The early evolutionary landscape of osteosarcoma provides clues for targeted treatment strategies

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes

Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchical cluster analysis, but these computational approaches disregard the heterogeneous composition of individual cancer samples. Here, we used a more sophisticated unsupervised Bayesian model termed latent process decomposition (LPD), which handles individual cancer sample heterogeneity and deconvolutes the structure of transcriptome data to provide clinically relevant information. The work was performed on the pediatric tumor osteosarcoma, which is a prototypical model for a rare and heterogeneous cancer. The LPD model detected three osteosarcoma subtypes. The subtype with the poorest prognosis was validated using independent patient datasets. This new stratification framework will be important for more accurate diagnostic labeling, expediting precision medicine, and improving clinical trial success. Our results emphasize the importance of using more sophisticated machine learning approaches (and for teaching deep learning and artificial intelligence) for RNA-seq data analysis, which may assist drug targeting and clinical management.

Unraveling molecular aberrations and pioneering therapeutic strategies in osteosarcoma

Osteosarcoma, a rare primary bone cancer, presents diverse molecular aberrations that underscore its complexity. Despite the persistent endeavors by researchers, the limited amelioration in the five-year survival rate indicates that current therapeutic strategies prove inadequate in addressing the clinical necessities. Advancements in molecular profiling have facilitated an enhanced comprehension of the biology of osteosarcoma, offering a promising outlook for treatment. There is an urgent need to develop innovative approaches to address the complex challenges of osteosarcoma, ultimately contributing to enhanced patient outcomes. This review explores the nexus between osteosarcoma and cancer predisposition syndromes, intricacies in its somatic genome, and clinically actionable alterations. This review covers treatment strategies, including surgery, chemotherapy, immune checkpoint inhibitors (ICIs), and tyrosine kinase inhibitors (TKIs). Innovative treatment modalities targeting diverse pathways, including multi-target tyrosine kinases, cell cycle, PI3K/mTOR pathway, and DNA damage repair (DDR), offer promising interventions. This review also covers promising avenues, including antibody-drug conjugates (ADCs) and immunotherapy strategies, such as cytokines, adoptive cellular therapy (ACT), ICIs, and cancer vaccines. This comprehensive exploration contributes to a holistic understanding, offering guidance for clinical applications to advance the management of osteosarcoma.

Biological Sample Collection to Advance Research and Treatment: A Fight Osteosarcoma Through European Research and Euro Ewing Consortium Statement

Osteosarcoma and Ewing sarcoma are bone tumors mostly diagnosed in children, adolescents, and young adults. Despite multimodal therapy, morbidity is high and survival rates remain low, especially in the metastatic disease setting. Trials investigating targeted therapies and immunotherapies have not been groundbreaking. Better understanding of biological subgroups, the role of the tumor immune microenvironment, factors that promote metastasis, and clinical biomarkers of prognosis and drug response are required to make progress. A prerequisite to achieve desired success is a thorough, systematic, and clinically linked biological analysis of patient samples, but disease rarity and tissue processing challenges such as logistics and infrastructure have contributed to a lack of relevant samples for clinical care and research. There is a need for a Europe-wide framework to be implemented for the adequate and minimal sampling, processing, storage, and analysis of patient samples. Two international panels of scientists, clinicians, and patient and parent advocates have formed the Fight Osteosarcoma Through European Research consortium and the Euro Ewing Consortium. The consortia shared their expertise and institutional practices to formulate new guidelines. We report new reference standards for adequate and minimally required sampling (time points, diagnostic samples, and liquid biopsy tubes), handling, and biobanking to enable advanced biological studies in bone sarcoma. We describe standards for analysis and annotation to drive collaboration and data harmonization with practical, legal, and ethical considerations. This position paper provides comprehensive guidelines that should become the new standards of care that will accelerate scientific progress, promote collaboration, and improve outcomes.

The Variable Genomic Landscape During Osteosarcoma Progression: Insights From a Longitudinal WGS Analysis

Osteosarcoma is a primary bone tumor that exhibits a complex genomic landscape characterized by gross chromosomal abnormalities. Osteosarcoma patients often develop metastatic disease, resulting in limited therapeutic options and poor survival rates. To gain knowledge on the mechanisms underlying osteosarcoma heterogeneity and metastatic process, it is important to obtain a detailed profile of the genomic alterations that accompany osteosarcoma progression. We performed WGS on multiple tissue samples from six patients with osteosarcoma, including the treatment naïve biopsy of the primary tumor, resection of the primary tumor after neoadjuvant chemotherapy, local recurrence, and distant metastases. A comprehensive analysis of single-nucleotide variants (SNVs), structural variants, copy number alterations (CNAs), and chromothripsis events revealed the genomic heterogeneity during osteosarcoma progression. SNVs and structural variants were found to accumulate over time, contributing to an increased complexity of the genome of osteosarcoma during disease progression. Phylogenetic trees based on SNVs and structural variants reveal distinct evolutionary patterns between patients, including linear, neutral, and branched patterns. The majority of osteosarcomas showed variable copy number profiles or gained whole-genome doubling in later occurrences. Large proportions of the genome were affected by loss of heterozygosity (LOH), although these regions remain stable during progression. Additionally, chromothripsis is not confined to a single early event, as multiple other chromothripsis events may appear in later occurrences. Together, we provide a detailed analysis of the complex genome of osteosarcomas and show that five of six osteosarcoma genomes are highly dynamic and variable during progression.

Subclonal Somatic Copy-Number Alterations Emerge and Dominate in Recurrent Osteosarcoma

Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease.

SIGNIFICANCE

The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

Osteosarcoma Multi-Omics Landscape and Subtypes

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.

Integrative Multi-OMICs Identifies Therapeutic Response Biomarkers and Confirms Fidelity of Clinically Annotated, Serially Passaged Patient-Derived Xenografts Established from Primary and Metastatic Pediatric and AYA Solid Tumors

Establishment of clinically annotated, molecularly characterized, patient-derived xenografts (PDXs) from treatment-naïve and pretreated patients provides a platform to test precision genomics-guided therapies. An integrated multi-OMICS pipeline was developed to identify cancer-associated pathways and evaluate stability of molecular signatures in a panel of pediatric and AYA PDXs following serial passaging in mice. Original solid tumor samples and their corresponding PDXs were evaluated by whole-genome sequencing, RNA-seq, immunoblotting, pathway enrichment analyses, and the drug−gene interaction database to identify as well as cross-validate actionable targets in patients with sarcomas or Wilms tumors. While some divergence between original tumor and the respective PDX was evident, majority of alterations were not functionally impactful, and oncogenic pathway activation was maintained following serial passaging. CDK4/6 and BETs were prioritized as biomarkers of therapeutic response in osteosarcoma PDXs with pertinent molecular signatures. Inhibition of CDK4/6 or BETs decreased osteosarcoma PDX growth (two-way ANOVA, p < 0.05) confirming mechanistic involvement in growth. Linking patient treatment history with molecular and efficacy data in PDX will provide a strong rationale for targeted therapy and improve our understanding of which therapy is most beneficial in patients at diagnosis and in those already exposed to therapy.

Identification of potential extracellular vesicle protein markers altered in osteosarcoma from public databases

PURPOSE

Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.

EXPERIMENTAL DESIGN

Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.

RESULTS

Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.

CONCLUSIONS AND CLINICAL RELEVANCE

This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.

Update of Key Clinical, Histological and Molecular Features of Malignant Bone Tumours Arising in the Craniofacial Skeleton

The craniofacial skeleton is a highly complex and specialized anatomic region containing and protecting the brain and sensory organs. Bone sarcomas arising here comprise a heterogeneous group of tumours, some of which differ in their biological behaviour compared to their peripheral counterparts. The reasons for this seem to lie, at least partially, in the embryonal development of the craniofacial bones. For reaching the correct diagnosis as the cornerstone of optimal personalised treatment planning, a multidisciplinary team of specialists, including pathologists, radiologists, oncologists, and head and neck surgeons needs to be involved. The most common tumours arising in the craniofacial bones are bone-forming tumours, cartilage-forming tumours, fibro-osseous lesions, giant cell-rich lesions, and notochordal tumours. While morphology remains the backbone for the diagnosis, the last decade has witnessed tremendous advances in the molecular characterization of tumours, and molecular testing is increasingly becoming a part of the diagnostic process. The integration of these new molecular markers into the diagnostic approach has undoubtedly increased the diagnostic accuracy and objectivity, and holds great promise to also identify new therapeutic targets for precision medicine in the future. Examples include HEY1-NCOA2 in mesenchymal chondrosarcoma, IDH1/2 mutations in chondrosarcoma and TFCP2 rearrangements in rhabdomyosarcoma. In this article, key clinical, histological and molecular features of malignant bone tumours arising in the craniofacial region are discussed, with a special focus on the differential diagnosis and prognostic considerations.

A murine mesenchymal stem cell model for initiating events in osteosarcomagenesis points to CDK4/CDK6 inhibition as a therapeutic target

Osteosarcoma is a high-grade bone-forming neoplasm, with a complex genome. Tumours frequently show chromothripsis, many deletions, translocations and copy number alterations. Alterations in the p53 or Rb pathway are the most common genetic alterations identified in osteosarcoma. Using spontaneously transformed murine mesenchymal stem cells (MSCs) which formed sarcoma after subcutaneous injection into mice, it was previously demonstrated that p53 is most often involved in the transformation towards sarcomas with complex genomics, including osteosarcoma. In the current study, not only loss of p53 but also loss of p16^Ink4a is shown to be a driver of osteosarcomagenesis: murine MSCs with deficient p15^Ink4b, p16^Ink4a, or p19^Arf transform earlier compared to wild-type murine MSCs. Furthermore, in a panel of nine spontaneously transformed murine MSCs, alterations in p15^Ink4b, p16^Ink4a, or p19^Arf were observed in eight out of nine cases. Alterations in the Rb/p16 pathway could indicate that osteosarcoma cells are vulnerable to CDK4/CDK6 inhibitor treatment. Indeed, using two-dimensional (n = 7) and three-dimensional (n = 3) cultures of human osteosarcoma cell lines, it was shown that osteosarcoma cells with defective p16^INK4A are sensitive to the CDK4/CDK6 inhibitor palbociclib after 72-hour treatment. A tissue microarray analysis of 109 primary tumour biopsies revealed a subset of patients (20-23%) with intact Rb, but defective p16 or overexpression of CDK4 and/or CDK6. These patients might benefit from CDK4/CDK6 inhibition, therefore our results are promising and might be translated to the clinic.

DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and young adults with poor prognosis for patients with refractory or metastatic disease. A common feature, so-called BRCAness, exists in multiple cancers including OS and is characterized by homologous recombination deficiency. Tumors exhibiting BRCAness have been shown to respond to therapy with PARP inhibitors. Currently, BRCAness is mostly assessed by the genomic instability score. This method based on the DNA sequencing requires normal tissue DNA as control and is vulnerable to subjective interpretation of "genomic scarring" events. In this study, we implemented a classifier based on DNA methylation patterns. It is capable of detecting BRCAness in OS samples and does not require control tissue DNA. Therefore, it has the potential to support clinical decision making on administering PARPi in OS patients. We further corroborated the presence of BRCAness in OS by detecting homologous recombination signatures through gene expression analysis.