Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Identification of immune-related gene signatures to evaluate immunotherapeutic response in cancer patients using exploratory subgroup discovery

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients[trademark] heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50 percent of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients[trademark] health outcome remains a challenging task. We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immunotargeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease. Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.Includes bibliographical references

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Tracking Cancer Evolution through the Disease Course.

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials

Clinical Relevance of Immune Cells in Pancreatic Adenocarcinoma and their Implication in Immunotherapy

This thesis summarizes the work I have done during my PhD training, focused on the immune microenvironment of pancreatic adenocarcinoma (PDAC). The aim was to delineate key features of immunologic and tumour components shaping the biology of pancreatic cancer. Immunotherapy has been largely explored as a promising anticancer approach but not convincingly introduced yet for this tumour type. The results here summarized could contribute to a better understanding of the microenvironment of human PDAC and foster the introduction of immunotherapeutic strategies into the clinical practice. The first section of the thesis summarizes the work done to investigate the occurrence of tertiary lymphoid tissue (TLT) in PDAC. TLT has been demonstrated to coordinate the recruitment and activation of adaptive immune cells in several tumour settings. This work documents the occurrence of TLT in pancreatic cancer, addresses the prognostic role of B cells according to their spatial distribution within TLT and tests the hypothesis that TLT can serve as immunological sites that contribute to efficacy of immunotherapeutic vaccination. The second section is focused on the intersection between cancer metabolism and immune function in PDAC. In order to unearth early metabolic alterations in PDAC, we have analyzed the pancreatic juice, a relatively unexplored liquid biopsy potentially enriched with tumor-derived metabolites. Our results indicate a specific metabolic signature that allows to single out PDAC from other pancreatic pathologies and reveal an important link between glucose metabolism and accumulation of PD-1+ cells in PDAC tumors

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCCrelated genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumorinfiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area

Development of a Novel miR-3648-Related Gene Signature as a Prognostic Biomarker in Esophageal Adenocarcinoma

Background: Esophageal adenocarcinoma (EA) is a typical immunogenic malignant tumor with a dismal 5-year survival rate lower than 20%. Although miRNA-3648 (miR-3648) is expressed abnormally in EA, its impact on the tumor immune microenvironment remains unknown. In this study, we sought to identify immune-related genes (IRGs) that are targeted by miR-3648 and develop an EA multigene signature. Methods: The gene expression data of 87 EA tumor samples and 67 normal tissue samples from The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database were downloaded, respectively. Weighted gene co-expression network analysis (WGCNA), the CIBERSORT algorithm, and Cox regression analysis were applied to identify IRGs and to construct a prognostic signature and nomogram. Results: MiR-3648 was expectedly highly expressed in EA tumor tissues (P=2.6e-8), and related to the infiltration of activated natural killer cells (NK cells) and activated CD4 T lymphocytes (CD4 cells). A total of 70 miR-3648-targeted genes related to immune cell infiltration were identified. Among them, 4 genes (C10orf55, DLL4, PANX2, and NKAIN1) were closely related to overall survival (OS), and were thus selected to construct a 4-gene risk score (RS). The RS had a superior capability to predict OS [area under the curve (AUC) =0.740 for 1 year; AUC =0.717 for 3 years; AUC =0.622 for 5 years]. A higher score was indicative of a poorer prognosis than a lower score [hazard ratio (HR) =2.71; 95% confidence interval (CI): 1.45–5.09; P=0.002]. Furthermore, the nomogram formed by combining the RS and the TNM classification of malignant tumors (TNM stage) improved the accuracy of survival prediction [Harrell’s concordance index (C-index) =0.698]. Conclusions: MiR-3648 may play a critical role in EA pathogenesis. The novel 4-gene signature may serve as a prognostic tool to manage patients with EA