Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients\u27 survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients\u27 survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients\u27 survival by integrating multi-omics data and clinical factors

Role of tissue and circulating microRNA and DNA as biomarkers in medullary thyroid cancer

Medullary thyroid carcinoma (MTC) is a rare neuroendocrine tumor comprising hereditary or sporadic form with frequent mutations in the rearranged during transfection (RET) or RAS genes. Diagnosis is based on presence of thyroid tumor mass with altered levels of calcitonin (Ctn) and carcinoembryonal antigen (CEA) in the serum and/or in the cytological smears from fine needle aspiration biopsies. Treatment consists of total thyroidectomy, followed by tyrosine kinase inhibitors (TKi) in case of disease persistence. During TKi treatment, Ctn and CEA levels can fluctuate regardless of tumor volume, metastasis or response to therapy. Research for more reliable non-invasive biomarkers in MTC is still underway. In this context, circulating nucleic acids, namely circulating microRNAs (miRNAs) and cell free DNA (cfDNA), have been evaluated by different research groups. Aiming to shed light on whether miRNAs and cfDNA are suitable as MTC biomarkers we searched three different databases, PubMed, Scopus, WOS and reviewed literature. We classified 83 publications fulfilling our search criteria and summarized the results. We report data on miRNA and cfDNA that can be evaluated for validation in independent studies and clinical application

Predicting microenvironment in CXCR4- and FAP-positive solid tumors - a pan-cancer machine learning workflow for theranostic target structures

Simple Summary Imaging based on positron emission tomography (PET) is a crucial part of up-to-date cancer care. For this purpose, PET employs and marks target structures at the cellular surface. Recently, C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) emerged as clinically relevant PET targets. However, it is unclear whether high levels of CXCR4 and FAP represent distinct cancer states—especially in solid tumors. Therefore, we established a machine learning model based on 9242 samples from 29 different cancer entities. Our analysis revealed that—in most solid tumors—high levels of CXCR4 were associated with immune cells infiltrating these tumors. Instead, FAP-positive tumors were characterized by high amounts of tumor vessels. Our machine learning approach potentially can identify the Achilles’ heel of tumors in a non-invasive manner—by performing PET without having to obtain tumor tissue beforehand. Abstract (1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database—representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets

The Interface of Cancer, Their Microenvironment and Nanotechnology

Cancer is one of the deadliest diseases with a cure far from being found. Despite the extraordinary advances in the therapy approaches, only a few patients respond to treatments. The tumor microenvironment (TME) plays a crucial role in cancer progression by contributing to the chemoresistance. Thus, emerging efforts are being made in nanotechnology research focusing on nanoparticles' potential role and their application in immune system modulation. Moreover, the omics have contributed to bioengineering and nanotechnology development by elucidating the mechanisms of cancer and specific biomarkers that could be used as new therapeutic targets. Furthermore, the non-coding microRNA as a target for cancer treatment and creation of organoids for the study of new treatments helped for the new therapeutics' era called personalized medicine. Here we will discuss the role played by TME in tumor initiation and progression we will describe the recent nanotechnology applied to cancer treatment. Specifically, we will describe the potential role of nanoparticles (NPs) and their application in immune system modulation, ultimately leading to circumventing tumor cell proliferation.publishersversionpublishe

Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia

IntroductionThe heterogeneity of treatment response in acute myeloid leukemia (AML) patients poses great challenges for risk scoring and treatment stratification. Carbohydrate metabolism plays a crucial role in response to therapy in AML. In this multicohort study, we investigated whether carbohydrate metabolism related genes (CRGs) could improve prognostic classification and predict response of immunity and treatment in AML patients.MethodsUsing univariate regression and LASSO-Cox stepwise regression analysis, we developed a CRG prognostic signature that consists of 10 genes. Stratified by the median risk score, patients were divided into high-risk group and low-risk group. Using TCGA and GEO public data cohorts and our cohort (1031 non-M3 patients in total), we demonstrated the consistency and accuracy of the CRG score on the predictive performance of AML survival.ResultsThe overall survival (OS) was significantly shorter in high-risk group. Differentially expressed genes (DEGs) were identified in the high-risk group compared to the low-risk group. GO and GSEA analysis showed that the DEGs were mainly involved in immune response signaling pathways. Analysis of tumor-infiltrating immune cells confirmed that the immune microenvironment was strongly suppressed in high-risk group. The results of potential drugs for risk groups showed that inhibitors of carbohydrate metabolism were effective.DiscussionThe CRG signature was involved in immune response in AML. A novel risk model based on CRGs proposed in our study is promising prognostic classifications in AML, which may provide novel insights for developing accurate targeted cancer therapies

Microarrays and NGS for Drug Discovery

Novel technologies and state of the art platforms developed and launched over the last two decades such as microarrays, next-generation sequencing, and droplet PCR have provided the medical field many opportunities to generate and analyze big data from the human genome, particularly of genomes altered by different diseases like cancer, cardiovascular, diabetes and obesity. This knowledge further serves for either new drug discovery or drug repositioning. Designing drugs for specific mutations and genotypes will dramatically modify a patient’s response to treatment. Among other altered mechanisms, drug resistance is of concern, particularly when there is no response to cancer therapy. Once these new platforms for omics data are in place, available information will be used to pursue precision medicine and to establish new therapeutic guidelines. Target identification for new drugs is necessary, and it is of great benefit for critical cases where no alternatives are available. While mutational status is of highest importance as some mutations can be pathogenic, screening of known compounds in different preclinical models offer new and quick strategies to find alternative frameworks for treating more diseases with limited therapeutic options