High mobility group box 1 as a predictive marker for radiation response in muscle-invasive bladder cancer
Bibliographic record
Abstract
According to statistics from the Canadian Cancer Society, about 8,000 people will be diagnosed with bladder cancer in 2014, making it the sixth most common cancer in Canada. Even today, radical cystectomy, which involves the complete removal of the bladder, remains the “gold standard” treatment for invasive bladder cancer. Unfortunately, complete removal of organ causes a serious deficit in the quality of life of the patients which leads to exploring radiation therapy as the treatment option. Radiation therapy preserves the bladder and allows for normal urinary and sexual functions; however, the lack of local control of the disease and the significant dose dependent toxicity associated with it remain problematic. As such, there is a need to increase the efficacy of radiotherapy. This can be done via radio-sensitization (process of finding drugs that make cancer cells more sensitive to radiation). The first step in developing a radio-sensitizer involves finding specific molecular markers which can predict radiation response. This project focused on trying to establish whether the expression levels of a particular protein, HMGB1 could be used to determine outcomes of radiotherapy in bladder cancer. Results in the study indicated that high levels of HMGB1 protein are linked to radio-resistant outcomes in bladder cancer. Moreover, HMGB1 was found to be a major player in two cellular processes (DNA damage repair and autophagy) which contribute to radio-resistance of bladder cancer. This meant that HMGB1 could potentially be used to predict radiation response outcomes in muscle-invasive bladder cancer. In future, the results of this project will lead to further studies that will help bring HMGB1 pathway targeting drugs into therapy. Also, based on its predictive potential, this research will positively impact the selection of better treatment plans for certain bladder cancer patient groups. Overall the ability to customize therapy according to patient needs would greatly advance the field of muscle-invasive bladder cancer treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".