Both immune profiling and tumor budding significantly correlate with colorectal cancer patient outcome but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II colorectal cancer. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3+CD8+ T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: n = 114, validation cohort 1: n = 56, validation cohort 2: n = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs [HR = 5.899; 95% confidence interval (CI) 1.875–18.55], low CD3+ T-cell density (HR = 9.964; 95% CI, 3.156–31.46), and low mean number of CD3+CD8+ T cells within 50 μm of TBs (HR = 8.907; 95% CI, 2.834–28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75; 95% CI, 6.46–54.43), than TBs, a lymphocytic infiltration score, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27; 95% CI, 3.524–42.73; HR = 15.61; 95% CI, 4.692–51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II colorectal cancer through an automated image analysis and machine learning workflow.

Multiple histopathologic features have been reported as candidates for predicting aggressive stage II colorectal cancer (CRC). These include tumor budding (TB), poorly differentiated clusters (PDC), Crohn-like lymphoid reaction and desmoplastic reaction (DR) categorization. Although their individual prognostic significance has been established, their association with disease-specific survival (DSS) has not been compared in stage II CRC. This study aimed to evaluate and compare the prognostic value of the above features in a Japanese (n=283) and a Scottish (n=163) cohort, as well as to compare 2 different reporting methodologies: analyzing each feature from across every tissue slide from the whole tumor and a more efficient methodology reporting each feature from a single slide containing the deepest tumor invasion. In the Japanese cohort, there was an excellent agreement between the multi-slide and single-slide methodologies for TB, PDC, and DR (κ=0.798 to 0.898) and a good agreement when assessing Crohn-like lymphoid reaction (κ=0.616). TB (hazard ratio [HR]=1.773; P=0.016), PDC (HR=1.706; P=0.028), and DR (HR=2.982; P<0.001) based on the single-slide method were all significantly associated with DSS. DR was the only candidate feature reported to be a significant independent prognostic factor (HR=2.982; P<0.001) with both multi-slide and single-slide methods. The single-slide result was verified in the Scottish cohort, where multivariate Cox regression analysis reported that DR was the only significant independent feature (HR=1.778; P=0.002) associated with DSS. DR was shown to be the most significant of all the analyzed histopathologic features to predict disease-specific death in stage II CRC. We further show that analyzing the features from a single-slide containing the tumor’s deepest invasion is an efficient and quicker method of evaluation.

Cellular subpopulations within the colorectal tumor microenvironment (TME) include CD3 + and CD8 + lymphocytes, CD68 + and CD163 + macrophages, and tumor buds (TBs), all of which have known prognostic significance in stage II colorectal cancer. However, the prognostic relevance of their spatial interactions remains unknown. Here, by applying automated image analysis and machine learning approaches, we evaluate the prognostic significance of these cellular subpopulations and their spatial interactions. Resultant data, from a training cohort retrospectively collated from Edinburgh, UK hospitals (n = 113), were used to create a combinatorial prognostic model, which identified a subpopulation of patients who exhibit 100% survival over a 5-year follow-up period. The combinatorial model integrated lymphocytic infiltration, the number of lymphocytes within 50-μm proximity to TBs, and the CD68 + /CD163 + macrophage ratio. This finding was confirmed on an independent validation cohort, which included patients treated in Japan and Scotland (n = 117). This work shows that by analyzing multiple cellular subpopulations from the complex TME, it is possible to identify patients for whom surgical resection alone may be curative.

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.

Mesothelin (MSLN) is a cell-surface glycoprotein present in many cancer types. Its expression is generally associated with an unfavorable prognosis. This study examined the prognostic significance of MSLN expression in different areas of individual colorectal cancers (CRCs) using tissue microarrays (TMAs) by enrolling 314 patients with stage II (T3-T4, N0, M0) CRCs. Using formalin-fixed paraffin-embedded tissue blocks from patients, TMA blocks were constructed. Tissue core specimens were obtained from submucosal invasive front [Fr-sm], subserosal invasive front [Fr-ss], central area [Ce], and rolled edge [Ro] of each tumor. Using these four-point TMA sets, MSLN expression was immunohistochemically surveyed. The area-specific prognostic significance of MSLN expression was evaluated. A deep-learning convolutional neural network algorithm was used for imaging analysis and evaluating our judgment's objectivity. MSLN staining ratio was positively correlated between the manual and machine-learning analyses (= 0.71). The correlation coefficient between Ro and Ce, Ro and Fr-sm, and Ro and Fr-ss was = 0.63, = 0.54, and = 0.61, respectively. Disease-specific survival curves for the MSLN-positive and MSLN-negative groups in Fr-sm, Fr-ss, and Ro were significantly different [five-year survival rates: 88.1% and 95.5% (= 0.024), 85.0 and 96.2% (= 0.0087), 87.8 and 95.5% (= 0.051), and 77.9 and 95.8% (= 0.046) for Fr-sm, Fr-ss, Ce, and Ro, respectively]. The analysis performed using area-specific four-point TMAs clearly demonstrated that MSLN expression in stage II CRC was relatively homogeneous within tumors. Additionally, high MSLN expression showed or tended to 4 show unfavorable prognostic significance regardless of the tumor area.