NAR Cancer最新文献

Ovarian cancer is driven by genetic alterations that necessitate protective DNA damage and replication stress responses through cell cycle control and genome maintenance. This creates specific vulnerabilities that may be exploited therapeutically. WEE1 kinase is a key cell cycle control kinase, and it has emerged as a promising cancer therapy target. However, adverse effects have limited its clinical progress, especially when tested in combination with chemotherapies. A strong genetic interaction between WEE1 and PKMYT1 led us to hypothesize that a multiple low-dose approach utilizing joint WEE1 and PKMYT1 inhibition would allow exploitation of the synthetic lethality. We found that the combination of WEE1 and PKMYT1 inhibition exhibited synergistic effects in eradicating ovarian cancer cells and organoid models at a low dose. The WEE1 and PKMYT1 inhibition synergistically promoted CDK activation. Furthermore, the combined treatment exacerbated DNA replication stress and replication catastrophe, leading to increase of the genomic instability and inflammatory STAT1 signalling activation. These findings suggest a new multiple low-dose approach to harness the potency of WEE1 inhibition through the synthetic lethal interaction with PKMYT1 that may contribute to the development of new treatments for ovarian cancer.

Tumors defective in DNA mismatch repair (dMMR) exhibit microsatellite instability (MSI). Currently, patients with dMMR tumors are benefitted from anti-PD-1/PDL1-based immune checkpoint inhibitor (ICI) therapy. Over the past several years, great progress has been made in understanding the mechanisms by which dMMR tumors respond to ICI, including the identification of mutator phenotype-generated neoantigens, cytosolic DNA-mediated activation of the cGAS-STING pathway, type-I interferon signaling and high tumor-infiltration of lymphocytes in dMMR tumors. Although ICI therapy shows great clinical benefits, ∼50% of dMMR tumors are eventually not responsive. Here we review the discovery, development and molecular basis of dMMR-mediated immunotherapy, as well as tumor resistant problems and potential therapeutic interventions to overcome the resistance.

TP53 is crucial for maintaining genome stability and preventing oncogenesis. Germline pathogenic variation in TP53 damages its function, causing genome instability and increased cancer risk. Despite extensive study in TP53, the evolutionary origin of the human TP53 germline pathogenic variants remains largely unclear. In this study, we applied phylogenetic and archaeological approaches to identify the evolutionary origin of TP53 germline pathogenic variants in modern humans. In the phylogenic analysis, we searched 406 human TP53 germline pathogenic variants in 99 vertebrates distributed in eight clades of Primate, Euarchontoglires, Laurasiatheria, Afrotheria, Mammal, Aves, Sarcopterygii and Fish, but we observed no direct evidence for the cross-species conservation as the origin; in the archaeological analysis, we searched the variants in 5031 ancient human genomes dated between 45045 and 100 years before present, and identified 45 pathogenic variants in 62 ancient humans dated mostly within the last 8000 years; we also identified 6 pathogenic variants in 3 Neanderthals dated 44000 to 38515 years before present and 1 Denisovan dated 158 550 years before present. Our study reveals that TP53 germline pathogenic variants in modern humans were likely originated in recent human history and partially inherited from the extinct Neanderthals and Denisovans.

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house series of 401 liver cancers, SpliceAI uncovers 1244 cryptic splice mutations, located outside essential splice sites, that validate at a high rate (66%) in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 17 714 tumors, revealing >100 000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing 126 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis suggests mutational processes that could give rise preferentially to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

Estrogen receptor α (ER) mutations occur in up to 30% of metastatic ER-positive breast cancers. Recent data has shown that ER mutations impact the expression of thousands of genes not typically regulated by wildtype ER. While the majority of these altered genes can be explained by constant activity of mutant ER or genomic changes such as altered ER binding and chromatin accessibility, as much as 33% remain unexplained, indicating the potential for post-transcriptional effects. Here, we explored the role of microRNAs in mutant ER-driven gene regulation and identified several microRNAs that are dysregulated in ER mutant cells. These differentially regulated microRNAs target a significant portion of mutant-specific genes involved in key cellular processes. When the activity of microRNAs is altered using mimics or inhibitors, significant changes are observed in gene expression and cellular proliferation related to mutant ER. An in-depth evaluation of miR-301b led us to discover an important role for PRKD3 in the proliferation of ER mutant cells. Our findings show that microRNAs contribute to mutant ER gene regulation and cellular effects in breast cancer cells.

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Translated non-canonical proteins derived from noncoding regions or alternative open reading frames (ORFs) can contribute to critical and diverse cellular processes. In the context of cancer, they also represent an under-appreciated source of targets for cancer immunotherapy through their tumor-enriched expression or by harboring somatic mutations that produce neoantigens. Here, we introduce the largest integration and proteogenomic analysis of novel peptides to assess the prevalence of non-canonical ORFs (ncORFs) in more than 900 patient proteomes and 26 immunopeptidome datasets across 14 cancer types. The integrative proteogenomic analysis of whole-cell proteomes and immunopeptidomes revealed peptide support for a nonredundant set of 9760 upstream, downstream, and out-of-frame ncORFs in protein coding genes and 12811 in noncoding RNAs. Notably, 6486 ncORFs were derived from differentially expressed genes and 340 were ubiquitously translated across eight or more cancers. The analysis also led to the discovery of thirty-four epitopes and eight neoantigens from non-canonical proteins in two cohorts as novel cancer immunotargets. Collectively, our analysis integrated both bottom-up proteogenomic and targeted peptide validation to illustrate the prevalence of translated non-canonical proteins in cancer and to provide a resource for the prioritization of novel proteins supported by proteomic, immunopeptidomic, genomic and transcriptomic data, available at https://www.maherlab.com/crypticproteindb.

Adenosine to inosine editing (A-to-I) in regions of double stranded RNA (dsRNA) is mediated by adenosine deaminase acting on RNA 1 (ADAR1) or ADAR2. ADAR1 and A-to-I editing levels are increased in many human cancers. Inhibition of ADAR1 has emerged as a high priority oncology target, however, whether ADAR1 overexpression enables cancer initiation or progression has not been directly tested. We established a series of in vivo models to allow overexpression of full-length ADAR1, or its individual isoforms, to test if increased ADAR1 expression was oncogenic. Widespread over-expression of ADAR1 or the p110 or p150 isoforms individually as sole lesions was well tolerated and did not result in cancer initiation. Therefore, ADAR1 overexpression alone is not sufficient to initiate cancer. We demonstrate that endogenous ADAR1 and A-to-I editing increased upon immortalization in murine cells, consistent with the observations from human cancers. We tested if ADAR1 over-expression could co-operate with cancer initiated by loss of tumour suppressors using a model of osteosarcoma. We did not see a disease potentiating or modifying effect of overexpressing ADAR1 or its isoforms in the models assessed. We conclude that increased ADAR1 expression and A-to-I editing in cancers is most likely a consequence of tumor formation.

RNA modifications are key regulatory factors for several biological and pathological processes. They are abundantly represented on ribosomal RNA (rRNA), where they contribute to regulate ribosomal function in mRNA translation. Altered RNA modification pathways have been linked to tumorigenesis as well as to other human diseases. In this study we quantitatively evaluated the site-specific pseudouridylation pattern in rRNA in breast cancer samples exploiting the RBS-Seq technique involving RNA bisulfite treatment coupled with a new NGS approach. We found a wide variability among patients at different sites. The most dysregulated positions in tumors turned out to be hypermodified with respect to a reference RNA. As for 2'O-methylation level of rRNA modification, we detected variable and stable pseudouridine sites, with the most stable sites being the most evolutionary conserved. We also observed that pseudouridylation levels at specific sites are related to some clinical and bio-pathological tumor features and they are able to distinguish different patient clusters. This study is the first example of the contribution that newly available high-throughput approaches for site specific pseudouridine detection can provide to the understanding of the intrinsic ribosomal changes occurring in human tumors.

Colorectal cancer (CRC) is the most common gastrointestinal malignancy and a leading cause of cancer deaths in the United States. More than half of CRC patients develop metastatic disease (mCRC) with an average 5-year survival rate of 13%. Circular RNAs (circRNAs) have recently emerged as important tumorigenesis regulators; however, their role in mCRC progression remains poorly characterized. Further, little is known about their cell-type specificity to elucidate their functions in the tumor microenvironment (TME). To address this, we performed total RNA sequencing (RNA-seq) on 30 matched normal, primary and metastatic samples from 14 mCRC patients. Additionally, five CRC cell lines were sequenced to construct a circRNA catalog in CRC. We detected 47 869 circRNAs, with 51% previously unannotated in CRC and 14% novel candidates when compared to existing circRNA databases. We identified 362 circRNAs differentially expressed in primary and/or metastatic tissues, termed circular RNAs associated with metastasis (CRAMS). We performed cell-type deconvolution using published single-cell RNA-seq datasets and applied a non-negative least squares statistical model to estimate cell-type specific circRNA expression. This predicted 667 circRNAs as exclusively expressed in a single cell type. Collectively, this serves as a valuable resource, TMECircDB (accessible at https://www.maherlab.com/tmecircdb-overview), for functional characterization of circRNAs in mCRC, specifically in the TME.