Synthetic lethal gene pairs: Experimental approaches and predictive models

Potential promising of synthetic lethality in cancer research and treatment

Cancer is a complex disease driven by multiple genetic changes, including mutations in oncogenes, tumor suppressor genes, DNA repair genes, and genes involved in cancer metabolism. Synthetic lethality (SL) is a promising approach in cancer research and treatment, where the simultaneous dysfunction of specific genes or pathways causes cell death. By targeting vulnerabilities created by these dysfunctions, SL therapies selectively kill cancer cells while sparing normal cells. SL therapies, such as PARP inhibitors, WEE1 inhibitors, ATR and ATM inhibitors, and DNA-PK inhibitors, offer a distinct approach to cancer treatment compared to conventional targeted therapies. Instead of directly inhibiting specific molecules or pathways, SL therapies exploit genetic or molecular vulnerabilities in cancer cells to induce selective cell death, offering benefits such as targeted therapy, enhanced treatment efficacy, and minimized harm to healthy tissues. SL therapies can be personalized based on each patient's unique genetic profile and combined with other treatment modalities to potentially achieve synergistic effects. They also broaden the effectiveness of treatment across different cancer types, potentially overcoming drug resistance and improving patient outcomes. This review offers an overview of the current understanding of SL mechanisms, advancements, and challenges, as well as the preclinical and clinical development of SL. It also discusses new directions and opportunities for utilizing SL in targeted therapy for anticancer treatment.

Heterogeneity of cancer-associated fibroblast subpopulations in prostate cancer: Implications for prognosis and immunotherapy

BACKGROUND

Prostate cancer stands as the second most common malignancy among men, notorious for its intricate heterogeneity, especially evident in metastatic disease. This complexity presents substantial challenges in treatment efficacy and patient prognosis.

OBJECTIVE

This study endeavors to elucidate the multifaceted roles of cancer-associated fibroblasts within the tumor microenvironment of prostate cancer, with a focus on their implications for disease prognosis and the potential for novel immunotherapeutic strategies.

METHODS

Leveraging advanced single-cell RNA sequencing technology, we meticulously characterized the diverse CAF subpopulations within prostate cancer samples. Our analysis identified four predominant subsets: C0 IER2+, C1 ABCA8+, C2 ABI3BP+, and C3 MEOX2+. We conducted comprehensive gene expression profiling to construct a robust prognostic model reflecting the clinical relevance of these subpopulations.

RESULTS

C1 ABCA8+ fibroblasts demonstrated heightened proliferative activity, underscoring their pivotal role in fostering tumor growth and metastasis via intricate signaling pathways. In vitro experiments verified that the T transcription factor NFAT5 of C1 ABCA8+ fibroblasts subpopulation was knocked down in LNCaP clone FGC and 22Rv1 cell lines, which was closely related to the proliferation of PC. Moreover, we identified key genes linked to patient outcomes and immune landscape alterations, reinforcing the prognostic significance of CAF characteristics in this context.

CONCLUSION

This investigation illuminates the critical potential of targeting CAFs to augment immunotherapeutic approaches in prostate cancer. Our findings contribute to a deeper understanding of the TME's complexity, advocating for further exploration into CAF-targeted therapies aimed at enhancing treatment responses and ultimately improving patient outcomes.

Inhibition of programmed cell death by melanoma cell subpopulations reveals mechanisms of melanoma metastasis and potential therapeutic targets

Melanoma is an aggressive type of skin cancer that arises from melanocytes, the cells responsible for producing skin pigment. In contrast to non-melanoma skin cancers like basal cell carcinoma and squamous cell carcinoma, melanoma is more invasive. Melanoma was distinguished by its rapid progression, high metastatic potential, and significant resistance to conventional therapies. Although it accounted for a small proportion of skin cancer cases, melanoma accounts for the majority of deaths caused by skin cancer due to its ability to invade deep tissues, adapt to diverse microenvironments, and evade immune responses. These unique features highlighted the challenges of treating melanoma and underscored the importance of advanced tools, such as single-cell sequencing, to unravel its biology and develop personalized therapeutic strategies. Thus, we conducted a single-cell analysis of the cellular composition within melanoma tumor tissues and further subdivided melanoma cells into subpopulations. Through analyzing metabolic pathways, stemness genes, and transcription factors (TFs) among cells in different phases (G1, G2/M, and S) as well as between primary and metastatic foci cells, we investigated the specific mechanisms underlying melanoma metastasis. We also revisited the cellular stemness and temporal trajectories of melanoma cell subpopulations, identifying the core subpopulation as C0 SOD3 + Melanoma cells. Our findings revealed a close relationship between the pivotal C0 SOD3 + Melanoma cells subpopulation and oxidative pathways in metastatic tumor tissues. Additionally, we analyzed prognostically relevant differentially expressed genes (DEGs) within the C0 SOD3 + Melanoma cells subpopulation and built a predictive model associated with melanoma outcomes. We selected the gene IGF1 with the highest coefficient (coef) value for further analysis, and experimentally validated its essential function in the proliferation and invasive metastasis of melanoma. In immune infiltration analysis, we discovered the critical roles played by M1/M2 macrophages in melanoma progression and immune evasion. Furthermore, the development and progression of malignant melanoma were closely associated with various forms of programmed cell death (PCD), including apoptosis, autophagic cell death, ferroptosis, and pyroptosis. Melanoma cells often resisted cell death mechanisms, maintaining their growth by inhibiting apoptosis and evading autophagic cell death. Meanwhile, the induction of ferroptosis and pyroptosis was thought to trigger immune responses that helped suppress melanoma dissemination. A deeper understanding of the relationship between melanoma and PCD pathways provided a critical foundation for developing novel targeted therapies, with the potential to enhance melanoma treatment efficacy. These findings contributed to the development of novel prognostic models for melanoma and shed light on research directions concerning melanoma metastasis mechanisms and therapeutic targets.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Targeting the Sodium-Potassium Pump as a Therapeutic Strategy in Acute Myeloid Leukemia

Tissue-specific differences in the expression of paralog genes, which are not essential in most cell types due to the buffering effect of the partner pair, can make for highly selective gene dependencies. To identify selective paralogous targets for acute myeloid leukemia (AML), we integrated the Cancer Dependency Map with numerous datasets characterizing protein-protein interactions, paralog relationships, and gene expression in cancer models. In this study, we identified ATP1B3 as a context-specific, paralog-related dependency in AML. ATP1B3, the β-subunit of the sodium-potassium pump (Na/K-ATP pump), interacts with the α-subunit ATP1A1 to form an essential complex for maintaining cellular homeostasis and membrane potential in all eukaryotic cells. When ATP1B3's paralog ATP1B1 is poorly expressed, elimination of ATP1B3 leads to the destabilization of the Na/K-ATP pump. ATP1B1 expression is regulated through epigenetic silencing in hematopoietic lineage cells through histone and DNA methylation in the promoter region. Loss of ATP1B3 in AML cells induced cell death in vitro and reduced leukemia burden in vivo, which could be rescued by stabilizing ATP1A1 through overexpression of ATP1B1. Thus, ATP1B3 is a potential therapeutic target for AML and other hematologic malignancies with low expression of ATP1B1. Significance: ATP1B3 is a lethal selective paralog dependency in acute myeloid leukemia that can be eliminated to destabilize the sodium-potassium pump, inducing cell death.

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction

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.

New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives

In recent years, synthetic lethality has been recognized as a solid paradigm for anticancer therapies. The discovery of a growing number of synthetic lethal targets has led to a significant expansion in the use of synthetic lethality, far beyond poly(ADP-ribose) polymerase inhibitors used to treat BRCA1/2-defective tumors. In particular, molecular targets within DNA damage response have provided a source of inhibitors that have rapidly reached clinical trials. This Perspective focuses on the most recent progress in synthetic lethal targets and their inhibitors, within and beyond the DNA damage response, describing their design and associated therapeutic strategies. We will conclude by discussing the current challenges and new opportunities for this promising field of research, to stimulate discussion in the medicinal chemistry community, allowing the investigation of synthetic lethality to reach its full potential.

The power and the promise of synthetic lethality for clinical application in cancer treatment

Synthetic lethality is a phenomenon wherein the simultaneous deficiency of two or more genes results in cell death, while the deficiency of any individual gene does not lead to cell death. In recent years, synthetic lethality has emerged as a significant topic in the field of targeted cancer therapy, with certain drugs based on this concept exhibiting promising outcomes in clinical trials. Nevertheless, the presence of tumor heterogeneity and the intricate DNA repair mechanisms pose challenges to the effective implementation of synthetic lethality. This review aims to explore the concepts, development, and ethical quandaries surrounding synthetic lethality. Additionally, it will provide an in-depth analysis of the clinical application and underlying mechanism of synthetic lethality.

NetVA: an R package for network vulnerability and influence analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.Communicated by Ramaswamy H. Sarma.

Paralog-based synthetic lethality: rationales and applications

Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell's survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of targeting these gene pairs and the difficulty in finding specific inhibitors that exclusively target a single but not both paralogs hinder further clinical development. Here, we review the potential mechanisms of paralog-based SL given their function and genetic combination, and discuss the challenge and application prospects of paralog-based SL in cancer therapeutic discovery.

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.