Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

Dhall, Anjali; Patiyal, Sumeet; Kaur, Harpreet; Bhalla, Sherry; Arora, Chakit; Raghava, Gajendra P. S.

doi:10.1101/850677

Cited by 11 publications

(13 citation statements)

References 45 publications

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“…In one of our previous studies [ 59 ], Human Leukocyte Antigen (HLA) alleles-based features were used in combination with clinical information, for the prognostication of melanoma patients. The study used a dataset of 401 patients for developing ML based models.…”

Section: Resultsmentioning

confidence: 99%

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Arora

Kaur

Lathwal

et al. 2020

Heliyon

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Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value~10 À8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value~10 À6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value~10 À9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-valuẽ 10 À15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

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Section: Resultsmentioning

confidence: 99%

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Arora

Kaur

Lathwal

et al. 2020

Heliyon

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“…HLA-DPB1 is derived from the β chain of HLA-II. In a previous research, Dhall et al [ 25 ] showed that HLA-DPB1 functioned in presenting abnormal antigens in MM, thus stimulating melanoma progression and metastasis. In gene polymorphism research, HLA-DPB1 mutations have been regarded as signatures associated with gastric and lung cancers [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma

Zhu

Gao

et al. 2021

Analytical Cellular Pathology

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In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK–STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages ( P < 0.01 ). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages ( P = 0.023 ) and activated mast cells ( P = 0.005 ).

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“…12, 29 In present study, to evaluate the developed machine learning models, we have applied 5-fold cross-validation on 80% of the training data for the internal training, testing and model evaluation. 30, 31 In 5-fold CV, the training data is divided into 5-sets, where four sets were used for the training and fifth set was utilized for the testing purposes. The same process is repeated five times, so that each set of positive and negative data is used for training and testing purposes.…”

Section: Methodsmentioning

confidence: 99%

ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

Sharma

Patiyal

Dhall

et al. 2021

Preprint

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Allergy is the abrupt reaction of the immune system that may occur after the exposure with allergens like protein/peptide or chemical allergens. In past number of methods of have been developed for classifying the protein/peptide based allergen. To the best of our knowledge, there is no method to classify the allergenicity of chemical compound. Here, we have proposed a method named ChAlPred, which can be used to fill the gap for predicting the chemical compound that might cause allergy. In this study, we have obtained the dataset of 403 allergen and 1074 non-allergen chemical compounds and used 2D, 3D and FP descriptors to train, test and validate our prediction models. The fingerprint analysis of the dataset indicates that PubChemFP129 and GraphFP1014 are more frequent in the allergenic chemical compounds, whereas KRFP890 is highly present in non-allergenic chemical compounds. Our XGB based model achieved the AUC of 0.89 on validation dataset using 2D descriptors. RF based model has outperformed other classifiers using 3D descriptors (AUC = 0.85), FP descriptors (AUC = 0.92), combined descriptors (AUC = 0.93), and hybrid model (AUC = 0.92) on validation dataset. In addition, we have also reported some FDA-approved drugs like Cefuroxime, Spironolactone, and Tioconazole which can cause the allergic symptoms. A user user-friendly web server named ChAlPred has been developed to predict the chemical allergens. It can be easily accessed at https://webs.iiitd.edu.in/raghava/chalpred/.

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Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

Cited by 11 publications

References 45 publications

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma

ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

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