Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics

Abstract

Gene editing has great potential in treating diseases caused by well-characterized molecular alterations. The introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based gene-editing tools has substantially improved the precision and efficiency of gene editing. The CRISPR/Cas9 system offers several advantages over the existing gene-editing approaches, such as its ability to target practically any genomic sequence, enabling the rapid development and deployment of novel CRISPR-mediated knock-out/knock-in methods. CRISPR/Cas9 has been widely used to develop cancer models, validate essential genes as druggable targets, study drug-resistance mechanisms, explore gene non-coding areas, and develop biomarkers. CRISPR gene editing can create more-effective chimeric antigen receptor (CAR)-T cells that are durable, cost-effective, and more readily available. However, further research is needed to define the CRISPR/Cas9 system's pros and cons, establish best practices, and determine social and ethical implications. This review summarizes recent CRISPR/Cas9 developments, particularly in cancer research and immunotherapy, and the potential of CRISPR/Cas9-based screening in developing cancer precision medicine and engineering models for targeted cancer therapy, highlighting the existing challenges and future directions. Lastly, we highlight the role of artificial intelligence in refining the CRISPR system's on-target and off-target effects, a critical factor for the broader application in cancer therapeutics.

Keywords: Artificial intelligence; CAR T-cells; CRISPR/Cas9; Cancer Immunotherapy; Cancer biomarker; Cancer precision medicine; Drug resistance; Epigenetics; Genome engineering.

Fig. 1

CRISPR/Cas9 in cancer research. A Schematic diagram illustrating cancer initiation and progression by involvement of multiple genetic and epigenetic alterations in cancer. B Different approaches used for genome editing in cancer include ZFNs, TALENs, and CRISPR/Cas9 systems. C CRISPR/Cas9 editing targets specific genes or growth factors regulating oncogenic processes. D Numerous mutations and dysregulated expression of oncogenes, tumor suppressor genes, chemotherapy-resistant genes, and cancer stem cell–related genes involved in tumorigenesis targeted by CRISPR/Cas9 system can be used for discovery of novel biomarkers and therapeutic targets in cancer research

Fig. 2

Schematic workflow of genome-wide CRISPR/Cas9 screening. A human genome-wide CRISPR/Cas9 knock-out library with sgRNAs is packed into lentiviral particles and transduced into Cas9-overexpressing cancer cells. The sgRNA-transduced cells are selected to generate mutant cells. Mutant cells are treated with drugs and DMSO (vehicle). DNA is extracted, and sgRNA is amplified via PCR. Whole-genome screening is conducted via next-generation sequencing before bioinformatics analysis. Volcano plots depicting genes selected with and without drug treatment and the corresponding networks are shown, with enriched genes on nodes and signaling pathways highlighted

Fig. 3

Implications of CRISPR/Cas9 genome engineering for personalized medicine in cancer treatment. Schematic showing the development of the CRISPR-Cas genome engineering platform to identify potential therapeutic targets and design cancer models specific to patient-specific genomic anomalies. CRISPR/Cas9-mediated knock-out, knockin or CRISPR Interference (CRISPRi) screens can be used to identify and validate novel drug targets, tumor-suppressor genes, cancer stem cell-related genes and to elucidate unknown drug resistance mechanisms, thus helping in perosnlized drug designing

Fig. 4

CRISPR/Cas9 in immunotherapy. The application of CRISPR/Cas9 system in editing CAR-T cells: A CRISPR/Cas9 system can be used to engineer CAR T-cells to make them more specific and to generate allogeneic universal CAR-T cells with reduced graft-versus-host disease (GVHD) responses. CRISPR/Cas9 system can simultaneously and efficiently knock out multiple gene loci to yield allogeneic universal T cells by incorporation of multiple guide RNAs in a CAR lentiviral vector, B CRISPR/Cas9 system can improve CAR T-cell functionality by avoiding off-target effects and making them more robust for enhanced proliferation and efficiency C CRISPR/Cas9 system can knock out inhibitory molecules (immune checkpoints) to enhance function of CAR-T cells, and D CRISPR/Cas9 system can modulate T-cell cytokine production to reduce the risk of cytokine release syndrome and inflammation for enhanced efficiency of cancer therapeutics

Fig. 5

CRISPR/Cas9 deep learning architecture. Artificial intelligence-based deep learning model architecture showing different steps for predicting on/off-targets in the CRISPR/Cas9 system. The model takes a 4 × 23 code matrix corresponding to 4 nucleotides of 23 sequence length as input. The input is passed to the convolutional layer for obtaining sgRNA-DNA matching information by applying different filters of varied sizes. The information is passed for batch normalization to reduce the effect of internal covariates. A pooling layer is connected to the normalization layer which filters out the non-informative values. The result of pooling layer is converted into a single vector by flattening which is connected to the fully connected layer for final model classification