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Record W2740970948 · doi:10.1158/1538-7445.am2017-3903

Abstract 3903: Targeting oral cancer stem cells in the hypoxic niche by BCG infected mesenchymal stem cells

2017· article· en· W2740970948 on OpenAlexaff
Bidisha Pal, Seema Bhuyan, Jaishree Garhyan, Hong Li, Rashmi Bhuyan, Herman Yeger, Bikul Das

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCancer stem cellMesenchymal stem cellCancer researchStem cellTumor microenvironmentTumor hypoxiaCancer cellBiologyImmunologyCancerMedicineImmune systemCell biologyInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

Abstract Background: Tumor hypoxia is a major contributing factor in cancer therapeutic failure. The microenvironment of hypoxia/oxidative stress may reprogram cancer cells to highly tumorigenic, metastatic, angiogenic and stem cell-like state. We have characterized in vitro and in vivo models of hypoxia-induced cancer cell reprogramming to cancer stem cell (CSCs) like cells including an immunocompetent model of oral cancer. These CSCs exhibit ABCG2 cell surface marker. Numerous approaches including targeted therapy using oncolytic virus and bacteria have been attempted to target cancer cells in their hypoxic niche without major therapeutic success. Major therapeutic challenges include the inaccessibility of hypoxic niche by therapy-agents and the poor replication of viruses or bacteria intracellular to hypoxic cancer cells. Furthermore, the immune privileged microenvironment of tumor hypoxia could pose a challenge to viral/bacterial-induced immunity against the tumor. In this context, here we have tested a novel stem cell-based approach to deliver BCG infected stem cells to the hypoxic core of tumors. Methods: Human SCC-25 cell line-derived xenograft in NOD/SCID mice exhibit hypoxic zones, where ABCG2+ resides (1). SCC-25 tumor-bearing mice were injected i.v. with CD271+ bone marrow mesenchymal stem cells (BM-MSCs). We recently found that CD271+ BM-MSCs could be infected with M. tuberculosis. The BCG infected CD271+ BM-MSCs were injected to SCC-25 xenograft-bearing mice. The mice were sacrificed on day-20 of stem cell injection and then evaluated for the hypoxic CSCs. Next, we have developed a 4-NQO induced oral cancer cell line, which was injected orthotropic to the tongue of congenic mice. The mice developed tumors, which was hypoxic. In these tumor-bearing mice, BCG-infected murine CD271 BM-MSCs were injected i.v., and animals were monitored for four weeks for tumor growth. On day-20 of injection, mice were sacrificed, and the tumors were collected. In a control group, BCG alone was injected. Results: In both the SCC-25 and an immunocompetent mouse model, we found that BCG or CD271+ BM-MSCs injection alone did not result in marked anti-tumor activity and or elimination of ABCG2+ cells from their hypoxic niche. In contrast, the injection of BCG infected CD271+ BM-MSCs led to marked reduction of tumor growth. Importantly, we observed marked replication of BCG intracellular to CSCs. These results indicate that CD271+ BM-MSCs facilitated the transfer of BCG to cancer cells residing in the hypoxic zone. Importantly, the transfer of BCG was seen more in the immunocompetent model, suggesting potential role of immune system. Conclusions: Our findings indicate that hypoxic CSCs may be targeted by a few BCG infected CD271 BM-MSCs. We propose this could be a novel therapeutic approach to target drug resistance cancer stem cells residing in the hypoxic niche of tumors.1. Bhuyan R et al, Cancer Research, 2016; Volume 76, Issue 14 Supplement, pp. 935. Note: This abstract was not presented at the meeting. Citation Format: Bidisha Pal, Seema Bhuyan, Jaishree Garhyan, Hong Li, Rashmi Bhuyan, Herman Yeger, Bikul Das. Targeting oral cancer stem cells in the hypoxic niche by BCG infected mesenchymal stem cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3903. doi:10.1158/1538-7445.AM2017-3903

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.399
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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