Distinct methylation profile of mucinous ovarian carcinoma reveals susceptibility to proteasome inhibitors

Unveiling mitochondrial and ribosomal gene deregulation and tumor microenvironment dynamics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant clonal hematopoietic disease with a poor prognosis. Understanding the interaction between leukemic cells and the tumor microenvironment (TME) can help predict the prognosis of leukemia and guide its treatment. Re-analyzing the scRNA-seq data from the CSC and G20 cohorts, using a Python-based pipeline including machine-learning-based scVI-tools, recapitulated the distinct hierarchical structure within the samples of AML patients. Weighted correlation network analysis (WGCNA) was conducted to construct a weighted gene co-expression network and to identify gene modules primarily focusing on hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), and natural killer (NK) cells. The analysis revealed significant deregulation in gene modules associated with aerobic respiration and ribosomal/cytoplasmic translation. Cell-cell communications were elucidated by the CellChat package, revealing an imbalance of activating and inhibitory immune signaling pathways. Interception of genes upregulated in leukemic HSCs & MPPs as well as in NKG2A-high NK cells was used to construct prognostic models. Normal Cox and artificial neural network models based on 10 genes were developed. The study reveals the deregulation of mitochondrial and ribosomal genes in AML patients and suggests the co-occurrence of stimulatory and inhibitory factors in the AML TME.

A subcomponent-guided deep learning method for interpretable cancer drug response prediction

Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design.

L1-CAM in Mucinous Ovarian Carcinomas and Borderline Tumors: Impact on Tumor Recurrence and Potential Role in Tumor Progression

Mucinous ovarian carcinoma (MOC) is a rare histotype of primary ovarian carcinoma. Frequent pathogenic molecular alterations include mutations in KRAS , TP53 , and overexpression of human epidermal growth factor receptor 2, but without having prognostic relevance. As L1-CAM (cell adhesion molecule) has previously shown prognostic relevance in other epithelial tumors of the female genital tract, we analyzed whether L1-CAM expression affected MOC prognosis. In addition, we investigated L1-CAM expression in mucinous borderline tumors (MBOTs) with and without adjacent MOC to identify its potential role in the pathogenesis of MOC. We examined a well-characterized collective of 39 MOCs by immunohistochemistry and compared their expression with clinicopathologic data. L1-CAM positivity was defined as any (even single-cell) positivity. Furthermore, we compared the L1-CAM expression in 20 MBOT regions adjacent to a MOC with that of 15 pure MBOTs. L1-CAM expression in MOC was significantly associated with recurrence, independent of tumor stage. Overall, 7/20 positive cases recurred versus 0/19 L1-CAM-negative cases ( P =0.032), showing a significant difference in time to progression. Furthermore, the presence of at least 1 defined molecular alteration (L1-CAM, aberrant p53, or human epidermal growth factor receptor 2) was found more frequently in the MBOT regions adjacent to a MOC (14/20) than in pure MBOTs (3/15) ( P =0.024). Expression of the tumor marker L1-CAM is frequent (51%) in MOC and is associated with tumor recurrence. The lack of L1-CAM may serve to characterize cases with a low risk of recurrence. Furthermore, the presence of specific molecular alterations in MBOTs is associated with adjacent carcinomas and may define potential pathways in tumor progression.

Update on Prognostic and Predictive Markers in Mucinous Ovarian Cancer

This review includes state-of-the-art prognostic and predictive factors of mucinous ovarian cancer (MOC), a rare tumor. Clinical, pathological, and molecular features and treatment options according to prognosis are comprehensively discussed. Different clinical implications of MOC are described according to the The International Federation of Gynecology and Obstetrics (FIGO) stage: early MOC (stage I-II) and advanced MOC (stage III-IV). Early MOC is characterized by a good prognosis. Surgery is the mainstay of treatment. Fertility-sparing surgery could be performed in patients who wish to become pregnant and that present low recurrence risk of disease. Adjuvant chemotherapy is not recommended, except in patients with high-risk clinical and pathological features. Regarding the histological features, an infiltrative growth pattern is the major prognostic factor of MOC. Furthermore, novel molecular biomarkers are emerging for tailored management of early-stage MOC. In contrast, advanced MOC is characterized by poor survival. Radical surgery is the cornerstone of treatment and adjuvant chemotherapy is recommended, although the efficacy is limited by the intrinsic chemoresistance of these tumors. Several molecular hallmarks of advanced MOC have been described in recent years (e.g., HER2 amplification, distinct methylation profiles, peculiar immunological microenvironment), but target therapy for these rare tumors is not available yet.

Bioinformatics analysis identifies PSMB8 as a key gene in the cutaneous malignant melanoma tumor microenvironment

Background

Cutaneous tumors are commonly seen in clinical practice, and malignant melanoma (MM) is the leading cause of cutaneous tumor-induced death. The tumor microenvironment (TME), a critical part of tumorigenesis, has been a research hotspot in recent years. However, the effects of the MM microenvironment components remain elusive. This study aimed to analyze the various components in the TME of MM to identify factors affecting the tumorigenesis, progression, and metastasis of MM and the survival of MM patients. We also aimed to identify biomarkers related to TME rehabilitation to provide a new direction for MM treatment.

Methods

We used bioinformatics to analyze the RNA-seq and somatic mutation data of 473 MM patients from The Cancer Genome Atlas database. Firstly, the patients' immunity and stroma were separately scored by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) method. According to the median score, the participants were split into high- and low-score groups. Then, Gene Set Enrichment Analysis (GSEA) was performed, showing that high-expression genes were highly abundant in biological and metabolic activities associated with the immune system.

Results

Differentially expressed genes (DEGs) and differentially mutated genes (DMGs) were identified and intersected to obtain the key immune-related genes PSMB8, FAM216B, DYSF, and FAM131C. PSMB8 was finally selected as the preferred immune-related prognostic marker; it was positively associated with overall survival and therefore considered a protective gene for MM patients. The GSEA analysis showed that PSMB8 with high expression had greater gene abundance in biological and metabolic processes related to immune system. In addition, CIBERSORT analysis showed an association between the proportion of tumor-infiltrating immune cells and PSMB8 expression.

Conclusions

Our results suggest that PSMB8 might be associated with tumorigenesis and MM progression and could serve as a biomarker for the TME rehabilitation of MM. Our findings provide a new perspective and direction for the treatment of MM.

The Functional and Mechanistic Roles of Immunoproteasome Subunits in Cancer

Cell-mediated immunity is driven by antigenic peptide presentation on major histocompatibility complex (MHC) molecules. Specialized proteasome complexes called immunoproteasomes process viral, bacterial, and tumor antigens for presentation on MHC class I molecules, which can induce CD8 T cells to mount effective immune responses. Immunoproteasomes are distinguished by three subunits that alter the catalytic activity of the proteasome and are inducible by inflammatory stimuli such as interferon-γ (IFN-γ). This inducible activity places them in central roles in cancer, autoimmunity, and inflammation. While accelerated proteasomal degradation is an important tumorigenic mechanism deployed by several cancers, there is some ambiguity regarding the role of immunoproteasome induction in neoplastic transformation. Understanding the mechanistic and functional relevance of the immunoproteasome provides essential insights into developing targeted therapies, including overcoming resistance to standard proteasome inhibition and immunomodulation of the tumor microenvironment. In this review, we discuss the roles of the immunoproteasome in different cancers.

Pan-cancer analysis of the prognostic and immunological role of PSMB8

Recently some evidence has demonstrated the significance of PSMB8 in various malignancies. Nevertheless, PSMB8 (proteasome subunit beta 8), more familiar in the field of immunology contributing to the process of antigen presentation, is indeterminate in the role as a survival predictor of human pan-cancer. Besides, how PSMB8 interacts with immune cell infiltration in the tumor microenvironment requires further research. We then penetrated into the analysis of the PSMB8 expression profile among 33 types of cancer in the TCGA database. The results show that overexpression of PSMB8 was associated with poor clinical outcomes in overall survival (Sartorius et al. in Oncogene 35(22):2881–2892, 2016), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in most cancer varieties. In addition, there existed distinctly positive correlations between PSMB8 and immunity, reflected straightforwardly in the form of immune scores, tumor-infiltrating immune cells (TIICs) abundance, microsatellite instability, tumor mutation burden, and neoantigen level. Notably, specific markers of dendrite cells exhibited the tightest association with PSMB8 expression in terms of tumor-related immune infiltration patterns. Moreover, gene enrichment analysis showed that elevated PSMB8 expression was related to multiple immune-related pathways. We finally validated the PSMB8 expression in our local breast samples via quantitative PCR assays and concluded that PSMB8 appeared to perform well in predicting the survival outcome of BRCA patients. These findings elucidate the pivotal role of the antigen presentation-related gene PSMB8, which could potentially serve as a robust biomarker for prognosis determination in multiple cancers.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Methylation profile of imprinted genes provides evidence for teratomatous origin of a subset of mucinous ovarian tumours

Mucinous ovarian tumours are sometimes associated with mature teratomas. It is suggested that the mucinous tumours in this setting are derived from teratomas, but there remains the possibility of collision or metastasis from extra-ovarian sites. Because mature ovarian teratomas are considered to be parthenogenetic tumours that arise from a single oocyte/ovum, they have only a maternal genome and therefore show maternal genome imprinting. If mucinous ovarian tumours originate from teratomas, their genome imprinting is theoretically maternal. One of the most important mechanisms of genome imprinting is DNA methylation. In the present study, we analysed a total of 28 mucinous ovarian tumours (7 with teratomas, 21 without teratomas; 14 malignant, 14 borderline) to clarify the methylation profiles of their imprinted genes using methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) of 21 imprinting control regions (ICRs) of nine imprinted genes/gene clusters using formalin-fixed, paraffin-embedded samples. All cases lacked evidence of an extra-ovarian primary mucinous tumour. In all seven mucinous tumours with teratomas, the overall methylation profile of mucinous tumours was comparable to that of teratomas, although some ICRs showed aberrant methylation. In contrast, all but one of the mucinous tumours without teratomas showed somatic or irregular methylation patterns. Morphologically, there was little teratomatous tissue in some mucinous tumours carrying teratoma-type methylation profiles, suggesting that mucinous tumours overwhelmed ancestral teratomas. In conclusion, the methylation profile of imprinted genes provides evidence that a subset of mucinous ovarian tumours originated from mature teratomas. Genome imprinting-based analysis is a promising strategy to verify the teratomatous origin of human tumours. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Ovarian cancer detection by DNA methylation in cervical scrapings

Background

Ovarian cancer (OC) is the most lethal gynecological cancer, worldwide, largely due to its vague and nonspecific early stage symptoms, resulting in most tumors being found at advanced stages. Moreover, due to its relative rarity, there are currently no satisfactory methods for OC screening, which remains a controversial and cost-prohibitive issue. Here, we demonstrate that Papanicolaou test (Pap test) cervical scrapings, instead of blood, can reveal genetic/epigenetic information for OC detection, using specific and sensitive DNA methylation biomarkers.

Results

We analyzed the methylomes of tissues (50 OC tissues versus 6 normal ovarian epithelia) and cervical scrapings (5 OC patients versus 10 normal controls), and integrated public methylomic datasets, including 79 OC tissues and 6 normal tubal epithelia. Differentially methylated genes were further classified by unsupervised hierarchical clustering, and each candidate biomarker gene was verified in both OC tissues and cervical scrapings by either quantitative methylation-specific polymerase chain reaction (qMSP) or bisulfite pyrosequencing. A risk-score by logistic regression was generated for clinical application.

One hundred fifty-one genes were classified into four clusters, and nine candidate hypermethylated genes from these four clusters were selected. Among these, four genes fulfilled our selection criteria and were validated in training and testing set, respectively. The OC detection accuracy was demonstrated by area under the receiver operating characteristic curves (AUCs) in 0.80–0.83 of AMPD3, 0.79–0.85 of AOX1, 0.78–0.88 of NRN1, and 0.82–0.85 of TBX15. From this, we found OC-risk score, equation generated by logistic regression in training set and validated an OC-associated panel comprising AMPD3, NRN1, and TBX15, reaching a sensitivity of 81%, specificity of 84%, and OC detection accuracy of 0.91 (95% CI, 0.82–1) in testing set.

Conclusions

Ovarian cancer detection from cervical scrapings is feasible, using particularly promising epigenetic biomarkers such as AMPD3/NRN1/TBX15. Further validation is warranted.

GATA3 as a master regulator and therapeutic target in ovarian high-grade serous carcinoma stem cells

Ovarian high-grade serous carcinoma (HGSC) is the most lethal gynecological malignancy. Prevailing evidences suggest that drug resistance and recurrence of ovarian HGSC are caused by the presence of cancer stem cells. Therefore, targeting cancer stems is appealing, however, all attempts to date, have failed. To circumvent this limit, we analyzed differential transcriptomes at early differentiation of ovarian HGSC stem cells and identified the developmental transcription factor GATA3 as highly expressed in stem, compared to progenitor cells. GATA3 expression associates with poor prognosis of ovarian HGSC patients, and was found to recruit the histone H3, lysine 27 (H3K27) demethylase, UTX, activate stemness markers, and promote stem-like phenotypes in ovarian HGSC cell lines. Targeting UTX by its inhibitor, GSKJ4, impeded GATA3-driven stemness phenotypes, and enhanced apoptosis of GATA3-expressing cancer cells. Combinations of gemcitabine or paclitaxel with GSKJ4, resulted in a synergistic cytotoxic effect. Our findings provide evidence for a new role for GATA3 in ovarian HGSC stemness, and demonstrate that GATA3 may serve as a biomarker for precision epigenetic therapy in the future.