Evidence map›Paper›PMID 37978271›Full record

ArticleScientific reports2023

Modeling phenotypic heterogeneity towards evolutionarily inspired osteosarcoma therapy.

Darcy L Welch, Brooke L Fridley, Ling Cen, Jamie K Teer, Sean J Yoder, Fredrik Pettersson, Liping Xu, Chia-Ho Cheng, Yonghong Zhang, Mark G Alexandrow and 6 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
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

16 authors at 1 institution in 1 country.

Darcy L WelchAdolescent and Young Adult Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Brooke L FridleyBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.ORCID 0000-0001-7739-7956
Ling CenBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Jamie K TeerBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.ORCID 0000-0003-4513-0282
Sean J YoderMolecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Fredrik PetterssonBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Liping XuBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Chia-Ho ChengBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Yonghong ZhangBiostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Mark G AlexandrowMolecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Shengyan XiangMolecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Mark Robertson-TessiIntegrative Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Joel S BrownIntegrative Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Jonathan MettsSarcoma Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Andrew S BrohlSarcoma Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Damon R ReedAdolescent and Young Adult Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. reedd1@mskcc.org.
Moffitt Cancer Center · US

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI John L. Cleveland · 1998 to 2026
$93.5M
Direct Control of the human CMG Helicase by Myc and RbR01GM140140 · NIGMS · H. LEE MOFFITT CANCER CTR & RES INST · PI ALEXANDROW, MARK G. · 2021 to 2024
$1.3M
NCI NIH HHS P30 CA076292NIGMS NIH HHS R01 GM140140
6 · The paper itself

Abstract

Osteosarcoma is the most common bone sarcoma in children and young adults. While universally delivered, chemotherapy only benefits roughly half of patients with localized disease. Increasingly, intratumoral heterogeneity is recognized as a source of therapeutic resistance. In this study, we develop and evaluate an in vitro model of osteosarcoma heterogeneity based on phenotype and genotype. Cancer cell populations vary in their environment-specific growth rates and in their sensitivity to chemotherapy. We present the genotypic and phenotypic characterization of an osteosarcoma cell line panel with a focus on co-cultures of the most phenotypically divergent cell lines, 143B and SAOS2. Modest environmental (pH, glutamine) or chemical perturbations dramatically shift the success and composition of cell lines. We demonstrate that in nutrient rich culture conditions 143B outcompetes SAOS2. But, under nutrient deprivation or conventional chemotherapy, SAOS2 growth can be favored in spheroids. Importantly, when the simplest heterogeneity state is evaluated, a two-cell line coculture, perturbations that affect the faster growing cell line have only a modest effect on final spheroid size. Thus the only evaluated therapies to eliminate the spheroids were by switching therapies from a first strike to a second strike. This extensively characterized, widely available system, can be modeled and scaled to allow for improved strategies to anticipate resistance in osteosarcoma due to heterogeneity.

Indexed as

Bone NeoplasmsOsteosarcomaCell Line, TumorChildCoculture TechniquesHumansPhenotypeYoung Adult

Identifiers

PMID37978271
PMCPMC10656496
OpenAlexW4388764504

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.