Luminal and basal subtyping of metastatic castration-resistant prostate cancer (mCRPC) and its clinical implications.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 197-197 ◽  
Author(s):  
Won Kim ◽  
Eric Jay Small ◽  
Rahul Raj Aggarwal ◽  
Robert Benjamin Den ◽  
Jonathan Lehrer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Drug Response ◽  
Metastatic Tumor ◽  
Localized Prostate Cancer ◽  
Patient Specific ◽  
Expression Data ◽  
Castration Resistant Prostate Cancer ◽  
Luminal A ◽  
Luminal B

197 Background: The PAM50 gene expression classifier (PAM50) identifies luminal and basal subtypes and predicts response to androgen deprivation therapy in localized prostate cancer. The clinical utility of using PAM50 to molecularly subtype mCRPC was evaluated. Methods: PAM50 was applied to RNA expression data from 86 metastatic tumor biopsies from the SU2C-AACR-PCF West Coast Prostate Cancer Dream Team (WCDT; NCT02432001). Overall survival (OS) differences between luminal A (LuA), luminal B (LuB), or basal patients (pts) were determined using Kaplan-Meier analyses. PAM50 was also applied to 15,136 prospectively collected radical prostatectomy (RP) samples from the Decipher GRiD database (NCT02609269). Drug response signatures (DRS) for 89 drugs were derived using publicly available data from the NCI60 cell line panel, and applied to gene expression data from the RP samples to predict patient-specific drug sensitivities. Differences in DRS as a function of PAM50 subtype were assessed using the Pearson’s chi-squared test. Results: The application of PAM50 to mCRPC transcriptional data segregated pts into LuA, LuB, and basal populations (43%, 14%, and 43%, respectively). The median OS for LuA, LuB, and basal pts was 20.6 months, 9.5 months, and 10.4 months, respectively (p=0.04), which was consistent with localized prostate cancer where LuB pts have the worst prognosis. DRS analyses revealed statistically significant differences in drug sensitivities, with LuA and LuB pts predicted to be markedly more sensitive to docetaxel than basal pts (p<0.00001), and basal pts predicted to be markedly more sensitive to platinums and etoposide than LuA and LuB pts (p<0.00001). Conclusions: PAM50 subtyping is prognostic in mCRPC, with LuA pts demonstrating the longest OS. Luminal and basal subtypes have distinct in silico drug response profiles that may be associated with response to mCRPC therapies. Prospective testing of DRS as a biomarker to guide treatment in mCRPC is warranted. Clinical trial information: NCT02432001, NCT02609269.

Luminal B subtype as a predictive biomarker of docetaxel benefit for newly diagnosed metastatic hormone sensitive prostate cancer (mHSPC): A correlative study of E3805 CHAARTED.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 162-162 ◽  
Author(s):  
Anis Hamid ◽  
Xin Victoria Wang ◽  
Yu-Hui Chen ◽  
Felix Y Feng ◽  
Robert Benjamin Den ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
De Novo ◽  
Local Therapy ◽  
Microarray Platform ◽  
High Volume ◽  
Predictive Biomarker ◽  
Luminal A ◽  
Luminal B ◽  
Basal Subtype

162 Background: Through gene expression profiling (GEP), the PAM50 classifier demonstrates prognostic value in localized prostate cancer (PCa). Pre-clinical drug response models predict increased taxane sensitivity in luminal subtypes compared to basal subtype. Men with mHSPC and high-risk features have greatest benefit from androgen deprivation therapy (ADT) plus docetaxel (D) vs ADT alone. We therefore sought to test the prognostic and predictive value of PAM50 in pre-ADT specimens from E3805 CHAARTED. Methods: Whole transcriptomic profiling of formalin-fixed, paraffin-embedded primary PCa biopsies from pts enrolled in the E3805 CHAARTED trial of ADT vs ADT+D was performed using the Human Exon 1.0 ST microarray platform (Decipher Biosciences). Normalized gene expression was used to classify subjects as luminal A, luminal B or basal subtype. Multivariable analyses (MVA) adjusted for ECOG status, de novo metastasis vs prior local therapy and volume of disease. The primary endpoint was overall survival (OS). Secondary endpoint was time to castration resistant PCa (TTCRPC). Results: Successful GEP was completed in 160 of 198 pts with available specimens. Eighty (50%), 77 (48%) and 3 (2%) pts were classified as luminal B, basal and luminal A, respectively. High volume disease was similarly present in luminal B (79%) and basal (78%) subtypes. In the ADT arm, luminal B subtype was associated with shorter OS vs basal (HR 1.75, p=0.05); consistent in MVA. Pts with luminal B subtype treated with ADT+D showed significant improvement in TTCRPC and OS (Table). By contrast, basal subtype showed no OS benefit from ADT+D even in pts with high volume disease. Conclusions: We demonstrate that GEP identifies tumor subtypes associated with differential benefit from chemohormonal therapy for mHSPC. Luminal B subtype is associated with poorer OS with ADT alone and benefits from addition of D. Basal subtype shows a lack of OS benefit from upfront ADT+D. We plan to validate these findings in independent trial cohorts.[Table: see text]

Development of a circulating tumor cell-based RNA classifier for patients with castration-resistant prostate cancer: CTC-PCS/PAM50.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17509-e17509
Author(s):  
Pai-Chi Teng ◽  
Yu Jen Jan ◽  
Minhyung Kim ◽  
Jie-Fu Chen ◽  
Junhee Yoon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Progression Free Survival ◽  
Circulating Tumor Cell ◽  
Clinical Test ◽  
Castration Resistant Prostate Cancer ◽  
Luminal A ◽  
Luminal B ◽  
Castration Resistant ◽  
Therapeutic Decisions ◽  
Blood Specimens

e17509 Background: Genomic profiling has strongly impacted the contemporary understanding of prostate cancer (PCa). Clinical trials are now testing the utility of genomic classifiers such as the PCS (You, Ca Res 2016) and PAM50 (Zhao, JAMA Onc 2017) systems to optimize therapy selection. As contemporary tissue is not always readily available, especially in metastatic, castration-resistant PCa (mCRPC), a blood-based test would be better suited for assessing patients and predicting treatment response. Methods: The CTC-RNA assay combines the Thermoresponsive (TR)-NanoVelcro system with the NanoString nCounter platform. This allows for CTC purification and RNA analysis. Using a novel bioinformatics approach that accounts for differences in background signals between tissue and blood, we reconstructed the PCS and PAM50 panels to recapitulate both classifiers in this blood-based assay. A weighted Z-score and nearest centroid classifier were used to calculate gene expression and to assign PCS and PAM50 subtypes. Performance of the revised signatures and CTC-RNA assay was benchmarked on simulated spiked-blood specimens. An initial clinical test was performed using clinically annotated, banked blood specimens within the Translational Oncology Program Blood and Biospecimen Bank. Results: CTC-RNA profiles of C4-2B AR signaling inhibitor (ARSI)-resistant sublines were compared to parental C4-2B. C4-2B ARSI-resistant cells had significantly higher PCS1 Z scores, PCS1 probability, and basal probability compared to the parental C4-2B cells. Blood samples from 34 mCRPC patients prior to initiation of therapy with ARSIs (abiraterone, enzalutamide, or apalutamide) were then analyzed. Samples were classified as PCS1 (n = 3), PCS2 (n = 20), and PCS3 (n = 11); luminal A (n = 12), luminal B (n = 11), and basal (n = 11). The biochemical progression-free survival (bPFS) on ARSI and overall survival (OS) for PCS1/Basal vs. other are shown in the table. Conclusions: The CTC-RNA assay is capable of generating luminal-basal classifications such as those in the PCS and PAM50 systems. Given early data of these classifiers and their potential to guide therapeutic decisions, this approach may be useful as an alternative to biopsy to facilitate such decisions. Larger prospective studies will be needed to confirm and validate its clinical utility. [Table: see text]

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Analysis of the Gene Expression Profile of Stromal Pro-Tumor Factors in Cancer-Associated Fibroblasts from Luminal Breast Carcinomas

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 865 ◽  
Author(s):  
Noemi Eiro ◽  
Sandra Cid ◽  
María Fraile ◽  
Jorge Ruben Cabrera ◽  
Luis O. Gonzalez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Cancer Associated Fibroblasts ◽  
Breast Carcinomas ◽  
Luminal A ◽  
Luminal B

Luminal tumors are the most frequent type of breast carcinomas showing less tumor aggressiveness, although heterogeneity exists in their clinical outcomes. Cancer-associated fibroblasts (CAFs) are a key component of the tumor stroma which contribute to tumor progression. We investigated by real-time PCR the gene expression of 19 factors implicated in tumor progression. Those factors included the calcium-binding protein S100A4, several growth factors (FGF2, FGF7, HGF, PDGFA, PDGFB, TGFβ, VEGFA, and IGF2), and we also studied inflammatory cytokines (IL6 and IL8), chemokines (CCL2, CXCL12), important proteases (uPA, MMP2, MMP9 and MMP11), the nuclear factor NFκB, and the metalloprotease inhibitor TIMP1, from luminal A and luminal B breast carcinoma CAFs. We performed a similar analysis after co-culturing CAFs with MCF-7 and MDA-MB-231 breast cancer cell lines. MMP-9 and CCL2 gene expressions were higher in CAFs from luminal B tumors. We also found different patterns in the induction of pro-tumoral factors from different CAFs populations co-cultured with different cancer cell lines. Globally, CAFs from luminal B tumors showed a higher expression of pro-tumor factors compared to CAFs from luminal A tumors when co-cultured with breast cancer cell lines. Moreover, we found that CAFs from metastatic tumors had higher IGF-2 gene expression, and we detected the same after co-culture with cell lines. Our results show the variability in the capacities of CAFs from luminal breast carcinomas, which may contribute to a better biological and clinical characterization of these cancer subtypes.

scholarly journals Leveraging TCGA gene expression data to build predictive models for cancer drug response

2020 ◽  
Vol 21 (S14) ◽  
Author(s):  
Evan A. Clayton ◽  
Toyya A. Pujol ◽  
John F. McDonald ◽  
Peng Qiu
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Predictive Models ◽  
Drug Response ◽  
Cancer Drug ◽  
Expression Data ◽  
Classification Methods ◽  
Clustering And Classification ◽  
Machine Learning Models

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

scholarly journals DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Ieva Rauluseviciute ◽  
Finn Drabløs ◽  
Morten Beck Rye
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Dna Methylation ◽  
Epigenetic Regulation ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Expression Data ◽  
Dna Hypermethylation ◽  
Genome Wide ◽  
A Genome

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

scholarly journals Integrative Genomic Analysis for the Discovery of Biomarkers in Prostate Cancer

2014 ◽  
Vol 9 ◽  
pp. BMI.S13729 ◽  
Author(s):  
Chindo Hicks ◽  
Tejaswi Koganti ◽  
Shankar Giri ◽  
Memory Tekere ◽  
Ritika Ramani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Genetic Variants ◽  
Association Studies ◽  
Biological Pathways ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Increased Risk

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

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