Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

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

Immune status is prognostic for poor survival in colorectal cancer patients and is associated with tumour hypoxia

Background Immunohistochemical quantification of the immune response is prognostic for colorectal cancer (CRC). Here, we evaluate the suitability of alternative immune classifiers on prognosis and assess whether they relate to biological features amenable to targeted therapy. Methods Overall survival by immune (CD3, CD4, CD8, CD20 and FOXP3) and immune-checkpoint (ICOS, IDO-1 and PD-L1) biomarkers in independent CRC cohorts was evaluated. Matched mutational and transcriptomic data were interrogated to identify associated biology. Results Determination of immune-cold tumours by combined low-density cell counts of CD3, CD4 and CD8 immunohistochemistry constituted the best prognosticator across stage II–IV CRC, particularly in patients with stage IV disease (HR 1.98 [95% CI: 1.47–2.67]). These immune-cold CRCs were associated with tumour hypoxia, confirmed using CAIX immunohistochemistry (P = 0.0009), which may mediate disease progression through common biology (KRAS mutations, CRIS-B subtype and SPP1 mRNA overexpression). Conclusions Given the significantly poorer survival of immune-cold CRC patients, these data illustrate that assessment of CD4-expressing cells complements low CD3 and CD8 immunohistochemical quantification in the tumour bulk, potentially facilitating immunophenotyping of patient biopsies to predict prognosis. In addition, we found immune-cold CRCs to associate with a difficult-to-treat, poor prognosis hypoxia signature, indicating that these patients may benefit from hypoxia-targeting clinical trials

Evolutionary genetic algorithm identifies <i>IL2RB</i> as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade

Evaluation of a synthetic CArG promoter for nitric oxide synthase gene therapy of cancer

Nitric oxide synthase gene therapy has been shown to be effective at inducing apoptosis in experimental tumours and sensitizing them to radiotherapy. We have also shown that expression of inducible nitric oxide synthase ( iNOS) can be effectively restricted to the tumour volume by the use of the radiation inducible promoter (WAF1) to drive the transgene in clinically relevant protocols. A synthetic construct (pE9), incorporating nine radiosensitive CArG elements from the Egr1 promoter, has recently been developed for cancer gene therapy. We have now investigated basal gene expression of transgenes driven by this promoter to assess its suitablility for use in iNOS gene therapy protocols in vivo. Transfection of human microvascular endothelial cells (HMEC-1) with pE9iNOS, using a cationic lipid vector, resulted in progressively increasing (<5-fold) levels of iNOS protein expression up to 8 h after transfection. Transfection of an ex vivo rat artery preparation with pE9iNOS caused 83% inhibition of response to the vasoconstrictor phenylephrine ( PE). CMVi-NOS transfection also reduced response to PE, but by only 52%. A single injection of 25 mu g of pE9iNOS DNA in a lipid vector into the centre of a murine sarcoma (RIF1) induced iNOS protein expression by four-fold and increased nitrite concentration eight-fold. This caused a 7-day delay in tumour growth and was more effective than the constitutive CMV-driven construct. Our data suggest that generation of NO center dot, as a result of iNOS overexpression, is capable of further activating the E9 promoter, through a positive feedback loop, yielding stronger and sustained levels of NO center dot. This pE9iNOS combination may, therefore, be particularly useful in an anticancer gene therapy strategy as its antitumour effect in vivo was clearly superior to that of the strong constitutive promoter, CMV