Table 1

Mitigating pitfalls and approaches for interpretation of TCRseq data

Potential confoundersPitfallMitigation
Batch effectCirculating T-cell clones may be ‘shared’ by multiple specimens collected at the same time point.Control normal tissue(s) collected at the same timepoint can be used to identify these background clones.
BloodDuring active immune responses,* both relevant and non-relevant clones circulate in blood.Functional assays enable identification of disease-relevant clones (vs batch background).
Tissue compartment effectSpecimens from the same organ share tissue resident T cells, including antitumor clones.19 Clonotype sharing with ‘paired’ normal tissue does not preclude biological relevance. Measurements such as abundance and antigen specificity (antigen-driven clustering/functional assays) are needed for further discernment.
Library size variationIncreased read count→more clones sampled→a larger proportion of shared clonesAnalyses must correct for sample size variation (eg, Morisita Overlap Index, normalization, etc)
LNs/lymphoid-rich tissuesIncreased probability of repertoire overlap given large, diverse T-cell populationsAvoid analysis of background lymphoid tissue in LN metastases; interpret LN data with caution.
Interpretation Definition Approach
Clonal abundance
(relative read count)
Relative proportion of sequencing reads for a unique clonotype (surrogate for clonal proliferation)Assess for signals of clonal proliferation in relevant tissues to suggest functional relevance.†
Low abundanceMeaningful threshold for exclusion of ‘background’ clones has not been rigorously definedExclude specimens with <1000 reads. Sample size informs interpretation of low-read clones.
High abundanceIncreasing abundance suggests clonal proliferation (and antigen exposure) in a given tissue.*Proliferation of shared clones in disease-relevant tissues supports potential mechanistic overlap.
TCR repertoire sharingMechanistic interpretations of TCR repertoire overlap are limited by several confounders.Multispecimen analyses, antigen-driven clustering (such as GLIPH2), and functional assays maximize interpretability of TCRseq data.
  • *During tumor killing (early in treatment) or active autoimmunity (immune-related adverse events).

  • †Assuming systemic clonal proliferation (batch effect) has been excluded.

  • GLIPH2, grouping lymphocyte interactions by paratope hotspots 2; LN, lymph node; TCR, T-cell receptor; TCRseq, T-cell receptor sequencing.