The hallmarks of cancer.

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

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Initial sequencing and analysis of the human genome.

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

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Statistical mechanics of complex networks

A genetic model for colorectal tumorigenesis

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

It is only recently that drugs targeting K-RAS and Tp53 missense mutations have been developed, and along with the allele specific nature of some of these drugs comes the possibility of combining them with the immunologic therapies for cancers. It has taken about 40 years since their discoveries to understand the pathways they command, how they function, and how they interact with the environment of the cells they control. This communication focuses on the transfer of some of the hard won information about the p53 protein, its mutations, structures, and activities learned in the basic science laboratory and translated to the clinic.

https://aacrjournals.org/cancerres/article-pdf/82/3/362/3031901/362.pdf

Targeting the P53 Protein for Cancer Therapies: The Translational Impact of P53 Research

During the 1960s, the field of cancer research lacked clear direction. Several facts appeared to be well-established and correct, but the relationships among these observations were not apparent. Fifty years of research had demonstrated that viruses with both DNA and RNA genomes could cause cancer in animals. Over the next 45 years six new viruses were to be discovered that were able to initiate cancers in humans (Epstein-Barr Virus, Human T-Cell Leukemia Virus, Hepatitis B and C Viruses, Kaposi Sarcoma Virus and the Papilloma Viruses) (McKinnel et al., 1998). It was equally clear from the perspective of the 1960s that certain chemicals, when applied to animals, were able to initiate cancers (Yamagawa et al., 1918). Chemical carcinogenesis was a field both separate and distinct (both in the experiments one did and the experimentalists who did them) from viral carcinogenesis and very few scientists thought to find a common ground between concepts generated in each field. Thirdly, the study of mouse genetics demonstrated that some cancers were clearly inherited and these observations confirmed many prior publications that suggested a role for cancer causing genes in humans and other animals (DeOme, 1965). Finally epidemiologists, studying a variety of important variables that predispose humans to developing cancers, had made the very striking observation that the rates of cancer incidence increase exponentially with age and begin to rise dramatically by the fifth and sixth decade of life (Miller, 1991). While these four observations were all accepted facts the relationship between

The First Twenty-Five Years of p53 Research

Immunoselection of simian virus 40 large T antigen messenger rnas from transformed cells.

Abstract Missense driver mutations in cancer are concentrated in a few hotspots 1 . Various mechanisms have been proposed to explain this skew, including biased mutational processes 2 , phenotypic differences 3–6 and immunoediting of neoantigens 7,8 ; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical ‘free fitness’ framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53 , the most mutated gene in cancer 1 , we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution. 

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Fundamental immune–oncogenicity trade-offs define driver mutation fitness

A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate. Training a Profile Hidden Markov Model with corresponding positions matching a combined-palindromic p53-binding motif creates the best p53-RE predictive model. The p53HMM algorithm is available on-line: http://tools.csb.ias.edu
 Using Profile Hidden Markov Models with training methods that exploit the redundant information of the homotetramer p53 binding site provides better predictive models than weight matrices (PSSMs). These methods may also boost performance when applied to other transcription factor binding sites.

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Arnold J. Levine

Papers

Targeting the P53 Protein for Cancer Therapies: The Translational Impact of P53 Research

The First Twenty-Five Years of p53 Research

Immunoselection of simian virus 40 large T antigen messenger rnas from transformed cells.

Fundamental immune–oncogenicity trade-offs define driver mutation fitness

The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes