Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning.

Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan-Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.

Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia.

Genomic profiling during the diagnosis of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification, and treatment decisions. Patients for whom diagnostic screening fails to identify disease-defining or risk-stratifying lesions are classified as having B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) of paired tumor-normal samples. For 52 patients with B-other, we compared the WGS findings with data from clinical and research cytogenetics. WGS identified a cancer-associated event in 51 of 52 patients, including an established subtype defining genetic alterations that were previously missed with standard-of-care (SoC) genetics in 5 of them. Of the 47 true B-other ALL, we identified a recurrent driver in 87% (41). A complex karyotype via cytogenetics emerges as a heterogeneous group, including distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r and IGK::BCL2). For a subset of 31 cases, we integrated the findings from RNA sequencing (RNA-seq) analysis to include fusion gene detection and classification based on gene expression. Compared with RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes; however, RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrated that WGS can identify clinically relevant genetic abnormalities missed with SoC testing as well as identify leukemia driver events in virtually all cases of B-other ALL.

ARPC1B Is Associated with Lethal Prostate Cancer and Its Inhibition Decreases Cell Invasion and Migration In Vitro.

ARPC1B (Actin Related Protein 2/3 Complex Subunit 1B) has been found to be involved in platelet abnormalities of immune-mediated inflammatory disease and eosinophilia. However, its role in prostate cancer (PCa) has not been established. We characterized the role of ARPC1B in PCa invasion and metastasis and investigated its prognosis using in vitro cellular models and PCa clinical data. Higher immunohistochemistry (IHC) expressions of ARPC1B were observed in localized and castrate resistant PCa (CRPC) vs. benign prostate tissue (p < 0.01). Additionally, 47% of patients with grade group 5 (GG) showed high ARPC1B expression vs. other GG patients. Assessing ARPC1B expression in association with two of the common genetic aberrations in PCa (ERG and PTEN) showed significant association to overall and cause-specific survival for combined assessment of ARPC1B and PTEN, and ARPC1B and ERG. Knockdown of ARPC1B impaired the migration and invasion of PC3 and DU145 PCa cells via downregulation of Aurora A kinase (AURKA) and resulted in the arrest of the cells in the G2/M checkpoint of the cell cycle. Additionally, higher ARPC1B expression was observed in stable PC3-ERG cells compared to normal PC3, supporting the association between ERG and ARPC1B. Our findings implicate the role of ARPC1B in PCa invasion and metastasis in association with ERG and further support its prognostic value as a biomarker in association with ERG and PTEN in identifying aggressive phenotypes of PCa cancer.