Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care

Personalized Medicine in Oncology in the Developing World: Barriers and Concepts to Improve Status Quo

Personalized medicine (PM) has revolutionized oncology management in high human development indexed countries. By interrogating both disease and host factors through a variety of tools, oncologists have been able to better target an individual's cancer, leading to improved outcomes. But both the tools used to define these variables, such as next generation sequencing, large immunohistochemical and fluorescence in situ hybridization (FISH) panels, and the weapons employed against each target are extremely expensive. The expenses have to be measured as not only the direct cost to the patient but also the cost to the system to develop and deploy the necessary infrastructure to optimally use them. However, the concepts of predictive, timely prevention and PM have demonstrated improvement in patient's satisfaction and cost effectiveness. In this paper we will summarize the relevant barriers and challenges that limit the implementation of PM in the developing world with an emphasis on the challenges in Nigeria and Nepal.

Approaches and genetic determinants in predicting response to neoadjuvant chemotherapy in locally advanced gastric cancer

Gastric cancer remains a major health burden worldwide. There is near-universal agreement that neoadjuvant chemotherapy (NAC) is a preferred management for locally advanced gastric cancer (LAGC). However, the optimal approach for an individual patient is still not clear and remains controversial, which could be at least partly explained by the lack of predictive tools. The ability to predict chemosensitivity from NAC in routine clinical practice is difficult and is an area of intense investigation, especially in the Precision-Medicine Era. Available consistent evidence suggests that a favorable tumor histopathological response to NAC may be a useful positive prognostic marker in gastric cancer. Hence, it is reasonable to speculate that making the histopathological response from NAC predictable will dramatically facility the NAC and improve patients' outcome. This review provides an overview on the current status of predictive biomarkers for histopathological response from NAC in LAGC, including clinicopathological variables, imaging and molecular testing. Furthermore, limitations and future perspectives are also discussed.

Breast cancer in young women: an overview

Despite dramatic advances in cancer research setting, breast cancer remains a major health problem and represents currently a top biomedical research priority. Worldwide, breast cancer is the most common cancer affecting women, and its incidence and mortality rates are expected to increase significantly the next years. Recently the researchers' interest has been attracted by breast cancer arising in young women. Current evidence suggests that in women aged <45 years, breast cancer is unquestionably the leading cause of cancer-related deaths. This type of cancer seems to be highly heterogeneous and has potentially aggressive and complex biological features. However, management strategies, recommendations and options are not age based and the 'complex' biology of this type of cancer remains uncertain and unexplored. In this review, we summarize the latest scientific information on breast cancer arising in young women highlighting the heterogeneity and the complex nature of this type of cancer.

Next-generation molecular diagnosis: single-cell sequencing from bench to bedside

Single-cell sequencing (SCS) is a fast-growing, exciting field in genomic medicine. It enables the high-resolution study of cellular heterogeneity, and reveals the molecular basis of complicated systems, which facilitates the identification of new biomarkers for diagnosis and for targeting therapies. It also directly promotes the next generation of genomic medicine because of its ultra-high resolution and sensitivity that allows for the non-invasive and early detection of abnormalities, such as aneuploidy, chromosomal translocation, and single-gene disorders. This review provides an overview of the current progress and prospects for the diagnostic applications of SCS, specifically in pre-implantation genetic diagnosis/screening, non-invasive prenatal diagnosis, and analysis of circulating tumor cells. These analyses will accelerate the early and precise control of germline- or somatic-mutation-based diseases, particularly single-gene disorders, chromosome abnormalities, and cancers.

Network inference from multimodal data: A review of approaches from infectious disease transmission

Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.

Molecular pathological epidemiology in diabetes mellitus and risk of hepatocellular carcinoma

Molecular pathological epidemiology (MPE) is a multidisciplinary and transdisciplinary study field, which has emerged as an integrated approach of molecular pathology and epidemiology, and investigates the relationship between exogenous and endogenous exposure factors, tumor molecular signatures, and tumor initiation, progression, and response to treatment. Molecular epidemiology broadly encompasses MPE and conventional-type molecular epidemiology. Hepatocellular carcinoma (HCC) is the third most common cause of cancer-associated death worldwide and remains as a major public health challenge. Over the past few decades, a number of epidemiological studies have demonstrated that diabetes mellitus (DM) is an established independent risk factor for HCC. However, how DM affects the occurrence and development of HCC remains as yet unclearly understood. MPE may be a promising approach to investigate the molecular mechanisms of carcinogenesis of DM in HCC, and provide some useful insights for this pathological process, although a few challenges must be overcome. This review highlights the recent advances in this field, including: (1) introduction of MPE; (2) HCC, risk factors, and DM as an established independent risk factor for HCC; (3) molecular pathology, molecular epidemiology, and MPE in DM and HCC; and (4) MPE studies in DM and risk of HCC. More MPE studies are expected to be performed in future and I believe that this field can provide some very important insights on the molecular mechanisms, diagnosis, personalized prevention and treatment for DM and risk of HCC.

Mathematical modeling for novel cancer drug discovery and development

INTRODUCTION

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.

AREAS COVERED

This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.

EXPERT OPINION

Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

The Impact of Molecular Morphology Techniques on the Expert Diagnosis in Melanocytic Skin Neoplasms

Thirteen melanocytic skin neoplasms with a consultation diagnosis by A. Bernard Ackerman were submitted to immunohistochemistry for HMB-45, Ki67, cyclin D1, e-cadherin, and p16; 9/13 cases underwent fluorescence in situ hybridization (FISH) test targeting 6p25 (RREB1), 6q23 (MYB), centromere 6 (Cep6), and 11q13 (CCND1), as well as the centromere 7 (Cep7). A “consensus diagnosis” among 3 experts was also advocated both before and after morphomolecular information. Three neoplasms with a consultation diagnosis of Spitz nevus showed at least 3 abnormal immunohistochemical patterns; 2 of these cases were also FISH-positive for CCND1 gain, but none of them had a final consensus diagnosis of melanoma. Two neoplasms with a consultation diagnosis of congenital nevus received a consensus diagnosis of melanoma. Molecular morphology techniques can highlight the atypical features of melanocytic neoplasms and support existence of a morphobiologic “spectrum”: This should be mirrored in the final report by abandoning the dichotomic (benign vs malignant) diagnostic approach.

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

Cancers are complex multifactorial diseases. For centuries, conventional organ-based classification system (i.e., breast cancer, lung cancer, colon cancer, colorectal cancer, prostate cancer, lymphoma, leukemia, and so on) has been utilized. Recently, molecular diagnostics has become an essential component in clinical decision-making. However, tumor evolution and behavior cannot accurately be predicted, despite numerous research studies reporting promising tumor biomarkers. To advance molecular diagnostics, a better understanding of intratumor and intertumor heterogeneity is essential. Tumor cells interact with the extracellular matrix and host non-neoplastic cells in the tumor microenvironment, which is influenced by genomic variation, hormones, and dietary, lifestyle and environmental exposures, implicated by molecular pathological epidemiology. Essentially, each tumor possesses its own unique characteristics in terms of molecular make-up, tumor microenvironment and interactomes within and between neoplastic and host cells. Starting from the unique tumor concept and paradigm, we can better classify tumors by molecular methods, and move closer toward personalized cancer medicine and prevention.

Differential expression of miRNAs in colorectal cancer: comparison of paired tumor tissue and adjacent normal mucosa using high-throughput sequencing

We present the results of a global study of dysregulated miRNAs in paired samples of normal mucosa and tumor from eight patients with colorectal cancer. Although there is existing data of miRNA contribution to colorectal tumorigenesis, these studies are typically small to medium scale studies of cell lines or non-paired tumor samples. The present study is to our knowledge unique in two respects. Firstly, the normal and adjacent tumor tissue samples are paired, thus taking into account the baseline differences between individuals when testing for differential expression. Secondly, we use high-throughput sequencing, thus enabling a comprehensive survey of all miRNAs expressed in the tissues. We use Illumina sequencing technology to perform sequencing and two different tools to statistically test for differences in read counts per gene between samples: edgeR when using the pair information and DESeq when ignoring this information, i.e., treating tumor and normal samples as independent groups. We identify 37 miRNAs that are significantly dysregulated in both statistical approaches, 19 down-regulated and 18 up-regulated. Some of these miRNAs are previously published as potential regulators in colorectal adenocarcinomas such as miR-1, miR-96 and miR-145. Our comprehensive survey of differentially expressed miRNAs thus confirms some existing findings. We have also discovered 16 dysregulated miRNAs, which to our knowledge have not previously been associated with colorectal carcinogenesis: the following significantly down-regulated miR-490-3p, -628-3p/-5p, -1297, -3151, -3163, -3622a-5p, -3656 and the up-regulated miR-105, -549, -1269, -1827, -3144-3p, -3177, -3180-3p, -4326. Although the study is preliminary with only eight patients included, we believe the results add to the present knowledge on miRNA dysregulation in colorectal carcinogenesis. As such the results would serve as a robust training set for validation of potential biomarkers in a larger cohort study. Finally, we also present data supporting the hypothesis that there are differences in miRNA expression between adenocarcinomas and neuroendocrine tumors of the colon.

Critical role of laser microdissection for genetic, epigenetic and proteomic analyses in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, and molecular studies to unravel novel biomarkers and therapeutic targets are warranted. However, PDAC is characterized by different precursor lesions, as well as by an intense desmoplastic reaction, with islet of neoplastic cells often representing a minor population. Moreover, normal ductal cells, which are considered to be the normal counterpart of pancreatic adenocarcinoma cells, comprise approximately 5% of the total population of cells making up this organ. For all these reasons, molecular techniques to identify critical mutations, as well as the pattern of altered mRNA/microRNA/protein expression should be performed on selected pancreatic cell subpopulations. Therefore, the use of the newest laser microdissection techniques is critical for the analysis of PDAC biological characteristics. This article highlights the most recent and clinically relevant aspects of genetic, epigenetic and proteomic analyses of PDAC from the perspective of the application of laser microdissection.

Systems biology of kidney diseases

Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.

In search of new therapeutic targets and strategies for heart failure: recent advances in basic science

Chronic heart failure continues to impose a substantial health-care burden, despite recent treatment advances. The key pathophysiological process that ultimately leads to chronic heart failure is cardiac remodelling in response to chronic disease stresses. Here, we review recent advances in our understanding of molecular and cellular mechanisms that play a part in the complex remodelling process, with a focus on key molecules and pathways that might be suitable targets for therapeutic manipulation. Such pathways include those that regulate cardiac myocyte hypertrophy, calcium homoeostasis, energetics, and cell survival, and processes that take place outside the cardiac myocyte--eg, in the myocardial vasculature and extracellular matrix. We also discuss major gaps in our current understanding, take a critical look at conventional approaches to target discovery that have been used to date, and consider new investigational avenues that might accelerate clinically relevant discovery.

Erlotinib or gefitinib for the treatment of relapsed platinum pretreated non-small cell lung cancer and ovarian cancer: a systematic review

BACKGROUND

Platinum-based chemotherapy is the standard of care for ovarian cancer and non-small cell lung cancer (NSCLC). However, resistance to platinum agents invariably develops. Targeted therapies, such as tyrosine kinase inhibitors (TKIs), have great potential here as they exert their anti-tumour effect via alternative mechanisms to platinum-based drugs and as such may remain unaffected by emergent resistance to platinum.

METHODS

A systematic review was conducted to investigate whether two EGFR-TKIs, erlotinib and gefitinib, have efficacy in the platinum-resistance setting. Preclinical studies of platinum-resistant cancer cell lines, which had been subsequently treated with EGFR-TKIs, were sought to establish proof-of-concept. Clinical trials reporting administration of EGFR-TKIs to ovarian cancer and NSCLC patients relapsed after therapy with platinum drugs were investigated to determine sensitivity of these cohorts to EGFR-TKI treatment. The role of EGFR mutation, copy number and protein expression on response to EGFR-TKIs after failure of platinum chemotherapy were also investigated.

RESULTS

Preclinical models of platinum-resistant cancer were found which display a spectrum of cross-resistance profiles to EGFR-TKIs. Sensitivity to EGFR-TKIs is dependent on the activation of the EGFR pathway or EGFR interacting proteins such as HER-2. EGFR-TKIs show favourable response rates in platinum-pretreated NSCLC, 11.14% and 15.25% for 150mg/day erlotinib and 250mg/day gefitinib, respectively. These response rates significantly improve in patients of Asian descent (28.3% and 29.17%, respectively) and patients with EGFR activation mutations (41.6% and 63.89%, respectively) or increased copy number (33.3% and 45.45%, respectively). Gefitinib significantly outperformed erlotinib and should therefore be the EGFR-TKI of choice in platinum-pretreated relapsed NSCLC. In contrast, response rates are very poor to both erlotinib and gefitinib in platinum pretreated ovarian cancer, 0-5.9% and they should not be used in this cohort of patients. Preclinical models demonstrate that, while cross resistance can occur between platinums and EGFR-TKIs, there is not a generalised cross-resistance phenotype. Erlotinib and gefitinib are suitable for the treatment of platinum-pretreated NSCLC, particularly in patients with EGFR mutations or increases in copy number. Unfortunately, the high rates of EGFR protein overexpression in ovarian cancer are not translating to a clinically useful therapeutic target for EGFR-TKIs; EGFR mutations are rare in ovarian cancer. Newer TKIs may improve response rates in these cohorts and future clinical trials need to collect tumour biopsies from all patients to ensure the success of personalised chemotherapy.

NF-κB addiction and its role in cancer: 'one size does not fit all'

Activation of nuclear factor (NF)-κB, one of the most investigated transcription factors, has been found to control multiple cellular processes in cancer including inflammation, transformation, proliferation, angiogenesis, invasion, metastasis, chemoresistance and radioresistance. NF-κB is constitutively active in most tumor cells, and its suppression inhibits the growth of tumor cells, leading to the concept of 'NF-κB addiction' in cancer cells. Why NF-κB is constitutively and persistently active in cancer cells is not fully understood, but multiple mechanisms have been delineated including agents that activate NF-κB (such as viruses, viral proteins, bacteria and cytokines), signaling intermediates (such as mutant receptors, overexpression of kinases, mutant oncoproteins, degradation of IκBα, histone deacetylase, overexpression of transglutaminase and iNOS) and cross talk between NF-κB and other transcription factors (such as STAT3, HIF-1α, AP1, SP, p53, PPARγ, β-catenin, AR, GR and ER). As NF-κB is 'pre-active' in cancer cells through unrelated mechanisms, classic inhibitors of NF-κB (for example, bortezomib) are unlikely to mediate their anticancer effects through suppression of NF-κB. This review discusses multiple mechanisms of NF-κB activation and their regulation by multitargeted agents in contrast to monotargeted agents, thus 'one size does not fit all' cancers.

Translating cancer genomes sequencing revolution into surgical oncology practice

BACKGROUND

Ten years after the first complete human genome sequencing, next-generation sequencing (NGS) technology has revolutionized genomics and biomedical research. Here we discuss potential emerging and future applications of NGS platforms and how cancer genome advances may change current surgical oncology practice.

MATERIALS AND METHODS

Publications in PubMed with partial or complete human and cancer genome sequencing over the last decade have been retrieved and analyzed. Recently launched international large-scale consortiums for systematic study of causal (driver) mutations underlying common complex diseases such as cancer and more recent opinion articles by leading scientists have been considered to predict the prospects of integration genomics data into surgical oncology.

RESULTS

Two dozen complete human genome sequences, and three full-genome sequencing in three cancer patients have recently been published. These studies and other cancer genome systematic studies evaluating genomic rearrangements and copy-number changes consistently reveal that cancer initiation and metastasis are much more complex than we thought. This high complexity and widespread variability of cancer genes, mutations, and deregulated signaling pathways among patients with the same cancer type, tumor stage, and clinicopathologic features explains the limited effectiveness of currently used biologically targeted agents.

CONCLUSIONS

Yet the impact of genomics explosion in surgical oncology is little. Nevertheless, "big" biology consortiums such as the International Cancer Genome Project and other systematic studies in the areas of genetics, genomics, and epigenetics are now providing in-depth understanding of cancer, and shape new more rational ways towards novel therapeutics for improving oncologic outcomes of millions of patients worldwide. At the beginning of the second post-genomic decade, an explosion in genomic technology has revolutionized biomedical sciences. The ability of latest DNA sequencing technology to identify, in an unprecedented level, causal mutations driving cancer initiation and metastasis raises significant expectations for improving oncologic outcomes. Which are the challenges and perspectives to translate this genomic revolution into a surgical oncology-changing era?