Abstract 19: Machine Learning Approach Identifies a Pattern of Gene Expression in Peripheral Blood Which Can Accurately Detect Ischemic Stroke

Objective: The identification of stroke-associated biomarkers represents a means by which prehospital triage could be expedited to increase the probability of successful intervention. Thus, the objective of this work was to use high-throughput transcriptomics in combination with basic machine learning techniques to identify a pattern of gene expression in peripheral whole blood which could be used to identify acute ischemic stroke (AIS) in the acute care setting. Methods: A two-stage study design was used which included a discovery cohort and an independent validation cohort. In the discovery cohort, peripheral whole blood samples were obtained from 39 AIS patients upon emergency department admission, and from 24 neurologically asymptomatic controls. Microarray was used to measure the expression of over 22,000 genes and a pattern recognition technique known as genetic algorithm k-nearest neighbors (GA/kNN) identified a pattern of gene expression that optimally discriminated between AIS and controls. In an independent validation cohort, the gene expression pattern was tested for its ability to discriminate between 39 AIS patients and each of two different control groups, one consisting of 30 neurologically asymptomatic controls, and the other consisting of 15 stroke mimics, with gene expression levels being assessed by qRT-PCR. Results: In the discovery cohort, GA/kNN identified ten transcripts (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) whose coordinate pattern of expression correctly identified 98.4% of subjects (97.4% sensitive, 100% specific). In the validation cohort, the same 10 transcripts correctly identified 95.6% of subjects when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 96.3% of subjects when comparing AIS patients to stroke mimics (97.4% specific, 93.3% sensitive). Conclusion: These results demonstrate that a highly accurate RNA-based companion diagnostic for AIS is plausible using a relatively small number of markers. The pattern of gene expression identified in this study shows strong diagnostic potential, and warrants further evaluation to determine true clinical efficacy.

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Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-000631 ◽

2020 ◽

Vol 8 (2) ◽

pp. e000631 ◽

Cited By ~ 1

Author(s):

Zhihao Lu ◽

Huan Chen ◽

Xi Jiao ◽

Wei Zhou ◽

Wenbo Han ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gastrointestinal Cancer ◽

Immune Checkpoint ◽

Learning Strategy ◽

Validation Cohort ◽

Support Vector ◽

Related Gene ◽

Discovery Cohort ◽

Immune Oncology

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the efficacy of ICIs in gastrointestinal cancer are still lacking. In this study, we performed 395-plex immune oncology (IO)-related gene target sequencing in tumor samples from 96 patients with metastatic gastrointestinal cancer patients treated with ICIs, and a linear support vector machine learning strategy was applied to construct a predictive model. ResultsAll 96 patients were randomly assigned into the discovery (n=72) and validation (n=24) cohorts. A 24-gene RNA signature (termed the IO-score) was constructed from 395 immune-related gene expression profiling using a machine learning strategy to identify patients who might benefit from ICIs. The durable clinical benefit rate was higher in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% vs 4.3%, p<0.001; validation cohort: 85.7% vs 17.6%, p=0.004). The IO-score may exhibit a higher predictive value in the discovery (area under the receiver operating characteristic curve (AUC)=0.97)) and validation (AUC=0.74) cohorts compared with the programmed death ligand 1 positivity (AUC=0.52), tumor mutational burden (AUC=0.69) and microsatellite instability status (AUC=0.59) in the combined cohort. Moreover, patients with a high IO-score also exhibited a prolonged overall survival compared with patients with a low IO-score (discovery cohort: HR, 0.29; 95% CI 0.15 to 0.56; p=0.003; validation cohort: HR, 0.32; 95% CI 0.10 to 1.05; p=0.04). Taken together, our results indicated the potential of IO-score as a biomarker for immunotherapy in patients with gastrointestinal cancers.

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A Combination of CD302 gene Expression and 3-Months BCR-ABL1 Level Predicts Inferior Achievement of Deep Molecular Response in CP-CML Patients Treated with Imatinib

Blood ◽

10.1182/blood-2019-125446 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 663-663

Author(s):

Chung Hoow Kok ◽

Liu Liu ◽

David T Yeung ◽

Verity A Saunders ◽

Phuong Dang ◽

...

Keyword(s):

Gene Expression ◽

Predictive Model ◽

Research Funding ◽

Validation Cohort ◽

Molecular Response ◽

Discovery Cohort ◽

Independent Validation ◽

Poor Risk ◽

Achievement Rate ◽

Deep Molecular Response

Introduction and Aim. Achievement of deep molecular response (DMR) is the prerequisite for treatment-free remission in chronic phase CML (CP-CML) patients (pts). Pts who fail to achieve early molecular response (BCR-ABL1 > 10% IS) at 3-months (mths), or have high ELTS score at diagnosis have inferior achievement of DMR. We and others have shown that the levels of NK-cell, T-cell, myeloid-derived suppressor cell, and neutrophils in the blood at diagnosis have an impact on DMR achievement. We hypothesized that Cluster of Differentiation (CD) (cell surface marker) gene expression might provide a surrogate marker to characterize immune cell composition. We aimed to identify pts who had a low probability of achieving DMR by 5 years (yrs) by combining 3-mths BCR-ABL1% and CD gene expression. This may enable clinicians to determine whether an individual patient is on a pathway towards DMR and potentially TFR or should be considered for a different therapeutic approach if TFR is the eventual goal. Methods. 119 blood samples from the imatinib-based TIDEL-II trial were subjected to transcriptomic microarray profiling. A total of 357 CD genes classified by the HUGO Gene Nomenclature Committee CD molecular gene group were assessed. We defined DMR as achieving MR4.5 (BCR-ABL1 < 0.0032%) at two consecutive time points. To construct a predictive model, the samples were randomly assigned to discovery and validation cohorts. Recursive partitioning and construction of a regression tree with tenfold cross-validation based on expression of 357 CD genes and 3-mths BCR-ABL1% were used as inputs in the discovery cohort. The performance was assessed based on accuracy of prediction of DMR by 5 yrs. The final model was validated using the independent validation cohort. All the analysis was performed using R statistical software. Results. Clinical variables (age, gender, ELTS, 3-mths BCR-ABL1%, MMR, and MR4.5) were well matched in the discovery (n=60) and independent validation cohort (n=59). The predictive model was constructed using the discovery cohort to reveal two risk groups: poor-risk (PR, 15% achieving MR4.5 at 5 yrs, n=19), and good-risk (GR, 88% achieving MR4.5 at 5 yrs, n=41) groups (Figure 1A-B). This model classified PR group by BCR-ABL1 ≥ 7.5% at 3 mths OR BCR-ABL1 < 7.5% at 3 months with high CD302 gene expression (≥7.9 log2 gene expression; top 15%) at diagnosis. GR group was defined as having both BCR-ABL1 < 7.5% and low CD302 gene expression (<7.9 log2 gene expression). These variables were chosen by the model based on accuracy performance in predicting DMR. CD302 is a C-type lectin receptor involved in cell adhesion and migration. It is expressed in myeloid populations as well as in blasts and leukemic stem cells (LSC) in AML. High expression of CD302 in PR pts may be a surrogate for increased LSC. The model was validated in the independent validation cohort. Pts identified as PR in the validation cohort had significantly lower 5-yrs MR4.5 achievement rate (14%, n=14) compared to those with GR (82%, n=45, p=0.0002, Figure 1C). We asked whether using the more conventional BCR-ABL1 10% cutoff instead of 7.5% in our model would give similar results, but the performance in predicting long-term DMR achievement was inferior: Pts predicted as PR with this criteria had ~2x higher achievement of DMR (e.g. 26% vs 14% using 3-mths BCR-ABL1 10% vs 7.5% cutoff respectively). ELTS score have been associated with the probability of DMR achievement. We compared the performance of ELTS in combination with 3-mths BCR-ABL1% by replacing CD302 gene expression with ELTS. The predictive accuracy was inferior. Pts with 3-mths BCR-ABL1 ≥7.5% OR BCR-ABL1 <7.5% with high/intermediate ELTS (PR-2) had about 3.3-3.5 fold higher DMR achievement rate than the PR group with CD302 in both discovery and validation cohorts (Figure 1D-E). In contrast, pts with 3-mths BCR-ABL1 <7.5% and low ELTS (GR-2) had approximately 1.1-1.2 fold lower DMR achievement rate than the GR group with CD302 in both discovery and validation cohorts (Figure 1D-E). Conclusion. We have constructed a predictive model for DMR achievement for pts who receive optimised frontline imatinib therapy. This model performs better than combining ELTS and 3-mths BCR-ABL1%. We postulate that this predictive model could enable identification of poor risk pts at 3 mths who would benefit from intensified therapeutic approaches to obtain eligibility for TFR and potentially optimal clinical outcome. Disclosures Yeung: Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Pfizer: Honoraria; Amgen: Honoraria. Hughes:Novartis: Other: Advisory Board and Symposia, Research Funding; BMS: Research Funding.

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Identification of CNGB1 as a Predictor of Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer

Cancers ◽

10.3390/cancers13153903 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3903

Author(s):

Anastasia C. Hepburn ◽

Nicola Lazzarini ◽

Rajan Veeratterapillay ◽

Laura Wilson ◽

Jaume Bacardit ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Bladder Cancer ◽

Gene Expression Analysis ◽

Validation Cohort ◽

Discovery Cohort ◽

Muscle Invasive Bladder Cancer ◽

Differential Gene Expression Analysis ◽

Differential Gene ◽

Muscle Invasive

Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5–10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (CNGB1, GGH, HIST1H4F, IDO1, KIF5A, MRPL4, NCDN, PRRT3, SLC35B3) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, n = 26, Log rank test, p = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, CNGB1, as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort (n = 30, p = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, n = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival (p < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients.

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Combining Gene Expression Profiling and Artificial Intelligence to Diagnose B-Cell Non-Hodgkin Lymphoma

Blood ◽

10.1182/blood-2019-122405 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1476-1476

Author(s):

Victor Bobée ◽

Fanny Drieux ◽

Vinciane Marchand ◽

Vincent Sater ◽

Liana Veresezan ◽

...

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

B Cell ◽

Board Of Directors ◽

Expression Profiling ◽

Marginal Zone ◽

Validation Cohort ◽

B Cell Lymphomas ◽

Advisory Committees ◽

Independent Validation

Introduction Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies associated with very diverse clinical behaviors. They rely on the activation of different signaling pathways for proliferation and survival which might be amenable to targeted therapies, increasing the need for precision diagnosis. Unfortunately, their accurate classification can be challenging, even for expert hemato-pathologists, and secondary reviews recurrently differ from initial diagnosis. To address this issue we have developed a pan-B-NHL classifier based on a middle throughput gene expression assay coupled with a random forest algorithm. Material and Methods Five hundred ten B-NHL diagnosed according to the WHO criteria were studied, with 325 diffuse large B-cell lymphomas (DLBCL), 43 primary mediastinal B-cell lymphomas (PMBL), 55 follicular lymphomas (FL), 31 mantle cell lymphomas (MCL), 17 small lymphocytic lymphomas (SLL), 20 marginal zone lymphomas (MZL), 11 marginal zone lymphomas of mucosa-associated lymphoid tissue (MALT) and 8 lymphoplasmacytic lymphomas (LPL). To train and validate the predictor the samples were randomly split into a training (2/3) and an independent validation cohort (1/3). A panel of 137 genes was designed by purposely selecting the differentiation markers identified in the WHO classification for their capacity to provide diagnostic and prognostic information in NHLs. Gene expression profiles were generated by ligation dependent RT-PCR applied to RNA extracted from frozen or FFPE tissue and analyzed on a MiSeq sequencer. For analysis, the sequencing reads were de-multiplexed, aligned with the sequences of the LD-RTPCR probes and counted. Results were normalized using unique molecular indexes counts to correct PCR amplification biases. Results In DLBCL, unsupervised gene expression analysis retrieved the expected GCB, ABC and PMBL signatures (Fig A). These tumors also showed higher expressions of the KI67 (proliferation), CD68 and CD163 (tumor associated macrophages), and PD-L1/2 (immune response) markers. We also observed that the dual expression of MYC and BCL2 at the mRNA level significantly associates with inferior PFS and OS, independent from the International Prognostic Index and from the GCB/ABC cell-of-origin signature, validating the capacity of the assay to identify these highly aggressive lymphomas (Fig C). Overall, low-grade lymphomas were characterized by a significant T cell component. FLs associated with the GCB (BCL6, MYBL1, CD10 and LMO2) and Tfh (CD3, CD5, CD28, ICOS, CD40L, CXCL13) signatures. Other small B-cell lymphomas tended to overexpress activated B-cell markers (LIMD1, TACI, IRF4,FOXP1...), and the expected CD5, CD10, CD23 and CCND1 differential expressions in SLL, MCL and MZL were correctly retrieved (Fig B). Surprisingly, our analysis revealed that the Ie-Ce sterile transcript, expressed from the IGH locus during IgE isotype switching, is almost exclusively expressed by FLs, constituting one of the most discriminant markers for this pathology. We next trained a random forest classifier to discriminate the 7 principal subtypes of B-NHLs. The training cohort comprised 162 DLBCLs (ABC or GCB), 28 PMBL, 35 FLs (grade 1-3A), 21 MCLs, 12 SLLs, and 25 NHLs grouped into the MZL category (13 MZLs, 8 MALT and 4 LPLs). The independent validation series comprised 90 DLBCLs classified as GCB or ABC DLBCLs by the Lymph2Cx assay, 15 PMBLs, 12 FLs (grade 1-3A), 10 MCLs, 5 SLLs and 14 MZLs (7 MZL, 3 MALT and 4 LPL). The RF algorithm classified all cases of the training series into the expected subtype, as well as 94.5% samples of the independent validation cohort (Fig D). For ABC and GCB DLBCLs, the concordance with the Lymph2Cx assay in the validation cohort was 94.3%. Conclusion We have developed a comprehensive gene expression based solution which allows a systematic evaluation of multiple diagnostic and prognostic markers expressed by the tumor and by the microenvironment in B-NHLs. This assay, which does not require any specific platform, could be implemented in complement to histology in many diagnostic laboratories and, with the current development of targeted therapies, enable a more accurate and standardized B-NHL diagnosis. Together, our data illustrate how the integration of gene expression profiling and artificial intelligence can increase precision diagnosis in cancers. Figure Disclosures Oberic: Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; Roche: Membership on an entity's Board of Directors or advisory committees. Haioun:Miltenyi: Honoraria; Takeda: Honoraria; Servier: Honoraria; F. Hoffmann-La Roche Ltd: Honoraria; Novartis: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Gilead: Honoraria; Janssen: Honoraria. Salles:Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Roche: Consultancy; Celgene: Consultancy, Research Funding; Astra-Zeneca: Consultancy; Karyopharm: Consultancy; BMS: Honoraria; Janssen: Honoraria; Gilead: Honoraria. Jardin:celgene: Honoraria; roche: Honoraria; amgen: Honoraria; Servier: Honoraria; janssen: Honoraria.

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Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

10.1101/647719 ◽

2019 ◽

Author(s):

William A Figgett ◽

Katherine Monaghan ◽

Milica Ng ◽

Monther Alhamdoosh ◽

Eugene Maraskovsky ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Systemic Lupus Erythematosus ◽

Clinical Trials ◽

Lupus Erythematosus ◽

Whole Blood ◽

Rna Seq ◽

Systemic Lupus ◽

Disease Heterogeneity ◽

Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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Abstract TP215: Machine Learning Analysis of Gene Expression Data and Predictive Stroke Outcomes in Acute Ischemic Stroke

Stroke ◽

10.1161/str.51.suppl_1.tp215 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Sarah R Martha ◽

Qiang Cheng ◽

Liyu Gong ◽

Lisa Collier ◽

Stephanie Davis ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Mechanical Thrombectomy ◽

Stroke Patients ◽

Stroke Outcomes ◽

Inflammatory Genes ◽

Patient Demographics ◽

Edema Volume

Background and Purpose: The ability to predict ischemic stroke outcomes in the first day of admission could be vital for patient counseling, rehabilitation, and care planning. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) collects blood samples distal and proximal to the intracranial thrombus during mechanical thrombectomy. These samples are a novel resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and patient demographics that are predictive of stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability, convenience sampling of subjects (≥ 18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion. We evaluated relative concentrations of mRNA for gene expression in 84 inflammatory molecules in static blood distal and proximal to the intracranial thrombus from adults who underwent thrombectomy. We employed a machine learning method, Random Forest, utilizing the first set of enrolled subjects, to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. Results: We analyzed the first 28 subjects (age = 66 ± 15.48, 11 males) in the BACTRAC registry. Results from machine learning analyses demonstrate that the genes CCR4, IFNA2, IL9, CXCL3, Age, DM, IL7, CCL4, BMI, IL5, CCR3, TNF, and IL27 predict infarct volume. The genes IFNA2, IL5, CCL11, IL17C, CCR4, IL9, IL7, CCR3, IL27, DM, and CSF2 predict edema volume. There is an intersection of genes CCR4, IFNA2, IL9, IL7, IL5, CCR3 to both infarct and edema volumes. Overall, these genes depicts a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop predictive biomarker signatures for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

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Identification and validation of a potent multi-mRNA signature for the prediction of early relapse in hepatocellular carcinoma

Carcinogenesis ◽

10.1093/carcin/bgz018 ◽

2019 ◽

Vol 40 (7) ◽

pp. 840-852 ◽

Cited By ~ 3

Author(s):

Jie Cai ◽

Ying Tong ◽

Lifeng Huang ◽

Lei Xia ◽

Han Guo ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Messenger Rna ◽

Validation Cohort ◽

Expression Profiles ◽

External Validation ◽

Early Recurrence ◽

Discovery Cohort ◽

Early Relapse ◽

Staging Systems

Abstract Early recurrence of hepatocellular carcinoma (HCC) is implicated in poor patient survival and is the major obstacle to improving prognosis. The current staging systems are insufficient for accurate prediction of early recurrence, suggesting that additional indicators for early recurrence are needed. Here, by analyzing the gene expression profiles of 12 Gene Expression Omnibus data sets (n = 1533), we identified 257 differentially expressed genes between HCC and non-tumor tissues. Least absolute shrinkage and selection operator regression model was used to identify a 24-messenger RNA (mRNA)-based signature in discovery cohort GSE14520. With specific risk score formula, patients were divided into high- and low-risk groups. Recurrence-free survival within 2 years (early-RFS) was significantly different between these two groups in discovery cohort [hazard ratio (HR): 7.954, 95% confidence interval (CI): 4.596–13.767, P < 0.001], internal validation cohort (HR: 8.693, 95% CI: 4.029–18.754, P < 0.001) and external validation cohort (HR: 5.982, 95% CI: 3.414–10.480, P < 0.001). Multivariable and subgroup analyses revealed that the 24-mRNA-based classifier was an independent prognostic factor for predicting early relapse of patients with HCC. We further developed a nomogram integrating the 24-mRNA-based signature and clinicopathological risk factors to predict the early-RFS. The 24-mRNA-signature-integrated nomogram showed good discrimination (concordance index: 0.883, 95% CI: 0.836–0.929) and calibration. Decision curve analysis demonstrated that the 24-mRNA-signature-integrated nomogram was clinically useful. In conclusion, our 24-mRNA signature is a powerful tool for early-relapse prediction and will facilitate individual management of HCC patients.

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Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

Journal of Clinical Medicine ◽

10.3390/jcm9051276 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1276

Author(s):

Pedro Martínez-Paz ◽

Marta Aragón-Camino ◽

Esther Gómez-Sánchez ◽

Mario Lorenzo-López ◽

Estefanía Gómez-Pesquera ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Mortality Risk ◽

Validation Cohort ◽

Expression Patterns ◽

Gene Expression Signature ◽

Mrna Levels ◽

Characteristic Analysis ◽

Discovery Cohort ◽

Risk Of Death

Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.

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A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection

Neurosurgery ◽

10.1093/neuros/nyaa355 ◽

2020 ◽

Vol 88 (1) ◽

pp. 202-210 ◽

Cited By ~ 1

Author(s):

William C Chen ◽

Harish N Vasudevan ◽

Abrar Choudhury ◽

Melike Pekmezci ◽

Calixto-Hope G Lucas ◽

...

Keyword(s):

Gene Expression ◽

Risk Score ◽

Gene Expression Analysis ◽

Validation Cohort ◽

Gene Expression Signature ◽

Gene Signature ◽

Who Grade ◽

Independent Validation ◽

Meningioma Recurrence ◽

Targeted Gene Expression

Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P < .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P < .0001), and overall survival (OS, P < .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.

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Discovery of a simple two-gene expression biomarker in whole blood predictive of the need for treatment escalation in inflammatory bowel disease

10.1101/2021.07.09.21259804 ◽

2021 ◽

Author(s):

Jan K Nowak ◽

Rahul Kalla ◽

Alex T Adams ◽

Jonas Halfvarson ◽

Jack Satsangi ◽

...

Keyword(s):

Gene Expression ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Validation Cohort ◽

Inception Cohort ◽

Treatment Intensification ◽

Discovery Cohort ◽

Inflammatory Bowel ◽

Treatment Escalation ◽

Risk Of Treatment

Background and aims: The IBD-Character consortium has recruited large internationally based inception cohorts of treatment-naive inflammatory bowel disease (IBD) patients, providing a unique resource to derive a simple transcriptome signature in the field of prognostication. Methods: The discovery cohort (n=160) was recruited in Norway, Sweden and Spain. The replication inception cohort from the United Kingdom (n=97) was followed-up for a mean (SD) of 350 (228) days. Treatment escalation was formally defined as the need for a biologic agent, ciclosporin and/or surgery, instituted for disease flare after initial remission, or colectomy during the index admission for ulcerative colitis. Whole blood RNA was subject to paired-end sequencing. In the discovery cohort a simple procedure was applied, which exploited differences of transcript ratios. The ten top performing ratios were tested using Cox regression models in the validation cohort. Results: Newly diagnosed IBD patients with high CACNA1E/LRRC42 expression ratio had an increased risk of treatment intensification (validation cohort: HR=19.3, 95%CI 2.6-143.9, p=0.000005; AUC 0.76, 95%CI 0.66-0.86). In 51 patients with CRP < 3.5 mg/L, CACNA1E/LRRC42 still predicted escalation (HR=10.4; 95%CI 1.2-86.5, p=0.007). The second best performing transcript ratio was CACNA1E/CEACAM21 yielding a HR of 10.9 (95%CI 2.5-46.7, p=0.00002) and an AUC of 0.76 (95%CI 0.65-0.86) in the validation cohort. Conclusion: Transcriptomic profiling of an IBD inception cohort identified gene expression ratios CACNA1E/LRRC42 and CACNA1E/CEACAM21 as prognostic biomarkers. These were validated in a replication cohort as strongly associated with short- and long-term risk of treatment intensification and may provide valuable information in clinical decision-making.

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