Abstract 2914: Phosphoproteomic and transcriptional biomarkers predict response to SAR302503, a JAK2 inhibitor, in human acute myeloid leukemia preclinical models
Bibliographic record
Abstract
Abstract Research to develop new anti-cancer treatments has recently shifted focus to identifying and targeting molecules and pathways essential for cancer stem cell survival. However, preclinical models that rely on cell lines for drug testing do not capture the heterogeneity of response typically seen in the clinic that likely reflects the underlying genetic and functional heterogeneity of the tumors. Moreover, the use of cell lines makes it difficult to develop companion biomarker tools for patient stratification. We have taken a novel approach that combines drug testing of a large cohort of patient samples in xenograft assays, with multiplexed phosphoflow cytometric and RNA-Seq analysis of each patient sample to develop biomarkers that predict drug response. We applied this approach to study the efficacy of SAR302503 (Sanofi), a small molecule inhibitor of JAK2, against leukemia stem cells (LSCs) in acute myeloid leukemia (AML). JAK2 inhibitors have demonstrated efficacy in clinical trials for treatment of myeloproliferative disorders. Activated JAK2 signaling has been reported in AML, however it is not clear whether JAK2 inhibitors are effective in this disease, particularly against the disease-sustaining LSCs. SAR302503 treatment reduced leukemic engraftment in 22 of 34 (65%) AML patient samples of multiple subtypes with heterogeneous cytogenetic and molecular abnormalities. Phosphoflow analysis showed that AML samples that were sensitive to JAK2 inhibition in xenotransplantation assays exhibited high basal levels of pSTAT5 that were rapidly decreased by SAR302503 treatment in vitro, whereas non-responding samples showed low levels of pSTAT5, indicating that pSTAT5 is a useful drug response biomarker. This biomarker has now been validated in an independent cohort of AML patient samples. Phosphoflow cytometric profiling also enabled the rational design and testing of a novel drug combination regimen (SAR302503+Dasatinib) with efficacy against LSCs. Additionally, RNA-Seq analysis of paired vehicle- and drug-treated patient samples revealed that samples that were responsive to JAK2 inhibition in vivo had distinct transcriptional profiles compared to those that were not, suggesting that a molecular signature predictive of response to JAK2 inhibition can be identified. Overall, our approach, involving large-scale analysis of patient samples using state-of-the-art xenograft assays, captures the heterogeneity of response typically seen in the clinic that likely reflects the underlying genetic and functional heterogeneity of the tumors and offers a new paradigm for development of both novel agents that effectively target LSCs and biomarker tools to identify the patients most likely to benefit from targeted treatment. Citation Format: Weihsu Claire Chen, Julie S. Yuan, Nathan Mbong, Andreea C. Popescu, Yan Xing, Gitte Gerhard, Wei Zhang, Yussanne Ma, Richard Moore, Marco Marra, Mark D. Minden, Donna E. Hogge, Cynthia Guidos, John E. Dick, Jean C.Y. Wang. Phosphoproteomic and transcriptional biomarkers predict response to SAR302503, a JAK2 inhibitor, in human acute myeloid leukemia preclinical models. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2914. doi:10.1158/1538-7445.AM2014-2914
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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 | 0.000 | 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 | 0.001 |
| 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".