Evidence map›Paper›PMID 38064127›Full record

ReviewClinical & experimental metastasis2024

Dissecting the tumor microenvironment in response to immune checkpoint inhibitors via single-cell and spatial transcriptomics.

Wendi Liu, Anusha Puri, Doris Fu, Lee Chen, Cassia Wang, Manolis Kellis, Jiekun Yang

Abstract readReview
In one paragraph

Review in Clinical & experimental metastasis, 2024. 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

7 authors.

Wendi Liu *Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Anusha Puri *Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Doris Fu *Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Lee ChenComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Cassia WangComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Manolis KellisComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jiekun YangComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. jkyang@mit.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a disease that undergoes selective pressure to evolve during its progression, becoming increasingly heterogeneous. Tumoral heterogeneity can dictate therapeutic response. Transcriptomics can be used to uncover complexities in cancer and reveal phenotypic heterogeneity that affects disease response. This is especially pertinent in the immune microenvironment, which contains diverse populations of immune cells, and whose dynamic properties influence disease response. The recent development of immunotherapies has revolutionized cancer therapy, with response rates of up to 50% within certain cancers. However, despite advances in immune checkpoint blockade specifically, there remains a significant population of non-responders to these treatments. Transcriptomics can be used to profile immune and other cell populations following immune-checkpoint inhibitor (ICI) treatment, generate predictive biomarkers of resistance or response, assess immune effector function, and identify potential immune checkpoints. Single-cell RNA sequencing has offered insight into mRNA expression within the complex and heterogeneous tumor microenvironment at single-cell resolution. Spatial transcriptomics has enabled measurement of mRNA expression while adding locational context. Here, we review single-cell sequencing and spatial transcriptomic research investigating ICI response within a variety of cancer microenvironments.

Indexed as

Immune Checkpoint InhibitorsNeoplasmsSingle-Cell AnalysisTranscriptomeTumor MicroenvironmentBiomarkers, TumorGene Expression ProfilingHumansImmunotherapyBiomarkers, TumorImmune Checkpoint InhibitorsImmunotherapySingle-cellTranscriptomicsTumor microenvironment

Identifiers

PMID38064127
PMCPMC11374862

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Registered trials

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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.