Abstract A11: A comprehensive and integrative omic analysis of multiply relapsed refractory pediatric pre-B cell acute lymphoblastic leukemia predicts response to CD19 CAR T-cell therapy
Bibliographic record
Abstract
Abstract Acute lymphoblastic leukemia (ALL) is the most common childhood cancer with a peak incidence at 3-5 years of age. Despite the improved survival rate of 90% for newly diagnosed children with ALL, the outcome for patients with relapsed disease is poor with a less than 30% overall survival. CD19 CAR T-cell therapy has shown remarkable response rates in relapsed/refractory disease. Long-term survival analysis has shown that initial response rates exceed 80%. However, durable response rates at one year are closer to 40%. Little is known about factors predicting durable response to CAR T therapy. We hypothesize that patients with CD19 CAR T-cell resistant ALL have a distinct disease compared to responders to therapy that can be identified in pretreatment leukemia. Utilizing advanced genomic, epigenetic, proteomic, and single-cell (sc) techniques, we characterized patient bone marrow aspirates (BMA) to identify mechanisms of resistance. Patients enrolled in PLAT-02 at Seattle Children’s Hospital were categorized according to the durability of their response to CD19 CAR T therapy. To characterize the molecular and genomic alterations specific to the therapy-resistant ALLs, we performed comprehensive analyses on pre-treatment therapy-resistant and sensitive BMAs using whole-exome sequencing, RNA- BMAs seq, scRNA-seq, sc B cell receptor (BCR)-seq, methylation array, H3K27ac ChIP-seq, ATAC-seq, and CyTOF. Additionally, we developed murine patient-derived xenografts (PDXs) for future studies. Initial mutation analyses revealed 5 hotspot mutations (ABL1, 2 x KRAS, IKZF1, and EP300) and actionable fusion (2 ABL1, 2 ETV6, 2 ETV5, KMT2A). Interestingly, we identified a KMT2A-AFF1 fusion in a sensitive leukemia, which has been demonstrated to predispose patients to CD19 CAR T resistance through lineage switching. Additionally, we identified a novel CREBBP-fusion in leukemias resistant to CD19 CAR T-induced B-cell aplasia. Alterations of CREBBP have previously been associated with ALL that is refractory to conventional therapies. Integrated gene expression and epigenetic analyses are ongoing to identify genes or pathways associated with resistant disease. scRNA- and scBCR-seq data are being analyzed and integrated with CyTOF analyses to detect mixed lineage and gene expression-based heterogeneity that may predict clonal selection by CAR T pressure. Finally, we developed and genetically analyzed murine PDXs for 64% of the patient samples, establishing a valuable resource for future studies and developing novel therapies for resistant leukemias. This study is one of the most integrative and comprehensive genomic profiling approaches to identify the molecular traits of therapy-resistant ALL in patient samples. We hope to identify and develop crucial biomarkers predicting responsiveness to CAR T-cell therapy. Citation Format: Katherine E. Masih, Rebecca Gardner, Berkley E. Gryder, Abdalla Abdelmaksoud, Ashley Wilson, Serifat Adebola, Benjamin Z. Stanton, Young K. Song, Justin Lack, Chaoyu Wang, Xinyu Wen, Zachary Rae, Adam Cheuk, Gregoire Altan-Bonnet, Michael Kelly, Jun S. Wei, Michael C. Jensen, Rimas J. Orentas, Javed Khan. A comprehensive and integrative omic analysis of multiply relapsed refractory pediatric pre-B cell acute lymphoblastic leukemia predicts response to CD19 CAR T-cell therapy [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A11.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".