Establishment and functional testing of a novel ex vivo extraskeletal osteosarcoma cell model (USZ20-ESOS1)

Extraskeletal osteosarcoma (ESOS) is a rare malignant mesenchymal tumor that originates in the soft tissue. ESOS accounts for less than 1% of all soft tissue sarcomas and exhibits an aggressive behavior with a high propensity for local recurrence and distant metastasis. Despite advances in treatment, the prognosis for ESOS remains poor, with a five-year survival rate of less than 50% and 27% for metastatic patients. Ex vivo models derived from patient samples are critical tools for studying rare diseases with poor prognoses, such as ESOS, and identifying potential new treatment strategies. In this work, we established a novel ESOS ex vivo sarco-sphere model from a metastatic lesion to the dermis for research and functional testing purposes. The ex vivo cell model accurately recapitulated the native tumor, as evidenced by histomorphology and molecular profiles. Through a functional screening approach, we were able to identify novel individual anti-cancer drug sensitivities for different drugs such as romidepsin, miverbresib and to multiple kinase inhibitors. Overall, our new ESOS ex vivo cell model represents a valuable tool for investigating disease mechanisms and answering basic and translational research questions

DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

Background: Drug synergy occurs when the combined effect of two drugs is greater than the sum of the individual drugs' effect. While cell line data measuring the effect of single drugs are readily available, there is relatively less comparable data on drug synergy given the vast amount of possible drug combinations. Thus, there is interest to use computational approaches to predict drug synergy for untested pairs of drugs. Methods: We introduce a Graph Neural Network (GNN) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We use information from the largest drug combination database available (DrugComb), combining drug synergy scores in order to construct high confidence benchmark datasets. Results: Our proposed solution for drug synergy predictions offers a number of benefits: 1) It utilizes a combination of 34 distinct drug synergy datasets to learn on a wide variety of drugs and cell lines representations. 2) It is trained on constructed high confidence benchmark datasets. 3) It learns task-specific drug representations, instead of relying on generalized and pre-computed chemical drug features. 4) It achieves similar or better prediction performance (AUPR scores ranging from 0.777 to 0.964) compared to state-of-the-art baseline models when tested on various benchmark datasets. Conclusions: We demonstrate that a GNN based model can provide state-of-the-art drug synergy predictions by learning task-specific representations of drugs

Molecular and immunophenotypic characterization of SMARCB1 (INI1) - deficient intrathoracic Neoplasms

The switch/sucrose-non-fermenting (SWI/SNF) complex is an ATP-dependent chromatin remodeling complex that plays important roles in DNA repair, transcription and cell differentiation. This complex consists of multiple subunits and is of particular interest in thoracic malignancies due to frequent subunit alteration of SMARCA4 (BRG1). Much less is known about SMARCB1 (INI1) deficient intrathoracic neoplasms, which are rare, often misclassified and understudied. In a retrospective analysis of 1479 intrathoracic malignant neoplasms using immunohistochemistry for INI1 (SMARCB1) on tissue micro arrays (TMA) and a search through our hospital sarcoma database, we identified in total nine intrathoracic, INI1 deficient cases (n = 9). We characterized these cases further by additional immunohistochemistry, broad targeted genomic analysis, methylation profiling and correlated them with clinical and radiological data. This showed that genomic SMARCB1 together with tumor suppressor alterations drive tumorigenesis in some of these cases, rather than epigenetic changes such as DNA methylation. A proper diagnostic classification, however, remains challenging. Intrathoracic tumors with loss or alteration of SMARCB1 (INI1) are highly aggressive and remain often underdiagnosed due to their rarity, which leads to false diagnostic interpretations. A better understanding of these tumors and proper diagnosis is important for better patient care as clinical trials and more targeted therapeutic options are emerging

Addressing Modern Diagnostic Pathology for Patient-Derived Soft Tissue Sarcosphere Models in the Era of Functional Precision Oncology

Responses to therapy often cannot be exclusively predicted by molecular markers, thus evidencing a critical need to develop tools for better patient selection based on relations between tumor phenotype and genotype. Patient-derived cell models could help to better refine patient stratification procedures and lead to improved clinical management. So far, such ex vivo cell models have been used for addressing basic research questions and in preclinical studies. As they now enter the era of functional precision oncology, it is of utmost importance that they meet quality standards to fully represent the molecular and phenotypical architecture of patients' tumors. Well-characterized ex vivo models are imperative for rare cancer types with high patient heterogeneity and unknown driver mutations. Soft tissue sarcomas account for a very rare, heterogeneous group of malignancies that are challenging from a diagnostic standpoint and difficult to treat in a metastatic setting because of chemotherapy resistance and a lack of targeted treatment options. Functional drug screening in patient-derived cancer cell models is a more recent approach for discovering novel therapeutic candidate drugs. However, because of the rarity and heterogeneity of soft tissue sarcomas, the number of well-established and characterized sarcoma cell models is extremely limited. Within our hospital-based platform we establish high-fidelity patient-derived ex vivo cancer models from solid tumors for enabling functional precision oncology and addressing research questions to overcome this problem. We here present 5 novel, well-characterized, complex-karyotype ex vivo soft tissue sarcosphere models, which are effective tools to study molecular pathogenesis and identify the novel drug sensitivities of these genetically complex diseases. We addressed the quality standards that should be generally considered for the characterization of such ex vivo models. More broadly, we suggest a scalable platform to provide high-fidelity ex vivo models to the scientific community and enable functional precision oncology

Unravelling homologous recombination repair deficiency and therapeutic opportunities in soft tissue and bone sarcoma.

Defects in homologous recombination repair (HRR) in tumors correlate with poor prognosis and metastases development. Determining HRR deficiency (HRD) is of major clinical relevance as it is associated with therapeutic vulnerabilities and remains poorly investigated in sarcoma. Here, we show that specific sarcoma entities exhibit high levels of genomic instability signatures and molecular alterations in HRR genes, while harboring a complex pattern of chromosomal instability. Furthermore, sarcomas carrying HRDness traits exhibit a distinct SARC-HRD transcriptional signature that predicts PARP inhibitor sensitivity in patient-derived sarcoma cells. Concomitantly, HRD$^{high}$ sarcoma cells lack RAD51 nuclear foci formation upon DNA damage, further evidencing defects in HRR. We further identify the WEE1 kinase as a therapeutic vulnerability for sarcomas with HRDness and demonstrate the clinical benefit of combining DNA damaging agents and inhibitors of DNA repair pathways ex vivo and in the clinic. In summary, we provide a personalized oncological approach to treat sarcoma patients successfully

DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

Background: Drug synergy occurs when the combined effect of two drugs is greater than the sum of the individual drugs' effect. While cell line data measuring the effect of single drugs are readily available, there is relatively less comparable data on drug synergy given the vast amount of possible drug combinations. Thus, there is interest to use computational approaches to predict drug synergy for untested pairs of drugs. Methods: We introduce a Graph Neural Network (GNN) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We use information from the largest drug combination database available (DrugComb), combining drug synergy scores in order to construct high confidence benchmark datasets. Results: Our proposed solution for drug synergy predictions offers a number of benefits: 1) It utilizes a combination of 34 distinct drug synergy datasets to learn on a wide variety of drugs and cell lines representations. 2) It is trained on constructed high confidence benchmark datasets. 3) It learns task-specific drug representations, instead of relying on generalized and pre-computed chemical drug features. 4) It achieves similar or better prediction performance (AUPR scores ranging from 0.777 to 0.964) compared to state-of-the-art baseline models when tested on various benchmark datasets. Conclusions: We demonstrate that a GNN based model can provide state-of-the-art drug synergy predictions by learning task-specific representations of drugs

Unravelling homologous recombination repair deficiency and therapeutic opportunities in soft tissue and bone sarcoma.

Defects in homologous recombination repair (HRR) in tumors correlate with poor prognosis and metastases development. Determining HRR deficiency (HRD) is of major clinical relevance as it is associated with therapeutic vulnerabilities and remains poorly investigated in sarcoma. Here, we show that specific sarcoma entities exhibit high levels of genomic instability signatures and molecular alterations in HRR genes, while harboring a complex pattern of chromosomal instability. Furthermore, sarcomas carrying HRDness traits exhibit a distinct SARC-HRD transcriptional signature that predicts PARP inhibitor sensitivity in patient-derived sarcoma cells. Concomitantly, HRDhigh sarcoma cells lack RAD51 nuclear foci formation upon DNA damage, further evidencing defects in HRR. We further identify the WEE1 kinase as a therapeutic vulnerability for sarcomas with HRDness and demonstrate the clinical benefit of combining DNA damaging agents and inhibitors of DNA repair pathways ex vivo and in the clinic. In summary, we provide a personalized oncological approach to treat sarcoma patients successfully