- DNA-based assays can miss nearly 50 percent of cancer.
-Bonnie Anderson, Veracyte Inc
- Cancer is not an unpredictable, shape-shifting disease against which all available weapons must be thrown.
-William Audeh, Cedars-Sinai Cancer Center
- No one is winning the war on cancer. It is mostly hype, the same rhetoric from the same self-important voices for the past half a century.
-Azra Raza, author, The First Cell
- Genome chaos drives major evolutionary transitions in cancer.
-Henry Heng, Wayne State University
- “The Cancer Cambrian is a phase of hyperspeciation.”
-Kenneth Pienta, Johns Hopkins University
- FACT: Cancer cells generate tumors with new and increasingly dangerous properties far faster than predicted by conventional, selection-centric evolution models.
- Cancer has a great deal to teach about the capacities of living cells to rapidly restructure their genomes.
-James Shapiro, University of Chicago; Author, Evolution: A View from the 21st Century
- FACT: Cancer, the #2 killer of humans, mimics the Cambrian Explosion – an unprecedented ignition of new animal species 540 million years ago.
- Evolution of resistance to systemic therapies is virtually inevitable.
-Robert Gatenby, Moffitt Cancer Center
- Evolutionary biologists have lessons for oncologists… and oncologists have deep knowledge to share with evolutionary biology.
- The prevailing ‘random mutations’ theory of organismal evolution turns out to be flawed. In fact, nature has made it mostly obsolete in contemporary cell biology.
-Frank Laukien, Author, Evolution 4.0: Feedback-Driven and Actively Accelerated Biological Evolution
- FACT: Massive genome reorganization produces new treatment-resistant species of cancer cells in weeks.
- FACT: Researchers have identified feedback-driven, active evolution mechanisms in cancer.
- Turning on the immune system isn’t enough; the key is to make sure it doesn’t get turned off AND that it is not over-activated.
-Adelene Perkins, CEO, Infinity Pharmaceuticals
- FACT: Many of cancer’s active mechanisms lie outside standard theories in evolutionary biology.
- FACT: Cancer progression and therapy are fundamentally evolutionary processes.
- Instead of toxic chemotherapy, we propose the use of computer model-selected existing, human-approved ion channel drugs as electroceuticals.
-Michael Levin, Tufts University
- I propose a unifying theory that explains the processes of evolution, and the transformation of both tumor types along our life cycle.
-Jinsong Liu, M.D. Anderson Cancer Center
- Cancer is evolution running out of control. The biology of cancer evolution suggests vital shifts in treatment strategies. This conference issues an urgent call for new ideas and approaches so lives can be saved.
- FACT: Prevention, early detection and early intervention are keys to dramatic progress in the war on cancer.
- Most of the gargantuan research effort across the world has been devoted to destroying cancer.
- Paul Davies, Director of the Beyond Center, Arizona State University
CANCER & EVOLUTION SYMPOSIUM
Online via Zoom
October 14-16, 2020
When we catch cancer early, we knock it out three-fourths of the time. But despite having spent $250 billion on cures, stage three and four patients’ chances of survival are no better today than in 1930.
Kenneth Pienta at Johns Hopkins refers to cancer hyper-speciation as “The Cancer Cambrian.” Cancer cells generate tumors with new and increasingly dangerous properties far faster than predicted by conventional, selection-centric evolution models.
Organizers of the Cancer & Evolution Symposium believe the phrase Cancer Cambrian accurately frames cancer cell transformations, as well as the conceptual basis for potential solutions to the burgeoning cancer problem.
The Cambrian Explosion was the unprecedented ignition of new animal species 540 million years ago. The pattern is eerily similar to today’s #2 killer of humans, right behind heart disease.
Evolutionary biologists have lessons for oncologists… and oncologists have deep knowledge to share with evolutionary biology. The time has come for a synergism of these two fields.
Conventional models in evolutionary biology fall short of adequately capturing the evolutionary capacity of cancer cells. Massive genome reorganization (“genome chaos”) produces new treatment-resistant species of cancer cells in weeks, thwarting oncologists’ efforts to combat the disease.
This symposium brings together creative evolutionary biologists, experienced clinical oncologists and innovative thinkers in basic cancer research, cancer diagnostics and cancer therapy. These cutting-edge researchers and authors have identified feedback-driven, active organismal evolution mechanisms.
Some of the relevant active mechanisms lie outside standard theories in evolutionary biology, especially concerning the origins of major genome reorganizations. These new insights of rapid and episodic cancer evolution have not yet entered mainstream thinking, either in basic research or clinical oncology.
Identifying major cell transitions in carcinogenesis, cancer progression and therapy as fundamentally evolutionary processes provides important novel insights into the nature of the disease. Cancer is evolution running out of control.
The biology of cancer evolution suggests vital shifts in treatment strategies. It identifies new targets for therapeutic intervention. It also can inform promising avenues of basic cancer research, as well as reprioritizations of translational efforts and clinical trial focus.
This conference issues an urgent call for new ideas and approaches so lives can be saved. Prevention, early detection and early intervention are key to dramatic progress in the war on cancer so we can reduce patient suffering and mortality on a broad societal scale.
This potentially historic symposium’s goal is to stimulate new strategies for comprehensive cancer prevention and early cancer detection, as well as adaptive cancer management and treatment.
We will facilitate constructive debate, strive for consensus where possible, and unearth new insights into how to reprioritize prevention, cancer research, screening, diagnostics and patient risk stratification to guide therapy improvements.
Symposium is online via Zoom October 14-16 with researchers from an array of top institutions.
Day 1: Wednesday Morning, October 14, 2020
Session Chair: Frank Laukien
Overview and Purpose of the Cancer & Evolution Symposium
The Patient Perspective on Modern Cancer Therapy
What Can Evolutionary Biology Learn from Cancer Biology?
Accelerated evolution, proliferation & mammalian development/cancer
Genome Chaos: creating new system information to drive macroevolution
A Systems-Approach to the Early Diagnosis and Prevention of Disease
Integrating high content imaging, deep learning with single cell proteomics for characterizing cancer heterogeneity
Harvard Origins of Life Initiative: Building Blocks, Protocells & UV-driven Evolution
Cancer evolution, immune evasion and metastasis
Adjourn for Day 1
Day 2: Thursday Morning, October 15, 2020
Session Chair: James Shapiro
The Next Challenge in Precision Cancer Medicine: Evolutionary Cancer Biomarkers
Cellular Darwinism: regulatory networks, stochasticity, and selection in cancer development
Can Adaptive Cancer Therapy Reduce Evolutionary Pressures?
Extracellular Vesicles in Metastasis, and Their Evolutionary Aspects
The Somatic Molecular Evolution of Cancer: Mutation, Selection, and Epistasis
Reverting to single-cell biology in cancer
Convergent Evolution and the Origins of Lethal Cancer
Can the “Cancer Species” be driven to extinction?
Cancer - Daring to Believe in a Cure and Why This Belief is Justified
The Evolution of Complex Traits in Diverse Populations
The Evolution of Cancer Suppression Across Life
Adjourn for Day 2
Day 3: Friday Morning, October 16, 2020
Session Chair: Perry Marshall
Cellular Cognition and the Volitional Turing Machine
Mechanisms of Malignant Progression of Carcinoma Cells
The Giant Cell Unifies Cancer Evolution, Development, and Human Life Cycle
Can Organismal Pattern Homeostasis Suppress Cancer?
Cancer Proteogenomics and Therapy Resistance
Introducing Quantum Onco-Therapeutics: The Path to Memory T-cells
BoRs (Biomarkers of Response) To Optimize Metastatic Breast Cancer Treatment
RNA Whole-Transcriptome Sequencing in Cancer Diagnostics: Reducing Unnecessary Surgeries and Informing Treatment Decisions
Cancer Cell Map Initiative: Interpreting cancer genomes with protein-protein interaction maps
The Evolution of Evolutionary Processes in Organismal and Cancer Evolution
Mathematical Modeling of Cancer Evolution
The Role of ‘Unnatural Evolution’ in Cancer and Beyond
Next Steps and Recommendations
Organizing and Advisory Committees
Adjourn for Day 3
Founder, Chairman & CEO, Veracyte, Inc.
Title: RNA Whole-Transcriptome Sequencing in Cancer Diagnostics: Reducing Unnecessary Surgeries and Informing Treatment Decisions
Abstract: Multiple data sets have shown that DNA-based assays can miss nearly 50 percent of cancers, often limiting their clinical utility in cancer diagnostics. This discussion will review why RNA whole-transcriptome sequencing – with its ability to measure gene expression, variants, fusions, copy numbers and loss of heterozygosity – is ideally suited for machine learning-based tests that answer specific clinical questions in cancer diagnosis and for guiding targeted therapy selection when the disease is found. Veracyte’s RNA Seq-developed tests are helping patients avoid unnecessary diagnostic surgeries, speeding time to appropriate treatment and reducing costs in thyroid cancer, lung cancer and idiopathic pulmonary fibrosis. RNA Seq forms the foundation of the company’s development of the first-of-its kind, noninvasive nasal swab test for early lung cancer detection and its identification of novel gene mutations that may be targeted by new oncology therapeutics.
Veracyte began with a focus on improving thyroid cancer diagnosis. Historically, up to a third of patients evaluated for thyroid nodules underwent diagnostic surgery on nodules that proved to be benign. To change this, Veracyte developed a machine learning-based genomic classifier to identify benign nodules using fine needle aspiration (FNA) samples so that patients could avoid unnecessary thyroid surgery. We determined that RNA expression, rather than DNA, would provide the optimal foundation for our machine learning classifier because RNA variant expression paints a clearer picture of disease manifestation while DNA only suggests the potential for oncogenesis. We call RNA “the living genome.”
We prospectively collected thousands of thyroid FNA samples – representing every type of thyroid subtype we anticipated the test would encounter once in the clinic – and let our algorithms scour the transcriptome for each sample to determine which genes were relevant for ruling out cancer. Our final test’s high sensitivity (92%) and negative predictive value (>95%) were demonstrated in a prospective, 49-site clinical validation study, with similar results shown in subsequent independent studies. We subsequently transferred our test to an RNA sequencing platform, which enables us to measure significantly more genomic content to further enhance our ability to tease apart complex thyroid biologies. Our next-generation test uses over 10,000 genes and encompasses multiple algorithms – i.e., algorithms within algorithms – to identify 30 percent more benign nodules while maintaining the original test’s high sensitivity. Additionally, our RNA sequencing platform enables us to identify gene alterations that may guide treatment decisions given the growing number of targeted therapies that are available or in development for thyroid cancer.
We are now also deploying RNA whole transcriptome sequencing and machine learning to improve diagnosis of lung cancer, the leading cancer killer worldwide. While early detection is key to saving lives, the discovery of lung nodules frequently leads to invasive and costly diagnostic procedures for nodules that prove to be benign. We are developing a novel, noninvasive nasal swab test to determine which patients with lung nodules found on CT scans are high risk for cancer and need further work-up and which are low risk and may be monitored. The test is based on proven “field of injury” science, whereby genomic changes caused by smoking and associated with lung cancer can be measured in the epithelial cells throughout the airway. Using RNA seq and machine learning, we are developing hundreds of candidate algorithms, using hundreds of patient samples, to accurately assess lung cancer risk.
We presented preliminary data in October 2019 showing strong performance of our in-development, “two cut-off” classifier, which suggested the test’s ability to identify low-risk patients with high sensitivity (>96%) so it would miss few cancers and to identify high-risk patients with high specificity (>94%), so it would give few false positive results. We are similarly using our RNA whole-transcriptome sequencing platform to develop a test that will provide gene alteration data to inform treatment decisions – at the time of diagnosis – based on the array of targeted therapies that are already available, as well as the more than 40 targets for which therapies are in development. We intend to make both tests available to physicians in the second half of 2021
M. William Audeh, MD, MS
Cedars-Sinai Cancer Center, Los Angeles
Title: Can the “Cancer Species” be driven to extinction?
Abstract: Cancer is not an unpredictable, shape-shifting disease against which all available weapons must be thrown, but more accurately a population of cells, a “rogue species”, seeking to survive in the ecosystem of the body and following the same principles of evolution as every other living thing. The means by which cancer cell populations develop, and ultimately survive the therapies aimed at killing them are now being understood, genomically mapped and predicted by the principles of evolutionary biology. Yet, this understanding has not translated into the cancer clinic as an enlightened evolutionary strategy of cancer therapy with the goal of cure. Is cure, or “extinction” of the cancer species, in any individual possible? Potential clinical strategies to approach this challenge will be discussed.
Anna D. Barker, Ph.D.
Chief Strategy Officer, Ellison Institute for Transformative Medicine, University of Southern California
Title: The Next Challenge in Precision Cancer Medicine: Evolutionary Cancer Biomarkers
Abstract: The National Cancer Act of 1971 will mark its 50th anniversary in December of 2021. A lot has happened in the last 50 years to unravel some of the mysteries that surrounded cancer in 1971. Progress is especially noteworthy in defining the “omics” of cancer which has enabled defining cancer subtypes based on using whole genome sequencing, RNA-Seq, epigenomic and other advanced molecular technologies to create molecular profiles of individual cancers. Data from these studies and additional patient data from sources such as digital devices and real world evidence, that were not considered significant or didn’t exist in 1971, has led to an unprecedented data “tsunami” in cancer research and care. Most of these data remain to be analyzed. Interestingly, beyond advances in technologies to interrogate cancer cells, the current data surge is driven in large part by the realization that we need data that reflects what is happening in individual patients in real time. This era of what we now refer to as “precision cancer medicine” represents an evolution in thinking about cancer that puts the patient (the phenotype) at the center of cancer research; and achieving broad success in precision cancer medicine is driving a need to understand how cancer evolves in the treatment setting.
Cancer is a complex adaptive system (CAS), where aberrant information (in the information theory sense) expressed across scales (molecular to organism) leads to several emergent properties. Evolution and robustness, two of these emergent properties, makes cancer very difficult to treat, and for metastatic cancer, nearly impossible to cure. Evolutionary pressure that result from nearly all cancer therapies results in resistance to current treatments that is an inevitable and predictable outcome. Solving this problem and improving the success of cancer treatment will depend to a major extent on understanding the effect of these evolutionary and ecological forces in terms of measurable changes (evolutionary biomarkers) in patients. Measurable (perhaps predictive) biomarkers of evolvability (clonal diversity, mutation rates, clonal expansion rates, etc.), represent examples of potential classes of biomarkers that hold promise for new strategies to drive decision making in selecting, monitoring and changing cancer therapies. New ideas and innovative approaches to discover and develop evolutionary biomarkers in various treatment settings requires new ideas and application of advances from the “omics” revolution and associated advanced technologies. Moreover, the development of innovative clinical trial models that enable collecting longitudinal samples and data to support the discovery and validation of evolutionary biomarkers is a critical next step in providing a foundation for the field.
The ISPY 2 Trial for high risk breast cancer is an adaptive ( Bayesian statistics driven) platform clinical trial that represents an excellent supportive model for providing the continuity and learning systems needed to both validate known evolutionary biomarkers and discover new ones. A bit more than 10 years old, ISPY 2 has demonstrated that multiple drugs and combinations can be tested rapidly and at significantly less costs. In doing so, the ISPY 2 trial design has allowed the network of ISPY centers to develop high value longitudinal data and samples that could be transformative to the field of evolutionary biomarkers. The presentation will touch on potential “best in class” biomarkers, the continuum for their development and the role of adaptive platform trials as a supportive model for the field.
In summary, given the promise of evolutionary biomarkers to inform new strategies for drug discovery, risk stratification and prognosis, and their potential to predict therapeutics response and combat multi-drug resistance (MDR), their discovery, development and commercialization is one of the highest value propositions in cancer medicine today.
Scott Bonner, DPhil Student
University of Oxford, Department of Paediatrics
Title: Extracellular Vesicles in Metastasis, and Their Evolutionary Aspects
Abstract: Introduction: Extracellular vesicles (EVs) are nano-sized, membrane enclosed vesicles that are released by cells. While initially thought to be detritus or involved in eliminating waste from cells, a landmark 1996 study demonstrated the functional capabilities of EVs in intercellular communication. Raposo et al. documented that B cell derived EVs could stimulate antigen specific immune responses in recipient T cells. Since then EVs have been recognised as important mediators of intercellular communication by transferring their bioactive cargoes to recipient cells. In this way, they are important physiological mediators in health and disease. The relevance of EVs in understanding biology and their potential as novel therapeutic and diagnostic tools has become clear, as evidenced by the large numbers of research groups studying EVs and the emergence and success of numerous companies. Additionally, the field has established its own thriving society; The International Society for Extracellular Vesicles (ISEV), and associated high impact journal; the Journal of Extracellular Vesicles (JEV).
Notably, over the last two decades, a substantial research effort has been undertaken to understand the role of EVs in cancer. It is now understood that tumour-derived EVs can transfer their contents to influence metastatic behaviour, as well as establish favourable microenvironments and pre-metastatic niches that support cancer development and progression. Furthermore, research has suggested that tumour cells with an increased propensity to adhere secrete more EVs in comparison to tumour cells that take longer to adhere and spread. This could be the domain of metastatic cells which have the capacity to rapidly adhere to various substrata and in this way gain a foothold for growth advantage.
However, notable heterogeneity exists among all secreted EVs due to variations in cellular cargo, cell type, epigenetic factors and cellular differentiation, making the study of EV biology much more complex due to the diverse array of biological effects they can exert on recipient cells. In this study we identified and characterised subpopulations of SKOV3 ovarian cancer cell derived EVs, assessed their role in cellular adhesion and highlight the significance of EV heterogeneity in this context.
Method: SKOV3 ovarian cancer EVs were purified from adherent cell culture by size exclusion chromatography (SEC). Small EVs were then further fractionated into four EV subpopulation pools based on the chromatographic profile of an extended SEC protocol. Separate cell culture surfaces were respectively coated with EVs from each pool and SKOV3 cell adhesion was assessed. EVs in each pool were characterised by nanoparticle tracking analysis, western blotting, MACS Plex Exosomes Flow Cytometry, Nanoview ExoView R100, peptide mass fingerprints via MALDI-TOF mass spectrometry and super-resolution structural illumination microscopy.
Results: Differential SKOV3 cell adhesion was observed to surfaces coated with different EV pools. When blocking CD29, SKOV3 cell adhesion was inhibited suggesting involvement of EV subpopulations in SKOV3 cell adhesion. Characterisation techniques revealed each of the EV pools to be phenotypically distinct and thusly a heterogeneous population of sub-150nm particles. Additionally, a high, yet fluctuating expression of cell adhesion proteins across each of the EV pools, including CD29 and other proteins previously shown to be involved in metastasis was observed. CD29 and the expression of other adhesion proteins correlated with the differential cell adhesion observed. However, CD29 was also shown to be completely anti-colocalised with the most abundantly present EV tetraspanin CD81.
Conclusion: Taken together the data highlights the significance of EV heterogeneity, and the existence of functionally distinct subpopulations of extracellular vesicles. With regard to cancer development and progression, cells may dictate the release of certain subpopulations of vesicles that are involved in these processes as opposed to the EV populations as a whole. Although further experiments remain to be performed to dissect roles, adhesion could be a very rapid way for EVs to contribute to cancer metastasis providing a foothold to migrating cells and offering growth advantages to adhered cells. This will of course be the result of an evolutionary process that has occurred gradually over time to enhance the growth and survival of cancer, and indeed this evolution may be as a result of EV mediated communication. Further studies are needed to investigate whether the EV subpopulations and their unique cargo result from distinct cellular biogenesis pathways or whether this is a stochastic process.
Michael C. Campbell, Ph.D.
Assistant Professor, Department of Biology, Howard University
Title: The evolution of complex traits in diverse populations
Abstract: The history of modern humans has consisted of a number of demographic events, including population structure, admixture and migration, which have shaped patterns of genetic variation in contemporary populations. In addition, novel genetic and phenotypic adaptations have also evolved in populations in response to dramatic variation in climate, diet, and/or exposure to infectious disease. In the end, present-day patterns of variation in human genomes are a product of both demographic and selective events. One of the “grand challenges” in genomics research is to better understand the connections between evolutionary history, genomic variation and complex traits, including disease susceptibility (e.g. cancers). Indeed, the characterization of genomic diversity, together with phenotype data, is informative for identifying variants that play a role in human adaptation and complex traits. This talk will explore the use of evolutionary and computational approaches to identify polymorphisms that contribute to the development of disease in human populations.
Professor, Harvard Medical School, Wyss Institute
Title: Accelerated evolution, proliferation & mammalian development/cancer
Abstract: We have developed methods using human transcription factor expression libraries to accelerate some developmental processes from 300 days to 4 days and have used these semi-synthetic tissues to accurately evaluate late onset (70 year) diseases like Alzheimer's and extendable to proliferative pathologies including cancer. We have easily engineerable proliferative systems with doubling times of only 15 minutes that we would like to adapt to display analogous developmental processes. We have accelerated evolution way of a combination of gigabase-scale computer-controlled DNA synthesis plus machine learning. We have used this to design and test millions of viral capsids for improved AAV gene therapies.
Regents' Professor at Arizona State University
Arizona State University
Title: Reverting to single-cell biology in cancer
Abstract: The hallmarks of cancer describe the functions that a cell or group of cells must express to become a cancerous tumor, including uncontrolled growth, uninhibited mobility, and resistance to cell death. The current paradigm ascribes the acquisition of such behavior to the gradual accumulation of random genomic changes. This gene-centric view has been useful up to a point, but it suffers from the problem that most oncogenic changes are neither necessary, sufficient, nor context-independent. Furthermore, such behaviors can be suppressed in a physiologically normal environment. We propose thinking about cancer as an atavism, in this case the re-expression of single-cell biology in the context of cells that have evolved to be part of a multicellular organism. Based on the atavism theory, we hypothesized the appearance of single-cell stress-responses in cancer genomes, such as stress-induced mutation (SIM). Our work has indeed uncovered evidence of SIM in cancer genomes. This discovery has important clinical ramifications for both patient survival and treatment approaches.
Robert A. Gatenby
Professor, Moffitt Cancer Center
Title: Can Adaptive Cancer Therapy Reduce Evolutionary Pressures?
Abstract: Clinical oncology investigations have largely focused on new drug discovery. Since Ehrlich’s pioneering work more than a century ago, his concept of a “magic bullet,” a drug that can kill cancer cells while sparing normal host cells, has dominated the ongoing quest for a cure for cancer. In the vast global drug development industry that has emerged from this concept, the focus is almost entirely on identifying new and better drugs. This approach has worked very well in developing many novel and successful agents for cancer therapy. However, most metastatic cancers remain fatal because even highly effective treatments usually fail due to evolution of resistance. Arguably, Darwinian dynamics are thus the proximate cause of death in many cancer patients.
An unintended consequence of the “magic bullet” research paradigm is little interest in investigating drug administration strategies beyond conventional Maximum Tolerated Dose (MTD) or integration of drugs from multiple manufacturers into broader treatment strategies that require their agents to be combined either in parallel or in sequence.
From an evolutionary perspective, the MTD strategy is often suboptimal because it imposes maximal selection pressure for resistance while eliminating the treatment-sensitive populations, which are potential competitors. An alternative approach in effective but not curative treatments, seeks to prolong response and tumor control by exploiting evolutionary principles through modulating the treatment schedule to suppress proliferation of resistant population. Pre-clinical and clinical studies have found this approach can be successfully applied.
More recent work has proposed evolutionary dynamics can be used to cure currently fatal metastatic cancers by strategic sequencing of available agents. This is based on a paradigm that curing cancer is equivalent to an Anthropocene extinction and can be achieved through a series of frequently small eco-evolutionary perturbations, none of which is by itself curative. This is analogous to the multistep process observed in Anthropogenic extinctions in nature and is observable in the empirically derived curative treatment for pediatric Acute Lymphocytic Leukemia. Clinical trials using this “extinction strategy” are now underway.
Michael A. Gillette, M.D, Ph.D.
Senior Group Leader and PI, Broad Institute of MIT and Harvard, Assistant Professor of Medicine, Harvard Medical School
Title: Cancer Proteogenomics and Therapy Resistance
Abstract: Cancer proteogenomics integrates data from mass spectrometry-based proteomics with next-generation DNA and RNA sequencing for more comprehensive tumor profiling. Proteomic and phosphoproteomic data illuminate biology downstream of copy number aberrations, somatic mutations and fusions, and identify therapeutic vulnerabilities associated with driver events and outlier kinase activities. When applied to core needle biopsies in the context of clinical trials, microscaled proteogenomics can provide early insights into target engagement and nominate potential mechanisms of resistance and alternative therapies. This talk will use data from breast and non-small cell lung cancer studies to illustrate these points and underscore the potential for integrating proteogenomic analysis into the clinical investigation of cancer.
Steve Gullans, Ph.D.
Co-author with Juan Enriquez of Evolving Ourselves: How Unnatural Selection and Non-Random Mutation are Changing Life on Earth
Title: The Role of ‘Unnatural Evolution’ in Cancer and Beyond
Abstract: Accelerating advances in biotechnology provide optimism for the future of cancer therapy and numerous other fields as well. But where are we today in this evolution? How fast will changes occur?
Interestingly, parallels to earlier human-driven advances in technology such as the industrial revolution and the more recent IT revolution offer a guide for where the biotech/life science revolution is today and where it could take us in the coming years.
By analogy, if we view the creation of the ENIAC computer in 1945 as the birth of the modern IT revolution, then today, 75 years later, smart devices and IT technologies are a major part of our everyday lives. However, the first 40+ years of this IT revolution had modest or even very little impact on our everyday lives or the macroeconomy. Beginning in the 1990’s, with the emergence of the internet and low-cost consumer devices, we have experienced enormous change in the way the world operates. Societies, institutions, and individuals who joined the IT revolution early have garnered unprecedented prosperity and influence. Today, 7 of the 10 largest companies in world are tech companies that did not exist before 1975 and several are less than 25 years old.
The birth of the modern life science revolution is often considered to be 1973 when Drs. Boyer and Cohen conducted the first gene transfer experiment. Hence, the biotech revolution lags behind the IT revolution by 28 years and is only 47 years old today, or the equivalent of 1992 in the IT revolution.
This talk will explore parallels between the IT and life science revolutions to suggest where biotechnology may be headed and how fast. How will the evolution of life science technologies affect our everyday lives? Will there be disruptions across most industries? Will some of the very largest companies in the world in 30 years be life science companies that are not yet created? And how will our ability to address cancer or even lifespan be impacted?
Henry H. Heng
Professor, Center for Molecular Medicine and Genetics, Wayne State University Medical School.
Author, Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine (2019), Debating Cancer: The Paradox in Cancer Research (2015)
Title: Genome Chaos: creating new system information to drive macroevolution
Abstract: Cancer is traditionally labelled a “cellular growth problem.” However, it is fundamentally an issue of new systems emergent from various constraints. These constraints, which range from the cellular and tissual to immune and individual health levels, can be overcome by multiple runs of cellular evolutionary “phase transitions” (including transformation, metastasis and drug resistance). To study how genomic and environmental factors affect these phase transitions, we used cellular immortalization and drug resistance models to “watch evolution in action” by comparing the profiles of karyotypes, transcriptome and cellular phenotypes longitudinally prior to, during and after each phase transition. These studies discovered two phased somatic evolution, comprising a punctuated phase and a gradual phase, dominated by karyotype changes and gene mutation/epigenetic alterations, respectively. While different stress-mediated trigger factors (internal and external) can promote similar two phased patterns of evolution, genome chaos plays a crucial role for rapid phase transitions by creating new system information.
Given that these observations are counterintuitive to gene-based genomic and evolutionary theory, we developed a new conceptual framework: the Genome Theory. This talk will discuss the following topics, which challenge longstanding, potentially flawed assumptions of research on cancer and organismal evolution:
1. Genome chaos – the process of rapid and massive genome reorganization induced by cellular crisis – represents a built-in mechanism for creating new genomic information packages with new phenotypes for survival. This seemingly random but productive process plays a key role in punctuated macroevolutionary phase transitions and can be easily observed during the rapid emergence of drug resistance. Strikingly, vital information can be passed from dying to newborn Genome Systems with novel survival potential, suggesting that cells can engage in active macroevolutionary change (not just passive selection). Such information passing via new genome creation explains the crucial role of various chaotic genomes, including giant nuclei, micronuclei clusters, chromothripisis, and other mechanisms that alter karyotype coding, in cancer evolution.
2. Karyotype Coding (KC) is a “systems inheritance” property endowing cells with novel genome regulatory architectures. Specifically, the order of genes and regulatory sequences along and among chromosomes represents a higher order systemic coding which defines genomic topology, the physical relationship of genes within a three-dimensional nucleus. Unlike the gene-defined “parts inheritance,” which specifies only specific proteins and RNAs a genome can encode, the karyotype organizes a species’ gene interaction network. The karyotype defines the species. This is the reason why CIN is the common “driver” for cancer evolution; cancers represent new cellular species. One surprising consequence of the KC view is that the main function of sexual reproduction is to constrain evolution by preserving a species’ karyotype through meiotic pairing, thereby maintaining the genomic boundary for a given species.
3. Cancer evolution does not fit the framework of neo-Darwinism, which relies on the gradual accumulation of small inherited changes over time. In contrast, the punctuated macroevolutionary phase is defined by rapid genome changes that generates novel KCs, and the gradual microevolutionary phase is defined by diverse localized mutations. Thus, macroevolution does not equal microevolution + time.
A new general cancer model is proposed to incorporate genome chaos and gene mutation: genome re-organization creates new KC systems, followed by localized cancer gene modifications during tumor cell population growth. This model not only provides a strategy to manage cancer by moderately constraining the microevolutionary phase (to avoid triggering genome chaos and macroevolution), but also suggests a framework applicable to organismal evolution. Plenty of new and complex information packages could be created during massive extinctions, as speciation (massive or not) is also an evolutionary phase transition issue.
Leroy Hood, M.D., Ph.D.
Co-founded Institute for Systems Biology (ISB), President 2000-2018, now Chief Strategy Officer, Professor at ISB
Title: A Systems-Approach to the Early Diagnosis and Prevention of Disease
Abstract: A systems-approach (global and holistic) to the diagnosis and prevention of disease employs a host of strategies and technologies for biomarker discovery as well as the use of longitudinal, multi-dimensional data generation in 1000s of patients. I will argue that the key to dealing with disease is early diagnosis before it becomes complex and then a systems approach to identify a therapy that can reverse the disease before it manifests itself in a clinical disease phenotype. I will talk about a number of systems-driven technologies and strategies for achieving these ends with specific disease examples that we are working on.
Natalia L. Komarova
Chancellor's Professor of Mathematics, University of California Irvine
Title: Mathematical modeling of cancer evolution
Abstract: Evolutionary dynamics is at the core of carcinogenesis. Mathematical methods can be used to study evolutionary processes, such as selection, mutation, and drift, and to shed light into cancer origins, progression, and mechanisms of treatment.
I will present two very general types of evolutionary patterns, loss-of-function and gain-of-function mutations, and discuss scenarios of population dynamics -- including stochastic tunneling and calculating the rate of evolution. Applications include origins of cancer, passenger and driver mutations, and how aspirin might help prevent cancer.
I will also talk about evolution in random environments. The presence of temporal or spatial randomness significantly affects the competition dynamics in populations and gives rise to some counterintuitive observations. I will present some recent results on the evolutionary dynamics in systems where spatial and temporal randomness affects division and/or death parameters of cells. Of particular interest are the dynamics of non-selected mutants, which exhibit counterintuitive properties.
Professor of Cellular and Molecular Pharmacology, Senior Investigator at the Gladstone Institutes, Founding Director of the Quantitative Biosciences Institute (QBI), University of California San Francisco (UCSF)
Title:Cancer Cell Map Initiative: Interpreting cancer genomes with protein-protein interaction maps
Abstract: Sequencing of tumor DNA has produced extensive lists of genetic alterations in cancer, many of which are rare and of uncertain significance. Interpreting cancer genomes and translating genomic changes to a molecular or clinical understanding of cancer is thus challenging. To bridge this gap, we have used affinity purification-mass spectrometry to generate interaction networks for proteins with recurrent genomic alterations in head-and-neck squamous cell carcinoma and breast cancer. The resulting maps are highly interconnected, reveal novel mechanisms of cancer pathogenesis, and point to potential therapeutic targets. We show that mutation-dependent rewiring of cancer networks can be exploited for therapeutic benefit and inform treatment decisions. Expanding our PPI network data with existing data sets of protein association allowed the construction of a hierarchical map spanning complexes to pathways to processes altered across 13 cancer types. Based on this, we propose an expanded catalog of cancer genes. Importantly, this multi-scale map reveals the impact of genomic alterations on cancer types where their significance had previously not been appreciated, informing future mechanistic and clinical investigations.
Frank H. Laukien, Ph.D.
Author, Natural Evolution 4.0: Feedback-Driven and Actively Accelerated Biological Evolution; Chairman, President & CEO, Bruker Corporation
Title: The Evolution of Evolutionary Processes in Organismal and Cancer Evolution
Abstract: Nature does not differentiate between ‘normal’ biology and pathobiology. These artificial distinctions obscure how flexibly and ‘agnostically’ cell biology will employ the most efficient (‘fittest’) mechanisms, regardless of whether they are adaptive in organismal evolution, or whether they promote infectious diseases or cancer within a host. Via the on-going evolution of the most efficient evolutionary processes, nature will leverage any available biological signaling or interaction process for new pathways and mechanisms of action.
In organismal evolution, billions of years of adaptive evolution of modern cell and organismal biology have facilitated more flexible, faster and more likely to succeed active change generation, prior to Darwinian selection. This compares to inefficient, infinitesimal and random mutations, which mostly have a neutral or deleterious effect in organismal evolution, and today likely have a negligible impact on adaptive evolution. The prevailing ‘random mutations’ theory of organismal evolution turns out to be flawed. In fact, nature has made it mostly obsolete in contemporary cell biology, after more than 3.5 billion years of evolution of life’s more efficient, feedback-driven or vector-mediated active evolutionary processes. The remaining, very visible exceptions are cases where random mutations have deleterious effects, e.g. by causing deformations, death, monogenic diseases, or by triggering driver mutations in oncogenes or tumor suppression genes.
More efficient and ‘fitter’ evolutionary processes have dramatically accelerated organismal evolution, in a manner that now can be reconciled with both the traditional fossil record, and the rapidly accumulating DNA record of organismal evolution. In Darwinian manner, these mostly non-random, often major and sometimes already directionally functional changes can be selected for by nature according to their differential phenome fitness. As selected, adaptive changes accumulate over time they also generate more efficient evolutionary processes, as well as advantageous novel traits and new species with improved fitness. In real-time cancer quasi-evolution, they frequently lead to therapy resistance.
An important recognition is that active, evolutionary cell biology processes themselves first had to evolve during the initial, approximately two billion years of single-cell evolution, after the origin(s) of life on Earth some 3.5-4.0 billion years ago. Subsequently, in the most recent 1.0-1.5 billion years of life on Earth, these evolved, active change generation processes have greatly accelerated adaptive evolution. This has enabled the emergence of eukaryotic single-cell life, and culminated in the rapid evolution of complex, multicellular life. After some 3.5-4 billion years of life and biological evolution on Earth, not only of traits and species, but importantly also of the evolutionary processes themselves, nature’s evolved feedback processes today can actively modify, restructure, rewrite and rearrange our genes, chromosomes and three-dimensional genomes (karyotypes) over time, via numerous intra-cellular, intra-organismal and vector-transmitted processes. In real-time cancer quasi-evolution, both random ‘driver’ mutations and active, major genome and karyotype modifications of heterogeneous cancer cells modify not only the cancer phenotypes, expressed by cancer cells as proteins, lipids, metabolites and exosomes, but can also shape the patient’s tumor microenvironment, in a form of ‘niche construction’ in cancer. Real-time co-evolution of the host, i.e. patient, plus various forms of therapy can suppress or facilitate tumorigenesis, progression or metastasis.
Vannevar Bush Professor, Biology Department, Tufts University; Director, Allen Discovery Center at Tufts
Title: Can Organismal Pattern Homeostasis Suppress Cancer?
Abstract: The key question isn’t why we get cancer; instead, it is: why do we ever have anything *but* cancer? How do otherwise independent, competent cells, which readily proliferate and survive as unicellular organisms, normally cooperate as swarms toward constrained group goals - the creation and regeneration of large complex organs?
Our lab studies this process from the perspective of biophysics and cognitive science. We have developed imaging and information-theory based analytical tools to begin to understand how cells establish bioelectrical networks that process organ-level patterning information. Our perspective on cancer is that it is a reversible breakdown of cellular communication with the global (in part, bioelectric) information cues that keep cells harnessed toward a large-scale anatomical outcome. We now have tools to read and write these pattern memories in vivo, which we use in regenerative medicine applications.
As a result, we have shown (in frog models) that we can initiate metastatic melanoma by disrupting endogenous bioelectric communication, track the appearance of pre-cancer by its aberrant bioelectrical signature, and best of all, normalize tumors caused by human oncogenes by forcibly reconnecting them to the patterning network in vivo.
Our approach suggests a new roadmap for cancer medicine: instead of toxic chemotherapy, we propose the use of computer model-selected existing, human-approved ion channel drugs as electroceuticals to normalize tumors via ancient morphogenetic pathways
Jinsong Liu, M.D, Ph.D., M.A.
Professor, Department of Anatomic Pathology, Molecular and Cellular Biology, Division of Pathology and Laboratory Medicine, The University of Texas M.D. Anderson Cancer Center
Title: The Giant Cell Unifies Cancer Evolution, Development, and Human Life Cycle
Abstract: Cancer has been considered as a disease via somatic evolution, either through punctuated macroevolution via whole genomic/epigenome reorganization (chaos) or, for some tumors, gradual microevolution via epigenetic or genetic alterations. On the other hand, cancer has long been considered as a disease of development at the level of tissue differentiation. Well-differentiated tumors are architecturally similar to the tissues from which they originate, whereas undifferentiated tumors exhibit high nuclear atypia and do not resemble their tissue of origin. However, the relationship between evolution and development has been far from clear.
Here I propose a unifying theory that explains the processes of evolution, and the transformation of both tumor types along our life cycle. Perpetuation of our life is mediated through the germ cells which display a cyclic change in cell size along their developmental life cycle. Following fertilization of a giant ovum by sperm, the zygote undergoes successive rounds of cleavage divisions, with progressive decreases in cell size and increases in the nuclear-to-cytoplasmic (N/C) ratio to form a 32-nuclei morula within zona pellucida, which then selectively erases the parental memory and activates a combined set of embryonic programs to initiate 1st differentiation in life to form a blastocyst, which generates inner cell mass for embryonic life and supporting trophoblasts for uterine invasion. The female germ cells arrest in utero and develop into a giant ovum or egg which is ready for the next life cycle. If the blastocyst implants in the uterus and subsequent differentiation proceeds to complete maturation, then a normal life result. However, if differentiation of any germ layer is asynchronously blocked after birth, a well-differentiated tumor develops in children and adults. If germ cells undergo parthenogenesis rather than differentiation into sperm or egg with or without developmental block, then different germ tumors result.
Similarly, somatic cells can initiate their oncogenesis via formation of polyploid giant cancer cells (PGCCs) that mimic the blastomere-stage embryo for de-differentiation to erase the memory of differentiated somatic cells. The giant cell life cycle, the evil twin of germ cell life cycle, can lead to progressive increases in the N/C ratio and awaken the suppressed embryonic program for tumorigenesis. The resulting somatic cell can transform into undifferentiated tumors due to lack of normal developmental program and microenvironment for differentiation. Thus, the giant cell-associated increase in ploidy explains not only normal embryogenesis for well-differentiated tumors and germ cell tumors but also “somatic embryogenesis” for undifferentiated tumors. The genomic chaos in early embryogenesis or PGCCs explains macroevolution and is likely the origin of well differentiated or undifferentiated tumors respectively. The microevolution due to sequent epigenetic alteration or somatic mutations can lead to differentiation arrest and transition into a proliferative phenotype. Both macroevolution and microevolution work synergistically with local microenvironment to facilitate the tumor initiation, progression, metastasis, and resistance to therapy. I refer to the number of ploidies in giant cells during early embryogenesis and tumorigenesis as the “life code”. This simple concept may provide a unifying view for cancer evolution, development, and human life cycle. It provides a new paradigm to guide our efforts to understand and fight this disease.
Carlo C. Maley, Ph.D.
Associate Professor, Biodesign Institute, Arizona State University; Director of the Arizona Cancer Evolution (ACE) Center; Author, with Mel Greaves: Frontiers in Cancer Research: Evolutionary Foundations, Revolutionary Directions
Title: The Evolution of Cancer Suppression Across Life
Abstract: Mechanisms to suppress cancer have been evolving for over a billion years, ever since the evolution of multicellularity. However, there are tradeoffs between cancer suppression and many other fitness relevant traits. The extent to which different species have developed and invested in cancer suppression mechanisms has depended on the ecology and selective pressures upon those species. The result has been a wide variety of cancer risks across species. Until recently, we only had cancer prevalence data on a few species (mostly humans and dogs). We have gathered data on hundreds of species of animals. I will present our discoveries of some of the most cancer resistant and cancer prone species, a series of natural experiments of closely related species with divergent cancer prevalences, and analyses of associations between life history factors and cancer prevalence. I will also summarize what we have discovered to date of the molecular mechanisms that have evolved to suppress cancer in particularly cancer resistant species.
Director, at the Max Planck Institute of Biochemistry, Germany; Director, Novo Nordisk Foundation Center for Protein Research, University of Copenhagan
Title: Integrating high content imaging, deep learning with single cell proteomics for characterizing cancer heterogeneity
Abstract: Defining features of cancers are their genetic and cellular heterogeneity, which can be studied by single cell genomics and advanced imaging analysis, respectively. Combining the two would be extremely interesting to connect genotype and cellular phenotype but present great technical challenges.
Here I introduce some of the great instrumental advances in mass spectrometry-based proteomics, including advances in algorithms and bioinformatics and focusing on the new tims-TOF technology, which we are co-developing with Bruker. The latter now allows ultra-sensitive analysis of only a few or even single cells.
A major effort of our group is the analysis of pathology samples, which can now be done in a streamlined way even from archived material (FFPE samples). Together with the group of Peter Horvath, we are now combining high content imaging, deep neural networks and single cell proteomics to determine the proteome of specific subsets of cells in human tissues. We call this technology ‘deep visual proteomics’. DVP inherently provides spatial information that can be mined for functional characterization of cells. Furthermore, deep visual proteomics can be used to characterize sub-cellular structures as well, which we demonstrated by capturing the cell cycle state by deep visual proteomics on the imaging and proteomics levels. These and other experimental advances are applied in our group in a wide range of biomedical settings, including the above mentioned fundamental problem of connecting genotype and phenotype in cancer at the cellular level, such as in ovarian cancer.
Author of several books, including Evolution 2.0: Breaking the Deadlock Between Darwin and Design; Industrial Ethernet and 80/20 Sales & Marketing; Founder, Evolution 2.0 Prize, the world's largest science research award at $10 million.
Title: Cellular Cognition and the Volitional Turing Machine
Abstract: Biology has scorned teleology for 120 years. But engineering has embraced it since the 1940s. All cells exhibit agency and seem to evolve purposefully. This is why cancer has outwitted doctors for 100 years and why stage 3-4 patients are no better in 2020 than in 1930. Evolution was presumed to be random and purposeless, yet cells possess cognition [REF Shapiro all cells are cognitive]. Cell behavior is normally algorithmic, but uniquely responds to novel situations. This is what makes evolution (and cancer) possible.
The origin of biological information is an unsolved cognition problem. Here we propose a new framework by modeling the cell as a Volitional Turing Machine. We model the agency of the cell as a computer that can choose “1” or “0” before writing its next output. This choice is a non-deterministic action of a free agent with sensory capacity and memory. It is not computable from prior states. As well as reading and reacting to its environment, it anticipates future threats, chooses goals and reasons inductively. Computers do none of these things. We replace Aristotle’s four causes (material, formal, efficient, and final) with three orthogonal components: Chemicals, Code and Cognition. The model is consistent with information theory and known limitations of AI, unlike the standard model of evolution. The Volitional Turing Machine encompasses Schrödinger’s “negative entropy” and Davies’ “Demon in the machine.” It explains the futility of reducing cancer to a single random or algorithmic component, and exposes the challenge of understanding this formidable autopoietic evolving life form.
Author, Dance to the Tune of Life: Biological Relativity (2016), The music of life (2006)
Title: Cellular Darwinism: regulatory networks, stochasticity, and selection in cancer development
A major theme of this symposium is that there are strong parallels between the evolutionary origin of species within populations and new concepts for the origin of cancers within the tissues of the body. The analogy is that cancers can be regarded as a new species developing within the host organism.
My presentation will show that to understand this parallel we need to turn the current popular theory of evolution, the Modern Synthesis (Huxley, 1942), on its head. That theory, also called neo-darwinism, is based on the idea that inherited variations are purely random. The better survival of fitter organisms is then attributed to blind (undirected) natural selection. I will show that the reverse is true. Cells and organisms control the stochasticity within them and do so in ways that favour their development and evolution. In doing so, they give directionality to what happens. The process is not completely blind. Many of the physiological control processes by which they achieve this are now fairly well understood.
Cell Selection in development
A theory of Darwinian selection amongst cells in developing tissues was first developed by Kupiec (1983, reprinted 2020), although it had a precursor in Weismann’s idea of selection amongst germ-line cells (Weismann, 1896). Kupiec (2009) has also emphasised the role of stochasticity in cellular development.
Writing a commentary on the 2020 republication of Kupiec’s 1983 paper, Andras Paldi (2020) has succinctly expressed the parallel:
“The apparently predetermined gene expression patterns that characterise the defined cell phenotypes are the results of a selective stabilization of some patterns through interactions between the cells. This way of framing one of the modern biology’s central questions calls for the same reasoning Charles Darwin proposed to explain the evolution of biological species. The idea that spontaneous variation followed by selective stabilization of some of these variants can account for the emergence of new cell types during ontogenesis places the evolution of the species and cell types on the same theoretical ground.”
I have emphasised the phrase relevant to this symposium. It speaks for itself.
Harnessing of chance
To these ideas I will add three further concepts:
1. Stochasticity becomes functional when it is harnessed. This idea was first presented at the “New Trends in Evolution” meeting of The Royal Society and The British Academy in 2016 (Noble 2017). There are many ways in which such harnessing occurs in organisms.
(a) DNA replication. The natural error rate in DNA replication is very high. In a genome of 3 billion base pairs nearly a million ‘chance’ errors occur. The final error rate when the genome is passed on to daughter cells is so small that hardly a single error is passed on. That high accuracy is achieved by the cell itself, which processes the raw error rate through a 3-stage process to achieve faithful transmission of hard genetic information. This is one of the reasons why DNA is dead outside an organism. Contrary to Schrödinger’s (1942) idea of the genetic molecule being an aperiodic crystal, DNA cannot self-replicate as a crystal does. It needs to be pampered by the cell to maintain its meaning and utility (Noble, 2018).
(b) Developing new antibodies. Cells also have the means to produce many new variant DNA sequences in a highly targeted region of the genome coding for the variable part of an immunoglobulin (Shapiro, 2012a,b).
(c) Developing anticipatory behaviour. A theory of neural selection was first developed by Edelman (1978). My brother and I have developed this idea to show how, by analogy with the harnessing of chance in the immune system, organisms can generate new forms of anticipatory behaviour (Noble & Noble, 2017, 2018, 2020).
2. Variation is not then random and can be at least partially directed (Noble & Noble, 2017).
This fact is ubiquitous in biology. Just consider what happens to the energy of photons randomly poured out into space by the sun. The few that hit the earth might encounter a green plant. The plant harnesses that energy through photosynthesis. The outcome, the growth of the plant and subsequently the growth of animals as they consume plants, is very far from random. That process, like the harnessing of randomness in the immune system, is unconscious. But, around the time of the Cambrian explosion (Ginsburg & Jablonka, 2019), the harnessing of random processes at all levels in organisms became an active, often conscious, process. Organisms that possess conscious agency then take part in social selection, originally identified by Darwin (1871) in the example of sexual selection.
There can therefore be two forms of directed evolution, unconscious and conscious. The form relevant to cancer is unconscious, but it is still directed.
3. Cells communicate their DNA, RNA and regulatory states. They can do this via intercellular exchange of information within extracellular vesicles, EVs (Edelstein et al 2019). This process includes the transmission of soma information to the germ-line and can therefore form a mechanism for the inheritance of acquired characteristics. This is almost the same theory that Darwin (1871) developed as the theory of gemmules. The EVs, I suggest, contain Darwin’s postulated gemmules (Noble, 2019).
The implications for the development of cancers are important. Those implications are the subject of another presentation in this symposium (Bonner, Willms & Noble, 2020).
Elizabeth O'Day, MPhil, Ph.D.
Founder and CEO, Olaris Therapeutics, Inc.
Title: BoRs (Biomarkers of Response) To Optimize Metastatic Breast Cancer Treatment
Abstract: Breast cancer is the most commonly diagnosed cancer and it is the second leading cause of cancer-related death in women worldwide1. More than 90% of breast cancer deaths are due to metastasis2,3. Although the exact number is unknown, estimates suggest in the US in 2020 over 168,292 women are living with metastatic breast cancer (mBC)4. Three out of every four of these women were initially diagnosed with Stage I-III disease who later progressed4. Despite advances in prevention and therapeutics, treatments to eradicate mBC do not exist. The median survival of patients with mBC is 3 years, with less than 22% of patients surviving beyond 5 years4. By 2050, the global incidence of female breast cancer is predicted to reach 3.2 million new cases per year5. As the number of cases increases, so will the cost to treat breast cancer. In 2018, the national cost to treat all cancer was $150.8B, with female breast cancer leading all cancer sites at an estimated $19.7B7.
The overwhelming majority of breast cancers (75%) are deemed hormone receptor positive (HR+) either expressing the estrogen receptor or progesterone receptor8. The backbone of HR+ breast cancer treatment remains endocrine therapy (ET)1. ET includes selective estrogen receptor modulators (SERMS: tamoxifen), aromatase inhibitors (AIs: letrozole, analstrozle, exemestane) and selective estrogen receptor down regulators (SERDs: fulvestrant). In the metastatic setting, ET is often supplemented with additional targeted therapies such as CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib), PI3K inhibitors (alpelisib) and/or mTOR inhibitors (evorlimus)1. There is no standard consensus for the optimal sequencing of these agents.
Resistance (intrinsic and acquired) are major issues with all known targeted therapies. The differences between intrinsic and acquired resistance are not clear, and each may operate through different mechanisms. Patient heterogeneity, tumor microenvironment, crosstalk between signaling pathways, the microbiome, environmental factors and more, contribute to resistance. Each therapy or class of therapies is also likely to have distinct mechanisms of resistance. Being able to detect intrinsic and/or acquired resistance to alter a patient’s therapy remains a long-standing goal of precision medicine (PM).
Biomarkers are the cornerstone of PM, enabling clinicians to use the altered expression of DNA, RNA, proteins, metabolites or combinations thereof to direct a clinical action. “Biomarkers of response” (BoR) are a subset of biomarkers that are capable of identifying patients that will respond to specific therapies and those that will not. Genomic-based BoR efforts have had limited success. This is not surprising as additional factors such as age, diet, environment, microbiome and other factors influence drug response. At present there are few, if any, BoRs available for clinicians to monitor drug response and resistance in mBC.
Recent advancements in metabolomics offer new promise to identify BoRs with bona fide clinical utility. Metabolism is influenced by both genetics and the environment. Through metabolomics, differences in metabolism between healthy and diseased states can be uncovered. Furthermore, by measuring metabolite changes, it is feasible to construct a metabolic fingerprint differentiating drug response and drug resistance. Here we will describe metabolomic approaches that have identified BoRs with high predictive accuracy to differentiate mBC patients that did and did not respond to CDK4/6 inhibitors. Upon validation in larger cohorts, the results of these studies could lead to a paradigm shift in medicine, moving away from stepwise medicine to one that is optimized for individual success. This has the potential to dramatically improve outcomes, reduce unnecessary exposure to side effects and diminish the financial toxicity.
1. Ballinger, T. J., Meier, J. B. & Jansen, V. M. Frontiers in Oncologyvol. 8 308 (2018). 2. Chaffer, C. L. & Weinberg, R. A. Sciencevol. 331 1559–1564 (2011). 3.The war on cancer Departments of Pharmacology and Medicine, Dartmouth Medical School, Hanover, NH 03755, USA (Prof M B Sporn MD).4.Mariotto, A. B., Etzioni, R., Hurlbert, M., Penberthy, L. & Mayer, M. Cancer Epidemiol. Biomarkers Prev.26, 809–815 (2017).5.Tao, Z. Q. et al. Cell Biochem. Biophys.72, 333–338 (2015).
Adelene Perkins, MBA
Chairman and CEO, Infinity Pharmaceuticals
Title: Cancer - Daring to Believe in a Cure and Why This Belief is Justified
Abstract: Three key questions set the context for daring to believe: Why is cancer so hard, How are we doing in the fight against it, and Is a cure realistic?
- Cancer is hard because it is a natural consequence of evolution. Cancer cells do what all cells are supposed to do, mutate, evolve, and increase in fitness compared to their neighbors to outlive them. To survive, cancer cells create a cancer friendly ecosystem to hijack our immune systems and suppress an immune response such that cancer is less a disease and more a phenomenon, the result of a basic compromise that is fundamental to evolution.
- How are we doing against this goliath? Following the advent of chemotherapy, there was a dearth of progress until 20 years ago with the introduction of targeted therapies. Over the past 10 years, the concept that the key to fighting cancer may already be inside each of us has led to extraordinary progress in the field of immuno-oncology. Yet we are far from declaring victory. Cancer cells mount their own Darwinian fight to survive against the body’s immune attack and, despite success with therapeutic approaches that re-activate T cells, only a small percentage of patients respond and most of those that do respond, ultimately relapse. It has become increasingly clear that turning on the immune system isn’t enough; the key is to make sure it doesn’t get turned off AND that it is not over-activated.
- Against these challenges, is a cure realistic? There is still much to be learned about how to best leverage our very dynamic immune systems in fighting cancer, balancing adaptive and innate immunity in killing cancer cells without triggering auto-immune reactions. The tremendous amount of translational analysis that is incorporated into clinical trials today makes it increasingly possible to identify those patients who benefit from immune activating therapies as well as those who are less susceptible to undesirable autoimmunity. These insights are an essential component of current clinical trial design to maximize the benefit: risk ratio in the treatment of cancer patients. AND these insights will be key to taking us to the next level: identifying patients at risk for cancer and developing cures!
Kenneth J. Pienta, M.D.
Professor, Johns Hopkins University School of Medicine; Director of Research, The James Buchanan Brady Urological Institute
Title: Convergent Evolution and the Origins of Lethal Cancer
Abstract: In patients, de novo lethal cancer evolves capacities for both metastasis and resistance. Therefore, cancers in different patients appear to follow the same eco-evolutionary path that independently manifests in affected patients. This convergent outcome, that always includes the ability to metastasize and exhibit resistance, demands an explanation beyond the slow and steady accrual of stochastic mutations. We suggest that cancer starts as a speciation event when a unicellular protist breaks away from its multicellular host and initiates a cancer clade within the patient. As the cancer cells speciate and diversify further, some evolve the capacity to evolve: evolvability. By generating and maintaining considerable heritable variation, the cancer clade can, with high certainty, serendipitously produce cells resistant to therapy and cells capable of metastasizing. Understanding that cancer cells can swiftly evolve responses to novel and varied stressors create opportunities for adaptive therapy, double-bind therapies, and extinction therapies; all involving strategic decision making that steers and anticipates the convergent coevolutionary responses of the cancers
Azra Raza, M.D.
Professor of Medicine, Director - MDS Center, Columbia University Medical Center; Author, The First Cell: And the Human Costs of Pursuing Cancer to the Last
Title: The patient perspective on modern cancer therapy
Abstract: Among newly diagnosed cancer patients today, 68% are cured (with surgery, chemotherapy and radiation therapy), while for the remaining 32% with advanced disease, the outcome is no different than it was fifty years ago. Despite a quarter of a trillion dollars invested in research, this level of failure is both staggering and perplexing. It is not for lack of molecular and biologic understanding of the disease which have advanced spectacularly. How does cancer remain one step ahead of every therapeutic strategy? What is the secret of its effortless superiority? Perhaps the apparent behavioral complexity of advanced disease is not the result of natural selection but an emergent property following endless iterations as cells follow simple algorithmic rules. The recurrent patterns of metastases support the lesser importance of natural selection. Rather, cancer grows to fill the limited forms available. The behavior and response of individual cells within the tumor will remain unpredictable because even random DNA copying errors could generate profound, arbitrary complexity and diversity. Discovering the initial algorithm cannot be predictive either because the complex behavior is an emergent property. The solution for the patient? Early detection of malignant behavior through biomarkers, scanning and imaging devices, swift elimination and prevention of the disease from morphing into the untreatable end-stage monstrosity.
Philips Professor of Astronomy, Harvard University; Director of the Harvard Origins of Life Initiative; Author, The Life of Super-Earths: How the Hunt for Alien Worlds and Artificial Cells Will Revolutionize Life on Our Planet
Title: Harvard Origins of Life Initiative: Building Blocks, Protocells & UV-driven Evolution
Abstract: A broad-based team at Harvard - the Origins of Life Initiative, has been exploring the interconnected issues in prebiotic chemistry leading to protocells, evolvability, and a robust biosphere sustained by planetary processes and detectable over cosmic distances. I will highlight a couple of our successes and will focus on the prebiotic UV-driven synthesis and selectivity of nucleic acids, and the early steps of evolvability.
The question of how prebiotic chemistry, on early Earth or other planetary bodies, led to the emergence of life remains wide open and many - and very different, scenarios are being explored. Most agree that this prebiotic chemistry must have been a natural and robust extension of the geochemical and environmental conditions available somewhere on the planet. Solar UV light is one of the most important environmental factors for origins of life scenarios on Earth's surface, like those by Sutherland (2016) where UVC light plays an important role in pathways to the synthesis of canonical nucleotides, as well as some amino acids and lipids.
UVC light has the right energy to both make and break bonds in those canonical monomers and can thus play two crucial roles: as a source of energy and as a very specific selection agent in chemical evolution (Sasselov, Grotzinger & Sutherland 2020). The latter role is essential in avoiding the harmful concomitant accumulation of a multitude of undesirable by-products (e.g., isomers) during the synthesis of life's building blocks.
The first step in this chemical evolution is that the canonical monomers emerge to be the most UVC photostable products of this synthesis, as lab experiments confirm.
The second step in this chemical evolution is the photostability of the oligomers and their functionality under UVC light, for example in non-enzymatic self-replication of RNA. Here we turn to exploring the phenomenon of "self-repair", as observed previously in the UV photodynamic of DNA oligomers. This is not simply a question of photostability, but of the subtle intrinsic ability of short DNA and RNA strands to make use of UV light to repair existing photo-damage. Certain adjacent bases are able to transition to a charge-separated state by UV excitation, which can repair photo-damage, and that ability strongly depends on the neighborhood of the base. Therefore, certain sequences have a higher probability of survival and thus may have determined early selection (Kufner, Zinth & Bucher 2020).
Finally, in this scenario we have the protocells - lipid membrane vesicles, which enable the encapsulated RNA strands to explore sequence space. They exist under the same UVC environment which synthesizes the necessary building blocks, at least until a time when the protocells become self-sufficient. Until that time, the balance between UV damage and UV self-repair holds the key to the protocells' survival and the form of their nascent biochemistry.
We muse that some vestiges of those early steps in life's emergence may be still present today in processes involving UV light and UV damage.
Professor of Biochemistry and Molecular Biology, University of Chicago; Author, Evolution: A View from the 21st Century
Title: What Can Evolutionary Biology Learn from Cancer Biology?
"The failure of current cancer treatments to successfully eradicate metastatic disease, likely results from a misunderstanding of the natural history of cancer. Rather than seeing malignancy as a consequence of Darwinian microevolution driven by stochastic mutations, it can be considered as the result of a programmed response illicitly accessed by a few key mutations…This programme appears to have been imprinted through evolution to cope with DNA damage and stored in the evolutionary memory of the genome."
Erenpreisa, J. and M. S. Cragg (2013). "Three steps to the immortality of cancer cells: senescence, polyploidy and self-renewal." Cancer Cell Int 13(1): 92.
Abstract: It is customary to think of evolutionary biology as providing lessons to cancer biologists. But contrary to common belief, cancer biology may well have a higher relative density of empirical observations about relationships between genotypes and phenotypes than do traditional evolutionary studies. It is clear that cancer has a great deal to teach about the capacities of living cells to rapidly restructure their genomes and generate novel reproductive capabilities. In addition to illustrating all the well-known processes of mutation, chromosome rearrangement, and transposition of mobile DNA elements, cancer genetics has revealed an unanticipated series of large-scale genome restructuring processes such as chromothripsis and chromoplexy.
After their discovery in cancer evolution, these complex, multi-site events have been observed to occur in normal human germlines as well. Although the sites of restructuring are quite diverse between tumors, each process displays its own characteristic pattern of what types of chromosomal rearrangements occur together. These results teach us that human cells have built-in routines to carry out recognizable classes of major changes to the organization and, consequently, the expression of the genome in a short period of time (clearly much less than a human lifetime). Recent observations indicate that these major genome restructuring events occur as a consequence of viral infection, as in HPV-induced tumors, and formation of amitotic polyploid giant cancer cells (PGCCs) formed by cell fusions or resulting from adaptive responses to DNA replication stress (e.g. as caused by chemotherapeutic agents). Notably, PGCCs experience massive chromosome breakage and repair, as they undergo aberrant multipolar mitoses to bud off pseudo-diploid mitotic cancer cells.
It is well-documented that all major groups of eukaryotic organisms form large polyploid cells during normal development and damage repair. Thus, analogous processes involving polyploid cells and genome restructuring, as they undergo diploidization, could occur in organismal germlines at moments of maximum reproductive stress (e.g. mass extinctions). Such a pathway to rapid and pervasive genome change has the potential to solve a great many evolutionary mysteries. The plausibility of a role for giant cell cycles in eukaryotic evolution is supported by the discovery of multiple independent cases of chromothripsis in the human germline.
Patrick Soon-Shiong, M.D.
Transplant surgeon, businessman, bioscientist, and media executive, Inventor of the drug Abraxane, known for its efficacy against lung, breast, and pancreatic cancer; Founder of NantWorks, a network of health and technology startups.
Title: Introducing Quantum Onco-Therapeutics: The Path to Memory T-cells
Abstract: Illusion of cancer as a single clone
The formation of transformed, potentially cancerous cells occurs routinely as part of the daily physiological process of regeneration. Such nascent cancer cells are kept in a dormant state by the natural killer (NK) cells of innate immune system. If this protective immune response is overwhelmed by a high rate of mutations or by cancer cell-generated immunosuppression, the escape phase ensues, resulting in clinical evidence of cancer.
Early oncologists developed methods for characterizing tumors based on tissue of origin, grade, stage, and cellular morphology; and this information has been the basis for choice of therapeutic approach for decades. The advent of multi-omic (genomic, transcriptomic, proteomic, etc.) analyses of tumor tissue has resulted in a significant advancement in tumor characterization, along with a rapidly expanding understanding of tumor interaction with the immune system. This has led to some advancement in therapeutic approaches, such as checkpoint inhibitors, but has not resulted in victory in the war against cancer.
As vital as these advancements have been in contributing to cancer therapy, in general they have been used to characterize a single biopsy or surgical sample to inform therapeutic choice. The illusion of this approach is that it assumes cancer is a single clone. As our knowledge increases, we understand now that cancer cells benefit from the same genome-encoded adaptive responses as normal cells, allowing a subpopulation of cells to expand that have resistance to therapy to which they have been exposed. Therefore, conventional chemotherapy or precision medicine, if based on detailed characterization of a single tumor sample only, are often doomed to fail.
‘Quantum Onco-therapeutics’ addresses the dynamic nature of cancer evolution head-on by combining longitudinal collection of tumor tissue with repeated analyses to reveal adaptive mechanisms for immune evasion by metastases or persisting lesions. These mechanisms (to name a few) - neoepitope silencing, gene editing, and/or expression of immuno-suppressing molecules and preponderance of ineffective immune cells such as M2 macrophages and Tregs – along with other information gleaned from analysis such as expression of neoepitopes, can then become targets for therapy.
The cancer biological match approach to drive T and NK cells memory
Armed with the knowledge of real-time immune/biology of not just the primary tumor, but of metastases, quantum oncotherapy seeks to overcome the immunosuppressive cross-talk of the tumor environment by longitudinal temporal spatial orchestration of low-dose chemotherapy that leverages nanoparticle penetrance of the tumor microenvironment with a tumor-specific adenovirus-vectored vaccine – exploiting the tumor itself as a source of specific antigens - interleukin-15-based immune enhancement and introduction of off-the-shelf high-affinity NK (haNK) cells to induce immunogenic cell death (ICD).
Key for the success of ICD, is the careful orchestration of innate and adaptive immunity resulting in immunological memory. We have already seen, and will report on, long-term durable ICD responses using this quantum oncotherapy approach in even difficult cancer types such as triple-negative breast, squamous cell head and neck, and pancreatic cancers.
Charles Swanton, M.D., Ph.D., FMedSci, FRS
Group Leader, Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute
Title: Cancer evolution, immune evasion and metastasis
Abstract: Increasing evidence supports complex subclonal relationships in solid tumours, manifested as intratumour heterogeneity. Parallel evolution of subclones, with distinct somatic events occurring in the same gene, signal transduction pathway or protein complex, suggests constraints to tumour evolution that might be therapeutically exploitable. Data from TRACERx, a longitudinal lung cancer evolution study will be presented. Drivers of tumour heterogeneity change during the disease course and contribute to the temporally distinct origins of lung cancer driver events. APOBEC driven mutagenesis appears to be enriched in subclones in multiple tumour types. Oncogene, tumour suppressor gene and drug induced DNA replication stress are found to drive APOBEC mutagenesis. Phylogenetic tracking detects minimal residual disease and clonal evolution of disease from primary to metastatic sites, presenting opportunities for drug development.
On-going chromosomal instability, manifested as Mirrored Subclonal Allelic Imbalance (MSAI) is found to be a major driver of intratumour heterogeneity across cancer types, contributing to parallel evolution and selection. The finding of subclonal driver events, evidence of ongoing selection within subclones, combined with genome instability driving cell-to-cell variation is likely to limit the efficacy of targeted monotherapies, suggesting the need for new approaches to drug development and clinical trial design and integration of cancer immunotherapeutic approaches. Multiple adaptive mechanisms to neo-antigen evolution have been found in TRACERx that emphasise the role of cancer chromosomal instability driving immune evasion and HLA/MHC loss and loss of clonal neo-antigens as well as epigenetic repression of neo-antigens. The clonal neo-antigenic architecture may act as a tumour vulnerability, targeting multiple clonal neo-antigens present in each tumour to mitigate resistance and treatment failure.
Elihu Professor of Biostatistics and Ecology & Evolutionary Biology, Yale School of Public Health, Yale University
Title: The Somatic Molecular Evolution of Cancer: Mutation, Selection, and Epistasis
Abstract: Modeling the somatic molecular evolution of cancer is aided by large data sets exemplifying its repeated occurrence. We have computed the underlying mutation rates for all sites in the genome using observed mutation distributions and known genomic correlations with gene expression level, chromatin state, and replication timing. Second, parameterized by those rates and the observed prevalences of mutations, we have derived analytical solutions for the scaled selection intensity. However, these cancer effect sizes of mutations are complicated by their interactions during the tumorigenic process. This complexity can be addressed in two ways: by estimating stage-specific scaled selection coefficients for somatic mutations and by estimating their epistatic interactions. Selective epistasis manifests in mutual exclusion and co-occurrence, which have been widely described in a phenomenological manner. Accurate prediction of the evolution of cancer in response to treatment requires analysis of rates of mutation and strength of selection in the context of epistasis. We have been able to use the underlying frequencies of mutual exclusivity and co-occurrence along with estimates of site-specific mutation rate to yield quantitative epistatic interactions between somatically selected variant sites, both in tumorigenesis and in the evolution of resistance to therapy. Together, our results provide stage-specific trajectories for the evolution of neoplasms, primary, and metastatic tumors and promise a full genetic fitness landscape for cancer evolution—a tool that will become fundamental to accurate and personalized prediction of therapeutic response.
Robert A. Weinberg
Member, Whitehead Institute for Biomedical Research; Professor of Biology, MIT; Director, MIT Ludwig Center for Molecular Oncology
Title: Mechanisms of Malignant Progression of Carcinoma Cells
Abstract: Many of the shifts in cell phenotype that occur during the malignant progression of carcinoma cells are achieved by shifts in the epigenetic programming of these cells after they have acquired the mutant alleles that are required, in aggregate, to enable them to become tumorigenic. In the case of carcinoma cells, these malignancy-associated phenotypes are largely conferred by the cell-biological program termed the epithelial-mesenchymal transition (EMT). In addition to conferring traits on carcinoma cells, such as motility and invasiveness, the EMT program may be critical to the dissemination of carcinoma cells to distant anatomical sites in the body. Moreover, the EMT program confers a variety of traits that are associated with resistance to existing therapies, including chemo- and immunotherapy, as well as stemness, i.e., the ability to seed new tumors which appears to be so critical to the outgrowth of metastases.
Frank H. Laukien
James A. Shapiro
Henry H. Heng
Symposium Planning & Execution Team
Tamra V. Thorne
M. William Audeh
Anna D. Barker
Steven A. Carr
Kenneth J. Pienta