Osteosarcoma (OS) patients exhibit poor overall survival, partly due to copy number variations (CNVs) resulting in dysregulated gene expression and therapeutic resistance. To identify actionable prognostic signatures of poor overall survival, we employed a systems biology approach using public databases to integrate CNVs, gene expression, and survival outcomes in pediatric, adolescent, and young adult OS patients. Chromosome 8 was a hotspot for poor prognostic signatures. The MYC-RAD21 copy number gain (8q24) correlated with increased gene expression and poor overall survival in 90% of the patients (n = 85). MYC and RAD21 play a role in replication-stress, which is a therapeutically actionable network. We prioritized replication-stress regulators, bromodomain and extra-terminal proteins (BETs), and CHK1, in order to test the hypothesis that the inhibition of BET + CHK1 in MYC-RAD21+ pediatric OS models would be efficacious and safe. We demonstrate that MYC-RAD21+ pediatric OS cell lines were sensitive to the inhibition of BET (BETi) and CHK1 (CHK1i) at clinically achievable concentrations. While the potentiation of CHK1i-mediated effects by BETi was BET-BRD4-dependent, MYC expression was BET-BRD4-independent. In MYC-RAD21+ pediatric OS xenografts, BETi + CHK1i significantly decreased tumor growth, increased survival, and was well tolerated. Therefore, targeting replication stress is a promising strategy to pursue as a therapeutic option for this devastating disease.

Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps for in vitro screening with 22Rv1-Cas9 and SaOS2-Cas9 cells followed by sequencing and data analysis. By introducing two backbones, we optimized the library construction process, facilitated standard pair-end sequencing, and provided options of screening on cells with or without modification of Cas9 expression.

Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes’ copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer.

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.

A key issue in semantic Web services is semantic matching which is important to facilitate to reuse of Web services. Existing semantic matching technique such as ontology based, do not guarantee fulfilling the preconditions and desired effect, and hence do not guarantee the correctness of the matching results, in this paper we prose a novel semantic matching approach based on service dynamic description logic (SDDL). SDDL is used to present services specification for capturing both static information and dynamic state aspect of service function, and then semantic matching take into account all these aspects to achieve a precise matching. Furthermore not only exact matching, but also various kinds of matching degree are defined.

Background/Objectives: Triple-negative breast cancer (TNBC) accounts for 15 to 20% of breast cancer cases and contributes to a disproportionate 35% of breast cancer deaths. Its resistance to chemotherapy presents a significant challenge. Methods: We firstly compared transcriptomic profiles between TNBC cell lines and patient samples and inferred the MDA-MB-231 cell line as the most representative model for TNBC with poor response to chemotherapy. We then conducted a genome-wide CRISPR-Cas9 screening and RNA-seq analysis in MDA-MB-231. Results: This analysis revealed 96 and 93 genes that could re-sensitize cisplatin and doxorubicin treatment, respectively, with 19 overlapping genes. Among these genes, 28 have been studied and published previously in chemoresistance research. MCM9 was found as a new TNBC chemoresistance target. Its target drug, KPT-185, showed an additive effect with cisplatin in treating TNBC cells. In the follow-up gene combination double-knockout experiment among 65 genes selected from cell death pathways, 242 synthetic lethal gene pairs were discovered to overcome chemoresistance in TNBC. Conclusions: In this study, we identified synthetic lethal targets in treating TNBC with cisplatin and doxorubicin through a genome-wide CRISPR-Cas9 screening and gene combination double-knockout screening.