Abstract
Tumour-infiltrating lymphocytes are associated with a survival benefit in several tumour types and with the response to immunotherapy1–8. However, the reason some tumours have high CD8 T cell infiltration while others do not remains unclear. Here we investigate the requirements for maintaining a CD8 T cell response against human cancer. We find that CD8 T cells within tumours consist of distinct populations of terminally differentiated and stem-like cells. On proliferation, stem-like CD8 T cells give rise to more terminally differentiated, effector-molecule-expressing daughter cells. For many T cells to infiltrate the tumour, it is critical that this effector differentiation process occur. In addition, we show that these stem-like T cells reside in dense antigen-presenting-cell niches within the tumour, and that tumours that fail to form these structures are not extensively infiltrated by T cells. Patients with progressive disease lack these immune niches, suggesting that niche breakdown may be a key mechanism of immune escape.
In many cancers, tumour-infiltrating CD8 T cells predict patient survival and response to immunotherapy1–8. These observations raise a fundamental question about the immune response to cancer and why some tumours have high CD8 T cell infiltration while others do not. A logical assumption has been made that T cell exhaustion drives a decline in the T cell response. T cell exhaustion has been extensively described in viral infections, in which persistent antigen exposure reduces the ability of the CD8 T cells to proliferate and kill target cells9,10. Acquisition of checkpoint molecules that inhibit T cell function are a hallmark of this exhausted state, and blockade of molecules such as PD-1 can rescue exhausted cells in these models11,12. Supporting the idea that T cell exhaustion is a factor that limits T cell function in cancer, many reports have found that T cells in tumours express high levels of these checkpoint molecules, and blockade of PD-1 and CTLA-4 are among the most successful treatments for many cancers13–17. However, the model of persistent antigen exposure driving T cell decline does not explain why some patients have a strong T cell response to their tumour for decades, or why patients with controlled disease may have many CD8 T cells that are phenotypically exhausted. Here we investigate the CD8 T cell response to human tumours to better explain the mechanisms that control the magnitude of the T cell response to cancer.
TCF1+ CD8 T cells reside in tumours
On the basis of the observation that CD8 infiltration into tumours predicts survival and response to immunotherapy in other cancers1–7,18,19, we measured this parameter in a cohort of patients with kidney cancer. To quantitate CD8 infiltration, tumour tissue was collected from patients undergoing surgery and analysed by flow cytometry (Extended Data Fig. 1a). CD8 T cell infiltration ranged from 0.002% to over 20% of the total tumour cells (Fig. 1a). For patients with disease at any stage, having less than 2.2% CD8 T cell infiltration predicted four-fold more rapid progression after surgery (hazard ratio (HR) = 3.84, P < 0.01) (Fig. 1b, Extended Data Figs 1b–e, 2a, b). CD8 T cell infiltration did not correlate with clinical parameters such as disease stage or patient age (Extended Data Fig. 2c–k), suggesting that other biological mechanisms control the degree of T cell infiltration into tumours.
Reasoning that the composition of the tumour-infiltrating CD8 T cells might offer insight into the mechanisms controlling - cell infiltration, we analysed expression of checkpoint molecules, co-stimulatory molecules and important transcription factors in tumour-infiltrating CD8 T cells. We detected a distinct population of cells that resembled exhausted CD8 cells by their expression of high levels of checkpoint molecules, TIM3, PD-1, CTLA4 and TIGIT (Fig. 1c, d, Extended Data Fig. 3a, b). We also identified a population of cells with low checkpoint molecule expression, but high expression of co-stimulatory molecule CD28 and transcription factor TCF1 (encoded by TCF7) (Fig. 1c, d, Extended Data Fig. 3a, b). TCF1 is a critical transcription factor that defines a stem-like T cell population in chronic murine lymphocytic choriomeningitis virus (LCMV) infection20–22. Of note, others have described a TCF1+ CD8 T cell population in human and mouse tumours that correlates with response to PD-1 blockade22–27. To functionally characterize the TCF1+ and checkpoint-high populations of CD8 T cells in tumours, checkpoint-high cells (PD-1+, TIM3+) and stem-like cells (TCF1+TIM3−CD28+) were sorted from tumours, labelled with CellTrace Violet, and incubated with anti-CD3/CD28 stimulation beads. The TCF1+TIM3−CD28+ stem-like population consistently proliferated in response to bead stimulus, whereas the checkpoint-high population lacked proliferative potential (Fig. 1e, f). Of note, after division, the stem-like T cells upregulated PD-1, TIM3 and CD244 to a similar level seen in vivo and downregulated TCF1, acquiring the phenotype of the checkpoint-high population (Fig. 1g, h, Extended Data Fig. 3c–f). Together, these data suggest that TIM3− CD28+ T cells possess a stem-like capability; they can proliferate and give rise to more terminally differentiated, checkpoint-molecule-expressing T cells.
To further investigate the relationship between the intra-tumoral stem-like and terminally differentiated CD8 T cells, we examined the T cell receptor (TCR) repertoires of each population in 11 tumour samples. We found that TCRs significantly overlapped between the stem-like and terminally differentiated cell populations in all patients examined, suggesting a clonal relationship between these populations (Fig. 1i, j, Extended Data Fig. 4h). In two patients from whom we recovered samples from distant sites within the same tumour, we found a high degree of TCR overlap between the stem-like and terminally differentiated populations at all locations (Extended Data Fig. 4g). These data are in contrast to reports finding that the CD39− population of tumour-infiltrating lymphocytes (TILs) are unrelated to tumour antigens, and instead support a model of T cell differentiation whereby stem-like T cells within the tumour are the precursors to the terminally differentiated CD8 T cell population28.
We next assessed how the composition of CD8 T cells in the tumour related to total T cell infiltration. Highly infiltrated tumours consistently had a distinct population of TIM3+ cells, which resemble phenotypically exhausted CD8 T cells, whereas poorly infiltrated tumours rarely had these cells (Fig. 1k, Extended Data Fig. 3g). The same relationship was evident in prostate and bladder tumours as well, where poorly infiltrated tumours contained few TIM3+ terminally differentiated cells (Fig. 1l). In poorly infiltrated tumours, the stem-like CD8 T cell population is consistently detectable at very low numbers (Extended Data Fig. 3h) but does not appear to be induced to differentiate into the TIM3+ cells (Fig. 1k, l, Extended Data Fig. 3g). These data suggest that the magnitude of the T cell response within a tumour is related to the ability of many terminally differentiated cells to be generated by the stem-like TCF1+ T cell population.
Transcription and epigenetics of CD8 T cell subsets
To further investigate the terminally differentiated and stem-like T cell populations in tumours, we performed RNA-sequencing (RNA-seq) on these populations. The terminally differentiated cells expressed more checkpoint molecules and much higher levels of granzymes and perforin (Fig. 2a). In contrast, TCF1+ stem-like CD8 T cells had higher levels of genes involved in survival such as IL7R and IL2RA (CD25), as well as co-stimulatory molecules such as CD28, CD226 and CD2 (Fig. 2a). We also compared these populations to stem-like and terminally differentiated CD8 T cell subsets previously described in murine chronic viral infection (that is, LCMV)20. Gene set enrichment (GSEA) found that the genes expressed by tumour-infiltrating populations were highly enriched with the analogous cell population described in LCMV (Fig. 2b). We compared these subsets to human effector and memory subsets, and both populations were more similar to the effector cells than memory29 (Extended Data Fig. 5a–e). These transcriptional data suggest key functional differences between the TCF1+ stem-like and TIM3+ terminally differentiated T cell subsets within human tumours, and that these functions appear to be similar to what has been described in stem and terminally differentiated CD8 T cells in mice.
To understand how epigenetic mechanisms affect the different functions of these subsets, we performed whole-genome DNA methylation analysis. As T cells underwent transition from naive cells to the stem-like and terminally differentiated states, demethylation events outweighed methylation events approximately 9 to 1 (Fig. 2c, d, Extended Data Fig. 5g–j). These epigenetic changes occurred near key genes involved in differentiation such as TCF7, TBX21, PDCD1 and many other checkpoint molecules (Fig. 2e, Extended Data Fig. 5k) Together these data highlight that two key functional characteristics of T cells – proliferative potential and cell killing – are compartmentalized into two distinct populations, and these functions are tightly regulated by transcriptional and epigenetic mechanisms to ensure that cells perform as required.
TCF1+ CD8 T cells reside in APC niches
Our finding of a stem-like CD8 T cell population within the tumour, rather than in lymphoid tissue, is unexpected. In mouse models of chronic infection, analogous TCF1+ stem-like T cells are found only in lymphoid tissue20,21. Thus, having identified these stem-like cells in tumour tissue, we reasoned that a lymphoid-like microenvironment within the tumour may support their survival in the tumour. We measured tumour-infiltrating antigen-presenting cell (APC) populations (Fig. 3a). This revealed a highly significant correlation – across kidney, prostate and bladder tumours – between the presence of dendritic cells and the number of stem-like CD8 T cells in the tumour (Fig. 3b, Extended Data Fig. 6h). The percentage of macrophages present did not correlate with the presence of TCF1+ CD8 T cells or the number of CD8 T cells (Fig. 3b). We then used immunofluorescence staining to determine the spatial relationship between APCs and stem-like CD8 T cells (Fig. 3c, d, Extended Data Fig. 6c–d). TCF1+ CD8 T cells were only found in regions with aggregations of major histocompatibility complex II (MHC-II)+ cells greater than 5 cells per 10,000 μm2 (Fig. 3e, f). In contrast, the TCF1− population was distributed across the tissue with no preference for APC dense zones (Fig. 3f). We expanded this analysis to large sections of tumour tissue and found that tumours had many regions with dense APC zones, and the stem-like CD8 cells preferentially resided there (Extended Data Fig. 6e–j). When we looked at prostate and bladder tumours, TCF1+ CD8 cells were also found in dense APC zones (Fig. 3g, Extended Data Fig. 6k, l). Lastly, we found a significant correlation (P < 0.05, R2 = 0.73) between the number of TCF1+ CD8 T cells in a tumour and the proportion of the tumour with sufficient APC density to support stem-like cells (Fig. 3g). This suggests that APC dense regions serve as an intra-tumoral niche for stem-like CD8 T cells, which sustain the terminally differentiated T cell population and thus of the anti-tumour immune response.
We next assessed whether these antigen-presenting niches were similar to tertiary lymphoid structures (TLS) previously described in other cancer types30,31. These structures were macroscopically visible with haemotoxylin and eosin staining in 5 out of 33 patients, with densely packed mononuclear cells compartmentalized and usually found outside the tumour border (Extended Data Fig. 7a, b). The presence of TLS did not correlate with CD8 T cell infiltration (Extended Data Fig. 7f–h). Visualized using immunofluorescence, TLS were predominantly very densely packed MHC-II+ cells, interspersed with few CD8 T cells (Extended Data Fig. 7d). On comparison to human tonsil tissue, these TLS much more closely resembled B cell follicles, which is consistent with several other reports30–32 (Extended Data Fig. 7c, d). In comparison, the antigen-presenting niches populated by TCF1+ CD8 T cells were predominantly found inside the stromal barrier of the tumour (Extended Data Fig. 7b). Of interest, these nests containing TCF1+ CD8 T cells closely resembled the extrafollicular regions of lymphoid tissue where T cells reside — moderately densely arranged APCs packed with many TCF1+ CD8 T cells (Extended Data Fig. 7c, e). In addition, we found a significantly higher level of blood and lymphatic endothelial cells (CD31+PDPN–, CD31+PDPN+, respectively) in tumours with CD8 infiltration, and these vessels were often closely associated with dense regions of T cell infiltration (Extended Data Fig. 8). Together these findings highlight key features of the CD8 T cell response to cancer. Regions exist in tumours that resemble a T cell zone of lymphatic tissue. These regions contain the TCF1+ CD8 T cells that seem to only reside in close proximity to APCs, and the generation of these immune niches is correlated to lymphatic and blood vessel infiltration into the tumour.
Loss of APC niche during immune escape
We next examined how the immune niche differs between patients with controlled disease after surgery compared to those whose tumours escaped immune control and rapidly progressed. We imaged large regions of tumour tissue from 26 patients with kidney cancer at the time of surgery to understand how the presence of immune niches in the tumour might correlate with disease progression (see Extended Data Fig. 9a for patient characteristics). Immunofluorescence quantification of CD8 T cells strongly correlated with flow cytometry quantification of CD8 T cell infiltration (Extended Data Fig. 9b, c). Across around 100,000 20× fields of view in these 26 samples, regardless of the level of CD8 T cell infiltration in the patient, we could generally identify a few dense regions of MHC-II where TCF1+ CD8 T cells resided (Fig. 4a–d). Most importantly, patients with controlled disease had significantly more of these dense regions (Fig. 4e, f). On stratifying patients above or below the median MHC-II density, we found that patients with low MHC-II+ cell density experience significantly impaired progression-free survival (Fig. 4g, P = 0.04, HR = 3.157). These factors were independent of PD-L1 expression in the tumour, which had no correlation to the level of CD8 or survival of patients (Extended Data Fig. 10). Importantly, when we specifically studied patients with stage III disease, around 50% of whom progress after surgery, there were >10-fold fewer immune niches in patients who progressed (Extended Data Fig. 9e–g). Patients with progressive disease also had lower proportions of MHC-II+ dense, CD8+ dense, and shared MHC-II+ and CD8+ dense regions in their tumour (Fig. 4h, i, Extended Data Fig. 9h, i), suggesting that for tumours to evade destruction by CD8 T cells, they must either prevent formation of intra-tumoral immune niches or find ways to destroy them.
Discussion
In this study, we sought to understand the mechanisms controlling CD8 T cell infiltration into human tumours. We found that tumour-infiltrating T cells are comprised of two functionally distinct subsets, a TCF1+ stem-like CD8 T cell population, and their progeny, a clonally related terminally differentiated population that express high levels of checkpoint molecules. These terminally differentiated cells fit the traditional definition of an exhausted CD8 T cell; they do not proliferate in response to re-stimulation and express high levels of checkpoint molecules. However, the presence of this terminally differentiated cell population positively correlates with the total number of tumour-infiltrating T cells and protection from disease progression. These observations are not well explained by a model of T cell exhaustion whereby continuous antigen exposure leads to accumulation of checkpoint molecules, resulting in a decline of the T cell response. Based on the functional characteristics we defined in these two cell populations and on the clonal relationship between stem-like and terminally differentiated cells, we propose that the stem-like CD8 T cell acts as a precursor to generate a terminally differentiated effector population, which is in agreement with other previous studies22–27. In this model, the stem-like cells require a region within the tumour that resembles the T cell zone of secondary lymphatic tissues, made up of dense areas of antigen-presenting cells. An unanswered question in this model is how stem-like CD8 T cells originate in the tumour. Previous studies have shown that CD8 T cells in tissue-draining lymph nodes are transcriptionally and phenotypically similar to the stem-like CD8 T cell described in chronic LCMV infection, suggesting this may be the source of the stem-like cells in tumours33.
On the basis of this model, we propose that the decline of the T cell response in human cancer is not caused by accumulation of checkpoint-expressing exhausted CD8 T cells or overexpression of PD-L1 in the tumour, but by the failure of stem-like CD8 T cells to be sufficiently stimulated by an antigen-presenting-cell niche to continuously produce terminally differentiated CD8 T cells in the tumour. Furthermore, the scarcity of these niches in tumours that rapidly progress after surgery suggests that tumours may be interfering with the formation or continued maintenance of immune niches and that this may be a novel mechanism of immune evasion requiring further investigation.
Online content
Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-019-1836-5.
Methods
Sample collection, preparation and storage
Patients were recruited in accordance with an approved IRB protocol, and all patients provided informed consent. Patient tumour samples were collected immediately after undergoing partial or radical nephrectomy or prostatectomy or undergoing transurethral resection of a bladder tumour (TURBT). Samples for flow cytometric analysis were harvested in Hank’s Balanced Salt Solution, minced into small pieces, digested using Liberase enzyme cocktail, and homogenized using a MACS Dissociator. Single cell suspensions were obtained, RBC ACK lysed, and stored at −80 °C in freezing media for batch analysis. Samples for immunofluorescence analysis were formaldehyde fixed and embedded in paraffin blocks by Emory Pathology. Unstained and haematoxylin/eosin stained sections of FFPE blocks were obtained from Emory Pathology.
Statistical analysis
Patients were selected to have at minimum 365 days of follow up. Follow up time was calculated as the number of days from the date of surgery to an event or to censorship. Progression and death were classified as events. Patients who had not progressed or are not deceased were censored, and the number of days is calculated from the date of surgery to 9 May 2018. Investigators were not blinded during outcome assessment. Statistical analysis was conducted using GraphPad Prism or using SAS Version 9.4 and SAS macros developed by the Biostatistics and Bioinformatics Shared Resource at Winship Cancer Institute. The significance level was set at P < 0.05. Descriptive statistics for each variable were reported. The univariate association with percentage of CD8 T cells was carried out by ANOVA/Kruskal–Wallis test for categorical covariates and by Pearson correlation coefficient for numerical covariates. The univariate association of each covariate with PFS was tested by proportional hazard model with hazard ratio and its 95% confidence interval being reported. We examined a possible nonlinear relationship between a continuous percentage of CD8 T cells and PFS through a martingale residual plot and identified an optimal cut-off value of percentage of CD8 T cells that maximizes the separation between the two groups by a bias adjusted log rank test34,35. The method enables the estimation and evaluation of the significance of the cut-off value and also is adjusted for the bias created by the data driven searching process. The optimal cut-off value was found to be 2.2% (Extended Data Fig. 1b & 1c). Using this same 2.2% cut-off for CD8 infiltration in patients with more aggressive, non-metastatic disease (T3N0M0), less CD8 T cell infiltration predicted a sixfold more rapid progression (Extended Data Fig. 1d). CD8 T cell infiltration also significantly predicted progression amongst patients categorized as high-risk by a conventional prognostic scoring system (SSIGN) (Extended Data Fig. 1e). Statistical methods were not used to pre-determine number of patients included.
Flow cytometry
Single cell suspensions from human tumours were stained with antibodies listed in Supplementary Information Table 1. Live/dead discrimination was performed using fixable Near-IR Dead Cell Stain Kit (Invitrogen). Samples were acquired with a Becton Dickinson LSRII and analysed using FlowJo. For intracellular staining, cells were fixed and permeabilized using the FOXP3 Transcription Factor Staining Buffer Set (eBioscience).
Proliferation assays
CD8 T cells subsets were sorted from tumours and labelled with Cell trace violet (Thermo) according to manufacturer’s instruction. Cells were incubated with anti-CD3/anti-CD28 T cell activation beads (Miltenyi) at a ratio of 1 bead to 2 T cells in U-bottom plates. 10 U ml−1 of human IL-2 (Peprotech) was included in culture media (RPMI + 10% FBS). After 4 days, cells were analysed by flow cytometry for proliferation and expression of various proteins. Proliferation index was assessed using FlowJo.
In vitro assays
Stem-like and terminally differentiated CD8 T cells were sorted from human tumours and incubated with T cell culture media (RPMI + 10%FBS) supplemented with human IL-2 (10 IU ml−1) in U-bottom plates. After 3 days, cells were analysed by flow cytometry for expression of various proteins.
TCR sequencing
Single cell suspensions from human tumours were stained with antibodies listed in Supplementary Information Table 1. Live/dead discrimination was performed using fixable Near-IR Dead Cell Stain Kit (Invitrogen). Populations of interest were isolated using a Becton Dickinson FACS Aria II Cell Sorter. Gating is shown in Extended Data Fig. 4a, c. DNA was isolated using a Qiagen AllPrep DNA/RNA Micro Isolation Kit. TCR sequencing was performed by Adaptive Biotechnologies Immunoseq technologies. TCR Sequencing analysis was performed using custom R scripts. The number of TCRs detected and degree of overlap detected was highly subject to the number of cells collected, highlighting the need to sufficiently sample the pools of cells to accurately understand the clonal relationship between them (Extended Data Fig. 4e, f).
To determine if there was significant overlap between populations, we first calculated the contamination of each population with the other so we could determine if overlap in TCRs could be explained by the contamination rate. To determine the overlap between the stem and terminally differentiated cells due to biological and technical variance, flow cytometry data was fit using an EM mixing model36. The characteristics of these fitted models are shown in Extended Data Fig. 4b. Shown on the plot are 80% and 95% confidence intervals for each population and the approximate position of gates used to sort populations. We then placed gates where we had for the sort and asked the question of what proportion of the cells in that gate were derived from the target and contaminating population. This contamination rate is highly subject to the ratio of the two populations. In our 2 most extreme patients shown in Extended Data Fig 4e, if 93% of the cells are the stem-like population, the contamination rate in the TD population is as high as 14%.
Extended Data Figure 4b shows how the purity changes as the ratio of stem to terminally differentiated cells changes. The two most extreme samples are highlighted on the figure to show what the inferred proportion of each population is in the sorted cells. In addition, we added 5% to this number for each sample to account for additional contamination from the sorting procedure. The summary of this analysis is included in Extended Data Fig. 4h.
To identify significance of TCR overlap we used the purity calculated for each patient we tested if the relative frequency of each TCR could be explained by contamination. For each specific TCR that was detected in both populations, we tested two hypotheses. First, can the number of a particular TCR in the stem-like population be accounted for by contamination from the TD cell population, and conversely, can the same TCR in the TD population be accounted for by contamination from the stem-like population. This was achieved by assuming each TCR detected in a sample was a Bernoulli trial with a probability of occurring equal to the expected frequency of the TCR due to contamination. For example, we assumed that if a TCR was found at a frequency of 10% in the stem population, and the inferred overlap into the TD was 10%, it would contaminate the terminally differentiated cells at a frequency of 1%. If we collected 1000 total TCRs for a particular sample, and detected 10 of this specific TCR, the probability of detecting at least this many TCRs due to this 1% contamination rate would be given by:
The general formula for testing if the overlap in the terminally differentiated population is caused by contamination from the stem-like cells is given by:
Where k = number of the specific TCR detected in the terminally differentiated population; P = frequency of the specific TCR in the stem population x contamination rate; n = total number of TCRs detected in a sample.
We applied this analysis to every TCR collected that had overlap detected and tested the converse hypothesis that the fraction of stem-like TCRs detected could be accounted for by contamination from the terminally differentiated cells. If both tests were under 0.05, we rejected the hypothesis that the overlap was caused by contamination. Figure 1l highlights the proportion of TCRs in each sample that meet these criteria. The supplementary table (Extended Data Fig. 4h) provided has these values used for every TCR and the P value calculated.
To identify significance of TCR overlap, we assumed 90% purity and conducted a Fisher’s exact test to test the hypothesis that the TCR overlap we detected could be explained by this contamination rate. To determine the probability that an overlap could have been detected given the number of cells recovered, we fit an exponential distribution of the observed stem and effector TCR clone frequency (shown in Extended Data Fig. 4b). We then used a bootstrapping approach to randomly sample the same number of TCRs from these two distributions as cells we had collected. We repeated this 1,000 times. If a 20% overlap was not detected at least 80% of the time, the sample was considered underpowered to detect an overlap. Analysis of the TCRs found that the TCR repertoires showed a high degree of immunodominance, where the ten most dominant clones account for 55% of the terminally differentiated repertoire and for 31% of the stem-like repertoire, indicating an expansion against a narrow range of antigens in the tumours (Extended Data Fig. 4d).
RNA sequencing and analysis
RNA was isolated from FACS sorted cells using QIAGEN All-prep kit. RNA was prepared using Contech SmartSeq2 (Bladder samples) or Nugen Ovation (Prostate, Kidney samples) library prep kits. Prostate and Kidney samples were sequenced at HudsonAlpha on a Hiseq25000, Bladder samples were sequenced at the Emory Yerkes Genomics Core on a HiSeq1000. Data was normalized and differential expression of genes identified using DESeq237. Raw fastq files and analysis of RNA-seq is uploaded to GEO under identifier GSE140430.
DNA-methylation analysis
Whole-genome DNA methylation was performed using the Illumina TruSeq DNA Methylation Kit. Sequence data was aligned using Bismark38, and data was analysed using custom R and Python scripts which are available upon request. Briefly, individual significantly differentially methylated CpG motifs were identified by Fisher’s exact test. Continuous regions of differentially methylated CpGs were identified by finding regions where at least 6 out of 10 CpGs in a continuous stretch were differentially methylated. These regions were then collapsed and analysed as single ‘differentially methylated regions’ (DMRs). Differentially expressed regions were identified as those that had a p value less than 1 × 10−4 by Fisher’s exact test and were at least 20% different to the comparison sample. Transcription factor binding enrichment analysis was also conducted, identifying TCF4, TCF7L2, and MYC as enriched in the stem-like cells and E2F, NRF2, and SP1 in the terminally differentiated cells (Extended Data Fig. 5l). Whole-genome DNA methylation data are uploaded to GEO under identifier GSE140430.
Deparaffinization and antigen retrieval
Sections were deparaffinized in successive incubations with xylene and decreasing concentrations (100, 95, 75, 50, 0%) of ethanol. Antigen retrieval was achieved using either Abcam 100× Citrate Antigen Retrieval Buffer (pH = 6.00) for 20 min at 100 °C, followed by 20 min at ambient temperature or Abcam 100× TrisEDTA Antigen Retrieval Buffer (pH = 9) heated to 115 °C under high pressure. Sections were then washed in either a solution of 10 mM glycine and 0.2% sodium azide in phosphate buffered saline or PBS + 0.1% Tween20 before antibody staining.
Immunofluorescence antibody staining
Sections were blocked for 15–30 min with a 5% goat serum, 1% bovine serum albumin blocking solution containing 10 mM glycine and 0.2% sodium azide or PBS + 0.1% Tween20. Sections were then stained with appropriate primary and secondary antibodies. Primary antibodies were used at a concentration of 1:100 and incubated for 1 h at room temperature. Secondary antibodies were used at a concentration of 1:250 and incubated for 30 min at room temperature. Detailed information about antibodies used is listed in Supplementary Information Table 2.
PD-L1 staining and scoring
FFPE slides for 45 patients were stained using Agilent Biotechnologies PD-L1 IHC (clone 22C3 pharmDx) Staining Kit by Emory Pathology Laboratories. Clinical-grade scoring of PD-L1 status was performed by two board-certified pathologists at Emory University Hospital. Slides with 1–49% of tumour cells expressing PD-L1 were scored ‘positive-low,’ slides with 50+% of tumour cells expressing PD-L1 were scored ‘positive-high,’ and slides with <1% of tumour cells expressing PD-L1 were scored ‘negative.’
Image capture and analysis
We selected a fluorophore panel which allowed for simultaneous visualization of three targets and a nuclear stain (DAPI). For images shown in Fig. 3, we used a Leica SP8 confocal microscope with a motorized stage for tiled imaging, and a 40x, 1.3NA, 0.24 mm WD oil immersion objective was used, allowing for highly resolved, smoothly tiled images. Fluorophores were excited with the 496, 561, and 594 laser lines or with a multiphoton Coherent Chameleon Vision II laser, tuned to 700 nm (DAPI). Emission-optimized wavelength ranges informed specific detector channels, which were used to detect fluorescence. Leica LASX software was used to create a maximum projection image, allowing us to obtain large tiled images regardless of a varying focal plane across each tissue section. For images shown in Fig. 4, we used a Zeiss Z.1 Slide Scanner equipped with a Colibri 7 Flexible Light Source. Zeiss ZenBlue software was used for post-acquisition image processing. For brightfield imaging, slides were scanned using a Hamamatsu’s Nanozoomer slide scanner.
CellProfiler, a free, open-source software for image analysis, was used for subsequent image manipulations. CellProfiler was used to define ‘primary objects’ within images, based upon user-defined parameters (diameter, fluorescence intensity, object clumping, etc.). We used this technique to define DAPI ‘primary objects’ (that is, all cells) and MHC+ ‘primary objects’ (that is, defining antigen presenting cells). We also used this technique to define CD8+ ‘primary objects,’ which we then used to create ‘secondary objects’ by extending the border of each object by 1 pixel in all directions. These CD8+ ‘secondary objects’ were used to define CD8+ T cells. Detailed review of parameters used to MHC-II+ antigen presenting cells and CD8+ T cells can be found in Supplementary Information Table 3. We then used CellProfiler to measure the intensity of TCF1 staining intensity in each CD8+ T cell object. Data exported from the CellProfiler pipeline included XY location of CD8+ objects, MHC-II+ objects, and mean intensity of TCF1 staining in CD8+ T cell objects. The remainder of image analysis was carried out using custom R and python scripts. MHC-II density and distance to nearest MHC-II+ neighbour were calculated in custom python scripts.
In order to determine the area of tissue necessary to be sampled to obtain an accurate and quantitative assessment of the CD8 T cell infiltration into tumours, large slide scanned images were dissected into areas the approximate size of a 20× field of view. Increasing number of random fields of view were sampled from images and the percent of cells that were CD8 positive by immunofluoresence correlated to FACS from the corresponding sample. The estimated number of 20× fields of view necessary to obtain an accurate assessment of level of CD8 T cell infiltration is 171 fields of view (Extended Data Fig. 9d). Histo-cytometric analysis approach employed similar to that reported previously39.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
Data availability
Raw fastq files and associated RNA and whole genome bisulphite sequencing have been uploaded to the NCBI Gene Expression Omnibus (GEO) database under identifier GSE140430. Other relevant data are available from the corresponding author upon reasonable request.
Code availability
Custom code for RNA-seq, whole genome methylation, and quantitative immunofluorescence are available from the corresponding author upon reasonable request.
Extended Data
Supplementary Material
Acknowledgements
This work was supported by funding from the Prostate Cancer Foundation, Swim Across America, the James M. Cox Foundation and James C. Kennedy, pilot funding from the Winship Cancer Institute supported by the Dunwoody Country Club Senior Men’s Association, and NCI grants 1-R00-CA197891 (H.K.) and U01-CA113913 (M.G.S.). We recognize Adaptive Biotechnologies for providing laboratory services as a part of an educational grant award. We would like to acknowledge the Yerkes NHP Genomics Core which is supported in part by NIH P51 OD011132, the Emory Flow Cytometry Core supported by the National Center for Georgia Clinical & Translational Science Alliance of the National Institutes of Health under award number UL1TR002378, the Intramural Research Program of the NIH, National Cancer Institute and the Emory University Integrated Cellular Imaging Microscopy Core of the Winship Cancer Institute of Emory University and NIH/NCI under award number 2P30CA138292-04.
Footnotes
Competing interests The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information is available for this paper at https://doi.org/10.1038/s41586-019-1836-5.
References
- 1.Galon J et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006). [DOI] [PubMed] [Google Scholar]
- 2.Pagès F et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N. Engl. J. Med 353, 2654–2666 (2005). [DOI] [PubMed] [Google Scholar]
- 3.Peranzoni E et al. Macrophages impede CD8 T cells from reaching tumor cells and limit the efficacy of anti-PD-1 treatment. Proc. Natl Acad. Sci. USA 115, E4041–E4050 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Azimi F et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J. Clin. Oncol 30, 2678–2683 (2012). [DOI] [PubMed] [Google Scholar]
- 5.Savas P et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med 24, 986–993 (2018). [DOI] [PubMed] [Google Scholar]
- 6.Herbst RS et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Tumeh PC et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Eroglu Z et al. High response rate to PD-1 blockade in desmoplastic melanomas. Nature 553, 347–350 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gallimore A, Dumrese T, Hengartner H, Zinkernagel RM, Rammensee H-G Protective immunity does not correlate with the hierarchy of virus-specific cytotoxic T cell responses to naturally processed peptides. J. Exp. Med 187, 1647–1657 (1998). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zajac AJ et al. Viral immune evasion due to persistence of activated T cells without effector function. J. Exp. Med 188, 2205–2213 (1998). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wherry EJ et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007). [DOI] [PubMed] [Google Scholar]
- 12.Barber DL et al. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 439, 682–687 (2006). [DOI] [PubMed] [Google Scholar]
- 13.Gros A et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest 124, 2246–2259 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Topalian SL et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med 366, 2443–2454 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Brahmer JR et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med 366, 2455–2465 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hodi FS et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med 363, 711–723 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ahmadzadeh M et al. Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood 114, 1537–1544 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mlecnik B et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016). [DOI] [PubMed] [Google Scholar]
- 19.Tosolini M et al. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (TH1, TH2, Treg, TH17) in patients with colorectal cancer. Cancer Res. 71, 1263–1271 (2011). [DOI] [PubMed] [Google Scholar]
- 20.Im SJ et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.He R et al. Follicular CXCR5-expressing CD8+ T cells curtail chronic viral infection. Nature 537, 412–416 (2016). [DOI] [PubMed] [Google Scholar]
- 22.Utzschneider DT et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016). [DOI] [PubMed] [Google Scholar]
- 23.Brummelman J et al. High-dimensional single cell analysis identifies stem-like cytotoxic CD8+ T cells infiltrating human tumors. J. Exp. Med 215, 2520–2535 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sade-Feldman M et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Siddiqui I et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019). [DOI] [PubMed] [Google Scholar]
- 26.Kurtulus S et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1–CD8+ tumor-infiltrating T cells. Immunity 50, 181–194 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Miller BC et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol 20, 326–336 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Simoni Y et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018). [DOI] [PubMed] [Google Scholar]
- 29.Akondy RS et al. Origin and differentiation of human memory CD8 T cells after vaccination. Nature 552, 362–367 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Dieu-Nosjean MC, Goc J, Giraldo NA, Sautès-Fridman C & Fridman WH Tertiary lymphoid structures in cancer and beyond. Trends Immunol. 35, 571–580 (2014). [DOI] [PubMed] [Google Scholar]
- 31.Sautès-Fridman C, Petitprez F, Calderaro J & Fridman WH Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019). [DOI] [PubMed] [Google Scholar]
- 32.Silina K et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 78, 1308–1320 (2017). [DOI] [PubMed] [Google Scholar]
- 33.Miron M et al. Human lymph nodes maintain TCF-1hi memory T cells with high f unctional potential and clonal diversity throughout life. J. Immunol 201, 2132–2140 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Mandrekar J, Cha S Cutpoint determination methods in survival analysis using SAS2003. SAS https://support.sas.com/resources/papers/proceedings/proceedings/sugi28/261-28.pdf (2003). [Google Scholar]
- 35.Contal C & O’Quigley J An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput. Stat. Data Anal 30, 253–270 (1999). [Google Scholar]
- 36.Scrucca L, Fop M, Murphy TB & Raftery AE mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J. 8, 289–317 (2016). [PMC free article] [PubMed] [Google Scholar]
- 37.Love MI, Huber W & Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Krueger F & Andrews SR Bismark: a flexible aligner and methylation caller for bisulfiteseq applications. Bioinformatics 27, 1571–1572 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Gerner MY, Kastenmuller W, Ifrim I, Kabat J & Germain RN Histo-cytometry: a method for highly multiplequantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364–376 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Raw fastq files and associated RNA and whole genome bisulphite sequencing have been uploaded to the NCBI Gene Expression Omnibus (GEO) database under identifier GSE140430. Other relevant data are available from the corresponding author upon reasonable request.