ArticleScientific reports2022
Design of combination therapy for engineered bacterial therapeutics in non-small cell lung cancer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
- Toward development of soil-derivedBiodesign research · 2026Article
- Microbial medicines: Unlocking the therapeutic potential of the microbiome in cancer treatment.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Microbiome-driven resistance in cervical cancer therapy: from mechanistic dissection to clinical translation.Expert reviews in molecular medicine · 2025Review
- Immune-oncology-microbiome axis in lung cancer: from carcinogenesis to therapy-a narrative review.Translational lung cancer research · 2025Review
- Designing live bacterial therapeutics for cancer.Advanced drug delivery reviews · 2025Review
- Modeling cancer-microbiome interactions in vitro: A guide to co-culture platforms.International journal of cancer · 2025Review
- A yeast-based oral therapeutic delivers immune checkpoint inhibitors to reduce intestinal tumor burden.Cell chemical biology · 2025Article
- Bacterial live therapeutics for human diseases.Molecular systems biology · 2024Review
- Bacteria colonization in tumor microenvironment creates a favorable niche for immunogenic chemotherapy.EMBO molecular medicine · 2024Article
- Dynamic Gene Expression Mitigates Mutational Escape in Lysis-Driven Bacteria Cancer Therapy.Biodesign research · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Synthetic biology enables the engineering of bacteria to safely deliver potent payloads to tumors for effective anti-cancer therapies. However, a central challenge for translation is determining ideal bacterial therapy candidates for specific cancers and integrating them with other drug treatment strategies to maximize efficacy. To address this, we designed a screening and evaluation pipeline for characterization of bacterial therapies in lung cancer models. We screened 10 engineered bacterial toxins across 6 non-small cell lung cancer patient-derived cell lines and identified theta toxin as a promising therapeutic candidate. Using a bacteria-spheroid co-culture system (BSCC), analysis of differentially expressed transcripts and gene set enrichment revealed significant changes in at least 10 signaling pathways with bacteria-producing theta toxin. We assessed combinatorial treatment of small molecule pharmaceutical inhibitors targeting 5 signaling molecules and of 2 chemotherapy drugs along with bacterially-produced theta toxin and showed improved dose-dependent response. This combination strategy was further tested and confirmed, with AKT signaling as an example, in a mouse model of lung cancer. In summary, we developed a pipeline to rapidly characterize bacterial therapies and integrate them with current targeted therapies for lung cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.