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There are more than 70 distinct sarcomas, and this diversity complicates the development of precision-based therapeutics for these cancers. Prospective comprehensive genomic profiling could overcome this challenge by providing insight into sarcomas' molecular drivers. Through targeted panel sequencing of 7494 sarcomas representing 44 histologies, we identify highly recurrent and type-specific alterations that aid in diagnosis and treatment decisions. Sequencing could lead to refinement or reassignment of 10.5% of diagnoses. Nearly one-third of patients (31.7%) harbor potentially actionable alterations, including a significant proportion (2.6%) with kinase gene rearrangements; 3.9% have a tumor mutational burden ≥10 mut/Mb. We describe low frequencies of microsatellite instability (

Lessons learnedThe results from the liposarcoma cohort of SARC024 confirm previously published data and do not support the routine use of regorafenib in this patient population. Continued exploration of novel therapies, including combination approaches, is warranted for a patient population in whom limited treatment options exist.BackgroundRegorafenib is a multitargeted kinase inhibitor with a kinase profile overlapping, but distinct from, pazopanib, an agent approved for recurrent and metastatic non-gastrointestinal stromal tumor (GIST), non-adipocytic soft tissue sarcoma. We conducted a randomized, phase II study of regorafenib versus placebo in refractory liposarcoma patients.MethodsPatients with advanced or metastatic, treatment-refractory liposarcoma were randomized 1:1 to receive regorafenib 160 mg or placebo once daily (3 weeks on, 1 week off). Patients with well-differentiated liposarcoma only were excluded. Crossover for placebo was allowed upon progression. The primary endpoint was progression-free survival (PFS), according to RECIST version 1.1.ResultsForty-eight subjects with liposarcoma (34 dedifferentiated, 12 myxoid/round cell, 2 pleomorphic) were enrolled. Median PFS was 1.87 (95% confidence interval [CI], 0.92-3.67) months for regorafenib versus 2.07 (95% CI, 1.64-3.44) months for placebo; stratified hazard ratio [HR], 0.85 (95% CI, 0.46, 1.58), p = .62. No responses were seen on regorafenib. One PR was observed on placebo. Median overall survival was 6.46 (95% CI, 4.16-23.48) months for regorafenib and 4.89 (95% CI, 3.02-9.77) months for placebo, stratified HR, 0.66 (95% CI, 0.31-1.40), p = .28). Treatment-related adverse events were similar to the known safety profile of regorafenib.ConclusionRegorafenib did not appear to improve PFS in treatment-refractory liposarcoma. No new significant safety signals were observed.

Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with diverse functions, including matrix deposition and remodelling, extensive reciprocal signalling interactions with cancer cells and crosstalk with infiltrating leukocytes. As such, they are a potential target for optimizing therapeutic strategies against cancer. However, many challenges are present in ongoing attempts to modulate CAFs for therapeutic benefit. These include limitations in our understanding of the origin of CAFs and heterogeneity in CAF function, with it being desirable to retain some antitumorigenic functions. On the basis of a meeting of experts in the field of CAF biology, we summarize in this Consensus Statement our current knowledge and present a framework for advancing our understanding of this critical cell type within the tumour microenvironment.

BackgroundCancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.MethodsTissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.ResultsOur computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).ConclusionsThese proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States, show remarkable histologic diversity, with more than 50 recognized subtypes. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.