Table 10 An overview of the emerging technologies in iPSC-based cancer research

CRISPR/Cas9

A gene-editing tool that allows precise modification of DNA

Enables targeted gene editing and functional genomics studies, leading to a better understanding of cancer biology and potential therapeutic targets

[644]

Single-cell RNA sequencing

Sequencing technique that provides transcriptomic data at the single-cell level

Allows identification of cellular heterogeneity within tumors, enabling the characterization of rare cell populations, identification of tumor subtypes, and monitoring of tumor evolution. It can provide insights into the mechanisms of drug resistance and aid in the development of personalized therapies

[611]

Organoid models

Three-dimensional cell cultures that mimic the structure and function of organs

Offers a more physiologically relevant model for studying tumor behavior and response to therapies. Organoids can be derived from patient-specific iPSCs, providing a platform for personalized medicine and drug screening

[95]

Liquid biopsy

Non-invasive analysis of circulating tumor DNA and other biomarkers in blood

Allows monitoring of tumor dynamics and genetic alterations through simple blood tests. Liquid biopsy can provide information on tumor heterogeneity, treatment response, and minimal residual disease, aiding in early detection, treatment selection, and disease monitoring

[645]

High-throughput screening

Automated methods to rapidly test large numbers of molecules or compounds

Accelerates the discovery of new therapeutic targets and drug candidates. High-throughput screening can identify compounds that selectively target cancer cells or modulate specific signaling pathways, facilitating the development of novel treatments and precision medicine approaches

[585]

Single-cell imaging

Advanced microscopy techniques for visualizing cellular processes at the single-cell level

Provides spatial and temporal information about cellular interactions, signaling pathways, and dynamic processes within tumors. Single-cell imaging can uncover new insights into tumor heterogeneity, microenvironment interactions, and therapeutic responses, informing the development of more effective cancer therapies

[634]

Machine learning

Artificial intelligence algorithms that learn from and make predictions or decisions based on data

Enables the analysis of large-scale datasets and extraction of patterns, facilitating the identification of novel biomarkers, prediction of treatment outcomes, and discovery of therapeutic targets. Machine learning can enhance precision medicine approaches by integrating diverse data types and optimizing treatment strategies for individual patients

[646]

Tumor-on-a-chip

Microfluidic platforms that recreate tumor microenvironments and interactions

Mimics the complex architecture and cellular interactions within tumors, allowing the study of tumor growth, invasion, and response to therapies. Tumor-on-a-chip models can aid in drug screening, personalized medicine, and understanding the mechanisms of metastasis, facilitating the development of targeted therapies and improving treatment efficacy

[647]

Spatial transcriptomics

Technique that combines spatial information with transcriptomic profiling

Enables the mapping of gene expression patterns within tissue sections, providing spatial context to molecular data. Spatial transcriptomics can reveal the cellular composition and organization of tumors, identify spatially restricted gene expression patterns, and uncover novel biomarkers or therapeutic targets. It enhances our understanding of tumor heterogeneity and microenvironmental interactions, guiding the development of more precise and effective cancer therapies

[648]

Nanotechnology

Application of nanoscale materials and devices in cancer research

Offers targeted drug delivery systems, imaging agents, and sensors for early detection. Nanotechnology can improve treatment efficacy by enhancing drug delivery to tumors, monitoring therapeutic response, and enabling personalized medicine. Additionally, nanoscale platforms can be used for diagnostic purposes, such as detecting circulating tumor cells or analyzing cancer-related biomarkers

[649]

Immunotherapy

Treatment approach that enhances the immune system's ability to fight cancer

Harnesses the body's immune response to target and destroy cancer cells. Immunotherapies, including immune checkpoint inhibitors and CAR-T cell therapy, have shown remarkable success in certain cancer types and have the potential to revolutionize cancer treatment by providing durable and specific responses

[217]

Epigenetic modifications

Alterations in gene expression patterns without changing the underlying DNA sequence

Studying epigenetic modifications can shed light on the regulatory mechanisms of cancer development and progression. Understanding the epigenetic landscape of tumors can lead to the identification of novel therapeutic targets and the development of epigenetic-based therapies

[650]

Metabolomics

Analysis of metabolites in biological systems

Provides insights into the metabolic rewiring of cancer cells, identifying metabolic vulnerabilities and potential targets for therapeutic intervention. Metabolomics can also aid in biomarker discovery, patient stratification, and monitoring treatment response

[651]

Artificial intelligence

Simulation of human intelligence in machines

Offers powerful tools for data analysis, image recognition, and decision-making in cancer research. AI can assist in image interpretation, drug discovery, treatment optimization, and predicting patient outcomes based on various data types. Integrating AI into clinical practice has the potential to enhance diagnostics, treatment planning, and patient management

[611]

3D bioprinting

Fabrication of three-dimensional tissues or organ-like structures

Enables the creation of patient-specific tumor models for drug screening and personalized medicine. 3D bioprinting can replicate the complex architecture and microenvironment of tumors, allowing researchers to study tumor biology, drug responses, and potential treatment strategies in a more clinically relevant context

[652]

Genome editing

Precision modification of DNA sequences

Techniques like CRISPR/Cas9 enable targeted editing of cancer-related genes, providing insights into their function and potential therapeutic interventions. Genome editing can be used to correct genetic mutations, disrupt oncogenes, or introduce therapeutic genes, opening up new possibilities for precise cancer treatments

[6]

Multi-omics integration

Integration of multiple types of omics data (genomics, transcriptomics, proteomics, etc.)

Combining different omics datasets allows for a comprehensive understanding of cancer biology. Multi-omics integration enables the identification of key molecular pathways, biomarkers, and potential therapeutic targets, paving the way for personalized treatment strategies and precision medicine

[653]

Tumor microenvironment

Study of the cellular and non-cellular components surrounding tumors

Investigating the tumor microenvironment provides insights into its role in tumor growth, invasion, metastasis, and response to therapies. Understanding the complex interactions between cancer cells, immune cells, stromal cells, and the extracellular matrix can lead to the development of targeted therapies that disrupt or modulate these interactions, improving treatment outcomes

[654]