How many molecular subtypes? Implications of the unique tumor principle in personalized medicine

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification

OBJECTIVES

Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simplify patient molecular portraits, neglecting the potential co-occurrence of traits from multiple subtypes. Yet, recognizing intra-sample heterogeneity is essential for more precise patient characterization and improved personalized treatments.

METHODS

We developed a novel computational workflow, named MULTI-STAR, which addresses current limitations and provides tailored solutions for reliable multi-label patient subtyping. MULTI-STAR uses state-of-the-art subtyping methods to obtain promising machine learning-based multi-label classifiers, leveraging gene expression profiles. It modifies standard single-label similarity-based techniques to obtain multi-label patient characterizations. Then, it employs these characterizations to train single-sample predictors using different multi-label strategies and find the best-performing classifiers.

RESULTS

MULTI-STAR classifiers offer advanced multi-label recognition of all the subtypes contributing to the molecular and clinical traits of a patient, also distinguishing the primary from the additional relevant secondary subtype(s). The efficacy was demonstrated by developing multi-label solutions for breast and colorectal cancer subtyping that outperform existing methods in terms of prognostic value, primarily for overall survival predictions, and ability to work on a single sample at a time, as required in clinical practice.

CONCLUSIONS

This work emphasizes the importance of moving to multi-label subtyping to capture all the molecular traits of individual patients, considering also previously overlooked secondary assignments and paving the way for improved clinical decision-making processes in diverse heterogeneous disease contexts. Indeed, MULTI-STAR novel, reproducible and generalizable approach provides comprehensive representations of patient inner heterogeneity and clinically relevant insights, contributing to precision medicine and personalized treatments.

Evaluating the utility of ZNF331 promoter methylation as a prognostic and predictive marker in stage III colon cancer: results from CALGB 89803 (Alliance)

While epigenomic alterations are common in colorectal cancers (CRC), few epigenomic biomarkers that risk-stratify patients have been identified. We thus sought to determine the potential of ZNF331 promoter hypermethylation (mZNF331) as a prognostic and predictive marker in colon cancer. We examined the association of mZNF331 with clinicopathologic features, relapse, survival, and treatment efficacy in patients with stage III colon cancer treated within a randomized adjuvant chemotherapy trial (CALGB/Alliance89803). Residual tumour tissue was available for genomic DNA extraction and methylation analysis for 385 patients. ZNF331 promoter methylation status was determined by bisulphite conversion and fluorescence-based real-time polymerase chain reaction. Kaplan-Meier estimator and Cox proportional hazard models were used to assess the prognostic and predictive role of mZNF331 in this well-annotated dataset, adjusting for clinicopathologic features and standard molecular markers. mZNF331 was observed in 267/385 (69.4%) evaluable cases. Histopathologic features were largely similar between patients with mZNF331 compared to unmethylated ZNF331 (unmZNFF31). There was no significant difference in disease-free or overall survival between patients with mZNF331 versus unmZNF331 colon cancers, even when adjusting for clinicopathologic features and molecular marker status. Similarly, there was no difference in disease-free or overall survival across treatment arms when stratified by ZNF331 methylation status. While ZNF331 promoter hypermethylation is frequently observed in CRC, our current study of a small subset of patients with stage III colon cancer suggests limited applicability as a prognostic marker. Larger studies may provide more insight and clarity into the applicability of mZNF331 as a prognostic and predictive marker.

Gene signatures for cancer research: A 25-year retrospective and future avenues

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.

Navigating Change in Tumor Naming: Exploring the Complexities and Considerations of Shifting Toward Molecular Classifications

Navigating change in tumor naming. Balance organ-based and molecular classifications for optimal treatment.

All-In-One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics

Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all-in-one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation-induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette-based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning-driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high-multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost-effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics.

Prolonged HPA axis dysregulation in postpartum depression associated with adverse early life experiences: A cross-species translational study

Childhood and adolescent stress increase the risk of postpartum depression (PPD), often providing an increased probability of treatment refractoriness. Nevertheless, the mechanisms linking childhood/adolescent stress to PPD remain unclear. Our study investigated the longitudinal effects of adolescent stress on the hypothalamic-pituitary-adrenal (HPA) axis and postpartum behaviors in mice and humans. Adolescent social isolation prolonged glucocorticoid elevation, leading to long-lasting postpartum behavioral changes in female mice. These changes were unresponsive to current PPD treatments but improved with post-delivery glucocorticoid receptor antagonist treatment. Childhood/adolescent stress significantly impacted HPA axis dysregulation and PPD in human females. Repurposing glucocorticoid receptor antagonists for some cases of treatment-resistant PPD may be considered.

Unlocking hidden potential: advancements, approaches, and obstacles in repurposing drugs for cancer therapy

High rates of failure, exorbitant costs, and the sluggish pace of new drug discovery and development have led to a growing interest in repurposing "old" drugs to treat both common and rare diseases, particularly cancer. Cancer, a complex and heterogeneous disease, often necessitates a combination of different treatment modalities to achieve optimal outcomes. The intrinsic polygenicity of cancer, intricate biological signalling networks, and feedback loops make the inhibition of a single target frequently insufficient for achieving the desired therapeutic impact. As a result, addressing these complex or "smart" malignancies demands equally sophisticated treatment strategies. Combinatory treatments that target the multifaceted oncogenic signalling network hold immense promise. Repurposed drugs offer a potential solution to this challenge, harnessing known compounds for new indications. By avoiding the prohibitive costs and long development timelines associated with novel cancer drugs, this approach holds the potential to usher in more effective, efficient, and cost-effective cancer treatments. The pursuit of combinatory therapies through drug repurposing may hold the key to achieving superior outcomes for cancer patients. However, drug repurposing faces significant commercial, technological and regulatory challenges that need to be addressed. This review explores the diverse approaches employed in drug repurposing, delves into the challenges faced by the drug repurposing community, and presents innovative solutions to overcome these obstacles. By emphasising the significance of combinatory treatments within the context of drug repurposing, we aim to unlock the full potential of this approach for enhancing cancer therapy. The positive aspects of drug repurposing in oncology are underscored here; encompassing personalized treatment, accelerated development, market opportunities for shelved drugs, cancer prevention, expanded patient reach, improved patient access, multi-partner collaborations, increased likelihood of approval, reduced costs, and enhanced combination therapy.

Estrogenic flavonoids and their molecular mechanisms of action

Flavonoids are a major group of phytoestrogens associated with physiological effects, and ecological and social impacts. Although the estrogenic activity of flavonoids was reported by researchers in the fields of medical, environmental and food studies, their molecular mechanisms of action have not been comprehensively reviewed. The estrogenic activity of the respective classes of flavonoids, anthocyanidins/anthocyanins, 2-arylbenzofurans/3-arylcoumarins/α-methyldeoxybenzoins, aurones/chalcones/dihydrochalcones, coumaronochromones, coumestans, flavans/flavan-3-ols/flavan-4-ols, flavanones/dihydroflavonols, flavones/flavonols, homoisoflavonoids, isoflavans, isoflavanones, isoflavenes, isoflavones, neoflavonoids, oligoflavonoids, pterocarpans/pterocarpenes, and rotenone/rotenoids, was summarized through a comprehensive literature search, and their structure-activity relationship, biological activities, signaling pathways, and applications were discussed. Although the respective classes of flavonoids contained at least one chemical mimicking estrogen, the mechanisms varied, such as those with estrogenic, anti-estrogenic, non-estrogenic, and biphasic activities, and additional activities through crosstalk/bypassing, which exert biological activities through cell signaling pathways. Such mechanistic variations of estrogen action are not limited to flavonoids and are observed among other broad categories of chemicals, thus this group of chemicals can be termed as the "estrogenome". This review article focuses on the connection of estrogen action mainly between the outer and the inner environments, which represent variations of chemicals and biological activities/signaling pathways, respectively, and form the basis to understand their applications. The applications of chemicals will markedly progress due to emerging technologies, such as artificial intelligence for precision medicine, which is also true of the study of the estrogenome including estrogenic flavonoids.

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.

ICOS/ICOSLG and PD-1 Co-Expression is Associated with the Progression of Colorectal Precancerous- Carcinoma Immune Microenvironment

Purpose

This study aimed to investigate the expression of inducible T-cell co-stimulator (ICOS) and its ligand (ICOSLG), along with their association with clinicopathological features and influence on the immune profile in colorectal cancer (CRC).

Patients and Methods

The Cancer Genome Atlas Colorectal Adenocarcinoma cohorts were used. We also analyzed 131 clinical samples of colon lesions, including precancerous lesions (hyperplastic polyps, low-grade dysplasia, and high-grade dysplasia) and CRC tissues. We conducted immunohistochemical (IHC) assays and multiple IHC (mIHC) of CD4+, Foxp3+ tumor-infiltrating lymphocytes (TILs), and PD-1/PD-L1 immune checkpoints in precancerous lesions and CRC samples from our patient subsets to determine changes and correlations in ICOS and ICOSLG expression during progression through the adenoma-carcinoma pathway.

Results

High expression of ICOS and ICOSLG was a significant factor in CRC in multiple analyses and was positively correlated with CD4+/Foxp3+ TIL density and PD-1/PD-L1 expression, which increased with the sequential progression of lesions from precancerous tissues to carcinoma. Multivariable logistic regression analysis suggested that the location and expression level of ICOS/ICOSLG may be involved in precancerous-carcinoma progression. The co-expression status of PD-1 and ICOS/ ICOSLG could stratify patients with colorectal lesions into three groups of low, moderate, and high risk of progression. According to this classification and mIHC assays, we found a strong correlation between increased PD-1+ICOS+ or PD-1+ICOSLG+ co-expression and CRC, which might be deemed an independent factor in carcinogenesis.

Conclusion

Increased ICOS/ICOSLG expression may be associated with the progressive formation of Foxp3+ TILs in the immune microenvironment and may further promote the development of the abnormal cytology of colorectal lesions from precancerous neoplasia to CRC. Our findings support the interpretation that enhanced co-expression of PD-1+ICOS+ or PD-1+ICOSLG+ contributes to the immune-active microenvironment of the colorectal adenoma-carcinoma sequence.

Chemotherapy induces feedback up-regulation of CD44v6 in colorectal cancer initiating cells through β-catenin/MDR1 signaling to sustain chemoresistance

Chemoresistance in colorectal cancer initiating cells (CICs) involves the sustained activation of multiple drug resistance (MDR) and WNT/β-catenin signaling pathways, as well as of alternatively spliced-isoforms of CD44 containing variable exon-6 (CD44v6). In spite of its importance, mechanisms underlying the sustained activity of WNT/β-catenin signaling have remained elusive. The presence of binding elements of the β-catenin-interacting transcription factor TCF4 in the MDR1 and CD44 promoters suggests that crosstalk between WNT/β-catenin/TCF4-activation and the expression of the CD44v6 isoform mediated by FOLFOX, a first-line chemotherapeutic agent for colorectal cancer, could be a fundamental mechanism of FOLFOX resistance. Our results identify that FOLFOX treatment induced WNT3A secretion, which stimulated a positive feedback loop coupling β-catenin signaling and CD44v6 splicing. In conjunction with FOLFOX induced WNT3A signal, specific CD44v6 variants produced by alternative splicing subsequently enhance the late wave of WNT/β-catenin activation to facilitate cell cycle progression. Moreover, we revealed that FOLFOX-mediated sustained WNT signal requires the formation of a CD44v6-LRP6-signalosome in caveolin microdomains, which leads to increased FOLFOX efflux. FOLFOX-resistance in colorectal CICs occurs in the absence of tumor-suppressor disabled-2 (DAB2), an inhibitor of WNT/β-catenin signaling. Conversely, in sensitive cells, DAB2 inhibition of WNT-signaling requires interaction with a clathrin containing CD44v6-LRP6-signalosome. Furthermore, full-length CD44v6, once internalized through the caveolin-signalosome, is translocated to the nucleus where in complex with TCF4, it binds to β-catenin/TCF4-regulated MDR1, or to CD44 promoters, which leads to FOLFOX-resistance and CD44v6 transcription through transcriptional-reprogramming. These findings provide evidence that targeting CD44v6-mediated LRP6/β-catenin-signaling and drug efflux may represent a novel approach to overcome FOLFOX resistance and inhibit tumor progression in colorectal CICs. Thus, sustained drug resistance in colorectal CICs is mediated by overexpression of CD44v6, which is both a functional biomarker and a therapeutic target in colorectal cancer.

Improve consensus partitioning via a hierarchical procedure

Consensus partitioning is an unsupervised method widely used in high-throughput data analysis for revealing subgroups and assigning stability for the classification. However, standard consensus partitioning procedures are weak for identifying large numbers of stable subgroups. There are two major issues. First, subgroups with small differences are difficult to be separated if they are simultaneously detected with subgroups with large differences. Second, stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitioning in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more meaningful subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a large deoxyribonucleic acid methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionalities for analysis and visualization. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis. The cola package is available at https://bioconductor.org/packages/cola/.

Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease

Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase gene. One of the main obstacles in studying AKU and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Based on that, a multi-purpose digital platform, called ApreciseKUre, was implemented to facilitate data collection, integration and analysis for patients affected by AKU. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and Quality of Life (QoL) scores that can be shared among registered researchers and clinicians to create a Precision Medicine Ecosystem. The combination of machine learning applications to analyse and re-interpret data available in the ApreciseKUre clearly indicated the potential direct benefits to achieve patients' stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In order to generate a comprehensive patient profile, computational modeling and database construction support the identification of potential new biomarkers, paving the way for more personalized therapy to maximize the benefit-risk ratio. In this work, different Machine Learning implemented approaches were described.

Contribution of Epithelial and Gut Microbiome Inflammatory Biomarkers to the Improvement of Colorectal Cancer Patients' Stratification

In order to ensure that primary endpoints of clinical studies are attained, the patients' stratification is an important aspect. Selection criteria include age, gender, and also specific biomarkers, such as inflammation scores. These criteria are not sufficient to achieve a straightforward selection, however, in case of multifactorial diseases, with unknown or partially identified mechanisms, occasionally including host factors, and the microbiome. In these cases, the efficacy of interventions is difficult to predict, and as a result, the selection of subjects is often random. Colorectal cancer (CRC) is a highly heterogeneous disease, with variable clinical features, outcomes, and response to therapy; the CRC onset and progress involves multiple sequential steps with accumulation of genetic alterations, namely, mutations, gene amplification, and epigenetic changes. The gut microbes, either eubiotic or dysbiotic, could influence the CRC evolution through a complex and versatile crosstalk with the intestinal and immune cells, permanently changing the tumor microenvironment. There have been significant advances in the development of personalized approaches for CRC screening, treatment, and potential prevention. Advances in molecular techniques bring new criteria for patients' stratification-mutational analysis at the time of diagnosis to guide treatment, for example. Gut microbiome has emerged as the main trigger of gut mucosal homeostasis. This may impact cancer susceptibility through maintenance of the epithelial/mucus barrier and production of protective metabolites, such as short-chain fatty acids (SCFAs) via interactions with the hosts' diet and metabolism. Microbiome dysbiosis leads to the enrichment of cancer-promoting bacterial populations, loss of protective populations or maintaining an inflammatory chronic state, all of which contribute to the development and progression of CRC. Meanwhile, variations in patient responses to anti-cancer immuno- and chemotherapies were also linked to inter-individual differences in intestine microbiomes. The authors aim to highlight the contribution of epithelial and gut microbiome inflammatory biomarkers in the improvement of CRC patients' stratification towards a personalized approach of early diagnosis and treatment.

Superior Overall Survival in Patients with Colorectal Cancer, Regular Aspirin Use, and Combined Wild-Type PIK3CA and KRAS-Mutated Tumors

Simple Summary

The impact of aspirin use after the diagnosis of colorectal cancer is unknown. Among others, PIK3CA mutational status was proposed as a molecular biomarker for the response to adjuvant aspirin therapy. The aim of this study was to retrospectively analyze whether the PIK3CA and KRAS mutational status had an impact on overall survival in patients with colorectal cancer and aspirin use. In a retrospective study, we obtained KRAS and PIK3CA mutational status in a cohort of 153 patients with a first diagnosis of colorectal cancer receiving tumor surgery with curative intent. Clinicopathological data and survival data were assessed using patient records and reporting registers. We observed a significant 10-year overall survival benefit in patients with aspirin use and combined wild-type PIK3CA and mutated-KRAS tumors (HR = 0.38; 95% CI = 0.17–0.87; p = 0.02). Our data indicated a benefit of aspirin usage particularly for patients with combined wild-type PIK3CA and mutated-KRAS tumor characteristics.

Abstract

The impact of aspirin use after the diagnosis of colorectal cancer is unknown. Among others, PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha) mutational status was proposed as a molecular biomarker for the response to adjuvant aspirin therapy. However, prognostic data on aspirin use after a colorectal cancer diagnosis in relation to KRAS mutational status is limited. In a single-center retrospective study, we obtained KRAS and PIK3CA mutational status in a cohort of 153 patients with a first diagnosis of colorectal cancer receiving tumor surgery with curative intent. PIK3CA mutational status was determined by pyrosequencing, and KRAS mutational status was determined by next-generation sequencing. Clinicopathological data and survival data were assessed using patient records and reporting registers. We observed a significant 10-year overall survival benefit in patients with aspirin use and combined wild-type PIK3CA and mutated-KRAS tumors (HR = 0.38; 95% CI = 0.17–0.87; p = 0.02), but not in patients without aspirin use. Our data indicate a benefit of aspirin usage particularly for patients with combined wild-type PIK3CA and mutated-KRAS tumor characteristics.

Recent advances in fecal gene detection for colorectal cancer diagnosis

There has been a gradual increase in the incidence of colorectal cancer (CRC) in recent years. Most patients lack obvious early symptoms, but are commonly in mid and advanced stages when the symptoms become evident, with rather high mortalities. Early diagnosis, treatment and recurrence monitoring are crucial to improving the recovery rate of CRC. Studies have shown that tumor-related genes can be detected in the feces of CRC patients. Owing to non-invasiveness, convenient sampling and continuous dynamic monitoring, fecal gene detection may be applicable to CRC screening, diagnosis, prognostic assessment and recurrence monitoring. Herein, we review the research advances in fecal gene detection for CRC diagnosis.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Recapitulating the Cancer Microenvironment Using Bioprinting Technology for Precision Medicine

The complex and heterogenous nature of cancer contributes to the development of cancer cell drug resistance. The construction of the cancer microenvironment, including the cell-cell interactions and extracellular matrix (ECM), plays a significant role in the development of drug resistance. Traditional animal models used in drug discovery studies have been associated with feasibility issues that limit the recapitulation of human functions; thus, in vitro models have been developed to reconstruct the human cancer system. However, conventional two-dimensional and three-dimensional (3D) in vitro cancer models are limited in their ability to emulate complex cancer microenvironments. Advances in technologies, including bioprinting and cancer microenvironment reconstruction, have demonstrated the potential to overcome some of the limitations of conventional models. This study reviews some representative bioprinted in vitro models used in cancer research, particularly fabrication strategies for modeling and consideration of essential factors needed for the reconstruction of the cancer microenvironment. In addition, we highlight recent studies that applied such models, including application in precision medicine using advanced bioprinting technologies to fabricate biomimetic cancer models. Furthermore, we discuss current challenges in 3D bioprinting and suggest possible strategies to construct in vitro models that better mimic the pathophysiology of the cancer microenvironment for application in clinical settings.

Proteogenomic analysis of pancreatic cancer subtypes

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

Cyclin-Dependent Kinase 4 and 6 Inhibitors in Cell Cycle Dysregulation for Breast Cancer Treatment

In cell development, the cell cycle is crucial, and the cycle progression’s main controllers are endogenous CDK inhibitors, cyclin-dependent kinases (CDKs), and cyclins. In response to the mitogenic signal, cyclin D is produced and retinoblastoma protein (Rb) is phosphorylated due to activated CDK4/CDK6. This causes various proteins required in the cell cycle progression to be generated. In addition, complexes of CDK1-cyclin A/B, CDK2-cyclin E/A, and CDK4/CDK6-cyclin D are required in each phase of this progression. Cell cycle dysregulation has the ability to lead to cancer. Based on its role in the cell cycle, CDK has become a natural target of anticancer therapy. Therefore, understanding the CDK structures and the complex formed with the drug, helps to foster the development of CDK inhibitors. This development starts from non-selective CDK inhibitors to selective CDK4/CDK6 inhibitors, and these have been applied in clinical cancer treatment. However, these inhibitors currently require further development for various hematologic malignancies and solid tumors, based on the results demonstrated. In drug development, the main strategy is primarily to prevent and asphyxiate drug resistance, thus a determination of specific biomarkers is required to increase the therapy’s effectiveness as well as patient selection suitability in order to avoid therapy failure. This review is expected to serve as a reference for early and advanced-stage researchers in designing new molecules or repurposing existing molecules as CDK4/CDK6 inhibitors to treat breast cancer.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Extracellular Vesicle-Mediated Chemoresistance in Oral Squamous Cell Carcinoma

Oral Squamous Cell Carcinoma (OSCC) remains a cancer with poor prognosis and high recurrence rate. Even with multimodal treatment options available for OSCC, tumor drug resistance is still a persistent problem, leading to increased tumor invasiveness among OSCC patients. An emerging trend of thought proposes that extracellular vesicles (EVs) play a role in facilitating tumor progression and chemoresistance via signaling between tumor cells. In particular, exosomes and microvesicles are heavily implicated in this process by various studies. Where primary studies into a particular EV-mediated chemoresistance mechanism in OSCC are limited, similar studies on other cancer cell types will be used in the discussion below to provide ideas for a new line of investigation into OSCC chemoresistance. By understanding how EVs are or may be involved in OSCC chemoresistance, novel targeted therapies such as EV inhibition may be an effective alternative to current treatment options in the near future. In this review, the current understandings on OSCC drug mechanisms under the novel context of exosomes and microvesicles were reviewed, including shuttling of miRNA content, drug efflux, alteration of vesicular pH, anti-apoptotic signaling, modulation of DNA damage repair, immunomodulation, epithelial-to-mesenchymal transition and maintenance of tumor by cancer stem cells.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Transcriptome of CD8+ tumor-infiltrating T cells: a link between diabetes and colorectal cancer

There is an increased risk of colorectal cancer (CRC) development in patients with non-insulin-dependent type 2 diabetes. CD8⁺ T cells have been implicated in diabetes and are crucial for anti-tumor immunity. However, transcriptomic profiling for CD8⁺ T cells from CRC diabetic patients has not been explored. We performed RNA sequencing and compared transcriptomic profiles of CD8⁺ tumor-infiltrating T lymphocytes (CD8⁺ TILs) in CRC diabetic patients with CRC nondiabetic patients. We found that genes associated with ribogenesis, epigenetic regulations, oxidative phosphorylation and cell cycle arrest were upregulated in CD8⁺ TILs from diabetic patients, while genes associated with PI3K signaling pathway, cytokine response and response to lipids were downregulated. Among the significantly deregulated 1009 genes, 342 (186 upregulated and 156 downregulated) genes were selected based on their link to diabetes, and their associations with the presence of specific CRC pathological parameters were assessed using GDC TCGA colon database. The 186 upregulated genes were associated with the presence of colon polyps history (P = 0.0007) and lymphatic invasion (P = 0.0025). Moreover, CRC patients with high expression of the 186 genes were more likely to have poorer disease-specific survival (DSS) (Mantel-Cox log-rank P = 0.024) than those with low score. Our data provide novel insights into molecular pathways and biological functions, which could be altered in CD8⁺ TILs from CRC diabetic versus nondiabetic patients, and reveal candidate genes linked to diabetes, which could predict DSS and pathological parameters associated with CRC progression. However, further investigations using larger patient cohorts and functional studies are required to validate these findings.

FOLFOX Therapy Induces Feedback Upregulation of CD44v6 through YB-1 to Maintain Stemness in Colon Initiating Cells

Cancer initiating cells (CICs) drive tumor formation and drug-resistance, but how they develop drug-resistance characteristics is not well understood. In this study, we demonstrate that chemotherapeutic agent FOLFOX, commonly used for drug-resistant/metastatic colorectal cancer (CRC) treatment, induces overexpression of CD44v6, MDR1, and oncogenic transcription/translation factor Y-box-binding protein-1 (YB-1). Our study revealed that CD44v6, a receptor for hyaluronan, increased the YB-1 expression through PGE2/EP1-mTOR pathway. Deleting CD44v6, and YB-1 by the CRISPR/Cas9 system attenuates the in vitro and in vivo tumor growth of CICs from FOLFOX resistant cells. The results of DNA:CD44v6 immunoprecipitated complexes by ChIP (chromatin-immunoprecipitation) assay showed that CD44v6 maintained the stemness traits by promoting several antiapoptotic and stemness genes, including cyclin-D1,BCL2,FZD1,GINS-1, and MMP9. Further, computer-based analysis of the clones obtained from the DNA:CD44v6 complex revealed the presence of various consensus binding sites for core stemness-associated transcription factors “CTOS” (c-Myc, TWIST1, OCT4, and SOX2). Simultaneous expressions of CD44v6 and CTOS in CD44v6 knockout CICs reverted differentiated CD44v6-knockout CICs into CICs. Finally, this study for the first time describes a positive feedback loop that couples YB-1 induction and CD44 alternative splicing to sustain the MDR1 and CD44v6 expressions, and CD44v6 is required for the reversion of differentiated tumor cells into CICs.

Patient-derived scaffolds as a model of colorectal cancer

Background

Colorectal cancer is the second most common cause of cancer‐related death worldwide and standardized therapies often fail to treat the more aggressive and progressive types of colorectal cancer. Tumor cell heterogeneity and influence from the surrounding tumor microenvironment (TME) contribute to the complexity of the disease and large variability in clinical outcomes.

Methods

To model the heterogeneous nature of colorectal cancer, we used patient‐derived scaffolds (PDS), which were obtained via decellularization of surgically resected tumor material, as a growth substrate for standardized cell lines.

Results

After confirmation of native cell absence and validation of the structural and compositional integrity of the matrix, 89 colorectal PDS were repopulated with colon cancer cell line HT29. After 3 weeks of PDS culture, HT29 cells varied their gene and protein expression profiles considerably compared to 2D‐grown HT29 cells. Markers associated with proliferation were consistently decreased, while markers associated with pluripotency were increased in PDS‐grown cells compared to their 2D counterparts. When comparing the PDS‐induced changes in HT29 cells with clinically relevant tumor information from individual patients, we observed significant associations between stemness/pluripotency markers and tumor location, and between epithelial‐to‐mesenchymal transition (EMT) markers and cancer mortality. Kaplan–Meier analysis revealed that low PDS‐induced EMT correlated with worse cancer‐specific survival.

Conclusions

The colorectal PDS model can be used as a simplified personalized tool that can potentially reveal important diagnostic and pathophysiological information related to the TME.

Anxiety, Depression, and Colorectal Cancer Survival: Results from Two Prospective Cohorts

Given the unalterable nature of most risk factors for colorectal cancer (CRC) survival (e.g., disease stage), identifying modifiable determinants is critical. We investigated whether anxiety and depression were related to CRC survival using data from the Nurses' Health Study (NHS) and Health Professional Follow-up Study (HPFS). Participants who received a CRC diagnosis and provided information about anxiety (nNHS = 335; nHPFS = 232) and depression (nNHS = 893; nHPFS = 272) within 4 years of diagnosis were included. Cox regression models estimated hazard ratios (HR) and 95% confidence intervals (CI) of overall mortality, while controlling for covariates (sociodemographics, cancer characteristics, and lifestyle factors). Pooled risk estimates were derived from fixed effects meta-analyses of the cohorts. Among 1732 CRC patients, 814 deaths occurred during the 28-year follow-up. Each 1 standard deviation increase in anxiety or depression symptoms was associated with a similar 16% higher mortality risk (anxiety: 95% CI = 1.05-1.29; depression: 95% CI = 1.07-1.26). Comparable results were observed across all sensitivity analyses (introducing a 1-year lag, restricting to CRC-related mortality, considering potential behavioral pathways) and stratified models (cancer stage, sex). Our findings suggest greater anxiety and depression symptoms can not only impede adherence to healthy habits and reduce quality of life in cancer patients but could also be a marker for accelerated CRC progression.

Differential gene expression of tumor-infiltrating CD8+ T cells in advanced versus early-stage colorectal cancer and identification of a gene signature of poor prognosis

Background

Cytotoxic CD8⁺ T cell-mediated response is the most important arm of adaptive immunity, which dictates the capacity of the host immune response in eradicating tumor cells. Due to tumor intrinsic and/or extrinsic factors, the density and function of CD8⁺ tumor-infiltrating lymphocytes (TILs) could be compromised, leading to poor prognosis and survival.

Methods

Using RNA-Seq, transcriptomes of sorted CD3⁺CD8⁺ TILs from treatment-naïve colorectal cancer (CRC) patients at advanced stages (III and IV) were compared with those from patients with early stages (I and II). A signature referred to as ‘poor prognosis CD8 gene signature (ppCD8sig)’ was identified and analyzed in The Cancer Genome Atlas CRC dataset. Scores for the ppCD8sig were calculated and classified as high, intermediate and low, and its prognostic significance was assessed using multivariate analysis and Cox proportional hazard model. Densities of CD3⁺ and CD8⁺ T cell infiltration in tumors from patients with high and low ppCD8sig scores were assessed by flow cytometry and immunostaining.

Results

Genes related to epigenetic regulation and response to hypoxia were upregulated in CD8⁺ TILs from patients with advanced stages, while genes related to T cell activation, cell proliferation and cell cycle were downregulated. Patients with high ppCD8sig score had poorer disease-specific survival (DSS) and shorter progression-free interval (PFI). The ppCD8sig was an independent prognostic indicator for DSS (HR 1.83, 95% CI 1.40 to 2.38, p<0.0001) and PFI (HR 1.42, 95% CI 1.04 to 1.93, p=0.026). Additionally, patients with high ppCD8sig score were more likely to have advanced stages (χ² p<0.0001) and residual disease after primary therapy (χ² p=0.046). Patients with high ppCD8sig score had reduced levels of CD3⁺ and CD8⁺ TILs and low Immunoscores (IS), compared to patients with low ppCD8sig score.

Conclusions

Our data provided insights into the altered regulation of biological mechanisms and signaling pathways in CD8⁺ TILs during CRC progression, and revealed a gene signature as an independent prognostic indicator. Patients with high ppCD8sig score had lower levels of TILs and low IS. These data further confirm the prognostic value of the identified ppCD8sig and potentially highlight its clinical relevance.

Exploring the classification of cancer cell lines from multiple omic views

BACKGROUND

Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.

METHODS

In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines.

RESULTS

Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines.

CONCLUSIONS

The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

Radiosynthesis, in vitro and preliminary biological evaluation of [18 F]2-amino-4-((2-((3-fluorobenzyl)oxy)benzyl)(2-((3-(fluoromethyl)benzyl)oxy)benzyl)amino)butanoic acid, a novel alanine serine cysteine transporter 2 inhibitor-based positron emission tomography tracer

The metabolic alterations in tumors make it possible to visualize the latter by means of positron emission tomography, enabling diagnosis and providing metabolic information. The alanine serine cysteine transporter-2 (ASCT-2) is the main transporter of glutamine and is upregulated in several tumors. Therefore, a good positron emission tracer targeting this transport protein would have substantial value. Hence, the aim of this study is to develop a fluorine-18-labeled version of a V-9302 analogue, one of the most potent inhibitors of ASCT-2. The precursor was labeled with fluorine-18 via a nucleophilic substitution of the corresponding benzylic bromide. The cold reference product was subjected to in vitro assays with [3 H]glutamine in a PC-3 and F98 cell line to determine the affinity for both the human and rat ASCT-2. To evaluate the tracer potential dynamic μPET, images were acquired in a mouse xenograft model for prostate cancer. The tracer could be synthesized with an overall nondecay corrected yield of 3.66 ± 1.90%. in vitro experiments show inhibitor constants Ki of 90 and 125 μM for the PC-3 and F98 cells, respectively. The experiments in the PC-3 xenograft demonstrate a low uptake in the tumor tissue. We have successfully synthesized the radiotracer [18 F]2-amino-4-((2-((3-fluorobenzyl)oxy)benzyl)(2-((3-(fluoromethyl)benzyl)oxy)benzyl)amino)butanoic acid. in vitro experiments show a good affinity for both the human and rat ASCT-2. However, the tracer suffers from poor in vivo tumor uptake in the PC-3 model. Briefly, we present the first fluorine-18-labeled derivative of compound V-9302, a promising novel ASCT-2 blocker used for inhibition of tumor growth.

Cancer-stromal cell fusion as revealed by fluorescence protein tracking

PURPOSE

We previously determined that cancer-stromal interaction was a direct route to tumor cell heterogeneity progression, since cancer-stromal cell fusion in coculture resulted in the creation of heterogeneous clones of fusion hybrid progeny. In this report, we modified the cancer-stromal coculture system to establish optimal experimental conditions for investigating cell fusion machinery and the mechanism of heterogeneity progression.

EXPERIMENTAL DESIGN

Red fluorescence protein-tagged LNCaP cells were cocultured with green fluorescence protein-labeled prostate stromal cells for cancer-stromal cell fusion, which was tracked as dual fluorescent cells by fluorescence microscopy.

RESULTS

We identified the most efficient strategy to isolate clones of fusion hybrid progenies. From the coculture, mixed cells including fusion hybrids were subjected to low-density replating for colony formation by fusion hybrid progeny. These colonies could propagate into derivative cell populations. Compared to the parental LNCaP cells, clones of the fusion hybrid progeny displayed divergent behaviors and exhibited permanent genomic hybridization.

CONCLUSIONS

Cancer-stromal cell fusion leads to cancer cell heterogeneity. The cancer-stromal coculture system characterized in this study can be used as a model for molecular characterization of cancer cell fusion as the mechanism behind the progression of heterogeneity observed in clinical prostate cancers.

Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study

BACKGROUND

Clustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.

RESULTS

In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained for n = 40 as for n = 330. The relative distribution of the subtypes had a noticeable effect on the ability to identify the disease subtypes and data with disproportionate cluster sizes turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.

CONCLUSIONS

The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.

Advances in Precision Health and Emerging Diagnostics for Women

During the Dutch winter famine of 1944–1945, an interesting observation was made about the offspring born during this time—They had an increased risk of developing metabolic syndrome and other chronic diseases. Subsequent research has confirmed this finding as well as noting that health outcomes for many diseases are different, and often worse, for women. These findings, combined with the lack of enrollment of women in clinical trials and/or analysis of sex-specific differences are important factors which need to be addressed. In fact, Women’s health research and sex differences have historically been overlooked or lumped together and assumed equivalent to those of men. Hence, a focus on women’s health and disease prevention is critical to improve the lives of women in the 21st Century. In this review, we point out the critical differences biologically and socially that present both challenges and opportunities for development of novel platforms for precision health. The technologic and scientific advances specific to women’s precision health have the potential to improve the health and wellbeing for all females across the world.

Systemic inflammation in colorectal cancer: Underlying factors, effects, and prognostic significance

Systemic inflammation is a marker of poor prognosis preoperatively present in around 20%-40% of colorectal cancer patients. The hallmarks of systemic inflammation include an increased production of proinflammatory cytokines and acute phase proteins that enter the circulation. While the low-level systemic inflammation is often clinically silent, its consequences are many and may ultimately lead to chronic cancer-associated wasting, cachexia. In this review, we discuss the pathogenesis of cancer-related systemic inflammation, explore the role of systemic inflammation in promoting cancer growth, escaping antitumor defense, and shifting metabolic pathways, and how these changes are related to less favorable outcome.

Precision Medicine in Non Communicable Diseases

Non-communicable diseases (NCDs) are the leading cause of death and disease burden globally, cardiovascular diseases (CVDs) account for the major part of death related to NCDs followed by different types of cancer, chronic obstructive pulmonary disease (COPD), and diabetes. As the World Health Organization (WHO) and the United Nations have announced a 25% reduction in mortality of NCDs by 2025, different communities need to adopt preventive strategies for achieving this goal. Personalized medicine approach as a predictive and preventive strategy aims for a better therapeutic goal to the patients to maximize benefits and reduce harms. The clinical benefits of this approach are already realized in cancer targeted therapy, and its impact on other conditions needs more studies in different societies. In this review, we essentially describe the concept of personalized (or precision) medicine in association with NCDs and the future of precision medicine in prediction, prevention, and personalized treatment.

Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary field that addresses heterogeneous effects of exogenous and endogenous factors (collectively termed 'exposures'), including microorganisms, on disease occurrence and consequences, utilising molecular pathological signatures of the disease. In parallel with the paradigm of precision medicine, findings from MPE research can provide aetiological insights into tailored strategies of disease prevention and treatment. Due to the availability of molecular pathological tests on tumours, the MPE approach has been utilised predominantly in research on cancers including breast, lung, prostate, and colorectal carcinomas. Mounting evidence indicates that the microbiome (inclusive of viruses, bacteria, fungi, and parasites) plays an important role in a variety of human diseases including neoplasms. An alteration of the microbiome may be not only a cause of neoplasia but also an informative biomarker that indicates or mediates the association of an epidemiological exposure with health conditions and outcomes. To adequately educate and train investigators in this emerging area, we herein propose the integration of microbiology into the MPE model (termed 'microbiology-MPE'), which could improve our understanding of the complex interactions of environment, tumour cells, the immune system, and microbes in the tumour microenvironment during the carcinogenic process. Using this approach, we can examine how lifestyle factors, dietary patterns, medications, environmental exposures, and germline genetics influence cancer development and progression through impacting the microbial communities in the human body. Further integration of other disciplines (e.g. pharmacology, immunology, nutrition) into microbiology-MPE would expand this developing research frontier. With the advent of high-throughput next-generation sequencing technologies, researchers now have increasing access to large-scale metagenomics as well as other omics data (e.g. genomics, epigenomics, proteomics, and metabolomics) in population-based research. The integrative field of microbiology-MPE will open new opportunities for personalised medicine and public health. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

High PTGS2 expression in post-neoadjuvant chemotherapy-treated oesophageal adenocarcinoma is associated with improved survival: a population-based cohort study

AIMS

High prostaglandin endoperoxide synthase-2 (PTGS2) enzyme expression in oesophageal adenocarcinoma has been shown to independently predict poor prognosis; however, the evidence is inconsistent. The aim of this study was to investigated the association between PTGS2 expression and prognosis in patients with oesophageal adenocarcinoma.

METHODS AND RESULTS

A cohort of 135 patients with oesophageal adenocarcinoma who received neoadjuvant chemotherapy and surgery from 2004 to 2012 was identified in the Northern Ireland Cancer Centre. Tissue microarrays were created in the Northern Ireland Biobank, with triplicate cores being sampled from each tumour. Immunohistochemical PTGS2 expression was scored by two independent assessors, with intensity and proportion of tumour staining being used to calculate H-scores for each patient. Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for overall and cancer-specific survival, and recurrence-free survival by PTGS2 expression, with adjustment for potential confounders. Patients were followed up for a mean of 3.0 years (standard deviation 1.8 years). The PTGS2 expression cut-off value was determined from the median H-score of the cohort (270/300). High (n = 79), as compared with low (n = 56), PTGS2 expression was associated with improved cancer-specific survival (adjusted HR 0.56, 95% CI 0.33-0.94; P = 0.03). PTGS2 expression was not significantly associated with recurrence-free survival (adjusted HR 0.85, 95% CI 0.52-1.38; P = 0.51).

CONCLUSIONS

High PTGS2 expression in oesophageal adenocarcinoma tissue was associated with improved overall and cancer-specific survival, in contrast to previous evidence. As this is the first study of its kind to include patients who had undergone neoadjuvant chemotherapy, further studies are needed to clarify these associations.

Cancer prevention and screening: the next step in the era of precision medicine

A primary mode of cancer prevention and early detection in the United States is the widespread practice of screening. Although many strategies for early detection and prevention are available, adverse outcomes, such as overdiagnosis and overtreatment, are prevalent among those utilizing these approaches. Broad use of mammography and prostate cancer screening are key examples illustrating the potential harms stemming from the detection of indolent lesions and the subsequent overtreatment. Furthermore, there are several cancers for which prevention strategies do not currently exist. Clinical and experimental evidence have expanded our understanding of cancer initiation and progression, and have instructed the development of improved, precise modes of cancer prevention and early detection. Recent cancer prevention and early detection innovations have begun moving towards the integration of molecular knowledge and risk stratification profiles to allow for a more accurate representation of at-risk individuals. The future of cancer prevention and early detection efforts should emphasize the incorporation of precision cancer prevention integration where screening and cancer prevention regimens can be matched to one's risk of cancer due to known genomic and environmental factors.

Insights into Pathogenic Interactions Among Environment, Host, and Tumor at the Crossroads of Molecular Pathology and Epidemiology

Evidence indicates that diet, nutrition, lifestyle, the environment, the microbiome, and other exogenous factors have pathogenic roles and also influence the genome, epigenome, transcriptome, proteome, and metabolome of tumor and nonneoplastic cells, including immune cells. With the need for big-data research, pathology must transform to integrate data science fields, including epidemiology, biostatistics, and bioinformatics. The research framework of molecular pathological epidemiology (MPE) demonstrates the strengths of such an interdisciplinary integration, having been used to study breast, lung, prostate, and colorectal cancers. The MPE research paradigm not only can provide novel insights into interactions among environment, tumor, and host but also opens new research frontiers. New developments-such as computational digital pathology, systems biology, artificial intelligence, and in vivo pathology technologies-will further transform pathology and MPE. Although it is necessary to address the rarity of transdisciplinary education and training programs, MPE provides an exemplary model of integrative scientific approaches and contributes to advancements in precision medicine, therapy, and prevention.

Physical Activity and Colorectal Cancer Prognosis According to Tumor-Infiltrating T Cells

Background

Evidence suggests that high-level physical activity may potentially reduce cancer mortality through its immune enhancement effect. We therefore hypothesized that survival benefits associated with physical activity might be stronger in colorectal carcinomas with lower immune reaction at diagnosis.

Methods

Using molecular pathological epidemiology databases of 470 colon and rectal carcinoma cases in the Nurses’ Health Study and the Health Professionals Follow-up Study, we assessed the prognostic association of postdiagnosis physical activity in strata of densities of CD3⁺ cells, CD8⁺ cells, CD45RO (PTPRC)⁺ cells, or FOXP3⁺ cells in tumor tissue. Cox proportional hazards regression model was used to adjust for potential confounders, including microsatellite instability, CpG island methylator phenotype, long interspersed nucleotide element-1 methylation, KRAS, BRAF, and PIK3CA mutations, and expression of CTNNB1 (beta-catenin), PTGS2 (cyclooxygenase-2), and IRS1.

Results

The association of postdiagnosis physical activity with colorectal cancer-specific mortality differed by CD3⁺ cell density (P_interaction < .001). Multivariable-adjusted colorectal cancer-specific mortality hazard ratios for a quartile-unit increase in physical activity were 0.56 (95% confidence interval = 0.38 to 0.83) among cases with the lowest quartile of CD3⁺ cell density compared with 1.14 (95% confidence interval = 0.79 to 1.65) in cases with the highest quartile. We observed no differential survival association of physical activity by densities of CD8⁺ cells, CD45RO⁺ cells, or FOXP3⁺ cells.

Conclusions

The association between postdiagnosis physical activity and colorectal cancer survival appeared stronger for carcinomas with lower T cell infiltrates, suggesting an interactive effect of exercise and immunity on colorectal cancer progression.

Oligonucleotide Aptamer-Mediated Precision Therapy of Hematological Malignancies

Precision medicine has recently emerged as a promising strategy for cancer therapy because it not only specifically targets cancer cells but it also does not have adverse effects on normal cells. Oligonucleotide aptamers are a class of small molecule ligands that can specifically bind to their targets on cell surfaces with high affinity. Aptamers have great potential in precision cancer therapy due to their unique physical, chemical, and biological properties. Therefore, aptamer technology has been widely investigated for biomedical and clinical applications. This review focuses on the potential applications of aptamer technology as a new tool for precision treatment of hematological malignancies, including leukemia, lymphoma, and multiple myeloma.

Accounting for tumor purity improves cancer subtype classification from DNA methylation data

Motivation

Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The 'tumor purity', or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.

Results

In this article, we developed a model-based clustering method and an R function which uses DNA methylation microarray data to infer tumor subtypes with the consideration of tumor purity. Simulation studies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with existing methods.

Availability and implementation

InfiniumClust is part of R package InfiniumPurify , which is freely available from CRAN ( https://cran.r-project.org/web/packages/InfiniumPurify/index.html ).

Contact

hao.wu@emory.edu or xqzheng@shnu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine

Immunotherapy strategies targeting immune checkpoints such as the CTLA4 and CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) T-cell coreceptor pathways are revolutionising oncology. The approval of pembrolizumab use for solid tumours with high-level microsatellite instability or mismatch repair deficiency by the US Food and Drug Administration highlights promise of precision immuno-oncology. However, despite evidence indicating influences of exogenous and endogenous factors such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures and microbiome on tumour-immune interactions, integrative analyses of those factors and immunity lag behind. Immune cell analyses in the tumour microenvironment have not adequately been integrated into large-scale studies. Addressing this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) offers research frameworks to integrate tumour immunology into population health sciences, and link the exposures and germline genetics (eg, HLA genotypes) to tumour and immune characteristics. Multilevel research using bioinformatics, in vivo pathology and omics (genomics, epigenomics, transcriptomics, proteomics and metabolomics) technologies is possible with use of tissue, peripheral blood circulating cells, cell-free plasma, stool, sputum, urine and other body fluids. This immunology-MPE model can synergise with experimental immunology, microbiology and systems biology. GI neoplasms represent exemplary diseases for the immunology-MPE model, given rich microbiota and immune tissues of intestines, and the well-established carcinogenic role of intestinal inflammation. Proof-of-principle studies on colorectal cancer provided insights into immunomodulating effects of aspirin, vitamin D, inflammatory diets and omega-3 polyunsaturated fatty acids. The integrated immunology-MPE model can contribute to better understanding of environment-tumour-immune interactions, and effective immunoprevention and immunotherapy strategies for precision medicine.

Obesity as risk factor for subtypes of breast cancer: results from a prospective cohort study

BACKGROUND

Earlier epidemiological studies indicate that associations between obesity and breast cancer risk may not only depend on menopausal status and use of exogenous hormones, but might also differ by tumor subtype. Here, we evaluated whether obesity is differentially associated with the risk of breast tumor subtypes, as defined by 6 immunohistochemical markers (ER, PR, HER2, Ki67, Bcl-2 and p53, separately and combined), in the prospective EPIC-Germany Study (n = 27,012).

METHODS

Formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 657 incident breast cancer cases were used for histopathological analyses. Associations between BMI and breast cancer risk across subtypes were evaluated by multivariable Cox regression models stratified by menopausal status and hormone therapy (HT) use.

RESULTS

Among postmenopausal non-users of HT, higher BMI was significantly associated with an increased risk of less aggressive, i.e. ER+, PR+, HER2-, Ki67_low, Bcl-2+ and p53- tumors (HR per 5 kg/m²: 1.44 [1.10, 1.90], p = 0.009), but not with risk of more aggressive tumor subtypes. Among postmenopausal users of HT, BMI was significantly inversely associated with less aggressive tumors (HR per 5 kg/m²: 0.68 [0.50, 0.94], p = 0.018). Finally, among pre- and perimenopausal women, Cox regression models did not reveal significant linear associations between BMI and risk of any tumor subtype, although analyses by BMI tertiles showed a significantly lower risk of less aggressive tumors for women in the highest tertile (HR: 0.55 [0.33, 0.93]).

CONCLUSION

Overall, our results suggest that obesity is related to risk of breast tumors with lower aggressiveness, a finding that requires replication in larger-scale analyses of pooled prospective data.

One-carbon metabolism biomarkers and genetic variants in relation to colorectal cancer risk by KRAS and BRAF mutation status

Disturbances in one-carbon metabolism, intracellular reactions involved in nucleotide synthesis and methylation, likely increase the risk of colorectal cancer (CRC). However, results have been inconsistent. To explore whether this inconsistency could be explained by intertumoral heterogeneity, we evaluated a comprehensive panel of one-carbon metabolism biomarkers and some single nucleotide polymorphisms (SNPs) in relation to the risk of molecular subtypes of CRC defined by mutations in the KRAS and BRAF oncogenes. This nested case-control study included 488 CRC cases and 947 matched controls from two population-based cohorts in the Northern Sweden Health and Disease Study. We analyzed 14 biomarkers and 17 SNPs in prediagnostic blood and determined KRAS and BRAF mutation status in tumor tissue. In a multivariate network analysis, no variable displayed a strong association with the risk of specific CRC subtypes. A non-synonymous SNP in the CTH gene, rs1021737, had a stronger association compared with other variables. In subsequent univariate analyses, participants with variant rs1021737 genotype had a decreased risk of KRAS-mutated CRC (OR per allele = 0.72, 95% CI = 0.50, 1.05), and an increased risk of BRAF-mutated CRC (OR per allele = 1.56, 95% CI = 1.07, 2.30), with weak evidence for heterogeneity (P_{heterogeneity} = 0.01). This subtype-specific SNP association was not replicated in a case-case analysis of 533 CRC cases from The Cancer Genome Atlas (P = 0.85). In conclusion, we found no support for clear subtype-specific roles of one-carbon metabolism biomarkers and SNPs in CRC development, making differences in CRC molecular subtype distributions an unlikely explanation for the varying results on the role of one-carbon metabolism in CRC development across previous studies. Further investigation of the CTH gene in colorectal carcinogenesis with regards to KRAS and BRAF mutations or other molecular characteristics of the tumor may be warranted.

Promoter CpG island methylation in ion transport mechanisms and associated dietary intakes jointly influence the risk of clear-cell renal cell cancer

Background

Sodium intake, but not potassium or fluid intake, has been associated with higher renal cell cancer (RCC) risk. However, risk factors may differ by molecular subtypes of the tumour. In renal physiology, electrolyte and water homeostasis is facilitated by ion transport mechanisms (ITM). Aberrant regulation of ITM genes, for example by promoter CpG island methylation, may modify associations between sodium, potassium and fluid intake and RCC risk.

Methods

We identified ARHGDIG , ATP1A1 , SCNN1B and SLC8A3 as ITM genes exhibiting RCC-specific promoter methylation and down-regulation. Methylation-specific polymerase chain reaction (PCR) was used to analyse promoter CpG island methylation in tumour DNA of 453 RCC cases from the Netherlands Cohort Study ( n = 120 852) after 20.3 years of follow-up. Diet was measured at baseline using food-frequency questionnaires. Cox regression analyses were restricted to clear-cell (cc)RCC ( n = 306) and stratified by tumours with no, low (1 gene) and high (≥ 2 genes) methylation.

Results

Sodium intake (high vs low) increased ccRCC risk particularly in tumours with a high methylation index: hazard ratio (HR) [95% confidence interval (CI)]: 2.04 (1.16-3.58), whereas heterogeneity across the methylation index was not significant ( P -heterogeneity = 0.26). Potassium intake was differentially associated with ccRCC risk ( P -heterogeneity = 0.008); the risk for high (vs low) potassium intake was low for unmethylated tumours [HR (95% CI): 0.60 (0.36-1.01)], but high for tumours with a high methylation index [HR (95% CI): 1.60 (0.96-2.65)]. Risks similarly differed for fluid intake, though not significantly ( P -heterogeneity = 0.54).

Conclusions

Our findings suggest for the first time that dietary intakes are differentially associated with ccRCC risk according to molecular subtypes defined by ITM gene-specific promoter methylation.

Mutation status and prognostic values of KRAS, NRAS, BRAF and PIK3CA in 353 Chinese colorectal cancer patients

Mutations in KRAS exon 2, BRAF and PIK3CA are commonly present in colorectal cancer (CRC) worldwide, but few data about RAS mutations outside KRAS exon 2 are available for Chinese CRCs. We, therefore, determined the mutation frequencies and prognostic values of KRAS exon 2, 3 and 4, NRAS exon 2 and 3, PIK3CA exon 9 and 20, and BRAF exon 15 by PCR and direct sequencing in 353 CRC patients from two Chinese clinical centers. KRAS exon 2, BRAF, PIK3CA mutations were identified in 42.2%, 4.5%, 12.3% of the cases, respectively. We found “rare mutations” in RAS genes in nearly 14% of CRCs-i.e., in almost a quarter (24.0%) of KRAS exon 2 wild type CRCs, including 2.3% in KRAS exon 3, 8.2% in KRAS exon 4 and 3.4% in NRAS. Stage I-III patients with PIK3CA or NRAS mutations developed more distant metastases (3-year risk in PIK3CA mutated and wild type patients: 23.3% vs 11.5%, P = 0.03; multivariate Hazard ratio (HR) = 3.129, P = 0.003; 3-year risk in NRAS mutated and wild type patients: 40.0% vs 12.2%, P = 0.012; multivariate HR = 5.152, P = 0.003). Our data emphasizes the importance of these novel molecular features in CRCs.

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.

Attitudes towards Personal Genomics and Sharing of Genetic Data among Older Swiss Adults: A Qualitative Study

OBJECTIVE

To assess the willingness of older Swiss adults to share genetic data for research purposes and to investigate factors that might impact their willingness to share data.

METHODS

Semi-structured interviews were conducted among 40 participants (19 male and 21 female) aged between 67 and 92 years, between December 2013 and April 2014 attending the Seniorenuniversität Zürich, Switzerland. All interviews were audio-recorded, transcribed verbatim, and anonymized. For the analysis of the interviews, an initial coding scheme was developed, refined over time, and applied afterwards to all interviews.

RESULTS

The majority of participants were in favor of placing genetic data to research's disposal. Participant's motivations to share data were mainly driven by altruistic reasons and by contributing to the greater good. Furthermore, several factors which might impact the willingness to share data such as sharing data with private companies, generational differences, differences between sharing genetic data or health data, and sharing due to financial incentives were highlighted. Last, some participants indicated concerns regarding data sharing such as misuse of data, the fear of becoming a transparent citizen, and data safety. However, 20% of the participants express confidence in data protection. Even participants who were skeptical in the beginning of the interviews admitted the benefits of data sharing.

DISCUSSION

Overall, this study suggests older citizens are willing to share their data for research purposes. However, most of them will only contribute if their data is appropriately protected and if they trust the research institution to use the shared data responsibly. More transparency and detailed information regarding the data usage are urgently needed. There is a great need to increase the engagement of older adults in research since they present a large segment of our society - one which is often underexamined in research.

CONCLUSION

Increased focus on general public engagement, especially of older adults, in scientific research activities known as "citizen science" is needed to further strengthen the uptake of personalized medicine.