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. 2013 Dec 4;5(214):214ra169.
doi: 10.1126/scitranslmed.3007247.

Digital Genomic Quantification of Tumor-Infiltrating Lymphocytes

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Free PMC article

Digital Genomic Quantification of Tumor-Infiltrating Lymphocytes

Harlan S Robins et al. Sci Transl Med. .
Free PMC article

Abstract

Infiltrating T lymphocytes are frequently found in malignant tumors and are suggestive of a host cancer immune response. Multiple independent studies have documented that the presence and quantity of tumor-infiltrating lymphocytes (TILs) are strongly correlated with increased survival. However, because of methodological factors, the exact effect of TILs on prognosis has remained enigmatic, and inclusion of TILs in standard prognostic panels has been limited. For example, some reports enumerate all CD3(+) cells, some count only cytotoxic CD8(+) T cells, and the criteria used to score tumors as TIL-positive or TIL-negative are inconsistent among studies. To address this limitation, we introduce a robust digital DNA-based assay, termed QuanTILfy, to count TILs and assess T cell clonality in tissue samples, including tumors. We demonstrate the clonal specificity of this approach by the diagnosis of T cell acute lymphoblastic leukemia and the accurate, sensitive, and highly reproducible measurement of TILs in primary and metastatic ovarian cancer. Our experiments demonstrate an association between higher TIL counts and improved survival among women with ovarian cancer, and are consistent with previous observations that the immune response against ovarian cancer is a meaningful and independent prognostic factor. Surprisingly, the TIL repertoire is diverse for all tumors in the study with no notable oligoclonal expansions. Furthermore, because variability in the measurement and characterization of TILs has limited their clinical utility as biomarkers, these results highlight the significant translational potential of a robust, standardizable DNA-based assay to assess TILs in a variety of cancer types.

Figures

Figure 1
Figure 1
Digital quantification and profiling of tumor-infiltrating lymphocytes. (A) Strategy for droplet digital PCR (ddPCR) amplification and probe hybridization of rearranged TCRβ CDR3 regions and RPP30. A generic rearranged TCR β CDR3 region PCR product is shown, indicating the constituent Variable (Vβ) segment, Diverse (Dβ) segment, Joining (Jβ) segment, and the nontemplated nucleotides inserted at the Vβ-Dβ and Dβ-Jβ junctions. Primers and Taqman® probes were designed for T-cell receptor β-chain loci and RPP30 control locus detection by ddPCR. To identify and quantify all possible TCR β chain loci resulting from somatic recombination, 45 forward primers were designed, each specific to a single functional Vβ segment or a small family of Vβ segments. The 3′ end of each Vβ forward primer is anchored at position −43 in the Vβ segment, relative to the recombination signal sequence, thereby providing a unique Vβ tag sequence within the amplified region. Thirty TaqMan® probes, each with a FAM fluorophore, were designed to bind to the 52 possible Vβ gene segments, while 13 reverse primers specific to each Jβ segment are anchored in the 3′ intron To measure the frequency of TCR β-chain loci in a tissue, we normalize to a reference gene, RPP30, a subunit of Ribonuclease P. Primers and a TaqMan® probe, with a VIC® fluorophore, were designed to specifically amplify and detect exon 1 of RPP30, allowing genome-normalized quantification. (B) ddPCR-based quantification of TCR β-chain and RPP30 loci. Primers and probes for Vβ gene segments are divided into 8 subgroups. In a 96 well format, these Vβ subgroup primers and probes are multiplexed with the 13 reverse Jβ primers, along with the RPP30-VIC® reference gene assay. These are combined ZLWK 3&5 PDVWHUPL[ DQG VDPSOH '1$ DQG WKHQ HPXOVLILHG LQWR D PL[WXUH RI ZDWHU LQ RLO droplets (~20,000 droplets per well), each droplet serving as an individual reaction chamber for PCR. These droplets are thermally cycled before being passed through a modified flow cytometer, where FAM and VIC® fluorescence is measured for each droplet. A FAM vs. VIC® plot allows gating of droplets with and without TCR β chain and RPP30 loci, and then Poisson statistics are applied to these populations for accurate quantification.
Figure 2
Figure 2
Spike-in validation of digital TCR quantification and profiling. (a) QuanTILfy was performed on mixtures of T-cells and lung fibroblasts with known ratios. A linear relationship was observed between the expected ratio of T-cells to total cells and the actual ratio of TCR to total cells (Linear regression, y = 1.0998x – 0.0007, R2 = 0.9926, n=6), illustrating the accurate quantification of TCR across several orders of magnitude. (b) To validate the profiling capabilities of QuanTILfy, TCRs were measured in DNA from T-ALL patient bone marrow. In each patient screened, a single TCRβ subgroup was found to dominate the T-cell population (>90%), corresponding perfectly with direct sequencing results. DNA from normal T-cells, however, displayed a relatively even distribution of TCRβ subgroups. (c) To illustrate the reproducibility of the assay, QuanTILfy was performed on 11 ovarian cancer tissues, which exhibit a wide range of TIL counts, in 2 independent experiments. The TIL detected per cell for experiment 1 and experiment 2 were plotted per TCRβ subgroup for each sample, and the data was assessed for conformation to a linear model of ideal reproducibility (R-squared/coefficient of determination to y=x, R2=0.9812, n=88).
Figure 3
Figure 3
Spatial characterization of TILs in primary ovarian carcinoma. To assess the heterogeneity of TIL frequency and profile throughout a tumor, a two-dimensional lattice was made for slices of primary tumors from 3 patients (a,b,c), with punch biopsies taken at one cm intervals from the interior (P) and margins (M), as indicated in the diagrams. DNA was isolated, and digital TIL quantification was performed for each biopsy. (a) In patient 1, a consistently low TIL fraction (< 0.5%) was observed across all biopsies. (b) Patient 2 displayed higher, but similarly homogenous TIL frequencies (6 – 8%). (c) Patient 3, however, showed a high degree of variability between biopsies, ranging from 2 to 15% TILs. Plotting these TIL fractions on the tumor diagram reveals that the biopsies with higher TIL fractions were all from one side, and the biopsies with lower TIL fractions were from the other.
Figure 4
Figure 4
TIL fraction in ovarian carcinoma and survival. (a) QuanTILfy was performed on primary tumors from 30 ovarian carcinoma patients with known survival outcomes, ranging from 1 to 122 months. A high degree of variability in TIL percent was observed among patients, ranging from 1 to 25%. Notably, no clonal expansion of specific T-cell populations was observed in the tumors, as each sample contained a normal distribution of TCRβ subgroups. (b) TIL fractions were averaged for tumors from patients with short-term (< 2 yrs., n = 14) and long-term survival (> 5 yrs. n = 16). A 2.5-fold higher average TIL fraction was observed for > 5 yr. survivors (two-tail unpaired t test, n=30, p = 0.0273).

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