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Enter the code below and hit Verify. Free Shipping All orders of Don't have an account? Update your profile Let us wish you a happy birthday! Make sure to buy your groceries and daily needs Buy Now. Let us wish you a happy birthday! In addition to mutations and other genomic markers, emphasis is currently being put on the protein composition of cancer cells. For a functional understanding of the effects of mutations, remaining at the nucleotide sequence level is not sufficient. Thus, protein-based approaches are advantageous over DNA sequencing-based approaches as biomarkers in blood samples.

Specifically, certain portions of proteins called domains — the structural and functional building blocks of proteins — have been shown to constitute mutation hotspots. Protein domain mutation hotspots provide important clues to understanding and classifying cancer. Mutation hotspots at the DNA level can be classified according to the effects these mutations have on the according structural and functional parts of proteins.

This way, a much more fine-grained molecular characterization and classification of cancer becomes possible. If certain classes of target protein domains can be identified, this also suggests ways to arm the immune system against these protein domains already at early stages of cancer. A better molecular understanding of the structural and functional properties of affected protein domains in cancer cells is the first step to understanding the processes on higher levels of organization.

Questions that need to be addressed include: Which hallmark protein domains are affected in which types of cancer? Which structural and functional properties of such domains cause cells to become malignant? Does protein-based classification deviate from cell-of-origin classification? The key proteins appear to be involved in regulating the balance between keeping neoplastic cells at bay and not overcautiously stopping cells from proliferating without need.

Apoptosis and cellular senescence, i. Frequently, it is exactly such proteins which are affected in different types of cancer.

An adequate cancer classification system needs to address these questions. Despite tremendous advances in cancer research, a stubborn gap exists between these advances and successful treatments that reduce mortality. One strategic way to address this gap is to model cancer as an infectious disease that we give ourselves.

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This conceptual maneuver shifts attention from cellular proliferation and tumor growth how cancer grows to cellular motility and metastasis how cancer spreads , and emphasizes properties of cancerous cells that are responsible for the majority of deaths. We use the case of cystic fibrosis as an analogy to show the value of conceptualizing a genetic disease that is recalcitrant to treatment as an infectious disease. Recent philosophical study of cancer identifies the functional role cancer plays in evolution by selection Germain , Lean and Plutynski , Germain and Laplane , Liu et al.

In this paper, I explore an alternative point of view: There is much to be learned through a philosophical study of cancer within a non-evolutionary framework, which concerns clinical classification programmes used in treatment practice. Clinical practice includes, but is not limited to, the work of oncologists administering diagnoses, prognoses, and treatment plans in hospitals and medical centers. I explore the importance of mutations and mutational patterns in classifying different cancer kinds, how medical intervention draws from those classifications, and other epistemic benefits of such endeavours.

By investigating classificatory practices in cancer biology, we can better understand how they guide epistemic inquiry and lead to success. Specifically, I investigate the influence of stochastic processes—processes not biased to the environment like mutations—for cancers. This moves away from functional adaptation-talk and selection, and towards a philosophical account of cancer that emphasizes intrinsic structural characteristics of oncogenic cells.

Cancer Biology Review: A Case-Based Approach

First, I discuss theoretical and empirical grounds for focusing on intrinsic cellular features, such as their role in cancer initiation and why mutations matter for distinguishing among cancer kinds. Then, I outline a particular case study in canine oncology to show how these details explain clinical success. Overall, I primarily focus on the practical advantages of a molecular approach to cancer, how it captures the classificatory and epistemic practices of cancer biology, and especially how a non-evolutionary framework identifies structural cancer classifications as means for intervention and positive clinical outcomes in medicine.

I propose to share and discuss the results of different surveys based on interview methodology where I questioned professionals on the integration of new technologies of genomic sequencing in their practice.

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  6. I will focus my talk on the exploration of two cases. The first will be based on the integration of sequencing strategy in cancer genetic services. The goal of those services is to predict the consequences of a hereditary mutation, and to propose a prevention strategy for the patients and eventually the familly: The scientific validity of these associations is questioned. These explorations combine the sequencing of the tumour DNA and of a matched constitutional DNA and can therefore reveal the presence of germline variants in genes involved in cancer susceptibility syndromes or lead to other incidental findings.

    These new approaches upset classical genetic practices logistically and temporally. This therapeutic strategy could be link to a possible genetic predisposition that would affect other members of the family. I will focus on results of this genetic analysis that are disconnected of what professionals expected to find: The implementation of genetic in oncology services questioned the representation we have on the genetic of cancer.

    Microfoundational or bottom-up models aim to reproduce the high-scale behavior of a system by modeling the interactions between lower-scale entities. Epstein and Forber posit five virtues of microfoundational models.

    Cancer Biology Review: A Case-Based Approach | Souq - UAE

    In this paper, I argue that bottom-up modeling is not so special. I consider the modeling strategies employed by in silico cancer researchers. Using this body of evidence, I demonstrate that none of the five virtues identified by Epstein and Forber are exclusive to bottom-up modeling. The middle-out, multiscale models preferred by cancer modelers also embody these virtues. Thus, I conclude, we have no good reason to privilege microfoundational models and instead ought to embrace pluralism when it comes to modeling complex biological phenomena, such as cancer. Because of the complexity of the disease and the difficulty of experimental interventions, cancer modeling has become a cornerstone within cancer research Deisboeck et al.

    The middle-out, multiscale models preferred by cancer modelers tend to integrate two distinct model types: The former are typically cellular automaton CA models, which update the state of each cell after discrete time steps in order to model tumor progression. The latter use the principles of continuum mechanics to represent variables that affect the dynamics of tumor growth.

    Unlike CA models, continuum models represent the model variables through sets of partial differential equations. Hybrid modeling approaches incorporate the strengths of both the discrete and continuum approaches to better simulate certain aspects of tumor growth. Using these modeling strategies, the cancer modeling community has recently begun to model cancer as a biological systems disease. This approach seeks to understand emergent behavior of the system rather than focusing on activities of individual components. Thus, many cancer modelers have shifted their attention from single-scale to middle-out, multiscale models.

    Middle-out modeling strategies begin by modeling a temporally and spatially intermediate scale and then gradually expand outward to include higher- and lower-scale entities and processes. The most common strategy of multiscale model development in cancer research involves integrating CA-based models with models of subcellular entities or processes. This creates a hybrid model, in which subcellular processes are represented by a continuum model and tumor cells are represented by automaton cells. The work of in silico cancer researchers undermines the claim that bottom-up modeling strategies are preferable to those models that explicitly include entities and interactions at higher-scales.

    In fact, I argue that the middle-out modeling strategy often marks an improvement over a strictly bottom-up approach. Hence, the lesson from cancer modeling seems clear: The goal of personalized medicine is to stratify patient populations into subgroups according to biologically relevant individual variations. In principle, these variations could be in lifestyle or environment; in practice, they are usually genetic. The hope is that subgroups will exhibit meaningful regularities that are directly relevant to individual patients.

    We may be able to explain why a risk exists or a disease develops in members of a particular subgroup; what course of disease that subgroup should expect; or how the subgroup will respond to different kinds of therapies. However, this project has turned out to be more challenging than early proponents expected.

    Around the time of the completion of the human genome project, it was expected that association studies would find a handful of genetic variations with relatively large effects that are relevant to explanation, prognosis, and therapy. But most of our data indicates that medically relevant genetic variations are for the most part rare and heterogeneous. This presents an unexpected epistemological challenge. Correlational studies, such as genome-wide association studies, are often insufficient for investigating causal structures in which the same effect can be produced by a large range of different causes, where each cause occurs only infrequently, and where each cause typically only has a small effect size.

    Cancer biology is a paradigm case of this problem. Over the past decades, the genetic causes of tumors have been found to be both extremely rare and strikingly heterogeneous. At the molecular level, the somatic mutations of most tumors differ from those of most other tumors, even when their clinical phenotypes are indistinguishable.

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    One goal of genetics research is to aid clinicians in predicting and preventing disease. To help predict and prevent cancer, we need to know the association between different genetic variants and the occurrence of disease. For most forms of cancer, having a deleterious mutation increases the likelihood that one will develop cancer. On the other hand, if one does not have a deleterious mutation, they only have a four percent chance of developing cancer. VUSs are variants whose association with cancer and other diseases are uncertain because there is either conflicting evidence or a lack of evidence about their association with cancer.

    This makes precise categorization difficult: How confident do we need to be that a specific variant is associated with cancer before we call it pathogenic? It also makes it more difficult to predict and prevent cancer: Given this uncertainty, what should clinicians and genetic counselors tell their patients about their risk of cancer and what steps, if any, they should take to deal with this risk?

    To aid in this task, several different schemes have been proposed to help categorize genetic variants. Despite the differences in these schemes, they all agree that one main goal should be to minimize errors. Given this shared goal, inductive risk is an important concept to help set these thresholds. Inductive risk is the chance that we accept mistaken conclusions. That is, there is always the chance that we will reject a conclusion when it is true, a false negative, and accept a conclusion when it is mistaken, a false positive. Dealing with inductive risk entails that we vary our thresholds for accepting and rejecting conclusions so that they balance the costs of these errors.

    All of the schemes to classify genetic variants treat the costs of false positives and false negatives as being equal. The cost of different mistakes varies according to the kind of cancer you are dealing with. First, over-diagnosis and treatment, the cost of false positives, means that we provide preventative care to patients who do not need it nor benefit from it. Second, missing patient who are at a higher risk of certain cancers, the cost of false negatives, means that we miss patients who could benefit from preventative care.

    Either way, these errors do not always have the same weight, and they should not be treated as such. Rather, we will want to adjust our thresholds for labeling genetic variants according to the weight of the costs that mistakes force patients to bear. Cancer is one of the main causes of death globally according to the World Health Organization.

    The biological complexity and heterogeneity of this disease or group of diseases make it very difficult to apprehend, control, and cure.