Dynamics of targeted cancer therapy

Super-enhancers in tumors: unraveling recent advances in their role in Oncogenesis and the emergence of targeted therapies

Super enhancers are a unique class of enhancers that possess a distinct structure and mechanism, which enable them to exhibit stronger gene transcription regulatory function than classical enhancers, thereby regulating cellular activities. In tumor samples, super enhancers have been identified as crucial players in the development and progression of tumor cells, opening up new avenues for cancer research and treatment. This review provides a concise overview of various models regarding super enhancer assembly and activation, examining the mechanisms through which tumor cells acquire or activate these enhancers and regulate carcinogenic transcription programs. Furthermore, we discuss the current landscape and challenges in developing cancer therapeutic drugs that target super enhancers.

Molecular mechanisms augmenting resistance to current therapies in clinics among cervical cancer patients

Cervical cancer (CC) is the fourth leading cause of cancer death (~ 324,000 deaths annually) among women internationally, with 85% of these deaths reported in developing regions, particularly sub-Saharan Africa and Southeast Asia. Human papillomavirus (HPV) is considered the major driver of CC, and with the availability of the prophylactic vaccine, HPV-associated CC is expected to be eliminated soon. However, female patients with advanced-stage cervical cancer demonstrated a high recurrence rate (50-70%) within two years of completing radiochemotherapy. Currently, 90% of failures in chemotherapy are during the invasion and metastasis of cancers related to drug resistance. Although molecular target therapies have shown promising results in the lab, they have had little success in patients due to the tumor heterogeneity fueling resistance to these therapies and bypass the targeted signaling pathway. The last two decades have seen the emergence of immunotherapy, especially immune checkpoint blockade (ICB) therapies, as an effective treatment against metastatic tumors. Unfortunately, only a small subgroup of patients (< 20%) have benefited from this approach, reflecting disease heterogeneity and manifestation with primary or acquired resistance over time. Thus, understanding the mechanisms driving drug resistance in CC could significantly improve the quality of medical care for cancer patients and steer them to accurate, individualized treatment. The rise of artificial intelligence and machine learning has also been a pivotal factor in cancer drug discovery. With the advancement in such technology, cervical cancer screening and diagnosis are expected to become easier. This review will systematically discuss the different tumor-intrinsic and extrinsic mechanisms CC cells to adapt to resist current treatments and scheme novel strategies to overcome cancer drug resistance.

Cell Competition Shapes Metastatic Latency and Relapse

Cell competition, a fitness-sensing process, is essential for tissue homeostasis. Using cancer metastatic latency models, we show that cell competition results in the displacement of latent metastatic (Lat-M) cells from the primary tumor. Lat-M cells resist anoikis and survive as residual metastatic disease. A memodeled extracellular matrix facilitates Lat-M cell displacement and survival in circulation. Disrupting cell competition dynamics by depleting secreted protein and rich in cysteine (SPARC) reduced displacement from orthotopic tumors and attenuated metastases. In contrast, depletion of SPARC after extravasation in lung-resident Lat-M cells increased metastatic outgrowth. Furthermore, multiregional transcriptomic analyses of matched primary tumors and metachronous metastases from patients with kidney cancer identified tumor subclones with Lat-M traits. Kidney cancer enriched for these Lat-M traits had a rapid onset of metachronous metastases and significantly reduced disease-free survival. Thus, an unexpected consequence of cell competition is the displacement of cells with Lat-M potential, thereby shaping metastatic latency and relapse.

SIGNIFICANCE

We demonstrate that cell competition within the primary tumor results in the displacement of Lat-M cells. We further show the impact of altering cell competition dynamics on metastatic incidence that may guide strategies to limit metastatic recurrences. This article is highlighted in the In This Issue feature, p. 1.

Entanglements and imagined futures: The subject(s) of precision in oncology

Precision oncology holds an increasingly powerful social function. In the era of precision, how people encounter, live with, and experience cancer, how they imagine their lives, how they navigate treatment regimens, and experience side effects, have been radically transformed. Innovations in oncology - in this case precision-related - are always more-than-clinical; their circulation exceeds the laboratory and the hospital, but what this 'circulation of innovation' produces has been thus far opaque. To begin to comprehend what is emergent at the cancer-precision nexus in people's everyday lives, we draw on qualitative interviews with twenty people diagnosed with metastatic non-small cell lung cancer undergoing immunotherapy and/or targeted therapy and we discuss how precision inflects survivorship, entangles subjects in chronic living, and induces novel temporalities. Through such inflections of survivorship, precision innovation re-shapes expectations and possibilities, and sometimes enacts new, unexpected (or, for some, unwanted) futures. Such illness and survivorship narratives indicate the importance of orientating the social science scholarship toward considerations of temporality and entanglements for comprehending precision innovation in oncology. And in doing so, provide a nuanced account of how innovations unsettle and recast, rather than unravel, the normative scene of cancer.

UCP2 as a Cancer Target through Energy Metabolism and Oxidative Stress Control

Despite numerous therapies, cancer remains one of the leading causes of death worldwide due to the lack of markers for early detection and response to treatment in many patients. Technological advances in tumor screening and renewed interest in energy metabolism have allowed us to identify new cellular players in order to develop personalized treatments. Among the metabolic actors, the mitochondrial transporter uncoupling protein 2 (UCP2), whose expression is increased in many cancers, has been identified as an interesting target in tumor metabolic reprogramming. Over the past decade, a better understanding of its biochemical and physiological functions has established a role for UCP2 in (1) protecting cells from oxidative stress, (2) regulating tumor progression through changes in glycolytic, oxidative and calcium metabolism, and (3) increasing antitumor immunity in the tumor microenvironment to limit cancer development. With these pleiotropic roles, UCP2 can be considered as a potential tumor biomarker that may be interesting to target positively or negatively, depending on the type, metabolic status and stage of tumors, in combination with conventional chemotherapy or immunotherapy to control tumor development and increase response to treatment. This review provides an overview of the latest published science linking mitochondrial UCP2 activity to the tumor context.

Demystifying extrachromosomal DNA circles: Categories, biogenesis, and cancer therapeutics

Since the advent of sequencing technologies in the 1990s, researchers have focused on the association between aberrations in chromosomal DNA and disease. However, not all forms of the DNA are linear and chromosomal. Extrachromosomal circular DNAs (eccDNAs) are double-stranded, closed-circled DNA constructs free from the chromosome that reside in the nuclei. Although widely overlooked, the eccDNAs have recently gained attention for their potential roles in physiological response, intratumoral heterogeneity and cancer therapeutics. In this review, we summarize the history, classifications, biogenesis, and highlight recent progresses on the emerging topic of eccDNAs and comment on their potential application as biomarkers in clinical settings.

Drug-Tolerant Persister Cells in Cancer Therapy Resistance

One of the current stumbling blocks in our fight against cancer is the development of acquired resistance to therapy, which is attributable to approximately 90% of cancer-related deaths. Undercutting this process during treatment could significantly improve cancer management. In many cases, drug resistance is mediated by a drug-tolerant persister (DTP) cell subpopulation present in tumors, often referred to as persister cells. This review provides a summary of currently known persister cell subpopulations and approaches to target them. A specific DTP cell subpopulation with elevated levels of aldehyde dehydrogenase (ALDH) activity has stem cell-like characteristics and a high level of plasticity, enabling them to switch rapidly between high and low ALDH activity. Further studies are required to fully elucidate the functions of ALDH-high DTP cells, how they withstand drug concentrations that kill other cells, and how they rapidly adapt under levels of high cellular stress and eventually lead to more aggressive, recurrent, and drug-resistant cancer. Furthermore, this review addresses the processes used by the ALDH-high persister cell subpopulation to enable cancer progression, the ALDH isoforms important in these processes, interactions of ALDH-high DTPs with the tumor microenvironment, and approaches to therapeutically modulate this subpopulation in order to more effectively manage cancer.

Quantification of the Selective Advantage of Driver Mutations Is Dependent on the Underlying Model and Stage of Tumor Evolution

Measuring the selective fitness advantages provided by driver mutations has the potential to facilitate a precise quantitative understanding of cancer evolution. However, accurately measuring the selective advantage of driver mutations has remained a challenge in the field. Early studies reported small selective advantages of drivers, on the order of 1%, whereas newer studies report much larger selective advantages, as high as 1,200%. In this article, we argue that the calculated selective advantages of cancer drivers are dependent on the underlying mathematical model and stage of cancer evolution and that comparisons of numerical values of selective advantage without regard for the underlying model and stage can lead to spurious conclusions.

The Genomic Processes of Biological Invasions: From Invasive Species to Cancer Metastases and Back Again

The concept of invasion is useful across a broad range of contexts, spanning from the fine scale landscape of cancer tumors up to the broader landscape of ecosystems. Invasion biology provides extraordinary opportunities for studying the mechanistic basis of contemporary evolution at the molecular level. Although the field of invasion genetics was established in ecology and evolution more than 50 years ago, there is still a limited understanding of how genomic level processes translate into invasive phenotypes across different taxa in response to complex environmental conditions. This is largely because the study of most invasive species is limited by information about complex genome level processes. We lack good reference genomes for most species. Rigorous studies to examine genomic processes are generally too costly. On the contrary, cancer studies are fortified with extensive resources for studying genome level dynamics and the interactions among genetic and non-genetic mechanisms. Extensive analysis of primary tumors and metastatic samples have revealed the importance of several genomic mechanisms including higher mutation rates, specific types of mutations, aneuploidy or whole genome doubling and non-genetic effects. Metastatic sites can be directly compared to primary tumor cell counterparts. At the same time, clonal dynamics shape the genomics and evolution of metastatic cancers. Clonal diversity varies by cancer type, and the tumors’ donor and recipient tissues. Still, the cancer research community has been unable to identify any common events that provide a universal predictor of “metastatic potential” which parallels findings in evolutionary ecology. Instead, invasion in cancer studies depends strongly on context, including order of events and clonal composition. The detailed studies of the behavior of a variety of human cancers promises to inform our understanding of genome level dynamics in the diversity of invasive species and provide novel insights for management.

Coupled Source-Sink Habitats Produce Spatial and Temporal Variation of Cancer Cell Molecular Properties as an Alternative to Branched Clonal Evolution and Stem Cell Paradigms

Intratumoral molecular cancer cell heterogeneity is conventionally ascribed to the accumulation of random mutations that occasionally generate fitter phenotypes. This model is built upon the “mutation-selection” paradigm in which mutations drive ever-fitter cancer cells independent of environmental circumstances. An alternative model posits spatio-temporal variation (e.g., blood flow heterogeneity) drives speciation by selecting for cancer cells adapted to each different environment. Here, spatial genetic variation is the consequence rather than the cause of intratumoral evolution. In nature, spatially heterogenous environments are frequently coupled through migration. Drawing from ecological models, we investigate adjacent well-perfused and poorly-perfused tumor regions as “source” and “sink” habitats, respectively. The source habitat has a high carrying capacity resulting in more emigration than immigration. Sink habitats may support a small (“soft-sink”) or no (“hard-sink”) local population. Ecologically, sink habitats can reduce the population size of the source habitat so that, for example, the density of cancer cells directly around blood vessels may be lower than expected. Evolutionarily, sink habitats can exert a selective pressure favoring traits different from those in the source habitat so that, for example, cancer cells adjacent to blood vessels may be suboptimally adapted for that habitat. Soft sinks favor a generalist cancer cell type that moves between the environment but can, under some circumstances, produce speciation events forming source and sink habitat specialists resulting in significant molecular variation in cancer cells separated by small distances. Finally, sink habitats, with limited blood supply, may receive reduced concentrations of systemic drug treatments; and local hypoxia and acidosis may further decrease drug efficacy allowing cells to survive treatment and evolve resistance. In such cases, the sink transforms into the source habitat for resistant cancer cells, leading to treatment failure and tumor progression. We note these dynamics will result in spatial variations in molecular properties as an alternative to the conventional branched evolution model and will result in cellular migration as well as variation in cancer cell phenotype and proliferation currently described by the stem cell paradigm.

Recent Advances of Nanotechnology-Facilitated Bacteria-Based Drug and Gene Delivery Systems for Cancer Treatment

Cancer is one of the most devastating and ubiquitous human diseases. Conventional therapies like chemotherapy and radiotherapy are the most widely used cancer treatments. Despite the notable therapeutic improvements that these measures achieve, disappointing therapeutic outcome and cancer reoccurrence commonly following these therapies demonstrate the need for better alternatives. Among them, bacterial therapy has proven to be effective in its intrinsic cancer targeting ability and various therapeutic mechanisms that can be further bolstered by nanotechnology. In this review, we will discuss recent advances of nanotechnology-facilitated bacteria-based drug and gene delivery systems in cancer treatment. Therapeutic mechanisms of these hybrid nanoformulations are highlighted to provide an up-to-date understanding of this emerging field.

Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening

High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.

CircRNA CircRIMS is Overexpressed in Esophageal Squamous Cell Carcinoma and Downregulate miR-613 Through Methylation to Increase Cell Proliferation

Purpose

CircRNA CircRIMS has been characterized as an oncogenic circRNA in gastric cancer, while its role in other cancers is unknown. This study aimed to explore the role of CircRIMS in esophageal squamous cell carcinoma (ESCC).

Patients and Methods

Tissues collected from 60 ESCC patients were subjected to extractions of total RNA and RT-qPCRs to analyze the differential expression of CircRIMS and miR-613. The 60 ESCC patients were followed up for 5 years to analyze the prognostic value of CircRIMS for ESCC. The interaction between CircRIMS and miR-613 was showed by luciferase activity assay and fluorescence in situ hybridization. The role of CircRIMS in regulating miR-613 expression and methylation was analyzed by overexpression experiments, RT-qPCRs and Western blot assay. The role of CircRIMS and miR-613 in regulating cell proliferation was analyzed using the BrdU assay. ESCC xenograft model was used to demonstrate the role of CircRIMS and miR-613 in vivo.

Results

We found that CircRIMS was overexpressed in ESCC and predicted poor survival. In addition, miR-613 was under expressed in ESCC and inversely correlated with CircRIMS. In ESCC cells, CircRIMS overexpression decreased the expression of miR-613 and increased the methylation of miR-613 gene. Cell proliferation assay showed that CircRIMS overexpression reduced the inhibitory effects of miR-613 overexpression on cell proliferation. Animal experience finally illustrated that CircRNA CircRIMS downregulated miR-613 through methylation to promote tumor growth.

Conclusion

Therefore, CircRIMS may downregulate miR-613 through methylation to increase cell proliferation in ESCC.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

Recent progress in TGF-β inhibitors for cancer therapy

Transforming growth factor-β (TGF-β) is a multifunctional cytokine that is involved in proliferation, metastasis, and many other important processes in malignancy. Inhibitors targeting TGF-β have been considered by pharmaceutical companies for cancer therapy, and some of them are in clinical trial now. Unfortunately, several of these programs have recently been relinquished, and most companies that remain in the contest are progressing slowly and cautiously. This review summarizes the TGF-β signal transduction pathway, its roles in oncogenesis and fibrotic diseases, and advancements in antibodies and small-molecule inhibitors of TGF-β.

Recent technologies enhancing the clinical utility of circulating tumor DNA

Circulating tumor DNA (ctDNA) is a promising blood based biomarker that is set to revolutionize cancer management. Non-invasive biopsy takes precedence over tissue biopsy for enabling longitudinal monitoring, providing a comprehensive profile of tumor heterogeneity and the ease of repeated sampling. Advanced genomic technologies enable real-time disease monitoring, detect minimal residual disease and recurrence at the earliest stages, the potential time points when treatment significantly reduces morbidity and mortality and enable tailored and personalized therapy. The review highlights evidence from literature that make ctDNA a potential liquid biopsy marker and the clinical utility of the recent techniques that leverage up on ctDNA.

Sigma-1 Receptor Positron Emission Tomography: A New Molecular Imaging Approach Using (S)-(-)-[18F]Fluspidine in Glioblastoma

Glioblastoma multiforme (GBM) is the most devastating primary brain tumour characterised by infiltrative growth and resistance to therapies. According to recent research, the sigma-1 receptor (sig1R), an endoplasmic reticulum chaperone protein, is involved in signaling pathways assumed to control the proliferation of cancer cells and thus could serve as candidate for molecular characterisation of GBM. To test this hypothesis, we used the clinically applied sig1R-ligand (S)-(-)-[18F]fluspidine in imaging studies in an orthotopic mouse model of GBM (U87-MG) as well as in human GBM tissue. A tumour-specific overexpression of sig1R in the U87-MG model was revealed in vitro by autoradiography. The binding parameters demonstrated target-selective binding according to identical KD values in the tumour area and the contralateral side, but a higher density of sig1R in the tumour. Different kinetic profiles were observed in both areas, with a slower washout in the tumour tissue compared to the contralateral side. The translational relevance of sig1R imaging in oncology is reflected by the autoradiographic detection of tumour-specific expression of sig1R in samples obtained from patients with glioblastoma. Thus, the herein presented data support further research on sig1R in neuro-oncology.

Modeling and Analyzing Stem-Cell Therapy toward Cancer: Evolutionary Game Theory Perspective

BACKGROUND

Immunotherapy is a recently developed method of cancer therapy, aiming to strengthen a patient's immune system in different ways to fight cancer. One of these ways is to add stem cells into the patient's body.

METHODS

The study was conducted in Kermanshah, western Iran, 2016-2017. We first modeled the interaction between cancerous and healthy cells using the concept of evolutionary game theory. System dynamics were analyzed employing replicator equations and control theory notions. We categorized the system into separate cases based on the value of the parameters. For cases in which the system converged to undesired equilibrium points, "stem-cell injection" was employed as a therapeutic suggestion. The effect of stem cells on the model was considered by reforming the replicator equations as well as adding some new parameters to the system.

RESULTS

By adjusting stem cell-related parameters, the system converged to desired equilibrium points, i.e., points with no or a scanty level of cancerous cells. In addition to the theoretical analysis, our simulation results suggested solutions were effective in eliminating cancerous cells.

CONCLUSION

This model could be applicable to different types of cancer, so we did not restrict it to a specific type of cancer. In fact, we were seeking a flexible mathematical framework that could cover different types of cancer by adjusting the system parameters.

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

Helping the Released Guardian: Drug Combinations for Supporting the Anticancer Activity of HDM2 (MDM2) Antagonists

The protein p53, known as the "Guardian of the Genome", plays an important role in maintaining DNA integrity, providing protection against cancer-promoting mutations. Dysfunction of p53 is observed in almost every cancer, with 50% of cases bearing loss-of-function mutations/deletions in the TP53 gene. In the remaining 50% of cases the overexpression of HDM2 (mouse double minute 2, human homolog) protein, which is a natural inhibitor of p53, is the most common way of keeping p53 inactive. Disruption of HDM2-p53 interaction with the use of HDM2 antagonists leads to the release of p53 and expression of its target genes, engaged in the induction of cell cycle arrest, DNA repair, senescence, and apoptosis. The induction of apoptosis, however, is restricted to only a handful of p53wt cells, and, generally, cancer cells treated with HDM2 antagonists are not efficiently eliminated. For this reason, HDM2 antagonists were tested in combinations with multiple other therapeutics in a search for synergy that would enhance the cancer eradication. This manuscript aims at reviewing the recent progress in developing strategies of combined cancer treatment with the use of HDM2 antagonists.

Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies

The importance of preexisting versus acquired drug resistance in patients with cancer treated with small-molecule tyrosine kinase inhibitors (TKI) remains controversial. The goal of this study is to provide a general estimate of the size and dynamics of a preexisting, drug-resistant tumor cell population versus a slow-growing persister population that is the precursor of acquired TKI resistance. We describe a general model of resistance development, including persister evolution and preexisting resistance, solely based on the macroscopic trajectory of tumor burden during treatment. We applied the model to 20 tumor volume trajectories of EGFR-mutant lung cancer patients treated with the TKI erlotinib. Under the assumption of only preexisting resistant cells or only persister evolution, it is not possible to explain the observed tumor trajectories with realistic parameter values. Assuming only persister evolution would require very high mutation induction rates, while only preexisting resistance would lead to very large preexisting populations of resistant cells at the initiation of treatment. However, combining preexisting resistance with persister populations can explain the observed tumor volume trajectories and yields an estimated preexisting resistant fraction varying from 10-4 to 10-1 at the time of treatment initiation for this study cohort. Our results also demonstrate that the growth rate of the resistant population is highly correlated to the time to tumor progression. These estimates of the size of the resistant and persistent tumor cell population during TKI treatment can inform combination treatment strategies such as multi-agent schedules or a combination of targeted agents and radiotherapy. SIGNIFICANCE: These findings quantify pre-existing resistance and persister cell populations, which are essential for the integration of targeted agents into the management of locally advanced disease and the timing of radiotherapy in metastatic patients.

Computational modelling of resistance and associated treatment response heterogeneity in metastatic cancers

Metastatic cancer patients invariably develop treatment resistance. Different levels of resistance lead to observed heterogeneity in treatment response. The main goal was to evaluate treatment response heterogeneity with a computation model simulating the dynamics of drug-sensitive and drug-resistant cells. Model parameters included proliferation, drug-induced death, transition and proportion of intrinsically resistant cells. The model was benchmarked with imaging metrics extracted from 39 metastatic prostate cancer patients who had ¹⁸F-NaF-PET/CT scans performed at baseline and at three cycles into chemotherapy or hormonal therapy. Two initial model assumptions were evaluated: considering only inter-patient heterogeneity and both inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells. The correlation between the median proportion of intrinsically resistant cells and baseline patient-level imaging metrics was assessed with Spearman's rank correlation coefficient. The impact of model parameters on simulated treatment response was evaluated with a sensitivity study. Treatment response after periods of six, nine, and 12 months was predicted with the model. The median predicted range of response for patients treated with both therapies was compared with a Wilcoxon rank sum test. For each patient, the time was calculated when the proportion of disease with a non-favourable response outperformed a favourable response. By taking into account inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells, the model performed significantly better ([Formula: see text]) than by taking into account only inter-patient heterogeneity ([Formula: see text]). The median proportion of intrinsically resistant cells showed a moderate correlation (ρ  =  0.55) with mean patient-level uptake, and a low correlation (ρ  =  0.36) with the dispersion of mean metastasis-level uptake in a patient. The sensitivity study showed a strong impact of the proportion of intrinsically resistant cells on model behaviour after three cycles of therapy. The difference in the median range of response (MRR) was not significant between cohorts at any time point (p   >  0.15). The median time when the proportion of disease with a non-favourable response outperformed the favourable response was eight months, for both cohorts. The model provides an insight into inter-patient and intra-patient heterogeneity in the evolution of treatment resistance.

The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer

Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor's edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.

Robustness and evolvability of heterogeneous cell populations

Biological systems are endowed with two fundamental but seemingly contradictory properties: robustness, the ability to withstand environmental fluctuations and genetic variability; and evolvability, the ability to acquire selectable and heritable phenotypic changes. Cell populations with heterogeneous genetic makeup, such as those of infectious microbial organisms or cancer, rely on their inherent robustness to maintain viability and fitness, but when encountering environmental insults, such as drug treatment, these populations are also poised for rapid adaptation through evolutionary selection. In this study, we develop a general mathematical model that allows us to explain and quantify this fundamental relationship between robustness and evolvability of heterogeneous cell populations. Our model predicts that robustness is, in fact, essential for evolvability, especially for more adverse environments, a trend we observe in aneuploid budding yeast and breast cancer cells. Robustness also compensates for the negative impact of the systems' complexity on their evolvability. Our model also provides a mathematical means to estimate the number of independent processes underlying a system's performance and identify the most generally adapted subpopulation, which may resemble the multi-drug-resistant "persister" cells observed in cancer.

Non-coding RNA networks in cancer

Thousands of unique non-coding RNA (ncRNA) sequences exist within cells. Work from the past decade has altered our perception of ncRNAs from 'junk' transcriptional products to functional regulatory molecules that mediate cellular processes including chromatin remodelling, transcription, post-transcriptional modifications and signal transduction. The networks in which ncRNAs engage can influence numerous molecular targets to drive specific cell biological responses and fates. Consequently, ncRNAs act as key regulators of physiological programmes in developmental and disease contexts. Particularly relevant in cancer, ncRNAs have been identified as oncogenic drivers and tumour suppressors in every major cancer type. Thus, a deeper understanding of the complex networks of interactions that ncRNAs coordinate would provide a unique opportunity to design better therapeutic interventions.

Stochastic model of contact inhibition and the proliferation of melanoma in situ

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL- 147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.

Sigma1 Pharmacology in the Context of Cancer

Sigma1 (also known as sigma-1 receptor, Sig1R, σ1 receptor) is a unique pharmacologically regulated integral membrane chaperone or scaffolding protein. The majority of publications on the subject have focused on the neuropharmacology of Sigma1. However, a number of publications have also suggested a role for Sigma1 in cancer. Although there is currently no clinically used anti-cancer drug that targets Sigma1, a growing body of evidence supports the potential of Sigma1 ligands as therapeutic agents to treat cancer. In preclinical models, compounds with affinity for Sigma1 have been reported to inhibit cancer cell proliferation and survival, cell adhesion and migration, tumor growth, to alleviate cancer-associated pain, and to have immunomodulatory properties. This review will highlight that although the literature supports a role for Sigma1 in cancer, several fundamental questions regarding drug mechanism of action and the physiological relevance of aberrant SIGMAR1 transcript and Sigma1 protein expression in certain cancers remain unanswered or only partially answered. However, emerging lines of evidence suggest that Sigma1 is a component of the cancer cell support machinery, that it facilitates protein interaction networks, that it allosterically modulates the activity of its associated proteins, and that Sigma1 is a selectively multifunctional drug target.

Evolutionary scalpels for dissecting tumor ecosystems

Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of "hit hard, hit early" treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

The challenges of tumor genetic diversity

The authors review and discuss the implications of genomic analyses documenting the diversity of tumors, both among patients and within individual tumors. Genetic diversity among solid tumors limits targeted therapies, because few mutations that drive tumors are both targetable and at high prevalence. Many more driver mutations and how they affect cellular signaling pathways must be identified if targeted therapy is to become widely useful. Genetic diversity within a tumor-intratumor genetic heterogeneity-makes the tumor a collection of subclones: related yet distinct cancers. Selection for pre-existing, resistant subclones by conventional or targeted therapies may explain many treatment failures. Immune therapy faces the same fundamental challenges. Nevertheless, the processes that generate and maintain heterogeneity might provide novel therapeutic targets. Addressing both types of diversity requires genomic tumor analyses linked to detailed clinical data. The trend toward sequencing restricted cancer gene panels, however, limits the ability to discover new driver mutations and assess intratumor heterogeneity. Clinical data currently collected with genomic analyses often lack critical information, substantially limiting their use in understanding tumor diversity. Now that diversity among and within tumors can no longer be ignored, research and clinical practice must adapt to take diversity into account. Cancer 2017;123:917-27. © 2016 American Cancer Society.

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt's lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.

(Chalcogen)semicarbazones and their cobalt complexes differentiate HL-60 myeloid leukaemia cells and are cytotoxic towards tumor cell lines

Cobalt complexes with semi- and thiosemicarbazones of 8-quinolinecarboxaldehyde have been synthesized and characterized by X-ray diffraction analysis. These novel complexes and a previously synthesized cobalt complex with a selenium-based selenosemicarbazone ligand showed myeloid differentiation activity on all trans retinoic acid resistant HL-60 acute myeloid leukaemia cells. They also showed varying levels of cytotoxicity on five human tumor cell lines: cervix carcinoma cells (HeLa), lung adenocarcinoma cells (A549), colorectal adenocarcinoma cells (LS-174), breast carcinoma cells (MDA-MB-361), and chronic myeloid leukaemia (K562) as well as one normal human cell line: fetal lung fibroblast cells (MRC-5). Leukaemia differentiation was most strongly induced by a metal-free oxygen ligand and the selenium ligand, whilst the latter and the cobalt(ii) complex with an oxygen ligand showed the strongest dose-dependent cytotoxic activity. In four out of five investigated tumor cell lines, it was of the same order of magnitude as cisplatin. These best compounds, however, had lower toxicity on non-transformed MRC-5 cells than cisplatin.

Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

Background

A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.

Results

To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use.

Conclusions

Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.

Reviewers

This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0143-4) contains supplementary material, which is available to authorized users.

Similar but different: distinct roles for KRAS and BRAF oncogenes in colorectal cancer development and therapy resistance

Colorectal cancer (CRC) is characterized by recurrent mutations deregulating key cell signaling cascades and providing the cancer cells with novel functional traits. Among the most frequent mutations in CRC are gain-of-function missense mutations in KRAS and BRAF. Oncogenic activation of KRAS and BRAF is mutually exclusive and occurs in approximately 40% and 10% of all CRCs, respectively. Here we summarize genetic alterations currently described in the literature and databases, indicating overlapping but also specific co-occurrences with either mutated BRAF or KRAS. We describe common and potentially specific biological functions of KRAS and BRAF oncoproteins in the intestinal epithelial cells and during initiation and progression of CRC. We discuss signal transduction networks, highlighting individual functions of oncogenic KRAS and BRAF in terms of feedback loops and their impact on treatment outcome. Finally, we give an update on current strategies of targeted therapeutic intervention in oncogenic RAS-RAF signaling networks for the treatment of metastatic CRC and outline future directions.

"Combo" nanomedicine: Co-delivery of multi-modal therapeutics for efficient, targeted, and safe cancer therapy

The dynamic and versatile nature of diseases such as cancer has been a pivotal challenge for developing efficient and safe therapies. Cancer treatments using a single therapeutic agent often result in limited clinical outcomes due to tumor heterogeneity and drug resistance. Combination therapies using multiple therapeutic modalities can synergistically elevate anti-cancer activity while lowering doses of each agent, hence, reducing side effects. Co-administration of multiple therapeutic agents requires a delivery platform that can normalize pharmacokinetics and pharmacodynamics of the agents, prolong circulation, selectively accumulate, specifically bind to the target, and enable controlled release in target site. Nanomaterials, such as polymeric nanoparticles, gold nanoparticles/cages/shells, and carbon nanomaterials, have the desired properties, and they can mediate therapeutic effects different from those generated by small molecule drugs (e.g., gene therapy, photothermal therapy, photodynamic therapy, and radiotherapy). This review aims to provide an overview of developing multi-modal therapies using nanomaterials ("combo" nanomedicine) along with the rationale, up-to-date progress, further considerations, and the crucial roles of interdisciplinary approaches.

Modeling Tumor Clonal Evolution for Drug Combinations Design

Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure towards drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review, we discuss promising opportunities these inter-disciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.

Quantifying Clonal and Subclonal Passenger Mutations in Cancer Evolution

The vast majority of mutations in the exome of cancer cells are passengers, which do not affect the reproductive rate of the cell. Passengers can provide important information about the evolutionary history of an individual cancer, and serve as a molecular clock. Passengers can also become targets for immunotherapy or confer resistance to treatment. We study the stochastic expansion of a population of cancer cells describing the growth of primary tumors or metastatic lesions. We first analyze the process by looking forward in time and calculate the fixation probabilities and frequencies of successive passenger mutations ordered by their time of appearance. We compute the likelihood of specific evolutionary trees, thereby informing the phylogenetic reconstruction of cancer evolution in individual patients. Next, we derive results looking backward in time: for a given subclonal mutation we estimate the number of cancer cells that were present at the time when that mutation arose. We derive exact formulas for the expected numbers of subclonal mutations of any frequency. Fitting this formula to cancer sequencing data leads to an estimate for the ratio of birth and death rates of cancer cells during the early stages of clonal expansion.

Cancer is the consequence of an evolutionary process, which lasts several decades, is impossible to observe during most of its time, and only becomes apparent in late stages. We use mathematical modeling to shed light on the evolutionary dynamics of cancer by studying the accumulation of passenger mutations. We show that the frequencies obtained by passenger mutations depend strongly on the ratio of death and birth rates of cancer cells. We use genetic data of colorectal cancer to estimate this important quantity in vivo. We estimate the size of the cancer cell population that was present when a specific mutation first emerged. Our theory informs the analysis of cancer sequencing data and the phylogenetic reconstruction of cancer evolution.

Toward a Better Understanding of the Complexity of Cancer Drug Resistance

Resistance to anticancer drugs is a complex process that results from alterations in drug targets; development of alternative pathways for growth activation; changes in cellular pharmacology, including increased drug efflux; regulatory changes that alter differentiation pathways or pathways for response to environmental adversity; and/or changes in the local physiology of the cancer, such as blood supply, tissue hydrodynamics, behavior of neighboring cells, and immune system response. All of these specific mechanisms are facilitated by the intrinsic hallmarks of cancer, such as tumor cell heterogeneity, redundancy of growth-promoting pathways, increased mutation rate and/or epigenetic alterations, and the dynamic variation of tumor behavior in time and space. Understanding the relative contribution of each of these factors is further complicated by the lack of adequate in vitro models that mimic clinical cancers. Several strategies to use current knowledge of drug resistance to improve treatment of cancer are suggested.

Vertical suppression of the EGFR pathway prevents onset of resistance in colorectal cancers

Molecular targeted drugs are clinically effective anti-cancer therapies. However, tumours treated with single agents usually develop resistance. Here we use colorectal cancer (CRC) as a model to study how the acquisition of resistance to EGFR-targeted therapies can be restrained. Pathway-oriented genetic screens reveal that CRC cells escape from EGFR blockade by downstream activation of RAS-MEK signalling. Following treatment of CRC cells with anti-EGFR, anti-MEK or the combination of the two drugs, we find that EGFR blockade alone triggers acquired resistance in weeks, while combinatorial treatment does not induce resistance. In patient-derived xenografts, EGFR-MEK combination prevents the development of resistance. We employ mathematical modelling to provide a quantitative understanding of the dynamics of response and resistance to these single and combination therapies. Mechanistically, we find that the EGFR-MEK Combo blockade triggers Bcl-2 and Mcl-1 downregulation and initiates apoptosis. These results provide the rationale for clinical trials aimed at preventing rather than intercepting resistance.

Cancer patients often respond well to primary treatment but then develop resistance. Here, Misale et al. show that dual treatment with EGFR and MEK inhibitors block resistance in mice containing patient-derived xenografts and provide a mathematical model that describes the temporal development of resistant tumour clones.

A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity

Most cancers in humans are large, measuring centimetres in diameter, and composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is that virtually every neoplastic cell within a large tumour often contains the same core set of genetic alterations, with heterogeneity confined to mutations that emerge late during tumour growth. How such alterations expand within the spatially constrained three-dimensional architecture of a tumour, and come to dominate a large, pre-existing lesion, has been unclear. Here we describe a model for tumour evolution that shows how short-range dispersal and cell turnover can account for rapid cell mixing inside the tumour. We show that even a small selective advantage of a single cell within a large tumour allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides insights into spatial and temporal aspects of tumour growth, but also suggests that targeting short-range cellular migratory activity could have marked effects on tumour growth rates.

The value of monitoring to control evolving populations

Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers

Targeted therapies provide an exciting new approach to combat human cancer. The immediate effect is a dramatic reduction in disease burden, but in most cases, the tumor returns as a consequence of resistance. Various mechanisms for the evolution of resistance have been implicated, including mutation of target genes and activation of other drivers. There is increasing evidence that the reason for failure of many targeted treatments is a small preexisting subpopulation of resistant cells; however, little is known about the genetic composition of this resistant subpopulation. Using the novel approach of ordering the resistant subclones according to their time of appearance, here we describe the full spectrum of resistance mutations present in a metastatic lesion. We calculate the expected and median number of cells in each resistant subclone. Surprisingly, the ratio of the medians of successive resistant clones is independent of any parameter in our model; for example, the median of the second clone divided by the median of the first is √2-1. We find that most radiographically detectable lesions harbor at least 10 resistant subclones. Our predictions are in agreement with clinical data on the relative sizes of resistant subclones obtained from liquid biopsies of colorectal cancer patients treated with epidermal growth factor receptor (EGFR) blockade. Our theory quantifies the genetic heterogeneity of resistance that exists before treatment and provides information to design treatment strategies that aim to control resistance.

A new model of time scheme for progression of colorectal cancer

BACKGROUND

tumourigenesis can be regarded as an evolutionary process, in which the transformation of a normal cell into a tumour cell involves a number of limiting genetic and epigenetic events. To study the progression process, time schemes have been proposed for studying the process of colorectal cancer based on extensive clinical investigations. Moreover, a number of mathematical models have been designed to describe this evolutionary process. These models assumed that the mutation rate of genes is constant during different stages. However, it has been pointed that the subsequent driver mutations appear faster than the previous ones and the cumulative time to have more driver mutations grows with the growing number of gene mutations. Thus it is still a challenge to calculate the time when the first mutation occurs and to determine the influence of tumour size on the mutation rate.

RESULTS

In this work we present a general framework to remedy the shortcoming of existing models. Rather than considering the information of gene mutations based on a population of patients, we for the first time determine the values of the selective advantage of cancer cells and initial mutation rate for individual patients. The averaged values of doubling time and selective advantage coefficient determined by our model are consistent with the predictions made by the published models. Our calculation showed that the values of biological parameters, such as the selective advantage coefficient, initial mutation rate and cell doubling time diversely depend on individuals. Our model has successfully predicted the values of several important parameters in cancer progression, such as the selective advantage coefficient, initial mutation rate and cell doubling time. In addition, experimental data validated our predicted initial mutation rate and cell doubling time.

CONCLUSIONS

The introduced new parameter makes our proposed model more flexible to fix various types of information based on different patients in cancer progression.

High-definition reconstruction of clonal composition in cancer

The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.

The impact of cell density and mutations in a model of multidrug resistance in solid tumors

In this paper we develop a mathematical framework for describing multidrug resistance in cancer. To reflect the complexity of the underlying interplay between cancer cells and the therapeutic agent, we assume that the resistance level is a continuous parameter. Our model is written as a system of integro-differential equations that are parameterized by the resistance level. This model incorporates the cell density and mutation dependence. Analysis and simulations of the model demonstrate how the dynamics evolves to a selection of one or more traits corresponding to different levels of resistance. The emerging limit distribution with nonzero variance is the desirable modeling outcome as it represents tumor heterogeneity.

Mathematical models for translational and clinical oncology

In the context of translational and clinical oncology, mathematical models can provide novel insights into tumor-related processes and can support clinical oncologists in the design of the treatment regime, dosage, schedule, toxicity and drug-sensitivity. In this review we present an overview of mathematical models in this field beginning with carcinogenesis and proceeding to the different cancer treatments. By doing so we intended to highlight recent developments and emphasize the power of such theoretical work.We first highlight mathematical models for translational oncology comprising epidemiologic and statistical models, mechanistic models for carcinogenesis and tumor growth, as well as evolutionary dynamics models which can help to describe and overcome a major problem in the clinic: therapy resistance. Next we review models for clinical oncology with a special emphasis on therapy including chemotherapy, targeted therapy, radiotherapy, immunotherapy and interaction of cancer cells with the immune system.As evident from the published studies, mathematical modeling and computational simulation provided valuable insights into the molecular mechanisms of cancer, and can help to improve diagnosis and prognosis of the disease, and pinpoint novel therapeutic targets.

Multiplex picodroplet digital PCR to detect KRAS mutations in circulating DNA from the plasma of colorectal cancer patients

BACKGROUND

Multiplex digital PCR (dPCR) enables noninvasive and sensitive detection of circulating tumor DNA with performance unachievable by current molecular-detection approaches. Furthermore, picodroplet dPCR facilitates simultaneous screening for multiple mutations from the same sample.

METHODS

We investigated the utility of multiplex dPCR to screen for the 7 most common mutations in codons 12 and 13 of the KRAS (Kirsten rat sarcoma viral oncogene homolog) oncogene from plasma samples of patients with metastatic colorectal cancer. Fifty plasma samples were tested from patients for whom the primary tumor biopsy tissue DNA had been characterized by quantitative PCR.

RESULTS

Tumor characterization revealed that 19 patient tumors had KRAS mutations. Multiplex dPCR analysis of the plasma DNA prepared from these samples identified 14 samples that matched the mutation identified in the tumor, 1 sample contained a different KRAS mutation, and 4 samples had no detectable mutation. Among the tumor samples that were wild type for KRAS, 2 KRAS mutations were identified in the corresponding plasma samples. Duplex dPCR (i.e., wild-type and single-mutation assay) was also used to analyze plasma samples from patients with KRAS-mutated tumors and 5 samples expected to contain the BRAF (v-raf murine sarcoma viral oncogene homolog B) V600E mutation. The results for the duplex analysis matched those for the multiplex analysis for KRAS-mutated samples and, owing to its higher sensitivity, enabled detection of 2 additional samples with low levels of KRAS-mutated DNA. All 5 samples with BRAF mutations were detected.

CONCLUSIONS

This work demonstrates the clinical utility of multiplex dPCR to screen for multiple mutations simultaneously with a sensitivity sufficient to detect mutations in circulating DNA obtained by noninvasive blood collection.