Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer

Threshold-awareness in adaptive cancer therapy

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.

In Vitro Microevolution and Co-Selection Assessment of Amoxicillin and Cefotaxime Impact on Escherichia coli Resistance Development

The global spread of antimicrobial resistance has become a prominent issue in both veterinary and public health in the 21st century. The extensive use of amoxicillin, a beta-lactam antibiotic, and consequent resistance development are particularly alarming in food-producing animals, with a focus on the swine and poultry sectors. Another beta-lactam, cefotaxime, is widely utilized in human medicine, where the escalating resistance to third- and fourth-generation cephalosporins is a major concern. The aim of this study was to simulate the development of phenotypic and genotypic resistance to beta-lactam antibiotics, focusing on amoxicillin and cefotaxime. The investigation of the minimal inhibitory concentrations (MIC) of antibiotics was performed at 1×, 10×, 100×, and 1000× concentrations using the modified microbial evolution and growth arena (MEGA-plate) method. Our results indicate that amoxicillin significantly increased the MIC values of several tested antibiotics, except for oxytetracycline and florfenicol. In the case of cefotaxime, this increase was observed in all classes. A total of 44 antimicrobial resistance genes were identified in all samples. Chromosomal point mutations, particularly concerning cefotaxime, revealed numerous complex mutations, deletions, insertions, and single nucleotide polymorphisms (SNPs) that were not experienced in the case of amoxicillin. The findings suggest that, regarding amoxicillin, the point mutation of the acrB gene could explain the observed MIC value increases due to the heightened activity of the acrAB-tolC efflux pump system. However, under the influence of cefotaxime, more intricate processes occurred, including complex amino acid substitutions in the ampC gene promoter region, increased enzyme production induced by amino acid substitutions and SNPs, as well as mutations in the acrR and robA repressor genes that heightened the activity of the acrAB-tolC efflux pump system. These changes may contribute to the significant MIC increases observed for all tested antibiotics. The results underscore the importance of understanding cross-resistance development between individual drugs when choosing clinical alternative drugs. The point mutations in the mdtB and emrR genes may also contribute to the increased activity of the mdtABC-tolC and emrAB-tolC pump systems against all tested antibiotics. The exceptionally high mutation rate induced by cephalosporins justifies further investigations to clarify the exact mechanism behind.

The evolution of diverse antimicrobial responses in vancomycin-intermediate Staphylococcus aureus and its therapeutic implications

Staphylococcus aureus causes endocarditis, osteomyelitis, and bacteremia. Clinicians often prescribe vancomycin as an empiric therapy to account for methicillin-resistant S. aureus (MRSA) and narrow treatment based on culture susceptibility results. However, these results reflect a single time point before empiric treatment and represent a limited subset of the total bacterial population within the patient. Thus, while they may indicate that the infection is susceptible to a particular drug, this recommendation may no longer be accurate during therapy. Here, we addressed how antibiotic susceptibility changes over time by accounting for evolution. We evolved 18 methicillin-susceptible S. aureus (MSSA) populations under increasing vancomycin concentrations until they reached intermediate resistance levels. Sequencing revealed parallel mutations that affect cell membrane stress response and cell-wall biosynthesis. The populations exhibited repeated cross-resistance to daptomycin and varied responses to meropenem, gentamicin, and nafcillin. We accounted for this variability by deriving likelihood estimates that express a population's probability of exhibiting a drug response following vancomycin treatment. Our results suggest antistaphylococcal penicillins are preferable first-line treatments for MSSA infections but also highlight the inherent uncertainty that evolution poses to effective therapies. Infections may take varied evolutionary paths; therefore, considering evolution as a probabilistic process should inform our therapeutic choices.

Characterization of dabrafenib-induced drug insensitivity via cellular barcoding and collateral sensitivity to second-line therapeutics

Drug insensitivity is arguably one of the biggest challenges in cancer therapeutics. Although effective therapeutic solutions in cancer are limited due to the emergence of drug insensitivity, exploiting evolutionary understanding in this context can provide potential second-line therapeutics sensitizing the drug insensitive populations. Targeted therapeutic agent dabrafenib is used to treat CRC patients with BRAF V600E genotype and insensitivity to dabrafenib is often observed. Understanding underlying clonal architecture of dabrafenib-induced drug insensitivity and identification of potential second-line therapeutics that could sensitize dabrafenib insensitive populations remain to be elucidated. For this purpose, we utilized cellular barcoding technology to decipher dabrafenib-induced clonal evolution in BRAF V600E mutant HT-29 cells. This approach revealed the detection of both pre-existing and de novo barcodes with increased frequencies as a result of dabrafenib insensitivity. Furthermore, our longitudinal monitoring of drug insensitivity based on barcode detection from floating DNA within used medium enabled to identify temporal dynamics of pre-existing and de novo barcodes in relation to dabrafenib insensitivity in HT-29 cells. Moreover, whole-exome sequencing analysis exhibited possible somatic CNVs and SNVs contributing to dabrafenib insensitivity in HT-29 cells. Last, collateral drug sensitivity testing demonstrated oxaliplatin and capecitabine, alone or in combination, as successful second-like therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells. Overall, our findings demonstrate clonal dynamics of dabrafenib-insensitivity in HT-29 cells. In addition, oxaliplatin and capecitabine, alone or in combination, were successful second-line therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells.

The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component

Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.

Diverse evolutionary pathways challenge the use of collateral sensitivity as a strategy to suppress resistance

Drug resistance remains a major obstacle to malaria control and eradication efforts, necessitating the development of novel therapeutic strategies to treat this disease. Drug combinations based on collateral sensitivity, wherein resistance to one drug causes increased sensitivity to the partner drug, have been proposed as an evolutionary strategy to suppress the emergence of resistance in pathogen populations. In this study, we explore collateral sensitivity between compounds targeting the Plasmodium dihydroorotate dehydrogenase (DHODH). We profiled the cross-resistance and collateral sensitivity phenotypes of several DHODH mutant lines to a diverse panel of DHODH inhibitors. We focus on one compound, TCMDC-125334, which was active against all mutant lines tested, including the DHODH C276Y line, which arose in selections with the clinical candidate DSM265. In six selections with TCMDC-125334, the most common mechanism of resistance to this compound was copy number variation of the dhodh locus, although we did identify one mutation, DHODH I263S, which conferred resistance to TCMDC-125334 but not DSM265. We found that selection of the DHODH C276Y mutant with TCMDC-125334 yielded additional genetic changes in the dhodh locus. These double mutant parasites exhibited decreased sensitivity to TCMDC-125334 and were highly resistant to DSM265. Finally, we tested whether collateral sensitivity could be exploited to suppress the emergence of resistance in the context of combination treatment by exposing wildtype parasites to both DSM265 and TCMDC-125334 simultaneously. This selected for parasites with a DHODH V532A mutation which were cross-resistant to both compounds and were as fit as the wildtype parent in vitro. The emergence of these cross-resistant, evolutionarily fit parasites highlights the mutational flexibility of the DHODH enzyme.

Identification of collateral sensitivity and evolutionary landscape of chemotherapy-induced drug resistance using cellular barcoding technology

Background: One of the most significant challenges impeding cancer treatment effectiveness is drug resistance. Combining evolutionary understanding with drug resistance can pave the way for the identification of second-line drug options that can overcome drug resistance. Although capecitabine and irinotecan are commonly used therapeutic agents in the treatment of CRC patients, resistance to these agents is common. The underlying clonal dynamics of resistance to these agents using high-resolution barcode technology and identification of effective second-line drugs in this context remain unclear. Methods and materials: Caco-2 and HT-29 cell lines were barcoded, and then capecitabine and irinotecan resistant derivatives of these cell lines were established. The frequencies of barcodes from resistant cell lines and harvested medium, longitudinally, were determined. Collateral drug sensitivity testing was carried out on resistant Caco-2 and HT-29 cell lines using single agents or drug combinations. The SyngeryFinder tool was used to analyse drug combination testing. Results: In Caco-2 and HT-29 cell lines, barcode frequency measurements revealed clonal dynamics of capecitabine and irinotecan formed by both pre-existing and de novo barcodes, indicating the presence of polyclonal drug resistance. The temporal dynamics of clonal evolution in Caco-2 and HT-29 cell lines were demonstrated by longitudinal analysis of pre-existing and de novo barcodes from harvested medium. In Caco-2 and HT-29 cell lines, collateral drug sensitivity revealed a number of drugs that were effective alone and in combination. Conclusion: The use of barcoding technology reveals the clonal dynamics of chemotherapy-induced drug resistance not only from harvested cell populations, but also from longitudinal sampling throughout the course of clonal evolution. Second-line drugs that sensitize drug-resistant CRC cell lines are identified through collateral drug testing.

Doxorubicin Activity Is Modulated by Traditional Herbal Extracts in a 2D and 3D Multicellular Sphere Model of Leukemia

The modulation of the tumor microenvironment by natural products may play a significant role in the response of tumor cells to chemotherapy. In this study, we evaluated the effect of extracts derived from P2Et (Caesalpinia spinosa) and Anamú-SC (Petiveria alliacea) plants, previously studied by our group, on the viability and ROS levels in the K562 cell line (Pgp- and Pgp+), endothelial cells (ECs, Eahy.926 cell line) and mesenchymal stem cells (MSC) cultured in 2D and 3D. The results show that: (a) the two botanical extracts are selective on tumor cells compared to doxorubicin (DX), (b) cytotoxicity is independent of the modulation of intracellular ROS for plant extracts, unlike DX, (c) the interaction with DX can be influenced by chemical complexity and the expression of Pgp, (d) the 3D culture shows a greater sensitivity of the tumor cells to chemotherapy, in co-treatment with the extracts. In conclusion, the effect of the extracts on the viability of leukemia cells was modified in multicellular spheroids with MSC and EC, suggesting that the in vitro evaluation of these interactions can contribute to the comprehension of the pharmacodynamics of the botanical drugs.

Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature

Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.

Evolution-Informed Strategies for Combating Drug Resistance in Cancer

The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.

MCB-613 exploits a collateral sensitivity in drug resistant EGFR-mutant non-small cell lung cancer through covalent inhibition of KEAP1

Targeted therapies have revolutionized cancer chemotherapy. Unfortunately, most patients develop multifocal resistance to these drugs within a matter of months. Here, we used a high-throughput phenotypic small molecule screen to identify MCB-613 as a compound that selectively targets EGFR-mutant, EGFR inhibitor-resistant non-small cell lung cancer (NSCLC) cells harboring diverse resistance mechanisms. Subsequent proteomic and functional genomic screens involving MCB-613 identified its target in this context to be KEAP1, revealing that this gene is selectively essential in the setting of EGFR inhibitor resistance. In-depth molecular characterization demonstrated that (1) MCB-613 binds KEAP1 covalently; (2) a single molecule of MCB-613 is capable of bridging two KEAP1 monomers together; and, (3) this modification interferes with the degradation of canonical KEAP1 substrates such as NRF2. Surprisingly, NRF2 knockout sensitizes cells to MCB-613, suggesting that the drug functions through modulation of an alternative KEAP1 substrate. Together, these findings advance MCB-613 as a new tool for exploiting the selective essentiality of KEAP1 in drug-resistant, EGFR-mutant NSCLC cells.

Silibinin Overcomes EMT-Driven Lung Cancer Resistance to New-Generation ALK Inhibitors

Epithelial-to-mesenchymal transition (EMT) may drive the escape of ALK-rearranged non-small-cell lung cancer (NSCLC) tumors from ALK-tyrosine kinase inhibitors (TKIs). We investigated whether first-generation ALK-TKI therapy-induced EMT promotes cross-resistance to new-generation ALK-TKIs and whether this could be circumvented by the flavonolignan silibinin, an EMT inhibitor. ALK-rearranged NSCLC cells acquiring a bona fide EMT phenotype upon chronic exposure to the first-generation ALK-TKI crizotinib exhibited increased resistance to second-generation brigatinib and were fully refractory to third-generation lorlatinib. Such cross-resistance to new-generation ALK-TKIs, which was partially recapitulated upon chronic TGFβ stimulation, was less pronounced in ALK-rearranged NSCLC cells solely acquiring a partial/hybrid E/M transition state. Silibinin overcame EMT-induced resistance to brigatinib and lorlatinib and restored their efficacy involving the transforming growth factor-beta (TGFβ)/SMAD signaling pathway. Silibinin deactivated TGFβ-regulated SMAD2/3 phosphorylation and suppressed the transcriptional activation of genes under the control of SMAD binding elements. Computational modeling studies and kinase binding assays predicted a targeted inhibitory binding of silibinin to the ATP-binding pocket of TGFβ type-1 receptor 1 (TGFBR1) and TGFBR2 but solely at the two-digit micromolar range. A secretome profiling confirmed the ability of silibinin to normalize the augmented release of TGFβ into the extracellular fluid of ALK-TKIs-resistant NSCLC cells and reduce constitutive and inducible SMAD2/3 phosphorylation occurring in the presence of ALK-TKIs. In summary, the ab initio plasticity along the EMT spectrum may explain the propensity of ALK-rearranged NSCLC cells to acquire resistance to new-generation ALK-TKIs, a phenomenon that could be abrogated by the silibinin-driven attenuation of the TGFβ/SMAD signaling axis in mesenchymal ALK-rearranged NSCLC cells.

Drug resistance mechanisms create targetable proteostatic vulnerabilities in Her2+ breast cancers

Oncogenic kinase inhibitors show short-lived responses in the clinic due to high rate of acquired resistance. We previously showed that pharmacologically exploiting oncogene-induced proteotoxic stress can be a viable alternative to oncogene-targeted therapy. Here, we performed extensive analyses of the transcriptomic, metabolomic and proteostatic perturbations during the course of treatment of Her2+ breast cancer cells with a Her2 inhibitor covering the drug response, resistance, relapse and drug withdrawal phases. We found that acute Her2 inhibition, in addition to blocking mitogenic signaling, leads to significant decline in the glucose uptake, and shutdown of glycolysis and of global protein synthesis. During prolonged therapy, compensatory overexpression of Her3 allows for the reactivation of mitogenic signaling pathways, but fails to re-engage the glucose uptake and glycolysis, resulting in proteotoxic ER stress, which maintains the protein synthesis block and growth inhibition. Her3-mediated cell proliferation under ER stress during prolonged Her2 inhibition is enabled due to the overexpression of the eIF2 phosphatase GADD34, which uncouples protein synthesis block from the ER stress response to allow for active cell growth. We show that this imbalance in the mitogenic and proteostatic signaling created during the acquired resistance to anti-Her2 therapy imposes a specific vulnerability to the inhibition of the endoplasmic reticulum quality control machinery. The latter is more pronounced in the drug withdrawal phase, where the de-inhibition of Her2 creates an acute surge in the downstream signaling pathways and exacerbates the proteostatic imbalance. Therefore, the acquired resistance mechanisms to oncogenic kinase inhibitors may create secondary vulnerabilities that could be exploited in the clinic.

Cross-Resistance Among Sequential Cancer Therapeutics: An Emerging Issue

Over the past two decades, cancer treatment has benefited from having a significant increase in the number of targeted drugs approved by the United States Food and Drug Administration. With the introduction of targeted therapy, a great shift towards a new era has taken place that is characterized by reduced cytotoxicity and improved clinical outcomes compared to traditional chemotherapeutic drugs. At present, targeted therapies and other systemic anti-cancer therapies available (immunotherapy, cytotoxic, endocrine therapies and others) are used alone or in combination in different settings (neoadjuvant, adjuvant, and metastatic). As a result, it is not uncommon for patients affected by an advanced malignancy to receive subsequent anti-cancer therapies. In this challenging complexity of cancer treatment, the clinical pathways of real-life patients are often not as direct as predicted by standard guidelines and clinical trials, and cross-resistance among sequential anti-cancer therapies represents an emerging issue. In this review, we summarize the main cross-resistance events described in the diverse tumor types and provide insight into the molecular mechanisms involved in this process. We also discuss the current challenges and provide perspectives for the research and development of strategies to overcome cross-resistance and proceed towards a personalized approach.

Collateral responses to classical cytotoxic chemotherapies are heterogeneous and sensitivities are sparse

Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity-the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug-has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19^ARF-/- cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.

Alectinib Together with Intracranial Therapies Improved Survival Outcomes in Untreated ALK-Positive Patients with Non-Small-Cell Lung Cancer and Symptomatic and Synchronic Brain Metastases: A Retrospective Study

Purpose

The performance of alectinib and crizotinib in untreated anaplastic lymphoma kinase (ALK)-positive patients with non-small-cell lung cancer (NSCLC) and symptomatic and synchronic brain metastases is largely unknown. This retrospective study assessed the effectiveness of alectinib and crizotinib, together with intracranial therapies in a cohort of these patients.

Patients and Methods

This study included 34 previously untreated ALK-positive NSCLC patients with three or fewer intracranial metastases. Of these patients, 13 received oral alectinib 600 mg twice daily, and 21 received oral crizotinib 250 mg twice daily, until progressive disease, unacceptable toxicity, or death. All intracranial metastases were treated with craniotomy, CyberKnife, or both.

Results

Median overall progression-free survival (PFS) was 32.8 months (95% CI 24.4–41.2 months) in patients treated with alectinib and 8.0 months (95% CI 7.3–8.7 months) in patients treated with crizotinib. Median PFS of brain lesions was not yet reached with alectinib (95% CI 30.1 months–not estimated) and was 8.5 months (95% CI 7.2–12.3 months) with crizotinib. Median PFS of lung lesions was 38.5 months (95% CI 27.5–49.5 months) with alectinib and 9.2 months (95% CI 7.4–11.0 months) with crizotinib. Median overall survival was not yet reached with alectinib (95% CI 31.0 months–not estimated) and 30.3 months (95% CI 27.3–37.1 months) with crizotinib.

Conclusion

Compared with crizotinib, alectinib showed superior efficacy and lower toxicity in the treatment of ALK-positive patients with NSCLC and symptomatic and synchronic brain metastases. The inclusion of intracranial therapies such as craniotomy or CyberKnife further improved the brain PFS and overall survival of these patients.

Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma

MicroRNA (miRNA)-based therapies are an emerging class of targeted therapeutics with many potential applications. Ewing Sarcoma patients could benefit dramatically from personalized miRNA therapy due to inter-patient heterogeneity and a lack of druggable (to this point) targets. However, because of the broad effects miRNAs may have on different cells and tissues, trials of miRNA therapies have struggled due to severe toxicity and unanticipated immune response. In order to overcome this hurdle, a network science-based approach is well-equipped to evaluate and identify miRNA candidates and combinations of candidates for the repression of key oncogenic targets while avoiding repression of essential housekeeping genes. We first characterized 6 Ewing sarcoma cell lines using mRNA sequencing. We then estimated a measure of tumor state, which we term network potential, based on both the mRNA gene expression and the underlying protein-protein interaction network in the tumor. Next, we ranked mRNA targets based on their contribution to network potential. We then identified miRNAs and combinations of miRNAs that preferentially act to repress mRNA targets with the greatest influence on network potential. Our analysis identified TRIM25, APP, ELAV1, RNF4, and HNRNPL as ideal mRNA targets for Ewing sarcoma therapy. Using predicted miRNA-mRNA target mappings, we identified miR-3613-3p, let-7a-3p, miR-300, miR-424-5p, and let-7b-3p as candidate optimal miRNAs for preferential repression of these targets. Ultimately, our work, as exemplified in the case of Ewing sarcoma, describes a novel pipeline by which personalized miRNA cocktails can be designed to maximally perturb gene networks contributing to cancer progression.

Theoretical modeling of collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

In previous work, we focused on the optimal therapeutic strategy with a pair of drugs which are collaterally sensitive to each other, that is, a situation in which evolution of resistance to one drug induces sensitivity to the other, and vice versa. Yoona (Bull Math Biol 8:1-34,Yoon et al. 2018) Here, we have extended this exploration to the optimal strategy with a collaterally sensitive drug sequence of an arbitrary length, N. To explore this, we have developed a dynamical model of sequential drug therapies with N drugs. In this model, tumor cells are classified as one of N subpopulations represented as [Formula: see text]. Each subpopulation, [Formula: see text], is resistant to '[Formula: see text]' and each subpopulation, [Formula: see text] (or [Formula: see text], if [Formula: see text]), is sensitive to it, so that [Formula: see text] increases under '[Formula: see text]' as it is resistant to it, and after drug-switching, decreases under '[Formula: see text]' as it is sensitive to that drug(s). Similar to our previous work examining optimal therapy with two drugs, we found that there is an initial period of time in which the tumor is 'shaped' into a specific makeup of each subpopulation, at which time all the drugs are equally effective ([Formula: see text]). After this shaping period, all the drugs are quickly switched with duration relative to their efficacy in order to maintain each subpopulation, consistent with the ideas underlying adaptive therapy. West(Canver Res 80(7):578-589Gatenby et al. 2009) and Gatenby (Cancer Res 67(11):4894-4903West et al. 2020). Additionally, we have developed methodologies to administer the optimal regimen under clinical or experimental situations in which no drug parameters and limited information of trackable populations data (all the subpopulations or only total population) are known. The therapy simulation based on these methodologies showed consistency with the theoretical effect of optimal therapy .

223-Radium for metastatic osteosarcoma: combination therapy with other agents and external beam radiotherapy

Background

Bone-seeking radiopharmaceuticals can deposit radiation selectively to some osteosarcoma tumours because of the bone-forming nature of this cancer.

Objectives

This is the first report of using 223-radium, an alpha-emitting calcium analogue with a high therapeutic index, in combination therapy with other agents in 15 patients with metastatic osteoblastic osteosarcoma.

Methods

Candidates for alpha-radiotherapy if ^99mTc-MDP bone scan had avid bone-forming lesions and no therapy of higher priority (eg, definitive surgery). Monthly 223-radium infusions (1.49 μCi/kg or 55.13 kBq/kg) were given.

Results

The median infusion number was three and the average time to progression was 4.3 months for this cohort receiving 223-radium+other agents. Agents provided during 223-radium included (1) drugs to reduce skeletal complications: monthly denosumab (n=13) or zolendronate (n=1); (2) agents with antivascular endothelial growth factor activity, pazopanib (n=8) or sorafenib (n=1), (3) alkylating agents: oral cyclophosphamide (n=1) or ifosfamide, given as a 14-day continuous infusion (n=1, two cycles), (4) high-dose methotrexate (n=1), pegylated liposomal doxorubicin (n=1); and (5) two other combinations: nivolumab and everolimus (n=1) and rapamycin and auranofin (n=1). Radiation therapy, including stereotactic body radiotherapy (SBRT), was also given to 11 patients concurrently with 223-radium (n=2), after 223-radium completion (n=3), or both concurrently and then sequentially for other sites (n=6). After 223-radium infusions, patients without RT had a median overall survival of 4.3 months compared with those with SBRT and/or RT, who had a median overall survival of 13.5 months.

Conclusion Although only 1/15 of patients with osteoblastic osteosarcoma still remain alive after 223-radium, overall survival

Chemotherapy confers a conserved secondary tolerance to EGFR inhibition via AXL-mediated signaling bypass

Drug resistance remains the major culprit of therapy failure in disseminated cancers. Simultaneous resistance to multiple, chemically different drugs feeds this failure resulting in cancer relapse. Here, we investigate co-resistance signatures shared between antimitotic drugs (AMDs) and inhibitors of receptor tyrosine kinases (RTKs) to probe mechanisms of secondary resistance. We map co-resistance ranks in multiple drug pairs and identified a more widespread occurrence of co-resistance to the EGFR-tyrosine kinase inhibitor (TKI) gefitinib in hundreds of cancer cell lines resistant to at least 11 AMDs. By surveying different parameters of genomic alterations, we find that the two RTKs EGFR and AXL displayed similar alteration and expression signatures. Using acquired paclitaxel and epothilone B resistance as first-line AMD failure models, we show that a stable collateral resistance to gefitinib can be relayed by entering a dynamic, drug-tolerant persister state where AXL acts as bypass signal. Delayed AXL degradation rendered this persistence to become stably resistant. We probed this degradation process using a new EGFR-TKI candidate YD and demonstrated that AXL bypass-driven collateral resistance can be suppressed pharmacologically. The findings emphasize that AXL bypass track is employed by chemoresistant cancer cells upon EGFR inhibition to enter a persister state and evolve resistance to EGFR-TKIs.

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.

The individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

Quantification and Optimization of Standard-of-Care Therapy to Delay the Emergence of Resistant Bone Metastatic Prostate Cancer

BACKGROUND

Bone metastatic prostate cancer (BMPCa), despite the initial responsiveness to androgen deprivation therapy (ADT), inevitably becomes resistant. Recent clinical trials with upfront treatment of ADT combined with chemotherapy or novel hormonal therapies (NHTs) have extended overall patient survival. These results indicate that there is significant potential for the optimization of standard-of-care therapies to delay the emergence of progressive metastatic disease.

METHODS

Here, we used data extracted from human bone metastatic biopsies pre- and post-abiraterone acetate/prednisone to generate a mathematical model of bone metastatic prostate cancer that can unravel the treatment impact on disease progression. Intra-tumor heterogeneity in regard to ADT and chemotherapy resistance was derived from biopsy data at a cellular level, permitting the model to track the dynamics of resistant phenotypes in response to treatment from biological first-principles without relying on data fitting. These cellular data were mathematically correlated with a clinical proxy for tumor burden, utilizing prostate-specific antigen (PSA) production as an example.

RESULTS

Using this correlation, our model recapitulated the individual patient response to applied treatments in a separate and independent cohort of patients (n = 24), and was able to estimate the initial resistance to the ADT of each patient. Combined with an intervention-decision algorithm informed by patient-specific prediction of initial resistance, we propose to optimize the sequence of treatments for each patient with the goal of delaying the evolution of resistant disease and limit cancer cell growth, offering evidence for an improvement against retrospective data.

CONCLUSIONS

Our results show how minimal but widely available patient information can be used to model and track the progression of BMPCa in real time, offering a clinically relevant insight into the patient-specific evolutionary dynamics of the disease and suggesting new therapeutic options for intervention.

TRIAL REGISTRATION

NCT # 01953640.

FUNDING

Funded by an NCI U01 (NCI) U01CA202958-01 and a Moffitt Team Science Award. CCL and DB were partly funded by an NCI PSON U01 (U01CA244101). AA was partly funded by a Department of Defense Prostate Cancer Research Program (W81XWH-15-1-0184) fellowship. LC was partly funded by a postdoctoral fellowship (PF-13-175-01-CSM) from the American Cancer Society.

Evolution in alternating environments with tunable interlandscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

Optimal control to reach eco-evolutionary stability in metastatic castrate-resistant prostate cancer

In the absence of curative therapies, treatment of metastatic castrate-resistant prostate cancer (mCRPC) using currently available drugs can be improved by integrating evolutionary principles that govern proliferation of resistant subpopulations into current treatment protocols. Here we develop what is coined as an 'evolutionary stable therapy', within the context of the mathematical model that has been used to inform the first adaptive therapy clinical trial of mCRPC. The objective of this therapy is to maintain a stable polymorphic tumor heterogeneity of sensitive and resistant cells to therapy in order to prolong treatment efficacy and progression free survival. Optimal control analysis shows that an increasing dose titration protocol, a very common clinical dosing process, can achieve tumor stabilization for a wide range of potential initial tumor compositions and volumes. Furthermore, larger tumor volumes may counter intuitively be more likely to be stabilized if sensitive cells dominate the tumor composition at time of initial treatment, suggesting a delay of initial treatment could prove beneficial. While it remains uncertain if metastatic disease in humans has the properties that allow it to be truly stabilized, the benefits of a dose titration protocol warrant additional pre-clinical and clinical investigations.

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance

Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.

Clone Wars: Quantitatively Understanding Cancer Drug Resistance

A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.

Chemical strategies to overcome resistance against targeted anticancer therapeutics

Emergence of resistance is a major factor limiting the efficacy of molecularly targeted anticancer drugs. Understanding the specific mutations, or other genetic or cellular changes, that confer drug resistance can help in the development of therapeutic strategies with improved efficacies. Here, we outline recent progress in understanding chemotype-specific mechanisms of resistance and present chemical strategies, such as designing drugs with distinct binding modes or using proteolysis targeting chimeras, to overcome resistance. We also discuss how targeting multiple binding sites with bifunctional inhibitors or identifying collateral sensitivity profiles can be exploited to limit the emergence of resistance. Finally, we highlight how incorporating analyses of resistance early in drug development can help with the design and evaluation of therapeutics that can have long-term benefits for patients.

Identifying States of Collateral Sensitivity during the Evolution of Therapeutic Resistance in Ewing's Sarcoma

Advances in the treatment of Ewing's sarcoma (EWS) are desperately needed, particularly in the case of metastatic disease. A deeper understanding of collateral sensitivity, where the evolution of therapeutic resistance to one drug aligns with sensitivity to another drug, may improve our ability to effectively target this disease. For the first time in a solid tumor, we produced a temporal collateral sensitivity map that demonstrates the evolution of collateral sensitivity and resistance in EWS. We found that the evolution of collateral resistance was predictable with some drugs but had significant variation in response to other drugs. Using this map of temporal collateral sensitivity in EWS, we can see that the path toward collateral sensitivity is not always repeatable, nor is there always a clear trajectory toward resistance or sensitivity. Identifying transcriptomic changes that accompany these states of transient collateral sensitivity could improve treatment planning for patients with EWS.

•

Ewing's sarcoma cell lines were evolved to become resistant to standard chemotherapy

•

A temporal collateral sensitivity map shows response to alternative drugs over time

•

Collateral drug response is repeatable in some instances and stochastic in others

•

Differential gene expression elucidates potential biomarkers of drug response

Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model

Background

CDK4/6 inhibitors such as ribociclib are becoming widely used targeted therapies in hormone-receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. However, cancers can advance due to drug resistance, a problem in which tumor heterogeneity and evolution are key features.

Methods

Ribociclib-resistant HR+/HER2- CAMA-1 breast cancer cells were generated through long-term ribociclib treatment. Characterization of sensitive and resistant cells were performed using RNA sequencing and whole exome sequencing. Lentiviral labeling with different fluorescent proteins enabled us to track the proliferation of sensitive and resistant cells under different treatments in a heterogeneous, 3D spheroid coculture system using imaging microscopy and flow cytometry.

Results

Transcriptional profiling of sensitive and resistant cells revealed the downregulation of the G2/M checkpoint in the resistant cells. Exploiting this acquired vulnerability; resistant cells exhibited collateral sensitivity for the Wee-1 inhibitor, adavosertib (AZD1775). The combination of ribociclib and adavosertib achieved additional antiproliferative effect exclusively in the cocultures compared to monocultures, while decreasing the selection for resistant cells.

Conclusions

Our results suggest that optimal antiproliferative effects in heterogeneous cancers can be achieved via an integrative therapeutic approach targeting sensitive and resistant cancer cell populations within a tumor, respectively.

Resistance to targeted therapies as a multifactorial, gradual adaptation to inhibitor specific selective pressures

Despite high initial efficacy, targeted therapies eventually fail in advanced cancers, as tumors develop resistance and relapse. In contrast to the substantial body of research on the molecular mechanisms of resistance, understanding of how resistance evolves remains limited. Using an experimental model of ALK positive NSCLC, we explored the evolution of resistance to different clinical ALK inhibitors. We found that resistance can originate from heterogeneous, weakly resistant subpopulations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence for a hybrid scenario involving the gradual, multifactorial adaptation to the inhibitors through acquisition of multiple cooperating genetic and epigenetic adaptive changes. Additionally, we found that during this adaptation tumor cells might present unique, temporally restricted collateral sensitivities, absent in therapy naïve or fully resistant cells, suggesting the potential for new therapeutic interventions, directed against evolving resistance.

Acquired resistance to cancer therapies reflects the ability of cancers to adapt to therapy-imposed selective pressures. Here, the authors elucidate the dynamics of developing resistance to ALK inhibitors in an ALK+ lung cancer cell line showing that resistance originates from drug-specific tolerant cancer cells and it develops as a gradual adaptation.

Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics

Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.

Targeting the Src Pathway Enhances the Efficacy of Selective FGFR Inhibitors in Urothelial Cancers with FGFR3 Alterations

Selective FGFR inhibitors such as infigratinib (BGJ398) and erdafitinib (JNJ-42756493) have been evaluated in clinical trials for cancers with FGFR3 molecular alterations, particularly in urothelial carcinoma patients. However, a substantial proportion of these patients (up to 50%) display intrinsic resistance to these drugs and receive minimal clinical benefit. There is thus an unmet need for alternative therapeutic strategies to overcome primary resistance to selective FGFR inhibitors. In this study, we demonstrate that cells expressing cancer-associated activating FGFR3 mutants and the FGFR3-TACC3 fusion showed primary resistance to infigratinib in long-term colony formation assays in both NIH-3T3 and urothelial carcinoma models. We find that expression of these FGFR3 molecular alterations resulted in elevated constitutive Src activation compared to wildtype FGFR3 and that cells co-opted this pathway as a means to achieve intrinsic resistance to infigratinib. Targeting the Src pathway with low doses of the kinase inhibitor dasatinib synergistically sensitized multiple urothelial carcinoma lines harbouring endogenous FGFR3 alterations to infigratinib. Our data provide preclinical rationale that supports the use of dasatinib in combination with selective FGFR inhibitors as a means to overcome intrinsic drug resistance in the salvage therapy setting in urothelial cancer patients with FGFR3 molecular alterations.

Celecoxib Prevents Doxorubicin-Induced Multidrug Resistance in Canine and Mouse Lymphoma Cell Lines

Background: Treatment of malignancies is still a major challenge in human and canine cancer, mostly due to the emergence of multidrug resistance (MDR). One of the main contributors of MDR is the overexpression P-glycoprotein (Pgp), which recognizes and extrudes various chemotherapeutics from cancer cells. Methods: To study mechanisms underlying the development of drug resistance, we established an in vitro treatment protocol to rapidly induce Pgp-mediated MDR in cancer cells. Based on a clinical observation showing that a 33-day-long, unplanned drug holiday can reverse the MDR phenotype of a canine diffuse large B-cell lymphoma patient, our aim was to use the established assay to prevent the emergence of drug resistance in the early stages of treatment. Results: We showed that an in vitro drug holiday results in the decrease of Pgp expression in MDR cell lines. Surprisingly, celecoxib, a known COX-2 inhibitor, prevented the emergence of drug-induced MDR in murine and canine lymphoma cell lines. Conclusions: Our findings suggest that celecoxib could significantly improve the efficiency of chemotherapy by preventing the development of MDR in B-cell lymphoma.

Model genotype-phenotype mappings and the algorithmic structure of evolution

Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.

Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa

Evolution is at the core of the impending antibiotic crisis. Sustainable therapy must thus account for the adaptive potential of pathogens. One option is to exploit evolutionary trade-offs, like collateral sensitivity, where evolved resistance to one antibiotic causes hypersensitivity to another one. To date, the evolutionary stability and thus clinical utility of this trade-off is unclear. We performed a critical experimental test on this key requirement, using evolution experiments with Pseudomonas aeruginosa, and identified three main outcomes: (i) bacteria commonly failed to counter hypersensitivity and went extinct; (ii) hypersensitivity sometimes converted into multidrug resistance; and (iii) resistance gains frequently caused re-sensitization to the previous drug, thereby maintaining the trade-off. Drug order affected the evolutionary outcome, most likely due to variation in the effect size of collateral sensitivity, epistasis among adaptive mutations, and fitness costs. Our finding of robust genetic trade-offs and drug-order effects can guide design of evolution-informed antibiotic therapy.

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.

Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle

Bacterial populations vary in their stress tolerance and population structure depending upon whether growth occurs in well-mixed or structured environments. We hypothesized that evolution in biofilms would generate greater genetic diversity than well-mixed environments and lead to different pathways of antibiotic resistance. We used experimental evolution and whole genome sequencing to test how the biofilm lifestyle influenced the rate, genetic mechanisms, and pleiotropic effects of resistance to ciprofloxacin in Acinetobacter baumannii populations. Both evolutionary dynamics and the identities of mutations differed between lifestyle. Planktonic populations experienced selective sweeps of mutations including the primary topoisomerase drug targets, whereas biofilm-adapted populations acquired mutations in regulators of efflux pumps. An overall trade-off between fitness and resistance level emerged, wherein biofilm-adapted clones were less resistant than planktonic but more fit in the absence of drug. However, biofilm populations developed collateral sensitivity to cephalosporins, demonstrating the clinical relevance of lifestyle on the evolution of resistance.

A bacterium known as Acinetobacter baumannii causes serious lung infections in people with weakened immune systems. These illnesses are becoming more common largely because A. baumannii is increasingly developing resistance to antibiotics.

Inside the airways, individual A. baumannii cells can stick together and coat themselves in a slimy substance to form a structure called biofilm, which physically protects bacteria from antibiotics. This may be one of the reasons why it is often harder to treat bacterial infections associated with biofilms.

Another possibility is that bacteria may evolve differently in biofilms compared with cells living independently. For example, A. baumannii may colonize several regions of the lungs during an infection, leading to distinct groups of bacteria that experience different conditions and evolve separately. Each population may therefore respond differently to an antibiotic. In contrast, bacteria living independently in a well-mixed population – such as in the bloodstream of their host – would be more likely to all evolve in the same way.

Santos-Lopez, Marshall et al. tested this theory by exposing populations of A. baumannii that lived either independently or in biofilms to increasing levels of an antibiotic called ciprofloxacin. The genetic information of these cells was examined as the populations were evolving, and the bacteria were also put in contact with other types of antibiotics.

The analyses revealed that bacteria in well-mixed populations shared the same limited number of mutations: these gave the bacteria high levels of resistance to the antibiotic’s primary target, an enzyme involved in DNA processes. The bacteria had also become resistant to other classes of antibiotics.

In contrast, the bacteria in biofilm populations evolved to be more genetically diverse, exhibiting different types of mutations that helped the cells to pump out the drug. These bacteria were less resistant to ciprofloxacin and more sensitive to other types of antibiotics.

Further experiments looked into the fitness of the bacteria – their ability to survive, reproduce and compete with each other. High levels of antibiotic resistance came with lower fitness: biofilm bacteria had evolved to become being fitter than those from well-mixed population. Even in the absence of drugs, these populations were in fact fitter than the original cells.

Overall, understanding how the lifestyles of bacteria affect the way they respond to drugs may help researchers to develop new approaches that limit the spread of antibiotic resistance and improve treatment.

Targeting tumor phenotypic plasticity and metabolic remodeling in adaptive cross-drug tolerance

Metastable phenotypic state transitions in cancer cells can lead to the development of transient adaptive resistance or tolerance to chemotherapy. Here, we report that the acquisition of a phenotype marked by increased abundance of CD44 (CD44^Hi) by breast cancer cells as a tolerance response to routinely used cytotoxic drugs, such as taxanes, activated a metabolic switch that conferred tolerance against unrelated standard-of-care chemotherapeutic agents, such as anthracyclines. We characterized the sequence of molecular events that connected the induced CD44^Hi phenotype to increased activity of both the glycolytic and oxidative pathways and glucose flux through the pentose phosphate pathway (PPP). When given in a specific order, a combination of taxanes, anthracyclines, and inhibitors of glucose-6-phosphate dehydrogenase (G6PD), an enzyme involved in glucose metabolism, improved survival in mouse models of breast cancer. The same sequence of the three-drug combination reduced the viability of patient breast tumor samples in an explant system. Our findings highlight a convergence between phenotypic and metabolic state transitions that confers a survival advantage to cancer cells against clinically used drug combinations. Pharmacologically targeting this convergence could overcome cross-drug tolerance and could emerge as a new paradigm in the treatment of cancer.

Limited Evolutionary Conservation of the Phenotypic Effects of Antibiotic Resistance Mutations

Multidrug-resistant clinical isolates are common in certain pathogens, but rare in others. This pattern may be due to the fact that mutations shaping resistance have species-specific effects. To investigate this issue, we transferred a range of resistance-conferring mutations and a full resistance gene into Escherichia coli and closely related bacteria. We found that resistance mutations in one bacterial species frequently provide no resistance, in fact even yielding drug hypersensitivity in close relatives. In depth analysis of a key gene involved in aminoglycoside resistance (trkH) indicated that preexisting mutations in other genes-intergenic epistasis-underlie such extreme differences in mutational effects between species. Finally, reconstruction of adaptive landscapes under multiple antibiotic stresses revealed that mutations frequently provide multidrug resistance or elevated drug susceptibility (i.e., collateral sensitivity) only with certain combinations of other resistance mutations. We conclude that resistance and collateral sensitivity are contingent upon the genetic makeup of the bacterial population, and such contingency could shape the long-term fate of resistant bacteria. These results underlie the importance of species-specific treatment strategies.

Quantifying local malignant adaptation in tissue-specific evolutionary trajectories by harnessing cancer's repeatability at the genetic level

Cancer is a potentially lethal disease, in which patients with nearly identical genetic backgrounds can develop a similar pathology through distinct combinations of genetic alterations. We aimed to reconstruct the evolutionary process underlying tumour initiation, using the combination of convergence and discrepancies observed across 2,742 cancer genomes from nine tumour types. We developed a framework using the repeatability of cancer development to score the local malignant adaptation (LMA) of genetic clones, as their potential to malignantly progress and invade their environment of origin. Using this framework, we found that premalignant skin and colorectal lesions appeared specifically adapted to their local environment, yet insufficiently for full cancerous transformation. We found that metastatic clones were more adapted to the site of origin than to the invaded tissue, suggesting that genetics may be more important for local progression than for the invasion of distant organs. In addition, we used network analyses to investigate evolutionary properties at the system-level, highlighting that different dynamics of malignant progression can be modelled by such a framework in tumour-type-specific fashion. We find that occurrence-based methods can be used to specifically recapitulate the process of cancer initiation and progression, as well as to evaluate the adaptation of genetic clones to given environments. The repeatability observed in the evolution of most tumour types could therefore be harnessed to better predict the trajectories likely to be taken by tumours and preneoplastic lesions in the future.

Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer

Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour’s strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells which are sensitive and resistant to the anaplastic lymphoma kinase inhibitor Alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played by the in-vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

Antibiotic collateral sensitivity is contingent on the repeatability of evolution

Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of collateral sensitivities, wherein evolution under a first drug induces susceptibility to a second. Here, we report that sequential drug regimens derived from in vitro evolution experiments may have overstated therapeutic benefit, predicting a collaterally sensitive response where cross-resistance ultimately occurs. We quantify the likelihood of this phenomenon by use of a mathematical model parametrised with combinatorially complete fitness landscapes for Escherichia coli. Through experimental evolution we then verify that a second drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted and whole genome sequencing. Taken together, these results highlight that the success of evolutionarily-informed therapies is predicated on a rigorous probabilistic understanding of the contingencies that arise during the evolution of drug resistance.

The evolution of resistance to an antibiotic can render bacteria more susceptible, or more resistant, to a second antibiotic. Here, Nichol et al. provide evidence that the final outcome can be fairly stochastic and depends on the shape of the evolutionary fitness landscape.

Proteins Regulating Microvesicle Biogenesis and Multidrug Resistance in Cancer

Microvesicles (MV) are emerging as important mediators of intercellular communication. While MVs are important signaling vectors for many physiological processes, they are also implicated in cancer pathology and progression. Cellular activation is perhaps the most widely reported initiator of MV biogenesis, however, the precise mechanism remains undefined. Uncovering the proteins involved in regulating MV biogenesis is of interest given their role in the dissemination of deleterious cancer traits. MVs shed from drug-resistant cancer cells transfer multidrug resistance (MDR) proteins to drug-sensitive cells and confer the MDR phenotype in a matter of hours. MDR is attributed to the overexpression of ABC transporters, primarily P-glycoprotein and MRP1. Their expression and functionality is dependent on a number of proteins. In particular, FERM domain proteins have been implicated in supporting the functionality of efflux transporters in drug-resistant cells and in recipient cells during intercellular transfer by vesicles. Herein, the most recent research on the proteins involved in MV biogenesis and in the dissemination of MV-mediated MDR are discussed. Attention is drawn to unanswered questions in the literature that may prove to be of benefit in ongoing efforts to improve clinical response to chemotherapy and circumventing MDR.

Case study: patient-derived clear cell adenocarcinoma xenograft model longitudinally predicts treatment response

There has been little progress in the use of patient-derived xenografts (PDX) to guide individual therapeutic strategies. In part, this can be attributed to the operational challenges of effecting successful engraftment and testing multiple candidate drugs in a clinically workable timeframe. It also remains unclear whether the ancestral tumor will evolve along similar evolutionary trajectories in its human and rodent hosts in response to similar selective pressures (i.e., drugs). Herein, we combine a metastatic clear cell adenocarcinoma PDX with a timely 3 mouse x 1 drug experimental design, followed by a co-clinical trial to longitudinally guide a patient's care. Using this approach, we accurately predict response to first- and second-line therapies in so far as tumor response in mice correlated with the patient's clinical response to first-line therapy (gemcitabine/nivolumab), development of resistance and response to second-line therapy (paclitaxel/neratinib) before these events were observed in the patient. Treatment resistance to first-line therapy in the PDX is coincident with biologically relevant changes in gene and gene set expression, including upregulation of phase I/II drug metabolism (CYP2C18, UGT2A, and ATP2A1) and DNA interstrand cross-link repair (i.e., XPA, FANCE, FANCG, and FANCL) genes. A total of 5.3% of our engrafted PDX collection is established within 2 weeks of implantation, suggesting our experimental designs can be broadened to other cancers. These findings could have significant implications for PDX-based avatars of aggressive human cancers.

Harnessing Tumor Evolution to Circumvent Resistance

High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.