A review of clinical and emerging biomarkers for breast cancers: towards precision medicine for patients
Bibliographic record
Abstract
Abstract Background: Breast cancer is the most commonly diagnosed malignancy among women and accounts for about 25% of all new cancer cases and 13% of all cancer deaths in Canadian women. It is a highly heterogeneous disease, encompassing multiple tumour entities, each characterised by distinct morphology, behaviour and clinical implications. Moreover, different breast tumour subtypes have different risk factors, clinical presentation, histopathological features, outcome and response to systemic therapies. Therefore, any strategies capable of the stratification of breast cancer by clinically relevant subtypes are an important requirement for personalised and targeted treatment. Therefore, in the advancement towards the concept of precision medicine that takes individual patient variability into account, several investigators have focused on the identification of effective clinical breast cancer biomarkers that interrogate key aberrant pathways potentially targetable with molecular targeted or immunological therapies. Methods and materials: This paper reports on a review of 11 current clinical and emerging biomarkers used in screening for early detection and diagnosis, to stratify patients by disease subtype, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment. Conclusion: The future holds promising for the use of effective clinical breast cancer biomarkers for early detection and personalised patient-specific targeted treatment and increased patient survival. Breast cancer biomarkers can potentially assist in early-staged, non-invasive, sensitive and specific breast cancer detection and screening, provide clinically useful information for identification of patients with a greater likelihood of benefiting from the specific treatment, offer a better understanding of the metastatic process in cancer patients, predict disease and for patients with the established disease can assist define the nature of the disease, monitor the success of treatment and guide the clinical management of the disease.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".