InTegrative Adaptive immune receptor repertoire atLas for dIsease progreSsion bioMArker: an application to primary Sjogren syndrom – TALISMAN

Primary sjögren's syndrome (pSS) is a systemic autoimmune disease (AD) characterized by lymphocytic infiltration of the exocrine glands that carries the highest risk of lymphoma among ADs. Both T and B cells play major roles in pSS and ADs’ pathophysiology. While autoantibodies are often used for AD diagnosis, including pSS, their biological relevance remains poorly understood. In contrast, while T-cell pathophysiological role is established in pSS, they are not considered as biomarkers in pSS, in part because they are more difficult to study.

TALISMAN aims at better characterizing the crosstalk between T- and B-cells in pSS, especially in regards with disease progression toward non-hodgkin lymphoma (NHL). To this end, TALISMAN proposes to characterize with an unprecedented accuracy the repertoires of T-cell receptors (TCR) and B-cell receptors (BCR) to identify composite TCR and BCR signatures as surrogate markers of pSS diagnosis and progression.

TALISMAN objectives will be to:
(i) establish the first database of combined TCR and BCR repertoires of pSS patients
(ii) identify cell subset- & disease-specific TCR and BCR signatures and elucidate their biological role in pSS pathophysiology
(iii) assert/predict patients disease status and progression using these signatures.

To this end, TALISMAN will capitalize on an established cohort of 250 pSS patients and will combine TCR and BCR i) bulk sequencing from peripheral effector (Teff) and regulatory (Treg) CD4 T-subsets, including follicular T-cells, and naïve and memory B-cell subsets, ii) single-cell sequencing from functionally distinct purified peripheral T- and B-cell subsets, i.e. TCR and transcriptome of Teffs and Tregs, and BCR and transcriptome of atypical CD21-/low B and typical CD21+ naïve and memory B-cell subsets. State-of-the-art data analyses based on machine learning and data mining will be applied to these datasets.

TALISMAN will be structured into three work-packages (WP).
WP1 will aim at collecting samples and generating the TCR and BCR bulk and single-cell data. For bulk analyses, we will use blood samples from sixty pSS patients selected according to their clinical status at the time of blood sampling - without lymphoproliferation, with lymphoproliferation or with marginal zone lymphoma - and from 50 healthy volunteers. For single-cell analyses of TCR, BCR and transcriptome, we will use blood and salivary glands biopsies from fifteen new pSS patients (5 for each clinical status).
WP2 will focus on TCR and BCR signature identification from blood bulk datasets, with the aim to (i) identify disease associated TCR and BCR signature for each patient subset analysed as compared to healthy individuals and (ii) identifying TCR and BCR signatures of disease progression (no lymphoproliferation to lymphoproliferation or lymphoma). This will be performed using machine learning approaches mastered by the team (such as sparse partial least squares discriminant analysis and generalized linear models).
WP3 will be analyzing the single-cell data to identify TCRs and BCRs enriched in the salivary glands of the different groups of patients and infer their specificity using deep-learning combined with annotated TCR and BCR databases screening.

The whole project will be handled by a team composed of immunologists and clinicians with expertise in autoimmune diseases, translational and fundamental research, as well as in high-dimensional data analysis notably in bulk and single cell TCR and BCR studies.

TALISMAN results will provide transformative mechanistic insights on the pathophysiology of pSS and progression to NHL, potentially leading to new biologics or cell therapies based on antibodies or TCRs. They will also identify novel biomarkers of pSS and NHL progression that should serve as prognostic and theranostic markers, improving patient care. Ultimately, these progresses should benefit to the entire field of ADs.