A multi-scale approach to colorectal cancer: from a biochemical- interaction signaling-network level, to multi-cellular dynamics of malignant transformation. Interplay with mutations and onco-protein inhibitor drugs

Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro‐RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1‐S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical‐trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314–336. doi: 10.1002/wsbm.1342

This article is categorized under: 1

Biological Mechanisms > Cell Signaling

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Analytical and Computational Methods > Computational Methods

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Translational, Genomic, and Systems Medicine > Translational Medicine

Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems

Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.

Systems biology approaches to adverse drug effects: the example of cardio-oncology

Increased awareness of the cardiovascular toxic effects of chemotherapy has led to the emergence of cardio-oncology (or onco-cardiology), which focuses on screening, monitoring and treatment of patients with cardiovascular dysfunctions resulting from chemotherapy. Anthracyclines, such as doxorubicin, and HER2 inhibitors, such as trastuzumab, both have cardiotoxic effects. The biological rationale, mechanisms of action and cardiotoxicity profiles of these two classes of drugs, however, are completely different, suggesting that cardiotoxic effects can occur in a range of different ways. Advances in genomics and proteomics have implicated several genomic variants and biological pathways that can influence the susceptibility to cardiotoxicity from these, and other drugs. Established pathways include multidrug resistance proteins, energy utilization pathways, oxidative stress, cytoskeletal regulation and apoptosis. Gene-expression profiles that have revealed perturbed pathways have vastly increased our knowledge of the complex processes involved in crosstalk between tumours and cardiac function. Utilization of mathematical and computational modelling can complement pharmacogenomics and improve individual patient outcomes. Such endeavours should enable identification of variations in cardiotoxicity, particularly in those patients who are at risk of not recovering, even with the institution of cardioprotective therapy. The application of systems biology holds substantial potential to advance our understanding of chemotherapy-induced cardiotoxicity.

Inhibition of KRAS codon 12 mutants using a novel DNA-alkylating pyrrole-imidazole polyamide conjugate

Despite extensive efforts to target mutated RAS proteins, anticancer agents capable of selectively killing tumour cells harbouring KRAS mutations have remained unavailable. Here we demonstrate the direct targeting of KRAS mutant DNA using a synthetic alkylating agent (pyrrole-imidazole polyamide indole-seco-CBI conjugate; KR12) that selectively recognizes oncogenic codon 12 KRAS mutations. KR12 alkylates adenine N3 at the target sequence, causing strand cleavage and growth suppression in human colon cancer cells with G12D or G12V mutations, thus inducing senescence and apoptosis. In xenograft models, KR12 infusions induce significant tumour growth suppression, with low host toxicity in KRAS-mutated but not wild-type tumours. This newly developed approach may be applicable to the targeting of other mutant driver oncogenes in human tumours.

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

Low percentage of KRAS mutations revealed by locked nucleic acid polymerase chain reaction: implications for treatment of metastatic colorectal cancer

Metastatic colorectal cancer (mCRC) is frequently characterized by the presence of mutations of the KRAS oncogene, which are generally associated with a poor response to treatment with anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibodies. With the methods currently used, a case is classified as KRAS-mutated when approximately 20% of the cells bear an activating KRAS mutation. These considerations raise the question of whether cells with a mutated KRAS can be found in mCRC cases classified as KRAS wild-type when more sensitive methods are used. In addition, the issue arises of whether these mCRC cases with low proportion of KRAS-mutated cells could account at least in part for the therapeutic failure of anti-EGFR therapies that occur in 40-60% of cases classified as KRAS wild type. In this study, we compared the classical assays with a very sensitive test, a locked nucleic acid (LNA) polymerase chain reaction (PCR), capable of detecting KRAS-mutated alleles at extremely low frequency (detection sensitivity limit 0.25% mutated DNA/wild-type DNA). By analyzing a cohort of 213 mCRC patients for KRAS mutations, we found a 20.6% discordance between the sequencing/TheraScreen methods and the LNA-PCR. Indeed, 44 mCRC patients initially considered KRAS wild type were reclassified as KRAS mutated by using the LNA-PCR test. These patients were more numerous among individuals displaying a clinical failure to anti-EGFR therapies. Failure to respond to these biological treatments occurred even in the absence of mutations in other EGFR pathway components such as BRAF.

Parameter space exploration within dynamic simulations of signaling networks

We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

On biomarkers and pathways in rectal cancer: What's the target?

In spite of tremendous progresses in surgical and chemo-radiotherapeutic regimens, rectal cancer still suffers from high relapse and mortality rates, and metastatic disease is incurable. Here we assess some of the most recent and validated biomarkers and potential targets studied in rectal cancer, and provide comments to a recent monographic topic covering several aspects of colorectal cancer, published in Current Cancer Drug Targets.

Expression of cyclin E in stage III colorectal carcinoma

Carcinogenesis is characterized by an abnormal regulation of the cell cycle. Regulators of the cell cycle such as cyclin E play an important role in neoplasia and may be correlated with prognosis. The clinical significance of the expression of cyclin E in stage III colorectal carcinoma has not yet been investigated. The expression of cyclin E was evaluated in 49 patients. Using a multivariate analysis, the expression of cyclin E in the tumor at diagnosis was compared with various clinicopathological variables, including age, gender, tumor site, tumor size, tumor differentiation and lymph node involvement. There were more node-positive cases in the cyclin E-negative group than in the cyclin E-positive group (P=0.003). However, there was no correlation between the degree of cyclin E expression and the clinical data. In conclusion, our data suggest that overexpression of cyclin E does not predict the clinical outcome in colorectal cancer stage III. Negative cyclin E staining may be associated with lymph node involvement.