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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Cancer Immunol Res. 2021 Dec 22;10(2):215–227. doi: 10.1158/2326-6066.CIR-21-0772

Spatial organization and prognostic significance of NK and NKT-like cells via multimarker analysis of the colorectal cancer microenvironment

Juha P Väyrynen 1,2,3,*, Koichiro Haruki 1,2,4,*, Mai Chan Lau 2,*, Sara A Väyrynen 1,*, Tomotaka Ugai 2,5, Naohiko Akimoto 2, Rong Zhong 2, Melissa Zhao 2, Andressa Dias Costa 1, Jennifer Borowsky 6, Phoenix Bell 2, Yasutoshi Takashima 2, Kenji Fujiyoshi 2, Kota Arima 2, Junko Kishikawa 2, Shan-shan Shi 2, Tyler S Twombly 2, Mingyang Song 7,8,9, Kana Wu 5,7,10, Andrew T Chan 8,9,10,11, Xuehong Zhang 10, Charles S Fuchs 12,13,14,15, Jeffrey A Meyerhardt 1,#, Marios Giannakis 1,16,17,#, Shuji Ogino 2,5,16,18,#, Jonathan A Nowak 2,#
PMCID: PMC8816895  NIHMSID: NIHMS1766462  PMID: 34937729

Abstract

Although tumor-infiltrating T cells hold a beneficial prognostic role in colorectal cancer, other lymphocytic populations are less characterized. We developed a multiplexed immunofluorescence assay coupled with digital image analysis and machine learning to identify NK cells (NCAM1+CD3−), NKT-like cells (NCAM1+CD3+), and T cells (NCAM1−CD3+) within the PTPRC+ (CD45+) cell population and to measure their GZMB (cytotoxicity marker) and FCGR3A (CD16a, NK cell maturity marker) expression. We evaluated immune cell densities and spatial configuration in 907 incident colorectal carcinoma cases within two prospective cohort studies. We found that T cells were approximately 100-times more abundant than NK and NKT-like cells. Overall, NK cells showed high GZMB expression and were located closer to tumor cells than T and NKT-like cells. In T and NKT-like cells, GZMB expression was enriched in cells in closer proximity to tumor cells. Higher densities of both T and NKT-like cells associated with longer cancer-specific survival, independent of potential confounders (Ptrend<0.0007). Higher stromal GZMB+ and FCGR3A+ NK cell densities associated with longer cancer-specific survival (Ptrend<0.003). For T and NKT-like cells, greater proximity to tumor cells associated with longer cancer-specific survival (Ptrend<0.0001). These findings indicate that cytotoxic NCAM1+CD3−GZMB+ NK cells and NCAM1+CD3+ NKT-like cells are relatively rare lymphocytic populations within the colorectal cancer microenvironment and show distinct spatial configuration and associations with patient outcome. The results highlight the utility of a quantitative multimarker assay for in situ, single-cell immune biomarker evaluation and underscore the importance of spatial context for tumor microenvironment characterization.

Keywords: colorectal cancer, cytotoxicity, lymphocyte, T cell, natural killer cell, spatial analysis, tumor microenvironment, multiplexed immunofluorescence, digital image analysis

Introduction

The prognostic classification of colorectal cancer largely relies upon anatomic extent, as captured using TNM staging, which is based on the invasion depth of the primary tumor (T) and the presence of nodal metastasis (N) and distant metastasis (M). However, accumulating evidence indicates that immune microenvironment features critically modulate tumor behavior and could be used to refine prognostic categories (1–3). Lymphocytes are a heterogenous group of adaptive and innate immune cells, and include T cells, B cells, and NK cells (4). Numerous studies have indicated that higher T-cell density in the colorectal cancer microenvironment associates with a favorable disease outcome (3), but the significance of other lymphocytic populations is less well-established.

NK cells, initially described in the 1970s based on their ability to mediate killing of certain tumors and virus-infected cells without prior antigen exposure (5), are cytotoxic lymphocytes of the innate immune system (4). The best-characterized NK cell subsets in humans include immature NK cells that express NCAM1 (CD56) but neither CD3 nor FCGR3A (Fc gamma receptor 3A, CD16)(i.e., NCAM1+CD3−FCGR3A− cells) and mature NK cells that express NCAM1 (CD56) and FCGR3A (CD16) but not CD3 (i.e., NCAM1+CD3−FCGR3A+ cells) within the total PTPRC+ (CD45+) leukocyte population (4). Although the identification of such cells has traditionally been possible using multiparameter flow cytometry (6) and, more recently, using in situ multiplexed immunohistochemical methods (7), most studies evaluating the significance of lymphocytic cells in the colorectal cancer microenvironment have been based on single-color immunohistochemistry (3) and have therefore been unable to accurately identify these cell populations.

In this study, we developed a customized, multiplex immunofluorescence assay to identify NK cells (PTPRC+NCAM1+CD3−), NKT-like cells (PTPRC+NCAM1+CD3+), and T cells (PTPRC+NCAM1−CD3+), along with expression of FCGR3A and GZMB (granzyme B) within these cell populations. Using digital image analysis and supervised machine learning, we applied this assay to an extensively characterized cohort of 907 colorectal cancers derived from two U.S. nationwide prospective cohort studies. To study the prognostic significance of these lymphocytic populations, we controlled for potential confounding and selection bias due to tissue data availability using the inverse probability weighting (IPW) method and covariate data from 4,465 colorectal cancer cases in the cohorts. We hypothesized that higher densities of all three cell types—in particular, their cytotoxic subpopulations—would associate with longer survival. We examined the spatial organization of these populations with respect to tumor cells and evaluated spatial organization prognostic value as an exploratory investigation.

Methods

Data availability

The data underlying this article cannot be shared publicly. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and Health Professionals Follow-up Study are described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.

Study population

We documented 4,465 colorectal cancer cases that had occurred during follow-up (until 2012) of two prospective cohort studies in the U.S., namely the Nurses’ Health Study (NHS, 121,701 women aged 30–55 years at enrollment in 1976) and the Health Professionals Follow-up Study (HPFS, 51,529 men aged 40–75 years at enrollment in 1986). Among those 4,465 cases, 907 tumors yielded multiplex immunofluorescence data that met all quality control metrics (Table 1, Supplementary Figure S1). Based upon the continuum of tumor characteristics across the colorectum, we included both colon and rectal carcinomas (8). The study was conducted in accordance with the U.S. Common Rule. All participants gave written informed consent for the study. The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health (Boston, MA), and those of participating registries as required.

Table 1.

Immune cell densities in relation to characteristics of colorectal cancer cases

Immune cell density (cells / mm2) Median (25th – 75th percentile)
Characteristic* Total N NCAM1+CD3− NK cells NCAM1+CD3+ NKT-like cells NCAM1−CD3+ T cells NCAM1−CD3−FCGR3A+ macrophages
All cases 907 2.4 (0.7–5.9) 2.9 (0.7–6.8) 434 (206–831) 550 (308–884)
Sex
 Female (NHS) 503 (55%) 2.5 (0.7–6.6) 3.1 (0.7–7.5) 440 (218–875) 580 (309–904)
 Male (HPFS) 404 (45%) 2.3 (0.7–5.4) 2.8 (0.8–6.0) 432 (193–799) 514 (305–857)
Age (years)
 < 65 280 (31%) 2.4 (0.7–5.8) 3.3 (0.9–7.8) 425 (210–799) 536 (293–871)
 ≥ 65 627 (69%) 2.4 (0.6–6.0) 2.7 (0.6–6.6) 439 (205–859) 559 (310–895)
Year of diagnosis
 1995 or before 296 (33%) 2.3 (0.8–5.6) 2.8 (0.6–7.5) 428 (195–811) 499 (265–930)
 1996–2000 296 (33%) 2.0 (0–6.5) 3.0 (0.8–6.8) 430 (213–805) 573 (320–920)
 2001–2008 315 (34%) 2.6 (0.7–5.8) 2.9 (0.7–6.7) 447 (206–892) 569 (314–840)
Family history of colorectal cancer in first-degree relative(s)
 Absent 713 (79%) 2.4 (0.6–5.9) 2.9 (0.7–6.8) 430 (207–805) 545 (308–895)
 Present 190 (21%) 2.5 (0.7–6.0) 3.1 (0.7–6.9) 491 (196–1,1) 572 (306–865)
Tumor location
 Cecum 161 (18%) 2.7 (0.7–6.7) 4.0 (1.5–8.5) 608 (241–1,051) 653 (378–1,162)
 Ascending to transverse colon 291 (32%) 2.6 (0.7–9.5) 3.2 (0.8–7.7) 429 (165–876) 577 (309–904)
 Descending to sigmoid colon 270 (30%) 2.0 (0.7–5.3) 2.9 (0.6–6.2) 418 (218–769) 468 (275–776)
 Rectum 181 (20%) 2.0 (0–4.5) 2.0 (0–4.5) 411 (209–723) 536 (309–802)
Tumor differentiation
 Well to moderate 823 (91%) 2.2 (0.6–5.5) 2.9 (0.7–6.8) 432 (206–809) 535 (297–849)
 Poor 83 (9.2%) 5.3 (2.2–12.7) 2.8 (1.2–7.6) 508 (207–1,122) 848 (479–1,180)
AJCC disease stage
 I 196 (23%) 3.1 (0.9–8.4) 3.4 (1.0–8.2) 610 (329–1,080) 558 (296–844)
 II 277 (33%) 2.7 (0.9–7.3) 3.3 (0.7–7.8) 445 (206–859) 604 (355–983)
 III 241 (29%) 1.9 (0.4–5.3) 2.6 (0.8–5.0) 411 (176–732) 558 (306–908)
 IV 131 (16%) 1.4 (0–4.0) 1.9 (0–4.8) 269 (120–505) 484 (293–752)
MSI status
 Non-MSI-high 727 (83%) 1.9 (0–4.5) 2.5 (0.4–5.7) 409 (193–768) 507 (281–811)
 MSI-high 153 (17%) 7.4 (3.0–18) 5.4 (2.1–15) 694 (280–1,180) 823 (469–1,162)
CIMP status
 Low/negative 684 (81%) 1.9 (0–4.5) 2.5 (0.5–5.8) 412 (195–757) 497 (281–796)
 High 156 (19%) 5.8 (2.2–16) 4.4 (1.4–13) 579 (248–1,134) 823 (457–1,117)
Mean LINE-1 methylation level
 ≥ 60% 552 (63%) 2.5 (0.7–6.3) 3.0 (0.7–7.0) 459 (212–921) 585 (318–961)
 < 60% 328 (37%) 1.8 (0.6–5.2) 2.6 (0.6–6.6) 404 (170–754) 482 (285–810)
KRAS mutation
 Wild-type 531 (60%) 2.6 (0.7–7.7) 3.2 (0.8–8.2) 448 (207–944) 572 (322–931)
 Mutant 348 (40%) 2.0 (0–4.1) 2.4 (0–5.6) 420 (196–746) 509 (264–850)
BRAF mutation
 Wild-type 749 (85%) 2.2 (0.5–5.3) 2.7 (0.6–6.2) 429 (201–797) 521 (296–842)
 Mutant 137 (15%) 4.5 (1.4–14) 3.9 (1.2–11) 488 (205–986) 725 (446–1,38)
PIK3CA mutation
 Wild-type 692 (84%) 2.4 (0.7–5.9) 2.8 (0.7–6.7) 423 (195–830) 537 (302–858)
 Mutant 134 (16%) 2.1 (0–5.6) 2.9 (0.6–6.8) 534 (258–980) 601 (323–997)
Neoantigen load
 Q1 (lowest) 103 (25%) 2.0 (0–4.1) 2.8 (0.8–5.0) 432 (241–907) 514 (301–774)
 Q2 103 (25%) 1.5 (0–3.1) 1.7 (0–4.1) 384 (156–721) 483 (264–808)
 Q3 103 (25%) 1.8 (0.6–5.3) 3.2 (1.3–6.8) 500 (247–937) 592 (369–1,13)
 Q4 (highest) 102 (25%) 5.5 (1.3–17) 4.5 (1.6–12) 588 (310–1,103) 723 (381–990)
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*

Percentage indicates the proportion of patients with a specific clinical, pathologic, or molecular characteristic among all patients.

Abbreviations: AJCC, American Joint Committee on Cancer; CIMP, CpG island methylator phenotype; HPFS, Health Professionals Follow-up Study; LINE-1, long-interspersed nucleotide element-1; MSI, microsatellite instability; NHS, Nurses’ Health Study; SD, standard deviation.

Study physicians reviewed medical records and gathered clinical information [such as tumor size, location, and the American Joint Committee on Cancer tumor, node, metastases (TNM) classification, as well as causes of deaths for deceased participants]. The National Death Index (National Center for Health Statistics, Hyattsville, MD) was utilized to confirm deaths and identify unreported lethal colorectal cancer cases. Survival time was defined as the period from the date of colorectal cancer diagnosis to death or the end of follow-up (January 1, 2016 for HPFS; June 1, 2016 for NHS). For analyses of colorectal cancer-specific survival, deaths from other causes were censored.

We collected formalin-fixed paraffin-embedded tumor blocks from 1,619 study participants with colorectal cancer from hospitals throughout the U.S. where participants had undergone tumor resection. Hematoxylin & eosin-stained sections were reviewed to confirm the presence of invasive cancer, evaluate tumor differentiation, and mark areas for tissue microarray construction (2–4 cores from each tumor of 0.6 mm diameter)(9). DNA was extracted using QIAmp DNA Mini Kit (Qiagen, Valencia, CA), and tumor microsatellite instability (MSI) status (based on ten microsatellites: D2S123, D5S346, D17S250, BAT25, BAT26, BAT40, D18S55, D18S56, D18S67, and D18S487), CpG island methylator phenotype (CIMP) status (based on eight CIMP-specific promoters: CACNA1G, CDKN2A, CRABP1, IGF2, MLH1, NEUROG1, RUNX3, and SOCS1), long interspersed nucleotide element-1 (LINE-1) methylation level, KRAS (codons 12, 13, 61 and 146), BRAF (codon 600), and PIK3CA (exons 9 and 20) mutation status, and neoantigen load were determined as previously described (10,11).

Multiplex immunofluorescence

To examine NK cells and other lymphocytic populations in the tumor microenvironment, we designed a custom multiplexed immunofluorescence (mIF) assay based on the tyramide signal amplification method (12). We first tested different antibody clones with chromogenic immunohistochemistry in colorectal cancer samples from eight patients (Supplementary Table S1), to evaluate the correspondence of the staining patterns to those described in the literature. We next evaluated immunofluorescent signal-to-noise ratios for antibody clones selected based on their performance in standard chromogenic immunohistochemistry, using Opal fluorophores (Akoya Biosciences, Hopkinton, MA). We then constructed a mIF assay targeting CD3 (T-cell surface marker (13)), FCGR3A (Fc gamma receptor 3A, CD16a; a receptor expressed by mature NK cells and macrophages involved in antibody-dependent cellular cytotoxicity (4)), GZMB (granzyme B; cytotoxicity marker (13)), NCAM1 (CD56; cell surface glycoprotein with a role in cell-cell adhesion; expressed by NK cells and some other cells, such as neurons (14)), PTPRC (CD45; pan-leukocyte marker (15)), KRT (keratin; epithelial cell marker), and DAPI (nuclear marker). We iteratively optimized antibody-fluorophore pairing and concentrations, as well as the sequence of the staining, and confirmed the correspondence of multiplex immunofluorescence staining patterns with those of single-marker chromogenic immunohistochemistry (Supplementary Figure S2). The final mIF protocol was automated using a Leica Bond RX Research Stainer (Leica Biosystems, Buffalo, IL)(Supplementary Figure S3).

Using our validated and automated assay, we processed two complete sets of tissue microarray sections (total: 20 sections), separated by a vertical depth of >20 μm, to increase the tissue area subjected to analysis, and included major histocompatibility complex class I antigen presentation molecules present in all nucleated cells (16)[either B2M (section 1) or HLA-A/HLA-B/HLA-C (section 2)] as additional quality controls to monitor the staining properties of individual samples. All slides were stained in a single batch to ensure uniform processing (Supplementary Fig. S3).

Image capture and analysis

We scanned the immunofluorescence slides using the Vectra Polaris Automated Quantitative Pathology Imaging System (Akoya Biosciences), equipped with seven imaging filters and a 20× objective. We separately constructed and profiled a spectral fluorophore and autofluorescence library to enable optimal multispectral unmixing (17,18). Spectrally unmixed images were generated using the inForm software package (v1.4.8; Akoya Biosciences) and further analyzed using pathologist-supervised machine learning algorithms built in the inForm software package (Figure 1) (17,18). We confirmed that staining intensities were consistent across the tissue microarrays, indicating uniform assay technical performance (Supplementary Figure S4). Tissue regions were categorized into epithelial, stromal, and “other” (such as empty space or mucin) categories. Individual cells were segmented into nuclear, cytoplasmic, and membranous regions. Cells were then phenotyped into the PTPRC+NCAM1+CD3− (NK cells), PTPRC+NCAM1+CD3+ (NKT-like cells), PTPRC+NCAM1−CD3+GZMB+ (GZMB+ T cells), PTPRC+NCAM1−CD3+GZMB− (GZMB− T cells), PTPRC+NCAM1−CD3−FCGR3A+ (FCGR3A+ macrophages), PTPRC+NCAM1−CD3−FCGR3A− (other PTPRC+ immune cells), KRT+PTPRC− (tumor epithelial cells), and KRT−PTPRC− (other cells) categories. The cell phenotype classification implemented in the inForm software package was based on multinomial logistic regression utilizing image features derived from texture analysis and cell segmentation. Single cell-level data from inForm were exported for further processing using the R statistical programming language (v. 4.0.1; R Foundation for Statistical Computing, Vienna, Austria). We further classified NK and NKT-like cells into GZMB+/GZMB− and FCGR3A+/FCGR3A− subsets based upon fluorophore signal intensity cut-points (cytoplasmic mean intensity 0.25 for GZMB and membranous mean intensity 8 for FCGR3A). We calculated immune cell density (cells/mm2) separately for tumor intraepithelial, stromal, and overall (intraepithelial and stromal) compartments. Core-to-core correlation for cell densities was moderate to high (Supplementary Figure S5) and was higher for cell types with higher average densities (Spearman rho=0.65 for NCAM1−CD3+ T cells; 0.64–0.65 for NCAM1−CD3−FCGR3A+ macrophages; 0.36–0.44 for NCAM1+CD3− NK cells, and 0.40 for NCAM1+CD3+ NKT-like cells). For each cell subset, cases were classified into quartile categories (C1–C4) if ≤25% of cases had a density of zero. If >25% of cases had a density of zero for a specific cell type, all zero-density cases were grouped together (C1 category), and the remaining (non-zero) cases were divided into tertiles (C2–C4).

Figure 1. Spatially informed analysis of immune cell populations in 907 colorectal cancer cases.

Figure 1.

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A. Example multiplex immunofluorescence image for detection of GZMB, NCAM1, KRT, CD3, FCGR3A, PTPRC, and DAPI (nuclear stain). Scale bar: 100 μm. B-C. The image was processed with pathologist-supervised machine learning algorithms to identify tumor intraepithelial and stromal regions (B) and identify and classify each cell (C). In addition to the phenotypic criteria designated in the image, PTPRC expression was required for all immune populations. D-E. The distribution of densities of various immune cell populations in tumor intraepithelial (D) and stromal (E) regions. F. Spearman’s rank correlation coefficients between the densities of intraepithelial (IEL) and stromal (S) immune cells.

We conducted spatial analysis using the spatstat R package (v 1.63–3)(19). We calculated the nearest neighbor distances between immune and tumor cells (NNDImmune cell:Tumor). For visualization, we plotted GZMB and FCGR3A intensities of individual immune cells across all cores (scaled across all immune cells by calculating Z-scores) as a function of NNDImmune cell:Tumor with the ggplot2 package (v. 3.3.0) using generalized additive model smoothing [formula y ~s(x)]. We evaluated co-localization of tumor and immune cells using the G-cross function [GTumor:Immune cell(r)] with Kaplan-Meier edge correction, evaluating the likelihood of any tumor cell in the sample having at least one immune cell of the specified type within radius r (20,21). For survival analysis, we utilized function values at 20 μm [GTumor:Immune cell(20 μm)] to identify immune cell populations likely capable of direct cell-to-cell interaction with tumor cells (20,21). This radius was pre-selected prior to analysis to maintain consistency with earlier studies (20–22).

Statistical analysis

We performed statistical analyses using SAS software (version 9.4, SAS Institute, Cary, NC). As our primary analysis, we evaluated the relationship between density of each lymphocytic cell type (within intraepithelial or stromal regions, categorized into ordinal quartiles C1–C4) and colorectal cancer-specific mortality using multivariable Cox proportional hazards regression models. We used the stringent two-sided α level of 0.005, as recommended by a panel of expert statisticians (23). Other analyses were secondary, and their results were interpreted cautiously. We analyzed overall mortality as a secondary outcome measure.

Using 4,465 incident colorectal cancer cases in the cohorts, we combined the inverse probability weighting (IPW) method with Cox proportional hazards regression to reduce selection bias due to the availability of tumor tissue (24). Using the multivariable logistic regression model for tissue availability vs. unavailability as an outcome variable in the 4,465 cases, we estimated the probability of the availability of tissue multiplex immunofluorescence data. Each patient with available tissue data was then weighted by the inverse of the availability probability. We set weights greater than the 95th percentile to the value of the 95th percentile to reduce outlier effects.

The covariates we assessed as potential confounders included sex (female vs. male), age at diagnosis (continuous), year of diagnosis (continuous), family history of colorectal cancer in any first-degree relative (present vs. absent), tumor location (proximal colon vs. distal colon vs. rectum), tumor differentiation (well to moderate vs. poor), disease stage (I/II vs. III/IV), MSI status (MSI-high vs. non-MSI-high), CIMP status (high vs. low/negative), LINE-1 methylation level (continuous), KRAS mutation status (mutant vs. wild-type), BRAF mutation status (mutant vs. wild-type), and PIK3CA mutation status (mutant vs. wild-type). We conducted a backward elimination with a threshold P=0.05 to select variables for the final models. We included cases with missing data in the majority category of a given categorical covariate to limit degrees of freedom: family history of colorectal cancer in a first-degree relative (0.4%), tumor location (0.4%), tumor differentiation (0.1%), disease stage (6.8%), MSI (3.0%), CIMP (7.4%), KRAS (3.1%), BRAF (2.3%), and PIK3CA (8.9%). For cases with missing LINE-1 methylation data (3.0%), we assigned a separate indicator variable. The proportionality of hazards assumption for colorectal cancer-specific survival was assessed by a time-varying covariate, which was an interaction term of survival time and immune cell densities (P>0.1). Although we observed evidence of violation of this assumption in the overall survival hazard, the Schoenfeld residual plots supported the proportionality of hazards during most of the follow-up period up to 10 years. Thus, we used Cox regression models limiting the follow-up period to 10 years.

In secondary analyses, we assessed the statistical interaction between immune cell densities (low vs. high) and MSI status (high vs. non-high) in relation to cancer-specific survival. We used the Wald test for the cross-product in multivariable Cox regression models. We estimated hazard ratio (HR) for colorectal cancer mortality comparing high vs. low immune cell densities in the two strata of MSI status using re-parameterization of the interaction term in a single regression model (25). We also estimated cumulative survival probabilities using the Kaplan-Meier method and compared the differences between categories using the log-rank test. We evaluated relationships between immune cell densities and clinicopathologic features using the Chi-square test and Spearman’s rank correlation test as appropriate.

Results

Spatial organization of NK and T-cell infiltrates in colorectal cancer

We applied our customized mIF assay to quantify NK cells, NKT-like cells, and T cells in colorectal carcinoma tissue from two prospective cohort studies. Across 3,234 core images (image per case; median 4, mean 3.6, range 1–8) from 907 colorectal cancer cases, we identified a total of 4,759,441 tumor cells, 6,224 PTPRC+NCAM1+CD3− NK cells, 6,043 PTPRC+NCAM1+CD3+ NKT-like cells, 672,271 PTPRC+NCAM1−CD3+ T cells, 700,234 PTPRC+NCAM1−CD3−FCGR3A+ macrophages, and 414,842 PTPRC+NCAM1−CD3−FCGR3A− “other” immune cells. T cells were approximately as common as FCGR3A+ macrophages, but approximately 100-times more abundant than NK and NKT-like cells (Figure 1D–E, Supplementary Figure S6). Whereas only 30% of T cells expressed the cytotoxic marker GZMB, 76% of NK and 54% of NKT-like cells expressed this marker (Supplementary Figure S6). Expression of FCGR3A, an NK cell maturity marker, was relatively uncommon among the NK and NKT-like cell populations (15% and 13%, respectively), and signal intensities were usually lower than those observed in FCGR3A+ macrophages (Figure 2F, Supplementary Figure S6). Overall, densities of different immune cell types showed low to moderate correlation (Figure 1F).

Figure 2. Immune cell-tumor cell proximity analyses with the nearest-neighbor distance (NND) function.

Figure 2.

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A-C. Multiplex immunofluorescence image of an example tissue microarray core (A), corresponding cell phenotype map (B), and immune cell to nearest tumor cell distance (NNDImmune cell:tumor) plot (C). D. Distributions of NNDImmune cell:tumor for different immune cell populations. E. Distributions of NNDImmune cell:tumor for lymphocytic populations according to GZMB expression. F. Scaled intensities of GZMB and FCGR3A according to NNDImmune cell:tumor. Scale bar in A: 100 μm. The plots in D-F are based on all immune cells identified in samples from 907 colorectal cancers (6,224 NCAM1+CD3−; 6,043 NCAM1+CD3+; 672,271 NCAM1−CD3+; 700,234 NCAM1−CD3−FCGR3A+; 414,842 NCAM1−CD3−FCGR3A−). The box indicates quartile 1 to quartile 3, with the middle line denoting the median and the whiskers indicating minimum (within a distance of 1.5 times the interquartile range below quartile 1) to maximum (within a distance of 1.5 times the interquartile range above quartile 3). P values determined using the Wilcoxon rank sum test. ****: P<0.0001, ns: P>0.005.

To study the spatial organization of lymphocytic infiltrates, we calculated distances from each immune cell to the closest tumor cell (Figure 2A–C). These analyses showed that the average distance to the nearest tumor cell from each NK cell was 30% or 15% shorter than that from each T cell or each NKT-like cell, respectively (Figure 2D). Within T and NKT-like cell populations, the distance from each GZMB+ cell to the closest tumor cell was 36% and 27% shorter than from each GZMB− cell (Figure 2E–F), respectively, whereas FCGR3A expression showed relatively less variation according to the distance to the closest tumor cell (Figure 2F). Taken together, these results indicate that NK cells, as well as cytotoxic GZMB+ T and NKT-like cell subpopulations, tend to be more closely co-localized with tumor cells than other lymphocytic cells or FCGR3A+ macrophages.

Associations with clinicopathologic features

Immune responses against colorectal cancer are known to associate with tumor molecular features (21,26,27). In particular, tumors with an MSI-high phenotype due to a defective mismatch repair (MMR) system typically accumulate larger numbers of immunogenic neoantigens, which may elicit an intense anti-tumor immune response driven primarily by T cells (10,26). We therefore assessed associations between molecular features and immune cell densities (Figure 3, Table 1). We found that an MSI-high phenotype associated with higher intraepithelial and stromal densities of numerous cell types, including NCAM1+CD3− NK cells, NCAM1+CD3+ NKT-like cells, NCAM1−CD3+ T cells, as well as NCAM1−CD3−FCGR3A+ macrophages (P<0.0007). Consistent with these results, higher neoantigen load associated with higher intraepithelial densities of all four cell types, as well as higher stromal densities of NK and NKT-like cells (P<0.0002). Analysis of additional key clinicopathologic features showed that high disease stage associated with lower intraepithelial and stromal densities of all four cell types (P<0.005), whereas high tumor grade associated with high intraepithelial and stromal densities of NK cells and FCGR3A+ macrophages, as well as high intraepithelial T-cell density (P<0.002).

Figure 3. Relationships between clinicopathologic features and immune cell densities in tumor intraepithelial and stromal regions.

Figure 3.

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The plots show data for 907 colorectal cancer cases. P values are based on the comparison of categorical data between the ordinal categories of immune cell density by Chi-square test. AJCC, American Joint Committee on Cancer; CIMP, CpG island methylator phenotype; MSI, microsatellite instability.

Survival analyses

As our main analysis, we evaluated the prognostic significance of immune cell densities using 4,465 incident colorectal cancer cases (including 907 cases with available immune cell data) in two prospective cohort studies and the IPW method to adjust for selection bias. During the median follow-up time of 16.3 years (IQR 12.8–20.3 years) for censored cases, there were 284 colorectal cancer-specific deaths among 640 all-cause deaths.

We first evaluated the prognostic significance of the total (PTPRC+) immune cell population (Supplementary Table S2, Supplementary Figure S7), and found that higher intraepithelial, stromal, and overall densities of total immune cells associated with lower cancer-specific mortality, independent of other characteristics such as disease stage or MSI status (Ptrend<0.0001). Compared to the overall tissue area, the associations were more significant for intraepithelial and stromal compartments, highlighting the value of spatially resolved analysis (Supplementary Table S2, Supplementary Figure S7).

Analysis of PTPRC+ subpopulations (Table 2, Supplementary Table S3, Figure 4) showed that higher intraepithelial and stromal densities of both NCAM1+CD3+ NKT-like and NCAM1−CD3+ T cells, as well as stromal densities of NCAM1−CD3−FCGR3A+ macrophages, associated with lower cancer-specific mortality (Ptrend<0.0007), whereas neither intraepithelial nor stromal NCAM1+CD3− NK cell populations exhibited prognostic significance in multivariable models (Ptrend>0.06). The most significant survival association was found for intraepithelial T cells [HR for the highest quartile (C4) (vs. the lowest C1) 0.25, 95% CI 0.15–0.40, Ptrend<0.0001].

Table 2.

Immune cell densities in tumor intraepithelial and stromal regions and patient survival with inverse probability weighting (IPW)

Colorectal cancer-specific survival Overall survival
No. of cases No. of events Univariable HR (95% CI)* Multivariable HR (95% CI)*,† No. of events Univariable HR (95% CI)* Multivariable HR (95% CI)*,†
NCAM1+CD3− NK cells
Intraepithelial cell density
 C1 535 194 1 (referent) 1 (referent) 385 1 (referent) 1 (referent)
 C2 124 32 0.68 (0.45–1.02) 0.59 (0.38–0.91) 88 0.67 (0.48–0.95) 0.60 (0.42–0.87)
 C3 125 36 0.91 (0.63–1.31) 1.06 (0.72–1.58) 86 0.98 (0.73–1.33) 1.06 (0.76–1.49)
 C4 123 22 0.48 (0.30–0.76) 0.83 (0.50–1.36) 81 0.60 (0.43–0.84) 0.77 (0.52–1.14)
 Ptrend‡ 0.0037 0.46 0.0099 0.27
Stromal cell density
 C1 261 92 1 (referent) 1 (referent) 187 1 (referent) 1 (referent)
 C2 216 90 1.26 (0.93–1.71) 1.16 (0.86–1.56) 162 1.08 (0.82–1.41) 1.01 (0.76–1.33)
 C3 214 63 0.84 (0.60–1.19) 1.01 (0.71–1.46) 153 0.94 (0.71–1.24) 1.10 (0.83–1.47)
 C4 216 39 0.52 (0.35–0.79) 0.64 (0.41–1.00) 138 0.63 (0.46–0.85) 0.68 (0.48–0.97)
 Ptrend‡ 0.0003 0.064 0.0025 0.098
NCAM1+CD3+ NKT-like cells
Intraepithelial cell density
 C1 530 195 1 (referent) 1 (referent) 391 1 (referent) 1 (referent)
 C2 126 40 0.79 (0.54–1.14) 0.81 (0.53–1.25) 88 0.88 (0.64–1.19) 0.93 (0.65–1.32)
 C3 125 34 0.69 (0.47–1.00) 0.74 (0.51–1.08) 81 0.67 (0.49–0.93) 0.73 (0.53–1.00)
 C4 126 15 0.30 (0.17–0.54) 0.40 (0.22–0.71) 80 0.55 (0.39–0.78) 0.63 (0.43–0.91)
 Ptrend‡ < 0.0001 0.0007 < 0.0001 0.0041
Stromal cell density
 C1 250 102 1 (referent) 1 (referent) 190 1 (referent) 1 (referent)
 C2 218 90 1.01 (0.75–1.36) 0.87 (0.65–1.16) 165 1.04 (0.80–1.36) 0.94 (0.72–1.23)
 C3 221 51 0.49 (0.34–0.71) 0.49 (0.34–0.70) 150 0.56 (0.41–0.76) 0.56 (0.41–0.76)
 C4 218 41 0.39 (0.27–0.58) 0.41 (0.27–0.62) 135 0.60 (0.45–0.80) 0.65 (0.48–0.88)
 Ptrend‡ < 0.0001 < 0.0001 < 0.0001 0.0002
NCAM1−CD3+ T cells
Intraepithelial cell density
 C1 226 117 1 (referent) 1 (referent) 178 1 (referent) 1 (referent)
 C2 227 77 0.64 (0.47–0.87) 0.67 (0.50–0.90) 164 0.80 (0.61–1.06) 0.81 (0.61–1.07)
 C3 227 60 0.44 (0.32–0.61) 0.52 (0.37–0.73) 154 0.55 (0.41–0.74) 0.61 (0.45–0.81)
 C4 227 30 0.19 (0.13–0.30) 0.25 (0.15–0.40) 144 0.42 (0.31–0.56) 0.43 (0.31–0.59)
 Ptrend‡ < 0.0001 < 0.0001 < 0.0001 < 0.0001
Stromal cell density
 C1 226 103 1 (referent) 1 (referent) 179 1 (referent) 1 (referent)
 C2 227 94 0.87 (0.65–1.17) 0.88 (0.65–1.18) 161 0.83 (0.63–1.09) 0.85 (0.64–1.13)
 C3 227 50 0.45 (0.31–0.64) 0.52 (0.37–0.74) 150 0.54 (0.41–0.73) 0.60 (0.45–0.80)
 C4 227 37 0.29 (0.19–0.44) 0.31 (0.20–0.47) 150 0.50 (0.37–0.66) 0.50 (0.36–0.68)
 Ptrend‡ < 0.0001 < 0.0001 < 0.0001 < 0.0001
NCAM1−CD3−FCGR3A+ macrophages
Intraepithelial cell density
 C1 226 79 1 (referent) 1 (referent) 164 1 (referent) 1 (referent)
 C2 227 80 1.02 (0.74–1.42) 0.98 (0.69–1.37) 161 1.06 (0.80–1.41) 0.99 (0.74–1.33)
 C3 227 71 0.92 (0.65–1.29) 1.01 (0.72–1.42) 165 1.00 (0.75–1.34) 1.01 (0.75–1.36)
 C4 227 54 0.69 (0.47–1.00) 0.74 (0.49–1.13) 150 0.80 (0.59–1.09) 0.82 (0.57–1.18)
 Ptrend‡ 0.044 0.25 0.17 0.37
Stromal cell density
 C1 226 95 1 (referent) 1 (referent) 177 1 (referent) 1 (referent)
 C2 227 84 0.88 (0.65–1.21) 0.88 (0.64–1.22) 156 0.88 (0.67–1.17) 0.84 (0.63–1.13)
 C3 227 67 0.70 (0.50–0.97) 0.78 (0.55–1.11) 163 0.82 (0.62–1.09) 0.78 (0.58–1.06)
 C4 227 38 0.37 (0.25–0.56) 0.43 (0.28–0.64) 144 0.56 (0.41–0.76) 0.60 (0.44–0.83)
 Ptrend‡ < 0.0001 < 0.0001 0.0002 0.0023
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*

IPW was applied to reduce bias due to the availability of tumor tissue after cancer diagnosis (see “Statistical Analysis” subsection for details).

†

The multivariable Cox regression model initially included sex, age, year of diagnosis, family history of colorectal cancer, tumor location, tumor differentiation, disease stage, microsatellite instability, CpG island methylator phenotype, KRAS, BRAF, and PIK3CA mutations, and long-interspersed nucleotide element-1 methylation level. A backward elimination with a threshold P of 0.05 was used to select variables for the final models.

‡

Ptrend value was calculated across the four ordinal categories of the density of each immune cell within tumor intraepithelial and stromal regions in the IPW-adjusted Cox regression model.

Abbreviations: CI, confidence interval; HR, hazard ratio; IPW, inverse probability weighting.

Figure 4. Kaplan-Meier estimates of colorectal cancer-specific survival.

Figure 4.

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A-F. Inverse probability weighting-adjusted Kaplan-Meier survival curves for colorectal cancer-specific survival according to ordinal categories (C1–C4, from low to high) of intraepithelial (A-C) and stromal (D-F) densities of NCAM1+CD3−, NCAM1+CD3+, and NCAM1−CD3+ lymphocytic populations.

When lymphocytic populations were further categorized according to their GZMB expression, cytotoxic GZMB+ subpopulations of NK and NKT-like cells were more significantly associated with favorable survival than GZMB− subpopulations, whereas both GZMB+ and GZMB− T-cell populations showed associations with lower cancer-specific mortality (Supplementary Table S4). For stromal GZMB+ NK cells, the HR for C4 (vs. C1) was 0.56 (95% CI 0.36–0.87, Ptrend=0.0022). Higher stromal densities of mature (FCGR3A+) NK cells also associated with lower cancer-specific mortality [HR for C4 (vs. C1) 0.23, 95% CI 0.09–0.56, Ptrend=0.0002] (Supplementary Table S5).

Given that MSI status is an important determinant of immune cell densities, we also performed survival analyses stratified by MSI-status (Supplementary Table S6). These analyses indicated that high NK cell density was more significantly associated with lower cancer-specific mortality in MSI-high tumors compared to non-MSI-high tumors (Pinteraction=0.0011), although the result needs to be interpreted cautiously due to low event numbers for MSI-high cases. For other cell types, there were no statistically significant differences at the α level of 0.005.

Finally, as secondary analyses, we assessed the prognostic significance of tumor cell-immune cell co-localization, as evaluated by the G-cross [GTumor:Immune cell(r)] function, where higher function values at a given radius r indicate a greater proportion of tumor cells in the sample being co-located with at least one immune cell within that radius (Figure 5). We found that greater co-localization between tumor cells and NCAM1+CD3+ NKT-like cells and NCAM1−CD3+ T cells was associated with lower cancer-specific mortality (Ptrend<0.0001) (Figure 5D). Because higher immune density itself can result in greater tumor and immune cell co-localization, we next evaluated whether these survival associations were independent of immune cell density (Supplementary Table S7). These analyses suggested that, for NCAM1−CD3+ T cells, G-cross function values measuring co-localization harbored greater prognostic significance than density (Ptrend=0.0009 and Ptrend of 0.35, respectively, when included in the same multivariable Cox regression model with reciprocal adjustment). Taken together, these findings not only support the prognostic significance of T and NK lineage lymphocyte densities, but also indicate that spatial proximity between tumor cells and certain immune cell types harbors independent prognostic significance.

Figure 5. Spatial analysis of immune cell infiltrates using the tumor cell-immune cell G-cross function [GTumor:Immune cell(r)].

Figure 5.

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The function evaluates the likelihood of any tumor cell in the sample having at least one neighboring immune cell of the specified type within an r μm radius. A. Example multiplex immunofluorescence image for detection of GZMB, NCAM1, KRT, CD3, FCGR3A, PTPRC, and DAPI (nuclear stain). Scale bar: 100 μm. B. Corresponding cell map. C. Corresponding GTumor:Immune cell(r) function curves for each immune cell type. D. Univariable (black) and multivariable (red) Cox proportional hazards regression models of cancer-specific survival according to ordinal categories (C1–C4, from low to high) of GTumor:Immune cell(20 μm) in 907 patients. The forest plots show hazard ratios (HRs) along with their 95% confidence intervals (95%CI) as whiskers. The multivariable Cox regression models initially included sex, age, year of diagnosis, family history of colorectal cancer, tumor location, tumor differentiation, disease stage, microsatellite instability, CpG island methylator phenotype, KRAS, BRAF, and PIK3CA mutations, and long-interspersed nucleotide element-1 methylation level. A backward elimination with a threshold P of 0.05 was used to select variables for the final models.

Discussion

Using a quantitative and multiplexed assay, we analyzed NK and T-cell infiltrates in more than 900 colorectal cancer cases from two U.S. nationwide prospective cohort studies. This approach enabled us to identify complex cellular phenotypes defined by combinations of multiple markers, as well as to precisely measure spatial immune cell infiltration patterns, none of which has been possible using traditional single-marker approaches. Our findings suggest that lymphocyte-subset specific spatial relationships between tumor and immune cells are associated with patient outcome independent of potential confounders such as disease stage and MSI status.

Although their anti-tumor potential has been appreciated for decades (5), the prognostic significance of NK cells in the colorectal cancer microenvironment has been much less studied than T cells, with only a small number of reports to date (3,28–33). Most of these studies, utilizing conventional single-color immunohistochemistry, have suggested that higher densities of NCAM1 (CD56)+ cells or B3GAT1+ (CD57+, a marker for NK cell terminal differentiation(4)) cells are associated with a favorable outcome (29–31), although other studies did not support this association (32,33). Notably, as single markers, neither NCAM1 nor B3GAT1 is specific for NK cells, since NCAM1 can be expressed by neurons, NKT cells, and tumor cells (34,35), and B3GAT1 can be expressed by some terminally differentiated T and NKT cells (36). This lack of specificity may explain why prior studies reached contradictory conclusions. Using our multimarker approach, we found that the overall PTPRC+NCAM1+CD3− NK cell population was not significantly prognostic in multivariable models, whereas both the cytotoxic (GZMB+) and mature (FCGR3A+) NK cell subpopulations associated with a favorable outcome. Studies have suggested that NK cell populations in gastrointestinal cancer are heterogeneous and are involved in both cytotoxic and immunomodulatory functions (37). Consistent with this observation, our evaluation of functionally distinct NK cell subsets suggests that the cytotoxic (GZMB+) and mature (FCGR3A+) subpopulations drive the association of NK cells with better survival.

NKT cells are a heterogenous population of lymphocytes with characteristics of both NK and T cells that exist at the boundary of the innate and adaptive immune systems (38). There are currently no specific markers that can identify the entirety of the NKT cell population using immunohistochemistry or flow cytometry, although the PTPRC+NCAM1+CD3+ combination has been frequently used in prior studies (38,39). Consequently, these cells are often referred to as “NKT-like” to acknowledge the fact that not all NKT cells express both NCAM1 and CD3 (39). To our knowledge, our study is the first to examine the significance of PTPRC+NCAM1+CD3+ NKT-like cells in colorectal cancer tissue. We found that these cells are approximately as common as PTPRC+NCAM1+CD3− NK cells but approximately 100-times less common than PTPRC+NCAM1−CD3+ T cells. Despite their relative rarity, higher densities of these cells associated with lower cancer-specific mortality, hinting at a potent functional role for this incompletely characterized cell type. Within the NKT-like cell population, cytotoxic GZMB+ cells, potentially capable of direct tumor cell lysis, appeared to drive prognostic associations. However, further studies using different methodologies are warranted given the heterogeneity in NKT cell populations (38) and resulting challenges for fully capturing this heterogeneity using multiplex immunofluorescence assays.

T cells had the most significant favorable prognostic association among the cell types analyzed in this study. Even though GZMB+ and GZMB− T cell subpopulations had significantly different spatial distributions, with the cytotoxic GZMB+ subset being preferentially located closer to tumor cells, both populations showed very similar prognostic significance, suggesting that both cytotoxic and non-cytotoxic roles for T cells are important for orchestrating an effective anti-tumor immune response. Numerous prior studies support the prognostic significance of T cells in colorectal cancer (3), and a prognostic parameter called Immunoscore, based on measurement of overall densities of CD3+ and CD8+ T cells in the invasive margin and tumor center has been validated in an international study involving more than 2,600 patients (40). Our exploratory, secondary analyses showed that co-localization between tumor cells and T cells harbors stronger prognostic value than overall T-cell density and is also independent of T-cell density. These findings suggest that incorporation of both immune cell density and spatial configuration into future analysis methods could drive creation of tumor-immune biomarkers that provide improved personalized treatment guidance.

Our study has some important limitations. First, the measurements were based on tissue microarrays. Although we fully recognize that immune cell infiltrates exhibit spatial heterogeneity, we analyzed a median of four core images per tumor and observed good core-to-core correlation, supporting the validity of our tissue microarray approach. The tissue microarrays also enabled us to evaluate more than 900 tumors in a single batch, eliminating batch effects from staining and sectioning, thereby significantly reducing technical variability. Second, although our assay enabled detection of more detailed cell phenotypes than single-color immunohistochemistry, the number of markers was still limited and did not allow for identification of some potential populations of interest, such as CD3− or NCAM1− NKT cells (38). However, our marker combinations and machine learning-based analysis algorithms did allow us to measure the key NK and NKT-like cell populations with high confidence across more than 900 separate tumors. Third, we tested multiple hypotheses, which might cause false-positive findings. However, we explicitly defined our main hypotheses and interpreted the results of the secondary analyses cautiously. We used the stringent α level of 0.005 to reduce false-positive findings, as recommended by an expert panel (23). Fourth, treatment information was not available in our cohort studies. However, treatment decisions for colorectal cancer are mainly based on disease stage, for which our multivariable models were adjusted. Finally, our subjects were predominantly non-Hispanic whites, and our findings need to be confirmed in different populations.

Our has some notable strengths. The multiplex immunofluorescence assay enabled us to examine multiple relevant lymphocyte markers at the single-cell level in situ in a single tissue section, enabling direct comparisons between various subpopulations. Using pathologist-supervised machine learning, we were able to identify individual tumor cells and lymphocytes with high precision, facilitating accurate measurements of cell densities in different tissue compartments, as well as detailed spatial point pattern analyses that were not possible using conventional manual evaluation. Our molecular pathological epidemiology database based on two prospective U.S.-wide cohort studies included extensive molecular and clinical information that allowed for correlative analysis with immune cells densities and detailed adjustment of multivariable survival models. The database, involving 4,465 incident colorectal cancer cases, also enabled us to use the IPW method to adjust for potential selection bias due to the availability of tumor tissue data. The patients received care at dozens of institutions across the U.S., minimizing potential bias associated with any single hospital or practice setting and increasing the generalizability of the findings.

In conclusion, higher densities of rare lymphocyte populations, NCAM1+CD3+ NKT-like cells and cytotoxic NCAM1+CD3−GZMB+ NK cells, in the colorectal cancer microenvironment are associated with lower cancer-specific mortality. The spatial configuration of lymphocytic infiltrates differs according to cell population and also harbors independent prognostic significance. These results highlight the utility of a quantitative multimarker assay for in situ biomarker evaluation in a manner that may guide personalized medicine.

Supplementary Material

1

Synopsis.

Authors develop a multiplex imaging protocol for quantifying lymphocyte populations in human colorectal cancer tissue. Cytotoxic NK cells and NKT-like cells are rare populations within the tumor microenvironment and show distinct spatial configuration and associations with patient outcome.

Acknowledgements

We would like to thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-up Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. This work was supported by U.S. National Institutes of Health (NIH) grants (P01 CA87969 to M.J. Stampfer; UM1 CA186107 to M.J. Stampfer; P01 CA55075 to W.C. Willett; UM1 CA167552 to W.C. Willett; U01 CA167552 to W.C. Willett and L.A. Mucci; P50 CA127003 to C.S.F.; R01 CA118553 to C.S.F.; R01 CA169141 to C.S.F.; R01 CA137178 to A.T.C.; K24 DK098311 to A.T.C.; R35 CA197735 to S.O.; R01 CA151993 to S.O.; R01 CA248857 to S.O., U. Peters, and A.I. Phipps; R03 CA197879 to K.W.; R21 CA222940 to K.W. and M.G.; R21 CA230873 to K.W. and S.O.; and K07 CA188126 to X.Z); by Nodal Award (2016-02) from the Dana-Farber Harvard Cancer Center (to S.O.); by the Stand Up to Cancer Colorectal Cancer Dream Team Translational Research Grant (SU2C-AACR-DT22-17 to C.S.F. and M.G.), administered by the American Association for Cancer Research, a scientific partner of SU2C; and by grants from the Project P Fund, The Friends of the Dana-Farber Cancer Institute, Bennett Family Fund, and the Entertainment Industry Foundation through National Colorectal Cancer Research Alliance. K.H. was supported by fellowship grants from the Uehara Memorial Foundation and the Mitsukoshi Health and Welfare Foundation. S.A.V. was supported by grants from the Finnish Cultural Foundation and Orion Research Foundation. J.B. was supported by a grant from the Australia Awards-Endeavour Scholarships and Fellowships Program. K.F. was supported by a fellowship grant from the Uehara Memorial Foundation. K.A. was supported by grants from Overseas Research Fellowship from Japan Society for the Promotion of Science (JP201860083). K.W. was supported by an Investigator Initiated Grant from the American Institute for Cancer Research (AICR). A.T.C. is a Stuart and Suzanne Steele MGH Research Scholar. J.A.M. research is supported by the Douglas Gray Woodruff Chair fund, the Guo Shu Shi Fund, Anonymous Family Fund for Innovations in Colorectal Cancer, Project P fund, and the George Stone Family Foundation. M.G. was supported by a Conquer Cancer Foundation of ASCO Career Development Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure of potential conflicts of interest:

A.T.C. reports grants and personal fees from Bayer Pharma AG and personal fees from Pfizer Inc. and Boehringer Ingelheim outside the submitted work. C.S.F. is currently employed by Genentech, a subsidiary of Roche; he reports consulting role for Agios, Amylin Pharmaceuticals, Astra-Zeneca, Bain Capital, CytomX Therapeutics, Daiichi-Sankyo, Eli Lilly, Entrinsic Health, Evolveimmune Therapeutics, Genentech, Merck, Taiho, and Unum Therapeutics; he also serves as a Director for CytomX Therapeutics and owns unexercised stock options for CytomX and Entrinsic Health; he is a co-Founder of Evolveimmune Therapeutics and has equity in this private company; he has provided expert testimony for Amylin Pharmaceuticals and Eli Lilly. J.A.M. reports personal fees from COTA Healthcare and Taiho Pharmaceutical (for NCCN Grant Review Panel) outside the submitted work. M.G. reports grants from Bristol-Myers Squibb, Merck, Servier, and Janssen outside the submitted work. J.A.N. reports grants from NanoString, Akoya Biosciences and Illumina outside the submitted work. The other authors declare that they have no conflicts of interest.

Abbreviations:

AJCC

American Joint Committee on Cancer

CI

confidence interval

CIMP

CpG island methylator phenotype

HPFS

Health Professionals Follow-up Study

HR

hazard ratio

IPW

inverse probability weighting

IQR

interquartile range

LINE-1

long-interspersed nucleotide element-1

MSI

microsatellite instability

NHS

Nurses’ Health Study

TNM

tumor, node, metastasis

Footnotes

Use of Standardized Official Symbols: We use HUGO (Human Genome Organization)-approved official symbols (or root symbols) for genes and gene products, including BRAF, CACNA1G, CD3, CDKN2A, CRABP1, FCGR3A, GZMB, IGF2, MLH1, NCAM1, NEUROG1, KRAS, KRT, PIK3CA, PTPRC, RUNX3, SOCS1; all of which are described at www.genenames.org. Gene symbols are italicized, whereas symbols for gene products are not italicized.

References

  • 1.Ogino S, Nowak JA, Hamada T, Milner DA, Nishihara R. Insights into Pathogenic Interactions Among Environment, Host, and Tumor at the Crossroads of Molecular Pathology and Epidemiology. Annu Rev Pathol. 2019;14:83–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Angell HK, Bruni D, Barrett JC, Herbst R, Galon J. The Immunoscore: Colon Cancer and Beyond. Clin Cancer Res. 2020;26:332–9. [DOI] [PubMed] [Google Scholar]
  • 3.Alexander PG, McMillan DC, Park JH. The local inflammatory response in colorectal cancer - Type, location or density? A systematic review and meta-analysis. Cancer Treat Rev. 2020;83:101949. [DOI] [PubMed] [Google Scholar]
  • 4.Miller JS, Lanier LL. Natural Killer Cells in Cancer Immunotherapy. Annu Rev Cancer Biol. 2019;3:77–103. [Google Scholar]
  • 5.Kiessling R, Klein E, Wigzell H. “Natural” killer cells in the mouse. I. Cytotoxic cells with specificity for mouse Moloney leukemia cells. Specificity and distribution according to genotype. Eur J Immunol. 1975;5:112–7. [DOI] [PubMed] [Google Scholar]
  • 6.Bryceson YT, Fauriat C, Nunes JM, Wood SM, Björkström NK, Long EO, et al. Functional analysis of human NK cells by flow cytometry. Methods Mol Biol. 2010;612:335–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Francisco-Cruz A, Parra ER, Tetzlaff MT, Wistuba II. Multiplex Immunofluorescence Assays. Methods Mol Biol. 2020;2055:467–95. [DOI] [PubMed] [Google Scholar]
  • 8.Yamauchi M, Morikawa T, Kuchiba A, Imamura Y, Qian ZR, Nishihara R, et al. Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum. Gut. 2012;61:847–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Chan AT, Ogino S, Fuchs CS. Aspirin and the risk of colorectal cancer in relation to the expression of COX-2. N Engl J Med. 2007;356:2131–42. [DOI] [PubMed] [Google Scholar]
  • 10.Giannakis M, Mu XJ, Shukla SA, Qian ZR, Cohen O, Nishihara R, et al. Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma. Cell Rep. 2016;15:857–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Liao X, Lochhead P, Nishihara R, Morikawa T, Kuchiba A, Yamauchi M, et al. Aspirin use, tumor PIK3CA mutation, and colorectal-cancer survival. N Engl J Med. 2012;367:1596–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Tóth ZE, Mezey E. Simultaneous visualization of multiple antigens with tyramide signal amplification using antibodies from the same species. J Histochem Cytochem. 2007;55:545–54. [DOI] [PubMed] [Google Scholar]
  • 13.Raskov H, Orhan A, Christensen JP, Gögenur I. Cytotoxic CD8+ T cells in cancer and cancer immunotherapy. Br J Cancer. 2021;124:359–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Gunesch JT, Dixon AL, Ebrahim TAM, Berrien-Elliott MM, Tatineni S, Kumar T, et al. CD56 regulates human NK cell cytotoxicity through Pyk2. Elife. 2020;9:1–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Rheinländer A, Schraven B, Bommhardt U. CD45 in human physiology and clinical medicine. Immunol Lett. 2018;196:22–32. [DOI] [PubMed] [Google Scholar]
  • 16.Neefjes J, Jongsma MLM, Paul P, Bakke O. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat Rev Immunol. 2011;11:823–36. [DOI] [PubMed] [Google Scholar]
  • 17.Parra ER, Uraoka N, Jiang M, Cook P, Gibbons D, Forget M-A, et al. Validation of multiplex immunofluorescence panels using multispectral microscopy for immune-profiling of formalin-fixed and paraffin-embedded human tumor tissues. Sci Rep. 2017;7:13380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Väyrynen JP, Haruki K, Lau MC, Väyrynen SA, Zhong R, Dias Costa A, et al. The Prognostic Role of Macrophage Polarization in the Colorectal Cancer Microenvironment. Cancer Immunol Res. 2021;9:8–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Baddeley A, Turner R. spatstat : An R Package for Analyzing Spatial Point Patterns. J Stat Softw. 2005;12:282–90. [Google Scholar]
  • 20.Barua S, Fang P, Sharma A, Fujimoto J, Wistuba I, Rao AUK, et al. Spatial interaction of tumor cells and regulatory T cells correlates with survival in non-small cell lung cancer. Lung Cancer. 2018;117:73–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Väyrynen JP, Lau MC, Haruki K, Väyrynen SA, Dias Costa A, Borowsky J, et al. Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections. Clin Cancer Res. 2020;26:4326–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Väyrynen JP, Haruki K, Väyrynen SA, Lau MC, Dias Costa A, Borowsky J, et al. Prognostic significance of myeloid immune cells and their spatial distribution in the colorectal cancer microenvironment. J Immunother cancer. 2021;9:e002297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Benjamin DJ, Berger JO, Johannesson M, Nosek BA, Wagenmakers E-J, Berk R, et al. Redefine statistical significance. Nat Hum Behav. 2018;2:6–10. [DOI] [PubMed] [Google Scholar]
  • 24.Liu L, Nevo D, Nishihara R, Cao Y, Song M, Twombly TS, et al. Utility of inverse probability weighting in molecular pathological epidemiology. Eur J Epidemiol. 2018;33:381–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Nosho K, Irahara N, Shima K, Kure S, Kirkner GJ, Schernhammer ES, et al. Comprehensive biostatistical analysis of CpG island methylator phenotype in colorectal cancer using a large population-based sample. PLoS One. 2008;3:e3698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Grasso CS, Giannakis M, Wells DK, Hamada T, Mu XJ, Quist M, et al. Genetic Mechanisms of Immune Evasion in Colorectal Cancer. Cancer Discov. 2018;8:730–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Haruki K, Kosumi K, Li P, Arima K, Väyrynen JP, Lau MC, et al. An integrated analysis of lymphocytic reaction, tumour molecular characteristics and patient survival in colorectal cancer. Br J Cancer. 2020;122:1367–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Zhang S, Liu W, Hu B, Wang P, Lv X, Chen S, et al. Prognostic Significance of Tumor-Infiltrating Natural Killer Cells in Solid Tumors: A Systematic Review and Meta-Analysis. Front Immunol. 2020;11:1242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Coca S, Perez-Piqueras J, Martinez D, Colmenarejo A, Saez MA, Vallejo C, et al. The prognostic significance of intratumoral natural killer cells in patients with colorectal carcinoma. Cancer. 1997;79:2320–8. [DOI] [PubMed] [Google Scholar]
  • 30.Chaput N, Svrcek M, Aupérin A, Locher C, Drusch F, Malka D, et al. Tumour-infiltrating CD68+ and CD57+ cells predict patient outcome in stage II-III colorectal cancer. Br J Cancer. 2013;109:1013–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Chen Y, Yuan R, Wu X, He X, Zeng Y, Fan X, et al. A Novel Immune Marker Model Predicts Oncological Outcomes of Patients with Colorectal Cancer. Ann Surg Oncol. 2016;23:826–32. [DOI] [PubMed] [Google Scholar]
  • 32.Sconocchia G, Zlobec I, Lugli A, Calabrese D, Iezzi G, Karamitopoulou E, et al. Tumor infiltration by FcγRIII (CD16)+ myeloid cells is associated with improved survival in patients with colorectal carcinoma. Int J cancer. 2011;128:2663–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Nagtegaal ID, Marijnen CA, Kranenbarg EK, Mulder-Stapel A, Hermans J, van de Velde CJ, et al. Local and distant recurrences in rectal cancer patients are predicted by the nonspecific immune response; specific immune response has only a systemic effect--a histopathological and immunohistochemical study. BMC Cancer. 2001;1:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Halama N, Braun M, Kahlert C, Spille A, Quack C, Rahbari N, et al. Natural killer cells are scarce in colorectal carcinoma tissue despite high levels of chemokines and cytokines. Clin Cancer Res. 2011;17:678–89. [DOI] [PubMed] [Google Scholar]
  • 35.Sconocchia G, Eppenberger S, Spagnoli GC, Tornillo L, Droeser R, Caratelli S, et al. NK cells and T cells cooperate during the clinical course of colorectal cancer. Oncoimmunology. 2014;3:e952197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kared H, Martelli S, Ng TP, Pender SLF, Larbi A. CD57 in human natural killer cells and T-lymphocytes. Cancer Immunol Immunother. 2016;65:441–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Wang F, Lau JKC, Yu J. The role of natural killer cell in gastrointestinal cancer: killer or helper. Oncogene. 2021;40:717–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Krijgsman D, Hokland M, Kuppen PJK. The Role of Natural Killer T Cells in Cancer-A Phenotypical and Functional Approach. Front Immunol. 2018;9:367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Krijgsman D, de Vries NL, Skovbo A, Andersen MN, Swets M, Bastiaannet E, et al. Characterization of circulating T-, NK-, and NKT cell subsets in patients with colorectal cancer: the peripheral blood immune cell profile. Cancer Immunol Immunother. 2019;68:1011–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pagès F, Mlecnik B, Marliot F, Bindea G, Ou F, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–39. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1766462-supplement-1.pdf (2.1MB, pdf)

Data Availability Statement

The data underlying this article cannot be shared publicly. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and Health Professionals Follow-up Study are described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.

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