Evidence map›Paper›PMID 33757216›Full record

ArticleMathematical biosciences and engineering : MBE2021

Immune classification of osteosarcoma.

Trang Le, Sumeyye Su, Leili Shahriyari

Abstract read
In one paragraph

Article in Mathematical biosciences and engineering : MBE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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

3 authors.

Trang LeDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA MA 01003-9305, USA.
Sumeyye SuDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA MA 01003-9305, USA.
Leili ShahriyariDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA MA 01003-9305, USA.

Funding

Data-driven QSP software for personalized colon cancer treatmentR21CA242933 · NCI · UNIVERSITY OF MASSACHUSETTS AMHERST · PI PAL, SUVRA, ROY, SOUVIK · 2019 to 2020
$547k
NCI NIH HHS R21 CA242933
6 · The paper itself

Abstract

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

Indexed as

Bone NeoplasmsOsteosarcomaCD8-Positive T-LymphocytesHumansMacrophagesTumor Microenvironmentgene expression analysisimmune patternosteosarcoma

Identifiers

PMID33757216
PMCPMC7992873

What OpenQuestion holds

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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.