When I went to Caltech as a young assistant professor in 1970, I began thinking of all the possible research choices before me. Molecular immunology and technology development (initially protein sequencing) were no brainers, as I had been active in these areas during my graduate and NIH careers. But I wanted something new. I was fascinated with human biology and disease, but the complexity of humans was daunting.
In terms of human complexity, I remember then thinking of the parable of the elephant and the six blind men (each felt a different part of the elephant and declared it was a trunk, a fan, a spear, etc.). In thinking more deeply about this parable, I was led to three observations. I describe them here, in clearer terms than I would have used at the time, as follows:
First, to deal with the complexity of the elephant, one needed lots of molecular as well as physiological and descriptive human data—for me, this was the beginning of the big data concept.
Second, one needed to assess the internal organs of the elephant as well as its exterior. This internal assessment was poorly done in medicine at that time. It was clear that blood measurements were the key to this internal assessment and that blood was a powerful window into health and disease as it bathes every organ and the organs secrete or shed informational molecules into the blood, which if properly translated, could inform one about health of different organs, including the brain.
Third, one needed to integrate all the measurements to visualize a more complete picture of the elephant—a process that later came to be called systems biology.
These insights led me to embark on a career trajectory in which I led or participated sequentially in seven paradigm changes that dealt with aspects of human complexity and eventually framed what 21st century healthcare should be.1,2 Several of my colleagues argued that this approach was vague and fuzzy and far too global for an individual scientist to tackle, but I persisted—big ideas are powerful drivers of novel science.
Let me make a few observations about paradigm changes to give my story a proper context. Paradigm changes arise from thinking outside the box about new directions or approaches to science and are always initially met with great skepticism. They require determined optimism to proceed in the face of opposition and frequently need new organizational structures to evolve properly—old organizations are constrained by bureaucracies of the past and have trouble dealing with the present, let alone the future. And skeptics can only be won over by unequivocal demonstrations of the power of the new paradigm.
Technology, biology, and big data
The quest for the ability to generate big data on humans led to the development of automated DNA sequencing and protein sequencing and their ability to decipher important information about the human DNA source code and its manifestation as mRNAs and proteins;3,4 instruments for synthesizing peptides and DNA for a multitude of uses (DNA probes for gene isolation and PCR, DNA arrays, specific DNA for gene editing, peptides for making antibodies to epitopes, etc.5-7); and an instrument for single-molecule RNA analyses in test tubes and tissues.8
These instruments opened exciting new opportunities, both through their applications to biology and healthcare and through the technological innovations they promoted (for example, our four-color Sanger capillary DNA sequencing technology led to next-generation DNA sequencing and its increasingly highly parallelized analyses, which eventual led to a million-fold decrease in the cost of DNA sequencing).
Biological needs should always drive the conceptualization of relevant new tools and their optimization, miniaturization, parallelization, and reduction in cost.
Just three years into my tenure as an assistant professor at Caltech, the chairman of biology, Bob Sinsheimer, approached me and said, “I advise you in the strongest possible terms to concentrate on biology and give up your technology development.” I did not understand this advice, as several months later I got tenure, which was early for Caltech. Hence, it was not based on complaints about the quality of my research.
Later, I found out that the senior biologists at Caltech were unhappy with engineering being practiced in biology (they suggested moving me to engineering—which Bob did not propose). They were also unhappy about the size of my laboratory, which was fueled by cross-disciplinary technology development that needed expertise in chemistry, engineering, computer science, and biology, and my management of individuals to run a microchemical facility available to the faculty.
I told Bob no. I would continue technology development. But years later, it also become evident that many of the biology faculty were also against the Human Genome Project, which by then became a passion of mine. Indeed, the opposition of engineering in biology and the Human Genome Project led me to realize I should practice my science at an institution that saw these interests as assets rather than deficiencies. This was a major factor in my moving to the University of Washington in 1992.
Blood as a window into wellness and disease
In keeping with the elephant parable, the confirmation of blood as a powerful window into health and disease came from many different strategies.
First, we developed a highly sensitive mass spectrometry–based approach9 to identify relevant blood diagnostic proteins.10 This approach led to a blood protein panel that could distinguish normal from neoplastic lung nodules, thus circumventing many unnecessary lung-nodule surgeries (potentially saving billions of healthcare dollars a year10,11) and leading to the discovery of blood proteins which could identify preterm birth early enough in pregnancy to allow measures to block this pathological development12 (a disease complication also costing billions of healthcare dollars a year). This approach could easily be applied to distinguishing drug responders from nonresponders.
Indeed, for the top 10 drugs sold in the United States today, only about 10% of patients respond.13 The amounts we could save by reducing our annual drug bill ($360 billion) would be staggering if the drugs were given only to responders (pharmaceutical companies may object).
Second, we analyzed large databases of the mRNA populations in 25 different
human tissues and organs and compared these to identify mRNAs present in just a single organ—organ-specific mRNAs. We then searched among the hundreds of
organ-specific transcripts we had identified—those whose corresponding organ-specific blood proteins were detectable in the blood by sensitive mass spectrometry techniques—and thereby identified organ-specific proteins for 25 organs. These organ-specific blood proteins were used to detect drug toxicities and several diseases, including liver disease9 and Lyme disease.14 We could use these organ-specific blood proteins to assess organ wellness or organ disease and to identify drug toxicities in both well and sick individuals.
Third, Nathan Price (a colleague at the Institute for Systems Biology) and I realized the potential power of the blood as a window into wellness in the context of whole genome sequencing (DNA from white blood cells) and longitudinal phenomic analyses.15 Such analyses are all measurements one can make on humans apart from the genome sequence itself, and include blood analytes like clinical chemistries, proteins and metabolites, the gut microbiome, and even digital health measurements. We carried these genome/phenome analyses out on two human populations to initiate our efforts in precision population health.
Fourth, the biotech company Grail has developed a panel of epigenetic probes against free DNA in blood that can be used for the early detection of about 50 different cancers.16 The fascinating results reported for the panel are now being validated.
Finally, we recently developed a blood analysis technique called deep immune phenotyping where we do single-cell transcriptome analyses—single-cell analyses of 250 cell-surface proteins and 40 secreted molecules for each of 5,000 individual white blood cells at each blood draw in a clinical trial for COVID-19. This allows us to define the type and state of differentiation (activation) of each cell. By integrating the states of 5,000 cells at each time point, we can assess the overall state of immunity.
These data have led to fascinating conclusions about acute COVID-19 and long COVID.17,18 Since the immune response is involved in virtually every acute and chronic disease, this approach will be invaluable in clinical trials studying these diseases in the future. Indeed, genome/phenome analyses (with or without deep immune phenotyping) lead to the concept of high-data dimensional clinical trials that can draw compelling conclusions even with small numbers of patients—100 or so.
Thus, developing blood as a window opened the way to studying both wellness and disease.19
Systems biology, precision population health and a wellness-driven healthcare
Applying systems thinking to biology was successful,20,21 and we applied systems thinking to what healthcare should be in the early 2000s. This led to the conclusion that medicine should be predictive, preventive, and personalized.22 Later, after a conversation with Google Co-founder Larry Page, participatory was added.23 The so-called P4 medicine has two domains—wellness (then and today largely ignored) and disease (current medicine’s dominant theme).
In 2014, Nathan Price and I felt we had sufficient blood analytic techniques available to recruit 100 wellness pioneers (P100) for genome/phenome analyses. We measured 600 blood analytes and the gut microbiome every three months for nine months and carried out digital health assessments. I described this project to several leaders at NIH and was told by one that “NIH was interested in disease, not wellness” and that no funding would be available.
We used philanthropy to raise the funds for this successful P100 trial . It validated the power correcting deficiencies or excesses of blood analytes for individuals to identify lists of actionable possibilities unique to each individual. This study had two important outcomes. The patients’ health improved significantly, and the analyses of the longitudinal data clouds led to the first stage in defining the science of wellness.
The success of the P100 study led Nathan and I to create a company, Arivale, which over four years gathered 5,000 patients, each with their genome and longitudinal phenome data clouds. The analyses of these data clouds has validated various concepts I include in the science of wellness: the power of scientific or quantitative wellness to optimize individual health,24 the derivation of a metric for healthy aging that leads to conclusions about how to optimize healthy aging for each individual,25 the ability to calculate genetic risks for 54 diseases for each individual26 and begin optimizing health treatments that are distinct for high and low risk individuals,27 and the ability to identify blood biomarkers that are specific for the transitions from wellness to many chronic disease years before the actual clinical diagnosis—with the possibility of reversing disease before it becomes clinically manifest.28
These and other observations about the gut microbiome and brain health29 lead to a collection of actionable possibilities that I have termed the Science of Wellness. They are a powerful proof-of-principle of the importance of precision population health. For a summary of these and other observations, see Omenn et al, 2021.30
A unique opportunity
In 2016, Rod Hochman, the CEO of Providence St. Joseph Health, asked me “would you like to become chief science officer of Providence and affiliate the Institute for Systems Biology with Providence” to bring scientific wellness and systems biology to the organization? This represented a dramatic change in career trajectory for me, as it made possible bringing precision population healthcare to a major healthcare system at a scale beyond my dreams.
I have spent the last three years developing an expanded vision of precision population health, termed Beyond the Human Genome (BHG)–the longitudinal phenome is what is beyond the first genome project–and attempting to persuade Providence to embrace this vision.
The vision is that we can develop the computational infrastructure to assess and optimize the lifetime health trajectory of each of 1 million patients over 10 years. The assessments will be carried by an extended version of the genome and longitudinal phenome analyses described above. We will identify patients from Providence and a second clinical partner, Guardian Research Network.
The idea is to return to patients and their physicians prioritized and unique lists of actionable possibilities for each individual. Providence has already initiated this effort through a program called Geno4Me, which will analyze the genomes and return their actionable possibilities to 5,000 patients in the first year.
We will emphasize brain health as well as body health, employing the digital assessment and management tools developed by our partner Posit Science (www.brainHQ.com) and integrating determinants of health and patient outcome (genome, longitudinal phenome, and electronic health records) to provide unique insights into patient health and the generation of thousands of new actionable possibilities that will be delivered to physicians by AI. We will use the electronic health records to select patients for high-data dimensional clinical trials for wellness, healthy aging, and selected expensive chronic diseases like diabetes and Alzheimer’s.
We will have industrial and academic partners to help us gain new knowledge (and translate it to society) from the data ecosystem and biobank arising from each patients’ data and samples. We will create a computational infrastructure that will enable us to automate this process to make it feasible to deliver this wellness-driven healthcare, first to the 1 million individuals and eventually to the entire US and the world.
We will ask for federal funding to support the BHG program, just as we did with the first human genome program. This program will open tremendous opportunities for biotechnology: optimizing DNA sequencing to bring the cost of whole genome sequences to well under $100; developing highly parallelized technologies for the analysis of the complete proteome rapidly and inexpensively; scaling the measurements of and decreasing the expense of metabolite and lipid analytes; exploring other body fluids, saliva, urine, cerebral spinal fluid, and the like to determine how they will provide unique views wellness and disease; and using AI together with domain expertise in biology and medicine to explore dimensions in big data that would be impossible for humans to visualize.
One key area is the 4th P—participatory.31 How can we persuade individuals to do what is good for their health? How are we going to persuade an entrenched healthcare system to adopt the largest change in the history of medicine—the ability to measure and optimize the health trajectory of each individual, including the ability to detect early and reverse chronic disease.
A wellness-driven healthcare will emerge that would take each of us into our 90s and 100s, mentally alert and physically active. In the end, health is the key to happiness and creativity for each of us.
Big visions and paradigm changes
In discussing how to move the BHG project forward to Congressional funding, two close friends who are both businessmen, argued passionately with me that the vision was too large, and that Congress would never support such an ambitious endeavor. They suggested that it be scaled back by a factor of 10 or more.
I remembered back to the first human genome project when at a key early meeting for this endeavor, six of us were discussing whether the project would cost $300 million or $3 billion over 10 years. Two of us argued passionately for $3 billion, and fortunately this view prevailed. The first human genome project would never have gotten anywhere with just $300 million.
In thinking about why my friends argued so passionately for a smaller approach, I realized that business is all about a focus that leads to profits. BHG’s profits will be that it will transform health for all humankind. A partial vision takes us nowhere and will not provide the validation and momentum necessary to realize the project.
Remember, for paradigm changes, one needs a bold and clear vision, determined optimism, the creation of a new organizational structure for execution, and definitive proof of the power of the paradigm change. BHG embodies these directives, and given appropriate government funding, will deliver the most transformational paradigm change in all of medical history.
Leroy Hood, MD, PhD, is senior vice president and CSO of Providence St. Joseph Health, and chief strategy officer, co-founder, and professor at the Institute for Systems Biology. He wishes to thank Simon Evans for his invaluable help in preparing the manuscript.
- Hood, L. A Personal Journey of Discovery: Developing Technology and Changing Biology. Annu. Rev. Anal. Chem. Palo Alto Calif 2008, 1, 1–43, doi:10.1146/annurev.anchem.1.031207.113113.
- Hood, L. Leroy Hood: Reflections on a Legendary Career. GEN – Genet. Eng. Biotechnol. News 2021.
- Hewick, R.M.; Hunkapiller, M.W.; Hood, L.E.; Dreyer, W.J. A Gas-Liquid Solid Phase Peptide and Protein Sequenator. J. Biol. Chem. 1981, 256, 7990–7997.
- Smith, L.M.; Sanders, J.Z.; Kaiser, R.J.; Hughes, P.; Dodd, C.; Connell, C.R.; Heiner, C.; Kent, S.B.; Hood, L.E. Fluorescence Detection in Automated DNA Sequence Analysis. Nature 1986, 321, 674–679, doi:10.1038/321674a0.
- Blanchard, A.P.; Kaiser, R.J.; Hood, L.E. High-Density Oligonucleotide Arrays. Biosens. Bioelectron. 1996, 11, 687–690, doi:10.1016/0956-5663(96)83302-1.
- Horvath, S.J.; Firca, J.R.; Hunkapiller, T.; Hunkapiller, M.W.; Hood, L. An Automated DNA Synthesizer Employing Deoxynucleoside 3’-Phosphoramidites. Methods Enzymol. 1987, 154, 314–326, doi:10.1016/0076-6879(87)54082-4.
- Kent, S.; Hood, L.; Beilan, H. A Novel Approach to Automated Peptide Synthesis Based on New Insights into Solid Phase Chemistry. Proc. Jpn. Pept. Symp. Osaka 1984, 217–222.
- Geiss, G.K.; Bumgarner, R.E.; Birditt, B.; Dahl, T.; Dowidar, N.; Dunaway, D.L.; Fell, H.P.; Ferree, S.; George, R.D.; Grogan, T.; et al. Direct Multiplexed Measurement of Gene Expression with Color-Coded Probe Pairs. Nat. Biotechnol. 2008, 26, 317–325, doi:10.1038/nbt1385.
- Qin, S.; Zhou, Y.; Gray, L.; Kusebauch, U.; McEvoy, L.; Antoine, D.J.; Hampson, L.; Park, K.B.; Campbell, D.; Caballero, J.; et al. Identification of Organ-Enriched Protein Biomarkers of Acute Liver Injury by Targeted Quantitative Proteomics of Blood in Acetaminophen- and Carbon-Tetrachloride-Treated Mouse Models and Acetaminophen Overdose Patients. J. Proteome Res. 2016, 15, 3724–3740, doi:10.1021/acs.jproteome.6b00547.
- Kearney, P.; Boniface, J.J.; Price, N.D.; Hood, L. The Building Blocks of Successful Translation of Proteomics to the Clinic. Curr. Opin. Biotechnol. 2018, 51, 123–129, doi:10.1016/j.copbio.2017.12.011.
- Li, X.; Hayward, C.; Fong, P.-Y.; Dominguez, M.; Hunsucker, S.W.; Lee, L.W.; McLean, M.; Law, S.; Butler, H.; Schirm, M.; et al. A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules. Sci. Transl. Med. 2013, 5, 207ra142, doi:10.1126/scitranslmed.3007013.
- Markenson, G.R.; Saade, G.R.; Laurent, L.C.; Heyborne, K.D.; Coonrod, D.V.; Schoen, C.N.; Baxter, J.K.; Haas, D.M.; Longo, S.; Grobman, W.A.; et al. Performance of a Proteomic Preterm Delivery Predictor in a Large Independent Prospective Cohort. Am. J. Obstet. Gynecol. MFM 2020, 2, doi:10.1016/j.ajogmf.2020.100140.
- Schork, N.J. Personalized Medicine: Time for One-Person Trials. Nature 2015, 520, 609–611, doi:10.1038/520609a.
- Zhou, Y.; Qin, S.; Sun, M.; Tang, L.; Yan, X.; Kim, T.-K.; Caballero, J.; Glusman, G.; Brunkow, M.E.; Soloski, M.J.; et al. Measurement of Organ-Specific and Acute-Phase Blood Protein Levels in Early Lyme Disease. J. Proteome Res. 2020, 19, 346–359, doi:10.1021/acs.jproteome.9b00569.
- Hood, L.; Price, N.D. Demystifying Disease, Democratizing Health Care. Sci. Transl. Med. 2014, 6, 225ed5, doi:10.1126/scitranslmed.3008665.
- Offman, J.; Hall, M.; Arvanis, A. GRAIL and the Quest for Earlier Multi-Cancer Detection. Nat. Portf.
- Su, Y.; Chen, D.; Yuan, D.; Lausted, C.; Choi, J.; Dai, C.L.; Voillet, V.; Duvvuri, V.R.; Scherler, K.; Troisch, P.; et al. Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19. Cell 2020, 183, 1479-1495.e20, doi:10.1016/j.cell.2020.10.037.
- Lee, J.; Su, Y.; Baloni, P.; Chen, D.; Pavlovitch-Bedzyk, A.; Yuan, D.; Duvvuri, V.; Ng, R.; Choi, J.; Xie, J.; et al. Integrated Analysis of Plasma and Single Immune Cells Uncovers Metabolic Changes in Individuals with COVID-19. Nat. Biotechnol., doi:10.1038/s41587-021-01020-4.
- Yurkovich, J.T.; Hood, L. Blood Is a Window into Health and Disease. Clin. Chem. 2019, 65, 1204–1206, doi:10.1373/clinchem.2018.299065.
- Hood, L.; Rowen, L.; Galas, D.J.; Aitchison, J.D. Systems Biology at the Institute for Systems Biology. Brief. Funct. Genomic. Proteomic. 2008, 7, 239–248, doi:10.1093/bfgp/eln027.
- Ideker, T.; Galitski, T.; Hood, L. A New Approach to Decoding Life: Systems Biology. Annu. Rev. Genomics Hum. Genet. 2001, 2, 343–372, doi:10.1146/annurev.genom.2.1.343.
- Hood, L.; Heath, J.R.; Phelps, M.E.; Lin, B. Systems Biology and New Technologies Enable Predictive and Preventative Medicine. Science 2004, 306, 640–643, doi:10.1126/science.1104635.
- Hood, L.; Friend, S.H. Predictive, Personalized, Preventive, Participatory (P4) Cancer Medicine. Nat. Rev. Clin. Oncol. 2011, 8, 184–187, doi:10.1038/nrclinonc.2010.227.
- Price, N.D.; Magis, A.T.; Earls, J.C.; Glusman, G.; Levy, R.; Lausted, C.; McDonald, D.T.; Kusebauch, U.; Moss, C.L.; Zhou, Y.; et al. A Wellness Study of 108 Individuals Using Personal, Dense, Dynamic Data Clouds. Nat. Biotechnol. 2017, 35, 747–756, doi:10.1038/nbt.3870.
- Hood, L.; Lovejoy, J.C.; Price, N.D. Integrating Big Data and Actionable Health Coaching to Optimize Wellness. BMC Med. 2015, 13, 4, doi:10.1186/s12916-014-0238-7.
- Wainberg, M.; Magis, A.T.; Earls, J.C.; Lovejoy, J.C.; Sinnott-Armstrong, N.; Omenn, G.S.; Hood, L.; Price, N.D. Multiomic Blood Correlates of Genetic Risk Identify Presymptomatic Disease Alterations. Proc. Natl. Acad. Sci. U. S. A. 2020, 117, 21813–21820, doi:10.1073/pnas.2001429117.
- Zubair, N.; Conomos, M.P.; Hood, L.; Omenn, G.S.; Price, N.D.; Spring, B.J.; Magis, A.T.; Lovejoy, J.C. Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program. Sci. Rep. 2019, 9, 6805, doi:10.1038/s41598-019-43058-0.
- Magis, A.T.; Rappaport, N.; Conomos, M.P.; Omenn, G.S.; Lovejoy, J.C.; Hood, L.; Price, N.D. Untargeted Longitudinal Analysis of a Wellness Cohort Identifies Markers of Metastatic Cancer Years Prior to Diagnosis. Sci. Rep. 2020, 10, 16275, doi:10.1038/s41598-020-73451-z.
- Arokiaraj, A.S.; Khairudin, R.; Sulaiman, W.S.W. The Impact of a Computerized Cognitive Training on Healthy Older Adults: A Systematic Review Focused on Processing Speed and Attention. Int. J. Acad. Res. Bus. Soc. Sci. 2020, 10, 645–685.
- Omenn, G.S.; Magis, A.T.; Price, N.D.; Hood, L. Personal Dense Dynamic Data Clouds Connect Systems Bio-Medicine to Scientific Wellness. In Systems Medicine; Methods in Molecular Biology; Springer, 2021.
- Hood, L.; Auffray, C. Participatory Medicine: A Driving Force for Revolutionizing Healthcare. Genome Med. 2013, 5, 110, doi:10.1186/gm514.
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