Background: Tattoos may cause a variety of adverse reactions in the body, including immune reactions and infections. However, it is unknown whether tattoos may increase the risk of lymphatic cancers such as non-Hodgkin Lymphoma (NHL) and multiple myeloma (MM).
Methods: Participants from two population-based case-control studies were including in logistic regression models to examine the association between tattoos and risk of NHL and MM.
Results: A total of 1518 participants from the NHL study (737 cases) and 742 participants from the MM study (373 cases) were included in the analyses. No statistically significant associations were found between tattoos and risk of NHL or MM after adjusting for age, sex, ethnicity, education, BMI, and family history.
Conclusions: We did not identify any significant associations between tattoos and risk of MM, NHL, or NHL subtypes in these studies.
Impact: Though biologically plausible, tattoos were not associated with increased risk of NHL or MM in this study. Future studies with greater detail regarding tattoo exposure may provide further insights.
Aim: To provide a comprehensive understanding of gene regulatory networks in the developing human brain and a foundation for interpreting pathogenic deregulation. Materials & methods: We generated reference epigenomes and transcriptomes of dissected brain regions and primary neural progenitor cells (NPCs) derived from cortical and ganglionic eminence tissues of four normal human fetuses. Results: Integration of these data across developmental stages revealed a directional increase in active regulatory states, transcription factor activities and gene transcription with developmental stage. Consistent with differences in their biology, NPCs derived from cortical and ganglionic eminence regions contained common, region specific, and gestational week specific regulatory states. Conclusion: We provide a high-resolution regulatory network for NPCs from different brain regions as a comprehensive reference for future studies.
Purpose: Previous studies indicate that breast cancer molecular subtypes differ with respect to their dependency on autophagy, but our knowledge of the differential expression and prognostic significance of autophagy-related biomarkers in breast cancer is limited.
Methods: Immunohistochemistry (IHC) was performed on tissue microarrays from a large population of 3992 breast cancer patients divided into training and validation cohorts. Consensus staining scores were used to evaluate the expression levels of autophagy proteins LC3B, ATG4B, and GABARAP and determine the associations with clinicopathological variables and molecular biomarkers. Survival analyses were performed using the Kaplan-Meier function and Cox proportional hazards regression models.
Results: We found subtype-specific expression differences for ATG4B, with its expression lowest in basal-like breast cancer and highest in Luminal A, but there were no significant associations with patient prognosis. LC3B and GABARAP levels were highest in basal-like breast cancers, and high levels were associated with worse outcomes across all subtypes (DSS; GABARAP: HR 1.43, LC3B puncta: HR 1.43). High ATG4B levels were associated with ER, PR, and BCL2 positivity, while high LC3B and GABARAP levels were associated with ER, PR, and BCL2 negativity, as well as EGFR, HER2, HER3, CA-IX, PD-L1 positivity, and high Ki67 index (p < 0.05 for all associations). Exploratory multi-marker analysis indicated that the combination of ATG4B and GABARAP with LC3B could be useful for further stratifying patient outcomes.
Conclusions: ATG4B levels varied across breast cancer subtypes but did not show prognostic significance. High LC3B expression and high GABARAP expression were both associated with poor prognosis and with clinicopathological characteristics of aggressive disease phenotypes in all breast cancer subtypes.
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/. This article is protected by copyright. All rights reserved.
The somatic missense point mutation c.402C>G (p.C134W) in the FOXL2 transcription factor is pathognomonic for adult-type granulosa cell tumors (AGCT) and a diagnostic marker for this tumor type. However, the molecular consequences of this mutation and its contribution to the mechanisms of AGCT pathogenesis remain unclear. To explore these mechanisms, we engineered V5-FOXL2WT- and V5-FOXL2C134W-inducible isogenic cell lines and performed ChIP-seq and transcriptome profiling. FOXL2C134W associated with the majority of the FOXL2 WT DNA elements as well as a large collection of unique elements genome-wide. This model enabled confirmation of altered DNA binding specificity for FOXL2C134W and identification of unique targets of FOXL2C134W including SLC35F2, whose expression increased sensitivity to YM155. Our results suggest FOXL2C134W drives AGCT by altering the binding affinity of FOXL2-containing complexes to engage an oncogenic transcriptional program.
Alignment-free classification tools have enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their computational efficiency. Originally k-mer based, such tools often lack sensitivity when faced with sequencing errors and polymorphisms. In response, some tools have been augmented with spaced seeds, which are capable of tolerating mismatches. However, spaced seeds have seen little practical use in classification because they bring increased computational and memory costs compared to methods that use k-mers. These limitations have also caused the design and length of practical spaced seeds to be constrained, since storing spaced seeds can be costly. To address these challenges, we have designed a probabilistic data structure called a multiindex Bloom Filter (miBF), which can store multiple spaced seed sequences with a low memory cost that remains static regardless of seed length or seed design. We formalize how to minimize the false-positive rate of miBFs when classifying sequences from multiple targets or references. Available within BioBloom Tools, we illustrate the utility of miBF in two use cases: read-binning for targeted assembly, and taxonomic read assignment. In our benchmarks, an analysis pipeline based on miBF shows higher sensitivity and specificity for read-binning than sequence alignment-based methods, also executing in less time. Similarly, for taxonomic classification, miBF enables higher sensitivity than a conventional spaced seed-based approach, while using half the memory and an order of magnitude less computational time.
Immunoglobulin (Ig) gene rearrangements and oncogenic translocations are routinely assessed during the characterization of B cell neoplasms and stratification of patients with distinct clinical and biological features, with the assessment done using Sanger sequencing, targeted next-generation sequencing, or fluorescence in situ hybridization (FISH). Currently, a complete Ig characterization cannot be extracted from whole-genome sequencing (WGS) data due to the inherent complexity of the Ig loci. Here, we introduce IgCaller, an algorithm designed to fully characterize Ig gene rearrangements and oncogenic translocations from short-read WGS data. Using a cohort of 404 patients comprising different subtypes of B cell neoplasms, we demonstrate that IgCaller identifies both heavy and light chain rearrangements to provide additional information on their functionality, somatic mutational status, class switch recombination, and oncogenic Ig translocations. Our data thus support IgCaller to be a reliable alternative to Sanger sequencing and FISH for studying the genetic properties of the Ig loci.
Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing.
The most aggressive B cell lymphomas frequently manifest extranodal distribution and carry somatic mutations in the poorly characterized gene TBL1XR1. Here, we show that TBL1XR1 mutations skew the humoral immune response toward generating abnormal immature memory B cells (MB), while impairing plasma cell differentiation. At the molecular level, TBL1XR1 mutants co-opt SMRT/HDAC3 repressor complexes toward binding the MB cell transcription factor (TF) BACH2 at the expense of the germinal center (GC) TF BCL6, leading to pre-memory transcriptional reprogramming and cell-fate bias. Upon antigen recall, TBL1XR1 mutant MB cells fail to differentiate into plasma cells and instead preferentially reenter new GC reactions, providing evidence for a cyclic reentry lymphomagenesis mechanism. Ultimately, TBL1XR1 alterations lead to a striking extranodal immunoblastic lymphoma phenotype that mimics the human disease. Both human and murine lymphomas feature expanded MB-like cell populations, consistent with a MB-cell origin and delineating an unforeseen pathway for malignant transformation of the immune system.