VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.

Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes

Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn's Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Machine learning analysis of TCGA cancer data

In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

Abstract 763: A gene expression signature of 37 genes improves survival prognosis in colon cancer patients

Colon cancer is one of the most prevalent and most deadly types of cancer. Identification of the patients with worse prognosis could improve the results of the therapeutic strategies. In order to achieve this, we present a 37 gene expression signature which improves the state-of-the-art signatures prediction.RNASeq data from the TCGA-COAD cohort have been used to create the model. In this case, a multivariate filtering based on correlation indexes approach was used for feature selection. The finally selected features present a high correlation with the dependent variable but a low correlation among them. These features, together with specific clinical covariates (stage, gender, MSI status, molecular subtype and age at diagnosis) have been used as dependent features in a Cox Proportional Hazard model (CPH). Subsequently, an internal and an external validation of the model were carried out. For the external validation, five independent external cohorts were used. In addition, our signature was compared with four top characterised signatures in colon cancer.In the internal validation, our signature shows higher accuracy than random gene sets, as well as with different known signatures including commercial gene expression based signatures. External validation shows how our signature achieves the best performance. The results obtained in the external validation are shown in Table 1.

Comparative analysis of colon risk signatures in external cohorts.CohortPerformance37-geneMDA114ColoGuideExColoGuideProColoPrintTCGA (test)AUC0,630,560,590,590,63n=33HR1,34 (1,02-1,77)1,14 (0,97-1,35)2,13 (1,03-4,40)2,37 (0,94-5,96)2,12 (0,96-4,68)C-Index0,72 (0,52-0,93)0,70 (0,50-0,90)0,65 (0,44-0,85)0,64 (0,43-0,84)0,67 (0,46-0,87)GSE17536AUC0,700,750,680,750,62n=177HR1,47 (1,30-1,67)1,14 (1,08-1,20)1,97 (1,56-2,48)2,86 (2,12-3,86)1,74 (1,37-2,22)C-Index0,70 (0,63-0,77)0,66 (0,59-0,73)0,70 (0,63-0,78)0,74 (0,67-0,81)0,67 (0,60-0,74)GSE17537AUC0,780,560,770,760,68n=55HR1,68 (1,31-2,14)1,05 (0,95-1,17)2,69 (1,67-4,31)2,47 (1,58-3,88)2,00 (1,24-3,22)C-Index0,76(0,63-0,89)0,57 (0,44-0,70)0,75 (0,62-0,88)0,75 (0,62-0,88)0,68 (0,55-0,81)GSE29621AUC0,750,600,690,750,72n=65HR3,47 (1,93-6,24)1,09 (0,97-1,22)2,32 (1,43-3,78)3,43 (1,88-6,23)2,05 (1,28-3,29)C-Index0,77 (0,65-0,89)0,58 (0,46-0,70)0,69 (0,57-0,81)0,76 (0,63-0,88)0,72 (0,60-0,85)GSE39582AUC0,720,640,700,730,63n=185HR1,38 (1,20-1,58)1,09 (1,03-1,16)2,84 (1,99-4,04)2,23 (1,65-3,03)1,36 (1,09-1,71)C-Index0,68 (0,60-0,76)0,61 (0,53-0,69)0,68 (0,60-0,77)0,68 (0,60-0,76)0,60 (0,51-0,68)SummaryAUC0,720,620,690,710,66HR1,87 (1,35-2,68)1,1 (1,00-1,22)2,39 (1,54-3,80)2,67 (1,63-4,59)1,86 (1,19-3,02)C-Index0,73 (0,61-0,85)0,62 (0,50-0,75)0,70 (0,57-0,82)0,71 (0,59-0,83)0,67 (0,54-0,79)

Citation Format: Jose Liñares-Blanco, Carlos Fernandez-Lozano, Jose A. Seoane. A gene expression signature of 37 genes improves survival prognosis in colon cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 763.

Molecular docking and machine learning analysis of Abemaciclib in colon cancer

BACKGROUND

The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD.

RESULTS

The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP - Nilotinib, PTPRN - Venetoclax, VEGFA - Venetoclax and FABP6 - Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle.

CONCLUSIONS

The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.