scholarly journals Topography of transcriptionally active chromatin in glioblastoma

2021 ◽  
Vol 7 (18) ◽  
pp. eabd4676
Author(s):  
Liang Xu ◽  
Ye Chen ◽  
Yulun Huang ◽  
Edwin Sandanaraj ◽  
John S. Yu ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Molecular Classification ◽  
Normal Brain ◽  
Active Chromatin ◽  
Primary Tumors ◽  
Genomic Variations ◽  
Transcriptional Enhancers ◽  
Super Enhancer ◽  
Druggable Targets ◽  
Regulatory Landscapes

Molecular profiling of the most aggressive brain tumor glioblastoma (GBM) on the basis of gene expression, DNA methylation, and genomic variations advances both cancer research and clinical diagnosis. The enhancer architectures and regulatory circuitries governing tumor-intrinsic transcriptional diversity and subtype identity are still elusive. Here, by mapping H3K27ac deposition, we analyze the active regulatory landscapes across 95 GBM biopsies, 12 normal brain tissues, and 38 cell line counterparts. Analyses of differentially regulated enhancers and super-enhancers uncovered previously unrecognized layers of intertumor heterogeneity. Integrative analysis of variant enhancer loci and transcriptome identified topographies of transcriptional enhancers and core regulatory circuitries in four molecular subtypes of primary tumors: AC1-mesenchymal, AC1-classical, AC2-proneural, and AC3-proneural. Moreover, this study reveals core oncogenic dependency on super-enhancer–driven transcriptional factors, long noncoding RNAs, and druggable targets in GBM. Through profiling of transcriptional enhancers, we provide clinically relevant insights into molecular classification, pathogenesis, and therapeutic intervention of GBM.

scholarly journals Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma

2019 ◽  
Vol 216 (5) ◽  
pp. 1071-1090 ◽  
Author(s):  
Stephen C. Mack ◽  
Irtisha Singh ◽  
Xiuxing Wang ◽  
Rachel Hirsch ◽  
Quilian Wu ◽  
...  
Keyword(s):  
Stem Cells ◽  
Transcription Factors ◽  
Erk Pathway ◽  
Therapeutic Approaches ◽  
Active Chromatin ◽  
Primary Tumors ◽  
The Core ◽  
Super Enhancer ◽  
Enhancer Regulation

Glioblastoma is an incurable brain cancer characterized by high genetic and pathological heterogeneity. Here, we mapped active chromatin landscapes with gene expression, whole exomes, copy number profiles, and DNA methylomes across 44 patient-derived glioblastoma stem cells (GSCs), 50 primary tumors, and 10 neural stem cells (NSCs) to identify essential super-enhancer (SE)–associated genes and the core transcription factors that establish SEs and maintain GSC identity. GSCs segregate into two groups dominated by distinct enhancer profiles and unique developmental core transcription factor regulatory programs. Group-specific transcription factors enforce GSC identity; they exhibit higher activity in glioblastomas versus NSCs, are associated with poor clinical outcomes, and are required for glioblastoma growth in vivo. Although transcription factors are commonly considered undruggable, group-specific enhancer regulation of the MAPK/ERK pathway predicts sensitivity to MEK inhibition. These data demonstrate that transcriptional identity can be leveraged to identify novel dependencies and therapeutic approaches.

scholarly journals Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

2020 ◽  
Vol 10 (7) ◽  
pp. 463 ◽  
Author(s):  
Muhaddisa Barat Ali ◽  
Irene Yu-Hua Gu ◽  
Mitchel S. Berger ◽  
Johan Pallud ◽  
Derek Southwell ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Negative Impact ◽  
Molecular Classification ◽  
Tumor Segmentation ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Molecular Type ◽  
Low Grade Gliomas ◽  
Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

scholarly journals Normal tissue content impact on the GBM molecular classification

2020 ◽  
Author(s):  
Rodrigo Madurga ◽  
Noemí García-Romero ◽  
Beatriz Jiménez ◽  
Ana Collazo ◽  
Francisco Pérez-Rodríguez ◽  
...  
Keyword(s):  
Normal Tissue ◽  
In Silico Analysis ◽  
Molecular Classification ◽  
Gene Signature ◽  
Normal Brain ◽  
Normal Cells ◽  
Final Model ◽  
Tumor Subtypes ◽  
Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

scholarly journals Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter W. Eide ◽  
Seyed H. Moosavi ◽  
Ina A. Eilertsen ◽  
Tuva H. Brunsell ◽  
Jonas Langerud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Principal Components ◽  
Prognostic Value ◽  
Tumor Heterogeneity ◽  
Molecular Subtypes ◽  
R Package ◽  
Data Set ◽  
Primary Tumors ◽  
External Data

AbstractGene expression-based subtypes of colorectal cancer have clinical relevance, but the representativeness of primary tumors and the consensus molecular subtypes (CMS) for metastatic cancers is not well known. We investigated the metastatic heterogeneity of CMS. The best approach to subtype translation was delineated by comparisons of transcriptomic profiles from 317 primary tumors and 295 liver metastases, including multi-metastatic samples from 45 patients and 14 primary-metastasis sets. Associations were validated in an external data set (n = 618). Projection of metastases onto principal components of primary tumors showed that metastases were depleted of CMS1-immune/CMS3-metabolic signals, enriched for CMS4-mesenchymal/stromal signals, and heavily influenced by the microenvironment. The tailored CMS classifier (available in an updated version of the R package CMScaller) therefore implemented an approach to regress out the liver tissue background. The majority of classified metastases were either CMS2 or CMS4. Nonetheless, subtype switching and inter-metastatic CMS heterogeneity were frequent and increased with sampling intensity. Poor-prognostic value of CMS1/3 metastases was consistent in the context of intra-patient tumor heterogeneity.

scholarly journals YAP/TAZ Signalling in Colorectal Cancer: Lessons from Consensus Molecular Subtypes

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3160
Author(s):  
Sophie Mouillet-Richard ◽  
Pierre Laurent-Puig
Keyword(s):  
Colorectal Cancer ◽  
Cancer Progression ◽  
Molecular Subtypes ◽  
Hippo Pathway ◽  
Molecular Classification ◽  
Colorectal Cancer Progression ◽  
Definition Of ◽  
The Hippo Pathway ◽  
Future Work

Recent advance in the characterization of the heterogeneity of colorectal cancer has led to the definition of a consensus molecular classification within four CMS subgroups, each associated with specific molecular and clinical features. Investigating the signalling pathways that drive colorectal cancer progression in relation to the CMS classification may help design therapeutic strategies tailored for each CMS subtype. The two main effectors of the Hippo pathway YAP and its paralogue TAZ have been intensively scrutinized for their contribution to colon carcinogenesis. Here, we review the knowledge of YAP/TAZ implication in colorectal cancer from the perspective of the CMS framework. We identify gaps in our current understanding and delineate research avenues for future work.

Molecular determinants of prostate specific antigen (PSA) kinetics and clinical response to apalutamide (APA) in patients (pts) with nonmetastatic castration-resistant prostate cancer (nmCRPC) in SPARTAN.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5521-5521 ◽  
Author(s):  
Fred Saad ◽  
Julie N Graff ◽  
Boris A. Hadaschik ◽  
Stephane Oudard ◽  
Paul N. Mainwaring ◽  
...  
Keyword(s):  
High Risk ◽  
Molecular Subtypes ◽  
Specific Antigen ◽  
Average Risk ◽  
Castration Resistant Prostate Cancer ◽  
Luminal A ◽  
Chi Square ◽  
Primary Tumors ◽  
Psa Kinetics ◽  
Castration Resistant

5521 Background: In SPARTAN, APA + androgen deprivation therapy (ADT) prolonged metastasis-free survival (MFS) and improved PSA kinetics over placebo (PBO) + ADT in high-risk nmCRPC. All molecular subtypes derived benefit in MFS from APA (Feng FY, et al. ASCO GU 2019; abstract 42). We evaluated the association of PSA decline and efficacy outcomes in SPARTAN pts with different molecular subtypes. Methods: Gene expression from archival primary tumors (biomarker population) was assessed with the DECIPHER platform (Decipher Biosciences, Inc.) and stratified into genomic classifier (GC) high- and low-to-average risk using GC score > 0.6 and ≤ 0.6, respectively, and ADT-resistant or -sensitive basal or luminal A/B (PAM50 classifier) subtypes. PSA nadir and confirmed PSA decline (Table) were assessed in APA pts overall and at 3, 6, and 12 mo. Associations between molecular subtypes and outcomes were assessed. Results: Of 233 available samples, 154 were from APA pts; 49% of APA pts had high GC score and 66% had basal subtype. PSA levels at baseline were similar across all subtypes. Regardless of GC score or basal/luminal subtype, > 50% of patients achieved ≥ 90% reduction in PSA with APA. PSA declined faster and PSA reduction was deeper at 6 mo (Table) in GC low to average vs GC high risk and luminal vs basal subtypes. Overall, only luminal vs basal subtypes had a significantly higher % of pts with ≥ 90% PSA decline (Chi square p = 0.037). In luminal pts, deeper PSA decline with APA was consistent with improved MFS vs basal pts. In GC high pts, MFS benefit with APA was similar to that in GC low to average pts despite lower PSA decline. Although GC low to average and luminal pts had more rapid and deeper PSA responses than GC high or basal pts, respectively, all pts derived MFS benefit. Association of long-term outcomes with PSA decline in these molecular subtypes will be presented. Conclusions: In SPARTAN, all molecular subtypes of pts with nmCRPC treated with APA + ADT had MFS benefit and rapid and sustained PSA decline. PSA responses were deepest and most rapid in GC low to average and luminal subtypes. Clinical trial information: NCT01946204 . [Table: see text]

scholarly journals The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer

2021 ◽  
Vol 13 ◽  
pp. 175883592110359
Author(s):  
Amy Jamieson ◽  
Tjalling Bosse ◽  
Jessica N. McAlpine
Keyword(s):  
Endometrial Cancer ◽  
Molecular Subtypes ◽  
Treatment Decision ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Molecular Profile ◽  
Therapeutic Implications ◽  
Treatment Decision Making ◽  
Exciting Time ◽  
Classification Tool

Following the discovery of the four molecular subtypes of endometrial cancer (EC) by The Cancer Genome Atlas (TCGA) in 2013, subsequent studies used surrogate markers to develop and validate a clinically relevant EC classification tool to recapitulate TCGA subtypes. Molecular classification combines focused sequencing ( POLE) and immunohistochemistry (mismatch repair and p53 proteins) to assign patients with EC to one of four molecular subtypes: POLEmut, MMRd, p53abn and NSMP (no specific molecular profile). Unlike histopathological evaluation, the molecular subtyping of EC offers an objective and reproducible classification system that has been shown to have prognostic value and therapeutic implications. It is an exciting time in EC care where we have moved beyond treatment based on histomorphology alone, and molecular classification will now finally allow assessment of treatment efficacy within biologically similar tumours. It is now recommended that molecular classification should be considered for all ECs, and should be performed routinely in all high grade tumours. It is also recommended to incorporate molecular classification into standard pathology reporting and treatment decision-making algorithms. In this review, we will discuss how the molecular classification of EC can be used to guide both conventional and targeted therapy in this new molecular era.

scholarly journals Translational applications of microRNA genes in medulloblastomas

2010 ◽  
Vol 33 (4) ◽  
pp. 223 ◽  
Author(s):  
Norbert F Ajeawung ◽  
Bin Li ◽  
Deepak Kamnasaran
Keyword(s):  
Biological Significance ◽  
Clinical Importance ◽  
Molecular Classification ◽  
Qualitative Assessment ◽  
Normal Brain ◽  
Protein Coding ◽  
Rna Molecules ◽  
Mirna Genes ◽  
Molecular Events ◽  
Microrna Genes

Purpose: To provide a critical assessment of the clinical translational applications of microRNA (miRNA) genes in medulloblastomas. Methods: Data were obtained from MEDLINE using Boolean-formatted keyword queries. Top articles were selected for critical analyses - depending on the novelty of findings, qualitative assessment of the citation index and relevance to the diagnosis, prognosis and therapeutic targeting of medulloblastomas. Results: MiRNAs, non-protein-coding RNA molecules, negatively regulate gene expression in a sequence–specific manner during biological processes. In the past few years, miRNA genes have emerged as key regulators of not only molecular events involved in normal brain development and function but also in the molecular pathogenesis of medulloblastomas. In this manner, microRNA genes are identified with functional roles as oncogenes and tumor suppressor genes. At least four miRNAs have proven useful in improving the molecular classification of medulloblastomas, and eight others have shown potential in predicting patients’ overall prognosis. Moreover, more than 10 miRNA genes can be potentially utilized in therapies against medulloblastomas, using nine recent methods of targetting miRNAs. Conclusion: The quest to identify miRNA genes that are of biological significance in medulloblastomas is on an ongoing venture. Most importantly, these miRNAs have been shown to be of clinical importance for improving the accuracy of diagnosis and prognosis and even developing therapies that can significantly improve patients’ overall survival from this deadly disease.

scholarly journals Simplified Molecular Classification of Lung Adenocarcinomas Based on EGFR, KRAS, and TP53 Mutations

2019 ◽  
Author(s):  
Roberto Ruiz-Cordero ◽  
Junsheng Ma ◽  
Abha Khanna ◽  
Genevieve Ray Lyons ◽  
Waree Rinsurongkawong ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Survival Data ◽  
Molecular Subtypes ◽  
Stage Iv ◽  
Molecular Classification ◽  
Smoking History ◽  
Egfr Mutations ◽  
Prognostic Features ◽  
Mutational Status

Abstract Introduction: Gene expression profiling has consistently identified three molecular subtypes of lung adenocarcinoma that have prognostic implications. To facilitate stratification of patients with this disease into similar molecular subtypes, we developed and validated a simple, mutually exclusive classification. Methods: Mutational status of EGFR, KRAS, and TP53 was used to define six mutually exclusive molecular subtypes. A development cohort of 283 cytology specimens of lung adenocarcinoma was used to evaluate the associations between the proposed classification and clinicopathologic variables including demographic characteristics, smoking history, fluorescence in situ hybridization and molecular results. For validation and prognostic assessment, 63 of the 283 cytology specimens with available survival data were combined with a separate cohort of 428 surgical pathology specimens of lung adenocarcinoma. Results: The proposed classification yielded significant associations between these molecular subtypes and clinical and prognostic features. We found better overall survival in patients who underwent surgery and had tumors enriched for EGFR mutations. Worse overall survival was associated with older age, stage IV disease, and tumors with co- mutations in KRAS and TP53. Interestingly, neither chemotherapy nor radiation therapy showed benefit to overall survival. Conclusions: The mutational status of EGFR, KRAS, and TP53 can be used to easily classify lung adenocarcinoma patients into six subtypes that show a relationship with prognosis, especially in patients who underwent surgery, and these subtypes are similar to classifications based on more complex genomic methods reported previously.

scholarly journals Simplified Molecular Classification of Lung Adenocarcinomas Based on EGFR, KRAS, and TP53 Mutations

2019 ◽  
Author(s):  
Roberto Ruiz-Cordero ◽  
Junsheng Ma ◽  
Abha Khanna ◽  
Genevieve Ray Lyons ◽  
Waree Rinsurongkawong ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Survival Data ◽  
Molecular Subtypes ◽  
Stage Iv ◽  
Molecular Classification ◽  
Smoking History ◽  
Egfr Mutations ◽  
Prognostic Features ◽  
Mutational Status

Abstract Background: Gene expression profiling has consistently identified three molecular subtypes of lung adenocarcinoma that have prognostic implications. To facilitate stratification of patients with this disease into similar molecular subtypes, we developed and validated a simple, mutually exclusive classification. Methods: Mutational status of EGFR, KRAS, and TP53 was used to define seven mutually exclusive molecular subtypes. A development cohort of 283 cytology specimens of lung adenocarcinoma was used to evaluate the associations between the proposed classification and clinicopathologic variables including demographic characteristics, smoking history, fluorescence in situ hybridization and molecular results. For validation and prognostic assessment, 63 of the 283 cytology specimens with available survival data were combined with a separate cohort of 428 surgical pathology specimens of lung adenocarcinoma. Results: The proposed classification yielded significant associations between these molecular subtypes and clinical and prognostic features. We found better overall survival in patients who underwent surgery and had tumors enriched for EGFR mutations. Worse overall survival was associated with older age, stage IV disease, and tumors with co-mutations in KRAS and TP53. Interestingly, neither chemotherapy nor radiation therapy showed benefit to overall survival. Conclusions: The mutational status of EGFR, KRAS, and TP53 can be used to easily classify lung adenocarcinoma patients into seven subtypes that show a relationship with prognosis, especially in patients who underwent surgery, and these subtypes are similar to classifications based on more complex genomic methods reported previously.

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