Author response: Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes
Bibliographic record
Abstract
Many cancers, including leukaemia, lymphoma and breast cancer, are treated with potent chemotherapy drugs such as anthracyclines. However, anthracyclines have strong side effects known as anthracycline cardiotoxicity, which affect the health of the heart. Almost half of the patients given high doses of anthracyclines develop chronic heart failure. While anthracycline cardiotoxicity is very common, people’s genes may contribute to how sensitive they are to these drugs but it is not understood which genes can cause this effect. Previous studies using only a small number of participants have not been able to pin down the genetic factors that make some patients respond well to anthracyclines, and others prone to developing heart failure when taking these drugs. To find out which genes affect anthracycline cardiotoxicity, Knowles, Burrows et al. transformed blood cells from 45 individuals into stem cells, which were then developed into heart muscle cells. Then, the activity of genes was analyzed by measuring the amount of RNA (the template molecules used to make proteins) produced by those genes. After the cells had been exposed for 24 hours to the anthracycline drug doxorubicin, hundreds of gene activity differences could be found in the heart muscle cells between individuals. Some of these differences were linked to poorer health of the cells after treatment with the drug. As a result, a number of genetic variants that could predispose patients to the side effects of doxorubicin were discovered. The experiments also revealed how doxorubicin disrupts an important process that separates ‘junk’ parts of the RNA from the parts that are used as a template for proteins. Being able to predict who is likely to be sensitive to drugs such as doxorubicin could help doctors to tailor chemotherapy treatments more effectively, minimising the risk of heart failure. In future, larger studies could lead to accurate predictions of a patient’s response to a particular chemotherapy drug to personalize their 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.053 |
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".