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.

Patient-reported outcome measurement implementation in cancer survivors: a systematic review.

PURPOSE: Patient-reported outcome measurements (PROMs) are increasingly used for cancer patients receiving active treatment, but little is known about the implementation and usefulness of PROMs in cancer survivorship care. This systematic review evaluates how cancer survivors and healthcare providers (HCPs) perceive PROM implementation in survivorship care, and how PROM implementation impacts cancer survivors' health outcomes. METHODS: We systematically searched PubMed/MEDLINE, Embase, CINAHL, Web of Science, and Cochrane Database of Systematic Reviews from database inception to February 2022 to identify randomized and nonrandomized studies of PROM implementation in cancer survivors. RESULTS: Based on prespecified eligibility criteria, we included 29 studies that reported on 26 unique PROMs. The studies were heterogeneous in study design, PROM instrument, patient demographics, and outcomes. Several studies found that cancer survivors and HCPs had favorable impressions of the utility of PROMs, and a few studies demonstrated that PROM implementation led to improvements in patient quality of life (QoL), with small to moderate effect sizes. CONCLUSIONS: We found implementation of PROMs in cancer survivorship care improved health outcomes for select patient populations. Future research is needed to assess the real-world utility of PROM integration into clinical workflows and the impact of PROMs on measurable health outcomes. IMPLICATIONS FOR CANCER SURVIVORS: Cancer survivors accepted PROMs. When successfully implemented, PROMs can improve health outcomes after completion of active treatment. We identify multiple avenues to strengthen PROM implementation to support cancer survivors.

Imaging and Pathology Correlations for Different Risk Stratification Models for Intermediate-risk Prostate Cancer.

BACKGROUND/AIM: We evaluated whether sub-stratifying intermediate-risk (IR) prostate cancer using the Memorial Sloan Kettering Cancer (MSKCC) or Prostate Cancer Risk Stratification (ProCaRS) model predicts for adverse imaging or pathologic features. PATIENTS AND METHODS: 56 consecutive IR patients who underwent multi-parametric MRI (mpMRI) and radical prostatectomy (RP) were studied. The different groups were tested for correlation with adverse findings. 2-sample T-tests assuming unequal variance were used. RESULTS: On mpMRI the MSKCC unfavorable group had higher index lesion suspicion scores (p=0.044), while the ProCaRS model showed a higher maximum tumor diameter (MTD) in the high-risk group (p=0.047). At RP, a higher pathologic MTD (23.3 vs. 17.6 mm, p=0.005) was present in the MSKCC unfavorable group as well as the ProCaRS high vs. low group (26.6 vs. 19.3 mm, p=0.022). CONCLUSION: Both models demonstrated a correlation with higher MTD for unfavorable IR patients. This is likely a driver of worse clinical outcomes.