Validation of the prognostic value of CD3 and CD8 cell densities analogous to the Immunoscore® by stage and location of colorectal cancer: an independent patient cohort study

Immunoproteomics Reveal Different Characteristics for the Prognostic Markers of Intratumoral-Infiltrating CD3+ T Lymphocytes and Immunoscore in Colorectal Cancer

Tumor-infiltrating lymphocytes (TILs) and immunoscoring based on densities of CD3+ and CD8+ TILs are both favorable prognostic markers in colorectal cancer (CRC). However, determination of the molecular features of TILs, particularly their immunoproteomic signatures would require the development of large scale in situ spatiotemporal technologies. Recently, a multiplex in situ digital spatial proteomic profiling (DSP) tool GeoMx DSP has been applied to identify biomarkers predictive of therapeutic responses and to understand disease mechanisms and progression. Taking advantage of this tool, we simultaneously characterized the spatial distribution and interactions of 42 immune proteins in tumor cells (TCs), CD3+ T stromal TILs (sTILs), and CD20+ B sTILs using tissue microarrays, and further studied their associations with CD3+ T TILs and immunoscores in CRC. First, our data showed that well-known immune checkpoints, such as PD-L1, PD-L2, and LAG3, were expressed at low levels, whereas some other immune proteins, such as CD11c, CD68, STING, and CD44, were highly expressed. Second, 8 spatial interactions were identified, including 5 interactions between TC and CD20+ B sTILs, 2 interactions between CD3+ T sTILs and CD20+ B sTILs, and 1 interaction among TC, CD3+ T sTILs, and CD20+ B sTILs. Third, the differential immune microlandscape in the spatial compartments was identified in tissues with positive CD3+ T intratumoral TILs and high immunoscores. Collectively, to our knowledge, our study is the first to provide in situ spatial immune characteristics at the proteomic level. Moreover, our findings provide direct evidence supporting the infiltration of CD3+ T sTILs from stoma to TC and shed important insights into better understanding and treating CRC patients related to different immune prognostic markers.

Clinical significance of combined tumour-infiltrating lymphocytes and microsatellite instability status in colorectal cancer: a systematic review and network meta-analysis

BACKGROUND

Microsatellite instability (MSI) status and tumour-infiltrating lymphocytes (TIL) are established prognostic factors in colorectal cancer. Previous studies evaluating the combination of TIL and MSI status identified distinct colorectal cancer subtypes with unique prognostic associations. However, these studies were often limited by sample size, particularly for MSI-high (MSI-H) tumours, and there is no comprehensive summary of the available evidence. We aimed to review the literature to compare the survival outcomes associated with the subtypes derived from the integrated MSI-TIL classification in patients with colorectal cancer.

METHODS

In this systematic review and network meta-analysis, we searched PubMed, Embase, Scopus, and the Cochrane Library without language restrictions, for articles published between Jan 1, 1990, and March 13, 2024. Patient cohorts comparing different combinations of TIL (high or low) and MSI status (MSI or microsatellite stable [MSS]) in patients with surgically resected colorectal cancer were included. Studies were excluded if they focused on neoadjuvant therapy or on other immune markers such as B cells or macrophages. Methodological quality assessment was done with the Newcastle-Ottawa scale; data appraisal and extraction was done independently by two reviewers. Summary estimates were extracted from published reports. The primary outcomes were overall survival, disease-free survival, and cancer-specific survival. A frequentist network meta-analysis was done to compare hazard ratios (HRs) and 95% CI for each outcome. The MSI-TIL subgroups were prognostically ranked based on P-score, bias, magnitude, and precision of associations with each outcome. The protocol is registered with PROSPERO (CRD42023461108).

FINDINGS

Of 302 studies initially identified, 21 studies (comprising 14 028 patients) were included in the systematic review and 19 (13 029 patients) in the meta-analysis. Nine studies were identified with a low risk of bias and the remaining ten had a moderate risk of bias. The MSI-TIL-high (MSI-TIL-H) subtype exhibited longer overall survival (HR 0·45, 95% CI 0·34-0·61; I2=77·7%), disease-free survival (0·43, 0·32-0·58; I2=61·6%), and cancer-specific survival (0·53, 0·43-0·66; I2=0%), followed by the MSS-TIL-H subtype for overall survival (HR 0·53, 0·41-0·69; I2=77·7%), disease-free survival (0·52, 0·41-0·64; I2=61·6%), and cancer-specific survival (0·55, 0·47-0·64; I2=0%) than did patients with MSS-TIL-low tumours (MSS-TIL-L). Patients with the MSI-TIL-L subtype had similar overall survival (0·88, 0·66-1·18; I2=77·7%) and disease-free survival (0·93, 0·69-1·26; I2=61·6%), but a modestly longer cancer-specific survival (0·72, 0·57-0·90; I2=0%) than did the MSS-TIL-L subtype. Results from the direct and indirect evidence were strongly congruous.

INTERPRETATION

The findings from this network meta-analysis suggest that better survival was only observed among patients with TIL-H colorectal cancer, regardless of MSI or MSS status. The integrated MSI-TIL classification should be further explored as a predictive tool for clinical decision-making in early-stage colorectal cancer.

FUNDING

German Research Council (HO 5117/2-2).

Based on Immune Microenvironment and Genomic Status, Exploring Immunotherapy in Advanced Hidradenocarcinoma: A Retrospective Analysis

There are no standard treatment guidelines for hidradenocarcinoma, and the immune microenvironment and genomic data are very limited. Thus, in this study the immune microenvironment and genomic indicators in hidradenocarcinoma was investigated, and immunotherapy for hidradenocarcinoma was initially explored. Forty-seven hidradenocarcinoma patients were retrospectively collected. Immunohistochemical staining was performed to identify CD3/CD8+ T cells and programmed death ligand-1 expression. In total, 89.4% and 10.6% of samples had Immunoscores of 0-25% and 25-70%. Tumour proportion score distribution was as follows: tumour proportion score < 1% in 72.4%, 1-5% in 17.0%, and > 5% in 10.6%. Combined positive score distribution was as follows: combined positive score < 1 in 63.8%, 1-5 in 14.9%, and > 5 in 21.3%. Next-generation sequencing revealed that TP53 (33%), PI3KCA (22%), and ERBB3 (22%) were the most frequently mutated genes. The PI3K-Akt signalling pathway, growth, and MAPK signalling pathways were significantly enriched. Five patients had a low TMB (< 10 muts/Mb), and 9 patients had MSS. Three patients treated with immune combined with chemotherapy achieved significant tumour regression, and the progression-free survival was 28.8 months. In conclusion, the hidradenocarcinoma immune microenvironment tends to be noninflammatory. Evidence-based targets for targeted therapy are lacking. Immunotherapy combined with chemotherapy may be better for most advanced hidradenocarcinoma patients with a noninflammatory microenvironment.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

BACKGROUND

Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

METHODS

In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables.

FINDINGS

We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors.

INTERPRETATION

Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work.

FUNDING

The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

Could immunoscore improve the prognostic and therapeutic management in patients with solid tumors?

The Immunoscore (ISc) is an emerging immune-based scoring system that has shown potential in improving the prognostic and therapeutic management of patients with solid tumors. The ISc evaluates the immune infiltrate within the tumor microenvironment (TME) and has demonstrated superior predictive ability compared to traditional histopathological parameters. It has been particularly promising in colorectal, lung, breast, and melanoma cancers. This review summarizes the clinical evidence supporting the prognostic value of the ISc and explores its potential in guiding therapeutic decisions, such as the selection of adjuvant therapies and recognizing patients likely to profit from immune checkpoint inhibitors (ICIs). The challenges and future directions of ISc implementation are also discussed, including standardization and integration into routine clinical practice.