Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy

Background In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. Methods In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. Results We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. Conclusion Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Microfluidic Multi-Drug and Multi-Dose Functional Profiling Towards Personalized Clinical Predictions in Pediatric Leukemia

Treatment of pediatric acute lymphoblastic leukemia (ALL) has seen dramatic improvements over the last several decades, leading to survival rates of over 90% for specific groups. However, for patients with relapsed/refractory disease, achieving complete remission remains a significant challenge. To further improve treatment outcomes for these patients, improved functional screens are needed to better match high risk leukemia patients with novel therapeutic strategies. Current functional screens are limited by the large patient samples required, particularly for combination drug screening and evaluation of higher order drug interactions. To that end, this work aims to develop a microfluidic multi-drug and multi-dose chemosensitivity assay to directly test candidate combinations of therapeutics in patient derived ALL cells. This will allow for analysis of response to combinations of 3 small molecule drugs while minimizing required patient sample and reducing experimental workload. Once validated, this assay can be used to identify biomarkers of response to novel combination regimens in pediatric ALL, and ultimately, could be used to prospectively guide therapy in newly diagnosed patients. Specifically, this is accomplished by generating stable, overlapping concentration gradients of small molecule drugs across 3D cultures of leukemia cells with and without human bone marrow derived mesenchymal stem cells (Fig 1A). We have demonstrated the ability to generate and maintain these gradients over 48 hours, and have shown that experimentally generated concentration gradients closely match steady state computational fluid dynamics (CFD) model predictions (Fig 1B). Moreover, exposing a T-ALL cell line (Jurkats) to a single gradient of cytarabine results in a gradient in cell viability. In drug treated devices, increasing drug dose led to decreasing averaged viability, while no appreciable difference was seen in vehicle control devices (Fig 1C). A commonly used 3 drug combination, Daunorubicin, Vincristine, and Prednisolone, was then used to validate the utility of the device for evaluating multi-dose drug combinations. Here, Jurkats were exposed to superimposed concentration gradients of Daunorubicin, Vincristine, and Prednisolone for 48 hours, after which cell viability as a function of concentration was examined. Specifically, a ternary contour map of the average viability across the device was generated, clearly depicting the range of response across the device (Fig 2B). Previous results have demonstrated Jurkats' sensitivity to Daunorubicin as well as Vincristine and relative resistance to Prednisolone, and these results are closely recapitulated in the device. Previous reports in literature have also demonstrated potential for antagonism between anthracyclines and vinca alkaloids, specifically between Doxorubicin and Vincristine in Jurkats (Ehrhardt, 2011). Our results also suggest that some amount of antagonism may exist between Daunorubicin and Vincristine in Jurkats at these concentration ranges tested, evidenced by the high average viability in regions of high vincristine concentrations and mid-range daunorubicin concentrations. Finally, we then used this system to evaluate response to drug combinations in a test patient sample. Specifically, combinations of Vincristine, Prednisolone, and Daunorubicin, as well as combinations of Nelarabine, Etoposide, and Cytoxan were evaluated in a standard risk B-ALL bone marrow aspirate sample. Drug treated devices resulted in significantly reduced viability relative to the vehicle control, with a differential in response observed between the 2 combinations tested (Fig 3A). Moreover, at these drug concentration ranges, it appears that combinations of Etoposide and Ara-G are particularly ineffective relative to other combinations in this sample (Fig 3B). Future studies will work to improve the baseline viability of banked samples in the device, and include testing of fresh leukemia samples as well. We are also working to develop methods to use data collected from these devices to identify synergistic or antagonistic drug combinations, and in the future, can correlate this with clinical outcome metrics as well as biomarkers of response. Tissue samples were provided by the Children's Healthcare of Atlanta Pediatric Bio-Repository. Other investigators may have received specimens from the same subjects. Figure 1 Figure 1. Disclosures Lam: Sanguina, Inc.: Current holder of individual stocks in a privately-held company.

Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens

Recent functional genomic screens -such as CRISPR-Cas9 or RNAi screening- have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem related to a large number of gene knockouts limits the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the <HUb effect in Genetic Essentiality> (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens -Project Score, CERES score, and DEMETER score- identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. HUGE shows an increased enrichment in a recent harmonized knowledgebase of clinical interpretations of somatic genomic variants in cancer (with an AUROC up to 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer.

Functional requirements of protein kinases and phosphatases in the development of the Drosophila melanogaster wing

Protein kinases and phosphatases constitute a large family of conserved enzymes that control a variety of biological processes by regulating the phosphorylation state of target proteins. They play fundamental regulatory roles during cell cycle progression and signaling, among other key aspects of multicellular development. The complement of protein kinases and phosphatases includes approximately 326 members in Drosophila, and they have been the subject of several functional screens searching for novel components of signaling pathways and regulators of cell division and survival. These approaches have been carried out mostly in cell cultures using RNA interference to evaluate the contribution of each protein in different functional assays, and have contributed significantly to assign specific roles to the corresponding genes. In this work we describe the results of an evaluation of the Drosophila complement of kinases and phosphatases using the wing as a system to identify their functional requirements in vivo. We also describe the results of several modifying screens aiming to identify among the set of protein kinases and phosphatases additional components or regulators of the activities of the Epidermal Growth Factor and Insulin receptors signaling pathways.

Genetic variations of DNA bindings of FOXA1 and co-factors in breast cancer susceptibility

Identifying transcription factors (TFs) whose DNA bindings are altered by genetic variants that regulate susceptibility genes is imperative to understand transcriptional dysregulation in disease etiology. Here, we develop a statistical framework to analyze extensive ChIP-seq and GWAS data and identify 22 breast cancer risk-associated TFs. We find that, by analyzing genetic variations of TF-DNA bindings, the interaction of FOXA1 with co-factors such as ESR1 and E2F1, and the interaction of TFs with chromatin features (i.e., enhancers) play a key role in breast cancer susceptibility. Using genetic variants occupied by the 22 TFs, transcriptome-wide association analyses identify 52 previously unreported breast cancer susceptibility genes, including seven with evidence of essentiality from functional screens in breast relevant cell lines. We show that FOXA1 and co-factors form a core TF-transcriptional network regulating the susceptibility genes. Our findings provide additional insights into genetic variations of TF-DNA bindings (particularly for FOXA1) underlying breast cancer susceptibility.

High-throughput functional variant screens via in vivo production of single-stranded DNA

Creating and characterizing individual genetic variants remains limited in scale, compared to the tremendous variation both existing in nature and envisioned by genome engineers. Here we introduce retron library recombineering (RLR), a methodology for high-throughput functional screens that surpasses the scale and specificity of CRISPR-Cas methods. We use the targeted reverse-transcription activity of retrons to produce single-stranded DNA (ssDNA) in vivo, incorporating edits at >90% efficiency and enabling multiplexed applications. RLR simultaneously introduces many genomic variants, producing pooled and barcoded variant libraries addressable by targeted deep sequencing. We use RLR for pooled phenotyping of synthesized antibiotic resistance alleles, demonstrating quantitative measurement of relative growth rates. We also perform RLR using the sheared genomic DNA of an evolved bacterium, experimentally querying millions of sequences for causal variants, demonstrating that RLR is uniquely suited to utilize large pools of natural variation. Using ssDNA produced in vivo for pooled experiments presents avenues for exploring variation across the genome.

Two-step functional screen on multiple proteinaceous substrates reveals temperature-robust proteases with a broad-substrate range

To support the bio-based industry in development of environment-friendly processes and products, an optimal toolbox of biocatalysts is key. Although functional screen of (meta)genomic libraries may potentially contribute to identifying new enzymes, the discovery of new enzymes meeting industry compliance demands is still challenging. This is particularly noticeable in the case of proteases, for which the reports of metagenome-derived proteases with industrial applicability are surprisingly limited. Indeed, proteolytic clones have been typically assessed by its sole activity on casein or skim milk and limited to mild screening conditions. Here, we demonstrate the use of six industry-relevant animal and plant by-products, namely bone, feather, blood meals, gelatin, gluten, and zein, as complementary substrates in functional screens and show the utility of temperature as a screening parameter to potentially discover new broad-substrate range and robust proteases for the biorefinery industry. By targeting 340,000 clones from two libraries of pooled isolates of mesophilic and thermophilic marine bacteria and two libraries of microbial communities inhabiting marine environments, we identified proteases in four of eleven selected clones that showed activity against all substrates herein tested after prolonged incubation at 55 °C. Following sequencing, in silico analysis and recombinant expression in Escherichia coli, one functional protease, 58% identical at sequence level to previously reported homologs, was found to readily hydrolyze highly insoluble zein at temperatures up to 50 °C and pH 9–11. It is derived from a bacterial group whose ability to degrade zein was unknown. This study reports a two-step screen resulting in identification of a new marine metagenome-derived protease with zein-hydrolytic properties at common biomass processing temperatures that could be useful for the modern biorefinery industry. Key points • A two-step multi-substrate strategy for discovery of robust proteases. • Feasible approach for shortening enzyme optimization to industrial demands. • A new temperature-tolerant protease efficiently hydrolyzes insoluble zein.

Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer

Recent technological advancements such as CRISPR/Cas-based systems enable multiplexed, high-throughput screening for new therapeutic targets in cancer. While numerous functional screens have been performed on protein-coding genes to date, long non-coding RNAs (lncRNAs) represent an emerging class of potential oncogenes and tumor suppressors, with only a handful of large-scale screens performed thus far. Here, we review in detail currently available screening approaches to identify new lncRNA drivers of tumorigenesis and tumor progression. We discuss the various approaches of genomic and transcriptional targeting using CRISPR/Cas9, as well as methods to post-transcriptionally target lncRNAs via RNA interference (RNAi), antisense oligonucleotides (ASOs) and CRISPR/Cas13. We discuss potential advantages, caveats and future applications of each method to provide an overview and guide on investigating lncRNAs as new therapeutic targets in cancer.

CRAGE-mediated insertion of fluorescent chromosomal markers for accurate and scalable measurement of co-culture dynamics in Escherichia coli

Monitoring population dynamics in co-culture is necessary in engineering microbial consortia involved in distributed metabolic processes or biosensing applications. However, it remains difficult to measure strain-specific growth dynamics high-throughput formats. This is especially vexing in plate-based functional screens leveraging whole-cell biosensors to detect specific metabolic signals. Here we develop an experimental high-throughput co-culture system to measure and model the relationship between fluorescence and cell abundance, combining chassis-independent recombinase-assisted genome engineering (CRAGE) and whole-cell biosensing with a PemrR-green fluorescent protein (GFP) monoaromatic reporter used in plate-based functional screening. CRAGE was used to construct E. coli EPI300 strains constitutively expressing red fluorescent protein (RFP) and the relationship between RFP expression and optical density (OD600) was determined throughout the EPI300 growth cycle. A linear equation describing the increase of normalized RFP fluorescence during deceleration phase was derived and used to predict biosensor strain dynamics in co-culture. Measured and predicted values were compared using flow cytometric detection methods. Induction of the biosensor lead to increased GFP fluorescence normalized to biosensor cell abundance, as expected, but a significant decrease in relative abundance of the biosensor strain in co-culture and a decrease in bulk GFP fluorescence. Taken together, these results highlight sensitivity of population dynamics to variations in metabolic activity in co-culture and the potential effect of these dynamics on the performance of functional screens in plate-based formats. The engineered strains and model used to evaluate these dynamics provide a framework for optimizing growth of synthetic co-cultures used in screening, testing and pathway engineering applications