Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning.

BACKGROUND: Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences. RESULTS: We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA. CONCLUSIONS: Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

Genomic landscape of treatment refractory metastatic colorectal cancer.

BACKGROUND: Metastatic colorectal cancer (mCRC) is a complex and heterogeneous disease with few standard and targeted treatment options. Next-generation sequencing of tumor tissue was performed to identify cancer driver mutations to discover possible personalized treatment options, as targeted treatment possibilities are limited for this patient population. Results of genomic sequencing in patients with treatment-refractory mCRC are described in this retrospective analysis. MATERIAL AND METHODS: Clinico-pathological characteristics and genomic sequence results of consecutive patients with refractory mCRC, referred to the Experimental Cancer Therapy Unit (ECTU) at Department of Oncology, Herlev & Gentofte Hospital in the period from 1 October 2015 to 14 December 2018 were reviewed in this retrospective analysis. Tumor tissue from the patients was analyzed by next-generation sequencing using the Oncomine Comprehensive primer panel to detect actionable variants of cancer driver mutations and microsatellite instability status. From August 2018 tumor mutational burden was also analyzed. RESULTS: A total of 80 patients with treatment-refractory mCRC and in a fairly good performance were referred to the ECTU during this period. Genomic sequencing of tumor tissue was performed for all 80 patients and a cancer driver mutation was identified in 90% (n = 72) of the patients. A total of 31.3% (n = 25) of the patients received therapy either as targetable therapy outside an available trial (n = 2), FDA approved therapy (n = 2), or treatment in phase 1 or 2 trials, independent of the genomic signature 26.3% (n = 21). CONCLUSION: Most mCRC patients refractory to standard anti-neoplastic therapies, presented with a cancer driver mutation, however, only a few of these mutations gave rise to matched therapies as only 2.5% of the patients from this period received targeted therapy.

Cancer driver mutations in endometriosis: Variations on the major theme of fibrogenesis.

BACKGROUND: One recent study reports cancer driver mutations in deep endometriosis, but its biological/clinical significance remains unclear. Since the natural history of endometriosis is essentially gradual progression toward fibrosis, it is thus hypothesized that the six driver genes reported to be mutated in endometriosis (the RP set) may play important roles in fibrogenesis but not necessarily malignant transformation. METHODS: Extensive PubMed search to see whether RP and another set of driver genes not yet reported (NR) to be mutated in endometriosis have any roles in fibrogenesis. All studies reporting on the role of fibrogenesis of the genes in both RP and NR sets were retrieved and evaluated in this review. RESULTS: All six RP genes were involved in various aspects of fibrogenesis as compared with only three NR genes. These nine genes can be anchored in networks linking with their upstream and downstream genes that are known to be aberrantly expressed in endometriosis, piecing together seemingly unrelated findings. CONCLUSIONS: Given that somatic driver mutations can and do occur frequently in physiologically normal tissues, it is argued that these mutations in endometriosis are not necessarily synonymous with malignancy or premalignancy, but the result of enormous pressure for fibrogenesis.

Discovery of Novel Potential Prognostic Markers and Targeted Therapy to Overcome Chemotherapy Resistance in an Advanced-Stage Wilms Tumor.

Wilms tumor (WT), the most prevalent type of renal cancer in children, exhibits overall survival rates exceeding 90%. However, chemotherapy resistance, which occurs in approximately 10% of WT cases, is a major challenge for the treatment of WT, particularly for advanced-stage patients. In this study, we aimed to discover potential mutation markers and drug targets associated with chemotherapy resistance in advanced-stage WT. We performed exome sequencing to detect somatic mutations and molecular targets in 43 WT samples, comprising 26 advanced-stage WTs, of which 7 cases were chemotherapy-resistant. Our analysis revealed four genes (ALPK2, C16orf96, PRKDC, and SVIL) that correlated with chemotherapy resistance and reduced disease-free survival in advanced-stage WT. Additionally, we identified driver mutations in 55 genes within the chemotherapy-resistant group, including 14 druggable cancer driver genes. Based on the mutation profiles of the resistant WT samples, we propose potential therapeutic strategies involving platinum-based agents, PARP inhibitors, and antibiotic/antineoplastic agents. Our findings provide insights into the genetic landscape of WT and offer potential avenues for targeted treatment, particularly for patients with chemotherapy resistance.

CarcSeq detection of lorcaserin-induced clonal expansion of Pik3ca H1047R mutants in rat mammary tissue.

Lorcaserin is a 5-hydroxytryptamine 2C (serotonin) receptor agonist and a non-genotoxic rat carcinogen, which induced mammary tumors in male and female rats in a two-year bioassay. Female Sprague Dawley rats were treated by gavage daily with 0, 30, or 100 mg/kg lorcaserin, replicating bioassay dosing but for shorter duration, 12 or 24 weeks. To characterize exposure and eliminate possible confounding by a potentially genotoxic degradation product, lorcaserin and N-nitroso-lorcaserin were quantified in dosing solutions, terminal plasma, mammary and liver samples using ultra high-performance liquid chromatography-electrospray tandem mass spectrometry. N-nitroso-lorcaserin was not detected, supporting lorcaserin classification as non-genotoxic carcinogen. Mammary DNA samples (n = 6/dose/timepoint) were used to synthesize PCR products from gene segments encompassing hotspot cancer driver mutations (CDMs), namely regions of Apc, Braf, Egfr, Hras, Kras, Nfe2l2, Pik3ca, Setbp1, Stk11, and Tp53. Mutant fractions (MFs) in the amplicons were quantified by CarcSeq, an error corrected next-generation sequencing approach. Considering all recovered mutants, no significant differences between lorcaserin dose groups were observed. However, significant dose-responsive increases in Pik3ca H1047R mutation were observed at both timepoints (ANOVA, p < 0.05), with greater numbers of mutants and mutants with greater MFs observed at 24 weeks as compared to 12 weeks. These observations suggest lorcaserin promotes outgrowth of spontaneously occurring Pik3ca H1047R mutant clones leading to mammary carcinogenesis. Importantly, this work reports approaches to analyze clonal expansion and demonstrates CarcSeq detection of the carcinogenic impact (selective Pik3ca H0147R mutant expansion) of a non-genotoxic carcinogen using a treatment duration as short as 3 months.

Assessment of Clonal Expansion Using CarcSeq Measurement of Lung Cancer Driver Mutations and Correlation With Mouse Strain- and Sex-Related Incidence of Spontaneous Lung Neoplasia.

Quantification of variation in levels of spontaneously occurring cancer driver mutations (CDMs) was developed to assess clonal expansion and predict future risk of neoplasm development. Specifically, an error-corrected next-generation sequencing method, CarcSeq, and a mouse CarcSeq panel (analogous to human and rat panels) were developed and used to quantify low-frequency mutations in a panel of amplicons enriched in hotspot CDMs. Mutations in a subset of panel amplicons, Braf, Egfr, Kras, Stk11, and Tp53, were related to incidence of lung neoplasms at 2 years. This was achieved by correlating median absolute deviation (MAD) from the overall median mutant fraction (MF) measured in the lung DNA of 16-week-old male and female, B6C3F1 and CD-1 mice (10 mice/sex/strain) with percentages of spontaneous alveolar/bronchioloalveolar adenomas and carcinomas reported in bioassay control groups. A total of 1586 mouse lung mutants with MFs >1 × 10-4 were recovered. The ratio of nonsynonymous to synonymous mutations was used to assess the proportion of recovered mutations conferring a positive selective advantage. The greatest ratio was observed in what is considered the most lung tumor-sensitive model examined, male B6C3F1 mice. Of the recurrent, nonsynonymous mouse mutations recovered, 55.5% have been reported in human tumors, with many located in or around the mouse equivalent of human cancer hotspot codons. MAD for the same subset of amplicons measured in normal human lung DNA samples showed a correlation of moderate strength and borderline significance with age (a cancer risk factor), as well as age-related cumulative lung cancer risk, suggesting MAD may inform species extrapolation.

CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia.

The ability to deduce carcinogenic potential from subchronic, repeat dose rodent studies would constitute a major advance in chemical safety assessment and drug development. This study investigated an error-corrected NGS method (CarcSeq) for quantifying cancer driver mutations (CDMs) and deriving a metric of clonal expansion predictive of future neoplastic potential. CarcSeq was designed to interrogate subsets of amplicons encompassing hotspot CDMs applicable to a variety of cancers. Previously, normal human breast DNA was analyzed by CarcSeq and metrics based on mammary-specific CDMs were correlated with tissue donor age, a surrogate of breast cancer risk. Here we report development of parallel methodologies for rat. The utility of the rat CarcSeq method for predicting neoplastic potential was investigated by analyzing mammary tissue of 16-week-old untreated rats with known differences in spontaneous mammary neoplasia (Fischer 344, Wistar Han, and Sprague Dawley). Hundreds of mutants with mutant fractions ≥ 10-4 were quantified in each strain, most were recurrent mutations, and 42.5% of the nonsynonymous mutations have human homologs. Mutants in the mammary-specific target of the most tumor-sensitive strain (Sprague Dawley) showed the greatest nonsynonymous/synonymous mutation ratio, indicative of positive selection consistent with clonal expansion. For the mammary-specific target (Hras, Pik3ca, and Tp53 amplicons), median absolute deviation correlated with percentages of rats that develop spontaneous mammary neoplasia at 104 weeks (Pearson r = 1.0000, 1-tailed p = .0010). Therefore, this study produced evidence CarcSeq analysis of spontaneously occurring CDMs can be used to derive an early metric of clonal expansion relatable to long-term neoplastic outcome.

ACB-PCR Quantification of Low-Frequency Hotspot Cancer-Driver Mutations.

Allele-specific competitive blocker PCR (ACB-PCR) is a sensitive and quantitative approach for the selective amplification of a specific base substitution. Using the ACB-PCR technique, hotspot cancer-driver mutations (tumor-relevant mutations in oncogenes and tumor suppressor genes, which confer a selective growth advantage) are being developed as quantitative biomarkers of cancer risk. ACB-PCR employs a mutant-specific primer (with a 3'-penultimate mismatch relative to the mutant DNA sequence, but a double 3'-terminal mismatch relative to the wild-type DNA sequence) to selectively amplify rare mutant DNA molecules. A blocker primer having a non-extendable 3'-end and a 3'-penultimate mismatch relative to the wild-type DNA sequence, but a double 3'-terminal mismatch relative to the mutant DNA sequence is included in ACB-PCR to selectively repress amplification from abundant wild-type molecules. Consequently, ACB-PCR can quantify the level of a single base pair substitution mutation in a DNA population when present at a mutant:wild-type ratio of 1 × 10-5 or greater. Quantification of rare mutant alleles is achieved by parallel analysis of unknown samples and mutant fraction (MF) standards (defined mixtures of mutant and wild-type DNA sequences). The ability to quantify specific mutations with known association to cancer has several important applications in evaluating the carcinogenic potential of chemical exposures in rodent models. Further, the measurement of cancer-driver mutant subpopulations is important for precision cancer treatment (selecting the most appropriate targeted therapy and predicting the development of therapeutic resistance). This chapter provides a step-by-step description of the ACB-PCR methodology as it has been used to measure human PIK3CA codon 1047, CAT→CGT (H1047R) mutation.

Lifespan Kras mutation levels in lung and liver of B6C3F1 mice.

Somatic mutations accumulate in the human genome and are correlated with increased cancer incidence as humans age. The standard model for studying the carcinogenic effects of exposures for human risk assessment is the rodent 2-year carcinogenicity assay. However, there is little information regarding the effect of age on cancer-driver gene mutations in these models. The mutant fraction (MF) of Kras codon 12 GGT to GAT and GGT to GTT mutations, oncogenic mutations orthologous between humans and rodents, was quantified over the lifespan of B6C3F1 mice. MFs were measured in lung and liver tissue, organs that frequently develop tumors following carcinogenic exposures. The MFs were evaluated at 4, 6, 8, 12, 21, and 85 weeks, with the 12-week and 21-week time points being coincident with the conclusion of 28-day and 90-day exposure durations used in short-term toxicity testing. The highly sensitive and quantitative Allele-specific Competitive Blocker PCR (ACB-PCR) assay was used to quantify the number of mutant Kras codon 12 alleles. The mouse lung showed a slight, but significant trend increase in the Kras codon 12 GAT mutation over the 85-week period. The trend with age can be equally well-fit by several non-linear functions, but not by a linear function. In contrast, the liver GAT mutation did not increase, and the GTT mutation did not increase for either organ. Even with the slight increase in the lung GAT MFs, our results indicate that the future use of Kras mutation as a biomarker of carcinogenic effect will not be confounded by animal age. Environ. Mol. Mutagen. 59:715-721, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces.

Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures.

High-grade serous ovarian cancer (HG-SOC), a major histologic type of epithelial ovarian cancer (EOC), is a poorly-characterized, heterogeneous and lethal disease where somatic mutations of TP53 are common and inherited loss-of-function mutations in BRCA1/2 predispose to cancer in 9.5-13% of EOC patients. However, the overall burden of disease due to either inherited or sporadic mutations is not known. We performed bioinformatics analyses of mutational and clinical data of 334 HG-SOC tumor samples from The Cancer Genome Atlas to identify novel tumor-driving mutations, survival-significant patient subgroups and tumor subtypes potentially driven by either hereditary or sporadic factors. We identified a sub-cluster of high-frequency mutations in 22 patients and 58 genes associated with DNA damage repair, apoptosis and cell cycle. Mutations of CHEK2, observed with the highest intensity, were associated with poor therapy response and overall survival (OS) of these patients (P = 8.00e-05), possibly due to detrimental effect of mutations at the nuclear localization signal. A 21-gene mutational prognostic signature significantly stratifies patients into relatively low or high-risk subgroups with 5-y OS of 37% or 6%, respectively (P = 7.31e-08). Further analysis of these genes and high-risk subgroup revealed 2 distinct classes of tumors characterized by either germline mutations of genes such as CHEK2, RPS6KA2 and MLL4, or somatic mutations of other genes in the signature. Our results could provide improvement in prediction and clinical management of HG-SOC, facilitate our understanding of this complex disease, guide the design of targeted therapeutics and improve screening efforts to identify women at high-risk of hereditary ovarian cancers distinct from those associated with BRCA1/2 mutations.