Multi-drug algorithm to accurately predict best first-line treatments in newly-diagnosed acute myeloid leukemia (AML).
Bibliographic record
Abstract
6525 Background: AML is a heterogeneous hematological malignancy with poor prognosis. Several treatments are approved for AML, but clinical trials have shown that current stratification approaches to determine patients’ eligibility produce false positives (treated patients that fail to respond) and negatives (patients not treated but could have responded). Venetoclax + azacitidine (VA) treatment is currently reserved for unfit patients, with younger patients stratified based on their FLT3status, and treated with either intensive chemotherapy (IC), or IC plus midostaurin (MIC). Here, we used phosphoproteomics to build a signature and algorithm that accurately predict which of these approved therapies may be more efficacious for a given patient. Methods: Routine bone marrow and peripheral blood diagnosis samples (s, n=182) were collected across the UK, Austria, Canada and the USA from 138 patients (p) subsequently treated with MIC (n=44/64 p/s), VA (n=40/48 p/s) or IC (n=54/70 p/s). Patients were grouped into Good Responders (GR) and Poor Responders (PR) based on treatment response. For VA, patients that achieved complete remission (CR) were considered GR, while refractory patients were considered PR. For MIC and IC, we considered patients that achieved CR without relapse within 6 months as GR, and those refractory or relapsed within 6 months as PR. Samples were processed for mass spectrometry-based phosphoproteomics. Phosphopeptide abundance data, generated with in-house PiQuant software, was used to identify phosphopeptides that distinguish GR and PR groups in each cohort. Statistical models based on these features were assessed via cross-validation. Results: We compared phosphoproteomes of 182 diagnosis samples from 138 AML patients, from three treatment cohorts (treated with IC, MIC or VA), with each cohort stratified by patients’ response to their respective treatment. Enrichment analysis in each cohort identified several phosphopeptides specific to one of the responder groups, mapping both to proteins with known roles in AML biology (e.g. DNMT3A or RUNX1) and proteins not yet implicated. Next, using machine learning, we identified phosphopeptides that could distinguish between PR and GR, and trained drug response prediction models based on the abundance of these phosphopeptides. In cross-validation, each model stratified patients with log rank p<0.001, HR<0.1 and more than 90% accuracy, greatly outperforming all currently-used stratification methods for first-line AML therapies. Conclusions: We built a suite of predictive models that accurately predict patient response to first-line AML treatment using phosphoproteomic data from routine diagnosis samples. Following validation in independent patient cohorts, this tool will be developed into a single test that predicts treatment response for AML patients, thus addressing an unmet clinical need in this 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".