Link synthetic lethality to drug sensitivity of cancer cells

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

The Current Therapeutic Landscape for Metastatic Prostate Cancer

In 2024, there will be an estimated 1,466,718 cases of prostate cancer (PC) diagnosed globally, of which 299,010 cases are estimated to be from the US. The typical clinical approach for PC involves routine screening, diagnosis, and standard lines of treatment. However, not all patients respond to therapy and are subsequently diagnosed with treatment emergent neuroendocrine prostate cancer (NEPC). There are currently no approved treatments for this form of aggressive PC. In this review, a compilation of the clinical trials regimen to treat late-stage NEPC using novel targets and/or a combination approach is presented. The novel targets assessed include DLL3, EZH2, B7-H3, Aurora-kinase-A (AURKA), receptor tyrosine kinases, PD-L1, and PD-1. Among these, the trials administering drugs Alisertib or Cabozantinib, which target AURKA or receptor tyrosine kinases, respectively, appear to have promising results. The least effective trials appear to be ones that target the immune checkpoint pathways PD-1/PD-L1. Many promising clinical trials are currently in progress. Consequently, the landscape of successful treatment regimens for NEPC is extremely limited. These trial results and the literature on the topic emphasize the need for new preventative measures, diagnostics, disease specific biomarkers, and a thorough clinical understanding of NEPC.

Diagnostic and prognostic significance of SLC50A1 expression in patients with primary early breast cancer

There is a lack of validated biomarkers for the diagnosis of early breast cancer (EBC). The current study aimed to determine the diagnostic and prognostic value of solute carrier family 50 member 1 (SLC50A1) in patients with EBC. Therefore, 123 patients with EBC, 30 patients with benign breast disease (BBD) and 26 healthy controls (HCs) were recruited. The serum levels of SLC50A1 in paired sera of 40 postoperative patients were assessed by ELISA. Immunohistochemical staining for SLC50A1 was performed in surgical tissue derived from 83 patients with EBC and 30 patients with BBD. mRNA expression of SLC50A1 and its diagnostic and prognostic value in patients with EBC was evaluated using an RNA-sequencing database. The results showed that serum levels of SLC50A1 in patients with EBC were significantly higher compared with those in patients with BBD and HCs (both P<0.001). Additionally, receiver operating characteristic curve analysis revealed that the serum levels of SLC50A1 distinguished patients with EBC from patients with BBD and HCs with a sensitivity of 76.42% and specificity of 76.79% [area under the curve (AUC)=0.783; P<0.001]. The diagnostic value of SLC50A1 was significantly greater than that of carcinoembryonic (P<0.005) and carbohydrate antigen 15-3 (P<0.029). Furthermore, the number of SLC50A1 positive cells significantly increased in tissue of patients with EBC compared with patients with BBD (P<0.001). A positive association between serum levels of SLC50A1 and its expression in tissue samples was observed in patients with EBC (ρ=0.700; P<0.001). Additionally, bioinformatics analysis verified the diagnostic value of SLC50A1, with an AUC of 0.983 (P<0.001). Multivariate analysis demonstrated that SLC50A1 was an independent prognostic factor in patients with EBC with a hazard ratio of 1.917 (P=0.013). These findings indicated that SLC50A1 may be a potential diagnostic biomarker for primary EBC and that SLC50A1 upregulation may be associated with unfavorable prognosis in patients with EBC.

A genetic map of the chromatin regulators to drug response in cancer cells

BACKGROUND

Diverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging.

METHODS

In order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets.

RESULTS

Totally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment.

CONCLUSIONS

The CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations

Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.

The tumor therapy landscape of synthetic lethality

Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.

Various methods have been proposed to identify synthetic lethality interactions, but selective treatment opportunities for various tumors have not yet been explored. Here, the authors develop the Synthetic Lethality Knowledge Graph webserver (SLKG, http://www.slkg.net) to explore the comprehensive tumor therapy landscape and uncover cancer-specific susceptibilities based on the principle of synthetic lethality.

In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC₅₀) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.

Gene regulatory network analysis with drug sensitivity reveals synergistic effects of combinatory chemotherapy in gastric cancer

The combination of docetaxel, cisplatin, and fluorouracil (DCF) is highly synergistic in advanced gastric cancer. We aimed to explain these synergistic effects at the molecular level. Thus, we constructed a weighted correlation network using the differentially expressed genes between Stage I and IV gastric cancer based on The Cancer Genome Atlas (TCGA), and three modules were derived. Next, we investigated the correlation between the eigengene of the expression of the gene network modules and the chemotherapeutic drug response to DCF from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The three modules were associated with functions related to cell migration, angiogenesis, and the immune response. The eigengenes of the three modules had a high correlation with DCF (−0.41, −0.40, and −0.15). The eigengenes of the three modules tended to increase as the stage increased. Advanced gastric cancer was affected by the interaction the among modules with three functions, namely cell migration, angiogenesis, and the immune response, all of which are related to metastasis. The weighted correlation network analysis model proved the complementary effects of DCF at the molecular level and thus, could be used as a unique methodology to determine the optimal combination of chemotherapy drugs for patients with gastric cancer.

The novel sugar transporter SLC50A1 as a potential serum-based diagnostic and prognostic biomarker for breast cancer

Background

The novel sugar transporter and membrane protein SLC50A1 has been identified as a potential candidate biomarker for breast cancer; however, its potential as a serum biomarker for breast cancer detection and prognosis is unclear. The aim of this study was to investigate the serum expression profile of SLC50A1 and to determine its diagnostic and prognostic significance in breast cancer.

Materials and methods

Bioinformatics analysis was conducted, and data for SLC50A1 expression in human breast cancer were collected. Semi-quantitative real-time PCR and ELISA were performed to compare SLC50A1 expression in several breast cancer cell lines, one paired tissue cohort (n=20) and two independent cohorts of human breast cancer patients (n=85) and healthy individuals (n=30). The results were analyzed statistically, and associations between clinicopathological and survival data were evaluated by multivariate Cox regression analysis.

Results

SLC50A1 was confirmed as a candidate breast cancer gene by bioinformatics analysis. SLC50A1 mRNA expression levels were significantly upregulated in breast cancer (P<0.001). Serum SLC50A1 levels were able to discriminate between women with breast cancer and healthy women with a sensitivity of 75.3% and a specificity of 100.0% (P<0.001; area under the curve=0.915). Interestingly, SLC50A1 protein expression was associated with estrogen receptor (P=0.016) and HER2 status (P=0.037). Furthermore, SLC50A1 levels were positively related to unfavorable 3-year outcomes in patients with high-grade breast cancer (HR =1.823, P=0.01), indicating its potential use as an independent prognostic factor.

Conclusion

SLC50A1 can be used as a serum-based diagnostic and prognostic biomarker in breast cancer. However, further studies are needed to clarify its potential role as a therapeutic target.