Biology subjects

Hwang, T. H.

Publications and source records attributed to Hwang, T. H..

26 records · Page 2Linked to original sources

bioRxiv2019

CRISPR/Cas9-based mutagenesis frequently provokes on-target mRNA misregulation

The introduction of insertion-deletions (INDELs) by activation of the error-prone non-homologous end-joining (NHEJ) pathway underlies the mechanistic basis of CRISPR/Cas9-directed genome editing. The ability of CRISPR/Cas9 to achieve gene elimination (knockouts) is largely attributed to the emergence of a pre-mature termination codon (PTC) from a frameshift-inducing INDEL that elicits non-sense mediated decay (NMD) of the mutant mRNA. Yet, the impact on gene expression as a consequence of CRISPR/Cas9-introduced INDELs into RNA regulatory sequences has been largely left uninvestigated. By tracking DNA-mRNA-protein relationships in a collection of CRISPR/Cas9-edited cell lines that harbor frameshift-inducing INDELs in various targeted genes, we detected the production of foreign mRNAs or proteins in [~]50% of the cell lines. We demonstrate that these aberrant protein products are derived from the introduction of INDELs that promote internal ribosomal entry, convert pseudo-mRNAs into protein encoding molecules, or induce exon skipping by disruption of exon splicing enhancers (ESEs). Our results using CRISPR/Cas9-introduced INDELs reveal facets of an epigenetic genome buffering apparatus that likely evolved to mitigate the impact of such mutations introduced by pathogens and aberrant DNA damage repair, and that more recently pose challenges to manipulating gene expression outcomes using INDEL-based mutagenesis.

molecular biology↗

bioRxiv2019

Using transfer learning on whole slide images to predict tumor mutational burden in bladder cancer patients

High-TMB (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patients own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Here we developed and applied computational approaches using digital whole slide images (WSIs) to investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, and its prognostic utility. In experiments using WSIs from The Cancer Genome Atlas bladder cancer (BLCA), our findings show that WSI-based approaches can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival indicating a prognostic role of spatial TMB and TILs information in BLCA.

bioinformatics↗

bioRxiv2019

Gaussian Process Based Heteroscedastic Noise Modeling for Tumor Mutation Burden Prediction from Whole Slide Images

Tumor mutation burden (TMB) is a quantitative measurement of how many mutations present in tumor cells from a patient tumor as assessed by next-generation sequencing (NGS) technology. High TMB is used as a predictive biomarker to select patients that likely respond to immunotherapy in many cancer types, thus it is critical to accurately measure TMB for cancer patients who need to receive the immunotherapy. Recent studies showed that image features from histopathology whole slide images can be used to predict genetic features (e.g., mutation status) or clinical outcome of cancer patients. In this study, we develop a computational method to predict the TMB level from cancer patients histopathology whole slide images. The prediction problem is formulated as multiple instance learning (MIL) because a whole slide image (a bag) has to be divided into multiple image blocks (instances) due to computational reasons but a single label is available only to an entire whole slide image not to each image block. In particular, we propose a novel heteroscedastic noise model for MIL based on the framework of Gaussian process (GP), where the noise variance is assumed to be a latent function of image level features. This noise variance can encode the confidence in predicting the TMB level from each training image and make the method to put different levels of effort to classify images according to how difficult each image can be correctly classified. The method tries to fit an easier image well while it does not put much effort in classifying a harder (ambiguous) image correctly. Expectation and propagation (EP) is employed to efficiently infer our model and to find the optimal hyper-parameters. We have demonstrated from synthetic and real-world data sets that our method outperforms on TMB prediction from whole slide images base-line methods, including a special case of our method that does not include the heteroscedastic noise modeling and multiple instance ordinal regression (MIOR) that is one of few algorithms to solve ordinal regression in the MIL setting.

bioinformatics↗

bioRxiv2018

STATISTICAL LOCAL BINARY PATTERNS (SLBP): APPLICATION TO PROSTATE CANCER GLEASON SCORE PREDICTION FROM WHOLE SLIDE PATHOLOGY IMAGES

Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more micro- and macro-structure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade [≤]7 vs grade [≥]8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-art LBP variants.

bioinformatics↗

bioRxiv2018

Divine: Prioritizing Genes for Rare Mendelian Disease in Whole Exome Sequencing Data

MotivationRecent studies showed that a phenotype-driven analysis of whole exome sequencing (WES) could provide more accurate and clinically relevant genetic variants.\n\nResultsWe develop a computational tool called Divine that integrates patients phenotype(s) and WES data with 30 prior biological knowledge (e.g., human phenotype ontology, gene ontology, pathway database, protein-protein interaction networks, pathogenicity by the amino acid change due to polymorphism, and hot-spot protein domains) to prioritize potential disease-causing genes. In a retrospective study with 22 real and four simulated data set, Divine ranks the same pathogenic genes confirmed by the original studies 5th on average out of a thousand of mutated genes and outperforms existing state-of-the-art methods.\n\nAvailabilityhttps://github.com/hwanglab/divine\n\nContacthwangt@ccf.org\n\nSupplementary informationSupplementary Document is attached at the end of the page.

bioinformatics↗

bioRxiv2018

Automatic Classification of Prostate Cancer Gleason Scores from Digitized Whole Slide Tissue Biopsies

Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we propose an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide biopsy images using a set of novel completed and statistical local bi-nary pattern (CSLBP) descriptors. First the technique divides the whole slide image into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are then extracted from salient image tiles to characterize different Gleason patterns. Finally, CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient biopsy with different Gleason score of 6, 7 or [≥]8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved more than 79% classification accuracies, which is superior to state-of-the-art textural descriptors for prostate cancer Gleason grading.

bioinformatics↗

bioRxiv2018

Molecular Subtypes of Anaplastic Gliomas Identified with Somatic-Mutation and Pathway Based Gene Signature

PurposeAnaplastic gliomas constitute heterogeneous population with variable outcomes and no consensus on therapeutic approach. Molecular profiling may provide prognostication beyond clinical and pathologic factors and help guide treatment decisions.\n\nExperimental DesignThe Cancer Genome Atlas (TCGA) was utilized to derive a 39-gene low grade glioma-specific gene signature. Consensus clustering based on expression of the signature identified subgroups for 176 patients with anaplastic glioma from TCGA. Overall survival (OS) was analyzed for each subgroup. A total of 68 patients from Repository for Molecular Brain Neoplasia Data (REMBRANDT) were used as an independent validation dataset.\n\nResultsConsensus clustering separated the TCGA group into two distinct cohorts. The OS was significantly different between two subgroups, 20 vs. 67 months (p<0.001). On univariate analysis, the molecular subgroup, age, KPS, IDH1/2 mutation, 1p19q-co-deletion, chemotherapy, and use of both chemotherapy and radiation-therapy were significantly associated with OS. On multivariable analysis, the molecular subgroup remained significant with HR of 2.6 (p=.047, 95%CI [1.01-6.68]). In an independent validation with REMBRANDT, consensus clustering based on the signature successfully identified similarly poor prognostic subgroup with median survival of 14 months and concordance of expression patterns in 21 of the genes.\n\nConclusionExpression patterns of the 39 gene stratified anaplastic gliomas into two distinct subgroups with substantially different OS. This molecular prognostication was validated in an external dataset. Utilization of molecular subgroup, in addition to known prognostic factors may help define those requiring aggressive therapeutic intervention. Characteristic genes within the poor prognostic group may represent potential targets for therapeutic intensification.\n\nSource code and dataset used in this work is available for reviewers at: https://www.taehyunlab.org/ntripath\n\nImportance of the studyDespite the revolution of tailored therapy, anaplastic gliomas represent a category of tumors without clear treatment recommendations. While current prognostic factors help guide therapy recommendations, further refinement with the addition of molecular markers can help physicians with treatment recommendations. In this study, we developed a 39 gene prognostic gene signature by utilizing Pan-Cancer TCGA mutation profiles of over 5,000 patients across 19 different TCGA cancer types to stratify patients with anaplastic gliomas. We performed consensus clustering based on gene expression profiles of our 39-gene signature without any consideration of clinical factors or outcomes to TCGA anaplastic gliomas patients as well as an independent dataset and successfully identified two molecular subgroups with distinct clinical outcome. We found that subgroups have remarkably different survivals with a clear poor prognostic group. Furthermore, the poor prognostic group showed significant benefits for aggressive multimodality therapy, justifying the use of intensive therapy based on molecular stratification.

genomics↗

bioRxiv2017

miR-16 suppresses growth of rhabdoid tumor cells

BackgroundRhabdoid tumor is a highly aggressive pediatric cancer characterized by biallelic loss and/or mutation of SMARCB1. Outcomes remain poor, and there are no established ways to target the tumorigenic pathways driven by SMARCB1 inactivation. SMARCB1 loss leads to an increase in cyclin D transcription.\n\nProcedureWe characterized the cell line WT-CLS1, which has been described previously as Wilms tumor, by whole-exome sequencing, RNA-seq, and xenograft histology. We measured the effect of microRNA overexpression on WT-CLS1, BT-12, and CHLA-06-ATRT.\n\nResultsWe found that WT-CLS1 demonstrates the histological, mutational, and transcriptional hallmarks of rhabdoid tumor. Because the microRNAs let-7 and miR-16 can target cyclin D genes, we next overexpressed each of these microRNAs in WT-CLS1. We found that miR-16 reduced cell accumulation. This was accompanied by a decrease in proliferation markers and an increase in apoptosis markers. These results were replicated in the BT-12 and CHLA-06-ATRT cell lines.\n\nConclusionsThe loss-of-function SMARCB1 mutation found in WT-CLS1, in conjunction with immunohistochemical and gene expression analysis, warrants reclassification of this cell line as rhabdoid tumor. Proliferation of WT-CLS1 and other rhabdoid tumor cell lines is significantly abrogated by miR-16 overexpression. Further studies are necessary to gain insight into the potential for miR-16 to be used as a novel therapeutic in rhabdoid tumor.\n\nAbbreviations

cancer biology↗