Dataset Information

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

ABSTRACT: PURPOSE:Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy. EXPERIMENTAL DESIGN:This study included 334 radical prostatectomy patients subdivided into training (VT, n = 127), validation 1 (V1, n = 62), and validation 2 (V2, n = 145). Hematoxylin and eosin-stained slides from resected prostates were digitized, and 242 quantitative descriptors of the intratumoral stroma were calculated using a computational algorithm. Machine learning and elastic net Cox regression models were constructed using VT to predict biochemical recurrence-free survival based on these features. Performance of these models was assessed using V1 and V2, both overall and in population-specific cohorts. RESULTS:An AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V1,AA: AUC = 0.87, HR = 4.71 (95% confidence interval (CI), 1.65-13.4), P = 0.003; V2,AA: AUC = 0.77, HR = 5.7 (95% CI, 1.48-21.90), P = 0.01]. AAstro outperformed clinical standard Kattan and CAPRA-S nomograms, and the underlying stromal descriptors were strongly associated with IHC measurements of specific tumor biomarker expression levels. CONCLUSIONS:Our results suggest that considering population-specific information and stromal morphology has the potential to substantially improve accuracy of prognosis and risk stratification in AA patients with prostate cancer.

SUBMITTER: Bhargava HK

PROVIDER: S-EPMC7165025 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

Bhargava Hersh K HK Leo Patrick P Elliott Robin R Janowczyk Andrew A Whitney Jon J Gupta Sanjay S Fu Pingfu P Yamoah Kosj K Khani Francesca F Robinson Brian D BD Rebbeck Timothy R TR Feldman Michael M Lal Priti P Madabhushi Anant A

Clinical cancer research : an official journal of the American Association for Cancer Research 20200305 8

<h4>Purpose</h4>Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral strom ...[more]

PMID: 32139401

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Project description:Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Methods: Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). Results: The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: P < 0.001, hazard ratio (HR) = 7.826; Validation set I: P = 0.006, HR = 2.655; Validation set II: P = 0.003, HR = 4.175; Validation set III: P < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. Conclusion: The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP.

Project description:BackgroundCurrent clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa.MethodsThe study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988-2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence ("controls") and 33 with clinical recurrence ("cases"). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation.ResultsA 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72-0.77) (MC), 0.90 (0.86-0.94) (MSKCC), and 0.82 (0.74-0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70-0.74), 0.86 (0.82-0.91), and 0.76 (0.67-0.85), respectively.ConclusionsWe report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP. Prostate 76:1239-1256, 2016. © 2016 Wiley Periodicals, Inc.

Project description:Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa. Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan-Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis. Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701-0.827). Finally, we constructed a nomogram to predict BCR of PCa. Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making. Subjects: Bioinformatics, oncology, urology.

Project description:Background: Decision-making regarding biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) mainly relies on clinicopathological parameters with a low predictive accuracy. Currently, accumulating evidence suggests that immune-associated genes (IAGs) play irreplaceable roles in tumorigenesis, progression and metastasis. Considering the critical role of immune in PCa, we therefore attempted to identify the novel IAGs signature and validate its prognostic value that can better forecast the risk for BCR and guide clinical treatment. Methods: RNA-sequencing and corresponding clinicopathological data were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was utilized to screen out the candidate module closely related to BCR, and univariate and LASSO Cox regression analyses were performed to build the gene signature. Kaplan-Meier (KM) survival analysis, time-dependent receiver operating curve (ROC), independent prognostic analysis and nomogram were also applied to evaluate the prognostic value of the signature. Besides, Gene ontology analysis (GO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore potential biological pathways. Results: A total of six IAGs (SSTR1, NFATC3, NRP1, TUBB3, IL1R1, GDF15) were eventually identified and used to establish a novel IAGs signature. The Kaplan-Meier analysis revealed that patients with low-risk scores had longer recurrence-free survival (RFS) than those with high-risk scores in both GSE70769 and TCGA cohorts. Further, our signature was also proven to be a valuable independent prognostic factor for BCR. We also constructed a nomogram based on the gene signature and related clinicopathologic features, which excellently predict 1-year, 3-year and 5-year prognosis of localized PCa patients after RP. Moreover, functional enrichment analysis demonstrated the vital biological processes, and stratified GSEA revealed that a crucial immune-related pathway (T cell receptor signaling pathway) was notably enriched in the high-risk group. Conclusions: We successfully developed a novel robust IAGs signature that is powerful in BCR prediction in localized PCa patients after RP, and created a prognostic nomogram. In addition, the signature might help clinicians in selecting high-risk subpopulation, predicting survival status of patients and promoting more individualized therapies than traditional clinical factors.

Dataset Information

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

Publications

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

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