TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data

Strategies and techniques for quality control and semantic enrichment with multimodal data: a case study in colorectal cancer with eHDPrep

BACKGROUND

Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.

FINDINGS

We developed an R package for electronic health data preparation, "eHDPrep," demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative "meta-variables" according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset.

CONCLUSIONS

eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package = eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

Immune Cell Networks Uncover Candidate Biomarkers of Melanoma Immunotherapy Response

The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, ‘immune hot’ status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine.

SynLeGG: analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationships

Achilles’ heel relationships arise when the status of one gene exposes a cell's vulnerability to perturbation of a second gene, such as chemical inhibition, providing therapeutic opportunities for precision oncology. SynLeGG (www.overton-lab.uk/synlegg) identifies and visualizes mutually exclusive loss signatures in ‘omics data to enable discovery of genetic dependency relationships (GDRs) across 783 cancer cell lines and 30 tissues. While there is significant focus on genetic approaches, transcriptome data has advantages for investigation of GDRs and remains relatively underexplored. SynLeGG depends upon the MultiSEp algorithm for unsupervised assignment of cell lines into gene expression clusters, which provide the basis for analysis of CRISPR scores and mutational status in order to propose candidate GDRs. Benchmarking against SynLethDB demonstrates favourable performance for MultiSEp against competing approaches, finding significantly higher area under the Receiver Operator Characteristic curve and between 2.8-fold to 8.5-fold greater coverage. In addition to pan-cancer analysis, SynLeGG offers investigation of tissue-specific GDRs and recovers established relationships, including synthetic lethality for SMARCA2 with SMARCA4. Proteomics, Gene Ontology, protein-protein interactions and paralogue information are provided to assist interpretation and candidate drug target prioritization. SynLeGG predictions are significantly enriched in dependencies validated by a recently published CRISPR screen.

Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling

Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted "neutral" (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington's epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.

Genome-scale CRISPR/Cas9 screen determines factors modulating sensitivity to ProTide NUC-1031

Gemcitabine is a fluoropyrimidine analogue that is used as a mainstay of chemotherapy treatment for pancreatic and ovarian cancers, amongst others. Despite its widespread use, gemcitabine achieves responses in less than 10% of patients with metastatic pancreatic cancer and has a very limited impact on overall survival due to intrinsic and acquired resistance. NUC-1031 (Acelarin), a phosphoramidate transformation of gemcitabine, was the first anti-cancer ProTide to enter the clinic. We find it displays important in vitro cytotoxicity differences to gemcitabine, and a genome-wide CRISPR/Cas9 genetic screening approach identified only the pyrimidine metabolism pathway as modifying cancer cell sensitivity to NUC-1031. Low deoxycytidine kinase expression in tumour biopsies from patients treated with gemcitabine, assessed by immunostaining and image analysis, correlates with a poor prognosis, but there is no such correlation in tumour biopsies from a Phase I cohort treated with NUC-1031.

Expression of glycolytic enzymes in ovarian cancers and evaluation of the glycolytic pathway as a strategy for ovarian cancer treatment

BACKGROUND

Novel therapeutic approaches are required to treat ovarian cancer and dependency on glycolysis may provide new targets for treatment. This study sought to investigate the variation of expression of molecular components (GLUT1, HKII, PKM2, LDHA) of the glycolytic pathway in ovarian cancers and the effectiveness of targeting this pathway in ovarian cancer cell lines with inhibitors.

METHODS

Expression of GLUT1, HKII, PKM2, LDHA were analysed by quantitative immunofluorescence in a tissue microarray (TMA) analysis of 380 ovarian cancers and associations with clinicopathological features were sought. The effect of glycolysis pathway inhibitors on the growth of a panel of ovarian cancer cell lines was assessed by use of the SRB proliferation assay. Combination studies were undertaken combining these inhibitors with cytotoxic agents.

RESULTS

Mean expression levels of GLUT1 and HKII were higher in high grade serous ovarian cancer (HGSOC), the most frequently occurring subtype, than in non-HGSOC. GLUT1 expression was also significantly higher in advanced stage (III/IV) ovarian cancer than early stage (I/II) disease. Growth dependency of ovarian cancer cells on glucose was demonstrated in a panel of ovarian cancer cell lines. Inhibitors of the glycolytic pathway (STF31, IOM-1190, 3PO and oxamic acid) attenuated cell proliferation in platinum-sensitive and platinum-resistant HGSOC cell line models in a concentration dependent manner. In combination with either cisplatin or paclitaxel, 3PO (a novel PFKFB3 inhibitor) enhanced the cytotoxic effect in both platinum sensitive and platinum resistant ovarian cancer cells. Furthermore, synergy was identified between STF31 (a novel GLUT1 inhibitor) or oxamic acid (an LDH inhibitor) when combined with metformin, an inhibitor of oxidative phosphorylation, resulting in marked inhibition of ovarian cancer cell growth.

CONCLUSIONS

The findings of this study provide further support for targeting the glycolytic pathway in ovarian cancer and several useful combinations were identified.

Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5– 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3–18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7– 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Characterisation of male breast cancer: a descriptive biomarker study from a large patient series

Male breast cancer (MBC) is rare. We assembled 446 MBCs on tissue microarrays and assessed clinicopathological information, together with data from 15 published studies, totalling 1984 cases. By immunohistochemistry we investigated 14 biomarkers (ERα, ERβ1, ERβ2, ERβ5, PR, AR, Bcl-2, HER2, p53, E-cadherin, Ki67, survivin, prolactin, FOXA1) for survival impact. The main histological subtype in our cohort and combined analyses was ductal (81%, 83%), grade 2; (40%, 44%), respectively. Cases were predominantly ERα (84%, 82%) and PR positive (74%, 71%), respectively, with HER2 expression being infrequent (2%, 10%), respectively. In our cohort, advanced age (>67) was the strongest predictor of overall (OS) and disease free survival (DFS) (p = 0.00001; p = 0.01, respectively). Node positivity negatively impacted DFS (p = 0.04). FOXA1 p = 0.005) and AR p = 0.009) were both positively prognostic for DFS, remaining upon multivariate analysis. Network analysis showed ERα, AR and FOXA1 significantly correlated. In summary, the principle phenotype of MBC was luminal A, ductal, grade 2. In ERα+ MBC, only AR had prognostic significance, suggesting AR blockade could be employed therapeutically.

Synthetic protein interactions reveal a functional map of the cell

To understand the function of eukaryotic cells, it is critical to understand the role of protein-protein interactions and protein localization. Currently, we do not know the importance of global protein localization nor do we understand to what extent the cell is permissive for new protein associations - a key requirement for the evolution of new protein functions. To answer this question, we fused every protein in the yeast Saccharomyces cerevisiae with a partner from each of the major cellular compartments and quantitatively assessed the effects upon growth. This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations, even if these associations lead to a proportion of the protein moving compartments within the cell. Furthermore, the interactions that do perturb growth provide a functional map of spatial protein regulation, identifying key regulatory complexes for the normal homeostasis of eukaryotic cells.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Epithelial-mesenchymal transition markers in lymph node metastases and primary breast tumors - relation to dissemination and proliferation

Epithelial-mesenchymal transition (EMT) was shown to enhance metastatic abilities of cancer cells, but it remains elusive in clinical samples. Moreover, EMT is rarely studied in lymph node metastases (LNM), thus limiting our understanding of its role outside of the primary tumors (PT). We collected a set of samples including triplets - PT, circulating tumor cells (CTCs)-enriched blood samples and LNM from 108 early breast cancer patients. With immunohistochemistry we analyzed levels of EMT effectors - E-cadherin, vimentin and N-cadherin in LNM, central areas and margins of PT. Additionally, expression of EMT core regulators TWIST1, SNAI1, SNAI2 was measured with RT-qPCR. Patients with E-cadherin loss had CTCs in 45% of the cases in comparison to 23% with normal E-cadherin level (P = 0.05). Mesenchymal phenotype of CTCs-enriched blood fractions was five-times more frequent in patients with E-cadherin loss in PT compared to PT with normal E-cadherin levels (P = 0.01). Epithelial/mesenchymal status of matched samples at different stages of dissemination was frequently discordant, especially for pairs involving CTCs, indicating high plasticity of tumor cells. LNM showed increased expression of TWIST1, SNAI1, SNAI2 accompanied by decreased Ki67 labeling index, with median Ki67 of 15% in PT and 10% in LNM (P = 0.0002). Our findings demonstrate that E-cadherin loss, not only in PT margin, might lead to seeding of especially malignant CTCs with mesenchymal phenotype. In comparison to PT, cells in LNM re-express E-cadherin, upregulate EMT transcription factors and reduce cell division rate, which could be viewed as their long-term survival strategy.

Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer

BACKGROUND

Tumour budding (TB), lymphatic vessel density (LVD) and lymphatic vessel invasion (LVI) have shown promise as prognostic factors in colorectal cancer (CRC) but reproducibility using conventional histopathology is challenging. We demonstrate image analysis methodology to quantify the histopathological features which could permit standardisation across institutes and aid risk stratification of Dukes B patients.

METHODS

Multiplexed immunofluorescence of pan-cytokeratin, D2-40 and DAPI identified epithelium, lymphatic vessels and all nuclei respectively in tissue sections from 50 patients diagnosed with Dukes A (n = 13), Dukes B (n = 29) and Dukes C (n = 8) CRC. An image analysis algorithm was developed and performed, on digitised images of the CRC tissue sections, to quantify TB, LVD, and LVI at the invasive front.

RESULTS

TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57-27.98) were successfully quantified through image analysis and all were shown to be significantly associated with poor survival, in univariate analyses. LVI (HR =6.08; 95% CI, 1.17-31.41) is an independent prognostic factor within the study and was correlated to both TB (Pearson r =0.71, p <0.0003) and LVD (Pearson r =0.69, p <0.0003).

CONCLUSION

We demonstrate methodology through image analysis which can standardise the quantification of TB, LVD and LVI from a single tissue section while decreasing observer variability. We suggest this technology is capable of stratifying a high risk Dukes B CRC subpopulation and we show the three histopathological features to be of prognostic significance.