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

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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Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer

BioMed Research International ◽

10.1155/2021/6057948 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Qianshi Zhang ◽

Zhen Feng ◽

Yongnian Zhang ◽

Shasha Shi ◽

Yu Zhang ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Poor Prognosis ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Prognostic Signature ◽

Discovery Cohort ◽

Immune Related Gene

Background. Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods. Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results. A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion. We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

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

Stroke ◽

10.1161/str.48.suppl_1.19 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Grant C O’Connell ◽

Ashley B Petrone ◽

Madison B Treadway ◽

Connie S Tennant ◽

Noelle Lucke-Wold ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Ischemic Stroke ◽

Whole Blood ◽

Validation Cohort ◽

Gene Expression Pattern ◽

Discovery Cohort ◽

Stroke Mimics ◽

Independent Validation ◽

Diagnostic Potential

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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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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Breast Cancer Type Classification Using Machine Learning

Journal of Personalized Medicine ◽

10.3390/jpm11020061 ◽

2021 ◽

Vol 11 (2) ◽

pp. 61

Author(s):

Jiande Wu ◽

Chindo Hicks

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Machine Learning ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Genomic Research ◽

Support Vector ◽

Cancer Type ◽

Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

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Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables

Health Informatics Journal ◽

10.1177/1460458216655188 ◽

2016 ◽

Vol 24 (1) ◽

pp. 54-65 ◽

Cited By ~ 3

Author(s):

Stefano Parodi ◽

Chiara Manneschi ◽

Damiano Verda ◽

Enrico Ferrari ◽

Marco Muselli

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Hodgkin’S Lymphoma ◽

Hodgkin's Lymphoma ◽

Support Vector ◽

Expression Data ◽

Clinical Variables ◽

Learning Machine

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin’s lymphoma patients.

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NIMG-63. MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS

Neuro-Oncology ◽

10.1093/neuonc/noab196.561 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi143-vi144

Author(s):

Omaditya Khanna ◽

Anahita Fathi Kazerooni ◽

Jose A Garcia ◽

Chiharu Sako ◽

Sherjeel Arif ◽

...

Keyword(s):

Machine Learning ◽

Skull Base ◽

Predictive Model ◽

Patient Treatment ◽

Support Vector ◽

Benign Tumors ◽

Replication Cohort ◽

Discovery Cohort ◽

Ki 67 ◽

Who Grade

Abstract PURPOSE Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 < 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model. RESULTS A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively. CONCLUSION The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.

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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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Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-217840 ◽

2020 ◽

pp. annrheumdis-2020-217840 ◽

Cited By ~ 1

Author(s):

Kimberly Showalter ◽

Robert Spiera ◽

Cynthia Magro ◽

Phaedra Agius ◽

Viktor Martyanov ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Systemic Sclerosis ◽

Clinical Improvement ◽

Smooth Muscle Actin ◽

Clinical Severity ◽

Support Vector ◽

Alpha Smooth Muscle Actin ◽

Modified Rodnan Skin Score ◽

Diffuse Cutaneous Systemic Sclerosis

ObjectiveWe sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).MethodsFifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).ResultsaSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.ConclusionCD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

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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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Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽

10.1371/journal.pone.0257343 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257343

Author(s):

Shaoshuo Li ◽

Baixing Chen ◽

Hao Chen ◽

Zhen Hua ◽

Yang Shao ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Network Analysis ◽

Postmenopausal Osteoporosis ◽

Molecular Mechanism ◽

Molecular Mechanisms ◽

Functional Enrichment ◽

Support Vector ◽

Hub Genes ◽

Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

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