Evidence map›Paper›PMID 41987302›Full record

ReviewCell communication and signaling : CCS2026

A conceptual blueprint for "turning cold to hot" in Osteosarcoma: from TME stratification hypotheses to adaptive therapeutic prospects.

Bai Yang, Shu Liu, Bingcheng Liu, Tianwen Ye, Xiao Ma, Tengfei Song

Abstract readReview
In one paragraph

Review in Cell communication and signaling : CCS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bai Yang *Department of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China.
Shu Liu *Department of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China.
Bingcheng Liu *Department of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China.
Tianwen YeDepartment of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China.
Xiao MaDepartment of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China. kugrdmx@163.com.
Tengfei SongDepartment of Orthopaedic Surgery, Changzheng Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, China. czsongtengfei@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteosarcoma (OS) is a quintessential “cold tumor,” and outcomes for patients with metastatic or recurrent disease have remained poor for decades. The failure of immune checkpoint inhibitors (ICIs) in OS reflects a multilayered immunosuppressive architecture rather than a single dominant lesion. This review deconstructs three principal barriers within that architecture: (1) physical T-cell exclusion driven by a dense fibrotic stroma and aberrant vasculature; (2) a myeloid-dominant suppressive network enriched for Tumor-Associated Macrophages (TAMs) and Myeloid-Derived Suppressor Cells (MDSCs); and (3) Antigen Presentation Machinery (APM) defects, most commonly loss of MHC-I/B2M. In parallel, the primary tumor actively engineers the pulmonary environment through exosomes and Neutrophil Extracellular Traps (NETs), establishing a Pre-metastatic Niche (PMN) that facilitates lung metastasis.Integrating evidence from single-cell and spatial omics, multi-modal imaging (radiomics, digital pathology), and liquid biopsy (ctDNA-minimal residual disease [MRD]), this review translates biological “decoding” of the OS microenvironment into a hypothesis-generating operational framework. We propose an “OS-TME Subtyping V1.0” model in which subtypes are treated as dynamic, dominant-barrier system states rather than fixed biological classes. In its current conceptual form, state assignment is envisioned as a semi-structured, rule-based process using concordant signals from pathology/spatial readouts, imaging surrogates, and ctDNA/immune context; mixed or discordant cases are intentionally retained as indeterminate states for reassessment rather than forcibly classified. On this basis, we outline a sequential “De-suppression → Priming → Checkpoint” logic tailored to different barrier-dominant states. For myeloid-dominant states, we prioritize myeloid reprogramming (e.g., CSF1R/CCR2-axis targeting) combined with immunogenic priming. For dense fibrotic stroma/angio-abnormal states, we emphasize up-front vessel/stroma remodeling before checkpoint therapy. For APM-defective states, we discuss MHC-independent approaches targeting B7-H3 or GD2 (e.g., CAR-T/NK cells, antibody-drug conjugates), while explicitly acknowledging target heterogeneity, trafficking barriers, and on-target/off-tumor risk.To narrow the translational gap, we further outline a perioperative “Window of Opportunity” (WoO) trial prototype and a conceptual “Cold-to-Hot Readiness Index (RI)” that integrates dynamic imaging, pathology, and MRD monitoring. The RI is presented only as an illustrative, hypothesis-generating summary variable intended for retrospective stratification, simulation modeling, or biomarker-guided early-phase trial design, rather than near-term routine clinical decision-making. Together, these elements define a theoretical blueprint for iterative state assessment and adaptive therapeutic sequencing in osteosarcoma.

Indexed as

Bone NeoplasmsOsteosarcomaTumor MicroenvironmentAnimalsHumansCold to HotImmunotherapy ResistanceOsteosarcomaTME StratificationTumor Microenvironment

Identifiers

PMID41987302
PMCPMC13188487

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.