Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets.

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colorectal cancers sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin, and DAPI by mIF. TMA slides were multi-spectrally imaged and analysed by cell-based and pixel-based marker analysis. We developed an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis. Applying this method effectively ameliorated inter- and intra-slide intensity variation improving the image analysis results compared with methods using a single global threshold. Correlation of CD8 data derived by our mIF analysis approach with single-plex chromogenic immunohistochemistry CD8 data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients ρ between 0.63 and 0.66, p ≪ 0.01) as compared with the current gold standard analysis approach. Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (ρ > 0.8, p ≪ 0.01, except for CD20 in the epithelial region) of both analytical approaches. These data suggest that our adaptive thresholding approach can enable analysis of mIF-stained clinical trial TMA datasets by digital pathology at scale for precision immunoprofiling.

The Glasgow Microenvironment Score associates with prognosis and adjuvant chemotherapy response in colorectal cancer.

BACKGROUND: The Glasgow Microenvironment Score (GMS) combines peritumoural inflammation and tumour stroma percentage to assess interactions between tumour and microenvironment. This was previously demonstrated to associate with colorectal cancer (CRC) prognosis, and now requires validation and assessment of interactions with adjuvant therapy. METHODS: Two cohorts were utilised; 862 TNM I-III CRC validation cohort, and 2912 TNM II-III CRC adjuvant chemotherapy cohort (TransSCOT). Primary endpoints were disease-free survival (DFS) and relapse-free survival (RFS). Exploratory endpoint was adjuvant chemotherapy interaction. RESULTS: GMS independently associated with DFS (p = 0.001) and RFS (p < 0.001). GMS significantly stratified RFS for both low risk (GMS 0 v GMS 2: HR 3.24 95% CI 1.85-5.68, p < 0.001) and high-risk disease (GMS 0 v GMS 2: HR 2.18 95% CI 1.39-3.41, p = 0.001). In TransSCOT, chemotherapy type (pinteraction = 0.013), but not duration (p = 0.64) was dependent on GMS. Furthermore, GMS 0 significantly associated with improved DFS in patients receiving FOLFOX compared with CAPOX (HR 2.23 95% CI 1.19-4.16, p = 0.012). CONCLUSIONS: This study validates the GMS as a prognostic tool for patients with stage I-III colorectal cancer, independent of TNM, with the ability to stratify both low- and high-risk disease. Furthermore, GMS 0 could be employed to identify a subset of patients that benefit from FOLFOX over CAPOX.

Histological phenotypic subtypes predict recurrence risk and response to adjuvant chemotherapy in patients with stage III colorectal cancer.

Histological 'phenotypic subtypes' that classify patients into four groups (immune, canonical, latent and stromal) have previously been demonstrated to stratify survival in a stage I-III colorectal cancer (CRC) pilot cohort. However, clinical utility has not yet been validated. Therefore, this study assessed prognostic value of these subtypes in additional patient cohorts along with associations with risk of recurrence and response to chemotherapy. Two independent stage I-III CRC patient cohorts (internal and external cohort) were utilised to investigate phenotypic subtypes. The primary endpoint was disease-free survival (DFS) and the secondary endpoint was recurrence risk (RR). Stage II-III patients, from the SCOT adjuvant chemotherapy trial, were utilised to further validate prognostic value and for exploratory analysis assessing associations with adjuvant chemotherapy. In an 893-patient internal cohort, phenotypic subtype independently associated with DFS (p = 0.025) and this was attenuated in stage III patients (p = 0.020). Phenotypic subtype also independently associated with RR (p < 0.001) in these patients. In a 146-patient external cohort, phenotypic subtype independently stratified patients by DFS (p = 0.028), validating their prognostic value. In 1343 SCOT trial patients, the effect of treatment type significantly depended on phenotypic subtype (pinteraction = 0.011). Phenotypic subtype independently associated with DFS in stage III patients receiving FOLFOX (p = 0.028). Furthermore, the immune subtype significantly associated with better response to FOLFOX compared to CAPOX adjuvant chemotherapy in stage III patients (p = 0.013). In conclusion, histological phenotypic subtypes are an effective prognostic classification in patients with stage III CRC that associates with risk of recurrence and response to FOLFOX adjuvant chemotherapy.