Date Published: March 14, 2019
Publisher: Public Library of Science
Author(s): Julie Rytlewski, Shibing Deng, Tao Xie, Craig Davis, Harlan Robins, Erik Yusko, Jadwiga Bienkowska, Paul A. Beavis.
Current methods to quantify T-cell clonal expansion only account for variance due to random sampling from a highly diverse repertoire space. We propose a beta-binomial model to incorporate time-dependent variance into the assessment of differentially abundant T-cell clones, identified by unique T Cell Receptor (TCR) β-chain rearrangements, and show that this model improves specificity for detecting clinically relevant clonal expansion. Using blood samples from ten healthy donors, we modeled the variance of T-cell clones within each subject over time and calibrated the dispersion parameters of the beta distribution to fit this variance. As a validation, we compared pre- versus post-treatment blood samples from urothelial cancer patients treated with atezolizumab, where clonal expansion (quantified by the earlier binomial model) was previously reported to correlate with benefit. The beta-binomial model significantly reduced the false-positive rate for detecting differentially abundant clones over time compared to the earlier binomial method. In the urothelial cancer cohort, the beta-binomial model enriched for tumor infiltrating lymphocytes among the clones detected as expanding in the peripheral blood in response to therapy compared to the binomial model and improved the overall correlation with clinical benefit. Incorporating time-dependent variance into the statistical framework for measuring differentially abundant T-cell clones improves the model’s specificity for T-cells that correlate more strongly with the disease and treatment setting of-interest. Reducing background-level clonal expansion, therefore, improves the quality of clonal expansion as a biomarker for assessing the T cell immune response and correlations with clinical measures.
High-throughput next-generation sequencing of the T cell receptor (TCR) repertoire, i.e., immunosequencing, enables precise molecular identification and tracking of tens to hundreds of thousands of T-cell clones in a single subject . A key component of the adaptive immune system is the clonal expansion of activated T cells. With immunosequencing, clonally expanded T cells can be identified by comparing the frequency of each T-cell clone at one time point versus another. One challenge with immunosequencing data is developing a systematic framework to determine if the increase in T-cell clone frequency meets the criteria for clonal expansion. Here, we describe a statistical framework that accounts for sampling and time-dependent repertoire variability to detect T-cell clones that are differentially abundant in an unbiased and quantitative manner.
In healthy individuals, we found that the number of T-cell clones detected as expanded increases as the time interval between Sample A and B increases. This time-dependence highlights the importance of establishing a prior on typical biological variance within the T-cell repertoire, especially over weekly and monthly time intervals commonly used for collecting clinical trial patient samples. We expect that the biological variance may further increase if much larger time intervals were used, e.g., months to years. By training a new beta-binomial model with data from healthy individuals, identification of differentially abundant T-cell clones now accounts for both random sampling from a diverse repertoire and normal time-dependent biological variability. In effect, the clonal expansion measured by the beta-binomial differential abundance model should display reduced biological noise and subsequently enrich for clones responding to the pharmacologic or pathologic setting-of-interest.