Efficiency of carcinogenesis: is the mutator phenotype inevitable?

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

Treatment resistance in urothelial carcinoma: an evolutionary perspective

The emergence of treatment-resistant clones is a critical barrier to cure in patients with urothelial carcinoma. Setting the stage for the evolution of resistance, urothelial carcinoma is characterized by extensive mutational heterogeneity, which is detectable even in patients with early stage disease. Chemotherapy and immunotherapy both act as selective pressures that shape the evolutionary trajectory of urothelial carcinoma throughout the course of the disease. A detailed understanding of the dynamics of evolutionary drivers is required for the rational development of curative therapies. Herein, we describe the molecular basis of the clonal evolution of urothelial carcinomas and the use of genomic approaches to predict treatment responses. We discuss various mechanisms of resistance to chemotherapy with a focus on the mutagenic effects of the DNA dC->dU-editing enzymes APOBEC3 family of proteins. We also review the evolutionary mechanisms underlying resistance to immunotherapy, such as the loss of clonal tumour neoantigens. By dissecting treatment resistance through an evolutionary lens, the field will advance towards true precision medicine for urothelial carcinoma.

Hepatitis B Virus DNA Integration Occurs Early in the Viral Life Cycle in an In Vitro Infection Model via Sodium Taurocholate Cotransporting Polypeptide-Dependent Uptake of Enveloped Virus Particles

Chronic infection by hepatitis B virus (HBV) is the major contributor to liver disease worldwide. Though HBV replicates via a nuclear episomal DNA (covalently closed circular DNA [cccDNA]), integration of HBV DNA into the host cell genome is regularly observed in the liver in infected patients. While reported as a prooncogenic alteration, the mechanism(s) and timing of HBV DNA integration are not well understood, chiefly due to the lack of in vitro infection models that have detectable integration events. In this study, we have established an in vitro system in which integration can be reliably detected following HBV infection. We measured HBV DNA integration using inverse nested PCR in primary human hepatocytes, HepaRG-NTCP, HepG2-NTCP, and Huh7-NTCP cells after HBV infection. Integration was detected in all cell types at a rate of >1 per 10,000 cells, with the most consistent detection in Huh7-NTCP cells. The integration rate remained stable between 3 and 9 days postinfection. HBV DNA integration was efficiently blocked by treatment with a 200 nM concentration of the HBV entry inhibitor Myrcludex B, but not with 10 μM tenofovir, 100 U of interferon alpha, or a 1 μM concentration of the capsid assembly inhibitor GLS4. This suggests that integration of HBV DNA occurs immediately after infection of hepatocytes and is likely independent of de novo HBV genome replication in this model. Site analysis revealed that HBV DNA integrations were distributed over the entire human genome. Further, integrated HBV DNA sequences were consistent with double-stranded linear HBV DNA being the major precursor. Thus, we have established an in vitro system to interrogate the mechanisms of HBV DNA integration.IMPORTANCE Hepatitis B virus (HBV) is a common blood-borne pathogen and, following a chronic infection, can cause liver cancer and liver cirrhosis. Integration of HBV DNA into the host genome occurs in all known members of the Hepadnaviridae family, despite this form not being necessary for viral replication. HBV DNA integration has been reported to drive liver cancer formation and persistence of virus infection. However, when and the mechanism(s) by which HBV DNA integration occurs are not clear. In this study, we have developed and characterized an in vitro system to reliably detect HBV DNA integrations that result from a true HBV infection event and that closely resemble those found in patient tissues. Using this model, we showed that integration occurs when the infection is first established. Importantly, we provide here a system to analyze molecular factors involved in HBV integration, which can be used to develop strategies to halt its formation.

Evolutionary dynamics and significance of multiple subclonal mutations in cancer

For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.

Long range personalized cancer treatment strategies incorporating evolutionary dynamics

Background

Current cancer precision medicine strategies match therapies to static consensus molecular properties of an individual’s cancer, thus determining the next therapeutic maneuver. These strategies typically maintain a constant treatment while the cancer is not worsening. However, cancers feature complicated sub-clonal structure and dynamic evolution. We have recently shown, in a comprehensive simulation of two non-cross resistant therapies across a broad parameter space representing realistic tumors, that substantial improvement in cure rates and median survival can be obtained utilizing dynamic precision medicine strategies. These dynamic strategies explicitly consider intratumoral heterogeneity and evolutionary dynamics, including predicted future drug resistance states, and reevaluate optimal therapy every 45 days. However, the optimization is performed in single 45 day steps (“single-step optimization”).

Results

Herein we evaluate analogous strategies that think multiple therapeutic maneuvers ahead, considering potential outcomes at 5 steps ahead (“multi-step optimization”) or 40 steps ahead (“adaptive long term optimization (ALTO)”) when recommending the optimal therapy in each 45 day block, in simulations involving both 2 and 3 non-cross resistant therapies. We also evaluate an ALTO approach for situations where simultaneous combination therapy is not feasible (“Adaptive long term optimization: serial monotherapy only (ALTO-SMO)”). Simulations utilize populations of 764,000 and 1,700,000 virtual patients for 2 and 3 drug cases, respectively. Each virtual patient represents a unique clinical presentation including sizes of major and minor tumor subclones, growth rates, evolution rates, and drug sensitivities.

While multi-step optimization and ALTO provide no significant average survival benefit, cure rates are significantly increased by ALTO. Furthermore, in the subset of individual virtual patients demonstrating clinically significant difference in outcome between approaches, by far the majority show an advantage of multi-step or ALTO over single-step optimization. ALTO-SMO delivers cure rates superior or equal to those of single- or multi-step optimization, in 2 and 3 drug cases respectively.

Conclusion

In selected virtual patients incurable by dynamic precision medicine using single-step optimization, analogous strategies that “think ahead” can deliver long-term survival and cure without any disadvantage for non-responders. When therapies require dose reduction in combination (due to toxicity), optimal strategies feature complex patterns involving rapidly interleaved pulses of combinations and high dose monotherapy.

Reviewers

This article was reviewed by Wendy Cornell, Marek Kimmel, and Andrzej Swierniak. Wendy Cornell and Andrzej Swierniak are external reviewers (not members of the Biology Direct editorial board). Andrzej Swierniak was nominated by Marek Kimmel.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0153-2) contains supplementary material, which is available to authorized users.

Adaptive Design for a Confirmatory Basket Trial in Multiple Tumor Types Based on a Putative Predictive Biomarker

Increasingly, tumors are defined on a molecular basis rather than only on histology, and targeted agents, which address these molecular subtypes, are being approved. This profusion of molecular subtypes creates "rare" diseases as subsets of common cancers, leading to difficulties in enrolling sufficiently large cohorts for confirmatory trials. However, if the molecular subtype is shared across various histologies, these may be pooled into a basket trial. To date, basket trials have been primarily for exploratory early development. In this perspective, we consider qualitative designs for confirmatory basket trials. These confirmatory basket designs will provide patients in niche indications with enhanced access to novel therapies, facilitate development and full approval for niche indications, allow accelerated approval for indications within a basket based on a surrogate endpoint, reduce development cost by combining trials, and enhance the ability of regulatory authorities to evaluate risk and benefit in niche indications.

Translating predictive biomarkers within oncology clinical development programs

Predictive biomarkers provide essential information to enable personalized medicine, and hold the promise for enhancing the effectiveness and value of cancer therapies. However, they do not always work. This review provides a framework for managing the risk of predictive biomarkers and maximally harvesting their benefit. Methods are provided which permit data-driven, adaptive decision making about the use of predictive biomarkers during clinical development, applying them to the extent they are validated by the clinical data. Techniques for optimizing overall development efficiency, measured as the number of successful drug indications approved per patient utilized, are also presented.

Conceptual models for the initiation of hepatitis B virus-associated hepatocellular carcinoma

Although chronic hepatitis B virus (HBV) infection is a known risk factor for the development of hepatocellular carcinoma (HCC), the steps involved in the progression from normal liver to HCC are poorly understood. In this review, we apply five conceptual models, previously proposed by Vineis et al. to explain carcinogenesis in general, to explore the possible steps involved in the initiation and evolution of HBV-associated HCC. Available data suggest that the most suitable and inclusive model is based on evolution of hepatocyte subpopulations. In this evolutionary model, HCC-associated changes are driven by selection and subsequent clonal expansion of phenotypically altered hepatocyte subpopulations in the microenvironment of the HBV-infected liver. This model can incorporate the wide range of mechanisms proposed to play a role in the initiation of HCC including oncogenic HBV proteins, integration of HBV DNA and chronic inflammation of the liver. The model may assist in the early prevention, detection and treatment of HCC and may guide future studies of the initiation of HBV-associated HCC.

Phase I Imaging and Pharmacodynamic Trial of CS-1008 in Patients With Metastatic Colorectal Cancer

PURPOSE

CS-1008 (tigatuzumab) is a humanized, monoclonal immunoglobulin G1 (IgG1) agonistic antibody to human death receptor 5. The purpose of this study was to investigate the impact of CS-1008 dose on the biodistribution, quantitative tumor uptake, and antitumor response in patients with metastatic colorectal cancer (mCRC).

PATIENTS AND METHODS

Patients with mCRC who had received at least one course of chemotherapy were assigned to one of five dosage cohorts and infused with a weekly dose of CS-1008. Day 1 and day 36 doses were trace-labeled with indium-111 ((111)In), followed by whole-body planar and regional single-photon emission computed tomography (SPECT) imaging at several time points over the course of 10 days.

RESULTS

Nineteen patients were enrolled. (111)In-CS-1008 uptake in tumor was observed in only 12 patients (63%). (111)In-CS-1008 uptake and pharmacokinetics were not affected by dose or repeated drug administration. (111)In-CS-1008 biodistribution showed gradual blood-pool clearance and no abnormal uptake in normal tissue. No anti-CS-1008 antibody development was detected. One patient achieved partial response (3.7 months duration), eight patients had stable disease, and 10 patients had progressive disease. Clinical benefit rate (stable disease + partial response) in patients with (111)In-CS-1008 uptake in tumor was 58% versus 28% in patients with no uptake. An analysis of individual lesions showed that lesions with antibody uptake were one third as likely to progress as those without antibody uptake (P = .07). Death-receptor-5 expression in archived tumor samples did not correlate with (111)In-CS-1008 uptake (P = .5) or tumor response (P = .6).

CONCLUSION

Death-receptor-5 imaging with (111)In-CS-1008 reveals interpatient and intrapatient heterogeneity of uptake in tumor, is not dose dependent, and is predictive of clinical benefit in the treatment of patients who have mCRC.

Genetic and non-genetic instability in tumor progression: link between the fitness landscape and the epigenetic landscape of cancer cells

Genetic instability is invoked in explaining the cell phenotype changes that take place during cancer progression. However, the coexistence of a vast diversity of distinct clones, most prominently visible in the form of non-clonal chromosomal aberrations, suggests that Darwinian selection of mutant cells is not operating at maximal efficacy. Conversely, non-genetic instability of cancer cells must also be considered. Such mutation-independent instability of cell states is most prosaically manifest in the phenotypic heterogeneity within clonal cell populations or in the reversible switching between immature "cancer stem cell-like" and more differentiated states. How are genetic and non-genetic instability related to each other? Here, we review basic theoretical foundations and offer a dynamical systems perspective in which cancer is the inevitable pathological manifestation of modes of malfunction that are immanent to the complex gene regulatory network of the genome. We explain in an accessible, qualitative, and permissively simplified manner the mathematical basis for the "epigenetic landscape" and how the latter relates to the better known "fitness landscape." We show that these two classical metaphors have a formal basis. By combining these two landscape concepts, we unite development and somatic evolution as the drivers of the relentless increase in malignancy. Herein, the cancer cells are pushed toward cancer attractors in the evolutionarily unused regions of the epigenetic landscape that encode more and more "dedifferentiated" states as a consequence of both genetic (mutagenic) and non-genetic (regulatory) perturbations-including therapy. This would explain why for the cancer cell, the principle of "What does not kill me makes me stronger" is as much a driving force in tumor progression and development of drug resistance as the simple principle of "survival of the fittest."

Cancer genomic research at the crossroads: realizing the changing genetic landscape as intratumoral spatial and temporal heterogeneity becomes a confounding factor

The US National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) created the Cancer Genome Atlas (TCGA) Project in 2006. The TCGA's goal was to sequence the genomes of 10,000 tumors to identify common genetic changes among different types of tumors for developing genetic-based treatments. TCGA offered great potential for cancer patients, but in reality has little impact on clinical applications. Recent reports place the past TCGA approach of testing a small tumor mass at a single time-point at a crossroads. This crossroads presents us with the conundrum of whether we should sequence more tumors or obtain multiple biopsies from each individual tumor at different time points. Sequencing more tumors with the past TCGA approach of single time-point sampling can neither capture the heterogeneity between different parts of the same tumor nor catch the heterogeneity that occurs as a function of time, error rates, and random drift. Obtaining multiple biopsies from each individual tumor presents multiple logistical and financial challenges. Here, we review current literature and rethink the utility and application of the TCGA approach. We discuss that the TCGA-led catalogue may provide insights into studying the functional significance of oncogenic genes in reference to non-cancer genetic background. Different methods to enhance identifying cancer targets, such as single cell technology, real time imaging of cancer cells with a biological global positioning system, and cross-referencing big data sets, are offered as ways to address sampling discrepancies in the face of tumor heterogeneity. We predict that TCGA landmarks may prove far more useful for cancer prevention than for cancer diagnosis and treatment when considering the effect of non-cancer genes and the normal genetic background on tumor microenvironment. Cancer prevention can be better realized once we understand how therapy affects the genetic makeup of cancer over time in a clinical setting. This may help create novel therapies for gene mutations that arise during a tumor's evolution from the selection pressure of treatment.

Nonstandard personalized medicine strategies for cancer may lead to improved patient outcomes

Cancer is an evolutionary process that is driven by mutation and selection. Tumors are genetically unstable, and research has shown that this is the most efficient way for cancers to evolve. Genetic instability leads to genetic heterogeneity and dynamic change within a single individual's tumor, in turn leading to therapeutic resistance. Cancer treatment has also evolved from an empirical science of killing dividing cells to the current era of 'personalized medicine', exquisitely targeting the molecular features of individual cancers. However, current personalized medicine regards a single individual's cancer as largely uniform and static. Moreover, from a strategic perspective, current personalized medicine thinks primarily of the immediate therapy selection. Ongoing research suggests that new, nonstandard personalized treatment strategies that plan further ahead and consider intratumoral heterogeneity and the evolving nature of cancer (due to genetic instability) may lead to the next level of therapeutic benefit beyond current personalized medicine.

New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy, increase the value of cancer medicines, and decrease the size and cost of clinical trials while increasing their chance of success. But predictive biomarkers do not always work. When unsuccessful, they add cost, complexity, and time to drug development. This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it can actually predict.

Modelling the evolution of genetic instability during tumour progression

The role of genetic instability in driving carcinogenesis remains controversial. Genetic instability should accelerate carcinogenesis by increasing the rate of advantageous driver mutations; however, genetic instability can also potentially retard tumour growth by increasing the rate of deleterious mutation. As such, it is unclear whether genetically unstable clones would tend to be more selectively advantageous than their genetically stable counterparts within a growing tumour. Here, we show the circumstances where genetic instability evolves during tumour progression towards cancer. We employ a Wright-Fisher type model that describes the evolution of tumour subclones. Clones can acquire both advantageous and deleterious mutations, and mutator mutations that increase a cell's intrinsic mutation rate. Within the model, cancers evolve with a mutator phenotype when driver mutations bestow only moderate increases in fitness: very strong or weak selection for driver mutations suppresses the evolution of a mutator phenotype. Genetic instability occurs secondarily to selectively advantageous driver mutations. Deleterious mutations have relatively little effect on the evolution of genetic instability unless selection for additional driver mutations is very weak or if deleterious mutations are very common. Our model provides a framework for studying the evolution of genetic instability in tumour progression. Our analysis highlights the central role of selection in shaping patterns of mutation in carcinogenesis.

Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable "patients" demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

Point mutation instability (PIN) mutator phenotype as model for true back mutations seen in hereditary tyrosinemia type 1 - a hypothesis

Hereditary tyrosinemia type 1 (HT1) is an autosomal recessive disorder affecting fumarylacetoacetate hydrolase (FAH), the last enzyme in the tyrosine catabolism pathway. The liver mosaicism observed in HT1 patients is due to the reversion to the wild type of one allele of the original point mutation in fah. It is generally accepted that these reversions are true back mutations; however, the mechanism is still unresolved. Previous reports excluded intragenic recombination, mitotic recombination, or homologous recombination with a pseudogene as possible mechanisms of mutation reversion in HT1. Sequence analysis did not reveal DNA motifs, tandem repeats or other sequence peculiarities that may be involved in mutation reversion. We propose the hypothesis that a point mutation instability mutator (PIN) phenotype brought about by the sustained stress environment created by the accumulating metabolites in the cell is the driver of the true back mutations in HT1. The metabolites accumulating in HT1 create a sustained stress environment by activating the extracellular signal-regulated kinase (ERK) and AKT survival pathways, inducing aberrant mitosis and development of death resistant cells, depleting glutathione, and impairing DNA ligase IV and possibly DNA polymerases δ and ε. This continual production of proliferative and stress-related survival signals in the cellular environment coupled with the mutagenicity of FAA, may instigate a mutator phenotype and could end in tumorigenesis and/or mutation reversion. The establishment of a PIN-mutator phenotype therefore not only seems to be a possible mechanism underlying the true back mutations, but also contributes to explaining the clinical heterogeneity seen in hereditary tyrosinemia type 1.

Lethal mutagenesis: targeting the mutator phenotype in cancer

The evolution of cancer and RNA viruses share many similarities. Both exploit high levels of genotypic diversity to enable extensive phenotypic plasticity and thereby facilitate rapid adaptation. In order to accumulate large numbers of mutations, we have proposed that cancers express a mutator phenotype. Similar to cancer cells, many viral populations, by replicating their genomes with low fidelity, carry a substantial mutational load. As high levels of mutation are potentially deleterious, the viral mutation frequency is thresholded at a level below which viral populations equilibrate in a traditional mutation-selection balance, and above which the population is no longer viable, i.e., the population undergoes an error catastrophe. Because their mutation frequencies are fine-tuned just below this error threshold, viral populations are susceptible to further increases in mutational load and, recently this phenomenon has been exploited therapeutically by a concept that has been termed lethal mutagenesis. Here we review the application of lethal mutagenesis to the treatment of HIV and discuss how lethal mutagenesis may represent a novel therapeutic approach for the treatment of solid cancers.