RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes

The importance of a well defined analytical strategy to solve complex murder cases

Abstract. Forensic techniques are becoming more and more powerful and affordable. This allows labs to utilise precise strategies, permitting multiple analytical approaches on the same evidence, thus obtaining precious information to solve criminal cases. This paper refers to a murder in which we received a plastic bottle and four latex gloves. These items were collected near a stolen car used to perpetrate the murder, and then burnt in order to destroy evidence linked to the murderer. We collected samples of saliva from the neck of the bottle and one glove underwent three different analyses, which were: ! Sampling and genetic analyses of sweat traces taken from the internal surface of the glove, corresponding to the lower palm area; ! Detection of palm-prints from the internal surface of the glove, corresponding to the upper palm area; ! Collection of gun shot residues (GSR) from the edge of the glove. Two full genetic profiles were obtained from the biological traces collected, one from the glove and the other one from the bottle. The analyses were instrumental in permitting the identification of the shooter who had played an important role in the murder. D 2005 Published by Elsevier B.V

Fluorescent Probes for Ecto-5′-nucleotidase (CD73)

Ecto-5′-nucleotidase (CD73) catalyzes the hydrolysis of AMP to anti-inflammatory, immunosuppressive adenosine. It is expressed on vascular endothelial, epithelial, and also numerous cancer cells where it strongly contributes to an immunosuppressive microenvironment. In the present study we designed and synthesized fluorescent-labeled CD73 inhibitors with low nanomolar affinity and high selectivity based on N6-benzyl-α,β-methylene-ADP (PSB-12379) as a lead structure. Fluorescein was attached to the benzyl residue via different linkers resulting in PSB-19416 (14b, Ki12.6 nM) and PSB-18332 (14a, Ki2.98 nM) as fluorescent high-affinity probes for CD73. These compounds are anticipated to become useful tools for biological studies, drug screening, and diagnostic applications

OMRT-3. Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment

Diffuse glioma is an aggressive brain cancer that is characterized by a poor prognosis and a universal resistance to therapy. The evolutionary processes behind this resistance remain unclear. Previous studies by the Glioma Longitudinal Analysis (GLASS) Consortium have indicated that therapy-induced selective pressures shape the genetic evolution of glioma in a stochastic manner. However, single-cell studies have revealed that malignant glioma cells are highly plastic and transition their cell state in response to diverse challenges, including changes in the microenvironment and the administration of standard-of-care therapy. Interactions between these factors remain poorly understood, making it difficult to predict how a patient’s tumor will evolve from diagnosis to recurrence. To interrogate the factors driving therapy resistance in diffuse glioma, we collected and analyzed RNA- and/or DNA-sequencing data from temporally separated tumor pairs of 292 adult patients with IDH-wild-type or IDH-mutant glioma. Recurrent tumors exhibited diverse changes that were attributable to changes in anatomic composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A homozygous deletions associated with an increase in proliferating stem-like malignant cells at recurrence in both glioma subtypes, reflecting active tumor expansion. IDH-wild-type tumors were more invasive at recurrence, and their malignant cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a specific myeloid cell state defined by unique ligand-receptor interactions with malignant cells, providing opportunities to target this transition through therapy. Collectively, our results uncover recurrence-associated changes in genetics and the microenvironment that can be targeted to shape disease progression following initial diagnosis

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The epigenetic evolution of glioma is determined by the IDH1 mutation status and treatment regimen

Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histological progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neo-angiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution towards an IDHwt-like phenotype