Table S1A: Activation Patterns of TCR Components in Human Mature T-Cell Tumors

Supplemental Data:

Table S1A: Activation patterns of TCR components in human mature T-cell tumors

* low absolute numbers in this category (2 of only 3 cases)

Protocols and detailed results of our extensive immunostaining for TCR components and TCR kinases on various PTCL/L are outlined in References [1–3]. This updated Table summarizes the data on 158 cases of the most sizable subsets, namely cutaneous T-cell lymphoma (CTCL; n=29), nodal and extranodal peripheral T-cell lymphoma of other types (PTCL; n=73), and anaplastic large cell lymphoma (ALCL; n=56). Expression and phosphorylation state of LCK, ZAP70, and SYK as well as expression of p(S473)AKT, CD3, and TCRβ were evaluated semi-quantitatively on a 3-tier system (negative, weak or partial expression, strongly expressed) in all identifiable (morphologically and by parallel CD3 stains) tumor cells.

1.Admirand J, Herling M, Patel K, et al. T-cell receptor signaling and growth pathways in T-cell tumors. Mod Pathol. 2005;18:220A.

2.Admirand J, Rassidakis G, Medeiros L, Jones D. CD3-independent activation of downstream effectors ZAP-70 and SYK in T-cell lymphomas. Lab Invest. 2004;84:237A.

3.Admirand JH, Rassidakis GZ, Abruzzo L V, et al. Immunohistochemical detection of ZAP-70 in 341 cases of non-Hodgkin and Hodgkin lymphoma. Mod Pathol. 2004;17(8):954–61.

Document S1: Protocol of the in-silico integrative analysis of gene expression data sets on human PTCL/L performed in this review

Out of 38 reports [1-38] on array-based mRNA expression profiling of PTCL/L tumor samples and normal T cells we identified 10 batches with available primary data sets from standardized oligonucleotide arrays from which 9 were integrated (6 on Affymetrix HG-U133 Plus 2.0, 2 on older U133A and 1 on U133A_2)[1-8,10]. One data set [9] was excluded as it strongly differed from all other datasets. This can at least in part be explained by the unique mode of sample extraction by single-cell micro-dissection applied there in contrast to all other studies. All data sets were separately background-corrected and pre-annotated using the BioConductor package „affy“ in R. Replicates were combined by mean. Data sets were quantile-normalized and the genes re-annotated via Ensembl ID using „biomaRt“. The resulting ambiguous probe sets assigned to a gene were also integrated by calculating their mean. The 48 selected marker genes for the clustering are listed in Table S2 (below). They were retrieved from the annotated datasets. A pool of these expression values from all data sets was then again quantile-normalized to account for variability in platform specifications and noise instead of working on gene overlap because of different probe sets (and their number or unavailability for certain genes). Together, this results in an approximate mRNA expression profile across multiple studies on a large number of PTCL/L. It provides the advantage to be able to compare and integrate individual studies / platforms without the need for control samples (normal T-cells) in each of these batches / experiments.

Abbreviations:

AB HSTL, Alpha-beta hepatosplenic T-cell lymphoma;

AITL, Angioimmunoblastic T-cell lymphoma;

ALCL-ALK-, Anaplastic large-cell lymphoma ALK negative;

ALCL- ALK+, Anaplastic large-cell lymphoma ALK positive;

ATLL, Adult T-cell lymphoma;

cALCL, primary cutaneous anaplastic large cell lymphoma;

CTCL, Cutaneous T-cell lymphoma;

DR, HLA-DR, human leukocyte antigen receptor;

EATL, Enteropathy-associated T-cell lymphoma;

GD HSTL, Gamma-delta hepatosplenic T-cell lymphoma;

LN, sort-isolated from lymph nodes;

MF, Mycosis fungoides;

PTCL-nos, Peripheral T-cell lymphoma not otherwise specified;

PB, sort-isolated from peripheral blood;

Tons., sort-isolated from tonsils;

T-PLL inv, T-cell prolymphocytic leukemia with inv(14) or t(14;14);

T-PLL no inv, T-cell-prolymphocytic leukemia without inv(14) or t(14;14);

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Table S2: Genes of the TCR pathway selected for the comprehensive in-silico meta-analysis of gene expression data in PTCL/L

The listed genes (categorized as functional classes) were initially selected based on literature-based implication in (proximal, intermediate, and distal) TCR signaling and its outcome. They were then limited to those that in first runs showed a differential expression across entities of PTCL/L. TCL1A and ALK served as controls for validating the reliability of the spontaneous TCR-based clustering. The clustering algorithms were applied using each functional category of these genes separately and in permutated combinations of each other. A summary of clustering of PTCL/L when including all gene categories at once is presented as a heatmap in Figure 1B of the main manuscript.