Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
Larry Blandford PharmD from Precision Medicine Group gave introduction about development of precision oncology medicine. Talked about value and value determination for partnerships.
Company Pitches:
Kernal Biologics: Preclinical immunotherapy company developing mRNA therapeutics. Their therapy only have activity in p53 deficient cells (messenger 2.0). They identified, by screening, multiple mRNAs that have oncoselectivity; ONC-333 is their lead mRNA active in AML and NSCLC. Looking for 5.5M seed $
Vaccibody AS: Vaccine technology from Oslo University to target antigen to antigen presenting cells. They are targeting the myocytes and dimerize the antigen to MHC. Targeting melanoma, certain cervical cancers, and hemotologic cancers. Technology based on identified neoantigens obtained from tumor biopsy.Three vaccines: VB10.neo VB10.16 against HPV cervical
Chimeric Therapeutics: developing CART to solid malignancies against CLEC14 (tumor endothelial marker), may make tumor susceptible to hypoxia. Targeting pancreatic cancer, prelim results in mice , efficacy of 15%, working on 3rd generation CART
Memo Therapeutics: Antibody therapeutics; based on Dropzylla single B cell sorting and subsequent screening for mAb. Targeting checkpoint inhibitors on solid tumors; have a new target other than PD1; target undisclosed on NK cells and T cells; Early stage have academic partners; seeking 20Million Swiss Francs
Takeda Oncology: Chris Hurff Senior Director Business Development; they depend on partnerships as they feel internal RD is less effective. They are diversifying their portfolio from small molecules. They have over 200 partnerships (132 in Boston). They are focusing on heme, lung, and Immunooncology. Partnering model: CEI (center external innovation) deals with both academic and small biotechs. They have numerous partners including Shatto and MD Anderson.
4.1.8 Newly Found Functions of B Cell, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
The importance of B cells to human health is more than what is already known. Vaccines capable of eradicating disease activate B cells, cancer checkpoint blockade therapies are produced using B cells, and B cell deficiencies have devastating impacts. B cells have been a subject of fascination since at least the 1800s. The notion of a humoral branch to immunity emerged from the work of and contemporaries studying B cells in the early 1900s.
Efforts to understand how we could make antibodies from B cells against almost any foreign surface while usually avoiding making them against self, led to Burnet’s clonal selection theory. This was followed by the molecular definition of how a diversity of immunoglobulins can arise by gene rearrangement in developing B cells. Recombination activating gene (RAG)-dependent processes of V-(D)-J rearrangement of immunoglobulin (Ig) gene segments in developing B cells are now known to be able to generate an enormous amount of antibody diversity (theoretically at least 1016 possible variants).
With so much already known, B cell biology might be considered ‘‘done’’ with only incremental advances still to be made, but instead, there is great activity in the field today with numerous major challenges that remain. For example, efforts are underway to develop vaccines that induce broadly neutralizing antibody responses, to understand how autoantigen- and allergen-reactive antibodies arise, and to harness B cell-depletion therapies to correct non-autoantibody-mediated diseases, making it evident that there is still an enormous amount we do not know about B cells and much work to be done.
Multiple self-tolerance checkpoints exist to remove autoreactive specificities from the B cell repertoire or to limit the ability of such cells to secrete autoantigen-binding antibody. These include receptor editing and deletion in immature B cells, competitive elimination of chronically autoantigen binding B cells in the periphery, and a state of anergy that disfavors PC (plasma cell) differentiation. Autoantibody production can occur due to failures in these checkpoints or in T cell self-tolerance mechanisms. Variants in multiple genes are implicated in increasing the likelihood of checkpoint failure and of autoantibody production occurring.
Autoantibodies are pathogenic in a number of human diseases including SLE (Systemic lupus erythematosus), pemphigus vulgaris, Grave’s disease, and myasthenia gravis. B cell depletion therapy using anti-CD20 antibody has been protective in some of these diseases such as pemphigus vulgaris, but not others such as SLE and this appears to reflect the contribution of SLPC (Short lived plasma cells) versus LLPC (Long lived plasma cells) to autoantibody production and the inability of even prolonged anti-CD20 treatment to eliminate the later. These clinical findings have added to the importance of understanding what factors drive SLPC versus LLPC development and what the requirements are to support LLPCs.
B cell depletion therapy has also been efficacious in several other autoimmune diseases, including multiple sclerosis (MS), type 1 diabetes, and rheumatoid arthritis (RA). While the potential contributions of autoantibodies to the pathology of these diseases are still being explored, autoantigen presentation has been posited as another mechanism for B cell disease-promoting activity.
In addition to autoimmunity, B cells play an important role in allergic diseases. IgE antibodies specific for allergen components sensitize mast cells and basophils for rapid degranulation in response to allergen exposures at various sites, such as in the intestine (food allergy), nose (allergic rhinitis), and lung (allergic asthma). IgE production may thus be favored under conditions that induce weak B cell responses and minimal GC (Germinal center) activity, thereby enabling IgE+ B cells and/or PCs to avoid being outcompeted by IgG+ cells. Aside from IgE antibodies, B cells may also contribute to allergic inflammation through their interactions with T cells.
B cells have also emerged as an important source of the immunosuppressive cytokine IL-10. Mouse studies revealed that B cell-derived IL-10 can promote recovery from EAE (Experimental autoimmune encephalomyelitis) and can be protective in models of RA and type 1 diabetes. Moreover, IL-10 production from B cells restrains T cell responses during some viral and bacterial infections. These findings indicate that the influence of B cells on the cytokine milieu will be context dependent.
The presence of B cells in a variety of solid tumor types, including breast cancer, ovarian cancer, and melanoma, has been associated in some studies with a positive prognosis. The mechanism involved is unclear but could include antigen presentation to CD4 and CD8 T cells, antibody production and subsequent enhancement of presentation, or by promoting tertiary lymphoid tissue formation and local T cell accumulation. It is also noteworthy that B cells frequently make antibody responses to cancer antigens and this has led to efforts to use antibodies from cancer patients as biomarkers of disease and to identify immunotherapy targets.
Malignancies of B cells themselves are a common form of hematopoietic cancer. This predilection arises because the gene modifications that B cells undergo during development and in immune responses are not perfect in their fidelity, and antibody responses require extensive B cell proliferation. The study of B cell lymphomas and their associated genetic derangements continues to be illuminating about requirements for normal B cell differentiation and signaling while also leading to the development of targeted therapies.
Overall this study attempted to capture some of the advances in the understanding of B cell biology that have occurred since the turn of the century. These include important steps forward in understanding how B cells encounter antigens, the co-stimulatory and cytokine requirements for their proliferation and differentiation, and how properties of the B cell receptor, the antigen, and helper T cells influence B cell responses. Many advances continue to transform the field including the impact of deep sequencing technologies on understanding B cell repertoires, the IgA-inducing microbiome, and the genetic defects in humans that compromise or exaggerate B cell responses or give rise to B cell malignancies.
Other advances that are providing insight include:
single-cell approaches to define B cell heterogeneity,
glycomic approaches to study effector sugars on antibodies,
new methods to study human B cell responses including CRISPR-based manipulation, and
the use of systems biology to study changes at the whole organism level.
With the recognition that B cells and antibodies are involved in most types of immune response and the realization that inflammatory processes contribute to a wider range of diseases than previously believed, including, for example, metabolic syndrome and neurodegeneration, it is expected that further
basic research-driven discovery about B cell biology will lead to more and improved approaches to maintain health and fight disease in the future.
A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information
Reporter: Stephen J. Williams, Ph.D.
Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome
The following is an open access article by Pedro Moreno on a methodology to analyze genetic information across species and in particular, the evolutionary trends of complex genomes, by a nonlinear analytic approach utilizing fractal geometry, coined “Nonlinear Bioinformatics”. This fractal approach stems from the complex nature of higher eukaryotic genomes including mosaicism, multiple interdispersed genomic elements such as intronic regions, noncoding regions, and also mobile elements such as transposable elements. Although seemingly random, there exists a repetitive nature of these elements. Such complexity of DNA regulation, structure and genomic variation is felt best understood by developing algorithms based on fractal analysis, which can best model the regionalized and repetitive variability and structure within complex genomes by elucidating the individual components which contributes to an overall complex structure rather than using a “linear” or “reductionist” approach looking at individual coding regions, which does not take into consideration the aforementioned factors leading to genetic complexity and diversity.
Indeed, many other attempts to describe the complexities of DNA as a fractal geometric pattern have been described. In a paper by Carlo Cattani “Fractals and Hidden Symmetries in DNA“, Carlo uses fractal analysis to construct a simple geometric pattern of the influenza A virus by modeling the primary sequence of this viral DNA, namely the bases A,G,C, and T. The main conclusions that
fractal shapes and symmetries in DNA sequences and DNA walks have been shown and compared with random and deterministic complex series. DNA sequences are structured in such a way that there exists some fractal behavior which can be observed both on the correlation matrix and on the DNA walks. Wavelet analysis confirms by a symmetrical clustering of wavelet coefficients the existence of scale symmetries.
suggested that, at least, the viral influenza genome structure could be analyzed into its basic components by fractal geometry.
This approach has been used to model the complex nature of cancer as discussed in a 2011 Seminars in Oncology paper
Abstract: Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by “phase space diagrams”, where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system s dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent “omics descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.
As authors conclude
” Transitions from one phenotype to another are reminiscent of phase transitions observed in physical systems. The description of such transitions could be obtained by a set of morphological, quantitative parameters, like fractal measures. These parameters provide reliable information about system complexity. “
the authors describe that gene expression networks display time series display fractal and long-range dependence characteristics.
Abstract: Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coliand Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.
Given that multitude of complex biomolecular networks and biomolecules can be described by fractal patterns, the development of bioinformatic algorithms would enhance our understanding of the interdependence and cross funcitonality of these mutiple biological networks, particularly in disease and drug resistance. The article below by Pedro Moreno describes the development of such bioinformatic algorithms.
Pedro A. Moreno
Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia
E-mail: pedro.moreno@correounivalle.edu.co
Eje temático: Ingeniería de sistemas / System engineering
Recibido: 19 de septiembre de 2012
Aceptado: 16 de diciembre de 2013
Abstract
The first draft of the human genome (HG) sequence was published in 2001 by two competing consortia. Since then, several structural and functional characteristics for the HG organization have been revealed. Today, more than 2.000 HG have been sequenced and these findings are impacting strongly on the academy and public health. Despite all this, a major bottleneck, called the genome interpretation persists. That is, the lack of a theory that explains the complex puzzles of coding and non-coding features that compose the HG as a whole. Ten years after the HG sequenced, two recent studies, discussed in the multifractal formalism allow proposing a nonlinear theory that helps interpret the structural and functional variation of the genetic information of the genomes. The present review article discusses this new approach, called: “Multifractal bioinformatics”.
Keywords: Omics sciences, bioinformatics, human genome, multifractal analysis.
1. Introduction
Omic Sciences and Bioinformatics
In order to study the genomes, their life properties and the pathological consequences of impairment, the Human Genome Project (HGP) was created in 1990. Since then, about 500 Gpb (EMBL) represented in thousands of prokaryotic genomes and tens of different eukaryotic genomes have been sequenced (NCBI, 1000 Genomes, ENCODE). Today, Genomics is defined as the set of sciences and technologies dedicated to the comprehensive study of the structure, function and origin of genomes. Several types of genomic have arisen as a result of the expansion and implementation of genomics to the study of the Central Dogma of Molecular Biology (CDMB), Figure 1 (above). The catalog of different types of genomics uses the Latin suffix “-omic” meaning “set of” to mean the new massive approaches of the new omics sciences (Moreno et al, 2009). Given the large amount of genomic information available in the databases and the urgency of its actual interpretation, the balance has begun to lean heavily toward the requirements of bioinformatics infrastructure research laboratories Figure 1 (below).
The bioinformatics or Computational Biology is defined as the application of computer and information technology to the analysis of biological data (Mount, 2004). An interdisciplinary science that requires the use of computing, applied mathematics, statistics, computer science, artificial intelligence, biophysical information, biochemistry, genetics, and molecular biology. Bioinformatics was born from the need to understand the sequences of nucleotide or amino acid symbols that make up DNA and proteins, respectively. These analyzes are made possible by the development of powerful algorithms that predict and reveal an infinity of structural and functional features in genomic sequences, as gene location, discovery of homologies between macromolecules databases (Blast), algorithms for phylogenetic analysis, for the regulatory analysis or the prediction of protein folding, among others. This great development has created a multiplicity of approaches giving rise to new types of Bioinformatics, such as Multifractal Bioinformatics (MFB) that is proposed here.
1.1 Multifractal Bioinformatics and Theoretical Background
MFB is a proposal to analyze information content in genomes and their life properties in a non-linear way. This is part of a specialized sub-discipline called “nonlinear Bioinformatics”, which uses a number of related techniques for the study of nonlinearity (fractal geometry, Hurts exponents, power laws, wavelets, among others.) and applied to the study of biological problems (http://pharmaceuticalintelligence.com/tag/fractal-geometry/). For its application, we must take into account a detailed knowledge of the structure of the genome to be analyzed and an appropriate knowledge of the multifractal analysis.
1.2 From the Worm Genome toward Human Genome
To explore a complex genome such as the HG it is relevant to implement multifractal analysis (MFA) in a simpler genome in order to show its practical utility. For example, the genome of the small nematode Caenorhabditis elegans is an excellent model to learn many extrapolated lessons of complex organisms. Thus, if the MFA explains some of the structural properties in that genome it is expected that this same analysis reveals some similar properties in the HG.
The C. elegans nuclear genome is composed of about 100 Mbp, with six chromosomes distributed into five autosomes and one sex chromosome. The molecular structure of the genome is particularly homogeneous along with the chromosome sequences, due to the presence of several regular features, including large contents of genes and introns of similar sizes. The C. elegans genome has also a regional organization of the chromosomes, mainly because the majority of the repeated sequences are located in the chromosome arms, Figure 2 (left) (C. elegans Sequencing Consortium, 1998). Given these regular and irregular features, the MFA could be an appropriate approach to analyze such distributions.
Meanwhile, the HG sequencing revealed a surprising mosaicism in coding (genes) and noncoding (repetitive DNA) sequences, Figure 2 (right) (Venter et al., 2001). This structure of 6 Gbp is divided into 23 pairs of chromosomes (diploid cells) and these highly regionalized sequences introduce complex patterns of regularity and irregularity to understand the gene structure, the composition of sequences of repetitive DNA and its role in the study and application of life sciences. The coding regions of the genome are estimated at ~25,000 genes which constitute 1.4% of GH. These genes are involved in a giant sea of various types of non-coding sequences which compose 98.6% of HG (misnamed popularly as “junk DNA”). The non-coding regions are characterized by many types of repeated DNA sequences, where 10.6% consists of Alu sequences, a type of SINE (short and dispersed repeated elements) sequence and preferentially located towards the genes. LINES, MIR, MER, LTR, DNA transposons and introns are another type of non-coding sequences which form about 86% of the genome. Some of these sequences overlap with each other; as with CpG islands, which complicates the analysis of genomic landscape. This standard genomic landscape was recently clarified, the last studies show that 80.4% of HG is functional due to the discovery of more than five million “switches” that operate and regulate gene activity, re-evaluating the concept of “junk DNA”. (The ENCODE Project Consortium, 2012).
Given that all these genomic variations both in worm and human produce regionalized genomic landscapes it is proposed that Fractal Geometry (FG) would allow measuring how the genetic information content is fragmented. In this paper the methodology and the nonlinear descriptive models for each of these genomes will be reviewed.
1.3 The MFA and its Application to Genome Studies
Most problems in physics are implicitly non-linear in nature, generating phenomena such as chaos theory, a science that deals with certain types of (non-linear) but very sensitive dynamic systems to initial conditions, nonetheless of deterministic rigor, that is that their behavior can be completely determined by knowing initial conditions (Peitgen et al, 1992). In turn, the FG is an appropriate tool to study the chaotic dynamic systems (CDS). In other words, the FG and chaos are closely related because the space region toward which a chaotic orbit tends asymptotically has a fractal structure (strange attractors). Therefore, the FG allows studying the framework on which CDS are defined (Moon, 1992). And this is how it is expected for the genome structure and function to be organized.
The MFA is an extension of the FG and it is related to (Shannon) information theory, disciplines that have been very useful to study the information content over a sequence of symbols. Initially, Mandelbrot established the FG in the 80’s, as a geometry capable of measuring the irregularity of nature by calculating the fractal dimension (D), an exponent derived from a power law (Mandelbrot, 1982). The value of the D gives us a measure of the level of fragmentation or the information content for a complex phenomenon. That is because the D measures the scaling degree that the fragmented self-similarity of the system has. Thus, the FG looks for self-similar properties in structures and processes at different scales of resolution and these self-similarities are organized following scaling or power laws.
Sometimes, an exponent is not sufficient to characterize a complex phenomenon; so more exponents are required. The multifractal formalism allows this, and applies when many subgroups of fractals with different scalar properties with a large number of exponents or fractal dimensions coexist simultaneously. As a result, when a spectrum of multifractal singularity measurement is generated, the scaling behavior of the frequency of symbols of a sequence can be quantified (Vélez et al, 2010).
The MFA has been implemented to study the spatial heterogeneity of theoretical and experimental fractal patterns in different disciplines. In post-genomics times, the MFA was used to study multiple biological problems (Vélez et al, 2010). Nonetheless, very little attention has been given to the use of MFA to characterize the content of the structural genetic information of the genomes obtained from the images of the Chaos Representation Game (CRG). First studies at this level were made recently to the analysis of the C. elegans genome (Vélez et al, 2010) and human genomes (Moreno et al, 2011). The MFA methodology applied for the study of these genomes will be developed below.
2. Methodology
The Multifractal Formalism from the CGR
2.1 Data Acquisition and Molecular Parameters
Databases for the C. elegans and the 36.2 Hs_ refseq HG version were downloaded from the NCBI FTP server. Then, several strategies were designed to fragment the genomic DNA sequences of different length ranges. For example, the C. elegans genome was divided into 18 fragments, Figure 2 (left) and the human genome in 9,379 fragments. According to their annotation systems, the contents of molecular parameters of coding sequences (genes, exons and introns), noncoding sequences (repetitive DNA, Alu, LINES, MIR, MER, LTR, promoters, etc.) and coding/ non-coding DNA (TTAGGC, AAAAT, AAATT, TTTTC, TTTTT, CpG islands, etc.) are counted for each sequence.
2.2 Construction of the CGR 2.3 Fractal Measurement by the Box Counting Method
Subsequently, the CGR, a recursive algorithm (Jeffrey, 1990; Restrepo et al, 2009) is applied to each selected DNA sequence, Figure 3 (above, left) and from which an image is obtained, which is quantified by the box-counting algorithm. For example, in Figure 3 (above, left) a CGR image for a human DNA sequence of 80,000 bp in length is shown. Here, dark regions represent sub-quadrants with a high number of points (or nucleotides). Clear regions, sections with a low number of points. The calculation for the D for the Koch curve by the box-counting method is illustrated by a progression of changes in the grid size, and its Cartesian graph, Table 1
The CGR image for a given DNA sequence is quantified by a standard fractal analysis. A fractal is a fragmented geometric figure whose parts are an approximated copy at full scale, that is, the figure has self-similarity. The D is basically a scaling rule that the figure obeys. Generally, a power law is given by the following expression:
Where N(E) is the number of parts required for covering the figure when a scaling factor E is applied. The power law permits to calculate the fractal dimension as:
The D obtained by the box-counting algorithm covers the figure with disjoint boxes ɛ = 1/E and counts the number of boxes required. Figure 4 (above, left) shows the multifractal measure at momentum q=1.
2.4 Multifractal Measurement
When generalizing the box-counting algorithm for the multifractal case and according to the method of moments q, we obtain the equation (3) (Gutiérrez et al, 1998; Yu et al, 2001):
Where the Mi number of points falling in the i-th grid is determined and related to the total number M0 and ɛ to box size. Thus, the MFA is used when multiple scaling rules are applied. Figure 4 (above, right) shows the calculation of the multifractal measures at different momentum q (partition function). Here, linear regressions must have a coefficient of determination equal or close to 1. From each linear regression D are obtained, which generate an spectrum of generalized fractal dimensions Dq for all q integers, Figure 4 (below, left). So, the multifractal spectrum is obtained as the limit:
The variation of the q integer allows emphasizing different regions and discriminating their fractal a high Dq is synonymous of the structure’s richness and the properties of these regions. Negative q values emphasize the scarce regions; a high Dq indicates a lot of structure and properties in these regions. In real world applications, the limit Dqreadily approximated from the data using a linear fitting: the transformation of the equation (3) yields:
Which shows that ln In(Mi )= for set q is a linear function in the ln(ɛ), Dq can therefore be evaluated as q the slope of a fixed relationship between In(Mi )= and (q-1) ln(ɛ). The methodologies and approaches for the method of box-counting and MFA are detailed in Moreno et al, 2000, Yu et al, 2001; Moreno, 2005. For a rigorous mathematical development of MFA from images consult Multifractal system, wikipedia.
2.5 Measurement of Information Content
Subsequently, from the spectrum of generalized dimensions Dq, the degree of multifractality ΔDq(MD) is calculated as the difference between the maximum and minimum values of D : ΔD qq = Dqmax– Dqmin(Ivanov et al, 1999). When qmaxqmin ΔDq is high, the multifractal spectrum is rich in information and highly aperiodic, when ΔDq is small, the resulting dimension spectrum is poor in information and highly periodic. It is expected then, that the aperiodicity in the genome would be related to highly polymorphic genomic aperiodic structures and those periodic regions with highly repetitive and not very polymorphic genomic structures. The correlation exponent t(q) = (q – 1)Dq, Figure 4 (below, right ) can also be obtained from the multifractal dimension Dq. The generalized dimension also provides significant specific information. D(q = 0) is equal to the Capacity dimension, which in this analysis is the size of the “box count”. D(q = 1) is equal to the Information dimension and D(q = 2) to the Correlation dimension. Based on these multifractal parameters, many of the structural genomic properties can be quantified, related, and interpreted.
2.6 Multifractal Parameters and Statistical and Discrimination Analyses
Once the multifractal parameters are calculated (Dq = (-20, 20), ΔDq, πq, etc.), correlations with the molecular parameters are sought. These relations are established by plotting the number of genome molecular parameters versus MD by discriminant analysis with Cartesian graphs in 2-D, Figure 5 (below, left) and 3-D and combining multifractal and molecular parameters. Finally, simple linear regression analysis, multivariate analysis, and analyses by ranges and clusterings are made to establish statistical significance.
3 Results and Discussion
3.1 Non-linear Descriptive Model for the C. elegans Genome
When analyzing the C. elegans genome with the multifractal formalism it revealed what symmetry and asymmetry on the genome nucleotide composition suggested. Thus, the multifractal scaling of the C. elegans genome is of interest because it indicates that the molecular structure of the chromosome may be organized as a system operating far from equilibrium following nonlinear laws (Ivanov et al, 1999; Burgos and Moreno-Tovar, 1996). This can be discussed from two points of view:
1) When comparing C. elegans chromosomes with each other, the X chromosome showed the lowest multifractality, Figure 5 (above). This means that the X chromosome is operating close to equilibrium, which results in an increased genetic instability. Thus, the instability of the X could selectively contribute to the molecular mechanism that determines sex (XX or X0) during meiosis. Thus, the X chromosome would be operating closer to equilibrium in order to maintain their particular sexual dimorphism.
2) When comparing different chromosome regions of the C. elegans genome, changes in multifractality were found in relation to the regional organization (at the center and arms) exhibited by the chromosomes, Figure 5 (below, left). These behaviors are associated with changes in the content of repetitive DNA, Figure 5 (below, right). The results indicated that the chromosome arms are even more complex than previously anticipated. Thus, TTAGGC telomere sequences would be operating far from equilibrium to protect the genetic information encoded by the entire chromosome.
All these biological arguments may explain why C. elegans genome is organized in a nonlinear way. These findings provide insight to quantify and understand the organization of the non-linear structure of the C. elegans genome, which may be extended to other genomes, including the HG (Vélez et al, 2010).
3.2 Nonlinear Descriptive Model for the Human Genome
Once the multifractal approach was validated in C. elegans genome, HG was analyzed exhaustively. This allowed us to propose a nonlinear model for the HG structure which will be discussed under three points of view.
1) It was found that the HG high multifractality depends strongly on the contents of Alu sequences and to a lesser extent on the content of CpG islands. These contents would be located primarily in highly aperiodic regions, thus taking the chromosome far from equilibrium and giving to it greater genetic stability, protection and attraction of mutations, Figure 6 (A-C). Thus, hundreds of regions in the HG may have high genetic stability and the most important genetic information of the HG, the genes, would be safeguarded from environmental fluctuations. Other repeated elements (LINES, MIR, MER, LTRs) showed no significant relationship,
Figure 6 (D). Consequently, the human multifractal map developed in Moreno et al, 2011 constitutes a good tool to identify those regions rich in genetic information and genomic stability. 2) The multifractal context seems to be a significant requirement for the structural and functional organization of thousands of genes and gene families. Thus, a high multifractal context (aperiodic) appears to be a “genomic attractor” for many genes (KOGs, KEEGs), Figure 6 (E) and some gene families, Figure 6 (F) are involved in genetic and deterministic processes, in order to maintain a deterministic regulation control in the genome, although most of HG sequences may be subject to a complex epigenetic control.
3) The classification of human chromosomes and chromosome regions analysis may have some medical implications (Moreno et al, 2002; Moreno et al, 2009). This means that the structure of low nonlinearity exhibited by some chromosomes (or chromosome regions) involve an environmental predisposition, as potential targets to undergo structural or numerical chromosomal alterations in Figure 6 (G). Additionally, sex chromosomes should have low multifractality to maintain sexual dimorphism and probably the X chromosome inactivation.
All these fractals and biological arguments could explain why Alu elements are shaping the HG in a nonlinearly manner (Moreno et al, 2011). Finally, the multifractal modeling of the HG serves as theoretical framework to examine new discoveries made by the ENCODE project and new approaches about human epigenomes. That is, the non-linear organization of HG might help to explain why it is expected that most of the GH is functional.
4. Conclusions
All these results show that the multifractal formalism is appropriate to quantify and evaluate genetic information contents in genomes and to relate it with the known molecular anatomy of the genome and some of the expected properties. Thus, the MFB allows interpreting in a logic manner the structural nature and variation of the genome.
The MFB allows understanding why a number of chromosomal diseases are likely to occur in the genome, thus opening a new perspective toward personalized medicine to study and interpret the GH and its diseases.
The entire genome contains nonlinear information organizing it and supposedly making it function, concluding that virtually 100% of HG is functional. Bioinformatics in general, is enriched with a novel approach (MFB) making it possible to quantify the genetic information content of any DNA sequence and their practical applications to different disciplines in biology, medicine and agriculture. This novel breakthrough in computational genomic analysis and diseases contributes to define Biology as a “hard” science.
MFB opens a door to develop a research program towards the establishment of an integrative discipline that contributes to “break” the code of human life. (http://pharmaceuticalintelligence. com/page/3/).
5. Acknowledgements
Thanks to the directives of the EISC, the Universidad del Valle and the School of Engineering for offering an academic, scientific and administrative space for conducting this research. Likewise, thanks to co authors (professors and students) who participated in the implementation of excerpts from some of the works cited here. Finally, thanks to Colciencias by the biotechnology project grant # 1103-12-16765.
6. References
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Moreno, P.A., Burgos, J.D., Vélez, P.E., Gutiérrez, J.M., & et al., (2000). Multifractal analysis of complete genomes. In P roceedings of the 12th International Genome Sequencing and Analysis Conference (pp. 80-81). Miami Beach (FL). [ Links ]
Moreno, P.A., Rodríguez, J.G., Vélez, P.E., Cubillos, J.R., & Del Portillo, P. (2002). La genómica aplicada en salud humana. Colombia Ciencia y Tecnología. Colciencias , 20, 14-21. [ Links ]
Moreno, P.A., Vélez, P.E., & Burgos, J.D. (2009). Biología molecular, genómica y post-genómica. Pioneros, principios y tecnologías. Popayán, Colombia: Editorial Universidad del Cauca. [ Links ]
Moreno, P.A., Vélez, P.E., Martínez, E., Garreta, L., Díaz, D., Amador, S., Gutiérrez, J.M., et. al. (2011). The human genome: a multifractal analysis. BMC Genomics , 12, 506. [ Links ]
Mount, D.W. (2004). Bioinformatics. Sequence and ge nome analysis. New York: Cold Spring Harbor Laboratory Press. [ Links ]
Peitgen, H.O., Jürgen, H., & Saupe D. (1992). Chaos and Fractals. New Frontiers of Science. New York: Springer-Verlag. [ Links ]
Restrepo, S., Pinzón, A., Rodríguez, L.M., Sierra, R., Grajales, A., Bernal, A., Barreto, E. et. al. (2009). Computational biology in Colombia. PLoS Computational Biology, 5 (10), e1000535. [ Links ]
The ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature , 489, 57-74. [ Links ]
Vélez, P.E., Garreta, L.E., Martínez, E., Díaz, N., Amador, S., Gutiérrez, J.M., Tischer, I., & Moreno, P.A. (2010). The Caenorhabditis elegans genome: a multifractal analysis. Genet and Mol Res , 9, 949-965. [ Links ]
Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., & et al. (2001). The sequence of the human genome. Science , 291, 1304-1351. [ Links ]
There is a good reference to read on The Hallmarks of Cancer published first in 2000 and then updated with 2 new hallmarks in 2011 (namely the ability of cancer cells to reprogram their metabolism and 2. the ability of cancer cells to evade the immune system)
Please also go to other articles on this site which are relevant to this lecture. You can use the search box in the upper right hand corner of the Home Page or these are few links you might find interesting
Gender affects the prevalence of the cancer type, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Gender of a person can affect the kinds of cancer-causing mutations they develop, according to a genomic analysis spanning nearly 2,000 tumours and 28 types of cancer. The results show striking differences in the cancer-causing mutations found in people who are biologically male versus those who are biologically female — not only in the number of mutations lurking in their tumours, but also in the kinds of mutations found there.
Liver tumours from women were more likely to carry mutations caused by a faulty system of DNA mending called mismatch repair, for instance. And men with any type of cancer were more likely to exhibit DNA changes thought to be linked to a process that the body uses to repair DNA with two broken strands. These biases could point researchers to key biological differences in how tumours develop and evolve across sexes.
The data add to a growing realization that sex is important in cancer, and not only because of lifestyle differences. Lung and liver cancer, for example, are more common in men than in women — even after researchers control for disparities in smoking or alcohol consumption. The source of that bias, however, has remained unclear.
In 2014, the US National Institutes of Health began encouraging researchers to consider sex differences in preclinical research by, for example, including female animals and cell lines from women in their studies. And some studies have since found sex-linked biases in the frequency of mutations in protein-coding genes in certain cancer types, including some brain cancers and advanced melanoma.
But the present study is the most comprehensive study of sex differences in tumour genomes so far. It looks at mutations not only in genes that code for proteins, but also in the vast expanses of DNA that have other functions, such as controlling when genes are turned on or off. The study also compares male and female genomes across many different cancers, which can allow researchers to pick up on additional patterns of DNA mutations, in part by increasing the sample sizes.
Researchers analysed full genome sequences gathered by the International Cancer Genome Consortium. They looked at differences in the frequency of 174 mutations known to drive cancer, and found that some of these mutations occurred more frequently in men than in women, and vice versa. When they looked more broadly at the loss or duplication of DNA segments in the genome, they found 4,285 sex-biased genes spread across 15 chromosomes.
There were also differences found when some mutations seemed to arise during tumour development, suggesting that some cancers follow different evolutionary paths in men and women. Researchers also looked at particular patterns of DNA changes. Such patterns can, in some cases, reflect the source of the mutation. Tobacco smoke, for example, leaves behind a particular signature in the DNA.
Taken together, the results highlight the importance of accounting for sex, not only in clinical trials but also in preclinical studies. This could eventually allow researchers to pin down the sources of many of the differences found in this study. Liver cancer is roughly three times as common in men as in women in some populations, and its incidence is increasing in some countries. A better understanding of its aetiology may turn out to be really important for prevention strategies and treatments.
Immuno-editing can be a constant defense in the cancer landscape, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
There are many considerations in the cancer immunoediting landscape of defense and regulation in the cancer hallmark biology. The cancer hallmark biology in concert with key controls of the HLA compatibility affinity mechanisms are pivotal in architecting a unique patient-centric therapeutic application. Selection of random immune products including neoantigens, antigens, antibodies and other vital immune elements creates a high level of uncertainty and risk of undesirable immune reactions. Immunoediting is a constant process. The human innate and adaptive forces can either trigger favorable or unfavorable immunoediting features. Cancer is a multi-disease entity. There are multi-factorial initiators in a certain disease process. Namely, environmental exposures, viral and / or microbiome exposure disequilibrium, direct harm to DNA, poor immune adaptability, inherent risk and an individual’s own vibration rhythm in life.
When a human single cell is crippled (Deranged DNA) with mixed up molecular behavior that is the initiator of the problem. A once normal cell now transitioned into full threatening molecular time bomb. In the modeling and creation of a tumor it all begins with the singular molecular crisis and crippling of a normal human cell. At this point it is either chop suey (mixed bit responses) or a productive defensive and regulation response and posture of the immune system. Mixed bits of normal DNA, cancer-laden DNA, circulating tumor DNA, circulating normal cells, circulating tumor cells, circulating immune defense cells, circulating immune inflammatory cells forming a moiety of normal and a moiety of mess. The challenge is to scavenge the mess and amplify the normal.
Immunoediting is a primary push-button feature that is definitely required to be hit when it comes to initiating immune defenses against cancer and an adaptation in favor of regression. As mentioned before that the tumor microenvironment is a “mixed bit” moiety, which includes elements of the immune system that can defend against circulating cancer cells and tumor growth. Personalized (Precision-Based) cancer vaccines must become the primary form of treatment in this case. Current treatment regimens in conventional therapy destroy immune defenses and regulation and create more serious complications observed in tumor progression, metastasis and survival. Commonly resistance to chemotherapeutic agents is observed. These personalized treatments will be developed in concert with cancer hallmark analytics and immunocentrics affinity and selection mapping. This mapping will demonstrate molecular pathway interface and HLA compatibility and adaptation with patientcentricity.
Immunotherapy may help in glioblastoma survival, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. But, in a glimmer of hope, a recent study found that a drug designed to unleash the immune system helped some patients live longer. Glioblastoma powerfully suppresses the immune system, both at the site of the cancer and throughout the body, which has made it difficult to find effective treatments. Such tumors are complex and differ widely in their behavior and characteristics.
A small randomized, multi-institution clinical trial was conducted and led by researchers at the University of California at Los Angeles involved patients who had a recurrence of glioblastoma, the most common central nervous system cancer. The aim was to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab (checkpoint inhibitor) in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone.
Neoadjuvant PD-1 blockade was associated with upregulation of T cell– and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhanced both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.
Immunotherapy has not proved to be effective against glioblastoma. This small clinical trial explored the effect of PD-1 blockade on recurrent glioblastoma in relation to the timing of administration. A total of 35 patients undergoing resection of recurrent disease were randomized to either neoadjuvant or adjuvant pembrolizumab, and surgical specimens were compared between the two groups. Interestingly, the tumoral gene expression signature varied between the two groups, such that those who received neoadjuvant pembrolizumab displayed an INF-γ gene signature suggestive of T-cell activation as well as suppression of cell-cycle signaling, possibly consistent with growth arrest. Although the study was not powered for efficacy, the group found an increase in overall survival in patients receiving neoadjuvant pembrolizumab compared with adjuvant pembrolizumab of 13.7 months versus 7.5 months, respectively.
In this small pilot study, neoadjuvant PD-1 blockade followed by surgical resection was associated with intratumoral T-cell activation and inhibition of tumor growth as well as longer survival. How the drug works in glioblastoma has not been totally established. The researchers speculated that giving the drug before surgery prompted T-cells within the tumor, which had been impaired, to attack the cancer and extend lives. The drug didn’t spur such anti-cancer activity after the surgery because those T-cells were removed along with the tumor. The results are very important and very promising but would need to be validated in much larger trials.
This article appeared on the web site of Harley Street Concierge, one of the U.K.’s leading independent providers of clinical, practical and emotion support for cancer patients.
Cancer at Work: An Interview With Barbara Wilson
Whether you’re supporting an employee through cancer at work. Or you’re a cancer patient struggling to get the support you need. Either way, this Q and A with Barbara Wilson will help you out. Read on for a glimpse into Barbara’s personal experience with breast cancer. Find out where companies are falling short of supporting employees. Discover what you need to do if you’re feeling unsupported at work. And learn what’s unacceptable for Barbara in a modern and civilised society.
In a 2013 interview about cancer at work, you expressed amazement at “the lack of understanding there is about cancer. And what the impact is on individuals”. How would you say this has improved in the last 4 years? And what do you feel still needs to change?
There’s greater awareness and understanding about cancer at work. More organisations are aware of the difficulties people face. But many organisations don’t appreciate that recovery isn’t straightforward or quick. They also tend to rely on generic return to work policies. And these are inappropriate when it comes to supporting people recovering from cancer. A lot still depends on how far the local line manager is prepared to support an employee. And whether they’ll bend rules if need be about leave or sick pay.
You werediagnosed with breast cancer in 2005 and given the all clear in 2010. What did you learn about yourself through treatment and recovery?
I learned that I wasn’t immortal or superhuman! And also that life is precious and so it’s important to make the best of it. That doesn’t actually mean counting off things on your bucket list. Or living each day as if it’s your last. It’s about appreciating what you have, family, friends and the sheer joy of being alive.
“Life is precious. It’s about appreciating what you have, family, friends and the sheer joy of being alive.”
It’s a common misperception that people in remission want more family time or to travel the world. What reasons do your clients share with you for wanting to get back to work?
Yes. Before I had cancer, I remember asking a terminally ill employee why she still wanted to work. And she worked until a fortnight before her death. The simple answer is that it’s about feeling normal. Using your brain. Being with friends and colleagues rather than on your own. And losing yourself in your work. There are also financial reasons. But typically – and I can say this based on my own experience – it’s about being ‘you’ again rather than a cancer patient.
“I remember asking a terminally ill employee why she still wanted to work. And she worked until a fortnight before her death. Typically – and I can say this based on my own experience – it’s about being ‘you’ again rather than a cancer patient.”
You share tips for employers and HR professionals in this article for Macmillan. And you set out how to support a colleague during and after cancer treatment. What would you say to an employee who isn’t feeling supported by their employer or colleagues in this way?
In my experience there are two main reasons why people often aren’t supported.
1. Bosses and colleagues don’t understand the full impact of cancer treatment. They won’t understand what fatigue is or chemo brain or peripheral neuropathy. So they often expect people to get ‘back to normal’ work after 6 to 8 weeks. But recovery can take many months. This isn’t helped by the person often looking fit and well.
2. People don’t like talking about cancer at work. They feel awkward. And as a result often decide to say nothing. We advise people to be open from the outset. To understand their right to reasonable adjustments. And their responsibility to update their employer about their recovery and support needs. Employees recovering from cancer often have to take the lead. They have to guide their colleagues about the specific help they need. You can’t expect others to do it for you. It sounds wrong but that’s how it is.
“Bosses and colleagues often expect people to get ‘back to normal’ work after 6 to 8 weeks. But recovery can take many months. “
More than 100,000 people had to wait more than 2 weeks to see a cancer specialist in the UK last year. 25,153 had to wait more than 62 days to start treatment. What’s your reaction to these statistics?
It’s shocking. The worry for patients and their families during this period is totally debilitating. And on top of this it means that the cancer is growing unchecked. Where the cancer is aggressive, the delay may threaten lives. And it will certainly add to the overall costs of care. We really have to address this. It’s just not acceptable in a modern and civilised society.
“The worry for patients and their families during this period is totally debilitating. We really have to address this.”
Finally, can you tell us more about Working With Cancer?
Working With Cancer is a social enterprise and was established in June 2014. We support people affected by cancer to lead fulfilling and rewarding working lives. That means helping people to successfully return to work or remain in work. Or sometimes it’s about helping people to find work – depending on their personal needs. We work with corporate, charities and other third sector organisations to support people throughout the UK.
We coach people diagnosed with cancer to re-establish their working lives. And we train employers to understand how to manage work and cancer. We’ll advise teams about how to support a colleague affected by cancer. And we help carers juggle work whilst supporting their loved ones. Working With Cancer also helps organisations to update or improve their policies.
About Barbara Wilson
Barbara Wilson is a senior HR professional with almost 40 years’ experience. Roles include Group Head of Strategic HR at Catlin Group Ltd. Deputy Head of HR at Schroders Investment Management. And Chief of Staff to the Group HR Director at Barclays. After a breast cancer diagnosis, Barbara launched Working With Cancer. It’s a Social Enterprise providing coaching, training and consultancy to employers, employees, carers and health professionals.
For more information about Working With Cancer, click here to visit the website. Follow this link to connect with Barbara on Twitter. Email admin@workingwithcancer.co.uk. Or call 07508 232257 or 07919 147784.
Inspiring Book for ALL Cancer Survivors, ALL Cancer Patients and ALL Cardiac Patients – The VOICES of Patients, Hospitals CEOs, Health Care Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures
The human body is often described as being ‘at war’. By this, it is meant that the body is constantly under attack from things that are trying to do it harm. These include toxins, bacteria, fungi, parasites and viruses. The human immune system is one of the most effective defense mechanisms known to nature and can sometimes can be overwhelmed by disease. Yet, on occasions our immune systems turn on our own tissue and attack it which can trigger conditions such as type I diabetes, rheumatoid arthritis and lupus.
In the case of rheumatoid arthritis, immune cells start to attack tissues in the joins which causes them to become painful, stiff and swollen. It is known that one third of those who develop rheumatoid arthritis, feel the horrible effects of the disease within two years of its onset. Immunologist Adrian Hayday, which is a researcher at Francis Crick Institute of London says that the current treatment for rheumatoid arthritis require patients to take the drugs for the rest of their lives. But, researchers such as Hayday found an unexpected ally in the battle against autoimmune disease, cancer.
However, there is a positive consequence to the discovery that cancer immunotherapies have the effect of triggering autoimmune diseases and for the first-time rheumatoid arthritis can be detected at the earliest stages. At present, people are not diagnosed with the condition until symptoms have already made their lives so unpleasant, they have gone to see their doctors. As a result, research backed by Cancer Research UK and Arthritis Research UK, has been launched with the aim of uncovering the roots of autoimmune disease from research on cancer patients.
The scientists mentioned stress that their work is only now start and warn that it will still take several years of research to get substantial results. Nevertheless, uncovering the first stages of an autoimmune disease emerging in a person’s body should give researchers a vital lead in ultimately developing treatments that will prevent or halt a range of conditions that currently cause a great deal of misery and require constant medication.
Our immune defenses consist of a range of cells and proteins that notice invading micro-organisms and attack them. The first line of defense, yet, consists of simple physical barriers similar to skin, which blocks invaders from entering your body. When this defense is penetrated, they are attacked by a number of agents. The key cells, leukocytes seek out and destroy disease-causing organisms. Neutrophils rush to the site of an infection and attack invading bacteria. Helper T-cells give instructions to other cells while killer T-cells punch holes in infected cells so that their contents ooze out. After these macrophages clean up the mess left behind.
Another significant agent is the B-cell, which produces antibodies that lock on to sites on the surface of bacteria or viruses and immobilize them until macrophages consume them. These cells can live a long time and can answer quickly following a second exposure to the same infections. In conclusion, suppressor T-cells act when an infection has been distributed with and the immune system needs to be reassured, the killer cells may keep on attacking, as they do in autoimmune diseases. By slowing down the immune system, regulatory T-cells prevent damage to “good” cells.
Following are a sampling of several relevant articles comprising health innovation and technology, which may ultimately lead to a good patient experience.
When a health journalist found out her 4-year-old son had a brain tumor, her family faced an urgent choice: proven but punishing rounds of chemotherapy, or a twice-a-day pill of a new “targeted” therapy with a scant track record.
Dr. Elaine Schattner has authored numerous articles on cancer — as a doctor and patient. She is a freelance journalist and former oncologist who lives in New York City. She is writing a book about public attitudes toward cancer.
A life-long patient with scoliosis and other chronic medical conditions, and a history of breast cancer, Elaine’s current interests include physicians’ health, cancer, and medical journalism.
A cancer researchers takes cancer personally: Dr. Tony Blau, who started All4Cure, an online platform for myeloma clinicians and researchers to interact directly with patients to come up with a customer treatment plan.
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Julia Louis-Dreyfus Acts Out: The actress on challenging comedy’s sexism, fighting cancer, and becoming the star of her own show.
Thanks to Wendy Lund, CEO of GCI Health (gcihealth.com) and her team for compiling part of this list.
Interoperability, patient matching could be fixed by smartphone apps, RAND says: Patients need quality information. A physician at George Washington University School of Medicine and Health Sciences believes that the healthcare community must improve reports by making them more accessible to patients.
Sometimes Patients Simply Need Other Patients: Finding a support community is also getting easier, through resources like the Database of Patients’ Experiences, which houses videos of patients speaking about their experiences.
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At These Hotels and Spas, Cancer is No Obstacle to Quality Care: A trend among spas and wellness resorts shows the increasing integration of safe wellness treatment options for cancer patients.
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