A systematic analysis of the landscape of synthetic lethality-driven precision oncology.

BACKGROUND: Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future. METHODS: We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology. FINDINGS: We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials. CONCLUSIONS: Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward. FUNDING: Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR).

Increased interleukin-6/C-reactive protein levels are associated with the upregulation of the adenosine pathway and serve as potential markers of therapeutic resistance to immune checkpoint inhibitor-based therapies in non-small cell lung cancer.

BACKGROUND: Systemic immune activation, hallmarked by C-reactive protein (CRP) and interleukin-6 (IL-6), can modulate antitumor immune responses. In this study, we evaluated the role of IL-6 and CRP in the stratification of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). We also interrogated the underlying immunosuppressive mechanisms driven by the IL-6/CRP axis. METHODS: In cohort A (n=308), we estimated the association of baseline CRP with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with NSCLC treated with ICIs alone or with chemo-immunotherapy (Chemo-ICI). Baseline tumor bulk RNA sequencing (RNA-seq) of lung adenocarcinomas (LUADs) treated with pembrolizumab (cohort B, n=59) was used to evaluate differential expression of purine metabolism, as well as correlate IL-6 expression with PFS. CODEFACS approach was applied to deconvolve cohort B to characterize the tumor microenvironment by reconstructing the cell-type-specific transcriptome from bulk expression. Using the LUAD cohort from The Cancer Genome Atlas (TCGA) we explored the correlation between IL-6 expression and adenosine gene signatures. In a third cohort (cohort C, n=18), plasma concentrations of CRP, adenosine 2a receptor (A2aR), and IL-6 were measured using ELISA. RESULTS: In cohort A, 67.2% of patients had a baseline CRP≥10 mg/L (CRP-H). Patients with CRP-H achieved shorter OS (8.6 vs 14.8 months; p=0.006), shorter PFS (3.3 vs 6.6 months; p=0.013), and lower ORR (24.7% vs 46.3%; p=0.015). After adjusting for relevant clinical variables, CRP-H was confirmed as an independent predictor of increased risk of death (HR 1.51, 95% CI: 1.09 to 2.11) and lower probability of achieving disease response (OR 0.34, 95% CI: 0.13 to 0.89). In cohort B, RNA-seq analysis demonstrated higher IL-6 expression on tumor cells of non-responders, along with a shorter PFS (p<0.05) and enrichment of the purinergic pathway. Within the TCGA LUAD cohort, tumor IL-6 expression strongly correlated with the adenosine signature (R=0.65; p<2.2e-16). Plasma analysis in cohort C demonstrated that CRP-H patients had a greater median baseline level of A2aR (6.0 ng/mL vs 1.3 ng/mL; p=0.01). CONCLUSIONS: This study demonstrates CRP as a readily available blood-based prognostic biomarker in ICI-treated NSCLC. Additionally, we elucidate a potential link of the CRP/IL-6 axis with the immunosuppressive adenosine signature pathway that could drive inferior outcomes to ICIs in NSCLC and also offer novel therapeutic avenues.

Prognostic implication of downregulated exosomal miRNAs in patients with sepsis: a cross-sectional study with bioinformatics analysis.

BACKGROUND: Despite the understanding of sepsis-induced extracellular vesicles (EVs), such as exosomes, and their role in intercellular communication during sepsis, little is known about EV contents such as microRNA (miRNA), which modulate important cellular processes contributing to sepsis in body fluids. This study aimed to analyze the differential expression of exosomal miRNAs in plasma samples collected from sepsis patients and healthy controls, and to identify potential miRNA regulatory pathways contributing to sepsis pathogenesis. METHODS: Quantitative real-time PCR-based microarrays were used to profile plasma exosomal miRNA expression levels in 135 patients with sepsis and 11 healthy controls from an ongoing prospective registry of critically ill adult patients admitted to the intensive care unit. The identified exosomal miRNAs were tested in an external validation cohort (35 sepsis patients and 10 healthy controls). And then, functional enrichment analyses of gene ontology, KEGG pathway analysis, and protein-protein interaction network and cluster analyses were performed based on the potential target genes of the grouped miRNAs. Finally, to evaluate the performance of the identified exosomal miRNAs in predicting in-hospital and 90-day mortalities of sepsis patients, receiver operating characteristic curve (ROC) and Kaplan-Meier analyses were performed. RESULTS: Compared with healthy controls, plasma exosomes from sepsis patients showed significant changes in 25 miRNAs; eight miRNAs were upregulated and 17 downregulated. Additionally, the levels of hsa-let-7f-5p, miR-331-3p miR-301a-3p, and miR-335-5p were significantly lower in sepsis patients than in healthy controls (p < 0.0001). These four miRNAs were confirmed in an external validation cohort. In addition, the most common pathway for these four miRNAs were PI3K-Akt and mitogen-activated protein kinase (MAPK) signaling pathways based on the KEGG analysis. The area under the ROC of hsa-let-7f-5p, miR-331-3p, miR-301a-3p, and miR-335-5p level for in-hospital mortality was 0.913, 0.931, 0.929, and 0.957, respectively (p < 0.001), as confirmed in an external validation cohort. Also, the Kaplan-Meier analysis showed a significant difference in 90-day mortality between sepsis patients with high and low miR-335-5p, miR-301a-3p, hsa-let-7f-5p, and miR-331-3p levels (p < 0.001, log-rank test). CONCLUSION: Among the differentially-expressed miRNAs detected in microarrays, the top four downregulated exosomal miRNAs (hsa-let-7f-5p, miR-331-3p miR-301a-3p, and miR-335-5p) were identified as independent prognostic factors for in-hospital and 90-day mortalities among sepsis patients. Bioinformatics analysis demonstrated that these four microRNAs might provide a significant contribution to sepsis pathogenesis through PI3K-Akt and MAPK signaling pathway.

A landscape of response to drug combinations in non-small cell lung cancer.

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma.

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. A better understanding of mesothelioma genomics and transcriptomics could advance therapies. Here, we present a mesothelioma cohort of 122 patients along with their germline and tumor whole-exome and tumor RNA sequencing data as well as phenotypic and drug response information. We identify a 48-gene prognostic signature that is highly predictive of mesothelioma patient survival, including CCNB1, the expression of which is highly predictive of patient survival on its own. In addition, we analyze the transcriptomics data to study the tumor immune microenvironment and identify synthetic-lethality-based signatures predictive of response to therapy. This germline and somatic whole-exome sequencing as well as transcriptomics data from the same patient are a valuable resource to address important biological questions, including prognostic biomarkers and determinants of treatment response in mesothelioma.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

AXL and Error-Prone DNA Replication Confer Drug Resistance and Offer Strategies to Treat EGFR-Mutant Lung Cancer.

Anticancer therapies have been limited by the emergence of mutations and other adaptations. In bacteria, antibiotics activate the SOS response, which mobilizes error-prone factors that allow for continuous replication at the cost of mutagenesis. We investigated whether the treatment of lung cancer with EGFR inhibitors (EGFRi) similarly engages hypermutators. In cycling drug-tolerant persister (DTP) cells and in EGFRi-treated patients presenting residual disease, we observed upregulation of GAS6, whereas ablation of GAS6's receptor, AXL, eradicated resistance. Reciprocally, AXL overexpression enhanced DTP survival and accelerated the emergence of T790M, an EGFR mutation typical to resistant cells. Mechanistically, AXL induces low-fidelity DNA polymerases and activates their organizer, RAD18, by promoting neddylation. Metabolomics uncovered another hypermutator, AXL-driven activation of MYC, and increased purine synthesis that is unbalanced by pyrimidines. Aligning anti-AXL combination treatments with the transition from DTPs to resistant cells cured patient-derived xenografts. Hence, similar to bacteria, tumors tolerate therapy by engaging pharmacologically targetable endogenous mutators. SIGNIFICANCE: EGFR-mutant lung cancers treated with kinase inhibitors often evolve resistance due to secondary mutations. We report that in similarity to the bacterial SOS response stimulated by antibiotics, endogenous mutators are activated in drug-treated cells, and this heralds tolerance. Blocking the process prevented resistance in xenograft models, which offers new treatment strategies. This article is highlighted in the In This Issue feature, p. 2483.

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets.

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.

USP15 negatively regulates lung cancer progression through the TRAF6-BECN1 signaling axis for autophagy induction.

TNF receptor-associated factor 6 (TRAF6)-BECN1 signaling axis plays a pivotal role in autophagy induction through ubiquitination of BECN1, thereby inducing lung cancer migration and invasion in response to toll-like receptor 4 (TLR4) stimulation. Herein, we provide novel molecular and cellular mechanisms involved in the negative effect of ubiquitin-specific peptidase 15 (USP15) on lung cancer progression. Clinical data of the TCGA and primary non-small cell lung cancer (NSCLC) patients (n = 41) revealed that the expression of USP15 was significantly downregulated in lung cancer patients. Importantly, USP15-knockout (USP15KO) A549 and USP15KO H1299 lung cancer cells generated with CRISPR-Cas9 gene-editing technology showed increases in cancer migration and invasion with enhanced autophagy induction in response to TLR4 stimulation. In addition, biochemical studies revealed that USP15 interacted with BECN1, but not with TRAF6, and induced deubiquitination of BECN1, thereby attenuating autophagy induction. Notably, in primary NSCLC patients (n = 4) with low expression of USP15, 10 genes (CCNE1, MMP9, SFN, UBE2C, CCR2, FAM83A, ETV4, MYO7A, MMP11, and GSDMB) known to promote lung cancer progression were significantly upregulated, whereas 10 tumor suppressor genes (FMO2, ZBTB16, FCN3, TCF21, SFTPA1B, HPGD, SOSTDC1, TMEM100, GDF10, and WIF1) were downregulated, providing clinical relevance of the functional role of USP15 in lung cancer progression. Taken together, our data demonstrate that USP15 can negatively regulate the TRAF6-BECN1 signaling axis for autophagy induction. Thus, USP15 is implicated in lung cancer progression.

The complete chloroplast genome sequence of a medicinal citrus landrace, Citrus erythrosa Hort. ex Tanaka in Jeju Island, Korea.

Citrus erythrosa (Dongjeongkyool in Korean) is a medicinal citrus landrace that grows in Korea. In this study, we characterized the complete chloroplast (Cp) genome (160,120 bp) of C. erythrosa. The Cp genome was consisted of 4 distinct regions: a large single copy (87,731 bp), a small single copy (18,393 bp), and a pair of inverted repeat regions (26,998 bp). The Cp genome encodes a total of 133 genes including 88 protein-coding genes, 37 tRNA genes and 8 rRNA genes. The phylogenetic analysis reveals that C. erythrosa is a sister group to the clade of species including C. reticulata within the genus Citrus.

Nucleoporin-93 reveals a common feature of aggressive breast cancers: robust nucleocytoplasmic transport of transcription factors.

By establishing multi-omics pipelines, we uncover overexpression and gene copy-number alterations of nucleoporin-93 (NUP93), a nuclear pore component, in aggressive human mammary tumors. NUP93 overexpression enhances transendothelial migration and matrix invasion in vitro, along with tumor growth and metastasis in animal models. These findings are supported by analyses of two sets of naturally occurring mutations: rare oncogenic mutations and inactivating familial nephrotic syndrome mutations. Mechanistically, NUP93 binds with importins, boosts nuclear transport of importins' cargoes, such as ß-catenin, and activates MYC. Likewise, NUP93 overexpression enhances the ultimate nuclear transport step shared by additional signaling pathways, including TGF-ß/SMAD and EGF/ERK. The emerging addiction to nuclear transport exposes vulnerabilities of NUP93-overexpressing tumors. Congruently, myristoylated peptides corresponding to the nuclear translocation signals of SMAD and ERK can inhibit tumor growth and metastasis. Our study sheds light on an emerging hallmark of advanced tumors, which derive benefit from robust nucleocytoplasmic transport.

Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti-PD-1 Therapy.

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type-specific gene expression in each sample from bulk expression, and LIRICS (Ligand-Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand-receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type-specific ligand-receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti-programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type-specific ligand-receptor interactions in the melanoma TME that stratify survival of patients receiving anti-PD-1 therapy better than some recently published bulk transcriptomics-based methods. SIGNIFICANCE: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type-specific gene expression profiles and identify cell type-specific ligand-receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873.

USP8 regulates liver cancer progression via the inhibition of TRAF6-mediated signal for NF-κB activation and autophagy induction by TLR4.

Herein, we aimed to elucidate the molecular and cellular mechanism in which ubiquitin-specific protease 8 (USP8) is implicated in liver cancer progression via TRAF6-mediated signal. USP8 induces the deubiquitination of TRAF6, TAB2, TAK1, p62, and BECN1, which are pivotal roles for NF-κB activation and autophagy induction. Notably, the LIHC patient with low USP8 mRNA expression showed markedly shorter survival time, whereas there was no significant difference in the other 18-human cancers. Importantly, the TCGA data analysis on LIHC and transcriptome analysis on the USP8 knockout (USP8KO) SK-HEP-1 cells revealed a significant correlation between USP8 and TRAF6, TAB2, TAK1, p62, and BECN1, and enhanced NF-κB-dependent and autophagy-related cancer progression/metastasis-related genes in response to LPS stimulation. Furthermore, USP8KO SK-HEP-1 cells showed an increase in cancer migration and invasion by TLR4 stimulation, and a marked increase of tumorigenicity and metastasis in xenografted NSG mice. The results demonstrate that USP8 is negatively implicated in the LIHC progression through the regulation of TRAF6-mediated signal for the activation of NF-κB activation and autophagy induction. Our findings provide useful insight into the LIHC pathogenesis of cancer progression.

A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing.

Recent studies have reported that genome editing by CRISPR-Cas9 induces a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to a selection of cells with pre-existing p53 mutations. In this study, employing an integrated computational and experimental framework, we systematically investigated the possibility of selection of additional cancer driver mutations during CRISPR-Cas9 gene editing. We first confirm the previous findings of the selection for pre-existing p53 mutations by CRISPR-Cas9. We next demonstrate that similar to p53, wildtype KRAS may also hamper the growth of Cas9-edited cells, potentially conferring a selective advantage to pre-existing KRAS-mutant cells. These selective effects are widespread, extending across cell-types and methods of CRISPR-Cas9 delivery and the strength of selection depends on the sgRNA sequence and the gene being edited. The selection for pre-existing p53 or KRAS mutations may confound CRISPR-Cas9 screens in cancer cells and more importantly, calls for monitoring patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations.

The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors.

Acute myeloid leukemia (AML) remains incurable, largely due to its resistance to conventional treatments. Here, we find that increased abundance of the ubiquitin ligase RNF5 contributes to AML development and survival. High RNF5 expression in AML patient specimens correlates with poor prognosis. RNF5 inhibition decreases AML cell growth in culture, in patient-derived xenograft (PDX) samples and in vivo, and delays development of MLL-AF9-driven leukemogenesis in mice, prolonging their survival. RNF5 inhibition causes transcriptional changes that overlap with those seen upon histone deacetylase (HDAC)1 inhibition. RNF5 induces the formation of K29 ubiquitin chains on the histone-binding protein RBBP4, promoting its recruitment to and subsequent epigenetic regulation of genes involved in AML maintenance. Correspondingly, RNF5 or RBBP4 knockdown enhances AML cell sensitivity to HDAC inhibitors. Notably, low expression of both RNF5 and HDAC coincides with a favorable prognosis. Our studies identify an ERAD-independent role for RNF5, demonstrating that its control of RBBP4 constitutes an epigenetic pathway that drives AML, and highlight RNF5/RBBP4 as markers useful to stratify patients for treatment with HDAC inhibitors.

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets.

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

Human delta like 1-expressing human mesenchymal stromal cells promote human T cell development and antigen-specific response in humanized NOD/SCID/IL-2R[Formula: see text]null (NSG) mice.

Human delta-like 1 (hDlk1) is known to be able to regulate cell fate decisions during hematopoiesis. Mesenchymal stromal cells (MSCs) are known to exhibit potent immunomodulatory roles in a variety of diseases. Herein, we investigated in vivo functions of hDlk1-hMSCs and hDlk1+hMSCs in T cell development and T cell response to viral infection in humanized NOD/SCID/IL-2Rγnull (NSG) mice. Co-injection of hDlk1-hMSC with hCD34+ cord blood (CB) cells into the liver of NSG mice markedly suppressed the development of human T cells. In contrast, co-injection of hDlk1+hMSC with hCD34+ CB cells into the liver of NSG dramatically promoted the development of human T cells. Human T cells developed in humanized NSG mice represent markedly diverse, functionally active, TCR V[Formula: see text] usages, and the restriction to human MHC molecules. Upon challenge with Epstein-Barr virus (EBV), EBV-specific hCD8+ T cells in humanized NSG mice were effectively mounted with phenotypically activated T cells presented as hCD45+hCD3+hCD8+hCD45RO+hHLA-DR+ T cells, suggesting that antigen-specific T cell response was induced in the humanized NSG mice. Taken together, our data suggest that the hDlk1-expressing MSCs can effectively promote the development of human T cells and immune response to exogenous antigen in humanized NSG mice. Thus, the humanized NSG model might have potential advantages for the development of therapeutics targeting infectious diseases in the future.