ReviewImmuno-oncology technology2023
Deciphering the tumour immune microenvironment cell by cell.
Review in Immuno-oncology technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Cancer drug response and resistance: molecular mechanisms and combating strategies.Signal transduction and targeted therapy · 2026Review
- Single-cell analysis reveals crosstalk between TREM1-positive myeloid cells and cancer-associated fibroblasts in colorectal cancer progression.Journal of gastroenterology · 2026Article
- Emerging role of microRNAs as modulators of the tumor immune microenvironment.Journal for immunotherapy of cancer · 2026Review
- Heterogeneity of Cancer-Associated Fibroblasts and Precision Targeting Strategies for Cancer Therapy.Thoracic cancer · 2026Review
- A Tissue Virus Microenvironment with Activated Stress Responses Underlies Durable SIV Persistence.bioRxiv : the preprint server for biology · 2026Article
- Integrative spatial profiling pipeline for determining TME architectures in archival clinical specimens using CmTSA superplex technology.Cell discovery · 2026Article
- T cells assemble lentivirus to solve local delivery of stable gene therapy.Scientific reports · 2026Article
- Adoptive cell therapies in solid tumors: current clinical landscape, challenges, and future directions.Frontiers in immunology · 2026Review
- A temporal model of tumor-immune dynamics during the metastatic progression of high-grade serous ovarian cancer.NPJ precision oncology · 2025Article
- Tumor-infiltrating lymphocytes in cancer immunotherapy: from chemotactic recruitment to translational modeling.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.