Abstract
Background
Despite heightened interest in early-onset colorectal cancer (CRC) diagnosed before age 50, little is known on immune cell profiles of early-onset CRC. It also remains to be studied whether CRCs diagnosed at or shortly after age 50 are similar to early-onset CRC. We therefore hypothesized that immune cell infiltrates in CRC tissue might show differential heterogeneity patterns between three age groups (< 50 “early onset,” 50–54 “intermediate onset,” ≥ 55 “later onset”).
Methods
We examined 1,518 incident CRC cases with available tissue data, including 35 early-onset and 73 intermediate-onset cases. To identify immune cells in tumor intraepithelial and stromal areas, we developed three multiplexed immunofluorescence assays combined with digital image analyses and machine learning algorithms, with the following markers: (1) CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 for T cells; (2) CD68, CD86, IRF5, MAF, and MRC1 (CD206) for macrophages; and (3) ARG1, CD14, CD15, CD33, and HLA-DR for myeloid cells.
Results
Although no comparisons between age groups showed statistically significant differences at the stringent two-sided α level of 0.005, compared to later-onset CRC, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes (P = 0.013), intratumoral periglandular reaction (P = 0.025), and peritumoral lymphocytic reaction (P = 0.044). Compared to later-onset CRC, intermediate-onset CRC tended to show lower densities of overall macrophages (P = 0.050), M1-like macrophages (P = 0.062), CD14+HLA-DR+ cells (P = 0.015), and CD3+CD4+FOXP3+ cells (P = 0.039).
Conclusions
This hypothesis-generating study suggests possible differences in histopathologic lymphocytic reaction patterns, macrophages, and regulatory T cells in the tumor microenvironment by age at diagnosis.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00262-021-03056-6.
Keywords: Immunology, Young onset, Colorectal neoplasms, Molecular pathological epidemiology
Introduction
Colorectal cancer (CRC) is a group of heterogeneous tumors with complex interactions between neoplastic and immune cells such as lymphocytes and tumor-associated macrophages in the tumor microenvironment [1–3]. Evidence indicates influences of tumor molecular features such as microsatellite instability (MSI) status on immune reactions to tumor. A strong adaptive immune response enriched with cytotoxic and memory T cells in tumor tissue has been associated with better survival in CRC patients [4–6], while the abundance of macrophages has also been associated with clinical outcome [7]. In addition, myeloid-derived suppressor cells (MDSCs) are considered to contribute to cancer immune evasion [8]. Improved understanding of tumor immune microenvironment will help advance immune-based cancer prevention and treatment strategies.
The incidence of early-onset CRC diagnosed before 50 years of age has been increasing in many parts of the world since the 1980s [9, 10]. The rise in early-onset cancers in various organs (including the colorectum) has been a growing global concern [11] and a topic of the top 2020 Provocative Question of the USA National Cancer Institute. Previous studies have shown heterogeneity of tumor molecular characteristics between early-onset CRC and CRC diagnosed at age ≥ 50 and within early-onset CRCs [12–20]. This suggests possible differences in immune cell infiltrates between early onset and CRC diagnosed at age ≥ 50. However, it is unclear whether there may or may not exist a sharp dichotomy in features of CRC at age 50. We speculated a possibility of an “age continuum” in features of CRC [9].
To clarify this issue, we examined characteristics of early-onset CRC as well as CRC diagnosed at age 50–54 (hereafter referred to as “intermediate-onset CRC”). We tested the hypothesis that profiles of immune cell infiltrates in tumor tissue might differ between three age groups (< 50 “early onset,” 50–54 “intermediate onset,” ≥ 55 “later onset”). We utilized a CRC database that included histopathologic lymphocytic reaction patterns as well as the densities of T-cell, macrophage, and other myeloid cell populations assessed by multiplex immunofluorescence assays combined with digital image analyses and machine learning algorithms.
Methods
Study population
We used a molecular pathological epidemiology database of 1518 incident CRC cases with available tissue data, including 35 early-onset cases and 73 intermediate-onset cases, that had occurred in two US-wide prospective cohort studies, namely the Nurses’ Health Study (NHS, 121,701 women aged 30–55 years at enrollment, followed since 1976) and the Health Professionals Follow-up Study (HPFS, 51,529 men aged 40 to 75 years at enrollment, followed since 1986) (Fig. 1) [21]. We included both colon and rectal cancer cases based on the colorectal continuum theory, i.e., a gradual change of clinical and tumor characteristics throughout the colorectum [22]. Study participants in NHS and HPFS have been sent questionnaires biennially to update information on their lifestyle and newly diagnosed diseases including incident CRC. The National Death Index was used to identify unreported lethal CRC cases. Study physicians reviewed medical records of CRC cases, confirmed the diagnosis, and collected data on tumor size, tumor anatomical location, and disease stage.
Fig. 1.
Flow diagram of study population in the Nurses’ Health Study and the Health Professionals Follow-up Study
Informed consent was obtained from all study participants. 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, and those of participating registries as required. We also obtained signed consents from patients (or next-of-kin, if patients died) to use tissue specimens.
Tumor tissue analyses including immune cell assessments
We obtained formalin-fixed paraffin-embedded (FFPE) tumor tissue samples from hospitals throughout the USA where CRC patients underwent surgical resection. A single pathologist (S.O.), blinded to other data, performed a centralized review of hematoxylin and eosin-stained tissue sections from all CRC cases. Tumor differentiation was categorized as well to moderate vs. poor (> 50% vs. ≤ 50% glandular area, respectively). Four components of lymphocytic reaction to tumors, including tumor-infiltrating lymphocytes, intratumoral periglandular reaction, peritumoral lymphocytic reaction, and Crohn’s-like lymphoid reaction were graded as absent/low, intermediate, and high, as previously described [23, 24].
Details of multiplex immunofluorescent assays have been described previously [25–28]. Briefly, we constructed tissue microarrays consisting of up to four tumor cores from each case. We developed three assays of multiplexed immunofluorescence with the following markers: (1) CD3, CD4, CD8, CD45RO (one isoform of PTPRC gene products), and FOXP3 for T cells, (2) CD68, CD86, IRF5, MAF, and MRC1 (CD206) for macrophages, and (3) ARG1, CD14, CD15, CD33, and HLA-DR for myeloid cells (Fig. 2) [following protein nomenclature recommendations by an expert panel [29]]. We obtained digital images at 200 × magnification using an automated multispectral imaging system (Vectra 3.0, Akoya Biosciences, Hopkinton, MA). Using machine learning algorithm with pathologist’s supervision (Inform 2.4.1, Akoya Biosciences), immune cell densities (cells/mm2) in tumor intraepithelial and stromal areas were calculated through the process of tissue segmentation (classifying tissue regions into tumor epithelium, stroma, and other), cell segmentation (detecting cells and their nuclear, cytoplasmic, and membranous compartments), and cell phenotyping (classifying cells based on cell phenotypic features including fluorophore intensities and cytomorphology). We used immune cell densities in overall tumor region (tumor intraepithelial and stromal areas combined) in our primary hypothesis testing.
Fig. 2.
Representative images of immune profiling of a colorectal adenocarcinoma using three multiplex immunofluorescence assays combined with digital image analysis. The scale bars are 100 µm
Genomic DNA was extracted from FFPE tissue blocks. Microsatellite instability (MSI) status was determined by 10 microsatellite markers (D2S123, D5S346, D17S250, BAT25, BAT26, BAT40, D18S55, D18S56, D18S67, and D18S487). High-level microsatellite instability (MSI) was defined as the presence of instability in ≥ 30% of the markers [22, 30]. CpG island methylator phenotype (CIMP)-high was defined as ≥ 6 methylated promoters of eight CIMP-specific promoters (CACNA1G, CDKN2A, CRABP1, IGF2, MLH1, NEUROG1, RUNX3, and SOCS1), and CIMP-low/negative as 0–5 methylated promoters, as previously described [22, 31]. Polymerase chain reaction and pyrosequencing were performed to assess mutations in KRAS (codons 12, 13, 61, and 146), BRAF (codon 600), and PIK3CA (exons 9 and 20) [30, 32, 33].
As previously described [34, 35], the quantitative polymerase chain reaction was conducted to measure the amount of Fusobacterium nucleatum (F. nucleatum) and Bifidobacterium genus DNA in tumor tissue, using SLCO2A1 (for F. nucleatum) or a universal 16S primer set (for Bifidobacterium genus) as reference genes. We categorized cases with any detectable F. nucleatum DNA and Bifidobacterium genus as low vs. high based on the median level of F. nucleatum and Bifidobacterium genus, while other cases were categorized as negative.
Statistical analyses
All statistical analyses were conducted using SAS software (version 9.4, SAS Institute, Cary, NC, USA). All P values were two-sided and we used the stringent α level of 0.005 as recommended by the expert statisticians due to multiple comparisons [36]. Our primary hypothesis testing was to determine whether levels of lymphocytic reaction and immune cell densities statistically significantly differed by age at diagnosis (< 50 vs. ≥ 55, 50–54 vs. ≥ 55). All other assessments, such as assessments of differences in clinical or molecular characteristic by age groups, were secondary analyses. We performed the chi-squared test or Fisher’s exact test (if appropriate) to compare categorical data between age groups. We performed the Wilcoxon rank-sum test to compare immune cell densities between age groups because immune cell densities were not normally distributed.
Results
Among 1,518 incident colorectal cancer (CRC) cases in the two cohorts, there were 35 early-onset cases diagnosed at age < 50, 73 intermediate-onset cases diagnosed at age 50–54, and 1410 later-onset cases diagnosed at age ≥ 55. Table 1 summarizes clinical, pathological, and molecular characteristics according to age at diagnosis.
Table 1.
Clinical, pathological, and molecular characteristics of colorectal cancer cases according to age at diagnosis
| Characteristicsa | Total No. (n = 1518) | Age at diagnosis | ||||
|---|---|---|---|---|---|---|
| < 50 (n = 35) | P value (< 50 vs. ≥ 55)b | 50–54 (n = 73) | P value (50–54 vs. ≥ 55)c | ≥ 55 (n = 1410) | ||
| Sex | ||||||
| Female (NHS) | 853 (56%) | 25 (71%) | 0.054 | 52 (71%) | 0.0066 | 776 (55%) |
| Male (HPFS) | 665 (44%) | 10 (29%) | 21 (29%) | 634 (54%) | ||
| Family history of colorectal cancer in a first-degree relative | ||||||
| Absent | 1215 (81%) | 27 (77%) | 0.62 | 59 (83%) | 0.59 | 1129 (81%) |
| Present | 293 (19%) | 8 (23%) | 12 (17%) | 273 (19%) | ||
| Tumor location | ||||||
| Proximal colon | 733 (48%) | 6 (17%) | 0.0020 | 31 (42%) | 0.25 | 696 (46%) |
| Distal colon | 447 (30%) | 17 (49%) | 24 (33%) | 446 (32%) | ||
| Rectum | 332 (22%) | 12 (34%) | 18 (25%) | 302 (22%) | ||
| pT stage | ||||||
| pT1 (submucosa) | 156 (11%) | 4 (13%) | 0.70 | 3 (4.4%) | 0.22 | 149 (12%) |
| pT2 (muscularis propria) | 289 (21%) | 8 (25%) | 14 (21%) | 267 (21%) | ||
| pT3 (subserosa) | 848 (62%) | 17 (53%) | 45 (66%) | 786 (633%) | ||
| pT4 (serosa or other organs) | 80 (5.8%) | 3 (9.4%) | 6 (8.3%) | 71 (5.6%) | ||
| pN stage | ||||||
| pN0 (0) | 848 (64%) | 18 (58%) | 0.15 | 30 (48%) | 0.036 | 800 (64%) |
| pN1 (1–3) | 294 (22%) | 5 (16%) | 19 (31%) | 270 (22%) | ||
| pN2 (≥ 4) | 192 (14%) | 8 (26%) | 13 (21%) | 171(14%) | ||
| AJCC disease stage | ||||||
| I | 358 (26%) | 10 (30%) | 0.71 | 11 (17%) | 0.034 | 337 (26%) |
| II | 441 (32%) | 8 (24%) | 16 (24%) | 417 (33%) | ||
| III | 385 (28%) | 11 (33%) | 26 (39%) | 348 (27%) | ||
| IV | 190 (14%) | 4 (12%) | 13 (20%) | 173 (14%) | ||
| Tumor differentiation | ||||||
| Well to moderate | 1350 (90%) | 31 (91%) | 0.99 | 65 (89%) | 0.87 | 1254 (90%) |
| Poor | 156 (10%) | 3 (9.0%) | 8 (11%) | 145 (10%) | ||
| MSI status | ||||||
| Non-MSI-high | 1105 (84%) | 27 (100%) | 0.015 | 55 (89%) | 0.22 | 1023 (83%) |
| MSI-high | 220 (16%) | 0 (0%) | 7 (11%) | 213 (17%) | ||
| CIMP status | ||||||
| Low/negative | 1050 (82%) | 28 (97%) | 0.029 | 61 (94%) | 0.0090 | 961 (81%) |
| High | 231 (18%) | 1 (3.5%) | 4 (6.2%) | 226 (19%) | ||
| KRAS mutation | ||||||
| Wild type | 733 (58%) | 17 (63%) | 0.57 | 41 (66%) | 0.18 | 675 (57%) |
| Mutant | 531 (42%) | 10 (37%) | 21 (34%) | 500 (43%) | ||
| BRAF mutation | ||||||
| Wild type | 1132 (85%) | 25 (93%) | 0.29 | 58 (89%) | 0.27 | 1049 (84%) |
| Mutant | 206 (15%) | 2 (7.4%) | 7 (11%) | 197 (16%) | ||
| PIK3CA mutation | ||||||
| Wild type | 1044 (84%) | 23 (85%) | 0.99 | 49 (86%) | 0.66 | 972 (84%) |
| Mutant | 200 (16%) | 4 (15%) | 8 (14%) | 188 (16%) | ||
aPercentage (%) indicates the proportion of cases with a specific clinical or pathological characteristic in all cases according to age categories
bTo compare categorical data between age groups (< 50 vs. ≥ 55), the chi-square test or Fisher’s exact test (if appropriate) was performed
cTo compare categorical data between age groups (50–54 vs. ≥ 55), the chi-square test or Fisher’s exact test (if appropriate) was performed
Abbreviations: AJCC, American Joint Committee on Cancer; CIMP, CpG island methylator phenotype; HPFS, Health Professionals Follow-up Study; MSI, microsatellite instability; NHS, Nurses’ Health Study
In our primary hypothesis testing, we assessed whether histopathologic lymphocytic reaction patterns statistically significantly differed by age at diagnosis (Table 2). Although no comparisons showed statistically significant differences at the stringent two-sided α level of 0.005, compared to later-onset CRC, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes (P = 0.013), intratumoral periglandular reaction (P = 0.025), and peritumoral lymphocytic reaction (P = 0.044). Considering the link between the gut microbiota and anti-tumor immunity, we investigated differences in F. nucleatum or Bifidobacterium genus positivity in tumor tissue between age groups (Table 2). However, we did not observe any significance difference. To investigate the potential influence of MSI status on our finding, we also conducted analyses limited to non-MSI-high tumors, which yielded similar patterns to those of the overall analyses although the sample size was limited (Supplementary Table 1).
Table 2.
Lymphocytic reaction patterns and microbial features according to age at diagnosis
| Characteristicsa | Total No. (n = 1518) | Age at diagnosis | ||||
|---|---|---|---|---|---|---|
| < 50 (n = 35) | P value (< 50 vs. ≥ 55)b | 50–54 (n = 73) | P value (50–54 vs. ≥ 55)c | ≥ 55 (n = 1410) | ||
| Tumor-infiltrating lymphocytes | ||||||
| Absent/low | 1125 (74%) | 33 (94%) | 0.013 | 53 (74%) | 0.78 | 1039 (74%) |
| Intermediate | 237 (16%) | 2 (5.7%) | 13 (18%) | 222 (16%) | ||
| High | 152 (10%) | 0 (0%) | 6 (8.3%) | 146 (10%) | ||
| Intratumoral periglandular reaction | ||||||
| Absent/low | 200 (13%) | 5 (14%) | 0.025 | 6 (8.2%) | 0.20 | 189 (13%) |
| Intermediate | 1112 (73%) | 30 (86%) | 60 (82%) | 1022 (73%) | ||
| High | 202 (13%) | 0 (0%) | 7 (9.6%) | 195 (14%) | ||
| Peritumoral lymphocytic reaction | ||||||
| Absent/low | 215 (14%) | 5 (14%) | 0.044 | 7 (10%) | 0.043 | 203 (15%) |
| Intermediate | 1048 (70%) | 29 (83%) | 60 (82%) | 959 (69%) | ||
| High | 245 (16%) | 1 (2.9%) | 6 (8.2%) | 238 (17%) | ||
| Crohn’s-like lymphoid reaction | ||||||
| Absent/low | 936 (75%) | 27 (93%) | 0.078 | 39 (80%) | 0.88 | 870 (75%) |
| Intermediate | 215 (17%) | 2 (6.9%) | 7 (14%) | 206 (18%) | ||
| High | 92 (7.4%) | 0 (0%) | 3 (6.1%) | 89 (7.6%) | ||
| Amount of F. nucleatum DNA | ||||||
| Negative | 1083 (87%) | 23 (92%) | 0.77 | 52 (90%) | 0.85 | 1008 (87%) |
| Low | 80 (6.4%) | 1 (4.0%) | 3 (5.2%) | 76 (6.6%) | ||
| High | 77 (6.2%) | 1 (4.0%) | 3 (5.2%) | 73 (6.3%) | ||
| Amount of Bifidobacterium genus DNA | ||||||
| Negative | 917 (70%) | 19 (73%) | 0.91 | 39 (61%) | 0.16 | 860 (71%) |
| Low | 190 (15%) | 4 (15%) | 14 (22%) | 172 (14%) | ||
| High | 189 (15%) | 3 (12%) | 11 (17%) | 175 (15%) | ||
aPercentage (%) indicates the proportion of cases with a specific clinical or pathological characteristic in all cases according to age categories
bTo compare categorical data between age groups (< 50 vs. ≥ 55), the Fisher’s exact test was performed
cTo compare categorical data between age groups (50–54 vs. ≥ 55), the chi-square test or Fisher’s exact test (if appropriate) was performed
Furthermore, we assessed whether densities of more specific immune cell subsets (cells/mm2) differed by age at diagnosis (Table 3). There were no significant differences in immune cell densities between early-onset CRC and later-onset CRC. Although statistical significance was not reached due to low statistical power, intermediate-onset CRC tended to show lower densities of overall macrophages (median [interquartile range, IQR], 357 [152–670]), M1-like macrophages (45 [18–114]), CD14+HLA-DR+ cells (mature macrophages) (479 [233–967]), and CD3+CD4+FOXP3+ cells (regulatory T cells) (0 [0–2.0]) compared to later-onset CRC (453 [254–811], P = 0.050; 91 [31–211], P = 0.062; 730 [377–1239], P = 0.015; 0.9 [0–8.5], P = 0.039, respectively). We observed similar findings in analyses limited to non-MSI-high tumors (Supplementary Table 2). Detailed data on four age groups (< 50, 50–54, 55–69, ≥ 70) and immune cell densities in tumor intraepithelial and stromal regions are shown in Supplementary Tables 3 and 4.
Table 3.
Immune cell densities of colorectal cancer cases according to age at diagnosis
| Immune cell densities (cells/mm2)a | Total No. (n = 966) | Age at diagnosis | ||||
|---|---|---|---|---|---|---|
| < 50 (n = 19) | P value (< 50 vs. ≥ 55)b | 50–54 (n = 46) | P value (50–54 vs. ≥ 55)c | ≥ 55 (n = 875) | ||
| T cells | ||||||
| CD3+ cells | 78 (18–252) | 132 (42–189) | 0.41 | 86 (11–251) | 0.78 | 76 (18–255) |
| CD3+CD4+ cells | 35 (3.4–151) | 48 (21–122) | 0.60 | 37 (1.6–107) | 0.66 | 35 (3.4–154) |
| CD3+CD4+FOXP3+ cells | 0.7 (0–8.2) | 1.6 (0–6.7) | 0.61 | 0 (0–2.0) | 0.039 | 0.9 (0–8.5) |
| CD3+CD4+CD45RO+ cells | 27 (2.7–119) | 41 (17–122) | 0.58 | 29 (0–81) | 0.44 | 25 (2.7–121) |
| CD3+CD4+CD45RO− cells | 3.5 (0–22) | 5.6 (0–19) | 0.73 | 4.1 (0–28) | 0.56 | 3.4 (0–22) |
| CD3+CD8+ cells | 6.8 (0–30) | 7.5 (1.5–24) | 0.97 | 8.0 (0–27) | 0.71 | 6.7 (0–31) |
| CD3+CD8+CD45RO+ cells | 4.4 (0–23) | 3.9 (0–9.2) | 0.54 | 3.7 (0–22) | 0.53 | 4.5 (0–23) |
| CD3+CD8+CD45RO− cells | 1.3 (0–5.5) | 2.5 (0–11) | 0.10 | 1.6 (0–9.6) | 0.33 | 1.3 (0–5.2) |
| CD3+CD45RO + cells | 43 (5.4–158) | 57 (26–128) | 0.70 | 43 (2.9–120) | 0.48 | 43 (5.5–161) |
| Macrophages | ||||||
| Overall macrophages | 449 (250–806) | 530 (248–852) | 0.91 | 357 (152–670) | 0.050 | 453 (254–811) |
| M1-like macrophages | 88 (30–210) | 65 (33–314) | 0.63 | 45 (18–114) | 0.062 | 91 (31–211) |
| M2-like macrophages | 107 (42–231) | 86 (38–209) | 0.58 | 91 (27–219) | 0.43 | 109 (42–233) |
| Other myeloid cells | ||||||
| CD14+ cells | 994 (609–1580) | 1135 (482–1400) | 0.60 | 812 (473–1352) | 0.13 | 997 (613–1589) |
| CD14+HLA-DR+ cells | 720 (366–1221) | 768 (369–1038) | 0.88 | 479 (233–967) | 0.015 | 730 (377–1239) |
| CD14+HLA-DR− cells | 232 (119–391) | 202 (108–328) | 0.21 | 274 (107–451) | 0.31 | 228 (119–395) |
| CD15+ cells | 97 (28–256) | 103 (14–527) | 0.73 | 81 (24–226) | 0.48 | 98 (29–258) |
| CD15+ARG1+ cells | 79 (21–233) | 93 (11–505) | 0.78 | 73 (22–177) | 0.49 | 80 (21–235) |
| CD15+ARG1− cells | 11 (3.2–24) | 6.1 (2.4–22) | 0.47 | 9.7 (2.4–23) | 0.80 | 11 (3.3–24) |
| CD15+CD33+ cells | 1.6 (0–11) | 4.3 (0–18) | 0.31 | 2.5 (0–15) | 0.64 | 1.5 (0–11) |
| CD15+CD33− cells | 89 (26–239) | 90 (14–454) | 0.74 | 78 (24–210) | 0.50 | 90 (28–570) |
aEach continuous variable is shown as median (IQR)
bTo compare continuous variables between age groups (< 50 vs. ≥ 55), the Wilcoxon rank-sum test was performed
cTo compare continuous variables between age groups (50–54 vs. ≥ 55), the Wilcoxon rank-sum test was performed
Discussion
Colorectal cancer (CRC) is a heterogenous group of neoplastic diseases influenced by tumor-host interactions in the tumor microenvironment [1–3]. Thus, comprehensive immunologic analyses of tumor tissue in CRC can shed light on the carcinogenesis process [27, 37–40]. In this study, we comprehensively evaluated immune cell infiltrates in CRC by age at diagnosis. The age of 50 years has been used as a cutoff to define early-onset CRC in the field. This cutoff might be at least in part derived from the recommended age to start screening for CRC in the past. However, it remains uncertain whether there is any reasonable cutoff point or there is an “age continuum” in a true biological sense. Especially, it remains to be studied whether CRCs diagnosed at or shortly after age 50 are similar to early-onset CRCs. To address this, we examined immune cell infiltrates in CRC tissue by three age groups (< 50, 50–54, ≥ 55). Although no comparisons showed statistically significant differences at the stringent two-sided α level of 0.005, several potentially interesting results were observed. Compared to later-onset cases diagnosed at age ≥ 55, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes, intratumoral periglandular reaction, and peritumoral lymphocytic reaction. Compared to later-onset cases, intermediate-onset cases tended to show lower densities of overall macrophages, M1-like macrophages, and CD3+CD4+FOXP3+ cells. Though our findings need to be replicated, this study generates several novel hypotheses for further investigation.
Previous studies showed that early-onset CRC was associated with certain pathological and molecular characteristics, including poor tumor differentiation, signet ring cell morphology, and tumor hypomethylation of long interspersed nucleotide element-1 [12, 15–20, 41, 42]. However, only few studies have investigated lymphocytic reaction patterns in early-onset CRC [17, 20]. One study reported that tumor-infiltrating lymphocytes were less common in CRCs diagnosed age < 40 compared to those diagnosed at age ≥ 40 [20]. Another study reported that CRCs diagnosed at age ≤ 40 less commonly showed Crohn’s-like lymphoid reaction compared to cases diagnosed at age > 40, whereas there was no significant difference in tumor-infiltrating lymphocytes between the two age groups [17]. Although statistical power was limited in CRCs before age 55, our data suggest that lymphocytic reactions may be less pronounced in early-onset cases. Lymphocytic reaction patterns reflect the anti-tumor immune response and have been associated with lower CRC mortality [24]. Therefore, survival outcomes of early-onset CRC may be at least partly adversely affected by lower levels of lymphocytic reaction.
Detailed features of immune cell infiltrates in early-onset CRC and intermediate-onset CRC diagnosed at age 50–54 are currently unknown. To detect detailed immune cell phenotypes, we employed three multiplex immunofluorescence assays that could simultaneously measure the expression levels of multiple protein markers [26–28]. For example, the detection of M1-like and M2-like macrophages requires multimarker combinations, as no single marker has appropriate specificity [43]. This study suggests possible differences in the densities of overall and M1-like macrophages and regulatory T cells according to the patient age at CRC diagnosis. Considering the established pro-inflammatory role of M1-like macrophages and the anti-inflammatory role of regulatory T cells [43, 44], our findings may suggest that there are differences in the immune microenvironment between intermediate-onset cases and later-onset cases. Studying the immune cell profiles by age at diagnosis can shed light on how CRC emerges and grows in young adults. Therefore, further investigation is warranted to replicate our findings and elucidate the underlying mechanisms.
We recognize limitations of this study. First, the small sample size of early-onset and intermediate-onset CRC cases limited the statistical power and precluded a conclusive interpretation of the results. Second, measurement errors exist in tissue analysis data. Third, not all incident CRC cases in the cohort studies could be included due to lack of available tumor tissue specimens. Nonetheless, our recent studies that controlled for selection bias through use of inverse probability weighting method [45] did show little evidence for the presence of substantial selection bias in our CRC tissue database [24]. Fourth, most of the study subjects were non-Hispanic Caucasians. Hence, independent studies on other populations are needed.
This study has notable strengths. First, the molecular pathological epidemiology database with detailed data on tumor molecular and immune characteristics, which was derived from the two US-wide prospective cohort studies, allowed us to conduct comprehensive analyses of lymphocytic reaction patterns and immune cell densities of early-onset, intermediate-onset, and later-onset CRC cases. Second, the study population was recruited from hospitals throughout the USA, which increases the generalizability of our results. Third, the current study was based on the assessment of immune cell densities by multiplex immunofluorescence, which is a powerful tool to simultaneously detect multiple epitopes in the context of immune cell biology.
In conclusion, this hypothesis-generating study suggests possible differences in histopathologic lymphocytic reaction patterns, macrophages, and regulatory T cells in the tumor microenvironment by age at diagnosis.
Use of standardized official symbols
We use HUGO (Human Genome Organisation)-approved official symbols for genes and gene products, including ARG1, BRAF, CACNA1G, CD3, CD4, CD8, CD14, CD33, CD68, CD86, CDKN2A, FOXP3, HLA-DR, IGF2, IL10, IRF5, KRAS, MAF, MLH1, MRC1, NEUROG1, PIK3CA, PTPRC, RUNX3, SOCS1; all of which are described at www.genenames.org. The gene symbols are italicized to differentiate from official protein symbols as well as non-italicized colloquial names that are used along with the official symbols.
Supplementary Information
Below is the link to the electronic supplementary material.
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, and WY. The authors assume full responsibility for analyses and interpretation of these data.
Abbreviations
- AJCC
American Joint Committee on Cancer
- CIMP
CpG island methylator phenotype
- CRC
Colorectal cancer
- FFPE
Formalin-fixed paraffin-embedded
- HPFS
Health Professionals Follow-up Study
- IQR
Interquartile range
- MSI
Microsatellite instability
- NHS
Nurses’ Health Study
Funding
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.;K07 CA188126 to X.Z., and R01 CA225655 to J.K.L.; R37 CA246175 to Y.C.); by Cancer Research UK’s Grand Challenge Initiative (UK C10674/A27140 to K.N., M.G., and S.O.); by Nodal Award (2016–20) from the Dana-Farber Harvard Cancer Center (to S.O.); by 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 and SU2C. K.F. was supported by fellowship grants from Uehara Memorial Foundation and Grant of The Clinical Research Promotion Foundation (2018). T.U. was supported by fellowship grants from Uehara Memorial Foundation and Yasuda Medical Foundation. K.A. and T.U. were supported by a grant from Overseas Research Fellowship from Japan Society for the Promotion of Science (201860083 to K.A.; 201960541 to T.U.). K.H. was supported by fellowship grants from the Uehara Memorial Foundation and the Mitsukoshi Health and Welfare Foundation. A.T.C. is a Stuart and Suzanne Steele MGH Research Scholar. M.G. is supported by an ASCO Conquer Cancer Foundation 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.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and the Health Professionals Follow-up Study is described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.
Code availability
The codes used in this analysis are not publicly available. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and the Health Professionals Follow-up Study is described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.
Declarations
Conflicts of interest
J.A.M. has received institutional research funding from Boston Biomedical, has served as an advisor/consultant to COTA Healthcare, and served on a grant review panel for the National Comprehensive Cancer Network funded by Taiho Pharmaceutical. M.G. receives research funding from Bristol-Myers Squibb, Merck, and Servier. This study was not funded by any of these commercial entities. A.T.C. previously served as a consultant for Bayer Healthcare and Pfizer Inc. This study was not funded by Bayer Healthcare or Pfizer Inc. C.S.F. is currently employed by Genentech, a subsidiary of Roche, and previously served as a consultant for Agios, Bain Capital, Bayer, Celgene, Dicerna, Five Prime Therapeutics, Gilead Sciences, Eli Lilly, Entrinsic Health, Genentech, KEW, Merck, Merrimack Pharmaceuticals, Pfizer Inc, Sanofi, Taiho, and Unum Therapeutics; C.S.F. also serves as a Director for CytomX Therapeutics and owns unexercised stock options for CytomX and Entrinsic Health. No other conflicts of interest exist. The other authors declare that they have no conflicts of interest.
Ethical approval
All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Consent to participate and for publication
Informed consent was obtained from all study participants, and the study 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.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Tomotaka Ugai, Juha P. Väyrynen, Mai Chan Lau, and Jennifer Borowsky have contributed equally as co-first authors.
Jeffrey A. Meyerhardt, Marios Giannakis, Jonathan A. Nowak, and Shuji Ogino have contributed equally as co-last authors.
References
- 1.Kamal Y, Schmit SL, Frost HR, Amos CI. The tumor microenvironment of colorectal cancer metastases: opportunities in cancer immunotherapy. Immunotherapy. 2020;12:1083–1100. doi: 10.2217/imt-2020-0026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kather JN, Halama N. Harnessing the innate immune system and local immunological microenvironment to treat colorectal cancer. Br J Cancer. 2019;120:871–882. doi: 10.1038/s41416-019-0441-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ogino S, Nowak JA, Hamada T, et al. Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine. Gut. 2018;67:1168–1180. doi: 10.1136/gutjnl-2017-315537. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Pages F, Mlecnik B, Marliot F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–2139. doi: 10.1016/S0140-6736(18)30789-X. [DOI] [PubMed] [Google Scholar]
- 5.Ling A, Edin S, Wikberg ML, Oberg A, Palmqvist R. The intratumoural subsite and relation of CD8(+) and FOXP3(+) T lymphocytes in colorectal cancer provide important prognostic clues. Br J Cancer. 2014;110:2551–2559. doi: 10.1038/bjc.2014.161. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Marisa L, Svrcek M, Collura A, et al. The balance between cytotoxic T-cell lymphocytes and immune checkpoint expression in the prognosis of colon Tumors. J Natl Cancer Inst. 2018 doi: 10.1093/jnci/djx136. [DOI] [PubMed] [Google Scholar]
- 7.Pinto ML, Rios E, Duraes C, Ribeiro R, Machado JC, Mantovani A, Barbosa MA, Carneiro F, Oliveira MJ. The two faces of Tumor-associated macrophages and their clinical significance in colorectal cancer. Front Immunol. 2019;10:1875. doi: 10.3389/fimmu.2019.01875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.De Cicco P, Ercolano G, Ianaro A. The new era of cancer immunotherapy: targeting myeloid-derived suppressor cells to overcome immune evasion. Front Immunol. 2020;11:1680. doi: 10.3389/fimmu.2020.01680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Akimoto N, Ugai T, Zhong R, et al. Rising incidence of early-onset colorectal cancer - a call to action. Nat Rev Clin Oncol. 2021;18:230–243. doi: 10.1038/s41571-020-00445-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020;70:145–164. doi: 10.3322/caac.21601. [DOI] [PubMed] [Google Scholar]
- 11.Sung H, Siegel RL, Rosenberg PS, Jemal A. Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry. Lancet Public Health. 2019;4:e137–e147. doi: 10.1016/S2468-2667(18)30267-6. [DOI] [PubMed] [Google Scholar]
- 12.Willauer AN, Liu Y, Pereira AAL, et al. Clinical and molecular characterization of early-onset colorectal cancer. Cancer. 2019;125:2002–2010. doi: 10.1002/cncr.31994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lieu CH, Golemis EA, Serebriiskii IG, et al. Comprehensive genomic landscapes in early and later onset colorectal cancer. Clin Cancer Res. 2019;25:5852–5858. doi: 10.1158/1078-0432.CCR-19-0899. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Soliman BG, Karagkounis G, Church JM, Plesec T, Kalady MF. Mucinous histology signifies poor oncologic outcome in young patients with colorectal cancer. Dis Colon Rectum. 2018;61:547–553. doi: 10.1097/DCR.0000000000001060. [DOI] [PubMed] [Google Scholar]
- 15.Rodriguez L, Brennan K, Karim S, Nanji S, Patel SV, Booth CM. Disease characteristics, clinical management, and outcomes of young patients with colon cancer: a population-based study. Clin Colorectal Cancer. 2018;17:e651–e661. doi: 10.1016/j.clcc.2018.06.007. [DOI] [PubMed] [Google Scholar]
- 16.Yeo H, Betel D, Abelson JS, Zheng XE, Yantiss R, Shah MA. Early-onset colorectal cancer is distinct from traditional colorectal cancer. Clin Colorectal Cancer. 2017;16(293–9):e6. doi: 10.1016/j.clcc.2017.06.002. [DOI] [PubMed] [Google Scholar]
- 17.Chang DT, Pai RK, Rybicki LA, et al. Clinicopathologic and molecular features of sporadic early-onset colorectal adenocarcinoma: an adenocarcinoma with frequent signet ring cell differentiation, rectal and sigmoid involvement, and adverse morphologic features. Mod Pathol. 2012;25:1128–1139. doi: 10.1038/modpathol.2012.61. [DOI] [PubMed] [Google Scholar]
- 18.Antelo M, Balaguer F, Shia J, et al. A high degree of LINE-1 hypomethylation is a unique feature of early-onset colorectal cancer. PLoS ONE. 2012;7:e45357. doi: 10.1371/journal.pone.0045357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Sultan I, Rodriguez-Galindo C, El-Taani H, Pastore G, Casanova M, Gallino G, Ferrari A. Distinct features of colorectal cancer in children and adolescents: a population-based study of 159 cases. Cancer. 2010;116:758–765. doi: 10.1002/cncr.24777. [DOI] [PubMed] [Google Scholar]
- 20.Yantiss RK, Goodarzi M, Zhou XK, Rennert H, Pirog EC, Banner BF, Chen YT. Clinical, pathologic, and molecular features of early-onset colorectal carcinoma. Am J Surg Pathol. 2009;33:572–582. doi: 10.1097/PAS.0b013e31818afd6b. [DOI] [PubMed] [Google Scholar]
- 21.Nishihara R, Wu K, Lochhead P, et al. Long-term colorectal-cancer incidence and mortality after lower endoscopy. N Engl J Med. 2013;369:1095–1105. doi: 10.1056/NEJMoa1301969. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Yamauchi M, Morikawa T, Kuchiba A, et al. Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum. Gut. 2012;61:847–854. doi: 10.1136/gutjnl-2011-300865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Ogino S, Nosho K, Irahara N, et al. Lymphocytic reaction to colorectal cancer is associated with longer survival, independent of lymph node count, microsatellite instability, and CpG island methylator phenotype. Clin Cancer Res. 2009;15:6412–6420. doi: 10.1158/1078-0432.CCR-09-1438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Haruki K, Kosumi K, Li P, et al. An integrated analysis of lymphocytic reaction, tumour molecular characteristics and patient survival in colorectal cancer. Br J Cancer. 2020;122:1367–1377. doi: 10.1038/s41416-020-0780-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Borowsky J, Haruki K, Lau MC, et al. Association of fusobacterium nucleatum with specific T-cell subsets in the colorectal carcinoma microenvironment. Clin Cancer Res. 2021;27:2816–2826. doi: 10.1158/1078-0432.CCR-20-4009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Vayrynen SA, Zhang J, Yuan C, et al. Composition, spatial characteristics, and prognostic significance of myeloid cell infiltration in pancreatic cancer. Clin Cancer Res. 2021;27:1069–1081. doi: 10.1158/1078-0432.CCR-20-3141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Vayrynen JP, Haruki K, Lau MC, et al. The prognostic role of macrophage polarization in the colorectal cancer microenvironment. Cancer Immunol Res. 2021;9:8–19. doi: 10.1158/2326-6066.CIR-20-0527. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Fujiyoshi K, Vayrynen JP, Borowsky J, et al. Tumour budding, poorly differentiated clusters, and T-cell response in colorectal cancer. EBioMedicine. 2020;57:102860. doi: 10.1016/j.ebiom.2020.102860. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Fujiyoshi K, Bruford EA, Mroz P, et al. Opinion: Standardizing gene product nomenclature-a call to action. Proc Natl Acad Sci U S A. 2021 doi: 10.1073/pnas.2025207118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Ogino S, Nosho K, Kirkner GJ, Kawasaki T, Meyerhardt JA, Loda M, Giovannucci EL, Fuchs CS. CpG island methylator phenotype, microsatellite instability, BRAF mutation and clinical outcome in colon cancer. Gut. 2009;58:90–96. doi: 10.1136/gut.2008.155473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Inamura K, Yamauchi M, Nishihara R, et al. Tumor LINE-1 methylation level and microsatellite instability in relation to colorectal cancer prognosis. J Natl Cancer Inst. 2014 doi: 10.1093/jnci/dju195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Liao X, Lochhead P, Nishihara R, et al. Aspirin use, tumor PIK3CA mutation, and colorectal-cancer survival. N Engl J Med. 2012;367:1596–1606. doi: 10.1056/NEJMoa1207756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Imamura Y, Lochhead P, Yamauchi M, et al. Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review. Mol Cancer. 2014;13:135. doi: 10.1186/1476-4598-13-135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Mima K, Sukawa Y, Nishihara R, et al. Fusobacterium nucleatum and T Cells in colorectal carcinoma. JAMA Oncol. 2015;1:653–661. doi: 10.1001/jamaoncol.2015.1377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Kosumi K, Hamada T, Koh H, et al. The amount of Bifidobacterium genus in colorectal carcinoma tissue in relation to Tumor characteristics and clinical outcome. Am J Pathol. 2018;188:2839–2852. doi: 10.1016/j.ajpath.2018.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Benjamin DJ, Berger JO, Johannesson M, et al. Redefine statistical significance. Nat Hum Behav. 2018;2:6–10. doi: 10.1038/s41562-017-0189-z. [DOI] [PubMed] [Google Scholar]
- 37.Mehrvarz Sarshekeh A, Alshenaifi J, Roszik J, et al. ARID1A mutation may define an immunologically active subgroup in patients with microsatellite stable colorectal cancer. Clin Cancer Res. 2021;27:1663–1670. doi: 10.1158/1078-0432.CCR-20-2404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Wang ST, Cui WQ, Pan D, Jiang M, Chang B, Sang LX. Tea polyphenols and their chemopreventive and therapeutic effects on colorectal cancer. World J Gastroenterol. 2020;26:562–597. doi: 10.3748/wjg.v26.i6.562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Inamura K. Colorectal cancers: an update on their molecular pathology. Cancers (Basel) 2018 doi: 10.3390/cancers10010026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Hughes LAE, Simons C, van den Brandt PA, van Engeland M, Weijenberg MP. Lifestyle, diet, and colorectal cancer risk according to (Epi) genetic Instability: current evidence and future directions of molecular pathological epidemiology. Curr Colorectal Cancer Rep. 2017;13:455–469. doi: 10.1007/s11888-017-0395-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Akimoto N, Zhao M, Ugai T, et al. Tumor long interspersed nucleotide element-1 (LINE-1) Hypomethylation in relation to age of colorectal cancer diagnosis and prognosis. Cancers (Basel) 2021 doi: 10.3390/cancers13092016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Holowatyj AN, Lewis MA, Pannier ST, et al. Clinicopathologic and racial/ethnic differences of colorectal cancer among adolescents and young adults. Clin Transl Gastroenterol. 2019;10:e00059. doi: 10.14309/ctg.0000000000000059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Murray PJ, Allen JE, Biswas SK, et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity. 2014;41:14–20. doi: 10.1016/j.immuni.2014.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Sharma A, Rudra D. Emerging functions of regulatory T Cells in tissue homeostasis. Front Immunol. 2018;9:883. doi: 10.3389/fimmu.2018.00883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Liu L, Nevo D, Nishihara R, et al. Utility of inverse probability weighting in molecular pathological epidemiology. Eur J Epidemiol. 2018;33:381–392. doi: 10.1007/s10654-017-0346-8. [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
The datasets generated and/or analyzed during the current study are not publicly available. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and the Health Professionals Follow-up Study is described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.
The codes used in this analysis are not publicly available. Further information including the procedures to obtain and access data from the Nurses’ Health Studies and the Health Professionals Follow-up Study is described at https://www.nurseshealthstudy.org/researchers/ and https://sites.sph.harvard.edu/hpfs/for-collaborators/.