Gene Expression Profile of Enriched T-Cells Prior to Chimeric Antigen Receptor (CAR) T-Cell Manufacturing Is Predictive of CAR T-Cell Response
Bibliographic record
Abstract
Introduction Anti-CD19 CAR T-cells have revolutionized treatment for relapsed, aggressive B-cell cancers. Our group has reported outcomes of our second generation, anti-CD19 CAR T-cell produced locally on demand utilizing a novel construct (aCD19/4-1BB/CD3z but utilizing TNFS19 hinge and transmembrane) tested in a phase 1b/2 clinical trial (ACIT001/EXC002). This construct demonstrates strong efficacy and safety akin to standard of care CAR T-cells. 42% of patients have failed to achieve long term remission on trial. CAR T-cells represent an expensive, time consuming and resource heavy therapy. Finding reliable and accurate methods of predicting outcomes to better identify appropriate candidates for CAR T-cells are still lacking. While T-cell phenotype has been helpful, exhausted T-cells is not a guarantee of failure. Here, we report our preliminary results of gene expression profile (GEP) of T-cells pre and post CAR T-cell manufacturing based on response to treatment. Methods In this trial, 30 patients have been accrued to date with 29 dosed. 25 patients have sufficient samples that are evaluable; 21 with non-Hodgkin lymphoma (NHL) and 5 with acute lymphoblastic leukemia (ALL). We performed gene expression profiling using Nanostring nCounter technology and the CAR-T Characterization panel on the sub-cohort of lymphoma patients. Sample included patient derived enriched CD3 T-cells pre-transfection, and the CAR-T cell product used for treatment on trial. Differentially expressed genes (DE) and gene set analysis (GSA) were identified using the Rosalind analysis platform among patients who had progressive disease compared to patients who had complete response. DE genes were selected using a false discovery rate (pAdj) cut of 0.05 and +/-1.5 fold change. A global significance score greater than 1.5 was set as a cut off for GSA. Results We identified DE genes and activated pathways in pre-CAR engineered enriched T-cells from patient who failed to respond (progressive disease, PD) to CAR-T infusion versus those who had a complete response (CR). The most significant genes that were upregulated in PD patients included MYL9, CXCL10, IL6, IFITM3, FCGR3A/B indicated inherent issues with interferon signaling, T-cell exhaustion, apoptosis and toxicity. DE genes and activated pathways were also in identified in transduced CAR-T cells from PD patients compared to CR patients. There were a higher number of differences in GEP following CAR T-cell production including up-regulation in 65 genes vs 30 that were down regulated. Top upregulated genes included LILRA5, CXCL8, CD14, IFITM3; while top down-regulated genes included ACSL5, TIMM17A, LAMP1, and NOTCH1. This suggests that PD patient CAR T-cells have issues with exhaustion, toxicity, and TCR diversity. Conclusion We identified DE genes between patients who had progressive disease or a complete response in both the CAR-T cell product and pre-transfection enriched T-cells in our anti-CD19 CAR-T trial. The results suggest that the state of the T cells prior to transfection play a role in determining the ability of CAR T-cells to generate an effective treatment response. These findings may be used to help identify patients more likely to have production of successful CAR-T cell product in future trials.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".