A modified fluctuation-test framework characterizes the population dynamics and mutation rate of colorectal cancer persister cells

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance

There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.

Intermittent or Continuous Panitumumab Plus Fluorouracil, Leucovorin, and Irinotecan for First-Line Treatment of RAS and BRAF Wild-Type Metastatic Colorectal Cancer: The IMPROVE Trial

PURPOSE

To investigate whether intermittent treatment after an induction phase of first-line schedule of fluorouracil, leucovorin, and irinotecan (FOLFIRI) plus panitumumab (PAN) prevents or delays the onset of resistance and improves safety and compliance with treatment in patients with unresectable RAS/BRAF wild-type (wt) metastatic colorectal cancer (mCRC).

PATIENTS AND METHODS

IMPROVE (ClinicalTrials.gov identifier: NCT04425239) was an open-label, multicenter, randomized phase II noncomparative trial. Patients with unresectable RAS/BRAF wt mCRC were randomly assigned (1:1) to receive FOLFIRI plus PAN continuously until progression (arm A) or intermittently, with treatment-free intervals (arm B) until progression on treatment, toxicity, or death. The primary end point was progression-free survival on treatment (PFSot) at 12 months. Assuming a null hypothesis of median PFSot time ≤7 months and target PFSot ≥10 months, 65 patients per arm were needed to achieve 80% power and 10% type I error, according to the binomial test.

RESULTS

Between May 2018 and June 2021, 69 patients were randomly assigned to arm A and 68 to arm B. The median number of treatment cycles was 13 in arm A and 16 in arm B. At a median follow-up of 43.2 months (IQR, 35.0-50.5), median PFSot was 11.2 and 17.5 months with 12-month PFSot rates of 45.7% and 58.5%, for arms A and B, respectively. The overall response rates were 68.1% and 61.2%, and median overall survival rates were 36.3 and 35.1 months in arms A and B, respectively. The overall rate of grade >2 skin PAN-related adverse events was 30.3% in arm A and 17.9% in arm B.

CONCLUSION

Intermittent FOLFIRI plus PAN after the induction phase was feasible, and the primary end point was met with reduced toxicity while allowing patients more time off treatment.

A stochastic population model for the impact of cancer cell dormancy on therapy success

Therapy evasion - and subsequent disease progression - is a major challenge in current oncology. An important role in this context seems to be played by various forms of cancer cell dormancy. For example, therapy-induced dormancy, over short timescales, can create serious obstacles to aggressive treatment approaches such as chemotherapy, and long-term dormancy may lead to relapses and metastases even many years after an initially successful treatment. In this paper, we focus on individual cancer cells switching into and out of a dormant state both spontaneously as well as in response to treatment. We introduce an idealized mathematical model, based on stochastic agent-based interactions, for the dynamics of cancer cell populations involving individual short-term dormancy, and allow for a range of (multi-drug) therapy protocols. Our analysis - based on simulations of the many-particle limit - shows that in our model, depending on the specific underlying dormancy mechanism, even a small initial population (of explicitly quantifiable size) of dormant cells can lead to therapy failure under classical single-drug treatments that would successfully eradicate the tumour in the absence of dormancy. We further investigate and quantify the effectiveness of several multi-drug regimes (manipulating dormant cancer cells in specific ways, including increasing or decreasing resuscitation rates or targeting dormant cells directly). Relying on quantitative results for concrete simulation parameters, we provide some general basic rules for the design of (multi-)drug treatment protocols depending on the types and processes of dormancy mechanisms present in the population.

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

The Potential Links between lncRNAs and Drug Tolerance in Lung Adenocarcinoma

Lung cancer patients treated with targeted therapies frequently respond well but invariably relapse due to the development of drug resistance. Drug resistance is in part mediated by a subset of cancer cells termed "drug-tolerant persisters" (DTPs), which enter a dormant, slow-cycling state that enables them to survive drug exposure. DTPs also exhibit stem cell-like characteristics, broad epigenetic reprogramming, altered metabolism, and a mutagenic phenotype mediated by adaptive mutability. While several studies have characterised the transcriptional changes that lead to the altered phenotypes exhibited in DTPs, these studies have focused predominantly on protein coding changes. As long non-coding RNAs (lncRNAs) are also implicated in the phenotypes altered in DTPs, it is likely that they play a role in the biology of drug tolerance. In this review, we outline how lncRNAs may contribute to the key characteristics of DTPs, their potential roles in tolerance to targeted therapies, and the emergence of genetic resistance in lung adenocarcinoma.

The impact of dormancy on evolutionary branching

In this paper, we investigate the consequences of dormancy in the 'rare mutation' and 'large population' regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the Polymorphic Evolution Sequence of the population, based on a previous work by Baar and Bovier (2018). After passing to a second 'small mutations' limit, we arrive at the Canonical Equation of Adaptive Dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and Méléard (2011). The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, quite an intuitive picture emerges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the 'speed of adaptation' of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations.

Unveiling the role of cellular dormancy in cancer progression and recurrence

PURPOSE OF REVIEW

Cellular dormancy is a major contributor to cancer progression and recurrence. This review explores recent findings on the molecular mechanisms implicated in cancer dormancy and investigates potential strategies to improve therapeutic interventions.

RECENT FINDINGS

Research on cancer dormancy reveals a complex and multifaceted phenomenon. Providing a latent reservoir of tumor cells with reduced proliferation and enhanced drug-tolerance, dormant cancer cells emerge from a clonally diverse population after therapy or at metastatic sites. These cells exhibit distinct transcriptional and epigenetic profiles, involving the downregulation of Myc and mechanistic target of rapamycin (mTOR) pathways, and the induction of autophagy. Senescence traits, under the control of factors such as p53, also contribute significantly. The tumor microenvironment can either promote or prevent dormancy establishment, notably through the involvement of T and NK cells within the dormant tumor niche. Strategies to combat dormancy-related relapse include direct elimination of dormant tumor cells, sustaining dormancy to prolong survival, or awakening dormant cells to re-sensitize them to antiproliferative drugs.

SUMMARY

Improving our understanding of cancer dormancy at primary and secondary sites provides valuable insights into patient care and relapse prevention.

Genetic and non-genetic drug resistance: Darwin or Lamarck?

Drug resistance represents a major limitation to the long-term efficacy of anti-cancer treatments. The commonly accepted view is that the selection of inheritable genetic mechanisms governs the development of secondary resistance. However, compelling evidence suggests an important role for adaptive cell plasticity and non-genetic mechanisms in the development of therapy resistance. The two phenomena are not mutually exclusive and the interplay between genetic and non-genetic mechanisms may affect tumor evolution during treatment. A broader characterization of the genetic and non-genetic mechanisms of drug resistance may pave the way for more precise and effective therapeutic strategies to overcome resistance.

Lineage Plasticity: The New Cancer Hallmark on the Block

Plasticity refers to the ability of cells to adopt a spectrum of states or phenotypes. In cancer, it is a critical contributor to tumor initiation, progression, invasiveness, and therapy resistance, and it has recently been recognized as an emerging cancer hallmark. Plasticity can occur as a result of cell-intrinsic factors (e.g., genetic, transcriptional, or epigenetic fluctuations), or through cell-extrinsic cues (e.g., signaling from components of the tumor microenvironment or selective pressure from therapy). Over the past decade, technological advances, analysis of patient samples, and studies in mouse model systems have led to a deeper understanding of how such plastic states come about. In this review, we discuss: (i) the definition of plasticity; (ii) methods to measure and quantify plasticity; (iii) the clinical relevance of plasticity; and (iv) therapeutic hypotheses to modulate plasticity in the clinic.

Drug-tolerant persister cells in cancer: the cutting edges and future directions

Drug-tolerant persister (DTP) cell populations were originally discovered in antibiotic-resistant bacterial biofilms. Similar populations with comparable features have since been identified among cancer cells and have been linked with treatment resistance that lacks an underlying genomic alteration. Research over the past decade has improved our understanding of the biological roles of DTP cells in cancer, although clinical knowledge of the role of these cells in treatment resistance remains limited. Nonetheless, targeting this population is anticipated to provide new treatment opportunities. In this Perspective, we aim to provide a clear definition of the DTP phenotype, discuss the underlying characteristics of these cells, their biomarkers and vulnerabilities, and encourage further research on DTP cells that might improve our understanding and enable the development of more effective anticancer therapies.

Transcriptional signature of early cisplatin drug-tolerant persister cells in lung adenocarcinoma

Resistance to cisplatin is the main cause of treatment failure in lung adenocarcinoma. Drug-tolerant-persister (DTP) cells are responsible for intrinsic resistance, since they survive the initial cycles of treatment, representing a reservoir for the emergence of clones that display acquired resistance. Although the molecular mechanisms of DTP cells have been described, few studies have investigated the earliest molecular alterations of DTP cells in intrinsic resistance to cisplatin. In this work, we report a gene expression signature associated with the emergence of cisplatin-DTP cells in lung adenocarcinoma cell lines. After a single exposure to cisplatin, we sequenced the transcriptome of cisplatin-DTPs to identify differentially expressed genes. Bioinformatic analysis revealed that early cisplatin-DTP cells deregulate metabolic and proliferative pathways to survive the drug insult. Interaction network analysis identified three highly connected submodules in which SOCS1 had a significant participation in controlling the proliferation of cisplatin-DTP cells. Expression of the candidate genes and their corresponding protein was validated in lung adenocarcinoma cell lines. Importantly, the expression level of SOCS1 was different between CDDP-susceptible and CDDP-resistant lung adenocarcinoma cell lines. Moreover, knockdown of SOCS1 in the CDDP-resistant cell line partially promoted its susceptibility to CDDP. Finally, the clinical relevance of the candidate genes was analyzed in silico, according to the overall survival of cisplatin-treated patients from The Cancer Genome Atlas. Survival analysis showed that downregulation or upregulation of the selected genes was associated with overall survival. The results obtained indicate that these genes could be employed as predictive biomarkers or potential targets to improve the effectiveness of CDDP treatment in lung cancer patients.

An Observatory for the MET Oncogene: A Guide for Targeted Therapies

The MET proto-oncogene encodes a pivotal tyrosine kinase receptor, binding the hepatocyte growth factor (HGF, also known as scatter factor, SF) and governing essential biological processes such as organogenesis, tissue repair, and angiogenesis. The pleiotropic physiological functions of MET explain its diverse role in cancer progression in a broad range of tumors; genetic/epigenetic alterations of MET drive tumor cell dissemination, metastasis, and acquired resistance to conventional and targeted therapies. Therefore, targeting MET emerged as a promising strategy, and many efforts were devoted to identifying the optimal way of hampering MET signaling. Despite encouraging results, however, the complexity of MET's functions in oncogenesis yields intriguing observations, fostering a humbler stance on our comprehension. This review explores recent discoveries concerning MET alterations in cancer, elucidating their biological repercussions, discussing therapeutic avenues, and outlining future directions. By contextualizing the research question and articulating the study's purpose, this work navigates MET biology's intricacies in cancer, offering a comprehensive perspective.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Multiple cancers escape from multiple MAPK pathway inhibitors and use DNA replication stress signaling to tolerate aberrant cell cycles

Many cancers harbor pro-proliferative mutations of the mitogen-activated protein kinase (MAPK) pathway. In BRAF-driven melanoma cells treated with BRAF inhibitors, subpopulations of cells escape drug-induced quiescence through a nongenetic manner of adaptation and resume slow proliferation. Here, we found that this phenomenon is common to many cancer types driven by EGFR, KRAS, or BRAF mutations in response to multiple, clinically approved MAPK pathway inhibitors. In 2D cultures and 3D spheroid models of various cancer cell lines, a subset of cells escaped drug-induced quiescence within 4 days to resume proliferation. These "escapee" cells exhibited DNA replication deficits, accumulated DNA lesions, and mounted a stress response that depended on the ataxia telangiectasia and RAD3-related (ATR) kinase. We further identified that components of the Fanconi anemia (FA) DNA repair pathway are recruited to sites of mitotic DNA synthesis (MiDAS) in escapee cells, enabling successful completion of cell division. Analysis of patient tumor samples and clinical data correlated disease progression with an increase in DNA replication stress response factors. Our findings suggest that many MAPK pathway-mutant cancers rapidly escape drug action and that suppressing early stress tolerance pathways may achieve more durable clinical responses to MAPK pathway inhibitors.

Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

Sequential mutations in exponentially growing populations

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Multiple cancer types rapidly escape from multiple MAPK inhibitors to generate mutagenesis-prone subpopulations

Many cancers harbor pro-proliferative mutations of the mitogen-activated protein kinase (MAPK) pathway and many targeted inhibitors now exist for clinical use, but drug resistance remains a major issue. We recently showed that BRAF-driven melanoma cells treated with BRAF inhibitors can non-genetically adapt to drug within 3-4 days to escape quiescence and resume slow proliferation. Here we show that this phenomenon is not unique to melanomas treated with BRAF inhibitors but rather is widespread across many clinical MAPK inhibitors and cancer types driven by EGFR, KRAS, and BRAF mutations. In all treatment contexts examined, a subset of cells can escape drug-induced quiescence within four days to resume proliferation. These escapee cells broadly experience aberrant DNA replication, accumulate DNA lesions, spend longer in G2-M cell cycle phases, and mount an ATR-dependent stress response. We further identify the Fanconi anemia (FA) DNA repair pathway as critical for successful mitotic completion in escapees. Long-term cultures, patient samples, and clinical data demonstrate a broad dependency on ATR- and FA-mediated stress tolerance. Together, these results highlight the pervasiveness with which MAPK-mutant cancers are able to rapidly escape drug and the importance of suppressing early stress tolerance pathways to potentially achieve more durable clinical responses to targeted MAPK pathway inhibitors.

Autophagy, molecular chaperones, and unfolded protein response as promoters of tumor recurrence

Tumor recurrence is a paradoxical function of a machinery, whereby a small proportion of the cancer cell population enters a resistant, dormant state, persists long-term in this condition, and then transitions to proliferation. The dormant phenotype is typical of cancer stem cells, tumor-initiating cells, disseminated tumor cells, and drug-tolerant persisters, which all demonstrate similar or even equivalent properties. Cancer cell dormancy and its conversion to repopulation are regulated by several protein signaling systems that inhibit or induce cell proliferation and provide optimal interrelations between cancer cells and their special niche; these systems act in close connection with tumor microenvironment and immune response mechanisms. During dormancy and reawakening periods, cell proteostasis machineries, autophagy, molecular chaperones, and the unfolded protein response are recruited to protect refractory tumor cells from a wide variety of stressors and therapeutic insults. Proteostasis mechanisms functionally or even physically interfere with the main regulators of tumor relapse, and the significance of these interactions and implications in the tumor recurrence phases are discussed in this review.

Dormancy, stemness, and therapy resistance: interconnected players in cancer evolution

The biological complexity of cancer represents a tremendous clinical challenge, resulting in the frequent failure of current treatment protocols. In the rapidly evolving scenario of a growing tumor, anticancer treatments impose a drastic perturbation not only to cancer cells but also to the tumor microenvironment, killing a portion of the cells and inducing a massive stress response in the survivors. Consequently, treatments can act as a double-edged sword by inducing a temporary response while laying the ground for therapy resistance and subsequent disease progression. Cancer cell dormancy (or quiescence) is a central theme in tumor evolution, being tightly linked to the tumor's ability to survive cytotoxic challenges, metastasize, and resist immune-mediated attack. Accordingly, quiescent cancer cells (QCCs) have been detected in virtually all the stages of tumor development. In recent years, an increasing number of studies have focused on the characterization of quiescent/therapy resistant cancer cells, unveiling QCCs core transcriptional programs, metabolic plasticity, and mechanisms of immune escape. At the same time, our partial understanding of tumor quiescence reflects the difficulty to identify stable QCCs biomarkers/therapeutic targets and to control cancer dormancy in clinical settings. This review focuses on recent discoveries in the interrelated fields of dormancy, stemness, and therapy resistance, discussing experimental evidences in the frame of a nonlinear dynamics approach, and exploring the possibility that tumor quiescence may represent not only a peril but also a potential therapeutic resource.

An mTOR feedback loop mediates the 'flare' ('rebound') response to MET tyrosine kinase inhibition

Targeted therapy significantly impairs tumour growth but suffers from limitations, among which the 'flare' ('rebound') effect. Among cancers driven by tyrosine kinase receptors, those relying on alterations of the MET oncogene benefit from treatment by specific inhibitors. Previously, we reported that discontinuation of MET tyrosine kinase receptor inhibition causes 'rebound' activation of the oncogene, with a post-treatment transient hyperphosphorylation phase that culminates into a dramatic increase in cancer cell proliferation. The molecular mechanisms behind the 'MET burst' after treatment cessation are unknown but critically important for patients. Here we identify a positive feedback loop mediated by the AKT/mTOR pathway leading to (a) enhanced MET translation by activating p70S6K and 4EBP1 and (b) MET hyper-phosphorylation by inactivation of the tyrosine-phosphatase PTP1B. The latter effect is due to m-TOR-driven PTP1B phosphorylation of the inhibitory residues Ser⁵⁰ and Ser³⁷⁸. These data provide in vitro evidence for the use of mTOR inhibitors to prevent the 'flare effect' in MET targeted therapy, with potential applicative ramifications for patient clinical management.

Stochastic Fluctuations Drive Non-genetic Evolution of Proliferation in Clonal Cancer Cell Populations

Evolutionary dynamics allows us to understand many changes happening in a broad variety of biological systems, ranging from individuals to complete ecosystems. It is also behind a number of remarkable organizational changes that happen during the natural history of cancers. These reflect tumour heterogeneity, which is present at all cellular levels, including the genome, proteome and phenome, shaping its development and interrelation with its environment. An intriguing observation in different cohorts of oncological patients is that tumours exhibit an increased proliferation as the disease progresses, while the timescales involved are apparently too short for the fixation of sufficient driver mutations to promote explosive growth. Here, we discuss how phenotypic plasticity, emerging from a single genotype, may play a key role and provide a ground for a continuous acceleration of the proliferation rate of clonal populations with time. We address this question by combining the analysis of real-time growth of non-small-cell lung carcinoma cells (N-H460) together with stochastic and deterministic mathematical models that capture proliferation trait heterogeneity in clonal populations to elucidate the contribution of phenotypic transitions on tumour growth dynamics.