Figure 1

<p><b>A.</b> Mutant read frequency in different samples. The patient's blood samples were all taken around the time of neuroblastoma diagnosis. Numbers of sequencing reads per sample are displayed in the top level. * p = 0.01, comparing mutant read count frequencies between samples PD9058b3 and PD9058b with Fisher's exact test. Differences between PD9058b2 and sample b/b3 are not significant (p>0.05). <b>B.</b> Capillary sequencing of control germline DNA (Reference sequence) and patient's blood samples extracted at three time points, showing very low level of adenine (black arrow). The guanine (black peak) dropped approximately between 8–17% relative to control, as given by the software, which would not usually be classed as significant when looking for heterozygous germline mutations. <b>C and D.</b> Immunohistochemistry to show strong nuclear staining p53 in neuroblastoma (C) and sarcoma NOS (D).</p

Additional file 1 of TIGIT is the central player in T-cell suppression associated with CAR T-cell relapse in mantle cell lymphoma

Additional file 1: Supplementary Fig. S1. Cellular composition of immune cells in tumor microenvironment. Supplementary Fig. S2. Endogenous T cell clones expanded during responsive stage but depleted post relapse. Supplementary Fig. S3. Endogenous T cells post relapse are functionally deficient. Supplementary Fig. S4. Checkpoint inhibitors, HLA II molecules and hallmark pathways in monocyte subsets. Supplementary Fig. S5. Expression of cell surface genes and enriched hallmark pathways in MCL tumor cells. Supplementary Fig. S6. CXCR3 is overexpression in exhausted CD8 T cells and sIL2R and IL-2 failed to induce ex vivo cell expansion of PBMC collected post relapse. Supplementary Table S1. Summary of clinical characteristics of 15 patients with MCL. Supplementary Table S2. Summary of clinical characteristics for patients with MCL. Supplementary Table S3. Analytes included in the 65-plex and 20-plex assays for cytokine profiling

Gating strategy for the detection of different immune cell subsets from malignant ascites of ovarian cancer patients.

Cells collected from ascites were stained with a 9-color flow cytometry panel and analyzed in an X-20 Fortessa flow cytometer. (A) The gating strategy is depicted starting with the detection of lymphocytes, followed by live cells and CD3+ T cells that are separated according to CD4 and CD8 expression. (B) Fluorescence-minus-one (FMO) staining is shown for the different markers analyzed on CD4+ and CD8+ T cells. (PDF)</p

Ascitic fluid cell CDR3β motifs associated with prognosis.

(A) Examples of CD4+ T cell receptor (TCR) motifs associated with different prognoses. (B) Examples of CD8+ TCR motifs associated with different prognoses. The figures show the logo and phylogenetic tree of the clustered CDR3β peptides for each motif. The phylogenetic tree also includes peptides found as the best hit from McPAS; the complete set of motifs associated with excellent or poor/worst prognosis is shown in S7A Table. (C) Association networks constructed to form the cluster of motifs associated with response to platinum therapy. Nodes in the networks indicate individual patients; the color of the node indicates prognosis. Edges in the networks indicate an association between a pair of samples if they share GLIPH specificity groups associated with prognosis. The networks validated the statistical algorithm used to identify prognosis-associated specificity groups.</p

Increased T cell receptor productive rearrangements in ascites of patients with <i>BRCA1/2</i> tumor mutations.

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Difference in flow subset (expressed as a percentage of CD3+ live lymphocytes) between ascites of patients grouped in Cluster A and ascites of patients grouped in the remaining clusters (others).

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Difference in T cell receptor (TCR) characteristics between ascites of patients grouped in Cluster A and ascites of patients grouped in the remaining clusters (others).

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