Molecular Risk Score Research Articles

The 3-Biomarker Classifier-A Novel and Simple Molecular Risk Score Predicting Overall Survival in Patients with Colorectal Cancer.

Several new molecular markers in colorectal carcinomas have been discovered; however, classical histopathological predictors are still being used to predict survival in patients. We present a novel risk score, which uses molecular markers, to predict outcomes in patients with colorectal carcinoma. The immunohistochemistry of tissue micro arrays was used to detect and quantify H2BUB1, RBM3 and Ki-67. Different intensities of staining were categorized for these markers and a score was established. A multivariate analysis was performed and survival curves were established. 1791 patients were evaluated, and multivariate analysis revealed that our risk score, the 3-biomarker classifier, is an independent marker to predict survival. We found a high risk-score to be associated with dismal median survival for the patients. A more personalized score might be able to better discriminate low- and high-risk patients and suggest adjuvant treatment compared to classical pathological staging. Our score can serve as a tool to predict outcomes in patients suffering from colorectal carcinoma.

An inflammation-related subtype classification for analyzing tumor microenvironment and clinical prognosis in colorectal cancer.

The inflammatory response plays an essential role in the tumor microenvironment (TME) of colorectal cancer (CRC) by modulating tumor growth, progression, and response to therapy through the recruitment of immune cells, production of cytokines, and activation of signaling pathways. However, the molecular subtypes and risk score prognostic model based on inflammatory response remain to be further explored. Inflammation-related genes were collected from the molecular signature database and molecular subtypes were identified using nonnegative matrix factorization based on the TCGA cohort. We compared the clinicopathological features, immune infiltration, somatic mutation profile, survival prognosis, and drug sensitivity between the subtypes. The risk score model was developed using LASSO and multivariate Cox regression in the TCGA cohort. The above results were independently validated in the GEO cohort. Moreover, we explored the biological functions of the hub gene, receptor interacting protein kinase 2 (RIPK2), leveraging proteomics data, in vivo, and in vitro experiments. We identified two inflammation-related subtypes (inflammation-low and inflammation-high) and have excellent internal consistency and stability. Inflammation-high subtype showed higher immune cell infiltration and increased sensitivity to common chemotherapeutic drugs, while inflammation-low subtype may be more suitable for immunotherapy. Besides, the two subtypes differ significantly in pathway enrichment and biological functions. In addition, the 11-gene signature prognostic model constructed from inflammation-related genes showed strong prognostic assessment power and could serve as a novel prognostic marker to predict the survival of CRC patients. Finally, RIPK2 plays a crucial role in promoting malignant proliferation of CRC cell validated by experiment. This study provides new insights into the heterogeneity of CRC and provides novel opportunities for treatment development and clinical decision making.

An integrated molecular risk score early in life for subsequent childhood asthma risk.

Numerous children present with early wheeze symptoms, yet solely a subgroup develops childhood asthma. Early identification of children at risk is key for clinical monitoring, timely patient-tailored treatment, and preventing chronic, severe sequelae. For early prediction of childhood asthma, we aimed to define an integrated risk score combining established risk factors with genome-wide molecular markers at birth, complemented by subsequent clinical symptoms/diagnoses (wheezing, atopic dermatitis, food allergy). Three longitudinal birth cohorts (PAULINA/PAULCHEN, n = 190 + 93 = 283, PASTURE, n = 1133) were used to predict childhood asthma (age 5-11) including epidemiological characteristics and molecular markers: genotype, DNA methylation and mRNA expression (RNASeq/NanoString). Apparent (ap) and optimism-corrected (oc) performance (AUC/R2) was assessed leveraging evidence from independent studies (Naïve-Bayes approach) combined with high-dimensional logistic regression models (LASSO). Asthma prediction with epidemiological characteristics at birth (maternal asthma, sex, farm environment) yielded an ocAUC = 0.65. Inclusion of molecular markers as predictors resulted in an improvement in apparent prediction performance, however, for optimism-corrected performance only a moderate increase was observed (upto ocAUC = 0.68). The greatest discriminate power was reached by adding the first symptoms/diagnosis (up to ocAUC = 0.76; increase of 0.08, p = .002). Longitudinal analysis of selected mRNA expression in PASTURE (cord blood, 1, 4.5, 6 years) showed that expression at age six had the strongest association with asthma and correlation of genes getting larger over time (r = .59, p < .001, 4.5-6 years). Applying epidemiological predictors alone showed moderate predictive abilities. Molecular markers from birth modestly improved prediction. Allergic symptoms/diagnoses enhanced the power of prediction, which is important for clinical practice and for the design of future studies with molecular markers.

Abstract 6416: Early stage endometrioid endometrial cancer recurrence risk stratification using a machine learning RNA-seq gene expression signature

Abstract Endometrioid endometrial cancer (EEC) adjuvant therapy decisions rely on risk stratification using histology, grade, stage, and lymphovascular space invasion (LVSI). Recently, molecular classification systems originating from TCGA, evaluated in GOG-210 and PORTEC-3 defined four prognostic subtypes based on POLE, MSI-H/MMR-D, and p53 alterations. Although valuable, this molecular approach still has significant limitations such as applicability to the majority of EEC patients categorized as no-specific molecular profile (NSMP) and the potential need to resolve pathogenic and prognostic heterogeneity within MMR-D, and TP53 subtypes. These unmet needs were key motivators for Tempus to develop a molecular classifier predicting distant recurrence risk in early-stage EEC with a focus on high intermediate-risk (HIR) patients. The RNA-seq-based gene expression profiler (GEP) was trained using a machine-learning pipeline with TCGA data which resulted in a 24 gene signature that classifies EEC patients as molecular risk (MR) high or low (MR-high, MR-low). The GEP MR test was then evaluated on a de-identified cohort of EEC patients (N=1037) from Tempus to test associations with known pathologic or molecular prognostic features. The GEP-MR risk predictor showed significant enrichment of MR high-risk in G3 versus G1/2 histology (p-value &amp;lt; 5e-8). A high correlation was found between the MR score and copy number alteration score (t-test p-value &amp;lt; 1e-5). Next, a clinical evaluation was performed in an early-stage EEC case-control cohort of patients with documented recurrence or no recurrence event at four years (N=109), from Stanford. In the entire cohort, the MR-high group had a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=4.8, N=109). Next, we performed a subgroup analysis in the clinically important HIR patients. In this subgroup, the MR-high group had a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=8.0, N=56). Lastly, given the significance of genomic biomarkers in the evolution of EEC FIGO staging, we stratified outcomes by the established TCGA subtypes as a reference standard and performed a subgroup analysis in patients classified as having no specific molecular profile (NSMP). Among patients who were NSMP, the MR-high group showed a significantly higher rate of distant recurrence in comparison to the MR-low group (HR=7.92, N=67). These evaluation studies demonstrate the performance of the GEP MR test in early-stage EEC distant recurrence risk stratification, specifically HIR patients, necessitating further studies to validate the test for clinical use in informing adjuvant clinical management. Citation Format: Rafi Pelossof, Talal Ahmed, Seung Won Hyun, Chithra Sangali, Ben Terdich, Calvin Chao, Michael Toboni, Casey Cosgrove, Elizabeth Kidd, Brook Howitt, Timothy Taxter. Early stage endometrioid endometrial cancer recurrence risk stratification using a machine learning RNA-seq gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6416.

Senescence-associated secretory phenotype constructed detrimental and beneficial subtypes and prognostic index for prostate cancer patients undergoing radical prostatectomy

BackgroundCellular senescence is growing in popularity in cancer. A dual function is played by the senescence-associated secretory phenotype (SASP) that senescent cells produce in the development of pro-inflammatory niches, tissue regeneration or destruction, senescence propagation, and malignant transformation. In this study, we conducted thorough bioinformatic analysis and meta-analysis to discover detrimental and beneficial subtypes and prognostic index for prostate cancer (PCa) patients using the experimentally confirmed SASP genes.MethodsWe identified differentially expressed and prognosis-related SASP genes and used them to construct two molecular subtypes and risk score. Another two external cohorts were used to confirm the prognostic effect of the above subtypes and risk score and meta-analysis was further conducted. Additionally, functional analysis, tumor stemness and heterogeneity and tumor microenvironment were also evaluated. We completed analyses using software R 3.6.3 and its suitable packages. Meta-analysis was performed by software Stata 14.0.ResultsThrough multivariate Cox regression analysis and consensus clustering analysis, we used VGF, IGFBP3 and ANG to establish detrimental and beneficial subtypes in the TCGA cohort, which was validated through other two independent cohorts. Meta-analysis showed that detrimental SASP group had significantly higher risk of biochemical recurrence (BCR) than beneficial SASP group (HR: 2.48). Moreover, we also constructed and validated risk score based on these genes to better guide clinical practice. DNA repair, MYC target, oxidative phosphorylation, proteasome and ribosome were highly enriched in detrimental SASP group. Detrimental SASP group had significantly higher levels of B cells, CD8+ T cells, homologous recombination deficiency, loss of heterozygosity, microsatellite instability, purity, tumor mutation burden, mRNAsi, differentially methylated probes and epigenetically regulated RNA expression than beneficial SASP group. The top mutation genes between detrimental and beneficial SASP groups were SPOP, FOXA1, KMT2C, APC, BSN, DNAH17, MYH6, EPPK1, ZNF536 and ZC3H13 with statistical significance.ConclusionsFrom perspective of SASP, we found detrimental and beneficial tumor subtypes which were closely associated with BCR-free survival for PCa patients, which might be important for the furture research in the field of PCa.

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma.

Telomere dysfunction has been identified as a biological marker of cancer progression in several types of cancer, including Head and Neck Squamous Cell Carcinoma (HNSCC). This study aimed to characterize the telomere maintenance genes (TMG)-related signature in prognosis and treatment response in HNSCC. The transcriptome and clinical data of HNSCC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases, respectively. Non-negative matrix factorization (NMF) was used to identify molecular subtypes derived from TMG. Gene set enrichment analysis (GSEA) was performed to analyze the differentially expressed pathways between subtypes, and a risk score model derived from TMG was established. Kaplan-Meier survival analysis was used to evaluate inter-group prognostic features, and the correlation between TMG-derived molecular subtypes and risk score model with immune infiltration, immunotherapy, and chemosensitivity was assessed. Two HNSCC subtypes were identified based on 59 TMG-related genes, which exhibit significant heterogeneity in prognosis, immune cell infiltration, and treatment response. Additionally, a TMG-derived risk signature containing 9 genes was developed to assess the prognosis of HNSCC patients. The signature had significant predictive ability for HNSCC prognosis and was significantly correlated with immune cell infiltration and immunotherapy response. A nomogram integrating the risk signature, N stage and radiotherapy was constructed to predict 1-, 3-, and 5-year overall survival (OS) of HNSCC patients, which had better performance than other prognostic models and included TMG-derived risk score, radiotherapy, and N stage. This study identified TMG-derived molecular subtypes in HNSCC and developed a novel prognostic score model, highlighting the potential value of TMG in HNSCC prognosis and immunotherapy.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

BackgroundMolecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient’s risk of harbouring aggressive disease. ObjectiveTo develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. Design, setting, and participantsExpression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). Outcome measurements and statistical analysisThe primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. Results and limitationsThe six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. ConclusionsThe six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. Patient summaryWe found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Abstract 3312: Comparison of risk factors to molecular risk stratification in Chinese early-stage non-squamous non-small cell lung cancer patients

Abstract Background: Improved risk stratification for non-small cell lung cancer (NSCLC) represents a critical unmet need. A Clinical Laboratory Improvement Amendments (CLIA)-certified, 14-gene, quantitative PCR (qPCR)-based expression assay was found to better stratify mortality risk. The association of molecular risk stratification with other clinicopathological, radiomic, and genetic risk factors have not been fully identified, especially in Chinese NSCLC patients (pts). Methods: We newly recruited 102 early-stage non-squamous NSCLC pts who had undergone complete surgical resection. RNA was extracted from tumor tissue specimens and prospective molecular risk stratification by the 14-gene prognostic assay was performed. Gene mutation status and tumor mutational burden (TMB) were evaluated. Histological sections and clinical records were reviewed for canonical prognostic indicators that have been reported to be associated with lung cancer risk. Results: Of the 102 pts, the mean age was 55 years, 66 (64.7%) were female, and 18 (17.7%) had a smoking history. The majority of pts (84, 82.4%) were stage I, with 1 atypical adenomatous hyperplasia (AAH), 6 adenocarcinomas in situ (AIS), 1 stage II, and 2 stage III. 64 pts were deemed low-risk by molecular testing, 24 were intermediate-risk, and 14 were high-risk. The pts of AAH and AIS were all in the low-risk group. The maximum tumor diameter (MTD) in the high-risk group was the highest. There were no significant differences in age, sex, smoking status, pathologic stage, NCCN risk criteria, micropapillary or solid (MP/S) pattern, or the number of lesions between the three groups. The most common driver mutations were EGFR (65%), TP53 (28%), and RBM10 (13%). The molecular risk stratification had a significant association with TP53 (p = 0.013), TP53 loss of function (LOF, p = 0.01), KRAS (p = 0.011), and APC mutation (p = 0.05), but not with EGFR mutation and co-mutational status. TMB in the low-risk pts was significantly lower than in other pts (p = 0.007). The low-risk pts had lower SUVmax (p = 0.076), significantly lower mean CT value (p = 0.023), and mean enhanced CT value (p = 0.005) compared with others. In the NCCN low-risk pts, 25 were molecular low-risk, 12 were intermediate-risk, and 9 were high-risk. The risk stratification showed a significant association with SUVmax, TP53 mutation, and TMB in the NCCN low-risk pts. Pearson correlation showed the molecular risk score was significantly associated with MTD, TMB, SUVmax, mean CT value, mean enhanced CT value, and max CT value. SUVmax showed the strongest correlation with the risk score. Conclusion: The molecular risk stratification by the 14-gene assay was associated with canonical prognostic indicators, like TP53 LOF, TP53, KRAS, APC mutation, TMB, mean CT value, and mean enhanced CT value. Our study provides more evidence of the clinical utility of the qPCR-based prognostic assay. Citation Format: Naixin Liang, Jianchao Xue, Ming Zhao, Bowen Li, Yadong Wang, Zhicheng Huang, Yankai Xia, Ruirui Li, Zhongxing Bing, Zhibo Zheng, Jianpeng Zhang, Bin Wang, Zhe Feng, Xinyu Liu, Haochen Li, Xiaoqing Yu, Yang Song. Comparison of risk factors to molecular risk stratification in Chinese early-stage non-squamous non-small cell lung cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3312.

MP17-08 CLINICAL VALIDATION OF A NOVEL SIX-GENE RISK-STRATIFICATION SIGNATURE TO SUPPORT TREATMENT DECISIONS FOR MEN DIAGNOSED WITH PROSTATE CANCER

You have accessJournal of UrologyCME1 Apr 2023MP17-08 CLINICAL VALIDATION OF A NOVEL SIX-GENE RISK-STRATIFICATION SIGNATURE TO SUPPORT TREATMENT DECISIONS FOR MEN DIAGNOSED WITH PROSTATE CANCER Agnieszka Krzyzanowska, Stephen Barron, Debra F. Higgins, Tony Loughman, Katherine M. Sheehan, Angel Chan-Ju Wang, Bozena Fender, Leah M. McGuire, David Galvin, Amanda O'Neil, Joanna Fay, Anthony O'Grady, Desmond O'Leary, R. William. G. Watson, Anders Bjartell, and William M. Gallagher Agnieszka KrzyzanowskaAgnieszka Krzyzanowska More articles by this author , Stephen BarronStephen Barron More articles by this author , Debra F. HigginsDebra F. Higgins More articles by this author , Tony LoughmanTony Loughman More articles by this author , Katherine M. SheehanKatherine M. Sheehan More articles by this author , Angel Chan-Ju WangAngel Chan-Ju Wang More articles by this author , Bozena FenderBozena Fender More articles by this author , Leah M. McGuireLeah M. McGuire More articles by this author , David GalvinDavid Galvin More articles by this author , Amanda O'NeilAmanda O'Neil More articles by this author , Joanna FayJoanna Fay More articles by this author , Anthony O'GradyAnthony O'Grady More articles by this author , Desmond O'LearyDesmond O'Leary More articles by this author , R. William. G. WatsonR. William. G. Watson More articles by this author , Anders BjartellAnders Bjartell More articles by this author , and William M. GallagherWilliam M. Gallagher More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003237.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PCa) is diagnosed in over 1.2 million men worldwide annually. Most men have indolent PCa and can safely defer treatment with active surveillance. However, many men are commonly treated with surgery or radiation therapy which are associated with reduced quality of life. Clinicians are increasingly using molecular information to further understand a patient’s risk of aggressive PCa, and while these signatures have shown promise, there remains a significant opportunity for improving prognostic capacity. METHODS: Expression of a novel six-gene signature was measured by RT-qPCR in prostate samples from an Irish cohort of men diagnosed with PCa and treated with radical prostatectomy (RP) (PCRC, n=426). Cross-validated logistic regression identified a six-gene molecular risk score (MRS) with strong prognostic performance to predict aggressive PCa. The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score to create a molecular plus clinical risk score (MCRS). The MRS and MCRS were clinically validated in an independent Swedish cohort of men diagnosed with PCa and treated by RP (UPCA, n=203 RP and n=100 biopsy samples). RESULTS: We identified a six-gene signature, four prognostic and two reference genes, with improved prognostic value over clinical features. In the clinical biopsy validation study, the AUC of the signature for AP was 0.72 (MRS) and 0.82 (MCRS) versus 0.72 (EAU) and 0.82 (CAPRA), and for high primary Gleason was 0.75 (MRS) and 0.85 (MCRS) versus 0.77 (EAU) and 0.83 (CAPRA). The C index for BCR was 0.62 (MRS) and 0.74 (MCRS) versus 0.65 (EAU) and 0.73 (CAPRA). In bivariable analysis, the six-gene signature added statistically significant (p<0.001) prognostic value to EAU and CAPRA. In the RP clinical validation study, the C index for BCR was 0.74 (MRS) and 0.83 (MCRS) versus 0.69 (EAU) and 0.77 (CAPRA). In bivariable analysis, the six-gene signature added statistically significant (p<0.0001) prognostic value to EAU and CAPRA. CONCLUSIONS: The six-gene molecular prognostic signature has strong performance for AP, high primary Gleason and BCR in independent clinical validation studies when tested on prostate biopsy or RP tissue. MCRS provides improved prognostic stratification and can inform optimal patient management post-diagnosis and post-treatment. Source of Funding: Support is acknowledged from the Swedish Cancer Society, the Swedish Scientific Council and Government Funding of Clinical Research within the Swedish National Health Service (ALF), the Irish Cancer Society, and OncoAssure Ltd © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e215 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Agnieszka Krzyzanowska More articles by this author Stephen Barron More articles by this author Debra F. Higgins More articles by this author Tony Loughman More articles by this author Katherine M. Sheehan More articles by this author Angel Chan-Ju Wang More articles by this author Bozena Fender More articles by this author Leah M. McGuire More articles by this author David Galvin More articles by this author Amanda O'Neil More articles by this author Joanna Fay More articles by this author Anthony O'Grady More articles by this author Desmond O'Leary More articles by this author R. William. G. Watson More articles by this author Anders Bjartell More articles by this author William M. Gallagher More articles by this author Expand All Advertisement PDF downloadLoading ...

Comprehensive analysis of cuproptosis-related genes on bladder cancer prognosis, tumor microenvironment invasion, and drug sensitivity.

Cuproptosis, a newly discovered form of programmed cell death, plays a vital role in the occurrence and development of tumors. However, the role of cuproptosis in the bladder cancer tumor microenvironment remains unclear. In this study, we developed a method for predicting the prognostic outcomes and guiding the treatment selection for patients with bladder cancer. We obtained 1001 samples and survival data points from The Cancer Genome Atlas database and Gene Expression Omnibus database. Using cuproptosis-related genes (CRGs) identified in previous studies, we analyzed CRG transcriptional changes and identified two molecular subtypes, namely high- and low-risk patients. The prognostic features of eight genes (PDGFRB, COMP, GREM1, FRRS1, SDHD, RARRES2, CRTAC1, and HMGCS2) were determined. The CRG molecular typing and risk scores were correlated with clinicopathological features, prognosis, tumor microenvironment cell infiltration characteristics, immune checkpoint activation, mutation burden, and chemotherapy drug sensitivity. Additionally, we constructed an accurate nomogram to improve the clinical applicability of the CRG_score. qRT-PCR was used to detect the expression levels of eight genes in bladder cancer tissues, and the results were consistent with the predicted results. These findings may help us to understand the role of cuproptosis in cancer and provide new directions for the design of personalized treatment and prediction of survival outcomes in patients with bladder cancer.

Development and clinical validation of a novel biopsy-based six-gene signature to accurately risk-stratify men with early-stage prostate cancer to aid treatment decision making.

374 Background: Prostate cancer (PCa) is diagnosed in over 1.2 million men worldwide annually, making it the second-most common non-cutaneous cancer in men. Most men have indolent PCa and can safely defer treatment with active surveillance. However, many patients are commonly treated with surgery (radical prostatectomy, RP) or radiation therapy, which are associated with a significant reduction in quality of life. Molecular signatures have been developed to assist clinicians in stratifying patients based on their risk of aggressive PCa; while these have shown promise, there remains a significant opportunity for improving prognostic capacity. Methods: A panel of fourteen prognostic genes was identified from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) database. Gene expression was measured by reverse-transcription quantitative polymerase chain reaction in RP samples from an Irish cohort of men diagnosed with PCa and treated with RP (PCRC, n=426). Cross-validated logistic regression analysis identified a six-gene molecular risk score (MRS) with strong prognostic performance to predict aggressive PCa, i.e., adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score to create a molecular plus clinical risk score (MCRS). The MRS and MCRS were clinically validated in biopsy samples from an independent Swedish cohort of men diagnosed with PCa and treated by RP (UPCA, n=100). Results: We identified a six-gene signature, consisting of four prognostic and two reference genes, with improved prognostic value over clinical features. In the clinical biopsy validation study univariable analysis, the AUC of the signature for AP was 0.72 (MRS) and 0.82 (MCRS) versus 0.72 for EAU risk categories and 0.82 for CAPRA. The C-index for BCR was 0.62 (MRS) and 0.74 (MCRS) versus 0.65 for EAU risk categories and 0.73 for CAPRA. Furthermore, in bivariable analysis, the six-gene signature added statistically significant (p &lt; 0.001) prognostic value to EAU risk categories and CAPRA. Conclusions: The six-gene prognostic signature has strong performance for AP and BCR in an independent clinical biopsy validation study. MCRS can provide improved prognostic stratification and inform optimal patient management post-diagnosis.

Glycosylation-related molecular subtypes and risk score of hepatocellular carcinoma: Novel insights to clinical decision-making.

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer deaths worldwide, seriously affecting human community health and care. Emerging evidence has shown that aberrant glycosylation is associated with tumor progression and metastasis. However, the role of glycosylation-related genes in HCC has notbeen reported. Weighted gene coexpression network analysis and non-negative matrix factorization analysis were applied to identify functional modules and molecularm subtypes in HCC. The least absolute shrinkage and selection operator Cox regression was used to construct the glycosylation-related signature. The independent prognostic value of the risk model was confirmed and validated by systematic techniques, including principal component analysis, T-distributed random neighbor embedding analysis, Kaplan-Meier survival analysis, the ROC curve, multivariate Cox regression, the nomogram, and the calibration curve. The single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were evaluated by the immune microenvironment and potential biological processes. The quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to verify the expression of five genes. We identified the glycosylation-related genes with bioinformatics analysis to construct and validate a five-gene signature for the prognosis of HCC patients. Patients with HCC in the high-risk group had a worse prognosis. The risk score could be an independent factor and was associated with clinical features, such as the grade and stage. The nomogram exhibited an accurate score that included the risk score and clinical parameters. The infiltration levels of antitumor cells were upregulated in the low-risk group, including B_cells, Mast_cells, neutrophils, NK_cells, and T_helper_cells. Moreover, glycosylation was more sensitive to immunotherapy, and may play a critical role in the metabolic processes of HCC, such as bile acid metabolism and fatty acid metabolism. In addition, the five-gene messenger RNA (mRNA) and protein expression were overexpressed in HCC cells and tissues. The glycosylation-related signature is effective for prognostic recognition, immune efficacy evaluation, and substance metabolism in HCC, providing a novel insight for therapeutic target prediction and clinical decision-making.

350P Postoperative histopathological, clinical and molecular risk score for patients with colorectal liver metastases

Hepatic resection is considered the optimal potentially curative treatment for colorectal liver metastases (LM). The Genetic And Morphological Evaluation (GAME) score, which includes KRAS status, tumor burden score, CEA, lymph node metastases and extrahepatic disease, has been proposed as a preoperative tool to select patients (pts) for liver resection. Nevertheless, histological characteristics of LM have been presented as potential prognostic biomarkers to be considered after resection. This study aimed to create a postoperative score including histological characteristics. Clinical and pathological data were collected from 146 metastatic colorectal cancer pts undergoing liver resection between 2004 and 2019 at Hospital de Sant Pau. Histopathological changes in liver parenchyma such as immune phenotype (IP) and histological growth pattern (HGP) were documented to identify potential prognostic factors. We calculated the GAME score of each patient and added the histological characteristics that had a prognostic value in the multivariable analysis. Of the 146 LM analyzed, twenty-three pts had a desmoplastic HGP (16%), thirty-one (22%) had a desertic IP and a fibrotic capsule was described in 22.5% of the cases. In the univariable analysis, the desmoplastic HGP and the fibrotic capsule was associated with a better overall survival (OS) (102 vs 49.3 months, HR 0.44 95% CI 0.23-0.86; p= 0.013 and 89.3 vs 46.9 months; HR 0.56 95% CI 0.33-0.98; p=0.037). The desertic IP was associated with a shorter OS (34.2 vs 58.3 months; HR 1.77 CI 1.1-2.8; p= 0.01) and remained significant in the multivariable analysis (HR 1.95, CI 95% 1.16-3.23; p= 0.012). We added 1 point to the original GAME score if a desertic immune phenotype was observed, and pts were classified in three risk groups with significant differences in survival. The high-risk group (score ≥4) had a recurrence free survival (RFS) and OS of 12.7 and 36.6 months vs 30.7 and 89.3 months in the low-risk group (score 0-1), respectively (p<0.001 and p=0.007). The desertic immune phenotype in liver metastasis of CRC has a bad prognostic value and could be considered to assess adjuvant treatment after hepatic resection.

A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

BackgroundSepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy.MethodsWe systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8).ResultsDatasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels.ConclusionsIn summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Identification of Previously Unrecognized Multiple Myeloma Risk Subgroups with a Novel Biological Disease Stratifier

Background: The prognosis of MM is determined by affected organs, tumor burden as measured by e.g., the international staging system (ISS), disease biology such as cytogenetic abnormalities, and response to therapy. The outcome of high-risk MM patients classified by ISS or adverse risk cytogenetics is not uniform and patients show heterogeneous survival. Recent insights into the pathogenesis of MM highlighted genome/transcriptome editing as well as inflammation as drivers for the onset and progression of MM. We hypothesized that inclusion of molecular features into risk stratification could potentially resolve the challenge of accurately distinguishing between high-risk and low-risk MM patients at initial diagnosis and improve outcome.Aim: We aimed to create a simple molecular risk score to identify unrecognized patient subgroups, who have been previously misclassified by current risk stratifiers.Method: The Multiple Myeloma Research Foundation CoMMpass study genomics dataset, combining mRNA Seq and clinical data from more than 700 MM patients, allowed us to evaluate the prognostic value of demographic and clinical parameters, cytogenetics, and gene expression levels of APOBEC genes as well as inflammation-modulating cytokines in MM patients. We calculated hazard ratios and Kaplan-Meier survival estimates for all extracted features. Combining clinical variables that were significantly associated with PFS and OS, we then applied machine learning approaches to identify the most accurate classification model to define a new risk score that is easy to compute and able to stratify NDMM patients more accurately than cytogenetics-based classifiers. Based on a Kaplan-Meier survival curve analysis, we then evaluated the performance of our newly built EI score in sub-classifying of current multiple myeloma risk stratifiers.Results: Based on machine learning models, we defined a weighted OS/PFS risk score (Editor-Inflammation (EI) score) based on mRNA expression of APOBEC2, APOBEC3B, IL11, TGFB1, TGFB3, as well as ß2-microglobulin and LDH serum levels. We showed that the EI score subclassified patients into high-risk, intermediate-risk, and low-risk prognostic groups and demonstrated superior performance (C-index: 0.76) compared to ISS (C-index: 0.66) and R-ISS (C-index: 0.64). We further showed that EI low-risk patients do not benefit from autograft and maintenance therapy. Re-classification of ISS (Figure 1a, b, c) and R-ISS risk groups further confirmed the superiority of the EI score. In addition, the EI score identified previously unrecognized distinct subgroups of MM patients with adverse risk cytogenetics but good prognosis (Figure 1d, e, f). For example, the EI score excellently subclassified del(17p) MM patients into three main risk subgroups including a super low-risk group (none of them has p53 mut) with 5-year OS of 100%, an intermediate-risk group (30% of these patients also have p53 mut) with 5-year OS rate of 75%, and a very poor prognosis group of patients (40% of these patients also have p53 mut) with 5-year OS rate of 0% (2y OS: 40%) (Figure 1f). In line, we could show that patients with del(17p) and high EI score exhibit an enrichment of APOBEC induced genomic mutations compared to intermediate-risk and low-risk patients supporting the hypothesis that del(17p) along with high APOBEC expression levels activate the APOBEC mutation program and thus create an optimal environment for tumor progression. These findings support the necessity of a prognostic score that more accurately reflects MM disease biology.Conclusion: Although MM is considered as an incurable disease, an improved risk stratification could help to identify previously unrecognized low- and high-risk patient subgroups that are over- or undertreated and lead to improved outcomes. Our EI score is a simple score that is based on recent insights into MM biology and accurately identifies high-risk and low-risk newly diagnosed MM patients as well as misclassified MM patients in different cytogenetic and ISS risk subgroups. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Proposal and clinical application of molecular genetic risk scoring system, “MRplus”, for BCR-ABL1 negative pediatric B-cell acute lymphoblastic leukemia- report from a single centre

IntroductionWe propose “MRplus”, a molecular genetic risk score and check its clinical application in the risk-stratification of pediatric B-ALL. MethodsThe genomic DNA of untreated pediatricBCR-ABL1 negative B-ALL patients was analyzed for deletions of IKZF1, PAX5, CDKN2A/B, BTG1, RB1, ETV6, EBF1, ERG, pseudoautosomal region(PAR) genes using multiplex ligation-dependent probe amplification, along with the routine genetic work-up. The patients were assigned an ‘M’score- 0 (M0) for low and 1 (M1) for high genetic-risk as per the criteria by Moorman et al., and another score “IKplus”-1 (IKplus1) for IKZF1plus as per the criteria by Stanulla et al., and 0 (IKplus0) for other patients. The final “MRplus” risk-score of 0 (MRplus0), 1 (MRplus1) or 2 (MRplus2) was obtained by adding both these scores. The association of risk scores with overall survival (OS) and event free survival(EFS) was seen using Cox proportion hazard model. The overall goodness of fit of the model was done using Cox-Snell residuals. ResultsThe median age of 320 patients was 6 years (1−18 years). The patients with score M1 were 139 (43.4 %), M0−181 (56.6 %); IKplus1−32 (10 %) and IKplus0−288 (90 %). The final “MRplus” score of 0,1,or 2 was obtained in 181(56.6 %), 107(33.4 %) and 32(10 %) patients respectively. The post-induction remission rate was 90.7 %, 77.8 %, 73.9 % (p = 0.004); 4-year OS 67 %, 48 %, 27 % (p < 0.001); and 4-year EFS 56 %, 34 %, 19 %(p < 0.001) in patients with “MRplus” score 0,1,and 2 respectively. ConclusionsThe proposed “MRplus” scoring at baseline could identify three distinct risk groups-good (MRplus0), intermediate (MRplus1) and poor (MRplus2), with different outcomes; in pediatricBCR-ABL1 negative B-ALL. This may help in better risk-stratification and selection of patients for alternative treatment approaches.

Retrospective analysis of a novel molecular genetic risk score, “MRplus”, in BCR-ABL1 negative pediatric B-ALL: A single-center experience.

7029 Background: Currently, the molecular risk scoring is variably used in the management algorithms of B-ALL across various clinical cohorts. Unlike AML, the usage of molecular genetic abnormalities in the risk-stratification of B-ALL is limited and a consensus is yet to be reached. We have retrospectively analyzed the utility of molecular genetic risk criteria, previously described by Moorman et al. (2014), and Stanulla et al. (2018), in our cohort and hereby propose a novel molecular risk score, “MRplus”, obtained by combining both criteria, as a useful way to improve the risk-stratification of BCR-ABL1 negative pediatric B-ALL. Methods: In this retrospective observational study, the genomic DNA of untreated BCR-ABL1 negative pediatric B-ALL cases was analyzed at baseline for copy number alterations of IKZF1, PAX5, CDKN2A/B, BTG1, RB1, ETV6, EBF1, ERG, pseudoautosomal region (PAR) genes– (CRLF2, CSF2RA, IL3RA), using multiplex ligation-dependent probe amplification (MLPA)- P335, P202 and P327 kits (MRC Holland). The cases were assigned a score- 0 for low and 1 for high genetic-risk as per the criteria by Moorman et al., and another score as per the criteria by Stanulla et al., 1 for IKZF1plus cases and 0 for other cases. The final “MRplus” risk-score of 0 (low), 1 (intermediate) or 2 (high) was given by adding both these scores. All patients were treated using Indian Childhood Collaborative Leukemia Group protocol (ICICLE). The post-induction remission status, overall survival (OS) and event free survival (EFS) was noted for all patients. Results: Out of 320 cases with median age of 6 years (1-18 years), 141 (44.1%) cases had high genetic-risk (score 1) as per the Moorman’s criteria. Thirty-two (10%) cases fulfilled the criteria of IKZF1plus (score 1) as per the criteria of Stanulla et al. The final “MRplus” score of 0, 1 and 2 was obtained in 179 (55.9%), 109 (34.1%) and 32 (10%) cases respectively. Out of these, 284 received treatment including 160, 101, and 23 cases with “MRplus” score 0, 1, and 2 respectively. The follow up period was upto 80 months with a median of 34.6 months. The post-induction remission rate was 90.6%, 78.2%, 73.9% (p = 0.008); 4-year OS 68%, 48%, 27% (p &lt; 0.001); and 4-year EFS 57%, 36%, 19% (p &lt; 0.001) in cases with “MRplus” score 0,1, and 2 respectively. Conclusions: The proposed novel “MRplus” scoring at baseline could identify three distinct risk- groups-low, intermediate and high, in BCR-ABL1 negative pediatric B-ALL. This may help in better identification of patients for alternative treatment approaches and also triage patients for further detailed genomic analysis for disease biology; with particular emphasis on those with intermediate and high “MRplus” score. Acknowledgement: Institute Research Grants [A-193 (2013-15), A-413 (2016-18) and A-600 (2018-20)] from All India Institute of Medical Sciences (AIIMS), New Delhi, INDIA.

O08 Developing a DNA methylation signature for predicting rheumatoid arthritis using a machine learning pipeline

Abstract Background/Aims Early diagnosis and intervention improves outcomes of immune mediated rheumatic and musculoskeletal diseases (RMDs) but may be hampered by diagnostic uncertainty. The extent to which rationally selected molecular parameters add value to clinical characteristics for diagnostic prediction in undifferentiated disease states warrants investigation. B lymphocytes play an increasingly recognised role in rheumatoid arthritis (RA) pathogenesis, and cell-specific methylation patterns link environmental exposures to genetic risk. We derived and tested the practical utility of a B lymphocyte-derived DNA methylation signature for predicting RA in an early arthritis clinic cohort. Methods CD19+ B cell and peripheral blood mononuclear cell (PBMC) whole genome DNA methylation array data were available, respectively, from 109 inflammatory arthritis patients naïve to immunomodulatory drugs (Newcastle, UK; 38% confirmed to have a diagnosis of RA within 1 year) and 50 untreated undifferentiated arthritis (UA) patients (Leiden, The Netherlands; 68% classifiable RA within 1 year by 1987 ACR criteria versus alternate diagnoses). A bespoke machine learning pipeline employed a sequential model-based optimisation (SMBO) procedure for selecting, tuning and applying methods amongst ten feature-selection, six data-sampling and two classification algorithms in the Newcastle “training cohort.” The predictive performance of the resultant optimised molecular classifier was assessed in the independent Leiden “test cohort” alongside a previously described clinical prediction rule, using comparative area under receiver operating characteristic (AUROC) curves. A modification to the clinical prediction rule that incorporated a single parameter to reflect molecular classification was also assessed. The pipeline was implemented using the R machine learning package mlr. Results Using the SMBO approach, 27 CpGs maximally discriminatory for RA were selected from B lymphocyte DNA methylome training data, and a molecular classifier was derived using the random forest algorithm. Applied to the independent PBMC methylome in UA patients, the classifier and the validated Leiden prediction rule performed similarly in predicting RA (AUROC [95% CI] = 0.8 [0.66-0.94] versus 0.78 [0.64-0.92]). Interestingly, incorporating a molecular risk score based on the 27-CpG signature into the validated Leiden clinical prediction rule significantly improved its performance (AUROC [95% CI] = 0.89 [0.79-0.98] versus 0.78 [0.64-0.92]; p = 0.048). When applied to the sub-cohort of 25 patients in the Leiden cohort who were negative for anti-citrullinated peptide autoantibodies (ACPA), enhanced performance of the modified over the un-modified clinical prediction rule was maintained (AUROC [95% CI] = 0.82 [0.65-1] versus 0.70 [0.45-0.95], respectively), although the difference did not reach statistical significance in this smaller cohort. Conclusion We provide a proof of principle for the application of a B lymphocyte-derived epigenetic signature to enhance prediction of RA in UA patients using stored PBMCs. Further refinement of our pipeline represents a plausible means to expedite the diagnosis in undifferentiated RMDs and could offer pathophysiological insight. Disclosure N. Naamane: None. E. Niemantsverdriet: None. N. Thalayasingam: None. N. Nair: None. A.D. Clark: None. K. Murray: None. B. Hargreaves: None. L.N. Reynard: None. S. Eyre: None. A. Barton: None. A.H.M. van der Helm-van Mil: None. A.G. Pratt: None.

A novel urinary mRNA signature using the droplet digital polymerase chain reaction platform improves discrimination between prostate cancer and benign prostatic hyperplasia within the prostate-specific antigen gray zone.

PurposeThe aim of this study was to identify a noninvasive urinary marker for prostate cancer (PCa) diagnosis and to validate the clinical performance of this novel urinary mRNA signature using the droplet digital polymerase chain reaction (ddPCR) approach.Materials and MethodsA gene expression microarray (HT-12, Illumina Inc., USA) was used to identify genes differentially expressed between 16 PCa and 8 benign prostatic hyperplasia (BPH) tissues; ddPCR (QX200; Bio-Rad Laboratories, USA) was carried out to quantify the expression of selected genes in urine. The urinary molecular PCa risk score (UMPCaRS) was calculated by using the sum of three upregulated genes as the numerator and the sum of three downregulated genes as the denominator. The diagnostic utility of the UMPCaRS was validated by using a screening set (10 PCa and 10 BPH samples) and a validation set (131 PCa and 105 BPH samples).ResultsThree upregulated genes (PDLIM5, GDF-15, THBS4) and three downregulated genes (UPK1A, SSTR3, NPFFR2) were selected from the microarray and subjected to ddPCR. The UMPCaRS for PCa in the screening and validation sets was significantly higher than that for BPH. For the validation set, the diagnostic accuracy of the UMPCaRS was comparable with that of prostate-specific antigen (PSA). Importantly, in the “PSA gray zone” (3–10 ng/mL), the AUC for the UMPCaRS was 0.843 and that for PSA was 0.628 (p<0.001).ConclusionsThe data demonstrate that the UMPCaRS is useful for discriminating between PCa and BPH in the “PSA gray zone”.

Clinically significant Prostate Cancer diagnosed using a urinary molecular biomarker-based risk score: two case reports

BackgroundIdentifying men for a repeat prostate biopsy is a conundrum to urologists. Risk calculators (RCs) such as the European Randomized Study of Screening for Prostate Cancer (ERSPC) RCs have been developed to predict the outcome of prostate biopsies and have been shown to improve diagnostic accuracy compared to PSA alone. However, it was recently shown that the outcome for high-grade prostate cancer (PCa) upon biopsy tended to be underestimated in men with previous negative biopsies using ERSPC RC model 4. For these men, an individualized approach combining the clinical information with the outcome of biomarker-related urine tests may help to make a more informed decision.Case presentationTwo men, aged 66 and 69 respectively when presented in the clinic, show the typical dilemma of urologist and patient for electing repeat prostate biopsy. Both men had normal DRE findings, did not have a family history of PCa, presented with serum PSA values between 3 and 10 ng/ml and the first biopsies were negative for disease. The ERSPC RC4 did not indicate a biopsy in these men. The urinary molecular biomarker-based test for HOXC6 and DLX1, combining biomarker-expression profiling with clinical risk factors, resulted in SelectMDx Risk scores for these men that were higher than the cut-off of the test. Based on this outcome, mpMRI was performed with an outcome of PI-RADS ≥4 in both men. Histopathological evaluation of TRUS-guided biopsies confirmed high-grade PCa.ConclusionsThe urinary molecular biomarker-based risk score played a pivotal role in the diagnosis of clinically significant PCa whereas ERSPC RC4 outcome would not have indicated further diagnostic follow-up in these two cases. The timely diagnosis was shown to be crucial for the curative treatment by radical retropubic prostatectomy and the potential life-years gained for these two vital males.