Abstract LBA013: Phosphoproteomics reveals active drug targets on pathways of resistance and predicts response to midostaurin plus chemotherapy in FLT3 mutant-positive acute myeloid leukemia
Bibliographic record
Abstract
Abstract Background: Midostaurin (mido) is approved for treatment of FLT3 mutant-positive (FLT3+) acute myeloid leukemia (AML). However, FLT3 mutation is not the only determinant of mido sensitivity. Here we report phosphoprotein signatures which predict response to chemotherapy (chemo) plus mido, and identify active drug targets on potential resistance pathways. Methods: Samples collected at diagnosis, post-treatment and relapse from FLT3+ patients treated with chemo+mido were obtained from the Leukemia Tissue Bank at the Princess Margaret Cancer Centre. Peptides and enriched phosphopeptides from bone marrow (BM) and peripheral blood (PB) mononuclear cells were quantified using liquid chromatography-tandem mass spectrometry. Signatures for BM/PB diagnosis samples were analyzed independently and used to train a classification machine learning algorithm to group patients (n=54) based on response to treatment. Additional features (e.g. genetic mutations) were also analyzed. Kaplan-Meier and Log-Rank test methods were used to assess differential survival between patient groups. To investigate pathways potentially driving resistance to chemo+mido, differential protein phosphorylation indexes were identified through comparison of post-treatment or relapse samples to paired diagnosis samples. To account for population heterogeneity, a filter was applied based on frequency of observation. Activated pathways potentially driving resistance were identified with functional enrichment tools and kinase-substrate enrichment analysis. Statistical significance of enrichment were determined using parametric methods and p-values adjusted for multiple testing using the Benjamini-Hochberg method. Results: Patients positive for a signature consisting of 26 phosphorylation sites showed a markedly longer survival time than negative patients (PB: 269 vs 76 weeks, Log-Rank p=1.30e-05; BM: 241 vs 56, Log-Rank p=2.13e-09). This signature partially overlapped with an ex-vivo signature of response to mido, described previously by Casado et al (Leukemia, 2018). A proteomic signature was also identified, with positive patients showing a longer survival time than negative patients (PB: 330 vs 173 weeks, Log-Rank p=5.0e-04; BM: 460 vs 156, Log-Rank p=5.2e-06). Key, diverging phosphorylation site signatures were identified between patients with refractory disease/early relapse and patients with complete response and no relapse or death within 2 years post-treatment. Pathways with increased activity in post-treatment or relapse specimens were associated with molecular functions such as regulation of cell proliferation, migration, differentiation and anti-apoptosis. Conclusions: We identified phosphoproteomic and proteomic signatures that differentiate survival mediated by response to chemo+mido. While the former was more predictive, both may enable further stratification of FLT3+ AML receiving mido treatment. Drug targets on pathways demonstrating increased activity in relapse/refractory cases may play a role in resistance; this will be determined in follow-up inhibitor studies. Citation Format: Luis Veiga Nobre, Celia Colomina Basanta, Salvatore Federico Pedicona, Arran David Dokal, Andrea Arruda, Ryan Smith, Calum Greenhalgh, Francesca Patella, Pedro Maria Casado-Izquierdo, Bela Wrench, Jane Theaker, Andrew Thompson, Mark D. Minden, John G. Gribben, David James Britton, Pedro Rodriguez Cutillas. Phosphoproteomics reveals active drug targets on pathways of resistance and predicts response to midostaurin plus chemotherapy in FLT3 mutant-positive acute myeloid leukemia [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA013.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".