Wang, Junwen

Abstract High-grade gliomas represent the most common type of primary adult malignant brain tumor historically diagnosed and graded from histologic criteria alone. Gliomas harboring isocitrate dehydrogenase (IDH) 1 or 2 mutations, which are present in more than 80% of World Health Organization (WHO) grade 2 or 3 tumors, portend a favorable prognosis as compared to IDH-wildtype tumors. However, recent molecular profiling has identified the presence of additional alterations in these lower grade tumors that transform the biology toward an aggressive high-grade phenotype clinically that are independent of histologic grading and confer a worse clinical prognosis. Of these, the loss of CDKN2A/B is the strongest implicated alteration and is sufficient to define a grade 4 tumor in IDH-mutant astrocytomas regardless of histologic appearance. However, there remain no effective therapies targeted at molecular subgroups in aggressive gliomas to date. Here we have profiled the intratumoral heterogeneity via whole exome and RNA sequencing and copy number alteration analysis within a cohort of de-identified IDH-mutant astrocytoma patient tumors collected through the Mayo Clinic and Barrow Neurological Institute (BNI) biorepositories to determine the spatial evolution of aggressive molecular features. Within this cohort we have collected 42 image-localized intratumoral biopsy specimens representing 13 IDH-mutant patient tumors. We identified 11 biopsies from 3 tumors that harbored loss of CDKN2A/B which included 1 tumor that displayed intratumoral heterogeneity of CDKN2A/B expression across subclones. We performed an unsupervised cluster analysis to profile the transcriptional signatures in IDH-mutant astrocytomas across both differential CDKN2A/B status and tumor geographic location comparing contrast enhancing versus non-enhancing regions, and we analyzed the MRI features in IDH-mutant tumors across CDKN2A/B status. We found that tumors with CDKN2A/B alteration demonstrated lower signal intensity on delayed post-contrast T2*W images, which could represent lower pre-contrast T2*, unique contrast agent kinetics, or dissimilar diffusion properties related to increased cellular density and/or greater heterogeneity of cell size. Interestingly, we uncovered spatial heterogeneity of additional molecular drivers of high-grade glioma including KRAS, MYCN, and PDGFRA. While EGFR alterations are rarely reported in IDH-mutant tumors, we identified one treatment naïve patient tumor that harbored EGFR alteration only in the contrast non-enhancing region. Based on this work we propose there is early spatial evolution of aggressive tumor subclones prior to primary tumor diagnosis, and multiregional molecular profiling may better inform tumor biology and predict therapeutic strategies in IDH-mutant astrocytomas. Citation Format: Shannon P. Fortin Ensign, Fulvio D'Angelo, Francesca P. Caruso, Christopher Sereduk, Kyle Singleton, Jennifer Eschbacher, Gina Mazza, Maciej Mrugala, Alyx Porter, Bernard Bendok, Richard Zimmerman, Kris Smith, Peter Nakaji, Kamala Swanson, Junwen Wang, Nathenael Semmineh, Christopher Quarles, Anna Lasorella, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Leland Hu, Nhan L. Tran. Multiregional imaging and genomic analysis in high-grade IDH-mutant astrocytoma identifies intratumor subclones with aggressive features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 770.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor with a median survival time of 12-15 months and 5-year survival rate of 5%. Despite the application of advanced genetics and biological breakthroughs in GBM, the outcome for GBM remains dismal. Previous genome-wide association studies (GWAS) for GBM shows that the incidence rate for GBM in males is 1.6 times higher in men than women, with men displaying overall worse outcomes. In-vivo studies in gender-matched isogenic murine astrocytic cell lines show that the rate of astrocytic transformation is higher in males than in females, corroborating the GWAS findings in human GBM. Thus, understanding how sex effects GBM biology may provide insight into the mechanism of gliomagenesis and response to therapy. To explore the molecular mechanisms by which transformation of GBM increases in males, we used DESeq2 to assess differentially expressed genes between male and female patients. We found that a long non-coding RNA WNT5A-AS1 is overexpressed in males [log2(fold change) of 1.40; FDR = 0.036], regardless of cofounding factors such as IDH1 status, age, and race. Next, we examined if expression of WNT5A-AS1 is correlated with survival within male patients versus female patients in the TCGA-GBM dataset. In males, WNT5A-AS1’s expression is significantly correlated with patient overall survival (P = 0.042, N=109), but not in female (P = 0.585, N=60). Male patients with high expression of WNT5A-AS1 display poor survival outcomes compared to males with low expression. WNT5A-AS1 is located in the upstream, promoter region of WNT5A. Q-PCR quantification of WNT5A expression increases in male GBM specimens as compared to GBM specimens from females. The role that WNT5A-AS1 plays in regulating WNT5A expression is unclear to date. Interestingly, WNT5A expression shows a positive correlation with patient survival in males, with high expression displaying better survival outcomes (p=0.0272). We have data obtained from a subset of patient samples showing an inverse relationship between WNT5A and WNT5A-AS1 expression. This suggests that there may be an inverse correlation between WNT5A-AS1 and WNT5A expression, which elucidates that WNT5A-AS1 may be regulating WNT5A expression. Current ongoing studies will evaluate the role of WNT5A-AS1/WNT5A in sex-differences of GBM progression. Citation Format: Aunay Miller, Junwen Wang, Joseph Loftus, Nhan L. Tran. Long non-coding RNA WNT5A-AS1 shows sex-dimorphic differences in survival for males with glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1556.

AbstractBackgroundImaging, cognitive and fluid data have been widely studied to identify quantitative biomarkers that can help predict the status and progression of Alzheimer’s disease (AD). However, it is still an underexplored topic whether there exist subpopulations with different genetic profiles across which the biomarker‐based prediction models may vary. We propose to use the Chow test (Chow 1960 Econometrica 28(3)) to perform genetically stratified analyses for identifying SNP‐based subpopulations coupled with precision AD biomarkers with varying effects on future diagnosis in these subpopulations. The investigation of such SNPs and precision biomarkers may eventually pave the way for increased customization of AD care.MethodParticipants included 1,324 subjects from the ADNI cohort with both AD biomarker and genotyping data available (http://www.pi4cs.org/qt‐pad‐challenge). 30 significant (P < 1.5E‐278) AD SNPs were sourced from (Jansen 2019 NatGen). Chow tests were performed to determine whether each of baseline visit measures of 16 AD biomarkers predicted AD diagnosis at the three‐year visit with varying slopes when stratifying upon the allelic dosage of each of 30 chosen SNPs. Bonferroni correction (P < 1.04E‐4) was employed to correct for multiple comparisons.ResultMultiple SNP‐biomarker pairs showed significant genetically driven deviations in the regression coefficients when predicting diagnosis in three years using baseline biomarkers (Figure 1). Top SNP hits involved rs769449 (Chr 19, APOE) and rs7561528 (Chr 2, LOC105373605), and almost all 16 studied biomarkers demonstrated differential slopes in different genotype groups to predict diagnosis in three years. To examine the details of these top findings, the regression coefficients calculated for each of the five most significant biomarkers of both SNPs were bootstrapped and plotted in Figure 2.ConclusionGenetic analysis of AD candidate SNPs in conjunction with AD biomarker data via the Chow test identified several SNPs coupled with precision AD biomarkers with varying prognosis effects in the corresponding genotype groups. These findings provide valuable information to reveal disease heterogeneity and help facilitate precision medicine.

Abstract Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.

AbstractDisturbed intrauterine development increases the risk of renal disease. Various studies have reported that Notch signalling plays a significant role in kidney development and kidney diseases. A disintegrin and metalloproteinase domain 10 (ADAM10), an upstream protease of the Notch pathway, is also reportedly involved in renal fibrosis. However, how ADAM10 interacts with the Notch pathway and causes renal fibrosis is not fully understood. In this study, using a prenatal chlorpyrifos (CPF) exposure mouse model, we investigated the role of the ADAM10/Notch axis in kidney development and fibrosis. We found that prenatal CPF‐exposure mice presented overexpression of Adam10, Notch1 and Notch2, and led to premature depletion of Six2+ nephron progenitors and ectopic formation of proximal tubules (PTs) in the embryonic kidney. These abnormal phenotypic changes persisted in mature kidneys due to the continuous activation of ADAM10/Notch and showed aggravated renal fibrosis in adults. Finally, both ADAM10 and NOTCH2 expression were positively correlated with the degree of renal interstitial fibrosis in IgA nephropathy patients, and increased ADAM10 expression was negatively correlated with decreased kidney function evaluated by serum creatinine, cystatin C, and estimated glomerular filtration rate. Regression analysis also indicated that ADAM10 expression was an independent risk factor for fibrosis in IgAN. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Background: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer’s disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. Objective: A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN. Methods: First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale cognitive subscale (ADAS-Cog). Results: We applied this novel CNN-MSCC system on the Alzheimer’s Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. Conclusion: Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Abstract Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability and implementation An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.