The prognostic effect of tumor-associated macrophages in stage I-III colorectal cancer depends on T cell infiltration

Background Tumor-associated macrophages (TAMs) are associated with unfavorable patient prognosis in many cancer types. However, TAMs are a heterogeneous cell population and subsets have been shown to activate tumor-infiltrating T cells and confer a good patient prognosis. Data on the prognostic value of TAMs in colorectal cancer are conflicting. We investigated the prognostic effect of TAMs in relation to tumor-infiltrating T cells in colorectal cancers. Methods The TAM markers CD68 and CD163 were analyzed by multiplex fluorescence immunohistochemistry and digital image analysis on tissue microarrays of 1720 primary colorectal cancers. TAM density in the tumor stroma was scored in relation to T cell density (stromal CD3+ and epithelial CD8+ cells) and analyzed in Cox proportional hazards models of 5-year relapse-free survival. Multivariable survival models included clinicopathological factors, MSI status and BRAFV600E mutation status. Results High TAM density was associated with a favorable 5-year relapse-free survival in a multivariable model of patients with stage I–III tumors (p = 0.004, hazard ratio 0.94, 95% confidence interval 0.90–0.98). However, the prognostic effect was dependent on tumoral T-cell density. High TAM density was associated with a good prognosis in patients who also had high T-cell levels in their tumors, while high TAM density was associated with poorer prognosis in patients with low T-cell levels (pinteraction = 0.0006). This prognostic heterogeneity was found for microsatellite stable tumors separately. Conclusions This study supported a phenotypic heterogeneity of TAMs in colorectal cancer, and showed that combined tumor immunophenotyping of multiple immune cell types improved the prediction of patient prognosis. Supplementary Information The online version contains supplementary material available at 10.1007/s13402-024-00926-w.

Describe the characteris7cs (e.g., disease stage or co-morbidi7es) of the study pa7ents, including their source and inclusion and exclusion criteria.
3+5, Sup.Table 1 3 Describe treatments received and how chosen (e.g., randomized or rule-based).3 Specimen characteris#cs 4 Describe type of biological material used (including control samples) and methods of preserva7on and storage. 3

5
Specify the assay method used and provide (or reference) a detailed protocol, including specific reagents or kits used, quality control procedures, reproducibility assessments, quan7ta7on methods, and scoring and repor7ng protocols.Specify whether and how assays were performed blinded to the study endpoint.

6
State the method of case selec7on, including whether prospec7ve or retrospec7ve and whether stra7fica7on or matching (e.g., by stage of disease or age) was used.Specify the 7me period from which cases were taken, the end of the follow-up period, and the median follow-up 7me.

3+1 7
Precisely define all clinical endpoints examined.5 8 List all candidate variables ini7ally examined or considered for inclusion in models.3 9 Give ra7onale for sample size; if the study was designed to detect a specified effect size, give the target power and effect size.

3,6
Sta#s#cal analysis methods 10 Specify all sta7s7cal methods, including details of any variable selec7on procedures and other modelbuilding issues, how model assump7ons were verified, and how missing data were handled.

5-6 11
Clarify how marker values were handled in the analyses; if relevant, describe methods used for cutpoint determina7on.

Data
12 Describe the flow of pa7ents through the study, including the number of pa7ents included in each stage of the analysis (a diagram may be helpful) and reasons for dropout.Specifically, both overall and for each subgroup extensively examined report the numbers of pa7ents and the number of events.6 Sup Fig. 1 13 Report distribu7ons of basic demographic characteris7cs (at least age and sex), standard (disease-specific) prognos7c variables, and tumor marker, including numbers of missing values.
S. Present univariable analyses showing the rela7on between the marker and outcome, with the es7mated effect (e.g., hazard ra7o and survival probability).Preferably provide similar analyses for all other variables being analyzed.For the effect of a tumor marker on a 7me-to-event outcome, a Kaplan-Meier plot is recommended.
Table 1 Figure 1-3 16 For key mul7variable analyses, report es7mated effects (e.g., hazard ra7o) with confidence intervals for the marker and, at least for the final model, all other variables in the model.
Table 1 17 Among reported results, provide es7mated effects with confidence intervals from an analysis in which the marker and standard prognos7c variables are included, regardless of their sta7s7cal significance.If done, report results of further inves7ga7ons, such as checking assump7ons, sensi7vity analyses, and internal valida7on.

Table 3 :
Reagents used for multiplex IHC.

Table 4 :
Thresholds for positivity of the markers within the two series.If a cell had a mean (normalized counts, total weighting in Inform software) nuclear signal above the threshold it was scored as positive, otherwise it was scored as negative for the marker.

Table 1
Interpret the results in the context of the pre-specified hypotheses and other relevant studies; include a discussion of limita7ons of the study.Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM, et al.Repor?ng recommenda?ons for tumor marker prognos?c studies (REMARK).J Natl Cancer Inst 97,1180-4 (2005).