Genomic Evidence Supports the Recognition of Endometriosis as an Inflammatory Systemic Disease and Reveals Disease-Specific Therapeutic Potentials of Targeting Neutrophil Degranulation.

Background: Endometriosis, classically viewed as a localized disease, is increasingly recognized as a systemic disease with multi-organ effects. This disease is highlighted by systemic inflammation in affected organs and by high comorbidity with immune-mediated diseases. Results: We provide genomic evidence to support the recognition of endometriosis as an inflammatory systemic disease. This was achieved through our genomics-led target prioritization, called 'END', that leverages the value of multi-layered genomic datasets (including genome-wide associations in disease, regulatory genomics, and protein interactome). Our prioritization recovered existing proof-of-concept therapeutic targeting in endometriosis and outperformed competing prioritization approaches (Open Targets and Naïve prioritization). Target genes at the leading prioritization revealed molecular hallmarks (and possibly the cellular basis as well) that are consistent with systemic disease manifestations. Pathway crosstalk-based attack analysis identified the critical gene AKT1. In the context of this gene, we further identified genes that are already targeted by licensed medications in other diseases, such as ESR1. Such analysis was supported by current interests targeting the PI3K/AKT/mTOR pathway in endometriosis and by the fact that therapeutic agents targeting ESR1 are now under active clinical trials in disease. The construction of cross-disease prioritization map enabled the identification of shared and distinct targets between endometriosis and immune-mediated diseases. Shared target genes identified opportunities for repurposing existing immunomodulators, particularly disease-modifying anti-rheumatic drugs (such as TNF, IL6 and IL6R blockades, and JAK inhibitors). Genes highly prioritized only in endometriosis revealed disease-specific therapeutic potentials of targeting neutrophil degranulation - the exocytosis that can facilitate metastasis-like spread to distant organs causing inflammatory-like microenvironments. Conclusion: Improved target prioritization, along with an atlas of in silico predicted targets and repurposed drugs (available at https://23verse.github.io/end), provides genomic insights into endometriosis, reveals disease-specific therapeutic potentials, and expands the existing theories on the origin of disease.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective.

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Identifying Potential Neoantigens for Cervical Cancer Immunotherapy Using Comprehensive Genomic Variation Profiling of Cervical Intraepithelial Neoplasia and Cervical Cancer.

Cervical cancer (CC) is one of the most common gynecological malignant tumors. The 5-year survival rate remains poor for the advanced and metastatic cervical cancer for the lack of effective treatments. Immunotherapy plays an important role in clinical tumor therapy. Neoantigens derived from tumor-specific somatic mutations are prospective targets for immunotherapy. Hence, the identification of new targets is of great significance for the treatment of advanced and metastatic cervical cancer. In this study, we performed whole-exome sequencing in 70 samples, including 25 cervical intraepithelial neoplasia (CINs) with corresponding blood samples and 10 CCs along with paired adjacent tissues to identify genomic variations and to find the potential neoantigens for CC immunotherapy. Using systematic bioinformatics pipeline, we found that C>T transitions were in both CINs and CCs. In contrast, the number of somatic mutations in CCs was significantly higher than those in CINs (t-test, P = 6.60E-04). Meanwhile, mutational signatures analysis revealed that signature 6 was detected in CIN2, CIN3, and CC, but not in CIN1, while signature 2 was only observed in CCs. Furthermore, PIK3CA, ARHGAP5 and ADGRB1 were identified as potential driver genes in this report, of which ADGRB1 was firstly reported in CC. Based on the genomic variation profiling of CINs and CCs, we identified 2586 potential neoantigens in these patients, of which 45 neoantigens were found in three neoantigen-related databases (TSNAdb, IEDB, and CTDatabase). Our current findings lay a solid foundation for the study of the pathogenesis of CC and the development of neoantigen-targeted immunotherapeutic measures.

The diagnostic and prognostic usage of circulating tumor DNA in operable hepatocellular carcinoma.

Circulating tumor DNA (ctDNA) is a promising noninvasive biomarker for hepatocellular carcinoma (HCC). In this study, we aimed to assess the diagnostic and prognostic value of ctDNA in HCC. Twenty-six operable HCC, 10 hepatitis and 10 cirrhosis patients were enrolled in this study. Treatment-naïve blood samples were collected from all patients, nevertheless resected tissue and postoperative blood samples were only collected from HCC patients. A custom-designed sequencing panel covering 354 genes was used to identify somatic mutations. Collectively, we identified 139 somatic mutations from 25 HCC baseline plasma samples (96.2%). TP53 (50.00%) was the most common mutant gene, and R249S was the most recurrent mutation (19.2%). Twenty-three patients (88.5%) carried at least one ctDNA mutation validated in matched tissue, and the driver mutations exhibited an advanced concordance than non-driver mutations (67.6% vs. 33.8%, P = 0.0002). For HCC patients, the number of mutations in ctDNA (R2 = 0.1682, P = 0.0375), maximal variant allele frequency (VAF) in ctDNA (R2 = 0.4974, P < 0.0001) and ctDNA concentration (R2 = 0.2676, P = 0.0068) were linearly correlated with tumor size. Multiple circulating cell-free DNA (cfDNA) parameters could be used in differentiating malignant lesions from benign lesions, and the performance was no less than blood alpha-fetoprotein (AFP). HCC patients with detectable mutation in postoperative plasma had a poor DFS than those without (17.5 months vs. 6.7 months, HR = 7.655, P < 0.0001), and postoperative cfDNA status (HR = 10.293, P < 0.0001) was an independent risk factors for recurrence. In conclusion, ctDNA profiling is potentially valuable in differential diagnosis and prognostic evaluation of HCC.

Comprehensive genomic variation profiling of cervical intraepithelial neoplasia and cervical cancer identifies potential targets for cervical cancer early warning.

BACKGROUND: To better understand the pathogenesis of cervical cancer (CC), we systematically analysed the genomic variation and human papillomavirus (HPV) integration profiles of cervical intraepithelial neoplasia (CIN) and CC. METHODS: We performed whole-genome sequencing or whole-exome sequencing of 102 tumour-normal pairs and human papillomavirus probe capture sequencing of 45 CCs, 44 CIN samples and 25 normal cervical samples, and constructed strict integrated workflow of genomic analysis. RESULTS: Mutational analysis identified eight significantly mutated genes in CC including four genes (FAT1, MLL3, MLL2 and FADD), which have not previously been reported in CC. Targetable alterations were identified in 55.9% of patients. In addition, HPV integration breakpoints occurred in 97.8% of the CC samples, 70.5% of the CIN samples and 42.8% of the normal cervical samples with HPV infection. Integrations of high-risk HPV strains in CCs, including HPV16, 18, 33 and 58, also occurred in the CIN samples. Moreover, gene mutations were detected in 52% of the CIN specimens, and 54.8% of these mutations occurred in genes that also mutated in CCs. CONCLUSION: Our results lay the foundation for a deep understanding of the molecular mechanisms and finding new diagnostic and therapeutic targets of CC.