Bacteria in Cancer Therapeutics: A Framework for Effective Therapeutic Bacterial Screening and Identification
Bibliographic record
Abstract
By 2030, the global incidence of cancer is expected to increase by approximately 50%. However, most conventional therapies still lack cancer selectivity, which can have severe unintended side effects on healthy body tissue. Despite being an unconventional and contentious therapy, the last two decades have seen a significant renaissance of bacterium-mediated cancer therapy (BMCT). Although promising, most present-day therapeutic bacterial candidates have not shown satisfactory efficacy, effectiveness, or safety. Furthermore, therapeutic bacterial candidates are available to only a few of the approximately 200 existing cancer types. Excitingly, the recent surge in BMCT has piqued the interest of non-BMCT microbiologists. To help advance these interests, in this paper we reviewed important aspects of cancer, present-day cancer treatments, and historical aspects of BMCT. Here, we provided a four-step framework that can be used in screening and identifying bacteria with cancer therapeutic potential, including those that are uncultivable. Systematic methodologies such as the ones suggested here could prove valuable to new BMCT researchers, including experienced non-BMCT researchers in possession of extensive knowledge and resources of bacterial genomics. Lastly, our analyses highlight the need to establish and standardize quantitative methods that can be used to identify and compare bacteria with important cancer therapeutic traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".