Evidence map›Paper›PMID 39113621›Full record

ArticleCancer research communications2024

Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood.

Yingying Cao, Tiangen Chang, Fiorella Schischlik, Kun Wang, Sanju Sinha, Sridhar Hannenhalli, Peng Jiang, Eytan Ruppin

Abstract read
In one paragraph

Article in Cancer research communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Towards liquid biopsy-based analysis of antitumour immunity.Nature reviews. Clinical oncology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yingying Cao *Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0009-0002-0604-5894
Tiangen Chang *Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0003-1485-1992
Fiorella Schischlik *Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0003-4299-7657
Kun WangCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0001-8923-8557
Sanju SinhaCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0002-2688-0603
Sridhar HannenhalliCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0001-9603-7569
Peng JiangCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0002-7828-5486
Eytan RuppinCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland.ORCID 0000-0002-7862-3940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications. SIGNIFICANCE: Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.

Indexed as

Head and Neck NeoplasmsSingle-Cell AnalysisSquamous Cell Carcinoma of Head and NeckTranscriptomeTumor MicroenvironmentBiomarkers, TumorGene Expression ProfilingHumansImmunotherapyLeukocytes, MononuclearMalePrognosisBiomarkers, Tumor

Identifiers

PMID39113621
PMCPMC11375407

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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.