A 23‐Gene Classifier urine test for prostate cancer prognosis

Guo, Jinan; Johnson, Heather; Zhang, Xuhui; Feng, Xiaoyan; Zhang, Heqiu; Simoulis, Athanasios; Wu, Alan H.B.; T, Xia; Li, Fēi; Tan, Wanlong; Johnson, Allan; Dizeyi, Nishtman; Abrahamsson, Per‐Anders; Kenner, Lukas; Chen, Lingwu; Zhong, Wanmei; Xiao, Kefeng; Persson, Jenny L.; Zou, Chang

doi:10.1002/ctm2.340

Cited by 6 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although some of the 18 genes have been identi ed previously as PCa diagnostic or prognostic biomarkers, or found to be important for cancer invasion and metastasis [18-22], the combination of these genes to form a classi er for predicting metastasis and mCRPC is novel. Although we previously developed a 23-Gene Classi er for predicting biochemical recurrence after treatment and its ability to predict metastasis was also assessed [20], the 18-Gene Algorithm is different from the 23-Gene Classi er in composition, and the prognostic performance of the 18-Gene Algorithm was thoroughly investigated in a prospective cohort with a median 6 year follow-up.…”

Section: Discussionmentioning

confidence: 99%

An 18-Gene Algorithm urine test for predicting prostate cancer metastasis and castration- resistance

Guo,

Gu,

Johnson

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background There is an urgent need to accurately predict the risk of distant metastasis and metastatic castration-resistant prostate cancer (mCRPC) for treatment decision-making and reducing mortality. An artificial intelliegnce machine learning screening method in combination with liquid biopsy urine test was used to develop a novel gene expression-based algorithm for predicting prostate cancer distant metastasis and mCRPC in newly diagnosed patients. Methods Random forest machine learning algorithm screening was conducted to develop and validate a gene expression-based algorithm to predict the risk of metastasis and mCRPC using liquid biopsy urine samples from the patients with distant metastasis and mCRPC collected from multi-center retrospective (n = 505) and prospective (n = 243) studies with a median follow-up period of 8 and 6 years respectively. The prognostic performance of the algorithm test was assessed using univariate/multivariate Cox regression analyses, Kaplan-Meier disease-free survival plot, and univariate/multivariate discriminant analyses. Results A novel 18-Gene Algorithm urine test was developed and validated. The algorithm showed high accuracy to predict distant metastasis with an area under the curve (AUC) of 0.96 (95% CI 0.87–1.05) and 0.98 (95% CI 0.96–1.02) in the retrospective and prospective cohort respectively. In the prospective cohort, a hazard ratio (HR) to predict metastasis-free survival was 93.8 (95% CI 29.3-300.6) (p < 0.0001). In a prospective mCRPC cohort (n = 205), the algorithm predicted mCRPC-free survival with a HR of 154.4 (95% CI 36.8-647.5) (p < 0.0001) and predicted mCRPC with AUC of 0.98 (95% CI 0.95–1.01). In contrast, currently using clinicopathological parameters, such as Gleason grade and pre-operative PSA, had much lower prognostic power. Conclusions The novel 18-Gene Algorithm is the first highly accurate and non-invasive liquid biopsy urine test to predict distant metastasis and mCRPC in newly diagnosed prostate cancer patients with the potential to improve prostate cancer treatment decision-making and reduce mortality.

show abstract

Section: Discussionmentioning

confidence: 99%

An 18-Gene Algorithm urine test for predicting prostate cancer metastasis and castration- resistance

Guo,

Gu,

Johnson

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, PHIbased predictive model predicted clinically significant cancer with AUC of 0.75 (22); a 12-gene proteomic biomarker panel predicted aggressive PCa with AUC of 0.72 (23); 17-gene Genomic Prostate Score (GPS) predicted aggressive PCa (high-grade and high stage) with odds ratio of 1.9-2.3 per 20 GPS units (24); a 31-gene cell cycle progression signature (Polaris) predicted BCR after prostatectomy with hazard ratio (HR) of 1.89 (25); Clinical Predictor (age, cancer stage, PSA, biopsy findings) predicted clinically significant cancer with AUC of 0.81, 4Kscore combined with the Clinical Predictor had AUC of 0.84 (26); Decipher genomic classifier predicted adverse pathology with odds ratio of 1.32, sensitivity of 88% and specificity of 36% at the threshold of 0.2, and sensitivity of 84% and specificity of 28% at the threshold of 0.45, Cancer of the Prostate Risk Assessment (CAPRA) predicted adverse pathology with AUC of 0.57 and Decipher combined with CAPRA had AUC of 0.65 (27); Decipher predicted distant metastasis after BCR with HR of 1.17 and AUC of 0.82 ( 28 (9). The diagnostic performance of these methods showed that none of them had robust accuracy, none had high sensitivity and specificity with AUC > 0.9, none had high HR or odds ratio, and none used urine samples collected without invasive DRE (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). In comparison, our 24-Gene Classifier urine test validated by large independent retrospective and prospective cohorts as well as various patient subgroups showed uniformly high diagnostic accuracy, thus, may serve as a better molecular classificatio...…”

Section: Discussionmentioning

confidence: 99%

Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients

Guo

Zhang²,

et al. 2021

Front. Med.

Self Cite

View full text Add to dashboard Cite

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available.Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups.Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups.Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

show abstract

“…A random forest machine learning algorithm screening was performed to select combinations of mutation profiles of the genes in the RAS-RAF-MEK-ERK and PI3K/Akt/PTEN/ mTOR pathways, as well as TP53 and APC, which are frequently mutated in CRC, to form classifiers by using the established methods previously described in [28][29][30]. Using the MSK cohort as a training set, the random forest algorithm classifiers, which combine different gene mutation profiles, were used to distinguish progression and non-progression using XLSTAT (Addinsoft, Paris, France).…”

Section: Algorithms For Prediction Of Cancer Progression After Treatmentmentioning

confidence: 99%

“…We have previously developed and used an ML-algorithms-based biomarker panel using gene expression profiles from primary tumor specimens and urine of large cohorts of prostate cancer patients [28][29][30]. However, there is no systematic implementation of ML algorithms-based on gene-mutation profiles for prediction of treatment response of mCRC.…”

Section: Introductionmentioning

confidence: 99%

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients

Johnson

El-Schich

Ali

et al. 2022

Cancers

Self Cite

View full text Add to dashboard Cite

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

show abstract

A 23‐Gene Classifier urine test for prostate cancer prognosis

Cited by 6 publications

References 9 publications

An 18-Gene Algorithm urine test for predicting prostate cancer metastasis and castration- resistance

An 18-Gene Algorithm urine test for predicting prostate cancer metastasis and castration- resistance

Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients

Contact Info

Product

Resources

About