PDF file - 235K, Clinical relevance of AXL in CRC tissues.

PDF file - 179K, Effect of AXL siRNA on migration, proliferation and survival of CRC cells.

Supplementary Table 2: Differentially expressed gene list contrasting tumour region of origin between Central Tumor and Invasive Front. Supplementary Table 3: Differentially expressed gene list contrasting tumour region of origin between CT-LN. Supplementary Table 4: Differentially expressed gene list contrasting tumour region of origin between LN-IF.

PDF file - 223K, Univariate and multivariate analyses for OS, DSS, DFS and AXL-LOW and AXL-HIGH in stage II/III CRC group (publicly-available CRC microarray dataset).

PDF file - 213K, Migration, proliferation rates and RTK profiling of HCT116 parental and invasive cells.

This Supplementary Figure indicates the project plan

This Supplementary Table indicates the sample source for the study

Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.

PDF file - 133K, Clinicopathological features of the colorectal cancer patient cohort.

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.

EphA2 and EFNA1 expression levels in colorectal cancer cells.

Role of p53, Src family kinases and RAS/MAPK pathway in regulating EphA2 expression levels.

The role of microenvironment-derived ligands in regulating EphA2 expression levels.

PDF file - 165K, Univariate and multivariate analyses in stage II-III CRC group (Singapore dataset) using Cox proportional hazards regression.

PDF file - 223K, Effect of 5-FU treatment on migration and proliferation of CRC cell lines.

PDF file - 53K, Statistical analysis.

PDF file - 190K, Effect of AXL small molecule inhibition on downstream signalling pathways and migration in CRC cell lines.

PDF file - 94K, Sensitivity of parental and invasive CRC cells to chemotherapy and EGFR targeted therapies.

Supplementary table 1. Clinical-pathological features of the resection-only and chemotherapy/resection stage II/III colorectal cancer patient cohorts with mature survival data in the Singapore dataset. Supplementary table 2. EphA2 and clinical-pathological correlates in CRC. Correlation between EphA2 and CD44, LGR5, CD133, Ki-67 and AXL in 509 stage I-IV CRC cases of the Singapore dataset. Supplementary table 3. Statistical associations between pairs of factors in the surgery only (A) and surgery/chemotherapy (B) stage II/III groups in the Singapore dataset. Supplementary table 4. A. Univariate and multivariate analysis (Cox proportional hazards regression) of resection-chemotherapy stage II/III patient group. B. Final multivariate model (Cox proportional hazards regression) of resection-chemotherapy stage II/III patient group. Supplementary table 5. Statistical association between expression levels of TGF-α and EphA2 using Spearman's Rank Correlation in GSE17536, GSE39582, GSE14333 and The Cancer Genome Atlas (TCGA). Within each dataset the value of Rho and associated p-value is reported.

Role of EFNA1 in invasion and migration of colorectal cancer cells.