Abstract 1128: Lineage tracing of CAR T cells in patients with B cell malignancies
Bibliographic record
Abstract
Abstract Autologous T cells genetically engineered to express a chimeric antigen receptor (CAR) targeting CD19 and/or CD22 have achieved high complete response rates in patients with hematologic malignancies, but >50% of patients progress following therapy. Here, we sought to understand key T cell intrinsic factors impacting efficacy: CAR T cell expansion, persistence, and homing to the tumor. Using an endogenous T cell receptor (TCR) sequence as a ‘barcode’, we followed individual T cell clonotypes at the single-cell level from pre-manufacture apheresis and infusion products to tumor-involved lymph node and blood at peak and late expansion in 22 adult patients with relapsed or refractory large B cell lymphoma (LBCL) or acute lymphoblastic leukemia (ALL) treated with axicabtagene ciloleucel, an FDA-approved CD19-CAR T cell immunotherapy, or bispecific CD19/CD22 CAR T cells on an investigator-initiated trial (NCT03233854). The resulting CAR T cell atlas comprises matched transcriptome (scRNA-seq) and surface protein expression (CITE-seq) for 846,344 cells from 97 samples, with 215,045 unique TCR clonotypes identified, including 8,747 clonotypes that could be traced across 2+ timepoints in CAR mRNA+ cells. This atlas enabled us to ask: “What were the phenotypes of ‘successful’ CAR T cell clonotypes with optimal homing, expansion, and persistence properties at the time of infusion or pre-manufacture apheresis?” We found that successful T cell clonotypes at apheresis had juvenile features, including IL7R expression. Conversely, successful clonotypes in the infusion product had elevated interferon pathway activity and effector signatures, including GZMB expression. Further, we built a cell-cell interactome using all live cells from on-treatment biopsies and identified a set of 149 specific ligand-receptor pairs significantly enriched in patients who progressed. Finally, we defined dynamics of TCR clonotypes with predicted specificities for viral and self-antigens. These analyses pinpoint the identities of source T cells and infusion CAR T cells with properties impacting efficacy, and also identify ligand-receptor pairs that could be modulated to enhance CAR T cell response in the tumor at the genetic or pharmacological level. This work was supported in part by the Parker Institute for Cancer Immunotherapy, California Institute for Regenerative Medicine, Kite Pharma, and Stanford Cancer Institute. Citation Format: Zinaida Good, Mark P. Hamilton, Jay Y. Spiegel, Sreevidya Kurra, Moksha H. Desai, Snehit Prabhu, Shin-Heng Chiou, Christine Y. Yeh, Yiyun Chen, Eric Yang, Michael G. Ozawa, Fang Wu, Matthew J. Frank, Lori Muffly, Gursharan K. Claire, Juliana Craig, Maria I. Iglesias, Sushma Bharadwaj, Katherine A. Kong, Dhananjay Wagh, John Coller, Mark M. Davis, Sylvia K. Plevritis, Bita Sahaf, David B. Miklos, Crystal L. Mackall. Lineage tracing of CAR T cells in patients with B cell malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1128.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".