The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition
Anzar, I.; Malone, B.; Samarakoon, P.; Vardaxis, I.; Simovski, B.; Fontenelle, H.; Meza-Zepeda, L. A.; Stratford, R.; Keung, E. Z.; Burgess, M.; Tawbi, H. A.; Myklebost, O.; Clancy, T.
Show abstract
Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes. To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME). A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important. The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multimodal profiling of chordoma immunity reveals distinct immune contextures 96%
- Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+ T cells in the tumor microenvironment and predicts clinical outcome in early- and late-phase clinical trials 95%
- Predictive value of preclinical models for CAR-T cell therapy clinical trials: a systematic review and meta-analysis 94%
Similar papers in this journal
- Cytokine Profiling of Children, Adolescents, and Young Adults Newly Diagnosed with Sarcomas Demonstrates a Role for IL-1β in Osteosarcoma Metastasis 95%
- ATRX alteration contributes to tumor growth and immune escape in pleomorphic sarcomas 94%
- Use of high-plex data reveals novel insights into the tumour microenvironment of clear cell renal cell carcinoma 94%
Similar papers in this journal
- Genetic and Epigenetic Characterization of Sarcoma Stem Cells Across Subtypes Identifies EZH2 as a Therapeutic Target 95%
- Development and validation of a gene expression score to account for tumour purity and improve prognostication in breast cancer 94%
- Regulatory FOXP3+ T cells in uterine sarcomas are associated with favorable prognosis, low extracellular matrix expression and reduced YAP activation 94%
Similar papers in this journal
- The Immune landscape of solid pediatric tumors. 94%
- Long-term patient-derived ovarian cancer organoids closely recapitulate tumor of origin and clinical response 93%
- Soluble TIM-3, likely produced by myeloid cells, predicts resistance to immune checkpoint inhibitors in metastatic clear cell renal cell carcinoma 93%
Similar papers in this journal
- A three-gene expression score for predicting clinical benefit to anti-PD-1 blockade in advanced renal cell carcinoma 95%
- Integrated immunogenomic analyses of high-grade serous ovarian cancer reveal vulnerability to combination immunotherapy 94%
- The immunogenic potential of recurrent cancer drug resistance mutations: an in silico study 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.