Abstract B63: Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment
Bibliographic record
Abstract
Abstract Background: Acute myeloid leukemia (AML) is characterized by clonal expansion of poorly differentiated myeloid precursors, resulting in impaired hematopoiesis and often bone marrow (BM) failure. The general therapeutic approach in patients with AML has not changed substantially in more than 30 years. Investigation of new strategies, including immunotherapy, remains a priority. Tumor phenotypes are dictated not only by major oncogene drivers, but also by the tumor immunologic microenvironment (TIME) which is inherently immunosuppressive, is equipped to hamper effector T-cell function and includes immune and inflammatory cells, soluble mediators such as interferon (IFN)-gamma and extracellular matrix components. Herein, we profiled the TIME of AML to identify gene signatures that are reflective of general immune status and predictive of antileukemia immune potential. Methods: We used the hybridization-based nCounter® system (NanoString Technologies®, Seattle, WA) and the RNA Pan-Cancer Immune Profiling PanelTM to analyze BM aspirates from 290 adults and 40 children with newly diagnosed, nonpromyelocytic AML. Data were normalized to a set of reference genes and log2-transformed for bioinformatics analysis. The clinical relevance of the association between immune gene signatures and patient outcome was further validated in silico using publicly available cancer transcriptomic datasets (The Cancer Genome Atlas; TCGA). Results: We identified distinct sets of co-expressed genes corresponding to innate immunity, adaptive immunity and IFN-gamma signaling. BM samples with IFN-gamma-dominant gene expression profiles showed up-regulation of actionable immune checkpoints, such as B7-H3, PD-L1 and CTLA-4. A set of 4 differentially expressed immune genes between children and adults with AML (FDR<0.005) was associated with adverse cytogenetic features, leukemia relapse and shorter patient survival, and also stratified survival in the TCGA cohort of adult patients with AML. Finally, all four genes were amplified, deleted or mutated in 7% of 51,175 TCGA solid tumors, with the highest frequency of mutations being detected in primary CNS lymphoma (40%) and diffuse large B-cell lymphoma (25%). Conclusions: Our study identified distinct immune gene programs in the TIME of patients with newly diagnosed AML. From a translational standpoint, immune-enriched and IFN-gamma-dominant AMLs may benefit from immune checkpoint blockade and from new immunotherapy approaches, including dual-affinity T-cell redirecting antibodies targeting CD123. Grant support: John and Lucille van Geest Foundation, UK; Roger Counter Foundation, UK; Qatar National Research Fund (#NPRP8-2297-3-494). Citation Format: Sergio Rutella, Jayakumar Vadakekolathu, Tressa Hood, Stephen Reeder, Sarah E. Warren, Patrick Danaher, Jan Davidson-Moncada, Alessandra Cesano, Joseph M. Beechem, Sarah K. Tasian, Mark D. Minden. Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B63.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".