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348 Interleukin 2(IL-2) systems immunology modeling: machine learning for cancer immunotherapy
  1. Jennifer Bone1,
  2. Newell Washburn1,
  3. Jian Han2,
  4. Miranda Steele2,
  5. Pranav Murthy3 and
  6. Michael Lotze4
  1. 1Carnegie Mellon University, Pittsburgh, PA, USA
  2. 3iRepertoire, Hunstville, AL, USA
  3. 4UPMC, Pittsburgh, PA, USA
  4. 5Nurix, PITTSBURGH, PA, USA


Background Clinical outcomes are correlated with aggregate B (BCR) and T cell receptor (TCR) diversity (the adaptome) in several infectious diseases and cancers. Advances in dimer avoidance multiplexed PCR (DAM-PCR) followed by next-generation sequencing (NGS) enable measurements of immune repertoire diversity and clonality, allowing prediction of cancer states and response to treatment. Clonotype-mediated predictions collapse a space of up to 1025 possible CDR3 variable region sequences into descriptors such as whole-sequence diversity. Broad descriptors, however, mask cancer-specific information embedded within clonotype sequences. Deep learning algorithms typically need large patient cohorts to make accurate predictions. We present a statistical model predicting response to IL-2 immunotherapy for small cohorts based on natural language processing (NLP) of CDR3 TCR and BCR clonotypes.

Methods In a completed Phase 2 trial (NCT01550367), the adaptome of 29 patients with metastatic clear cell renal carcinoma (RCC) treated with high dose (HD) IL-2 and the autophagy inhibitor, hydroxychloroquine (HCQ) were measured from peripheral blood samples by DAM-PCR. All seven TCR and BCR chains were measured at three treatment points (pre-treatment, 14D after HCQ initiation, and following recovery from the first cycle of IL-2 on D15). Outcomes were assessed by assigning two states (responder or non-responder) one year following treatment based on radiographic changes in tumor size. Cancer-specific amino acid motifs from TCR and BCR CDR3 sequences on D15 were mined by counting amino acid pairs and calculating a 400-feature transition probability matrix, scoring the likelihood of a motif belonging to the responder or non-responder cohort.

Results Seven-chain NLP analysis of CDR3 amino acid motifs at > 90% accuracy for each chain independently predicted patient response to IL-2 by D15 (figure 1). Furthermore, longitudinal monitoring of patient CDR3s across the three timepoints revealed a dichotomy in repertoire orchestration. Responding patients, convincingly, were more likely to demonstrate either a TCR-driven (p<0.01) or a BCR-driven (p<0.001) entropy bias while non-responding patients unanimously showed no significant bias.

Abstract 348 Figure 1

Classification of nonresponding (Non-Res) and responding (Res) patients based on scoring from Feature Selection Filter and Analysis. ****, p<0.0001; ***, p<0.001; **, p<0.01

Conclusions NLP of both TCR and BCR repertoires can provide early predictions of cancer response to treatment. Furthermore, seven-chain longitudinal monitoring across treatment revealed a surprisingly robust repertoire orchestration in responders that was not observed in non-responders, suggesting that the methodology proposed here can be used to gain new mechanistic insight into the role of repertoire evaluation in cancer immunotherapy.

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