BACKGROUND: High expression of ubiquitin ligase MDM2 is a primary cause of p53 inactivation in many tumors, making it a promising therapeutic target. However, MDM2 inhibitors have failed in clinical trials due to p53-induced feedback that enhances MDM2 expression. This underscores the urgent need to find an effective adaptive genotype or combination of targets.
METHODS: Kinome-wide CRISPR/Cas9 knockout screen was performed to identify genes that modulate the response to MDM2 inhibitor using TP53 wild type cancer cells and found ULK1 as a candidate. The MTT cell viability assay, flow cytometry and LDH assay were conducted to evaluate the activation of pyroptosis and the synthetic lethality effects of combining ULK1 depletion with p53 activation. Dual-luciferase reporter assay and ChIP-qPCR were performed to confirm that p53 directly mediates the transcription of GSDME and to identify the binding region of p53 in the promoter of GSDME. ULK1 knockout / overexpression cells were constructed to investigate the functional role of ULK1 both in vitro and in vivo. The mechanism of ULK1 depletion to activate GSMDE was mainly investigated by qPCR, western blot and ELISA.
RESULTS: By using high-throughput screening, we identified ULK1 as a synthetic lethal gene for the MDM2 inhibitor APG115. It was determined that deletion of ULK1 significantly increased the sensitivity, with cells undergoing typical pyroptosis. Mechanistically, p53 promote pyroptosis initiation by directly mediating GSDME transcription that induce basal-level pyroptosis. Moreover, ULK1 depletion reduces mitophagy, resulting in the accumulation of damaged mitochondria and subsequent increasing of reactive oxygen species (ROS). This in turn cleaves and activates GSDME via the NLRP3-Caspase inflammatory signaling axis. The molecular cascade makes ULK1 act as a crucial regulator of pyroptosis initiation mediated by p53 activation cells. Besides, mitophagy is enhanced in platinum-resistant tumors, and ULK1 depletion/p53 activation has a synergistic lethal effect on these tumors, inducing pyroptosis through GSDME directly.
CONCLUSIONS: Our research demonstrates that ULK1 deficiency can synergize with MDM2 inhibitors to induce pyroptosis. p53 plays a direct role in activating GSDME transcription, while ULK1 deficiency triggers upregulation of the ROS-NLRP3 signaling pathway, leading to GSDME cleavage and activation. These findings underscore the pivotal role of p53 in determining pyroptosis and provide new avenues for the clinical application of p53 restoration therapies, as well as suggesting potential combination strategies.

Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. However, lack of scalable SL properties prevents generalizability of SL interactions to out-of-sample data, thereby hindering modeling efforts. In this paper, we recognize that SL connectivity is a scalable and generalizable SL property. We develop a novel two-step multilayer encoder for individual sample-specific SL prediction model (MLEC-iSL), which predicts SL connectivity first and SL interactions subsequently. MLEC-iSL has three encoders, namely, gene, graph, and transformer encoders. MLEC-iSL achieves high SL prediction performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells, while no existing methods exceed 0.62 AUPR and AUC. The prediction performance of MLEC-iSL is validated in a CDKO experiment in 22Rv1 cells, yielding a 46.8% SL rate among 987 selected gene pairs. The screen also reveals SL dependency between apoptosis and mitosis cell death pathways.

BACKGROUND: Pathogenic BRCA1 or BRCA2 germline mutations contribute to hereditary breast, ovarian, prostate, and pancreatic cancer. Paradoxically, bi-allelic inactivation of BRCA1 or BRCA2 (bBRCA1/2) is embryonically lethal and decreases cellular proliferation. The compensatory mechanisms that facilitate oncogenesis in bBRCA1/2 tumors remain unclear.
METHODS: We identified recurrent genetic alterations enriched in human bBRCA1/2 tumors and experimentally validated if these improved proliferation in cellular models. We analyzed mutations and copy number alterations (CNAs) in bBRCA1/2 breast and ovarian cancer from the TCGA and ICGC. We used Fisher\'s exact test to identify CNAs enriched in bBRCA1/2 tumors compared to control tumors that lacked evidence of homologous recombination deficiency. Genes located in CNA regions enriched in bBRCA1/2 tumors were further screened by gene expression and their effects on proliferation in genome-wide CRISPR/Cas9 screens. A set of candidate genes was functionally validated with in vitro clonogenic survival and functional assays to validate their influence on proliferation in the setting of bBRCA1/2 mutations.
RESULTS: We found that bBRCA1/2 tumors harbor recurrent large-scale genomic deletions significantly more frequently than histologically matched controls (n = 238 cytobands in breast and ovarian cancers). Within the deleted regions, we identified 277 BRCA1-related genes and 218 BRCA2-related genes that had reduced expression and increased proliferation in bBRCA1/2 but not in wild-type cells in genome-wide CRISPR screens. In vitro validation of 20 candidate genes with clonogenic proliferation assays validated 9 genes, including RIC8A and ATMIN (ATM-Interacting protein). We identified loss of RIC8A, which occurs frequently in both bBRCA1/2 tumors and is synthetically viable with loss of both BRCA1 and BRCA2. Furthermore, we found that metastatic homologous recombination deficient cancers acquire loss-of-function mutations in RIC8A. Lastly, we identified that RIC8A does not rescue homologous recombination deficiency but may influence mitosis in bBRCA1/2 tumors, potentially leading to increased micronuclei formation.
CONCLUSIONS: This study provides a means to solve the tumor suppressor paradox by identifying synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.

暂无摘要。

暂无摘要。

Neurofibromatosis type 1, a genetic disorder caused by pathogenic germline variations in NF1, predisposes individuals to the development of tumors, including cutaneous and plexiform neurofibromas (CNs and PNs), optic gliomas, astrocytomas, juvenile myelomonocytic leukemia, high-grade gliomas and malignant peripheral nerve sheath tumors (MPNSTs), which are chemotherapy- and radiation-resistant sarcomas with poor survival. Loss of NF1 also occurs in sporadic tumors, such as glioblastoma (GBM), melanoma, breast, ovarian and lung cancers. We performed a high-throughput screen for compounds that were synthetic lethal with NF1 loss, which identified several leads, including the small molecule Y102. Treatment of cells with Y102 perturbed autophagy, mitophagy and lysosome positioning in NF1-deficient cells. A dual proteomics approach identified BLOC-one-related complex (BORC), which is required for lysosome positioning and trafficking, as a potential target of Y102. Knockdown of a BORC subunit using siRNA recapitulated the phenotypes observed with Y102 treatment. Our findings demonstrate that BORC might be a promising therapeutic target for NF1-deficient tumors.

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of \'maps\' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

Mutations in the tumor-suppressor genes BRCA1 and BRCA2 resulting in BRCA1/2 deficiency are frequently identified in breast, ovarian, prostate, pancreatic, and other cancers. Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) selectively kill BRCA1/2-deficient cancer cells by inducing synthetic lethality, providing an effective biomarker-guided strategy for targeted cancer therapy. However, a substantial fraction of cancer patients carrying BRCA1/2 mutations do not respond to PARPis, and most patients develop resistance to PARPis over time, highlighting a major obstacle to PARPi therapy in the clinic. Recent studies have revealed that changes of specific functional defects of BRCA1/2-deficient cells, particularly their defects in suppressing and protecting single-stranded DNA gaps, contribute to the gain or loss of PARPi-induced synthetic lethality. These findings not only shed light on the mechanism of action of PARPis, but also lead to revised models that explain how PARPis selectively kill BRCA-deficient cancer cells. Furthermore, new mechanistic principles of PARPi sensitivity and resistance have emerged from these studies, generating potentially useful guidelines for predicting the PARPi response and design therapies for overcoming PARPi resistance. In this Review, we will discuss these recent studies and put them in context with the classic views of PARPi-induced synthetic lethality, aiming to stimulate the development of new therapeutic strategies to overcome PARPi resistance and improve PARPi therapy.

Despite the success of programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibition in tumor therapy, many patients do not benefit. This failure may be attributed to the intrinsic functions of PD-L1. We perform a genome-wide CRISPR synthetic lethality screen to systematically explore the intrinsic functions of PD-L1 in head and neck squamous cell carcinoma (HNSCC) cells, identifying ferroptosis-related genes as essential for the viability of PD-L1-deficient cells. Genetic and pharmacological induction of ferroptosis accelerates cell death in PD-L1 knockout cells, which are also more susceptible to immunogenic ferroptosis. Mechanistically, nuclear PD-L1 transcriptionally activates SOD2 to maintain redox homeostasis. Lower reactive oxygen species (ROS) and ferroptosis are observed in patients with HNSCC who have higher PD-L1 expression. Our study illustrates that PD-L1 confers ferroptosis resistance in HNSCC cells by activating the SOD2-mediated antioxidant pathway, suggesting that targeting the intrinsic functions of PD-L1 could enhance therapeutic efficacy.