
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01979-5
10.1016/j.isci.2024.110754
110754
Article
T cell factor 1 (TCF-1) defines T cell differentiation in colorectal cancer
Tran Kelly 127
Kumari Anita N. 12
Raghu Dinesh 12
Cox Daniel R.A. 34
Goh Su Kah 34
Perini Marcos V. 34
Muralidharan Vijayaragavan 34
Tebbutt Niall C. 1235
Behren Andreas 12
Mariadason John 12
Williams David S. 126
Mielke Lisa A. lisa.mielke@onjcri.org.au
1278∗
1 Olivia Newton-John Cancer Research Institute, Heidelberg, VIC, Australia
2 School of Cancer Medicine, La Trobe University, Heidelberg, VIC, Australia
3 Department of Surgery (Austin Precinct), University of Melbourne, Melbourne, VIC, Australia
4 HPB & Liver Transplant Surgery Unit, Department of Surgery, Austin Health, Heidelberg, VIC, Australia
5 Department of Medical Oncology, Austin Health, Heidelberg, VIC, Australia
6 Department of Pathology, Austin Health, Heidelberg, VIC, Australia
7 La Trobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia
∗ Corresponding author lisa.mielke@onjcri.org.au
8 Lead contact

22 8 2024
20 9 2024
22 8 2024
27 9 11075427 12 2023
11 3 2024
13 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

The presence of precursor to exhausted (Tpex) CD8+ T cells is important to maintain robust immunity following treatment with immune checkpoint inhibition (ICI). Impressive responses to ICI are emerging in patients with stage II-III mismatch repair (MMR)-deficient (dMMR) colorectal cancer (CRC). We found 64% of dMMR and 15% of mismatch repair-proficient (pMMR) stage III CRCs had a high frequency of tumor infiltrating lymphocytes (TIL-hi). Furthermore, expression of TCF-1 (Tcf7) by CD8+ T cells predicted improved patient prognosis and Tpex cells (CD3+CD8+TCF-1+PD-1+) were abundant within lymphoid aggregates of stage III CRCs. In contrast, CD3+CD8+TCF-1−PD-1+ cells were more abundant at the invasive front and tumor core, while γδ T cells were equally abundant in all tumor areas. Interestingly, no differences in the frequency of Tpex cells were observed between TIL-hi dMMR and TIL-hi pMMR CRCs. Therefore, Tpex cell function and ICI response rates in TIL-hi CRC warrants further investigation.

Graphical abstract

Highlights

• 15% of stage III pMMR CRCs have a high frequency of TILs

• CD3+CD8+TCF-1+ T cells predict improved survival of stage III CRC patients

• Lymphoid aggregates harbor CD3+CD8+TCF-1+PD-1+ Tpex cells in primary CRCs

• Tpex cells are abundant in lymphoid aggregates of TIL-hi pMMR and dMMR CRCs

Immunology; Components of the immune system; Cancer

Subject areas

Immunology
Components of the immune system
Cancer
Published: August 22, 2024
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pmcIntroduction

Colorectal cancer (CRC) is a leading cause of cancer death worldwide and T cells play a key role in preventing the development and progression of these tumors.1,2 CRCs that have defective DNA mismatch repair (dMMR) capacity (also referred to as microsatellite instability high (MSI-H)) generally produce more neoantigens and elicit a better immune response.3 In recent years, immune checkpoint inhibition (ICI) with anti-PD-1 and anti-CTLA4 antibodies has been widely used for the successful treatment of dMMR/MSI-H tumors in stage IV metastatic CRC (mCRC).4,5 Furthermore, ICI is now gaining traction as an effective treatment in earlier stage dMMR CRC, with the first clinical trials revealing impressive response rates in the neoadjuvant setting.3,6,7,8,9,10,11,12,13,14,15

A critical role of immune cells in the defense against CRC progression is further highlighted by recent studies showing that tumors with high numbers of lymphoid aggregates16,17,18 or increased tumor infiltrating lymphocytes (TILs) are associated with better survival outcomes irrespective of MMR status.19,20,21,22 In addition, TILs are predictive of ICI response in mCRC.23 Interestingly, one of the first clinical trials using neoadjuvant ICI in non-mCRC treating 40 stage I-III CRC patients with ICI, reported pathological responses in 100% of dMMR tumors and 27% of proficient DNA mismatch repair (pMMR) tumors.6 These response rates are higher than the response rates observed in mCRC.5,24 Notably, the presence of CD8+PD-1+ T cell infiltration was predictive of ICI response, in both the dMMR and pMMR CRC patient groups.6 In addition to CD8+ T cells, other T cell subsets, including γδ T cells have recently emerged as important players capable of responding to ICI in stage II-III CRC, particularly in dMMR tumors.25 Together, these studies show that multiple T cell subsets play an important role in defense against CRC and both dMMR and some pMMR CRCs can respond to ICI.6

Extending on the findings that γδ T cell and CD8+ T cell infiltration is associated with better patient prognosis,26,27,28 recent work from numerous labs has shown that distinct subsets of γδ T cells, including Vδ1 cells in humans and Vγ1 and Vγ7 subsets in mice, play an important role to limit colon tumor growth.25,28,29,30 On the other hand, alternate IL-17 producing γδ T cell subsets act to promote tumor growth.30,31 Intrinsic factors and microenvironment signals regulating γδ T cell anti-tumor properties are now starting to emerge, with microbial, T cell receptor, butyrophilin-like molecules, Natural Killer cell receptors and integrins all recently implicated.25,29,30,32,33 In addition, recent work by our laboratory and others showed the transcription factor, T cell factor 1 (TCF-1) (encoded by the gene Tcf7) acts as a potent suppressor of γδ T cell anti-tumor effector functions.28,34 However, more work is required to determine how these different γδ T cell subsets infiltrate CRCs, their distribution in different tumor subtypes and how inhibitory receptors influence γδ T cell function and response to treatment.

In recent years, detailed investigation of CD8+ T cell differentiation in situations of chronic stimulation, such as cancer or chronic viral infection, have unveiled numerous populations of exhausted CD8+ T cells (Tex) that play a critical role in tumor defense.2 To mediate early tumor control, naive CD8+ T cells differentiate into short-lived effector T (Teff) cells that express cytotoxic molecules, including granzymes, perforin and cytokines, critical for tumor cell killing.2 Naive CD8+ T cells also differentiate into precursors of exhausted T (Tpex) cells. This cell population is marked by the transcription factor TCF-1, inhibitory receptor PD-1, and as antigen stimulation persists, Tpex cells are capable of differentiating into terminally differentiated exhausted CD8+ T cell (Ttex) populations.2,35,36,37 Ttex cells are short-lived terminally differentiated cells and although not as efficient as Teff cells, can produce cytotoxic molecules and significantly contribute to overall tumor control. Investigation of tumor models and chronic viral infections show that Tpex cells retain stem-like or progenitor features, expand dramatically in response to ICI and are essential for the overall maintenance of Ttex cells.2,35,36,37,38 The identification of Tpex cells resolved the long-standing question of how CD8+ T cell populations could be reinvigorated following ICI to eliminate tumor cells. Building on these discoveries in a clinical setting, increased ratios of TCF-1+CD8+ cells were shown to be an important predictive factor of response to ICI in melanoma.39 Continued investigation of Tpex cells, also known as stem-like exhausted CD8+ T cells, have confirmed the importance of these cell populations in reinvigorating T cell responses following ICI in numerous cancer types.39,40,41,42,43,44 In addition, recent studies have identified Tpex cells preferentially localize to lymphoid aggregate or tertiary lymphoid structures in lung cancers,45,46,47 however, the presence of Tpex cells in the colon and CRC has only recently begun to be explored.48,49 Reports showing that some Tpex cells, particularly those residing in secondary lymphoid organs, express CXCR5.48,49 The presence of these CD8+CXCR5+ cells in tumor draining lymph nodes were predictive of better prognosis in stage III CRC.48,49

We set out to investigate the overall TIL frequency and T cell differentiation in pMMR vs. dMMR CRCs. We found that 24% of stage III CRC patients had robust TIL infiltration, including 64% of dMMR and 15% of pMMR tumors. Survival analysis demonstrated that TCF-1-expressing CD8+ T cells correlated with better patient survival outcomes in stage III CRC. CD8+ T cell phenotyping in different locations within and surrounding tumors, revealed that a population of CD8+TCF-1+PD-1+ cells encompassing Tpex cells, were significantly more abundant in lymphoid aggregates compared with other areas of the tumor, whereas CD8+TCF-1−PD-1+ containing populations of activated and Tex cells, were most abundant in the tumor core and invasive front. In contrast, analysis of γδ T cells revealed equal distribution of these cells in each tumor area analyzed. These results highlight differences in CD8+ T cell and γδ T cell spatial distribution in stage III CRC. Our analysis of TIL-hi CRCs revealed no difference in the frequency of CD8+TCF-1+PD-1+ populations between dMMR and pMMR cohorts. These results suggest that both TIL-hi dMMR and pMMR tumors are capable inducing CD8+ T cell differentiation, including differentiation of CD8+TCF-1+PD-1+ Tpex cells. Overall, our findings warrant further research with larger datasets dissecting the role and mechanisms controlling Tpex formation and function in lymphoid aggregates, in addition to γδ T cells in defense against CRC and treatment response.

Results

TILs correlate with MMR status in stage III CRC

The presence of TILs, in particular CD8+ T cells, γδ T cells, and dMMR status are established predictors of improved outcomes for CRC patients.19,26,27,28 To begin to establish a relationship between these factors, we determined and classified the TIL status of 411 stage III CRCs as TIL-hi or TIL-lo as previously described19 and determined TIL association with MMR status. We assessed hematoxylin and eosin (H&E) staining of 411 tumors for the presence of TILs and found that 98 (24%) had a TIL-hi phenotype and 313 (76%) had a TIL-lo phenotype (Figure 1A). Of the 411 tumors analyzed, 74 (18%) had a dMMR phenotype, and 337 (82%) had a pMMR phenotype (Figure 1B; Table S1). Analysis of TILs in conjunction with MMR status, revealed that 64% of dMMR tumors displayed a TIL-hi phenotype. Intriguingly, we observed that 15% of pMMR patients also displayed a TIL-hi phenotype (Figure 1C). Our analysis of MMR status within the overall TIL-hi group, revealed that of the 98 TIL-hi tumors identified, they equally encompassed both dMMR and pMMR tumors (Figure 1D). These results indicate that although the majority of stage III CRCs are TIL-low and pMMR, 24% of tumors exhibited robust TIL infiltration.Figure 1 TIL frequency correlates with MMR status in stage III CRC

(A and B) Stage III CRCs were analyzed and stratified as A) TIL-hi or TIL-lo infiltration and for B) MMR status. Data are presented as number of patients and the frequency on each graph represents the percent of patients in each subgroup.

(C) Frequency of TIL-hi and TIL-lo tumors shown for dMMR and pMMR subgroups in stage III CRC.

(D) Number and frequency of dMMR or pMMR patients among TIL-hi stage III CRCs. A total of 411 stage III CRCs were analyzed.

CD8+ T cells are abundant in CRC lymphoid aggregates, while γδ T cells are equally distributed in all tumor regions

Given the robust infiltration of TILs observed in a proportion of stage III CRCs, we next set out to perform a more detailed analysis of lymphocyte subsets in these CRCs. CD8+ T cells and γδ T cells play a critical role in defense against CRC and are two of the major cell types involved in immunotherapy response in CRC.6,25,28 To determine whether the TIL-hi phenotype was also associated with increased T cell infiltration, we assessed total T cells and CD8+ T cells in a small cohort of TIL-hi and TIL-lo stage III CRCs (Figures S1A and S1B). As expected, total T cells and CD8+ T cells were increased in TIL-hi tumors (Figures S1A and S1B). In addition, the number of γδ T cells were also increased in TIL-hi tumors (Figure S1C). We next analyzed both the differentiation status and spatial distribution of γδ T cells and CD8+ T cells in 20 stage III TIL-hi tumors, of which 10 were dMMR and 10 were pMMR. This analysis allowed us to directly compare immune responses in dMMR and pMMR tumors. We assessed the following tumor locations; tumor core, invasive front, lymphoid aggregates (defined by a cluster of >100 CD3+ cells that are in direct contact with one another), tumor stroma and normal-adjacent tissue (Figure 2A). The tumor samples were stained and analyzed using multiplex immunohistochemistry (mIHC) for the following cell markers; CD3, CD8, TCRδ, PanCK and DAPI (Figures 2B–2F).Figure 2 Differences in CD8+ T cell and γδ T cell spatial distribution in stage III CRC

(A) Representative hematoxylin and eosin (H&E) staining of a stage III CRC tumor whole tissue section. Areas of interest for analysis include tumor stroma, tumor core, normal-adjacent tissue, lymphoid aggregates and invasive front. Scale bar: 2000 μm.

(B–F) Representative images of mIHC staining of the five compartments of interest, i.e., B) tumor core (T), C) invasive front (IF), D) lymphoid aggregate (LA), E) tumor stroma (S) and F) normal-adjacent tissue (N). Staining represented as merged and single channel staining for CD3, CD8, TCRδ and PanCK and are shown with DAPI staining. White square boxes are magnified to the right (middle panel). Scale bar: 50 μm.

(G–I) Densities (mm2, upper panel) and frequencies (lower panel) of G) total CD3+, H) CD3+CD8+TCRδ− and I) CD3+CD8−TCRδ+ cells across the five tumor areas of interest. Orange data points show dMMR cases, blue data points show pMMR cases. Each point represents the average of five multispectral images per patient. Error bars represent SEMs. p-values calculated using one-way ANOVA test and only p-values < 0.05 are displayed.

T cell subset density and percentage frequencies were analyzed for all CD3+ cells (Figure 2G), CD3+CD8+TCRδ− cells (Figure 2H) and CD3+CD8−TCRδ+ (Figure 2I). We analyzed each cell subset across the five tumor areas of interest, revealing as expected that CD3+ T cells were significantly enriched in lymphoid aggregates (Figure 2G). Lymphoid aggregates were also enriched in TIL-hi vs. TIL-lo tumors (Figure S1D). Similarly, CD3+CD8+TCRδ− cells were increased in lymphoid aggregates, compared to the tumor stroma and normal-adjacent tissue (Figure 2H). Comparatively, equivalent frequencies of γδ T cells (CD3+CD8−TCRδ+) were observed across all tumor regions analyzed (Figure 2I). Interestingly, we observed a significant increase in total CD3+ T cells in dMMR lymphoid aggregates and increased frequency of γδ T cells in dMMR tumors, consistent with previous reports25,50 (Figures 2G–2I; Table S2). These data demonstrate that CD8+ T cells accumulate in dense lymphoid aggregates, whereas γδ T cells are more dispersed throughout the tumor microenvironment.

Different γδ T cell effector subsets localize within the tumor and in the tumor periphery

While our analysis demonstrated that γδ T cells are equally abundant in all tumor regions, we next sought to assess their phenotype in different tumor regions. Recent reports from our laboratory and others identified TCF-1 as a key suppressor of γδ T cell effector function in multiple cancer types, and that low TCF-1 expression efficiently marked effector γδ T cells in tumors.28,34 The function of inhibitory receptors on γδ T cells, has only recently begun to emerge, with PD-1 expression being associated with enhanced tumor cell killing by human γδ T cells in in vitro CRC co-cultures.25 In addition, studies in mice linked PD-1 expression with IL-17 production and γδ T cell exhaustion.30,51 Therefore, we analyzed γδ T cells for TCF-1 and PD-1 expression in different tumor regions of our TIL-hi CRC cohort (Figure S2A). Interestingly, mIHC staining showed that TCF-1+ γδ T cells were more abundant in normal colon tissue adjacent to the tumor (Figure S2B left panel). Comparatively, TCF-1- γδ T cells were equally abundant in all tumor areas analyzed (Figure S2B right panel). These results reveal that γδ T cells with reduced TCF-1 expression, that is linked to increased effector function, are differentially abundant in distinct regions of CRCs, with TCF-1- effector γδ T cells present in all tumor regions, including the tumor core.

Abundance of TCF-1+CD8+ T cells is associated with improved survival in stage III CRC

Unlike γδ T cells, the role of TCF-1 in CD8+ T cell differentiation has been intensely researched in recent years. CD8+ T cell differentiation in tumors is complex, however recent reports have shown that under conditions of chronic stimulation, co-expression of TCF-1 and PD-1 marks a stem-like CD8+ T cell population, known as Tpex cells. Due to their stem-like properties, Tpex cells are essential for maintaining CD8+ T cell differentiation in response to ICI, and CD8+TCF-1+ T cells have been identified as an important predictor of clinical response to ICI in numerous cancer types.39,41,43,44 Detailed investigation of TCF-1 expressing CD8+ T cell subsets has not been previously examined in CRC. To begin, we set out to examine whether the presence of CD8+TCF-1+ T cells correlated with patient prognosis. We assessed tissue microarrays (TMAs) comprising colon tumors from 63 stage III patients, these were stained and analyzed using mIHC for CD3, CD8, TCF-1 and Pan Cytokeratin (PanCK) (Figure 3A). Analysis of CD3+CD8+TCF-1+ tumor infiltrating T cells, revealed that the abundance of these cells correlated with improved disease-free (Figure 3B) and overall survival (Figure 3C). In contrast, total T cells or total CD8+ T cells in this small patient cohort did not reveal significant association with patient outcome. Collectively, these results suggest that the abundance of CD8+TCF-1+ T cells could serve as a prognostic marker in CRC.Figure 3 TCF-1-expressing CD8+ T cells predict improved survival in stage III CRC

(A) Flowchart depicting overview of experiment setup.

(B and C) Percentage disease-free and C) overall survival of stage III CRC patients with high or low CD3+CD8+TCF-1+, total CD3+ or total CD3+CD8+ T cell infiltration. Staining and analysis is performed on 3 replicate tissue microarray samples per tumor from 63 stage III CRC patients. p-values calculated using Log rank Mantel-Cox test.

CD8+TCF-1+PD-1+ Tpex cells are more abundant in lymphoid aggregates in stage III CRC

The experiments in Figure 3 analyzed total CD8+TCF-1+ T cells, the TMAs used contain only a small region of the total tumor, making it difficult to analyze smaller populations of cells including Tpex populations, and different structures, including lymphoid aggregates. Therefore, we next assessed CD8+ T cell differentiation status in more detail within different tumor areas in both dMMR and pMMR tumors. CD3+CD8+TCRδ− T cells were characterized according to their TCF-1 and PD-1 expression as follows; TCF-1+PD-1+ encompassing Tpex cells, TCF-1−PD-1+ cells that will contain subsets of Ttex, Teff and early-exhausted populations, TCF-1−PD-1- that will contain Teff cells and finally TCF-1+PD-1- containing naive or memory CD8+ T cells. Representative CD8+TCF-1+PD-1+ Tpex and CD8+TCF-1−PD-1+ cell subsets located in lymphoid aggregates and tumor core, displayed positive staining for PD-1 (Figure 4A). Analysis of the frequency of each CD3+CD8+TCRδ− subset revealed that CD8+TCF-1+PD-1+ Tpex cells were significantly enriched in lymphoid aggregates compared to the tumor core and tumor stroma (Figure 4B). In contrast, the frequencies of CD8+TCF-1−PD-1+ and CD8+TCF-1−PD-1- cell subsets were similarly observed across all tumor areas, with the highest abundance in the tumor core (Figures 4C and 4D). Finally, CD8+TCF-1+PD-1- naive or memory CD8+ T cell subsets were enriched in lymphoid aggregates compared to other tumor regions (Figure 4E). Strikingly, similar frequencies of CD8+TCF-1+PD-1+ Tpex cells and all other CD3+CD8+TCRδ− cell subsets were observed in both TIL-hi dMMR and TIL-hi pMMR tumors. In some circumstances, including in lymph nodes, CD8+TCF-1+PD-1+ Tpex cells have been shown to express the chemokine receptor CXCR5.38,48,49 We observed CXCR5 expression on CD8+ T cells in lymphoid aggregates of tumors from stage III CRC, but not in the tumor core (Figure S3A). Densities and the frequency of CD3+CD8+CXCR5+ T cells were analyzed across the five tumor locations, which revealed these cells were primarily located in lymphoid aggregates, rather than any other tumor region (Figure S3B).Figure 4 CD8+TCF-1+PD-1+ Tpex cells are more abundant in lymphoid aggregates in stage III CRC

(A) Representative CD8+TCF-1+PD-1+ Tpex (upper panel) and CD8+TCF-1−PD-1+ T cells (lower panel) mIHC staining in a lymphoid aggregate and tumor core of stage III CRC whole tumor section. Merged images display overlay of CD3, CD8, TCRδ, PD-1, TCF-1, PanCK and DAPI staining. Single stains of CD3, CD8, TCRδ, PD-1, TCF-1 and PanCK are shown with DAPI staining. White square boxes are magnified to the right (middle panel). Scale bar: 50 μm.

(B–E) Percentage frequencies of B) Tpex (CD8+TCF-1+PD-1+), C) CD8+TCF-1−PD-1+, D) CD8+TCF-1−PD-1- and E) CD8+TCF-1+PD-1- T cell subsets across the five tumor areas of interest; tumor core (T), invasive front (IF), lymphoid aggregates (LA), tumor stroma (S) and normal-adjacent tissue (N). Percentage frequencies for each CD8+ T cell subset is calculated based on the total CD3+ T cell count. Orange data points represent dMMR cases, blue data points represent pMMR cases. Each point represents the average of five multispectral images per patient. Error bars represent SEMs. p-values calculated using one-way ANOVA test and only p-values < 0.05 are shown.

Discussion

Surgery and chemotherapy have been the standard of care for non-mCRC patients for many years. However, relapse following treatment remains high at 20–40% for stage III CRC patients, and in some cases, treatment is associated with long term and serious side effects.52 Therefore, more effective and safer treatment options are desperately needed to manage CRC. ICI has revolutionized the treatment of some cancer types, including melanoma and lung cancer. In CRC, ICI is currently used to treat patients with dMMR mCRC or dMMR rectal cancers.9,23 ICI is yet to be widely used to treat early stage dMMR disease, however, recent clinical trials using ICI to treat dMMR and pMMR stage II and III CRC have demonstrated promising early results.6,8,9,10,11,12,13 Although, more clinical trials with long-term outcome data and improved methods of tracking disease progression and treatment response are needed. We assessed CD8+ T cell differentiation status, focusing on Tpex cells, which have recently been characterized as the major cell population responsible for reinvigorating T cell responses to promote tumor clearance following ICI.2,38 Our analysis of CD8+ T cell differentiation in stage III CRC elucidates patient populations with the presence of Tpex cells in lymphoid aggregates, indicating potential to respond to ICI.

Overall, we found that 24% of stage III CRC patients had robust TIL infiltration. A focused analysis of CD8+ T cell differentiation revealed that CD8+TCF-1+PD-1+ Tpex populations were present in tumors of stage III CRC patients and are specifically enriched in lymphoid aggregates, whereas CD8+TCF-1−PD-1+ cells were most abundant in the tumor core and invasive tumor front. Little is known about the factors controlling recruitment, differentiation and maintenance of CD8+TCF-1+PD-1+ Tpex cells in colon tumors. An important relationship between dendritic cells (DCs) and Tpex cells is emerging. Tpex cells have the potential to secrete XCL1, which is important for dendritic cell recruitment. Analysis of lung, kidney, bladder and prostate cancers showed DCs co-reside with Tpex cells within distinct anatomical niches.42 Two recent studies further interrogated the localization of DCs with Tpex cells within intratumoral “lymphonets” or “immunity hubs” that are abundant in early-stage human lung adenocarcinoma.45,46 These structures are often less organized and lack distinct T and B cell zones compared with tertiary lymphoid structures. These finding are consistent with our results showing that CD8+TCF-1+PD-1+ Tpex cells are largely restricted to lymphoid aggregates and these findings are consistent across tumor types. In addition, in mouse models of lung cancer, migratory type 1 conventional DCs (cDC1) facilitated the recruitment and maintenance of Tpex cells within the lung and the draining lymph node.53,54 Further studies investigating Tpex development within the tumor and intestinal microenvironment, will be essential to elucidate new opportunities to exploit these cells and maintain robust immune responses in defense against CRC.

Our analysis focused on TIL-hi CRCs and in this cohort revealed no significant differences in Tpex cell differentiation, including CD8+TCF-1+PD-1+ Tpex frequency between the dMMR and pMMR patient cohorts. Our selection of TIL-hi cases provides a unique outlook of the CRC landscape providing evidence that some TIL-high pMMR patients can exert CD8+ T cell responses that could be comparable to patients with dMMR tumors. These results in combination with recent clinical trials3,6 and reports showing that high immune cell infiltration is superior to MMR status in predicting CRC patient survival outcomes,19,21 raises an emerging hypothesis for the field, that additional cohorts of CRC patients may respond to ICI.

In addition to our analysis of CD8+ T cell differentiation, we also investigated spatial distribution of γδ T cells. Unlike CD8+ T cell subsets, the γδ T cells were evenly distributed throughout the tumor microenvironment. In agreement with our previous study, TCF-1+ γδ T cells were most abundant in normal colon epithelium compared with tumor areas. TCF-1 acts to suppress colon γδ T cell anti-tumor defense and effector functions, limiting molecules important for DC recruitment and cytotoxicity, including XCL1 and Granzyme B.28 The role of PD-1 on colon γδ T cells is beginning to emerge, its expression has been associated with tumor reactive colon γδ T cells,25 however much more work is needed to determine the function of PD-1 and other checkpoint receptors on colon γδ T cells and how they influence the anti-tumor defense of the two major human γδ T cell subsets, Vδ1 and Vδ2 in tissues and circulation. Overall, our data reveals differences in CD8+ T cell and γδ T cell spatial distribution in stage III CRC, highlighting the importance of analyzing immune responses in lymphoid aggregates in addition to the tumor core. Further studies investigating factors regulating development of lymphoid aggregates in CRC, may provide insights for future immunotherapy strategies55 and provide opportunities to exploit Tpex differentiation or identify new biomarkers to predict treatment response in CRC.

Limitations of the study

The aim of this study was to investigate T cell differentiation in TIL-hi stage III CRC and compare T cell differentiation between the two major disease subtypes, dMMR and pMMR CRC. We found that in a subgroup of stage III CRCs with high TIL infiltration, the presence of CD3+CD8+TCF-1+ T cells predicted improved survival of patients and CD3+CD8+TCF-1+PD-1+ Tpex cells were enriched in lymphoid aggregates in both subtypes of dMMR and pMMR TIL-hi tumors. However, there are a number of limitations with our study and further investigation of T cell differentiation in CRC will shed light on the role of CD8+ T cells in CRC and whether they can be harnessed with immunotherapies to benefit patients. Limitations include the TIL scoring method used; only lymphocytes infiltrating the tumor epithelium are counted to generate the TIL score, however, other TIL or immune scoring protocols exist and future comparison of these different methods would be useful to determine effective methods to predict patient outcomes.56 In addition, our analyses of T cell differentiation in different tumor regions highlight lymphoid aggregates as a region of interest; however, very recent reports describe similar regions of interest and a future comprehensive analysis of these structures in comparison to tertiary lymphoid structures should be performed. Finally, increasing patient numbers in the analysis and the inclusion of additional markers of exhaustion and T cell activation, including TIM3 and Granzyme B, would allow us to confirm our findings in independent patient cohorts and more accurately distinguish exhausted and effector CD8+ T cell subsets in CRC. Increasing the patient cohort will also allow us to better correlate the number of Tpex cells with MMR or TIL status in association with patient survival. Currently, this is not possible with the small sample size.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, L.A.M. (lisa.mielke@onjcri.org.au).

Materials availability

This study did not generate new unique reagents.

Data and code availability

Data reported in this paper will be shared by the lead contact upon request. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

We thank our consumers and patient advocates for their advice, patients and their families who donated human tissues for use in this study and the Austin Hospital ethics committee for their guidance. We thank the Victorian Cancer Biobank through the Cancer Council Victoria as Lead Agency is supported by the Victorian Government through the Victorian Cancer Agency, a business unit of the Department of Health. This research was funded by The Australian National Health and Medical Research Council Ideas Grants 2011558 (L.A.M.) and 1185513 (L.A.M. and D.W.), Victorian Cancer Agency mid-career and early career fellowships (L.A.M. and D.R.), 10.13039/501100001184 Cure Cancer Australia , Priority Driven Young Investigator Grant 1123388 (L.A.M.) and Veski Career Recovery Grant (L.A.M.).

Author contributions

Investigation: K.T., A.N.K., D.R., D.S.W., and L.A.M. Resources: D.S.W. and A.B. Supervision: L.A.M., D.S.W., V.M., D.R.A.C., S.K.G., M.V.P., J.M., and N.C.T. Funding acquisition: L.A.M., D.S.W., D.R., and N.C.T. Writing original draft: K.T. and L.A.M. Conceptualization: L.A.M.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Antibodies	
	
CD3 [SP7]	Abcam	Cat #: AB16669; RRID: AB_443425	
CD8 [4B11]	GeneTex	Cat #: GTX75394; RRID: AB_376778	
CXCR5 [Polyclonal]	Atlas Antibodies	Cat #: HPA042432; RRID: AB_2677995	
PanCK [AE1/AE7]	Leica Biosystems	Cat #: PA0094	
PD-1 [NAT105]	Abcam	Cat #: AB52587; RRID: AB_881954	
TCF-1 [C63D9]	Cell Signaling Technology	Cat #: 2203S	
TCRδ [H-41]	Santa Cruz Biotechnology	Cat #: SC-100289; RRID: AB_1130061	
	
Biological samples	
	
All Stage III CRC primary tumor samples were retrospectively collected from patients recruited at Austin Health from Austin pathology or from Victorian Cancer Biobank	Austin Pathology or Victorian Cancer Biobank		
	
Critical commercial assays	
	
Opal 7-Colour Immunohistochemistry Kit	Akoya Biosciences	NEL811001KT	
	
Software and algorithms	
	
Inform Advanced Image Analysis Software	PerkinElmer		
HALO	Indica Labs		
	
Other	
	
VectaShield HardSet Mounting Medium	Vector Labs	H-1400-10	

Experimental model and study participant details study participants

Stage III CRC primary tumor samples were retrospectively collected from patients including 218 females and 193 males with an average age of 61 (Table S1). Patients were recruited at Austin Health between 2008 and 2013 or provided by Victorian Cancer Biobank, for whom clinical, treatment and follow-up data were collected. TIL counts were derived from hematoxylin and eosin (H&E) stained whole tumor sections as previously described19 and categorized as follows; TIL high tumors are described as >10 lymphocytes per 5 high powered fields (HPFs) and a low TIL count is defined as <10 lymphocytes per 5 HPFs. Only lymphocytes infiltrating the tumor epithelium were counted to generate the TIL score; stromal lymphocytes and lymphoid aggregates are not included or used to generate the TIL score. The 5 representative HPF were chosen at random, in accordance with the above criteria by an independent pathologist who is blinded to the sample and patient identification.

Tissue microarrays from 63 stage III CRC tumor samples were used for multiplex immunohistochemistry staining (31 female, 32 male, average age 68). For all other multiplex immunohistochemistry experiments involving human samples, FFPE whole tumor sections were used from both male and female patients. 20 whole tissue sections with high TIL counts were selected, of which ten were dMMR/microsatellite instability-high (MSI-H) and ten were mismatch repair proficient pMMR/microsatellite instability-low (MSI-L). Analysis of DNA mismatch repair status was performed using the Bethesda consensus panel of microsatellite markers as previously described.19

All human samples were collected and used in accordance with the procedures approved by the Austin Health Human Ethics Committee (Heidelberg, Australia protocol HREC/15/Austin/359 and 75462).

Method details

Multiplex immunohistochemistry (IHC)

Formalin-fixed paraffin-embedded (FFPE) whole tissue sections and TMAs of 0.4 μm thickness were stained using the Opal 7-Colour Immunohistochemistry Kit (Akoya Biosciences), according to the manufacturer’s protocol, manually or using the Leica Bond RX Autostainer. Slides were stained with a combination of antibodies including CD3, CD8, Pan Cytokeratin, PD-1, TCF-1, TCRδ, CXCR5 and DAPI.

In summary, sections were baked, dewaxed and rehydrated. Antigen retrieval was performed at the corresponding pH, then slides were incubated with hydrogen peroxide (3%) and blocked with blocking buffer. The first cycle of primary antibody staining was performed, followed by HRP anti-mouse and rabbit-conjugated secondary antibody incubation and Opal signal development. The process was repeated with the next sequential antibodies until complete, then nuclei were stained with DAPI. Upon completion, slides were set with VectaShield HardSet Mounting Medium (Vector Labs).

Imaging and analysis

Slides were imaged using the Vectra Imaging System (PerkinElmer). Whole slide scans of 10x magnification were acquired. Regions of interest were stamped using PhenoChart (PerkinElmer) to facilitate scanning of multispectral images (MSIs) of 20x magnification. For the whole tissue sections, MSIs were obtained for the tumor core, invasive front, lymphoid aggregates, tumor stroma and normal-adjacent tissue. For the TMAs, MSIs were acquired for every core. Image files were processed on the Inform Analysis software (PerkinElmer), then transferred to HALO (Indica Labs) to perform cell phenotyping. Cell frequencies and densities were calculated using cell counts, obtained from Halo. Lymphoid aggregates were defined as clusters of CD3+ cells that are in direct contact with one another as previously described.46 The cut off was set at >100 CD3+ cells. This definition includes large lymphonets, immune hubs and tertiary lymphoid structures16,17,18,19,45,46. Frequencies of the CD3+ cell populations were calculated based on the number of total cells, whereas the frequencies of CD3+CD8+TCRδ− and CD3+CD8−TCRδ+ cell populations were calculated based on the total number of CD3+ cells. This was performed for every region of interest (5 per patient), then the average per patient was plotted.

Quantification and statistical analysis

For all statistical analyses, GraphPad Prism 9.0 (GraphPad Software, San Diego, CA) was used. One-way analysis of variance (ANOVA), T-test or log rank (Mantel-Cox) tests were performed to determine statistical significance. All data are plotted as the means ± SEM, unless otherwise specified. p values <0.05 were considered statistically significant.

Supplemental information

Document S1. Figures S1–S3

Table S1. Raw data summary for Figure 1, related to Figure 1

Table S2. Raw data summary for Figure 2, related to Figure 2

Table S3. Raw data summary for Figure 3, related to Figure 3

Table S4. Raw data summary for Figure 4, related to Figure 4

Table S5. Raw data summary for Figure S1, related to Figure 2

Table S6. Raw data summary for Figure S2, related to Figure 4

Table S7. Raw data summary for Figure S3, related to Figure 4

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110754.
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