Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy.

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

Monitoring Sarcoma Response to Immune Checkpoint Inhibition and Local Cryotherapy with Circulating Tumor DNA Analysis.

PURPOSE: Immune checkpoint inhibition has led to promising responses in soft tissue sarcomas (STS), but the majority of patients do not respond and biomarkers of response will be crucial. Local ablative therapies may augment systemic responses to immunotherapy. We evaluated circulating tumor DNA (ctDNA) as a biomarker of response in patients treated on a trial combining immunotherapy with local cryotherapy for advanced STS. PATIENTS AND METHODS: We enrolled 30 patients with unresectable or metastatic STS to a phase II clinical trial. Patients received ipilimumab and nivolumab for four doses followed by nivolumab alone with cryoablation performed between cycles 1 and 2. The primary endpoint was objective response rate (ORR) by 14 weeks. Personalized ctDNA analysis using bespoke panels was performed on blood samples collected prior to each immunotherapy cycle. RESULTS: ctDNA was detected in at least one sample for 96% of patients. Pretreatment ctDNA allele fraction was negatively associated with treatment response, progression-free survival (PFS), and overall survival (OS). ctDNA increased in 90% of patients from pretreatment to postcryotherapy, and patients with a subsequent decrease in ctDNA or undetectable ctDNA after cryotherapy had significantly better PFS. Of the 27 evaluable patients, the ORR was 4% by RECIST and 11% by irRECIST. Median PFS and OS were 2.7 and 12.0 months, respectively. No new safety signals were observed. CONCLUSIONS: ctDNA represents a promising biomarker for monitoring response to treatment in patients with advanced STS, warranting future prospective studies. Combining cryotherapy and immune checkpoint inhibitors did not increase the response rate of STS to immunotherapy.