Identification of Novel Antigens for Normal Karyotype Triple Mutated Acute Myeloid Leukemia
Bibliographic record
Abstract
Introduction Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults. AML is divided in several subgroups based on genetic alterations and prognosis varies among AML subgroups. Normal karyotype AML with NPM1, DNMT3A and FLT3(ITD) mutations (NK triple mutated AML) is associated with a long-term overall survival of ~20% and represents ~6% of AML cases (Papaemmanuil et al., NEJM, 2016). No curative chemotherapy for this subgroup of AML patients currently exists, highlighting the need to develop novel therapeutic strategies for this group of AML patients. Methods To identify novel cell surface antigens for NK triple mutated AML that could ultimately be targeted by immunotherapeutic approaches, we developed a method to map the surface proteome of primary human AML specimens based on biotinylation of cell surface proteins, affinity purification and mass spectrometry. To further enrich mass spectrometry datasets with surface proteins, we developed an algorithm that assesses the probability of a protein to be located on the cell surface based on annotations from various databases. Using this approach, the surface proteome of 100 primary human AML specimens was analysed, which included 12 NK triple mutated AML samples. Differential surfaceome analyses identified several surface proteins specifically expressed by NK triple mutated AML specimens. Expression analyses were performed using publicly available datasets to select proteins with limited expression in normal tissues and hematopoietic cells. Surface expression and AML subgroup specificity for these potential antigens was evaluated by flow cytometry using primary human AML specimens from various AML subgroups (n=76). Moreover, to assess the intra-specimen expression profile of these antigens, single cell RNA-sequencing of 15 primary human NK triple mutated AML specimens was performed. Finally, we evaluated the prognostic impact of antigen expression on overall survival in a subgroup of patients from the Leucegene cohort composed of patients diagnosed with de novo AML with intermediate risk cytogenetics who have been treated with intensive induction chemotherapy (n = 316). Results Surface proteome analysis of NK triple mutated AML specimens and hit filtering according to selection pipeline described above identified several promising NK triple mutated AML antigens. Surface expression analysis of these antigens by flow cytometry showed specificity of antigen expression for NK triple mutated AML specimens. Transcriptomic analyses using RNA sequencing data from primary human AML specimens of the Leucegene cohort (n=691) showed significantly higher antigen transcript levels in NK triple mutated AML specimens compared to samples from other AML subgroups. Moreover, multivariate transcriptome analysis comparing the impact of most frequent mutations in AML on antigen expression revealed that FLT3-ITD mutation is the only significant factor associated with antigen overexpression in AML samples. Single cell RNA sequencing data from NK triple mutated AML specimens revealed that these antigens are uniformly expressed across the vast majority of blast populations, including blast cells with an expression profile reminiscent of leukemic stem cells. Interestingly, in patients with intermediate cytogenetic risk AML, high antigen expression was significantly associated with worse overall survival. Patients with high antigen expression who proceeded to allogeneic hematopoietic stem cell transplantation (HSCT) in first complete remission also had a significantly lower 5-year overall survival rate post-HSCT compared to patients with low antigen expression. Conclusion We identified novel antigens for FLT3-ITD mutated and NK triple mutated AML, which show homogeneous expression in blast populations of AML samples including most primitive ones, making it ideal antigens for immunotherapeutic targeting. Patients with intermediate cytogenetic risk AML and high antigen expression show poor prognosis even after HSCT in first complete remission, highlighting the need to develop novel therapeutic strategies for these patients. Different immunotherapeutic approaches targeting these antigens are currently being developed by our group, with the hope of identifying novel therapeutic strategies for the treatment of NK triple mutated AML.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".