Predicting Chemotherapy Treatment Outcomes in Ovarian Cancer Patients Using Gene Expression Analysis
Bibliographic record
Abstract
Ovarian cancer is the abnormal development of cells found in the ovaries. It is the fifth most fatal cancer amongst woman and has an overall five-year survival rate of 45% (American Cancer Society, 2016). For women with newly-diagnosed, advanced stage ovarian cancer, the current standard of care is surgery – to remove as much of the cancer as possible – followed by chemotherapy – to kill the remaining tumour cells (Cancer.Net Editorial Board, 2016). However, chemotherapy can have devastating side-effects such as infection, nausea, reduced cognitive
 function, and death (Sun, et al., 2005). Using patients’ genomic profiles to predict how well they will respond to the standard of care will be valuable for patients when deciding whether to pursue standard or alternative forms of treatment. This study uses ovarian cancer patient data compiled by The Cancer Genome Atlas (TCGA). Clinical data – such as patient age, gender, ethnicity, disease severity and treatment undergone – is used to define which patients responded well to chemotherapy. Patient gene expression data – which gives insight into which genes are up- or down-regulated – will be used to identify markers of chemotherapy response. This will be done using differential gene expression analysis – to identify individual genes that contribute to chemotherapy-response – and network analysis – to understand how the expression of these genes functions as a system. To make the results of the study clinically relevant, chemotherapy-response markers will be correlated to single nucleotide polymorphisms – a form of genetic variation that is much quicker to test for in a patient than gene expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".