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Cited by 251CRISPR and Genetic EngineeringCAR-T cell therapy researchVirus-based gene therapy research

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Comprehensive review of CRISPR-based gene editing: mechanisms, challenges, and applications in cancer therapy

Open access
Chehelgerdi, Mohammad, Chehelgerdi, Matin, Khorramian-Ghahfarokhi, Milad, Shafieizadeh, Marjan, Mahmoudi, Esmaeil, Eskandari, Fatemeh, Rashidi, Mohsen, Arshi, Asghar, Mokhtari-Farsani, Abbas·2024·Molecular Cancer
DOI 10.1186/s12943-023-01925-5PMC PMC10775503
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Abstract

The CRISPR system is a revolutionary genome editing tool that has the potential to revolutionize the field of cancer research and therapy. The ability to precisely target and edit specific genetic mutations that drive the growth and spread of tumors has opened up new possibilities for the development of more effective and personalized cancer treatments. In this review, we will discuss the different CRISPR-based strategies that have been proposed for cancer therapy, including inactivating genes that drive tumor growth, enhancing the immune response to cancer cells, repairing genetic mutations that cause cancer, and delivering cancer-killing molecules directly to tumor cells. We will also summarize the current state of preclinical studies and clinical trials of CRISPR-based cancer therapy, highlighting the most promising results and the challenges that still need to be overcome. Safety and delivery are also important challenges for CRISPR-based cancer therapy to become a viable clinical option. We will discuss the challenges and limitations that need to be overcome, such as off-target effects, safety, and delivery to the tumor site. Finally, we will provide an overview of the current challenges and opportunities in the field of CRISPR-based cancer therapy and discuss future directions for research and development. The CRISPR system has the potential to change the landscape of cancer research, and this review aims to provide an overview of the current state of the field and the challenges that need to be overcome to realize this potential.

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Figures

Fig. 1 The evolution of CRISPR tools that have been harnessed in the investigation of cancer biology. Since the inception of CRISPR-associated 9 (Cas9) gene editing in mammalian cells, there has been a rapid expansion in the field of CRISPR technology. This expansion has led to the development of various specialized CRISPR variants designed to tackle specific challenges. Scientists have created these variants through deliberate design and evolutionary processes, resulting in improved flexibility in recognizing protospacer adjacent motifs (PAMs) and increased precision in target selection. Additionally, they've harnessed naturally occurring variants from different bacterial species, like Cas12a (Cpf1) and Cas13, for effective combinatorial knockout (KO) and precise RNA targeting, respectively. To broaden the range of CRISPR applications, researchers have combined transcriptional effectors with catalytically inactive Cas9 (dCas9), allowing precise targeting of the transcriptome and epigenome. Furthermore, CRISPR base editing has enabled the introduction of specific transition mutations using a Cas9 nickase (Cas9n) fused with adenine or cytosine deaminase. In the case of cytosine base editing enzymes (BEs), they use a uracil glycosylase inhibitor (UGI) to prevent base excision repair and promote C > T transition mutations. A significant advancement known as prime editing has emerged, which involves fusing a dCas9 with a reverse transcriptase, enabling the engineering of various mutation types, such as missense mutations, insertions, and deletions. This is guided by a sequence template and an extended prime editing guide RNA (pegRNA). Additionally, to facilitate unbiased proteome mapping, researchers have employed engineered ascorbate peroxidase (APEX2) tethered to dCas9, enabling targeted biotinylation at specific genomic locations. Reprinted from [11] with permission from Springer Nature
Fig. 1 The evolution of CRISPR tools that have been harnessed in the investigation of cancer biology. Since the inception of CRISPR-associated 9 (Cas9) gene editing in mammalian cells, there has been a rapid expansion in the field of CRISPR technology. This expansion has led to the development of various specialized CRISPR variants designed to tackle specific challenges. Scientists have created these variants through deliberate design and evolutionary processes, resulting in improved flexibility in recognizing protospacer adjacent motifs (PAMs) and increased precision in target selection. Additionally, they've harnessed naturally occurring variants from different bacterial species, like Cas12a (Cpf1) and Cas13, for effective combinatorial knockout (KO) and precise RNA targeting, respectively. To broaden the range of CRISPR applications, researchers have combined transcriptional effectors with catalytically inactive Cas9 (dCas9), allowing precise targeting of the transcriptome and epigenome. Furthermore, CRISPR base editing has enabled the introduction of specific transition mutations using a Cas9 nickase (Cas9n) fused with adenine or cytosine deaminase. In the case of cytosine base editing enzymes (BEs), they use a uracil glycosylase inhibitor (UGI) to prevent base excision repair and promote C > T transition mutations. A significant advancement known as prime editing has emerged, which involves fusing a dCas9 with a reverse transcriptase, enabling the engineering of various mutation types, such as missense mutations, insertions, and deletions. This is guided by a sequence template and an extended prime editing guide RNA (pegRNA). Additionally, to facilitate unbiased proteome mapping, researchers have employed engineered ascorbate peroxidase (APEX2) tethered to dCas9, enabling targeted biotinylation at specific genomic locations. Reprinted from [11] with permission from Springer NatureCC BY · PubMed Central
Fig. 2 Different workflows used in CRISPR screening and mutagenesis. The CRISPR screening procedures commence by selecting the appropriate screening system, offering various options: A CRISPRko, where Cas9 is employed to disrupt genes, resulting in the generation of premature stop codons or frameshift mutations; CRISPRa, involving the attachment of activation domains (e.g., VPR, VP64) to dCas9, resulting in enhanced transcription of target genes; CRISPRi, on the contrary, employs repression domains (e.g., KRAB) tethered to dCas9, leading to a reduction in the transcription of target genes; Base editing screen, which uses a base editor (e.g., cytosine deaminase or adenine deaminase) with or without a uracil DNA glycosylase inhibitor to induce mutations without causing double-strand breaks. Once the suitable CRISPR screening method is chosen, the gRNA library is introduced into cells, creating a genetically altered cell population. These cells are exposed to drugs to select for drug-resistant populations. Subsequently, the gRNAs are extracted from the cells, amplified via PCR, and their target genes are determined using next-generation sequencing. B On the other hand, CRISPR mutagenesis screening begins with a gRNA library designed to induce in-frame mutations in the target protein coding sequence. After transducing the cells with the gRNA library, viable cells with protein variants are subjected to drug treatment, both with and without the drug. Activity-based cell sorting is used to enrich cells carrying mutations that make the drug ineffective, thereby identifying drug-resistant cells. Finally, the enriched cells are genotyped using deep sequencing to analyze structural changes and detect any escape mutants. Reprinted from [14] with permission from Cell Press
Fig. 2 Different workflows used in CRISPR screening and mutagenesis. The CRISPR screening procedures commence by selecting the appropriate screening system, offering various options: A CRISPRko, where Cas9 is employed to disrupt genes, resulting in the generation of premature stop codons or frameshift mutations; CRISPRa, involving the attachment of activation domains (e.g., VPR, VP64) to dCas9, resulting in enhanced transcription of target genes; CRISPRi, on the contrary, employs repression domains (e.g., KRAB) tethered to dCas9, leading to a reduction in the transcription of target genes; Base editing screen, which uses a base editor (e.g., cytosine deaminase or adenine deaminase) with or without a uracil DNA glycosylase inhibitor to induce mutations without causing double-strand breaks. Once the suitable CRISPR screening method is chosen, the gRNA library is introduced into cells, creating a genetically altered cell population. These cells are exposed to drugs to select for drug-resistant populations. Subsequently, the gRNAs are extracted from the cells, amplified via PCR, and their target genes are determined using next-generation sequencing. B On the other hand, CRISPR mutagenesis screening begins with a gRNA library designed to induce in-frame mutations in the target protein coding sequence. After transducing the cells with the gRNA library, viable cells with protein variants are subjected to drug treatment, both with and without the drug. Activity-based cell sorting is used to enrich cells carrying mutations that make the drug ineffective, thereby identifying drug-resistant cells. Finally, the enriched cells are genotyped using deep sequencing to analyze structural changes and detect any escape mutants. Reprinted from [14] with permission from Cell PressCC BY · PubMed Central
Fig. 3 The various mechanisms employed for gene editing. In the first part (a), Zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR-Cas systems are capable of generating double-strand breaks (DSBs) at precise locations within the genome. Moving on to the second part (b), the introduction of DNA sequences or mutations into the DNA can be achieved by means of homology-directed repair (HDR) or non-homologous end joining (NHEJ) processes with the aid of a donor template. In mammalian cells, CRISPR-induced DSBs are generally mended via NHEJ, which can result in the incorporation of small insertions and/or deletions (indels), leading to gene inactivation due to frameshift mutations. When two DSBs occur on the same chromosome, a substantial segment can be deleted, whereas DSBs on different chromosomes can give rise to chromosomal rearrangements. The abbreviations found in the figure include dsDNA (double-stranded DNA), PAM (protospacer adjacent motif), sgRNA (single-guide RNA), ssDNA (single-stranded DNA), and TALE (transcription activator-like effector). Reprinted from [15] with permission from Springer Nature
Fig. 3 The various mechanisms employed for gene editing. In the first part (a), Zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR-Cas systems are capable of generating double-strand breaks (DSBs) at precise locations within the genome. Moving on to the second part (b), the introduction of DNA sequences or mutations into the DNA can be achieved by means of homology-directed repair (HDR) or non-homologous end joining (NHEJ) processes with the aid of a donor template. In mammalian cells, CRISPR-induced DSBs are generally mended via NHEJ, which can result in the incorporation of small insertions and/or deletions (indels), leading to gene inactivation due to frameshift mutations. When two DSBs occur on the same chromosome, a substantial segment can be deleted, whereas DSBs on different chromosomes can give rise to chromosomal rearrangements. The abbreviations found in the figure include dsDNA (double-stranded DNA), PAM (protospacer adjacent motif), sgRNA (single-guide RNA), ssDNA (single-stranded DNA), and TALE (transcription activator-like effector). Reprinted from [15] with permission from Springer NatureCC BY · PubMed Central
Fig. 4 The diverse applications of CRISPR technology within cancer research. In section a, the paragraph explains that the inhibition of a specific gene can be accomplished by combining Deactivated Cas9 (dCas9) with repressor domains. In section b, it discusses how the fusion of dCas9 with activation domains can stimulate the expression of a particular gene. Furthermore, it mentions that augmenting the binding of additional transcription activators to a single-guide RNA or dCas9 can intensify the expression of target exons. In section c, it states that when dCas9 is fused with epigenetic regulators, it can either repress or activate transcription. In section d, the paragraph talks about the focused introduction of point mutations in the genome, which is made possible by combining dCas9 with adenosine deaminase or cytidine deaminase, allowing for precise genetic modifications. Additionally, it provides explanations for the abbreviations KRAB (Kruppel-associated box) and scFv (single-chain variable fragment). Reprinted from [15] with permission from Springer Nature
Fig. 4 The diverse applications of CRISPR technology within cancer research. In section a, the paragraph explains that the inhibition of a specific gene can be accomplished by combining Deactivated Cas9 (dCas9) with repressor domains. In section b, it discusses how the fusion of dCas9 with activation domains can stimulate the expression of a particular gene. Furthermore, it mentions that augmenting the binding of additional transcription activators to a single-guide RNA or dCas9 can intensify the expression of target exons. In section c, it states that when dCas9 is fused with epigenetic regulators, it can either repress or activate transcription. In section d, the paragraph talks about the focused introduction of point mutations in the genome, which is made possible by combining dCas9 with adenosine deaminase or cytidine deaminase, allowing for precise genetic modifications. Additionally, it provides explanations for the abbreviations KRAB (Kruppel-associated box) and scFv (single-chain variable fragment). Reprinted from [15] with permission from Springer NatureCC BY · PubMed Central
Fig. 5 Different strategies for editing cells using CRISPR technology in patients. On the left, ex vivo applications involve first isolating cells, then expanding and editing them before transplanting them back. On the right, in vivo editing occurs by administering CRISPR-Cas9 (or dCas9, not shown) locally or systemically using viral packaging or nanoparticles. The figure also highlights specific clinical trials. Abbreviations used include CRISPR (clustered regularly interspaced short palindromic repeats), dCas9 (dead Cas9), and HPV (human papillomavirus). Reprinted from [14] with permission from Cell Press
Fig. 5 Different strategies for editing cells using CRISPR technology in patients. On the left, ex vivo applications involve first isolating cells, then expanding and editing them before transplanting them back. On the right, in vivo editing occurs by administering CRISPR-Cas9 (or dCas9, not shown) locally or systemically using viral packaging or nanoparticles. The figure also highlights specific clinical trials. Abbreviations used include CRISPR (clustered regularly interspaced short palindromic repeats), dCas9 (dead Cas9), and HPV (human papillomavirus). Reprinted from [14] with permission from Cell PressCC BY · PubMed Central
Fig. 6 Employing CRISPR for creating cancer models in cells and mice. In the initial case (a), cultured cells or organoids undergo genome editing through CRISPR, which facilitates the incorporation of alterations or adjustments in transcriptional control at one or more phases. In the latter case (b), CRISPR mechanisms can be transferred to animal models using diverse methods, thereby enabling them to target numerous tissues and organs. One such approach involves utilizing adeno-associated viruses (AAV) for delivery. Reprinted from [15] with permission from Springer Nature
Fig. 6 Employing CRISPR for creating cancer models in cells and mice. In the initial case (a), cultured cells or organoids undergo genome editing through CRISPR, which facilitates the incorporation of alterations or adjustments in transcriptional control at one or more phases. In the latter case (b), CRISPR mechanisms can be transferred to animal models using diverse methods, thereby enabling them to target numerous tissues and organs. One such approach involves utilizing adeno-associated viruses (AAV) for delivery. Reprinted from [15] with permission from Springer NatureCC BY · PubMed Central
Fig. 7 A The CTX001 molecular approach and preclinical studies. Panel A illustrates the shift from fetal hemoglobin (HbF) to adult hemoglobin (HbA) shortly after birth and the role of the transcription factor BCL11A in suppressing γ-globin, a component of fetal hemoglobin. When fetal hemoglobin levels decrease approximately 3 months after birth, individuals who cannot produce enough functional β-globin experience symptoms. This has implications for conditions like sickle cell disease (SCD) and transfusion-dependent β-thalassemia (TDT). Moving to Panel B, it showcases the specific editing site targeted by the single guide RNA (sgRNA) that guides CRISPR-Cas9 to the erythroid-specific enhancer region of BCL11A. The five BCL11A exons are represented as gold boxes, and GATA1 is the binding site for the GATA1 transcription factor. PAM, the protospacer adjacent motif (NGG), is a specific DNA sequence required immediately following the Cas9 target DNA sequence. Panel C displays preclinical data that reveals the percentage of fetal hemoglobin as a portion of total hemoglobin after editing and the differentiation of erythroid cells. This data was obtained from samples taken from 10 healthy donors, with error bars indicating the standard deviation. Finally, Panel D presents the results of an off-target evaluation. GUIDE-seq (genomewide unbiased identification of double-strand breaks enabled by sequencing) was independently performed on three CD34 + HSPC (hematopoietic stem and progenitor cell) healthy donor samples to nominate sites. Subsequently, hybrid capture was conducted on four CD34 + HSPC healthy donor samples to confirm these sites. The on-target allelic editing was confirmed in each experiment with an average of 57%, and no detectable off-target editing was observed at any of the sites identified by GUIDE-seq and sequence homology. Panel A was adapted with permission from Canver and Orkin. B The data related to hemoglobin fractionation, F-cell levels, and transfusion events in the two groups of patients under study. Panel A depicts the results of CRISPR-Cas9 treatment for transfusion-dependent β-thalassemia in Patient 1, while Panel D presents data for Patient 2, who received treatment for sickle cell disease, showcasing various hemoglobin adducts and variants. The changes in F-cell percentages over time can be observed in Panel B for Patient 1 and in Panel E for Patient 2. Baseline levels of hemoglobin and F-cells were established during the initial assessment prior to treatment. Additionally, Panel C shows the progression of transfusion events over time in Patient 1, and Panel F displays vaso-occlusive crises (VOCs) or episodes and transfusion events in Patient 2. It's worth noting that exchange transfusions performed according to the study protocol before the infusion of CTX001 during the on-study period are not included in the figures. Reprinted from [152] with permission from the New England Journal of Medicine
Fig. 7 A The CTX001 molecular approach and preclinical studies. Panel A illustrates the shift from fetal hemoglobin (HbF) to adult hemoglobin (HbA) shortly after birth and the role of the transcription factor BCL11A in suppressing γ-globin, a component of fetal hemoglobin. When fetal hemoglobin levels decrease approximately 3 months after birth, individuals who cannot produce enough functional β-globin experience symptoms. This has implications for conditions like sickle cell disease (SCD) and transfusion-dependent β-thalassemia (TDT). Moving to Panel B, it showcases the specific editing site targeted by the single guide RNA (sgRNA) that guides CRISPR-Cas9 to the erythroid-specific enhancer region of BCL11A. The five BCL11A exons are represented as gold boxes, and GATA1 is the binding site for the GATA1 transcription factor. PAM, the protospacer adjacent motif (NGG), is a specific DNA sequence required immediately following the Cas9 target DNA sequence. Panel C displays preclinical data that reveals the percentage of fetal hemoglobin as a portion of total hemoglobin after editing and the differentiation of erythroid cells. This data was obtained from samples taken from 10 healthy donors, with error bars indicating the standard deviation. Finally, Panel D presents the results of an off-target evaluation. GUIDE-seq (genomewide unbiased identification of double-strand breaks enabled by sequencing) was independently performed on three CD34 + HSPC (hematopoietic stem and progenitor cell) healthy donor samples to nominate sites. Subsequently, hybrid capture was conducted on four CD34 + HSPC healthy donor samples to confirm these sites. The on-target allelic editing was confirmed in each experiment with an average of 57%, and no detectable off-target editing was observed at any of the sites identified by GUIDE-seq and sequence homology. Panel A was adapted with permission from Canver and Orkin. B The data related to hemoglobin fractionation, F-cell levels, and transfusion events in the two groups of patients under study. Panel A depicts the results of CRISPR-Cas9 treatment for transfusion-dependent β-thalassemia in Patient 1, while Panel D presents data for Patient 2, who received treatment for sickle cell disease, showcasing various hemoglobin adducts and variants. The changes in F-cell percentages over time can be observed in Panel B for Patient 1 and in Panel E for Patient 2. Baseline levels of hemoglobin and F-cells were established during the initial assessment prior to treatment. Additionally, Panel C shows the progression of transfusion events over time in Patient 1, and Panel F displays vaso-occlusive crises (VOCs) or episodes and transfusion events in Patient 2. It's worth noting that exchange transfusions performed according to the study protocol before the infusion of CTX001 during the on-study period are not included in the figures. Reprinted from [152] with permission from the New England Journal of MedicineCC BY · PubMed Central
Fig. 8 A The process and results of high-throughput quantification of gRNA efficiency in cells. In panel (a), a graphic illustrates the sequence of actions involved, which includes employing a lentiviral surrogate vector, synthesizing an oligo pool, performing PCR amplification, using golden-gate assembly, packing the genetic material into lentiviruses, and then introducing it. Panel (b) showcases the editing efficiency of gRNA at all surrogate locations, assessed through targeted amplicon sequencing. The results are presented for HEK293T-SpCas9 cells at 2, 8, and 10 days following the introduction. Panel (c) displays the correlation between gRNA editing efficiency on days 8 and 10 post-transduction. Panel (d) presents the patterns of indels (deletions ranging from 1–30 bp and insertions ranging from 1–10 bp) introduced by SpCas9 in HEK293T-SpCas9 cells at 2, 8, and 10 days after the transduction. Panel (e) depicts the agreement between the observed indel patterns in cells and those predicted by inDelphi, visualized as a violin plot with medians and quartiles. In panel (f), a scatter plot portrays the frequency of 1-bp insertion indels (mean ± 95% confidence interval), categorized based on the nucleotide at position N17 of the protospacer and the type of inserted nucleotide. Lastly, panel (g) exhibits the association between gRNA editing efficiencies in this study and those from other significant research, with a particular emphasis on common gRNA + PAM (23 nt) cases, presented using a Venn diagram. B The CRISPR on model and its ability to generalize on independent test sets. Panel a displays a visual depiction of the input DNA sequence for CRISPRon, including the prediction algorithm. The deep learning network receives inputs in the form of a one-hot encoded 30mer and the binding energy (ΔGB). It's worth noting that only the filtering (convolutional) layers and the three fully connected layers are explicitly depicted, with the thin vertical bars representing the output of one layer, serving as the input for the next layer. In panel b, a performance evaluation comparing CRISPRon to other existing models is presented, specifically focusing on independent test sets containing over 1000 gRNAs. Reprinted from [223] with permission from Springer Nature
Fig. 8 A The process and results of high-throughput quantification of gRNA efficiency in cells. In panel (a), a graphic illustrates the sequence of actions involved, which includes employing a lentiviral surrogate vector, synthesizing an oligo pool, performing PCR amplification, using golden-gate assembly, packing the genetic material into lentiviruses, and then introducing it. Panel (b) showcases the editing efficiency of gRNA at all surrogate locations, assessed through targeted amplicon sequencing. The results are presented for HEK293T-SpCas9 cells at 2, 8, and 10 days following the introduction. Panel (c) displays the correlation between gRNA editing efficiency on days 8 and 10 post-transduction. Panel (d) presents the patterns of indels (deletions ranging from 1–30 bp and insertions ranging from 1–10 bp) introduced by SpCas9 in HEK293T-SpCas9 cells at 2, 8, and 10 days after the transduction. Panel (e) depicts the agreement between the observed indel patterns in cells and those predicted by inDelphi, visualized as a violin plot with medians and quartiles. In panel (f), a scatter plot portrays the frequency of 1-bp insertion indels (mean ± 95% confidence interval), categorized based on the nucleotide at position N17 of the protospacer and the type of inserted nucleotide. Lastly, panel (g) exhibits the association between gRNA editing efficiencies in this study and those from other significant research, with a particular emphasis on common gRNA + PAM (23 nt) cases, presented using a Venn diagram. B The CRISPR on model and its ability to generalize on independent test sets. Panel a displays a visual depiction of the input DNA sequence for CRISPRon, including the prediction algorithm. The deep learning network receives inputs in the form of a one-hot encoded 30mer and the binding energy (ΔGB). It's worth noting that only the filtering (convolutional) layers and the three fully connected layers are explicitly depicted, with the thin vertical bars representing the output of one layer, serving as the input for the next layer. In panel b, a performance evaluation comparing CRISPRon to other existing models is presented, specifically focusing on independent test sets containing over 1000 gRNAs. Reprinted from [223] with permission from Springer NatureCC BY · PubMed Central
Fig. 9 The functional domains of different CRISPR effectors and their applications in genome-scale screens. Multiple CRISPR effectors are accessible for disrupting coding and noncoding DNA and RNA segments. One commonly employed CRISPR effector is the CRISPR-associated 9 (Cas9) nuclease, which precisely cuts DNA at a specified target site guided by a guide RNA (gRNA). Noncoding regions can be suppressed with CRISPR interference (CRISPRi) by directing the catalytically inactive Cas9 (dCas9) to promoters and enhancer regions. It can be fused with repressor domains like methyl-CpG-binding protein 2 (MeCP2) and Krüppel-associated box (KRAB). Conversely, gene expression can be enhanced by directing dCas9 fusion proteins to regions around transcription start sites (TSSs). One approach is to fuse dCas9 with transcriptional activators such as VP64, p65, and Rta (VPR). Another method is fusing dCas9 with VP64 and using a modified single gRNA (sgRNA) to recruit the activator fusion complex MS2–p65–HSF1, collectively known as the synergistic activation modulator (SAM). In addition to targeting DNA, the Cas13 nuclease can be employed to cleave RNA at a specific site indicated by a gRNA. Furthermore, dCas9 can be combined with methyltransferases (e.g., DNA methyltransferase 3A or DNMT3A) to enable targeted DNA methylation or with proteins involved in DNA demethylation (e.g., tet methylcytosine dioxygenase 1 or TET1) to facilitate targeted DNA demethylation. Moreover, linking dCas9 to acetyltransferases like p300 or histone deacetylase proteins like histone deacetylase 3 (HDAC3) enables targeted histone acetylation or deacetylation, respectively. The design of gRNAs depends on the specific CRISPR effector and the intended targets of the CRISPR screen. When focusing on protein-coding genes, gRNAs can be designed to target either exons (using CRISPR nucleases) or regions near the transcription start site (TSS) of the gene (for CRISPRi or CRISPR activation (CRISPRa)). For saturation mutagenesis using nucleases, gRNAs are designed to target multiple noncoding regions around a gene of interest. In noncoding genome-wide screens using CRISPR nucleases, CRISPRi, or CRISPRa, gRNAs are tailored to specific genomic features like cis-regulatory elements. When silencing or amplifying noncoding RNAs with CRISPRi and CRISPRa, respectively, sgRNAs are directed to regions flanking the transcription start site (TSS) of a noncoding RNA gene. Reprinted from [11] with permission from Springer Nature
Fig. 9 The functional domains of different CRISPR effectors and their applications in genome-scale screens. Multiple CRISPR effectors are accessible for disrupting coding and noncoding DNA and RNA segments. One commonly employed CRISPR effector is the CRISPR-associated 9 (Cas9) nuclease, which precisely cuts DNA at a specified target site guided by a guide RNA (gRNA). Noncoding regions can be suppressed with CRISPR interference (CRISPRi) by directing the catalytically inactive Cas9 (dCas9) to promoters and enhancer regions. It can be fused with repressor domains like methyl-CpG-binding protein 2 (MeCP2) and Krüppel-associated box (KRAB). Conversely, gene expression can be enhanced by directing dCas9 fusion proteins to regions around transcription start sites (TSSs). One approach is to fuse dCas9 with transcriptional activators such as VP64, p65, and Rta (VPR). Another method is fusing dCas9 with VP64 and using a modified single gRNA (sgRNA) to recruit the activator fusion complex MS2–p65–HSF1, collectively known as the synergistic activation modulator (SAM). In addition to targeting DNA, the Cas13 nuclease can be employed to cleave RNA at a specific site indicated by a gRNA. Furthermore, dCas9 can be combined with methyltransferases (e.g., DNA methyltransferase 3A or DNMT3A) to enable targeted DNA methylation or with proteins involved in DNA demethylation (e.g., tet methylcytosine dioxygenase 1 or TET1) to facilitate targeted DNA demethylation. Moreover, linking dCas9 to acetyltransferases like p300 or histone deacetylase proteins like histone deacetylase 3 (HDAC3) enables targeted histone acetylation or deacetylation, respectively. The design of gRNAs depends on the specific CRISPR effector and the intended targets of the CRISPR screen. When focusing on protein-coding genes, gRNAs can be designed to target either exons (using CRISPR nucleases) or regions near the transcription start site (TSS) of the gene (for CRISPRi or CRISPR activation (CRISPRa)). For saturation mutagenesis using nucleases, gRNAs are designed to target multiple noncoding regions around a gene of interest. In noncoding genome-wide screens using CRISPR nucleases, CRISPRi, or CRISPRa, gRNAs are tailored to specific genomic features like cis-regulatory elements. When silencing or amplifying noncoding RNAs with CRISPRi and CRISPRa, respectively, sgRNAs are directed to regions flanking the transcription start site (TSS) of a noncoding RNA gene. Reprinted from [11] with permission from Springer NatureCC BY · PubMed Central
Fig. 10 Illustrates the application of CRISPR in immuno-oncology. In scenario a, primary T cells extracted and purified from cancer patients can have a chimeric antigen receptor (CAR) inserted using CRISPR technology, instead of lentiviral-mediated transduction. CRISPR can also be employed to deactivate immune-checkpoint genes, such as PD-1 and CTLA-4, within these T cells. Alternatively, scenario b involves the isolation and purification of primary T cells from healthy donors not diagnosed with cancer. CRISPR systems are used to introduce a CAR into these cells, and they can also be utilized to inactivate the genes responsible for T cell receptor (TCR) and HLA components. This process generates 'universal' allogeneic CAR T cells, which can be infused into cancer patients. Reprinted from [15] with permission from Springer Nature
Fig. 10 Illustrates the application of CRISPR in immuno-oncology. In scenario a, primary T cells extracted and purified from cancer patients can have a chimeric antigen receptor (CAR) inserted using CRISPR technology, instead of lentiviral-mediated transduction. CRISPR can also be employed to deactivate immune-checkpoint genes, such as PD-1 and CTLA-4, within these T cells. Alternatively, scenario b involves the isolation and purification of primary T cells from healthy donors not diagnosed with cancer. CRISPR systems are used to introduce a CAR into these cells, and they can also be utilized to inactivate the genes responsible for T cell receptor (TCR) and HLA components. This process generates 'universal' allogeneic CAR T cells, which can be infused into cancer patients. Reprinted from [15] with permission from Springer NatureCC BY · PubMed Central
Fig. 11 The ex vivo CRISPR manipulation of human T cells for adoptive T cell therapy. Ongoing clinical trials are currently dedicated to assessing the safety and effectiveness of CRISPR-engineered T cells through ex vivo modification and subsequent transfer. The goal is to enhance the anti-cancer response of T cells taken from healthy donors or patients. These trials investigate the potential of both allogeneic (from different donors) and autologous (from the patient themselves) T cells in various approaches, including tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T cells. One of the methods involves using CRISPR-Cas9 to remove immunosuppressive factors, like the programmed cell death protein 1 (PD1) ligand, from human primary T cells. This approach is being tested for adoptive T cell therapy involving both TILs and CAR T cells. The delivery of CRISPR-Cas9 ribonucleoproteins (RNPs) allows precise editing of immunosuppressive factors such as PD1 by guiding Cas9 to specific locations. Researchers are also exploring the deletion of the endogenous T cell receptor (TCR) using CRISPR-Cas9 to prevent TCR priming or immune rejection in the case of allogeneic T cells. Another avenue being explored is the replacement of the endogenous TCR with a cancer antigen-specific TCR, either through a TCR transgene or a CAR element. This has been shown to enhance the killing of cancer cells by T cells. In clinical trials, CRISPR-Cas9 homology-directed repair (HDR)-mediated knock-in to the T cell receptor α-chain constant (TRAC) locus is used to deliver CAR elements, and its efficacy is being tested. Additionally, CRISPR is used to delete the endogenous T cell receptor-β constant (TRBC) locus and endogenous major histocompatibility complex class I (MHC-I) to prevent immune rejection after transplant, and to remove immunosuppressive factors, all aimed at improving T cell activity in CAR T cells. Next-generation sequencing (NGS) is employed to confirm the engineered T cells, ensuring accurate on-target editing with minimal off-target effects. The expanded and validated T cells are then transplanted into the cancer patient, and disease progression is closely monitored to assess the safety and efficacy of the engineered T cells. Reprinted from [11] with permission from Springer Nature
Fig. 11 The ex vivo CRISPR manipulation of human T cells for adoptive T cell therapy. Ongoing clinical trials are currently dedicated to assessing the safety and effectiveness of CRISPR-engineered T cells through ex vivo modification and subsequent transfer. The goal is to enhance the anti-cancer response of T cells taken from healthy donors or patients. These trials investigate the potential of both allogeneic (from different donors) and autologous (from the patient themselves) T cells in various approaches, including tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T cells. One of the methods involves using CRISPR-Cas9 to remove immunosuppressive factors, like the programmed cell death protein 1 (PD1) ligand, from human primary T cells. This approach is being tested for adoptive T cell therapy involving both TILs and CAR T cells. The delivery of CRISPR-Cas9 ribonucleoproteins (RNPs) allows precise editing of immunosuppressive factors such as PD1 by guiding Cas9 to specific locations. Researchers are also exploring the deletion of the endogenous T cell receptor (TCR) using CRISPR-Cas9 to prevent TCR priming or immune rejection in the case of allogeneic T cells. Another avenue being explored is the replacement of the endogenous TCR with a cancer antigen-specific TCR, either through a TCR transgene or a CAR element. This has been shown to enhance the killing of cancer cells by T cells. In clinical trials, CRISPR-Cas9 homology-directed repair (HDR)-mediated knock-in to the T cell receptor α-chain constant (TRAC) locus is used to deliver CAR elements, and its efficacy is being tested. Additionally, CRISPR is used to delete the endogenous T cell receptor-β constant (TRBC) locus and endogenous major histocompatibility complex class I (MHC-I) to prevent immune rejection after transplant, and to remove immunosuppressive factors, all aimed at improving T cell activity in CAR T cells. Next-generation sequencing (NGS) is employed to confirm the engineered T cells, ensuring accurate on-target editing with minimal off-target effects. The expanded and validated T cells are then transplanted into the cancer patient, and disease progression is closely monitored to assess the safety and efficacy of the engineered T cells. Reprinted from [11] with permission from Springer NatureCC BY · PubMed Central

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Research works printed but not linked: 4

  1. De A, Biswas AR. Nanotechnology and Computational tool based study of CRISPR/Cas-9 research in Biomedical Engineering. J Nano Res Adv Mater Polym Sci. 2020;1:6–1.
  2. Reis A, Hornblower B, Robb B, Tzertzinis G. CRISPR/Cas9 and targeted genome editing: a new era in molecular biology. NEB expressions. 2014;1:3–6.
  3. Pomeroy E, Hunzeker J, Kluesner M, Crosby M, Bendzick L, Geller M, et al. Genetically engineered natural killer cells for cancer immunotherapy. Mol Ther. 2018;26(5):355–6.
  4. Gong Z, Zhang X, Dai Z. Progress of nanoparticles inhibiting tumor metastasis. Kexue Tongbao/Chinese Sci Bull. 2018;63(15):1482–92.

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Metadata

Authors
Chehelgerdi, M., Chehelgerdi, M., Khorramian-Ghahfarokhi, M., Shafieizadeh, M., Mahmoudi, E., Eskandari, F., Rashidi, M., Arshi, A., & Mokhtari-Farsani, A.
Affiliations
  • Islamic Azad University, Tehran (IR)
  • Shahid Bahonar University of Kerman (IR)
  • Islamic Azad University of Falavarjan (IR)
  • Mazandaran University of Medical Sciences (IR)
  • NourDanesh Institute of Higher Education (IR)
Corresponding author
Chehelgerdi, Mohammad
ORCID
Venue
Molecular Cancer
Publisher
Springer Science and Business Media LLC
Published
2024-01-09
Volume
23
Issue
1
Pages
9-9
ISSN
1476-4598 (ISSN-L)
DOI
10.1186/s12943-023-01925-5
PMC
PMC10775503
OpenAlex
W4390732271
Cited by
251
References
338
Citations by year
2026: 80 · 2025: 128 · 2024: 42
FWCI
10.17
Type
journal article
Language
en
Update notices
correction (2024-02-27)
Open access
Licence
Locations
  • Molecular Cancer (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Health Sciences › Medicine › Oncology › CAR-T cell therapy research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Virus-based gene therapy research
Keywords
CRISPR gene editing, cancer therapy, genome editing, tumor suppressor inactivation, cancer immunotherapy, off-target effects, precision oncology
MeSH terms
Gene Editing, Humans, Mutation, Neoplasms

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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