancer and Autism: How PTEN Mutations Degrade Function at the Membrane and Isoform Expression in the Human Brain

Mutations causing loss of PTEN lipid phosphatase activity can promote cancer, benign tumors (PHTS), and neurodevelopmental disorders (NDDs). Exactly how they preferentially trigger distinct phenotypic outcomes has been puzzling. Here, we demonstrate that PTEN mutations differentially allosterically bias P loop dynamics and its connection to the catalytic site, affecting catalytic activity. NDD-related mutations are likely to sample conformations of the functional wild-type state, while sampled conformations for the strong, cancer-related driver mutation hotspots favor catalysis-primed conformations, suggesting that NDD mutations are likely to be weaker, and our large-scale simulations show why. Prenatal PTEN isoform expression data suggest exons 5 and 7, which harbor NDD mutations, as cancer-risk carriers. Since cancer requires more than a single mutation, our conformational and genomic analysis helps discover how same protein mutations can foster different clinical manifestations, articulates a role for co-occurring background latent driver mutations, and uncovers relationships of splicing isoform expression to life expectancy.

Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregulation and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression constitutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcriptional and translational changes, shedding light on the tumor's epigenetic landscape and the role of its cell-of-origin.

Pan-cancer clinical impact of latent drivers from double mutations

Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.

Spatial genomic diversity associated with APOBEC mutagenesis in squamous cell carcinoma arising from ovarian teratoma

Although the gross and microscopic features of squamous cell carcinoma arising from ovarian mature cystic teratoma (MCT-SCC) vary from case to case, the spatial spreading of genomic alterations within the tumor remains unclear. To clarify the spatial genomic diversity in MCT-SCCs, we performed whole-exome sequencing by collecting 16 samples from histologically different parts of two MCT-SCCs. Both cases showed histological diversity within the tumors (case 1: nonkeratinizing and keratinizing SCC and case 2: nonkeratinizing SCC and anaplastic carcinoma) and had different somatic mutation profiles by histological findings. Mutation signature analysis revealed a significantly enriched apolipoprotein B mRNA editing enzyme catalytic subunit (APOBEC) signature at all sites. Intriguingly, the spread of genomic alterations within the tumor and the clonal evolution patterns from nonmalignant epithelium to cancer sites differed between cases. TP53 mutation and copy number alterations were widespread at all sites, including the nonmalignant epithelium, in case 1. Keratinizing and nonkeratinizing SCCs were differentiated by the occurrence of unique somatic mutations from a common ancestral clone. In contrast, the nonmalignant epithelium showed almost no somatic mutations in case 2. TP53 mutation and the copy number alteration similarities were observed only in nonkeratinizing SCC samples. Nonkeratinizing SCC and anaplastic carcinoma shared almost no somatic mutations, suggesting that each locally and independently arose in the MCT. We demonstrated that two MCT-SCCs with different histologic findings were highly heterogeneous tumors with clearly different clones associated with APOBEC-mediated mutagenesis, suggesting the importance of evaluating intratumor histological and genetic heterogeneity among multiple sites of MCT-SCC.

Solute Carrier Family 7 Member 11 (SLC7A11) is a Potential Prognostic Biomarker in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a common type of gynecological cancers, second only to cervical cancer in incidence. Thus, it is necessary to develop effective therapies and identify biomarkers for its prognosis. Solute carrier family 7 member 11 (SLC7A11) is well known for its role in maintaining the intracellular glutathione level and preventing oxidative-stress-induced cell death. However, the association between SLC7A11 expression and prognosis as well as the correlation between tumor-infiltrating immune cells (TIICs) and immunotherapy of UCEC has rarely been reported. This study aims to evaluate the prognostic significance and immune cell infiltration level of SLC7A11 in UCEC.

Methods

Bioinformatics analysis tools and databases, including R software, National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), GEPIA2, Sangerbox, Kaplan-Meier (K-M) Plotter, TISIDB, and TIMER2, were utilized to measure the expression level and clarify the clinical significance of SLC7A11 in UCEC.

Results

SLC7A11 expression was dramatically up-regulated in UCEC patients and associated with prognosis. DNA methylation levels in the SLC7A11-promoter region were significantly higher in normal participants than in patients with UCEC. We also showed that SLC7A11 overexpression was associated with TIICs, immune checkpoint blockers (ICBs), and immunotherapy response in UCEC. The half-maximal inhibitory concentration (IC50) results obtained with the cohort from TCGA showed that Z-VAD-FMK (Caspase inhibitor), S-Triphenylmethyl-L-cysteine (S-Trityl-L-cysteine), and TAE684 (ALK inhibitor) had higher IC50 values in low-expression patient (p < 0.05).

Conclusion

SLC7A11 overexpression is associated with favorable prognosis of patients with UCEC and is associated with TIICs and the responses to immunotherapy.

miR-30d-5p: A Non-Coding RNA With Potential Diagnostic, Prognostic and Therapeutic Applications

Cancer is a great challenge facing global public health. Scholars have made plentiful efforts in the research of cancer therapy, but the results are still not satisfactory. In relevant literature, the role of miRNA in cancer has been widely concerned. MicroRNAs (miRNAs) are a non-coding, endogenous, single-stranded RNAs that regulate a variety of biological functions. The abnormal level of miR-30d-5p, a type of miRNAs, has been associated with various human tumor types, including lung cancer, colorectal cancer, esophageal cancer, prostate cancer, liver cancer, cervical cancer, breast cancer and other types of human tumors. This reflects the vital function of miR-30d-5p in tumor prognosis. miR-30d-5p can be identified either as an inhibitor hindering the development of, or a promoter accelerating the occurrence of tumors. In addition, the role of miR-30d-5p in cell proliferation, motility, apoptosis, autophagy, tumorigenesis, and chemoresistance are also noteworthy. The multiple roles of miR-30d-5p in human cancer suggest that it has broad feasibility as a biomarker and therapeutic target. This review describes the connection between miR-30d-5p and the clinical indications of tumors, and summarizes the mechanisms by which miR-30d-5p mediates cancer progression.

Multiple mutations within individual oncogenes

Recent studies of the cancer genome have identified numerous patients harboring multiple mutations (MM) within individual oncogenes. These MM (de novo MM) in cis synergistically activate the mutated oncogene and promote tumorigenesis, indicating a positive epistatic interaction between mutations. The relatively frequent de novo MM suggest that intramolecular positive epistasis is widespread in oncogenes. Studies also suggest that negative and higher-order epistasis affects de novo MM. Comparison of de novo MM and MM associated with drug-resistant secondary mutations (secondary MM) revealed several similarities with respect to allelic configuration, mutational selection and functionality of individual mutations. Conversely, they have several differences, most notably the difference in drug sensitivities. Secondary MM usually confer resistance to molecularly targeted therapies, whereas several de novo MM are associated with increased sensitivity, implying that both can be useful as therapeutic biomarkers. Unlike secondary MM in which specific secondary resistant mutations are selected, minor (infrequent) functionally weak mutations are convergently selected in de novo MM, which may provide an explanation as to why such mutations accumulate in cancer. The third type of MM is MM from different subclones. This type of MM is associated with parallel evolution, which may contribute to relapse and treatment failure. Collectively, MM within individual oncogenes are diverse, but all types of MM are associated with cancer evolution and therapeutic response. Further evaluation of oncogenic MM is warranted to gain a deeper understanding of cancer genetics and evolution.