Drug dependence in cancer is exploitable by optimally constructed treatment holidays

PKN2 Is a Dependency of the Mesenchymal-like Cancer Cell State

Cancer cells exploit a mesenchymal-like transcriptional state (MLS) to survive drug treatments. Although the MLS is well characterized, few therapeutic vulnerabilities targeting this program have been identified. In this study, we systematically identify the dependency network of mesenchymal-like cancers through an analysis of gene essentiality scores in ∼800 cancer cell lines, nominating a poorly studied kinase, PKN2, as a top therapeutic target of the MLS. Coessentiality relationships, biochemical experiments, and genomic analyses of patient tumors revealed that PKN2 promotes mesenchymal-like cancer growth through a PKN2-SAV1-TAZ signaling mechanism. Notably, pairing genetic PKN2 inhibition with clinically relevant targeted therapies against EGFR, KRAS, and BRAF suppresses drug resistance by depleting mesenchymal-like drug-tolerant persister cells. These findings provide evidence that PKN2 is a core regulator of the Hippo tumor suppressor pathway and highlight the potential of PKN2 inhibition as a generalizable therapeutic strategy to overcome drug resistance driven by the MLS across cancer contexts. Significance: This work identifies PKN2 as a core member of the Hippo signaling pathway, and its inhibition blocks YAP/TAZ-driven tumorigenesis. Furthermore, this study discovers PKN2-TAZ as arguably the most selective dependency of mesenchymal-like cancers and supports specific inhibition of PKN2 as a provocative strategy to overcome drug resistance in diverse cancer contexts. See related commentary by Shen and Tan, p. 458.

Validation of polymorphic Gompertzian model of cancer through in vitro and in vivo data

Mathematical modeling plays an important role in our understanding and targeting therapy resistance mechanisms in cancer. The polymorphic Gompertzian model, analyzed theoretically and numerically by Viossat and Noble to demonstrate the benefits of adaptive therapy in metastatic cancer, describes a heterogeneous cancer population consisting of therapy-sensitive and therapy-resistant cells. In this study, we demonstrate that the polymorphic Gompertzian model successfully captures trends in both in vitro and in vivo data on non-small cell lung cancer (NSCLC) dynamics under treatment. Additionally, for the in vivo data of tumor dynamics in patients undergoing treatment, we compare the goodness of fit of the polymorphic Gompertzian model to that of the classical oncologic models, which were previously identified as the models that fit this data best. We show that the polymorphic Gompertzian model can successfully capture the U-shape trend in tumor size during cancer relapse, which can not be fitted with the classical oncologic models. In general, the polymorphic Gompertzian model corresponds well to both in vitro and in vivo real-world data, suggesting it as a candidate for improving the efficacy of cancer therapy, for example, through evolutionary/adaptive therapies.

Current and emerging treatment options for BRAFV600-mutant melanoma

INTRODUCTION

BRAF mutations are the most common driver mutation in cutaneous melanoma, present in 40% of cases. Rationally designed BRAF targeted therapy (TT) has been developed in response to this, and alongside immune checkpoint inhibitors (ICI), forms the backbone of systemic therapy options for BRAF-mutant melanoma. Various therapeutic approaches have been studied in the neoadjuvant, adjuvant and advanced settings, and there is a wealth of information to guide clinicians managing these patients. Despite this, certain challenges remain.

AREAS COVERED

We reviewed the available literature regarding BRAF mutation types and resistance mechanisms, neoadjuvant and adjuvant approaches for patients with early-stage disease, management of advanced disease, including patients with brain metastases, as well as identified areas of further research.

EXPERT OPINION

Although there is a significant amount of literature to guide the management of BRAF-mutant melanoma, several questions remain. Thus far, the management of stage III BRAF-mutant patients following neoadjuvant ICI, treatment de-escalation in long-term TT responders in the advanced setting and the management of symptomatic brain metastases remain areas of debate. Further work on predictive and prognostic biomarkers for patients with BRAF-mutant melanoma patients will assist in clinical decision-making.

Survival Resonances during Fractional Killing of Cell Populations

Fractional killing in response to drugs is a hallmark of nongenetic cellular heterogeneity. Yet how individual lineages evade drug treatment, as observed in bacteria and cancer cells, is not quantitatively understood. We study a stochastic population model with age-dependent division and death rates, allowing for persistence. In periodic drug environments, we discover peaks in the survival probabilities at division or death times that are multiples of the environment duration. The survival resonances are unseen in unstructured populations and are amplified by persistence.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. With clusterCleaver, ESAM and BST2/tetherin were experimentally validated as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification of surface markers for tracking and isolating transcriptomically distinct subpopulations within cell lines. This tool paves the way for studies on coexisting cancer cell subpopulations in well-defined in vitro systems.

To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.