Base editor screens for in situ mutational scanning at scale

Accelerated drug-resistant variant discovery with an enhanced, scalable mutagenic base editor platform

Personalized cancer therapeutics bring directed treatment options to patients based on their tumor's genetic signature. Unfortunately, tumor genomes are remarkably adaptable, and acquired resistance through gene mutation frequently occurs. Identifying mutations that promote resistance within drug-treated patient populations can be cost, resource, and time intensive. Accordingly, base editing, enabled by Cas9-deaminase domain fusions, has emerged as a promising approach for rapid, large-scale gene variant screening in situ. Here, we adapt and optimize a conditional activation-induced cytidine deaminase (AID)-dead Cas9 (dCas9) system, which demonstrates greater heterogeneity of edits with an expanded footprint compared to the most commonly utilized cytosine base editor, BE4. In combination with a custom single guide RNA (sgRNA) library, we identify individual and compound variants in epidermal growth factor receptor (EGFR) and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) that confer resistance to established EGFR inhibitors. This system and analytical pipeline provide a simple, highly scalable platform for cis or trans drug-modifying variant discovery and for uncovering valuable insights into protein structure-function relationships.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

A mutational atlas for Parkin proteostasis

Proteostasis can be disturbed by mutations affecting folding and stability of the encoded protein. An example is the ubiquitin ligase Parkin, where gene variants result in autosomal recessive Parkinsonism. To uncover the pathological mechanism and provide comprehensive genotype-phenotype information, variant abundance by massively parallel sequencing (VAMP-seq) is leveraged to quantify the abundance of Parkin variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, shows that most low abundance variants are proteasome targets and are located within the structured domains of the protein. Half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) reveals an exposed degron region proximal to the so-called "activation element". This work provides examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation.

Forward Genetic Screens Identify Mechanisms of Resistance to Small-Molecule Lactate Dehydrogenase Inhibitors

Altered metabolism is a hallmark of cancer; however, it has been difficult to specifically target metabolism in cancer for therapeutic benefit. Cancers with genetically defined defects in metabolic enzymes constitute a subset of cancers where targeting metabolism is potentially accessible. Hürthle cell carcinoma of the thyroid (HTC) tumors frequently harbor deleterious mitochondrial DNA (mtDNA) mutations in subunits of complex I of the mitochondrial electron transport chain (ETC). Previous work has shown that HTC models with deleterious mtDNA mutations exhibit mitochondrial ETC defects that expose lactate dehydrogenase (LDH) as a therapeutic vulnerability. Here, we performed forward genetic screens to identify mechanisms of resistance to small-molecule LDH inhibitors. We identified two distinct mechanisms of resistance: upregulation of an LDH isoform and a compound-specific resistance mutation. Using these tools, we demonstrate that the anticancer activity of LDH inhibitors in cell line and xenograft models of complex I mutant HTC is through on-target LDH inhibition.

Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review

The advent of targeted therapies has led to tremendous improvements in treatment options and their outcomes in the field of oncology. Yet, many cancers outsmart precision drugs by developing on-target or off-target resistance mechanisms. Gaining the ability to resist treatment is the rule rather than the exception in tumors, and it remains a major healthcare challenge to achieve long-lasting remission in most cancer patients. Here, we discuss emerging strategies that take advantage of innovative high-throughput screening technologies to anticipate on- and off-target resistance mechanisms before they occur in treated cancer patients. We divide the methods into non-systematic approaches, such as random mutagenesis or long-term drug treatment, and systematic approaches, relying on the clustered regularly interspaced short palindromic repeats (CRISPR) system, saturated mutagenesis, or computational methods. All these new developments, especially genome-wide CRISPR-based screening platforms, have significantly accelerated the processes for identification of the mechanisms responsible for cancer drug resistance and opened up new avenues for future treatments.

Forward genetic screens identify mechanisms of resistance to small molecule lactate dehydrogenase inhibitors

Altered metabolism is a hallmark of cancer; however, it has been difficult to specifically target metabolism in cancer for therapeutic benefit. Cancers with genetically defined defects in metabolic enzymes constitute a subset of cancers where targeting metabolism is potentially accessible. Hürthle cell carcinoma of the thyroid (HTC) tumors frequently harbor deleterious mitochondrial DNA (mtDNA) mutations in subunits of complex I of the mitochondrial electron transport chain (ETC). Previous work has shown that HTC models with deleterious mtDNA mutations exhibit mitochondrial ETC defects that expose lactate dehydrogenase (LDH) as a therapeutic vulnerability. Here, we performed forward genetic screens to identify mechanisms of resistance to small molecule LDH inhibitors. We identified two distinct mechanisms of resistance: upregulation of an LDH isoform and a compound-specific resistance mutation. Using these tools, we demonstrate that the anti-cancer activity of LDH inhibitors in cell line and xenograft models of complex I-mutant HTC is through on-target LDH inhibition.

Base Editor Scanning Reveals Activating Mutations of DNMT3A

DNA methyltransferase 3A (DNMT3A) is a de novo cytosine methyltransferase responsible for establishing proper DNA methylation during mammalian development. Loss-of-function (LOF) mutations to DNMT3A, including the hotspot mutation R882H, frequently occur in developmental growth disorders and hematological diseases, including clonal hematopoiesis and acute myeloid leukemia. Accordingly, identifying mechanisms that activate DNMT3A is of both fundamental and therapeutic interest. Here, we applied a base editor mutational scanning strategy with an improved DNA methylation reporter to systematically identify DNMT3A activating mutations in cells. By integrating an optimized cellular recruitment strategy with paired isogenic cell lines with or without the LOF hotspot R882H mutation, we identify and validate three distinct hyperactivating mutations within or interacting with the regulatory ADD domain of DNMT3A, nominating these regions as potential functional target sites for pharmacological intervention. Notably, these mutations are still activating in the context of a heterozygous R882H mutation. Altogether, we showcase the utility of base editor scanning for discovering functional regions of target proteins.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.