Chromatin Accessibility Profiling to Increase Diagnostic Accuracy and Refine Cell-of-Origin Classification of Mature T-Cell Lymphomas
Authors: Julia Edith , Mareschal Sylvain , Chebel Amel , Golfier Camille , Müller Tony Andreas , Lours Camille , Hadj-Hamou Sofiane , Bissay Nathalie , Tschanz Manon , Ceribelli Michele , Chartoire Dimitri , Vernay Aurélie , Carras Sylvain , Sibon David , Marçais Ambroise , Laurent Camille , Martin Laurent , Asnafi Vahid , Payen Lea , Sanlaville Damien , Bardel-Danjean Claire , Roussel Mikael , Szablewski Vanessa , Drieux Fanny , Ruminy Philippe , Herling Marco , Chassagne-Clément Catherine , Salles Gilles , Sujobert Pierre , Traverse-Glehen Alexandra , Baseggio Lucile , De Leval Laurence , Staudt Louis M. , Gaulard Philippe , Genestier Laurent , Bachy Emmanuel 
Published: November 2021

Background:

Mature T-cell lymphomas and leukemias (MTCL) are heterogeneous diseases with dismal prognosis. Differentiating between the numerous entities requires specialized pathology expertise and studies show up to 20% change in diagnosis after expert review of cases (Laurent, JCO, 2017). Assay for transposase accessible chromatin sequencing (ATAC-seq) is a simple technique to profile open chromatin regions (OCR) proven to be highly discriminant for cell-of-origin identification regardless of cell activation status (Shih, Cell, 2016). We applied ATAC-seq to MTCL in order to explore the epigenetic landscape of these diverse entities, compared them to normal T-cell subtypes and built a predictive model to help diagnosis.

Method:

Ten-thousand FACS-sorted single cells from primary MTCL samples and 50µm section of frozen tumoral tissue from the TENOMIC French T-cell Lymphoma Consortium were processed according to the previously published FAST-ATAC and OMNI-ATAC protocols respectively (Corces, Nat Genetics, 2016 & Nat. Methods, 2017). Concurrently we applied FAST ATAC to different normal T- and NK-cell subsets sorted from healthy donor PBMC or lymph node suspensions. Sequencing data were processed by an adapted version of ENCODE ATAC-seq pipeline. Matrix of insertion events in peaks by sample was obtained, normalized and most variant peaks were selected for UMAP projection.

Results:

In total, 678 normal and tumoral samples were sequenced to provide a comprehensive landscape of chromatin accessibility in MTCL. Epigenetic profiling by ATAC-seq of FACS-sorted tumoral samples resulted in a complete segregation of the known MTCL entities (AITL, TFH-PTCL, ALK+ and ALK- ALCL, HSTL, CTCL, ATLL, LGL and T-PLL). Most PTCL-NOS (13/17) clustered with a pre-defined MTCL subtype (mainly AITL/TFH-phenotype PTCL, CTCL and lymphomas exhibiting cytotoxic features). All but one discordant diagnosis between pathology and ATAC-seq (1/11) led to revised diagnosis after pathology review.

Unsupervised clustering of normal NK- and T-cell subtypes (N=49) and sorted tumoral lymphoma cells (N=104) confirmed that AITL derive from TFH cells. HSTL and LGL closely segregated with NK- and gamma-delta T cells, in line with their known innate-like phenotype.

Surprisingly, the cell-of-origin of T-PLL seems to be naïve T cells despite the known expression of central memory markers on leukemic cells.

Beyond epigenetic classification, background reads from ATAC-seq profiles were used to detect copy number variation (CNV), such as isochromosome 7q in HSTL. In addition, HTLV1 and EBV viral sequence detection in ATAC-seq reads strengthened identification of ATLL and NKTCL cases.

Finally, using unsupervised deconvolution approaches, we were able to discriminate different MTCL subtypes from 223 processed bulk frozen samples. All known MTCL subtypes were differentiated (AITL/PTCL-TFH, HSTL, NKTCL, ATLL, ALK- and ALK+ ALCL, MEITL, EATL). A subgroup of PTCL-NOS harboring GATA3 OCRs and a distinctly high CNV number was isolated that might correspond to previously described PTCL-GATA3 subtypes (Iqbal, Blood, 2019).

A random forest model was trained to predict diagnosis based on chromatin-accessibility clusters defined in the discovery cohort of patients. The model showed accurate prediction performance by cross-validation. External validation on 172 samples collected from 5 tertiary care centers will be presented at the meeting.

Conclusion:

ATAC-seq is a fast and cost-effective technique to help and refine MTCL pathological classification and allows for putative cell-of-origin identification in lymphoma. Training of a machine learning model to predict MTCL entity diagnosis based on ATAC-seq analysis of fresh or frozen samples shows promising results.

Figure 1 Figure 1.

Disclosures

Sibon: Janssen: Consultancy; Abbvie: Consultancy; iQone: Consultancy; Takeda: Consultancy; Roche: Consultancy. Drieux: Genexpath: Patents & Royalties: The author is a potential inventor on a patent application for the LymphoSign, which has been licensed for by Genexpath Patents & Royalties.. Ruminy: Genexpath: Patents & Royalties: The author is a potential inventor on a patent application for the LymphoSign, which has been licensed for by Genexpath Patents & Royalties. . Salles: Takeda: Consultancy; Velosbio: Consultancy; Ipsen: Consultancy; Allogene: Consultancy; Miltneiy: Consultancy; Genentech/Roche: Consultancy; Genmab: Consultancy; Janssen: Consultancy; Loxo: Consultancy; Kite/Gilead: Consultancy; Regeneron: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Novartis: Consultancy; Incyte: Consultancy; Rapt: Consultancy; Epizyme: Consultancy, Honoraria; Debiopharm: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria. Gaulard: Alderaan: Research Funding; Sanofi: Research Funding; Innate Pharma: Research Funding; Gilead: Consultancy; Takeda: Consultancy, Honoraria. Bachy: Kite, a Gilead Company: Honoraria; Novartis: Honoraria; Daiishi: Research Funding; Roche: Consultancy; Takeda: Consultancy; Incyte: Consultancy.