Chasing Mendel: five questions for personalized medicine

The Spiral Model of Evolution: Stable Life Forms of Organisms and Unstable Life Forms of Cancers

If one must prioritize among the vast array of contributing factors to cancer evolution, environmental-stress-mediated chromosome instability (CIN) should easily surpass individual gene mutations. CIN leads to the emergence of genomically unstable life forms, enabling them to grow dominantly within the stable life form of the host. In contrast, stochastic gene mutations play a role in aiding the growth of the cancer population, with their importance depending on the initial emergence of the new system. Furthermore, many specific gene mutations among the many available can perform this function, decreasing the clinical value of any specific gene mutation. Since these unstable life forms can respond to treatment differently than stable ones, cancer often escapes from drug treatment by forming new systems, which leads to problems during the treatment for patients. To understand how diverse factors impact CIN-mediated macroevolution and genome integrity-ensured microevolution, the concept of two-phased cancer evolution is used to reconcile some major characteristics of cancer, such as bioenergetic, unicellular, and multicellular evolution. Specifically, the spiral of life function model is proposed, which integrates major historical evolutionary innovations and conservation with information management. Unlike normal organismal evolution in the microevolutionary phase, where a given species occupies a specific location within the spiral, cancer populations are highly heterogenous at multiple levels, including epigenetic levels. Individual cells occupy different levels and positions within the spiral, leading to supersystems of mixed cellular populations that exhibit both macro and microevolution. This analysis, utilizing karyotype to define the genetic networks of the cellular system and CIN to determine the instability of the system, as well as considering gene mutation and epigenetics as modifiers of the system for information amplification and usage, explores the high evolutionary potential of cancer. It provides a new, unified understanding of cancer as a supersystem, encouraging efforts to leverage the dynamics of CIN to develop improved treatment options. Moreover, it offers a historically contingent model for organismal evolution that reconciles the roles of both evolutionary innovation and conservation through macroevolution and microevolution, respectively.

Further illusions: On key evolutionary mechanisms that could never fit with Modern Synthesis

In the light of illusions of the Modern Synthesis (MS) listed by Noble (2021a), its key concept, that gradual accumulation of gene mutations within microevolution leads to macroevolution, requires reexamination. In this article, additional illusions of the MS are identified as being caused by the absence of system information and correct history. First, the MS lacks distinction among the two basic types of information: genome-defined system and gene-defined parts-information, as its treatment was based mostly on gene information. In contrast, it is argued here that system information is maintained by species-specific karyotype code, and macroevolution is based on the whole genome information package rather than on specific genes. Linking the origin of species with system information shows that the creation and accumulation of the latter in evolution is the fundamental question omitted from the MS. Second, modern evidence eliminates the MS's preferred theory that present evolutionary events can be linearly extrapolated to the past to reconstruct Life's history, wrongly assuming that most of the fossil record supports the gradual change while ignoring the true karyotype/genome patterns. Furthermore, stasis, as the most prominent pattern of the deep history of Life, remains a puzzle to the MS, but can be explained by the mechanism of karyotype-preservation-via-sex. Consequently, the concept of system-information is smoothly integrated into the two-phased evolutionary model that paleontology requires (Eldredge and Gould, 1972). Finally, research on genome-level causation of evolution, which does not fit the MS, is summarized. The availability of alternative concepts further illustrates that it is time to depart from the MS.

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

A Postgenomic Perspective on Molecular Cytogenetics

BACKGROUND

The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging.

DISCUSSION

In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies.

CONCLUSION

Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since "system inheritance" is defined by the genome context (gene content and genomic topology) while "parts inheritance" is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Evolution viewed from physics, physiology and medicine

Stochasticity is harnessed by organisms to generate functionality. Randomness does not, therefore, necessarily imply lack of function or 'blind chance' at higher levels. In this respect, biology must resemble physics in generating order from disorder. This fact is contrary to Schrödinger's idea of biology generating phenotypic order from molecular-level order, which inspired the central dogma of molecular biology. The order originates at higher levels, which constrain the components at lower levels. We now know that this includes the genome, which is controlled by patterns of transcription factors and various epigenetic and reorganization mechanisms. These processes can occur in response to environmental stress, so that the genome becomes 'a highly sensitive organ of the cell' (McClintock). Organisms have evolved to be able to cope with many variations at the molecular level. Organisms also make use of physical processes in evolution and development when it is possible to arrive at functional development without the necessity to store all information in DNA sequences. This view of development and evolution differs radically from that of neo-Darwinism with its emphasis on blind chance as the origin of variation. Blind chance is necessary, but the origin of functional variation is not at the molecular level. These observations derive from and reinforce the principle of biological relativity, which holds that there is no privileged level of causation. They also have important implications for medical science.

Treating the host response to emerging virus diseases: lessons learned from sepsis, pneumonia, influenza and Ebola

There is an ongoing threat of epidemic or pandemic diseases that could be caused by influenza, Ebola or other emerging viruses. It will be difficult and costly to develop new drugs that target each of these viruses. Statins and angiotensin receptor blockers (ARBs) have been effective in treating patients with sepsis, pneumonia and influenza, and a statin/ARB combination appeared to dramatically reduce mortality during the recent Ebola outbreak. These drugs target (among other things) the endothelial dysfunction found in all of these diseases. Most scientists work on new drugs that target viruses, and few accept the idea of treating the host response with generic drugs. A great deal of research will be needed to show conclusively that these drugs work, and this will require the support of public agencies and foundations. Investigators in developing countries should take an active role in this research. If the next Public Health Emergency of International Concern is caused by an emerging virus, a "top down" approach to developing specific new drug treatments is unlikely to be effective. However, a "bottom up" approach to treatment that targets the host response to these viruses by using widely available and inexpensive generic drugs could reduce mortality in any country with a basic health care system. In doing so, it would make an immeasurable contribution to global equity and global security.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders

Genome-wide association studies (GWAS) have been extensively used to study common complex diseases such as coronary artery disease (CAD), revealing 153 suggestive CAD loci, of which at least 46 have been validated as having genome-wide significance. However, these loci collectively explain <10% of the genetic variance in CAD. Thus, we must address the key question of what factors constitute the remaining 90% of CAD heritability. We review possible limitations of GWAS, and contextually consider some candidate CAD loci identified by this method. Looking ahead, we propose systems genetics as a complementary approach to unlocking the CAD heritability and etiology. Systems genetics builds network models of relevant molecular processes by combining genetic and genomic datasets to ultimately identify key "drivers" of disease. By leveraging systems-based genetic approaches, we can help reveal the full genetic basis of common complex disorders, enabling novel diagnostic and therapeutic opportunities.

Evidence of altered autonomic cardiac regulation in breast cancer survivors

PURPOSE

Surgery and adjuvant therapy improved prognosis of breast cancer survivors. This improvement risks being offset by potential late-occurring cardiovascular toxicity of oncologic treatment and increased cardiometabolic risk profile associated with lifestyle changes. We address the hypothesis that in breast cancer survivors, multiple functional alterations might define a phenotype, characterized by vagal impairment, diminished aerobic fitness, increased metabolic risk, and reduced wellbeing.

METHODS

We studied 171 sedentary asymptomatic women (106 cancer survivor-65 controls) of similar age (53 ± 8.6; 51 ± 8.1 years). Autonomic regulation was evaluated by autoregressive spectral analysis of R wave to R wave (RR) interval and systolic arterial pressure variability. Aerobic fitness was directly assessed by cardiopulmonary exercise test. Body mass index (BMI) and waist circumference served as proxies of metabolism. Fatigue and stress-related symptoms were evaluated with validated questionnaire.

RESULTS

Patients showed significantly smaller total RR variance (1644 ± 2363 vs. 2302 ± 1561 msec2), smaller absolute power of low frequency (LF) (386 ± 745 vs. 810 ± 1300 msec2) and high frequency (HF) (485 ± 1202 vs. 582 ± 555 msec2) of RR interval variability and smaller spontaneous baroreflex sensitivity (15.0 ± 8.9 vs. 21.9 ± 10 msec/mmHg), suggesting vagal impairment. VO2 peak and O2 pulse were lower in cancer survivors than in controls. Fatigue and stress-related somatic symptoms scores were higher, as was BMI and waist circumference.

CONCLUSION

Breast cancer survivors show multiple dysfunctions: vagal impairment, lower aerobic fitness, signs of altered metabolism, and higher perception of fatigue.

IMPLICATIONS FOR CANCER SURVIVORS

We propose that the concept of clinical phenotype, which may accommodate multiple functional disturbances, might be useful in long-term personalized prevention programs for breast cancer survivors.

The childhood obesity epidemic as a result of nongenetic evolution: the maternal resources hypothesis

Over the past century, socioenvironmental evolution (eg, reduced pathogenic load, decreased physical activity, and improved nutrition) led to cumulative increments in maternal energy resources (ie, body mass and adiposity) and decrements in energy expenditure and metabolic control. These decrements reduced the competition between maternal and fetal energy demands and increased the availability of energy substrates to the intrauterine milieu. This perturbation of mother-conceptus energy partitioning stimulated fetal pancreatic β-cell and adipocyte hyperplasia, thereby inducing an enduring competitive dominance of adipocytes over other tissues in the acquisition and sequestering of nutrient energy via intensified insulin secretion and hyperplastic adiposity. At menarche, the competitive dominance of adipocytes was further amplified via hormone-induced adipocyte hyperplasia and weight-induced decrements in physical activity. These metabolic and behavioral effects were propagated progressively when obese, inactive, metabolically compromised women produced progressively larger, more inactive, metabolically compromised children. Consequently, the evolution of human energy metabolism was markedly altered. This phenotypic evolution was exacerbated by increments in the use of cesarean sections, which allowed both the larger fetuses and the metabolically compromised mothers who produced them to survive and reproduce. Thus, natural selection was iatrogenically rendered artificial selection, and the frequency of obese, inactive, metabolically compromised phenotypes increased in the global population. By the late 20th century, a metabolic tipping point was reached at which the postprandial insulin response was so intense, the relative number of adipocytes so large, and inactivity so pervasive that the competitive dominance of adipocytes in the sequestering of nutrient energy was inevitable and obesity was unavoidable.

Has Neo-Darwinism failed clinical medicine: does systems biology have to?

In this essay I argue that Neo-Darwinism ultimately led to an oversimplified genotype equals phenotype view of human disease. This view has been called into question by the unexpected results of the Human Genome Project which has painted a far more complex picture of the genetic features of human disease than was anticipated. Cell centric Systems Biology is now attempting to reconcile this complexity. However, it too is limited because most common chronic diseases have systemic components not predicted by their intracellular responses alone. In this context, congestive heart failure is a classic example of this general problem and I discuss it as a systemic disease vs. one solely related to dysfunctional cardiomyocytes. I close by arguing that a physiological perspective is essential to reconcile reductionism with what is required to understand and treat disease.