Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Giudice, Marco Del; Peirone, Serena; Perrone, Sarah; Priante, Francesca; Varese, F.; Tirtei, Elisa; Cereda, Matteo

doi:10.3390/ijms22094563

Cited by 18 publications

(7 citation statements)

References 133 publications

(180 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assemblage of novel technologies and computational algorithms, however, promises to reduce this breach and help to infer relevant pathophysiological characteristics of rare diseases, such as FA. On the one hand scRNAseq is producing datasets of single cell resolution transcriptional profiles from multiple healthy and diseased tissues, where rare and small populations can be identified 38,39 ; on the other hand, multiple machine learning algorithms exist that can be implemented to identify patterns on these datasets, or that can be trained with labelled datasets for identification of transcriptional profiles of interest in inquiry datasets 7,[40][41][42] .…”

Section: Discussionmentioning

confidence: 99%

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Flores-Mejía,

Siliceo-Portugal,

Figueroa

et al. 2024

Preprint

View full text Add to dashboard Cite

Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main tumor risks for patients with FA, including high-risk myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Although bone marrow transplantation is the treatment of choice for FA patients that develop aplastic anemia, patients with a more stable bone marrow remain at a high risk of presenting MDS/AML and should be monitored for appearance of myeloid malignant clones. Markers for an as-early-as-possible identification of emerging myeloid malignant cells are needed for the monitoring of patients with FA, since quick medical action after detection of neoplastic transformation is needed.In this work we have leveraged publicly available single cell RNA seq (scRNAseq) datasets of patients with MDS and AML for training deep neural networks (DNN). We have generated two machine learning models aimed to identify myeloid malignant transcriptional profiles in scRNAseq datasets from the bone marrow of patients with FA, one for detection of MDS and a second one for AML. Both predictors displayed high sensitivity, specificity, and accuracy for detection of single cell resolution myeloid malignant transcriptional profiles.Multiple tools for analysis of single cell transcriptional data were implemented to characterize the predicted MDS and AML cells. Our analysis suggests that the predicted MDS and AML cells from FA patients are enriched in the lympho-myeloid-primed progenitor (LMPP) and the granulocyte-monocyte progenitor (GMP) populations. The predicted MDS and AML cells have gene expression and master transcriptional factor profiles that suggest malignant transformation and that differ from the rest of FA cells. Also cues of immune evasion were detected using single cell pathway analysis (SCPA) and cell-cell communication profiles. Next work will be aimed to find potential cell surface markers on the predicted MDS and AML cells as well as to assess our predictions in primary samples from FA patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Flores-Mejía,

Siliceo-Portugal,

Figueroa

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Secondly, collaboration with artificial intelligence can enhance research and application of single-cell sequencing by providing comprehensive data processing and analysis methods, thereby lowering the threshold for data processing and analysis [ 111 ]. For instance, artificial intelligence can leverage existing information in genome big data to ultimately deliver precise drugs [ 112 ]. Thirdly, increased investment in single-cell sequencing technology can help reduce its cost and promote its broader use in clinical applications.…”

Section: Opportunities For Precision Medicinementioning

confidence: 99%

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Lin,

Zhang,

et al. 2024

J Transl Med

View full text Add to dashboard Cite

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

show abstract

“…To date, high-throughput sequencing technologies have made thousands of transcriptome profiles available. The high heterogeneity and complexity of transcriptomic data is the major challenge that hinders researchers from gaining novel scientific insights [ 29 ]. As transcriptome data become larger, the need for AI algorithms is even more evident.…”

Section: Artificial Intelligence Fosters the Application Of Transcrip...mentioning

confidence: 99%

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

Gui

et al. 2023

JCM

View full text Add to dashboard Cite

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

show abstract

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Cited by 18 publications

References 133 publications

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

Contact Info

Product

Resources

About