Molecular Heterogeneity in Leiomyosarcoma and Implications for Personalised Medicine.

OPINION STATEMENT: Leiomyosarcoma (LMS) is one of the more common subtypes of soft tissue sarcomas (STS), accounting for about 20% of cases. Differences in anatomical location, risk of recurrence and histomorphological variants contribute to the substantial clinical heterogeneity in survival outcomes and therapy responses observed in patients. There is therefore a need to move away from the current one-size-fits-all treatment approach towards a personalised strategy tailored for individual patients. Over the past decade, tissue profiling studies have revealed key genomic features and an additional layer of molecular heterogeneity among patients, with potential utility for optimal risk stratification and biomarker-matched therapies. Furthermore, recent studies investigating intratumour heterogeneity and tumour evolution patterns in LMS suggest some key features that may need to be taken into consideration when designing treatment strategies and clinical trials. Moving forward, national and international collaborative efforts to aggregate expertise, data, resources and tools are needed to achieve a step change in improving patient survival outcomes in this disease of unmet need.

Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.

OPINION STATEMENT: Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques.

A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis.

BACKGROUND: Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. METHODS: A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. FINDINGS: 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. INTERPRETATION: Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. FUNDING: Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.

Functional genomics of human clear cell sarcoma: genomic, transcriptomic and chemical biology landscape for clear cell sarcoma.

BACKGROUND: Systemic therapy for metastatic clear cell sarcoma (CCS) bearing EWSR1-CREB1/ATF1 fusions remains an unmet clinical need in children, adolescents, and young adults. METHODS: To identify key signaling pathway vulnerabilities in CCS, a multi-pronged approach was taken: (i) genomic and transcriptomic landscape analysis, (ii) integrated chemical biology interrogations, (iii) development of CREB1/ATF1 inhibitors, and (iv) antibody-drug conjugate testing (ADC). The first approach encompassed DNA exome and RNA deep sequencing of the largest human CCS cohort yet reported consisting of 47 patient tumor samples and 8 cell lines. RESULTS: Sequencing revealed recurrent mutations in cell cycle checkpoint, DNA double-strand break repair or DNA mismatch repair genes, with a correspondingly low to intermediate tumor mutational burden. DNA multi-copy gains with corresponding high RNA expression were observed in CCS tumor subsets. CCS cell lines responded to the HER3 ADC patritumab deruxtecan in a dose-dependent manner in vitro, with impaired long term cell viability. CONCLUSION: These studies of the genomic, transcriptomic and chemical biology landscape represent a resource 'atlas' for the field of CCS investigation and drug development. CHK inhibitors are identified as having potential relevance, CREB1 inhibitors non-dependence of CCS on CREB1 activity was established, and the potential utility of HER3 ADC being used in CCS is found.

Machine learning for rhabdomyosarcoma histopathology.

Correctly diagnosing a rare childhood cancer such as sarcoma can be critical to assigning the correct treatment regimen. With a finite number of pathologists worldwide specializing in pediatric/young adult sarcoma histopathology, access to expert differential diagnosis early in case assessment is limited for many global regions. The lack of highly-trained sarcoma pathologists is especially pronounced in low to middle-income countries, where pathology expertise may be limited despite a similar rate of sarcoma incidence. To address this issue in part, we developed a deep learning convolutional neural network (CNN)-based differential diagnosis system to act as a pre-pathologist screening tool that quantifies diagnosis likelihood amongst trained soft-tissue sarcoma subtypes based on whole histopathology tissue slides. The CNN model is trained on a cohort of 424 centrally-reviewed histopathology tissue slides of alveolar rhabdomyosarcoma, embryonal rhabdomyosarcoma and clear-cell sarcoma tumors, all initially diagnosed at the originating institution and subsequently validated by central review. This CNN model was able to accurately classify the withheld testing cohort with resulting receiver operating characteristic (ROC) area under curve (AUC) values above 0.889 for all tested sarcoma subtypes. We subsequently used the CNN model to classify an externally-sourced cohort of human alveolar and embryonal rhabdomyosarcoma samples and a cohort of 318 histopathology tissue sections from genetically engineered mouse models of rhabdomyosarcoma. Finally, we investigated the overall robustness of the trained CNN model with respect to histopathological variations such as anaplasia, and classification outcomes on histopathology slides from untrained disease models. Overall positive results from our validation studies coupled with the limited worldwide availability of sarcoma pathology expertise suggests the potential of machine learning to assist local pathologists in quickly narrowing the differential diagnosis of sarcoma subtype in children, adolescents, and young adults.

Proteomic and Metabolomic Profiling in Soft Tissue Sarcomas.

OPINION STATEMENT: Advances in proteomic and metabolomic technologies have accelerated our understanding of multiple aspects of cancer biology across distinct tumour types. Here we review the current state-of-the-art in the use of proteomics and metabolomics in soft tissue sarcomas. We highlight the utility of these Omics-based methodologies to identify new drug targets, synthetic lethal interactions, candidate therapeutics and novel biomarkers to facilitate patient stratification. Due to the unbiased and global nature of these profiling methods to assess the levels of protein expression, post-translational modifications such as phosphorylation and glycosylation as well as key metabolites, many of these findings have broad applications not just in specific histotypes but across multiple STS subtypes. Specific examples of proteomic and metabolomic findings that have led to the development of early phase clinical trials of investigational agents will be discussed. While promising, the use of these technologies in the study of sarcoma is still limited, and there is a need for further research in this area. In particular, it would be important to integrate these approaches with other Omics strategies such as genomics and epigenomics as well as implement these tools alongside clinical trials in order to maximize the impact of these tools on our biological understanding and treatment of this group of rare diseases of unmet need.

KIT Exon 9-Mutated Gastrointestinal Stromal Tumours: Biology and Treatment.

BACKGROUND: The majority of gastroinstestinal stromal tumours (GISTs) harbour oncogenic mutations in the gene encoding for the tyrosine kinase (TK) KIT. The most common mutations are found in exon 11, followed by mutations in exon 9. The latter mutations are associated more frequently with GISTs in extra-gastric locations and with a more aggressive clinical behaviour. SUMMARY: Here, we review the unique and often poorly recognized molecular, biological, and clinical characteristics that differentiate KIT exon 9-mutant GISTs from other GIST subtypes. In particular, KIT exon 9 mutations are associated to KIT mutants with retained sensitivity to stimulation by stem cell factor and localization to the cell membrane. Moreover, KIT exon 9-mutant GISTs display significant activation of KIT-independent oncogenic pathways. These characteristics may explain the limited activity of the TK inhibitor imatinib in the adjuvant setting in KIT exon 9-mutant GISTs, as well as their lower sensitivity to standard dose imatinib in the advanced setting. In contrast, the multi-TK inhibitor sunitinib displays better activity in KIT exon 9-mutant GISTs compared to others. KEY MESSAGES: KIT exon 9-mutant GISTs represent a subtype of GIST distinct from other GISTs, including the more common KIT exon 11-mutant GISTs. A better understanding of the molecular biology and clinical behaviour of KIT exon 9-mutant GISTs may help identify more improved treatment options.

Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer.

Epidermal growth factor receptor (EGFR) mutations are the second most common oncogenic driver event in non-small cell lung cancer (NSCLC). Classical activating mutations (exon 19 deletions and the L858R point mutation) comprise the vast majority of EGFR mutations and are well defined as strong predictors for good clinical response to EGFR tyrosine kinase inhibitors (EGFRi). However, low frequency mutations including point mutations, deletions, insertions and duplications occur within exons 18-25 of the EGFR gene in NSCLC and are associated with poorer responses to EGFRi. Despite an increased uptake of more sensitive detection methods to identify rare EGFR mutations in patients, our understanding of the biology of these rare EGFR mutations is poor compared to classical mutations. In particular, clinical data focused on these mutations is lacking due to their rarity and challenges in trial recruitment, resulting in an absence of effective treatment strategies for many low frequency EGFR mutations. In this review, we describe the structural and mechanistic features of rare EGFR mutations in NSCLC and discuss the preclinical and clinical evidence for EGFRi response for individual rare EGFR mutations. We also discuss EGFRi sensitivity for complex EGFR mutations, and conclude by offering a perspective on the outstanding questions and future steps required to make advances in the treatment of NSCLC patients that harbour rare EGFR mutations.

Next-generation sequencing for the management of sarcomas with no known driver mutations.

PURPOSE OF REVIEW: Next-generation sequencing (NGS) has enabled fast, high-throughput nucleotide sequencing and has begun to be implemented into clinical practice for genomic-guided precision medicine in various cancer types. This review will discuss recent evidence that highlights opportunities for NGS to improve outcomes in sarcomas that have complex genomic profiles with no known driver mutations. RECENT FINDINGS: Global genomic signatures detectable by NGS including tumour mutational burden and microsatellite instability have potential as biomarkers for response to immunotherapy in certain sarcoma subtypes including angiosarcomas. Identification of hallmarks associated with 'BRCAness' and homologous recombination repair defects in leiomyosarcomas and osteosarcomas may predict sensitivity to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors. Lastly, the use of NGS for evaluating cancer predisposition in sarcomas may be useful for early detection, screening and surveillance. SUMMARY: Currently, the implementation of NGS for every sarcoma patient is not practical or useful. However, adopting NGS as a complementary approach in sarcomas with complex genomics and those with limited treatment options has the potential to deliver precision medicine to a subgroup of patients, with novel therapies such as immune checkpoint and PARP inhibitors. Moving forward, molecular tumour boards incorporating multidisciplinary teams of pathologists, oncologists and genomic specialists to interpret NGS data will complement existing tools in diagnosis and treatment decision making in sarcoma patients.

Advances in the proteomic profiling of the matrisome and adhesome.

INTRODUCTION: The matrisome and adhesome comprise proteins that are found within or are associated with the extracellular matrix (ECM) and adhesion complexes, respectively. Interactions between cells and their microenvironment are mediated by key matrisome and adhesome proteins, which direct fundamental processes, including growth and development. Due to their underlying complexity, it has historically been challenging to undertake mass spectrometry (MS)-based profiling of these proteins. New developments in sample preparative workflows, informatics databases, and MS techniques have enabled in-depth proteomic characterization of the matrisome and adhesome, resulting in a comprehensive understanding of the interactomes, and cellular signaling that occur at the cell-ECM interface. AREA COVERED: This review summarizes recent advances in proteomic characterization of the matrisome and adhesome. It focuses on the importance of curated databases and discusses key strengths and limitations of different workflows. EXPERT OPINION: MS-based proteomics has shown promise in characterizing the matrisome and topology of adhesome networks in health and disease. Moving forward, it will be important to incorporate integrative analysis to define the bidirectional signaling between the matrisome and adhesome, and adopt new methods for post-translational modification and in vivo analyses to better dissect the critical roles that these proteins play in human pathophysiology.

Tropomyosin receptor kinase inhibitors in the management of sarcomas.

PURPOSE OF REVIEW: Genetic aberrations resulting in tropomyosin receptor kinase (TRK) fusion proteins can drive oncogenesis and are postulated to occur in up to 1% of solid tumours. However, TRK fusions in adult sarcomas are rare and there is a significant challenge in identifying patients with sarcomas harbouring TRK fusions in the clinical setting. Despite a recent European Society of Medical Oncology consensus article regarding screening of tumours for TRK fusions, economical and practical limitations present a barrier to widespread screening of sarcomas. RECENT FINDINGS: Larotrectinib and entrectinib are pan-TRK inhibitors which have both received FDA approval for the management of solid tumours harbouring NTRK fusions. Initial results of a number of clinical trials have demonstrated promising efficacy and safety data, including dramatic and durable responses in patients with sarcomas. As such, TRK inhibitors represent a promising treatment option in a small cohort of adult sarcoma patients, where currently treatment options are limited. The emergence of acquired resistance is a concern associated with TRK inhibitor therapy and a number of second-generation agents targeting TRK kinase mutations driving acquired resistance have entered early-phase clinical trials. SUMMARY: With the growing appreciation of the implications of TRK fusions, this review will summarize the emerging clinical trial data of TRK inhibitors in sarcomas. Although in their infancy, clinical trial results are encouraging, and as further results and analyses are released, we will have a greater understanding of their impact on clinical practice and the management of patients with sarcomas.

Targeting the Src Pathway Enhances the Efficacy of Selective FGFR Inhibitors in Urothelial Cancers with FGFR3 Alterations.

Selective FGFR inhibitors such as infigratinib (BGJ398) and erdafitinib (JNJ-42756493) have been evaluated in clinical trials for cancers with FGFR3 molecular alterations, particularly in urothelial carcinoma patients. However, a substantial proportion of these patients (up to 50%) display intrinsic resistance to these drugs and receive minimal clinical benefit. There is thus an unmet need for alternative therapeutic strategies to overcome primary resistance to selective FGFR inhibitors. In this study, we demonstrate that cells expressing cancer-associated activating FGFR3 mutants and the FGFR3-TACC3 fusion showed primary resistance to infigratinib in long-term colony formation assays in both NIH-3T3 and urothelial carcinoma models. We find that expression of these FGFR3 molecular alterations resulted in elevated constitutive Src activation compared to wildtype FGFR3 and that cells co-opted this pathway as a means to achieve intrinsic resistance to infigratinib. Targeting the Src pathway with low doses of the kinase inhibitor dasatinib synergistically sensitized multiple urothelial carcinoma lines harbouring endogenous FGFR3 alterations to infigratinib. Our data provide preclinical rationale that supports the use of dasatinib in combination with selective FGFR inhibitors as a means to overcome intrinsic drug resistance in the salvage therapy setting in urothelial cancer patients with FGFR3 molecular alterations.

Spatial localisation of Discoidin Domain Receptor 2 (DDR2) signalling is dependent on its collagen binding and kinase activity.

Discoidin Domain Receptor 2 (DDR2) is a collagen-binding receptor tyrosine kinase that initiates delayed and sustained tyrosine phosphorylation signalling. To understand the molecular basis of this unique phosphorylation profile, here we utilise fluorescence microscopy to map the spatiotemporal localisation of DDR2 and tyrosine phosphorylated proteins upon stimulation with collagen. We show that cellular phosphorylated proteins are localised to the interface where DDR2 is in contact with collagen and not in the early endosomes or lysosomes. We find that DDR2 localisation is independent of integrin activation and the key DDR2 signalling effector SHC1. Structure-function analysis reveals that DDR2 mutants defective for collagen binding or kinase activity are unable to localise to the cell surface, demonstrating for the first time that both collagen binding and kinase functions are required for spatial localisation of DDR2. This study provides new insights into the underlying structural features that control DDR2 activation in space and time.

Novel therapeutic approaches in chondrosarcoma.

Chondrosarcoma is a malignant tumor of bones, characterized by the production of cartilage matrix. Due to lack of effective treatment for advanced disease, the clinical management of chondrosarcomas is exceptionally challenging. Current research focuses on elucidating the molecular events underlying the pathogenesis of this rare bone malignancy, with the goal of developing new molecularly targeted therapies. Signaling pathways suggested to have a role in chondrosarcoma include Hedgehog, Src, PI3k-Akt-mTOR and angiogenesis. Mutations in IDH1/2, present in more than 50% of primary conventional chondrosarcomas, make the development of IDH inhibitors a promising treatment option. The present review discusses the preclinical and early clinical data on novel targeted therapeutic approaches in chondrosarcoma.

Comparative proteomic assessment of matrisome enrichment methodologies.

The matrisome is a complex and heterogeneous collection of extracellular matrix (ECM) and ECM-associated proteins that play important roles in tissue development and homeostasis. While several strategies for matrisome enrichment have been developed, it is currently unknown how the performance of these different methodologies compares in the proteomic identification of matrisome components across multiple tissue types. In the present study, we perform a comparative proteomic assessment of two widely used decellularisation protocols and two extraction methods to characterise the matrisome in four murine organs (heart, mammary gland, lung and liver). We undertook a systematic evaluation of the performance of the individual methods on protein yield, matrisome enrichment capability and the ability to isolate core matrisome and matrisome-associated components. Our data find that sodium dodecyl sulphate (SDS) decellularisation leads to the highest matrisome enrichment efficiency, while the extraction protocol that comprises chemical and trypsin digestion of the ECM fraction consistently identifies the highest number of matrisomal proteins across all types of tissue examined. Matrisome enrichment had a clear benefit over non-enriched tissue for the comprehensive identification of matrisomal components in murine liver and heart. Strikingly, we find that all four matrisome enrichment methods led to significant losses in the soluble matrisome-associated proteins across all organs. Our findings highlight the multiple factors (including tissue type, matrisome class of interest and desired enrichment purity) that influence the choice of enrichment methodology, and we anticipate that these data will serve as a useful guide for the design of future proteomic studies of the matrisome.

Phosphoproteomics: unraveling the signaling web.

In recent years, phosphoproteomic technologies have increased our understanding of cellular signaling networks. Here, we frame recent phosphoproteomics-based advances in the context of the DNA damage response and ErbB receptor family signaling and offer a perspective on how the molecular insights arising from the integration of such proteomic approaches might be used for clinical applications.