Loss of peptidase D binding restores the tumor suppressor functions of oncogenic p53 mutants

Tumor suppressor p53, a critical regulator of cell fate, is frequently mutated in cancer. Mutation of p53 abolishes its tumor-suppressing functions or endows oncogenic functions. We recently found that p53 binds via its proline-rich domain to peptidase D (PEPD) and is activated when the binding is disrupted. The proline-rich domain in p53 is rarely mutated. Here, we show that oncogenic p53 mutants closely resemble p53 in PEPD binding but are transformed into tumor suppressors, rather than activated as oncoproteins, when their binding to PEPD is disrupted by PEPD knockdown. Once freed from PEPD, p53 mutants undergo multiple posttranslational modifications, especially lysine 373 acetylation, which cause them to refold and regain tumor suppressor activities that are typically displayed by p53. The reactivated p53 mutants strongly inhibit cancer cell growth in vitro and in vivo. Our study identifies a cellular mechanism for reactivation of the tumor suppressor functions of oncogenic p53 mutants.

Prediction of cancer mutation states using multiple data modalities reveals the utility and consistency of gene expression and DNA methylation

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal may be unclear. In this study, we consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem. Since functional signatures of cancer mutation have been identified across many data types, this problem presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. The TCGA Pan-Cancer Atlas contains genetic alteration data including somatic mutations and copy number variants (CNVs), as well as several -omics data types. From TCGA, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of cancer-associated genetic alterations, RNA sequencing and DNA methylation were the most effective predictors of alteration state. Surprisingly, we found that for most alterations, they were approximately equally effective predictors. The target gene was the primary driver of performance, rather than the data type, and there was little difference between the top data types for the majority of genes. We also found that combining data types into a single multi-omics model often provided little or no improvement in predictive ability over the best individual data type. Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, we recommend focusing on gene expression or DNA methylation as first-line readouts.

Next generation sequencing identifies potential PARP inhibitor sensitive mutations

Introduction/Objective Poly ADP-ribose polymerase (PARP) inhibitors are a novel and important drug class targeting homologous recombination DNA repair defects (HRD) and have been approved for use in breast, pancreatic and ovarian cancers. Originally targeted for loss of function mutations in BRCA1 and BRCA2, many other genes are involved in the HRD pathway; Rimar et al detailed 19 DNA repair genes associated with homologous recombination and PARP inhibitor sensitivity. Our 214 gene NGS panel, Iowa Cancer Mutation Profile, includes BRCA1, BRCA2 and 15 other HRD pathway genes. We reviewed cases from the prior 12 months to determine the frequency of HRD7 pathway gene variants in various tumor types with potential PARP inhibitor sensitivity. Methods/Case Report Iowa Cancer Mutation Profile NGS test results from June 4, 2020 through May 7, 2021 were reviewed for variants involving ATM, ATR, BAP1, BLM, BRCA1, BRCA2, CDK12, CHEK1, CHEK2, FANCA, FANCC, FANCD2, MRE11A, NBN, PALB2, RAD51c and RAD51d, categorized as pathogenic, likely pathogenic or of unknown significance and had the tumor type identified. Additional chart review for PARP inhibitor therapy was performed in cases of breast, pancreatic, and ovarian cancer. Results (if a Case Study enter NA) A total of 599 cases were reviewed with 234 found to have variants in genes with possible PARP inhibitor sensitivity. Of these 2% (n=8) and 11% (n=43) of variants were categorized as pathogenic or likely pathogenic while most (n=334) were categorized as variants of unknown significance. The pathogenic and likely pathogenic variants included mutations in ATM (n=13), BRCA2 (n=12), BAP1 (n=8), FANCA (n=5), BRCA1 (n=4), NBM (n=3), FANCD2 (n=2), ATR (n=1), CDK12 (n=1), FANCC (n=1), and RAD51d (n=1), and frameshift (n=19) and nonsense (n=19) alterations were most common. Non-small cell lung cancer was the most frequent tumor type identified (n=78). At this time no PARP inhibitors were identified for use in cases of breast (n=9), pancreatic (n=3) and ovarian cancer (n=33). Conclusion Review of our institutional results for mutations in HRD pathway genes identified possible PARP inhibitor sensitivity in 8% (46 of 599) of cases during the period of review in a wide variety of tumor types. Our results suggest that variants with possible sensitivity to PARP inhibitor therapy are frequently identified from many tumor types and should be a component of solid tumor mutation profiling.

Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers

A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker.

Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.

Approximation of the age distribution of cancer incidence using a mutational model

The approximation of the age distributions of cancer was carried out using a complex mutational model presented in our work [1]. Datasets from the American National Cancer Institute (SEER program) were used. We approximated the data of age distributions of lung, stomach, colon and breast cancer in women; cancer of the lung, stomach, colon and prostate in men. The average number of mutations (required for cancer formation) averaged over the four types of cancer is 5.25 mutations per cell in women and 5.5 mutations per cell in men. The average (over the four types of cancer) mutation rate is estimated as 0.0004 mutations per year per cell for women and 0.0008 mutations per year per cell for men. This article is a continuation of work [1].