ArticleAdvances in experimental medicine and biology2022
Multi-Omics Profiling of the Tumor Microenvironment.
Article in Advances in experimental medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Mechanism of cancer-associated fibroblast-driven thyroid cancer dedifferentiation via the ZFP57-PKM2 axis-mediated lactate secretion and therapeutic intervention with resveratrol.Journal of experimental & clinical cancer research : CR · 2026Article
- Single-cell multi-omics in biliary tract cancers: decoding heterogeneity, microenvironment, and treatment strategies.Molecular biomedicine · 2025Review
- SERPINA1 is a new frontier in cancer immunotherapy and drug targeting by pan-cancer analysis.Discover oncology · 2025Article
- Explainable AI for classifying vertebral fracture histology in digital spine pathology.Frontiers in medicine · 2025Article
- Cancer-associated fibroblast spatial heterogeneity andTheranostics · 2024Article
- Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.Genome biology · 2023Article
- The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity.Frontiers in oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
All solid tumors and many hematological malignancies grow and proliferate in a tumor microenvironment (TME), a spectrum of continuous and highly dynamic interactions with different immune and stromal cells. This ecosystem contributes to the extensive heterogeneity that exists between and within cancer patients. Understanding the characteristics of this intricate network could significantly improve cancer prognosis, as was demonstrated already for a subset of patients by the advent of immunotherapies (including monoclonal antibodies, bispecific antibodies, and chimeric antigen receptor (CAR) T cells. The development of multimodal omics technologies has allowed researchers to document and characterize the TME at single-cell resolution, which provides an unprecedent opportunity to understand the full complexity of the tumor microenvironment. In this chapter, we highlight the paradigm shift that has brought the TME to the forefront of cancer research and discuss its composition. In addition, we summarize the available multimodal single-cell omics methods that allow studying the TME from different angles, as well as their advantages and limitations. We discuss computational analysis tools, data integration, and methods to specifically study crosstalk between TME components. Finally, we touch upon the implications of studying the TME for ongoing or future clinical studies and how these can lead to more effective treatments for cancer patients.
Indexed as
Identifiers
35230695What 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.