Evidence map›Paper›PMID 31708031›Full record

ArticleAnalytica chimica acta2019

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Renmeng Liu, Mei Sun, Genwei Zhang, Yunpeng Lan, Zhibo Yang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Analytica chimica acta, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
1.3field-weighted citation impact, top 23% of its field
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

27 citing papers in PubMed, 42 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Renmeng LiuDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Mei SunDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Genwei ZhangDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Yunpeng LanDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Zhibo YangDepartment of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA. Electronic address: zhibo.yang@ou.edu.
University of Oklahoma · US

Funding

From Single Cells to Tissues: a Novel Mass Spectrometry Approach for BioanalysisR01GM116116 · NIGMS · UNIVERSITY OF OKLAHOMA · PI YANG, ZHIBO · 2015 to 2019
$1.5M
Mass Spectrometry Detection of Drugs in Single Bladder Cancer Cells from PatientsR21CA204706 · NCI · UNIVERSITY OF OKLAHOMA · PI BURGETT, ANTHONY WG · 2016 to 2018
$599k
NCI NIH HHS R21 CA204706NIGMS NIH HHS R01 GM116116
6 · The paper itself

Abstract

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Indexed as

Machine LearningAntineoplastic AgentsBiomarkersDrug ResistanceHCT116 CellsHumansIrinotecanMass SpectrometryMetabolomicsNeural Networks, ComputerProof of Concept StudyROC CurveSingle-Cell AnalysisAntineoplastic AgentsBiomarkersIrinotecanDrug resistanceMachine learningMetabolomicsSingle cell mass spectrometryThe single-probe

Identifiers

PMID31708031
PMCPMC6878984
OpenAlexW2975345404

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.