Molecular Characterization of Astrocytoma Progression Towards Secondary Glioblastomas Utilizing Patient-Matched Tumor Pairs

FANCM Gene Variants in a Male Diagnosed with Sertoli Cell-Only Syndrome and Diffuse Astrocytoma

Azoospermia is a form of male infertility characterized by a complete lack of spermatozoa in the ejaculate. Sertoli cell-only syndrome (SCOS) is the most severe form of azoospermia, where no germ cells are found in the tubules. Recently, FANCM gene variants were reported as novel genetic causes of spermatogenic failure. At the same time, FANCM variants are known to be associated with cancer predisposition. We performed whole-exome sequencing on a male patient diagnosed with SCOS and a healthy father. Two compound heterozygous missense mutations in the FANCM gene were found in the patient, both being inherited from his parents. After the infertility assessment, the patient was diagnosed with diffuse astrocytoma. Immunohistochemical analyses in the testicular and tumor tissues of the patient and adequate controls showed, for the first time, not only the existence of a cytoplasmic and not nuclear pattern of FANCM in astrocytoma but also in non-mitotic neurons. In the testicular tissue of the SCOS patient, cytoplasmic anti-FANCM staining intensity appeared lower than in the control. Our case report raises a novel possibility that the infertile carriers of FANCM gene missense variants could also be prone to cancer development.

Personalized identification and characterization of genome-wide gene expression differences between patient-matched intracranial and extracranial melanoma metastasis pairs

Melanoma is the most serious type of skin cancer that frequently spreads to other organs of the human body. Especially melanoma metastases to the brain (intracranial metastases) are hard to treat and a major cause of death of melanoma patients. Little is known about molecular alterations and altered mechanisms that distinguish intra- from extracranial melanoma metastases. So far, almost all existing studies compared intracranial metastases from one set of patients to extracranial metastases of an another set of melanoma patients. This neglects the important facts that each melanoma is highly individual and that intra- and extracranial melanoma metastases from the same patient are more similar to each other than to melanoma metastases from other patients in the same organ. To overcome this, we compared the gene expression profiles of 16 intracranial metastases to their corresponding 21 patient-matched extracranial metastases in a personalized way using a three-state Hidden Markov Model (HMM) to identify altered genes for each individual metastasis pair. This enabled three major findings by considering the predicted gene expression alterations across all patients: (i) most frequently altered pathways include cytokine-receptor interaction, calcium signaling, ECM-receptor interaction, cAMP signaling, Jak-STAT and PI3K/Akt signaling, (ii) immune-relevant signaling pathway genes were downregulated in intracranial metastases, and (iii) intracranial metastases were associated with a brain-like phenotype gene expression program. Further, the integration of all differentially expressed genes across the patient-matched melanoma metastasis pairs led to a set of 103 genes that were consistently down- or up-regulated in at least 11 of the 16 of the patients. This set of genes contained many genes involved in the regulation of immune responses, cell growth, cellular signaling and transport processes. An analysis of these genes in the TCGA melanoma cohort showed that the expression behavior of 11 genes was significantly associated with survival. Moreover, a comparison of the 103 genes to three closely related melanoma metastasis studies revealed a core set of eight genes that were consistently down- or upregulated in intra- compared to extracranial metastases in at least two of the three related studies (down: CILP, DPT, FGF7, LAMP3, MEOX2, TMEM119; up: GLDN, PMP2) including FGF7 that was also significantly associated with survival. Our findings contribute to a better characterization of genes and pathways that distinguish intra- from extracranial melanoma metastasis and provide important hints for future experimental studies to identify potential targets for new therapeutic approaches.

Long-term survivors of glioblastoma: Tumor molecular, clinical, and imaging findings

Background

Glioblastoma (GBM) is the most aggressive primary brain malignancy with <45% living a year beyond diagnosis. Previously published investigations of long-term survivors (LTS) provided clinical data but rarely incorporated a comprehensive clinical and molecular analysis. Herein, we identify clinical, imaging, molecular, and outcome features for 23 GBM-LTS patients and compare them with a matched cohort of short-term survivors (STS).

Methods

Molecularly confirmed Isocitrate Dehydrogenase (IDH) wildtype GBM patients living ≥3 years post-diagnosis (NLTS = 23) or <3 years (NSTS = 75) were identified from our Natural History study. Clinical and demographic characteristics were compared. Tumor tissue was analyzed with targeted next generation sequencing (NGS) (NLTS = 23; NSTS = 74) and methylation analysis (NLTS = 18; NSTS = 28). Pre-surgical MRI scans for a subset of LTS (N = 14) and STS control (N = 28) matched on sex, age, and extent of resection were analyzed.

Results

LTS tended to be younger. Diagnostic MRIs showed more LTS with T1 tumor hypointensity. LTS tumors were enriched for MGMTp methylation and tumor protein 53 (TP53) mutation. Three patients with classic GBM histology were reclassified based on NGS and methylation testing. Additionally, there were LTS with typical poor prognostic molecular markers.

Conclusions

Our findings emphasize that generalized predictions of prognosis are inaccurate for individual patients and underscore the need for complete clinical evaluation including molecular work-up to confirm the diagnosis. Continued accrual of patients to LTS registries that containcomprehensive clinical, imaging, tumor molecular data, and outcomes measures may pro\vide important insights about individual patient prognosis.

DUBing Primary Tumors of the Central Nervous System: Regulatory Roles of Deubiquitinases

The ubiquitin proteasome system (UPS) utilizes an orchestrated enzymatic cascade of E1, E2, and E3 ligases to add single or multiple ubiquitin-like molecules as post-translational modification (PTM) to proteins. Ubiquitination can alter protein functions and/or mark ubiquitinated proteins for proteasomal degradation but deubiquitinases (DUBs) can reverse protein ubiquitination. While the importance of DUBs as regulatory factors in the UPS is undisputed, many questions remain on DUB selectivity for protein targeting, their mechanism of action, and the impact of DUBs on the regulation of diverse biological processes. Furthermore, little is known about the expression and role of DUBs in tumors of the human central nervous system (CNS). In this comprehensive review, we have used publicly available transcriptional datasets to determine the gene expression profiles of 99 deubiquitinases (DUBs) from five major DUB families in seven primary pediatric and adult CNS tumor entities. Our analysis identified selected DUBs as potential new functional players and biomarkers with prognostic value in specific subtypes of primary CNS tumors. Collectively, our analysis highlights an emerging role for DUBs in regulating CNS tumor cell biology and offers a rationale for future therapeutic targeting of DUBs in CNS tumors.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

The diagnostic and prognostic potential of the EGFR/MUC4/MMP9 axis in glioma patients

Glioblastoma is the most aggressive form of brain cancer, presenting poor prognosis despite current advances in treatment. There is therefore an urgent need for novel biomarkers and therapeutic targets. Interactions between mucin 4 (MUC4) and the epidermal growth factor receptor (EGFR) are involved in carcinogenesis, and may lead to matrix metalloproteinase-9 (MMP9) overexpression, exacerbating cancer cell invasiveness. In this study, the role of MUC4, MMP9, and EGFR in the progression and clinical outcome of glioma patients was investigated. Immunohistochemistry (IHC) and immunofluorescence (IF) in fixed tissue samples of glioma patients were used to evaluate the expression and localization of EGFR, MMP9, and MUC4. Kaplan-Meier survival analysis was also performed to test the prognostic utility of the proteins for glioma patients. The protein levels were assessed with enzyme-linked immunosorbent assay (ELISA) in serum of glioma patients, to further investigate their potential as non-invasive serum biomarkers. We demonstrated that MUC4 and MMP9 are both significantly upregulated during glioma progression. Moreover, MUC4 is co-expressed with MMP9 and EGFR in the proliferative microvasculature of glioblastoma, suggesting a potential role for MUC4 in microvascular proliferation and angiogenesis. The combined high expression of MUC4/MMP9, and MUC4/MMP9/EGFR was associated with poor overall survival (OS). Finally, MMP9 mean protein level was significantly higher in the serum of glioblastoma compared with grade III glioma patients, whereas MUC4 mean protein level was minimally elevated in higher glioma grades (III and IV) compared with control. Our results suggest that MUC4, along with MMP9, might account for glioblastoma progression, representing potential therapeutic targets, and suggesting the 'MUC4/MMP9/EGFR axis' may play a vital role in glioblastoma diagnostics.

Computational gene expression analysis reveals distinct molecular subgroups of T-cell prolymphocytic leukemia

T-cell prolymphocytic leukemia (T-PLL) is a rare blood cancer with poor prognosis. Overexpression of the proto-oncogene TCL1A and missense mutations of the tumor suppressor ATM are putative main drivers of T-PLL development, but so far only little is known about the existence of T-PLL gene expression subtypes. We performed an in-depth computational reanalysis of 68 gene expression profiles of one of the largest currently existing T-PLL patient cohorts. Hierarchical clustering combined with bootstrapping revealed three robust T-PLL gene expression subgroups. Additional comparative analyses revealed similarities and differences of these subgroups at the level of individual genes, signaling and metabolic pathways, and associated gene regulatory networks. Differences were mainly reflected at the transcriptomic level, whereas gene copy number profiles of the three subgroups were much more similar to each other, except for few characteristic differences like duplications of parts of the chromosomes 7, 8, 14, and 22. At the network level, most of the 41 predicted potential major regulators showed subgroup-specific expression levels that differed at least in comparison to one other subgroup. Functional annotations suggest that these regulators contribute to differences between the subgroups by altering processes like immune responses, angiogenesis, cellular respiration, cell proliferation, apoptosis, or migration. Most of these regulators are known from other cancers and several of them have been reported in relation to leukemia (e.g. AHSP, CXCL8, CXCR2, ELANE, FFAR2, G0S2, GIMAP2, IL1RN, LCN2, MBTD1, PPP1R15A). The existence of the three revealed T-PLL subgroups was further validated by a classification of T-PLL patients from two other smaller cohorts. Overall, our study contributes to an improved stratification of T-PLL and the observed subgroup-specific molecular characteristics could help to develop urgently needed targeted treatment strategies.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

Simple Summary

The diagnostic and treatment strategies of cancer remain generally suboptimal resulting in over-diagnosis or under-treatment. Though many attempts on optimizing treatment decisions by early prediction of disease progression have been undertaken, these efforts yielded only modest success so far due to the heterogeneity of cancer with multifactorial etiology. Here, we propose a deep-learning based data integration model capable of predicting disease progression by integrating collective information available through multiple studies with different cohorts and heterogeneous data types. The results have shown that the proposed data integration pipeline is able to identify disease progression with higher accuracy and robustness compared to using a single cohort, by offering a more complete picture of the specific disease on patients with brain, blood, and pancreatic cancers.

Abstract

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

C-C motif chemokine ligand 2/C-C receptor 2 is associated with glioma recurrence and poor survival

Several studies have explored the mechanisms of C-C motif chemokine ligand (CCL)2/CC receptor (R)2 function in tumorigenesis and inflammation. However, little is known about the role of CCL2/CCR2 in tumor recurrence, especially after radiotherapy. The present study aimed to determine the association between CCL2/CCR2 and glioma relapse. Moreover, the difference in the expression of CCL2/CCR2 between post-radiation and non-radiation recurrent glioma tissues was compared. A retrospective analysis of 80 patients with glioma who underwent tumor resection twice was performed. Primary group refers to glioma patients who received glioma resection surgery for the first time. Recurrent group refers to glioma patients who received glioma resection surgery after first relapse. In total, 10 patients with brain trauma who underwent partial resection of the normal brain as decompression treatment were used as controls. Protein expression levels of CCL2 and CCR2 were evaluated using immunohistochemistry. Prognostic analyses of patient survival using Kaplan-Meier curves and Cox regression models were performed. The expression levels of CCL2 and CCR2 were higher in recurrent glioma compared with the primary group. There was a positive correlation between tumor grade and protein expression of CCL2/CCR2. Furthermore, irradiation had a significant effect on CCR2 protein expression (P=0.014), but not on CCL2 protein expression (P=0.626). However, the expression of CCL2 and CCR2 showed no significant difference between primary and secondary glioblastoma. After adjusting for sex, radiotherapy and location of tumors in these gliomas, CCL2 was a prognostic factor for disease-free and overall survival (OS) times, as well as age and tumor grade. In the multivariate Cox modeling for glioma, CCR2 was significantly associated with OS rather than DFI. The significant correlations between CCL2/CCR2 expression and glioma tumor grade suggested that CCL2/CCR2 has a role in glioma progression. Combined with previous in vitro experiments, it was proposed that irradiation (radiotherapy)-induced expression of CCL2 is transient, while irradiation-induced expression of CCR2 is lasting. Therefore, CCL2/CCR2 is a potential therapeutic target for patients with glioma.