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Pancreatic ductal adenocarcinoma is a highly metastatic disease and macrophages support liver metastases. Efferocytosis, or engulfment of apoptotic cells by macrophages, is an essential process in tissue homeostasis and wound healing, but its role in metastasis is less well understood. Here, we found that the colonization of the hepatic metastatic site is accompanied by low-grade tissue injury and that efferocytosis-mediated clearance of parenchymal dead cells promotes macrophage reprogramming and liver metastasis. Mechanistically, progranulin expression in macrophages is necessary for efficient efferocytosis by controlling lysosomal acidification via cystic fibrosis transmembrane conductance regulator and the degradation of lysosomal cargo, resulting in LXRα/RXRα-mediated macrophage conversion and upregulation of arginase 1. Pharmacological blockade of efferocytosis or macrophage-specific genetic depletion of progranulin impairs macrophage conversion, improves CD8 + T cell functions, and reduces liver metastasis. Our findings reveal how hard-wired functions of macrophages in tissue repair contribute to liver metastasis and identify potential targets for prevention of pancreatic ductal adenocarcinoma liver metastasis., (© 2024. The Author(s).)

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease for which better therapies are urgently needed. Fibroblasts and macrophages are heterogeneous cell populations able to enhance metastasis, but the role of a macrophage-fibroblast crosstalk in regulating their pro-metastatic functions remains poorly understood. Here we deconvolve how macrophages regulate metastasis-associated fibroblast (MAF) heterogeneity in the liver. We identify three functionally distinct MAF populations, among which the generation of pro-metastatic and immunoregulatory myofibroblastic-MAFs (myMAFs) critically depends on macrophages. Mechanistically, myMAFs are induced through a STAT3-dependent mechanism driven by macrophage-derived progranulin and cancer cell-secreted leukaemia inhibitory factor (LIF). In a reciprocal manner, myMAF secreted osteopontin promotes an immunosuppressive macrophage phenotype resulting in the inhibition of cytotoxic T cell functions. Pharmacological blockade of STAT3 or myMAF-specific genetic depletion of STAT3 restores an anti-tumour immune response and reduces metastases. Our findings provide molecular insights into the complex macrophage-fibroblast interactions in tumours and reveal potential targets to inhibit PDAC liver metastasis., (© 2024. The Author(s).)

Colorectal cancer (CRC) is the third leading cause of cancer-related death worldwide. Metastasis is the prime driver of CRC-related mortality, and the liver is the organ most frequently involved. Despite the overall success of current treatments, colorectal liver metastasis (CRLM) is associated with poor prognoses and a survival rate of only 14%. Recent studies have highlighted the importance of the tumor microenvironment (TME) and the crosstalk within it in determining the invasion of distant organs by circulating cancer cells. In the TME, cellular communication is mediated via soluble molecules, among which cytokines have recently emerged as key regulators, involved in every aspect of tumor progression and the metastatic cascade. Indeed, in the serum of CRC patients elevated levels of several cytokines are associated with cancer development and progression. The current review evaluates the role of different cytokines during CRLM development. Additionally, considering the increasing amount of data concerning the importance of cytokine complex networks, we outline the potential of combination treatments using targeted cytokines together with other well-established therapies, such as immune checkpoint blockades, chemotherapy, or gene therapy, to improve therapeutic outcomes.

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies., Competing Interests: VQ is an Academic co-Founder and equity holder for Parthenon Therapeutics, Inc. and Duet BioSystems, Inc. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Groves and Quaranta.)

Polyglycerol fatty acid esters (PGFAEs) are gaining interest in several industrial sectors due to their excellent surfactant properties and their wide range of hydrophilic-lipophilic balance (HLB) values. Moreover, they can be prepared from renewable resources, i.e., fatty acids and glycerol. In this study, polyglycerol-2 stearic acid esters (PG2SAEs) were synthesized by the enzymatic esterification of polyglycerol-2 (PG2) and stearic acid (SA) using the immobilized lipase Novozym 435 as a biocatalyst in a solvent-free system. Reaction conditions, i.e. , temperature (80 °C), reactant ratio (1:1.8), and enzyme loading (2.7% w/w), were finely optimized; furthermore, biocatalyst recycling was studied by assessing the residual activity of the lipase after each reaction cycle, up to 20 times. The composition of the enzymatically synthesized products ( E ) was roughly evaluated by chromatographic methods and mass spectrometry and compared with that of the esters obtained by acid-catalyzed esterification ( C ). Then, the surfactant properties of the prepared polyglycerol-based surfactants were investigated by interfacial tension studies. Specifically, the emulsifying capacity and stability and the rheological behavior of O/W emulsions prepared in the presence of E were deeply investigated in comparison with those of the chemically synthesized and commercially available product C ., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Beik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

The drug-induced proliferation (DIP) rate is a metric of in vitro drug response that avoids inherent biases in commonly used metrics such as 72 h viability. However, DIP rate measurements rely on direct cell counting over time, a laborious task that is subject to numerous challenges, including the need to fluorescently label cells and automatically segment nuclei. Moreover, it is incredibly difficult to directly count cells and accurately measure DIP rates for cell populations in suspension. As an alternative, we use real-time luminescence measurements derived from the cellular activity of NAD(P)H oxidoreductase to efficiently estimate drug response in both adherent and suspension cell populations to a panel of known anticancer agents. For the adherent cell lines, we collect both luminescence reads and direct cell counts over time simultaneously to assess their congruency. Our results demonstrate that the proposed approach significantly speeds up data collection, avoids the need for cellular labels and image segmentation, and opens the door to significant advances in high-throughput screening of anticancer drugs.

Objective: Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease and cytotoxic chemotherapy is the standard of care treatment for patients with advanced disease. Here, we investigate how the microenvironment in PDAC liver metastases reacts to chemotherapy and its role in metastatic disease progression post-treatment, an area which is poorly understood., Design: The impact of chemotherapy on metastatic disease progression and immune cell infiltrates was characterised using flow and mass cytometry combined with transcriptional and histopathological analysis in experimental PDAC liver metastases mouse models. Findings were validated in patient derived liver metastases and in an autochthonous PDAC mouse model. Human and murine primary cell cocultures and ex vivo patient-derived liver explants were deployed to gain mechanistical insights on whether and how chemotherapy affects the metastatic tumour microenvironment., Results: We show that in vivo, chemotherapy induces an initial infiltration of proinflammatory macrophages into the liver and activates cytotoxic T cells, leading only to a temporary restraining of metastatic disease progression. However, after stopping treatment, neutrophils are recruited to the metastatic liver via CXCL1 and 2 secretion by metastatic tumour cells. These neutrophils express growth arrest specific 6 (Gas6) which leads to AXL receptor activation on tumour cells enabling their regrowth. Disruption of neutrophil infiltration or inhibition of the Gas6/AXL signalling axis in combination with chemotherapy inhibits metastatic growth. Chemotherapy increases Gas6 expression in circulating neutrophils from patients with metastatic pancreatic cancer and recombinant Gas6 is sufficient to promote tumour cell proliferation ex vivo, in patient-derived metastatic liver explants., Conclusion: Combining chemotherapy with Gas6/AXL or neutrophil targeted therapy could provide a therapeutic benefit for patients with metastatic pancreatic cancer., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Crimean-Congo haemorrhagic fever (CCHF) is an emerging tick-borne disease caused by the arbovirus Crimean-Congo haemorrhagic fever virus (CCHFV; family Nairoviridae). Given the public health impact, CCHF is considered a priority disease for the European Union. This study describes the first detection of anti-CCHFV antibodies in transhumant bovines in Italy. Sera from 794 cattle collected across Basilicata region (Southern Italy) were screened using a commercial ELISA kit. The animal-level and herd-level seroprevalences detected were 1.89% [95%CI: 1.12-3.1] and 29.63% [95%CI: 15.68-48.65], respectively. Results of the χ 2 test for trend show that the exposure to CCHFV was significantly associated with increasing age, with the odds 5 times higher in 11-22-year old cattle than 1-4-year old cattle. The detection of antibodies against CCHFV in indigenous cattle indicates that the infection occurred in the study area and may warrant further consideration. Additionally, no significant spatial clustering of CCHF infection was detected, supporting the hypothesis that the disease is widespread in the region. Further studies at larger scale are needed to identify the areas at higher risk of zoonotic infection. A One Health approach should be implemented to better understand the disease risk and dynamics in the country, which effectively address the related public health threat., (© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.)

Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum. Here, we used mass-action-based models to describe post-transcriptional regulations, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. Furthermore, the newly identified intermediates-enabling circuits are common in the EMT regulatory network, and they can synergize with transcriptional feedback to support phenotypic continuum. Finally, our model predicted a role of miR-101 in multistate EMT, and we identified evidence from single-cell RNA-sequencing data that support the prediction. Our work reveals a previously unknown role of cooperative RNA degradation and microRNAs in EMT, providing a framework that can bridge the gap between mechanistic models and single-cell experiments., Competing Interests: V.Q. is co-founder of Parthenon Therapeutics, Inc., and Duet BioSytems, Inc. The other authors declare no conflict of interest., (© 2022 The Author(s).)

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information., Competing Interests: Declaration of interests C.M.L. is a consultant/advisory board member for Pfizer, Novartis, Astra Zeneca, Genoptix, Sequenom, Ariad, Takeda, Blueprints Medicine, Cepheid, Foundation Medicine, Roche, Achilles Therapeutics, Genentech, Syros, Amgen, EMD Serono, and Eli Lilly and reports receiving commercial research grants from Xcovery, Astra Zeneca, and Novartis. W.T.I. is a consultant/advisory board member for Genentech, Jazz Pharma, G1 Therapeutics, Mirati, OncLive, Clinical Care Options, Chardan, Outcomes Insights, Cello Health, and Curio Science. T.G.O. is a consultant/advisory board member for Known Medicine. J.S. receives research funding from Pfizer. V.Q. is an Academic co-Founder and equity holder for Parthenon Therapeutics, Inc. and Duet BioSystems, Inc., (Copyright © 2022. Published by Elsevier Inc.)

With the clinical approval of T-cell-dependent immune checkpoint inhibitors for many cancers, therapeutic cancer vaccines have re-emerged as a promising immunotherapy. Cancer vaccines require the addition of immunostimulatory adjuvants to increase vaccine immunogenicity, and increasingly multiple adjuvants are used in combination to bolster further and shape cellular immunity to tumor antigens. However, rigorous quantification of adjuvants' synergistic interactions is challenging due to partial redundancy in costimulatory molecules and cytokine production, leading to the common assumption that combining both adjuvants at the maximum tolerated dose results in optimal efficacy. Herein, we examine this maximum dose assumption and find combinations of these doses are suboptimal. Instead, we optimized dendritic cell activation by extending the Multidimensional Synergy of Combinations (MuSyC) framework that measures the synergy of efficacy and potency between two vaccine adjuvants. Initially, we performed a preliminary in vitro screening of clinically translatable adjuvant receptor targets (TLR, STING, NLL, and RIG-I). We determined that STING agonist (CDN) plus TLR4 agonist (MPL-A) or TLR7/8 agonist (R848) as the best pairwise combinations for dendritic cell activation. In addition, we found that the combination of R848 and CDN is synergistically efficacious and potent in activating both murine and human antigen-presenting cells (APCs) in vitro . These two selected adjuvants were then used to estimate a MuSyC-dose optimized for in vivo T-cell priming using ovalbumin-based peptide vaccines. Finally, using B16 melanoma and MOC1 head and neck cancer models, MuSyC-dose-based adjuvating of cancer vaccines improved the antitumor response, increased tumor-infiltrating lymphocytes, and induced novel myeloid tumor infiltration changes. Further, the MuSyC-dose-based adjuvants approach did not cause additional weight changes or increased plasma cytokine levels compared to CDN alone. Collectively, our findings offer a proof of principle that our MuSyC-extended approach can be used to optimize cancer vaccine formulations for immunotherapy., Competing Interests: DT and MK report grants from DoD USAMRAA and NIH, as well as other support from Barry Baker Biorepository Fund and James Rowen Fund during the conduct of the study. YK reports grants from NCI and NIDCR during the conduct of the study, as well as personal fees from Aduro, AstraZeneca, Sanofi, Takeda, and Mersanna outside the submitted work. Author YK was employed by Regeneron Pharmaceuticals, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Taylor, Meyer, Graves, Sen, Fu, Tran, Mirza, Rodriguez, Lang, Feng, Quaranta, Wilson, Kim and Korrer.)

Inhibitors of the mitotic kinesin Kif11 are anti-mitotics that, unlike vinca alkaloids or taxanes, do not disrupt microtubules and are not neurotoxic. However, development of resistance has limited their clinical utility. While resistance to Kif11 inhibitors in other cell types is due to mechanisms that prevent these drugs from disrupting mitosis, we find that in glioblastoma (GBM), resistance to the Kif11 inhibitor ispinesib works instead through suppression of apoptosis driven by activation of STAT3. This form of resistance requires dual phosphorylation of STAT3 residues Y705 and S727, mediated by SRC and epidermal growth factor receptor (EGFR), respectively. Simultaneously inhibiting SRC and EGFR reverses this resistance, and combined targeting of these two kinases in vivo with clinically available inhibitors is synergistic and significantly prolongs survival in ispinesib-treated GBM-bearing mice. We thus identify a translationally actionable approach to overcoming Kif11 inhibitor resistance that may work to block STAT3-driven resistance against other anti-cancer therapies as well., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Small Cell Lung Cancer (SCLC) is a highly aggressive, neuroendocrine tumor. Traditional reductionist approaches have proven ineffective to ameliorate the uniformly dismal outcomes for SCLC - survival at 5 years remains less than 5%. A major obstacle to improving treatment is that SCLC tumor cells disseminate early, with a strong propensity for metastasizing to the brain. Accumulating evidence indicates that, contrary to previous textbook knowledge, virtually every SCLC tumor is comprised of multiple subtypes. Important questions persist regarding the role that this intra-tumor subtype heterogeneity may play in supporting the invasive properties of SCLC. A recurrent hypothesis in the field is that subtype interactions and/or transition dynamics are major determinants of SCLC metastatic seeding and progression. Here, we review the advantages of cerebral organoids as an experimentally accessible platform for SCLC brain metastasis, amenable to genetic manipulations, drug perturbations, and assessment of subtype interactions when coupled, e.g., to temporal longitudinal monitoring by high-content imaging or high-throughput omics data generation. We then consider systems approaches that can produce mathematical and computational models useful to generalize lessons learned from ex vivo organoid cultures, and integrate them with in vivo observations. In summary, systems approaches combined with ex vivo SCLC cultures in brain organoids may effectively capture both tumor-tumor and host-tumor interactions that underlie general principles of brain metastasis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Quaranta and Linkous.)

The correct type and time of follow-up for patients affected by COVID19 ARDS is still unclear. The aim of this study was to evaluate at the survivors to COVID19 ARDS requiring non-invasive respiratory support (NRS) admitted to a Respiratory Intensive care unit (RICU) from March 8th till May 31th 2020 looking at all sequelae via a comprehensive follow up. All patients underwent a multi-disciplinary instrumental and clinical assessment within three months form admission to evaluate all infection related sequelae. Thirty-eight patients were enrolled Lung-Ultrasound (LUS) showed an outstanding discrimination ability (ROC AUC: 0.95) and a substantial agreement rate (Cohen's K: 0.74) compared to chest CT-scan detecting improvement of lung consolidations. Youden's test showed a cut-off pressure of 11 cmH2O ExpiratoryPAP-Continuous-PAP-max (EPAP-CPAP) applied at the airways during hospitalization to be significantly correlated (p value: 0.026) to the increased pulmonary artery common trunk diameter. A total of 8/38 patients (21.8%), 2 of whom during follow-up, were diagnosed with Pulmonary Emboli (PE) and started anticoagulant treatment. Patients with PE had a statistically significant shorter length of time of hospitalization, time to negative swab, CPAP/NIV duration, P/F ratio and D-dimers at follow-up compared to non PE. A comprehensive approach to patients with ARDS COVID19 requiring NRS is necessary. This study highlighted cardiopulmonary impairment related to the ARDS and to the high-EPAP-CPAP-max greater than 11mmHg provided during admission, the usefulness of LUS in monitoring post-infection recovery and the correct identification and  treatment of patients with PE during follow up.

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies., (© 2021. The Author(s).)

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose-response curves, and derived dose-response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Introduction: The programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors, atezolizumab and durvalumab, have received regulatory approval for the first-line treatment of patients with extensive-stage SCLC. Nevertheless, when used in combination with platinum-based chemotherapy, these PD-L1 inhibitors only improve overall survival by 2 to 3 months. This may be due to the observation that less than 20% of SCLC tumors express PD-L1 at greater than 1%. Evaluating the composition and abundance of checkpoint molecules in SCLC may identify molecules beyond PD-L1 that are amenable to therapeutic targeting., Methods: We analyzed RNA-sequencing data from SCLC cell lines (n = 108) and primary tumor specimens (n = 81) for expression of 39 functionally validated inhibitory checkpoint ligands. Furthermore, we generated tissue microarrays containing SCLC cell lines and patient with SCLC specimens to confirm expression of these molecules by immunohistochemistry. We annotated patient outcomes data, including treatment response and overall survival., Results: The checkpoint protein B7-H6 (NCR3LG1) exhibited increased protein expression relative to PD-L1 in cell lines and tumors (p < 0.05). Higher B7-H6 protein expression correlated with longer progression-free survival (p = 0.0368) and increased total immune infiltrates (CD45+) in patients. Furthermore, increased B7-H6 gene expression in SCLC tumors correlated with a decreased activated natural killer cell gene signature, suggesting a complex interplay between B7-H6 expression and immune signature in SCLC., Conclusions: We investigated 39 inhibitory checkpoint molecules in SCLC and found that B7-H6 is highly expressed and associated with progression-free survival. In addition, 26 of 39 immune checkpoint proteins in SCLC tumors were more abundantly expressed than PD-L1, indicating an urgent need to investigate additional checkpoint targets for therapy in addition to PD-L1., (Published by Elsevier Inc.)

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment., Competing Interests: The authors have declared that no competing interests exist.

ASCL1 is a neuroendocrine lineage-specific oncogenic driver of small cell lung cancer (SCLC), highly expressed in a significant fraction of tumors. However, ∼25% of human SCLC are ASCL1-low and associated with low neuroendocrine fate and high MYC expression. Using genetically engineered mouse models (GEMMs), we show that alterations in Rb1/Trp53/Myc in the mouse lung induce an ASCL1 + state of SCLC in multiple cells of origin. Genetic depletion of ASCL1 in MYC-driven SCLC dramatically inhibits tumor initiation and progression to the NEUROD1 + subtype of SCLC. Surprisingly, ASCL1 loss promotes a SOX9 + mesenchymal/neural crest stem-like state and the emergence of osteosarcoma and chondroid tumors, whose propensity is impacted by cell of origin. ASCL1 is critical for expression of key lineage-related transcription factors NKX2-1, FOXA2, and INSM1 and represses genes involved in the Hippo/Wnt/Notch developmental pathways in vivo. Importantly, ASCL1 represses a SOX9/RUNX1/RUNX2 program in vivo and SOX9 expression in human SCLC cells, suggesting a conserved function for ASCL1. Together, in a MYC-driven SCLC model, ASCL1 promotes neuroendocrine fate and represses the emergence of a SOX9 + nonendodermal stem-like fate that resembles neural crest., (© 2021 Olsen et al.; Published by Cold Spring Harbor Laboratory Press.)

Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2. Cisplatin treatment of SCLC-A patient-derived xenografts induces intratumoral shifts toward SCLC-I, supporting subtype switching as a mechanism of acquired platinum resistance. We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients., Competing Interests: Declaration of interests C.M.G. reports research funding from AstraZeneca. S.H. reports consulting/honoraria from Qiagen, Boehringer Ingelheim and travel funding from Roche. B.Y.Z. and D.S.S. are employees of Genentech, Inc. G.F. is an employee of AstraZeneca and has stock ownership in AstraZeneca. B.G. reports research funding from ISA Pharm, Cue Bio, Pfizer, and Medimmune. I.I.W. reports consulting/advisory roles for Medscape, MSD, Genentech/Roche, PlatformQ Health, Pfizer, Bayer, Bristol-Myers Squibb, AstraZeneca/Medimmune, HTG Molecular, Asuragen, Merck, GlaxoSmithKline, Guardant Health, Oncocyte, and MSD, research support from Genentech, Oncoplex, HTG Molecular, DepArray, Merck, Bristol-Myers Squibb, Medimmune, Adapative, Adaptimmune, EMD Serono, Pfizer, Takeda, Amgen, Karus, Johnson & Johnson, Bayer, Iovance, 4D, Novartis and Akoya. J.V.H. serves on advisory committees for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Guardant Health, Kairos Venture Investments, BrightPath Biotherapeutics, Hengrui Therapeutics, Eli Lilly, Spectrum, EMD Serono, Roche, and Foundation One Medicine and has research support from AstraZeneca, Spectrum, and Checkmate Pharmaceuticals, as well as royalties/licensing fees from Spectrum and Bio-Tree Systems. L.A.B. serves on advisory committees for AstraZeneca, AbbVie, GenMab, BergenBio, Pharma Mar SA, Sierra Oncology, Merck, Bristol-Myers Squibb, Genentech, and Pfizer and has research support from AbbVie, AstraZeneca, GenMab, Sierra Oncology, Tolero Pharmaceuticals. C.M.G., L.A.B., and J.V.H. have pending patent applications for SCLC subtyping U.S. 62/895,322. Otherwise, there are no pertinent financial or non-financial conflicts of interest to report., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Melanomas harboring BRAF mutations can be treated with BRAF inhibitors ( BRAF i), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF -mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAF i sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAF i sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAF i in any melanoma, regardless of mutational status. SIGNIFICANCE: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma., (©2020 American Association for Cancer Research.)

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Cancer cells adjust their metabolic profiles to evade treatment. Metabolic adaptation is complex and hence better understood by an integrated theoretical-experimental approach. Using a minimal kinetic model, we predicted a previously undescribed Low/Low (L/L) phenotype, characterized by low oxidative phosphorylation (OXPHOS) and low glycolysis. Here, we report that L/L metabolism is observed in BRAF -mutated melanoma cells that enter a drug-tolerant "idling state" upon long-term MAPK inhibition (MAPKi). Consistently, using publicly available RNA-sequencing data of both cell lines and patient samples, we show that melanoma cells decrease their glycolysis and/or OXPHOS activity upon MAPKi and converge toward the L/L phenotype. L/L metabolism is unfavorable for tumor growth, yet supports successful cell division at ~50% rate. Thus, L/L drug-tolerant idling cells are a reservoir for accumulating mutations responsible for relapse, and it should be considered as a target subpopulation for improving MAPKi outcomes in melanoma treatment., (Copyright © 2020 Jia, Paudel, Hayford, Hardeman, Levine, Onuchic and Quaranta.)

The outcomes of patients with SCLC have not yet been substantially impacted by the revolution in precision oncology, primarily owing to a paucity of genetic alterations in actionable driver oncogenes. Nevertheless, systemic therapies that include immunotherapy are beginning to show promise in the clinic. Although, these results are encouraging, many patients do not respond to, or rapidly recur after, current regimens, necessitating alternative or complementary therapeutic strategies. In this review, we discuss ongoing investigations into the pathobiology of this recalcitrant cancer and the therapeutic vulnerabilities that are exposed by the disease state. Included within this discussion, is a snapshot of the current biomarker and clinical trial landscapes for SCLC. Finally, we identify key knowledge gaps that should be addressed to advance the field in pursuit of reduced SCLC mortality. This review largely summarizes work presented at the Third Biennial International Association for the Study of Lung Cancer SCLC Meeting., (Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.S. receives research funding from StemCentrx/Abbvie, Revolution Medicines, and from Pfizer, and owns stock in Forty Seven Inc.

Metabolic plasticity in cancer has been linked to the development of drug-tolerant populations. In this issue of Science Signaling , Goldman et al integrate metabolic plasticity with phenotypic state transitions to examine how this reprogramming can be therapeutically exploited., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Despite the incredible clinical benefits obtained by the use of immune checkpoint blockers (ICBs), resistance is still common for many types of cancer. Central for ICBs to work is activation and infiltration of cytotoxic CD8 + T cells following tumour-antigen recognition. However, it is now accepted that even in the case of immunogenic tumours, the effector functions of CD8 + T cells are highly compromised by the presence of an immunosuppressive tumour microenvironment (TME) at the tumour site. Tumour-associated macrophages (TAMs) are among the most abundant non-malignant stromal cell types within the TME and they are crucial drivers of tumour progression, metastasis and resistance to therapy. TAMs are able to regulate either directly or indirectly various aspects of tumour immunity, including T cell recruitment and functions. In this review we discuss the mechanisms by which TAMs subvert CD8 + T cell immune surveillance and how their targeting in combination with ICBs represents a very powerful therapeutic strategy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the "equivalent dose metric." This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response.

Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors. Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease.

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Competing Interests: The authors declare no conflict of interest.

Aim: To evaluate the conditioning capabilities of the DAR™ Hygrobac™ S, a Heat and Moisture Exchanger (HME), using a new device to measure the temperature (T) and the absolute humidity (AH) of the ventilated gases in vivo during mechanical ventilation in Intensive Care Unit (ICU) patients., Materials and Methods: In 49 mechanically ventilated ICU patients, we evaluated T and AH, indicating the HME efficacy, during the inspiratory phase upstream and downstream the HME and the ratio of inspired AH to expired AH and the difference between expired T and inspired T indicated the HME efficiency. Efficacy and efficiency were assessed at three time points: at baseline (t 0 , HME positioning time), at 12 hours (t 1 ), and at 24 hours (t 2 ) using a dedicated, ad hoc built wireless device. Differences over time were evaluated using one-way ANOVA for repeated measures, whereas differences between in vivo and laboratory values (declared by the manufacturer according to UNI® EN ISO 9360 international standard) were evaluated using one-sample Student t-test., Results: 49 HMEs were analysed in vivo during mechanical ventilation. T and AH means (SD) of the inspired gas (the efficacy) were 31.5°C (1.54) and 32.3 mg/l (2.60) at t 0 , 31.1°C (1.34) and 31.7 mg/l (2.26) at t 1 , and 31°C (1.29) and 31.4 mg/l (2.27) at t 2 . Both efficiency parameters were constant over time (inspired AH/expired AH=89%, p=0.24; and expired T-inspired T = 2.2°C, p=0.81). Compared with laboratory values, in vivo T and AH indicating efficacy were significantly lower (p<0.01), whereas the efficiency was significantly higher (p<0.01)., Conclusions: HME performances can be accurately assessed for prolonged periods in vivo during routine mechanical ventilation in ICU patients. Temperature and absolute humidity of ventilated gases in vivo were maintained within the expected range and remained stable over time. HME efficacy and efficiency in vivo significantly differed from laboratory values.

Whether patients with glioblastoma that contacts the ventricular-subventricular zone stem cell niche (VSVZ + GBM) have a distinct survival profile from VSVZ - GBM patients independent of other known predictors or molecular profiles is unclear. Using multivariate Cox analysis to adjust survival for widely-accepted predictors, hazard ratios (HRs) for overall (OS) and progression free (PFS) survival between VSVZ + GBM and VSVZ - GBM patients were calculated in 170 single-institution patients and 254 patients included in both The Cancer Genome (TCGA) and Imaging (TCIA) atlases. An adjusted, multivariable analysis revealed that VSVZ contact was independently associated with decreased survival in both datasets. TCGA molecular data analyses revealed that VSVZ contact by GBM was independent of mutational, DNA methylation, gene expression, and protein expression signatures in the bulk tumor. Therefore, while survival of GBM patients is independently stratified by VSVZ contact, with VSVZ + GBM patients displaying a poor prognosis, the VSVZ + GBMs do not possess a distinct molecular signature at the bulk sample level. Focused examination of the interplay between the VSVZ microenvironment and subsets of GBM cells proximal to this region is warranted.

Long-range charge transport is important for many applications like batteries, fuel cells, sensors, and catalysis. Obtaining microscopic insights into the atomistic mechanism is challenging, in particular if the underlying processes involve protons as the charge carriers. Here, large-scale reactive molecular dynamics simulations employing an efficient density-functional-theory-based neural network potential are used to unravel long-range proton transport mechanisms at solid-liquid interfaces, using the zinc oxide-water interface as a prototypical case. We find that the two most frequently occurring ZnO surface facets, (101[combining macron]0) and (112[combining macron]0), that typically dominate the morphologies of zinc oxide nanowires and nanoparticles, show markedly different proton conduction behaviors along the surface with respect to the number of possible proton transfer mechanisms, the role of the solvent for long-range proton migration, as well as the proton transport dimensionality. Understanding such surface-facet-specific mechanisms is crucial for an informed bottom-up approach for the functionalization and application of advanced oxide materials.

The ability of disseminated cancer cells to evade the immune response is a critical step for efficient metastatic progression. Protection against an immune attack is often provided by the tumor microenvironment that suppresses and excludes cytotoxic CD8 + T cells. Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive metastatic disease with unmet needs, yet the immunoprotective role of the metastatic tumor microenvironment in pancreatic cancer is not completely understood. In this study, we find that macrophage-derived granulin contributes to cytotoxic CD8 + T-cell exclusion in metastatic livers. Granulin expression by macrophages was induced in response to colony-stimulating factor 1. Genetic depletion of granulin reduced the formation of a fibrotic stroma, thereby allowing T-cell entry at the metastatic site. Although metastatic PDAC tumors are largely resistant to anti-PD-1 therapy, blockade of PD-1 in granulin-depleted tumors restored the antitumor immune defense and dramatically decreased metastatic tumor burden. These findings suggest that targeting granulin may serve as a potential therapeutic strategy to restore CD8 + T-cell infiltration in metastatic PDAC, thereby converting PDAC metastatic tumors, which are refractory to immune checkpoint inhibitors, into tumors that respond to immune checkpoint inhibition therapies. Significance: These findings uncover a mechanism by which metastatic PDAC tumors evade the immune response and provide the rationale for targeting granulin in combination with immune checkpoint inhibitors for the treatment of metastatic PDAC. Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/15/4253/F1.large.jpg Cancer Res; 78(15); 4253-69. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101¯0) interface as a prototypical case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrödinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 Å from the surface; (ii) the spectrum contains a number of overlapping signals arising from different chemical species, with the frequencies decreasing in the order ν(adsorbed hydroxide) > ν(non-adsorbed water) > ν(surface hydroxide) > ν(adsorbed water); (iii) stretching frequencies are strongly influenced by the hydrogen bond pattern of these interfacial species. Finally, we have been able to identify substantial correlations between the stretching frequencies and hydrogen bond lengths for all species.

We develop a combined experimental-mathematical framework to investigate heterogeneity in the context of breast cancer treated with doxorubicin. We engineer a cell line to over-express the multi-drug resistance 1 protein (MDR1), an ATP-dependent pump that effluxes intracellular drug. Co-culture experiments mixing the MDR1-overexpressing line with its parental line are evaluated via fluorescence microscopy. To quantify the impact of population heterogeneity on therapy response, these data are analyzed with a coupled pharmacokinetics/pharmacodynamics model. The proliferation and death rates of each line vary with co-culture condition (the relative fraction of each cell line at the time of seeding). For example, the death rate in the parental line under low-dose doxorubicin treatment is increased from 0.64 (± 0.22) × 10 -2 to 1.46 (± 0.58) × 10 -2  h -1 with increasing fractions of MDR1-overexpressing cells. The growth rate of the MDR1-overexpressing line increases 29% as its relative fraction is decreased. Simulations of the pharmacokinetics/pharmacodynamics model suggest increased efflux from MDR1-overexpressing cells contributes to the increased death rate in the parental cells. Experimentally, the death rate of parental cells is constant across co-culture conditions under co-treatment with an MDR1 inhibitor. These data indicate that intercellular pharmacokinetic variability should be considered in analyzing treatment response in heterogeneous populations.

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy., Methods and Materials: Post-radiation therapy response is modeled using a cell death model (M d ), a reduced proliferation rate model (M p ), and cell death and reduced proliferation model (M dp ). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number., Results: For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M dp and M p models compared with the M d model. The M dp model fit, however, had significantly lower sum squared error compared with the M p and M d models., Conclusions: The results of this study indicate that for both doses, the M p and M dp models result in accurate predictions of tumor growth, whereas the M d model poorly describes response to radiation therapy., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Aim: Potentially inappropriate prescriptions (PIP) have been highly reported in older patients, but few studies have investigated their association with adverse clinical outcomes. The present study aimed to evaluate the prevalence and predictors of PIP in hospital-discharged older adults, and to explore the association of PIP with death and rehospitalization., Methods: We carried out a multicenter prospective cohort study on hospital-discharged patients aged ≥65 years. Each patient underwent a comprehensive geriatric assessment, and the prevalence of PIP was obtained by applying Beers Criteria 2015 to discharge documents. Telephone follow up was carried out at 6 months., Results: The prevalence of PIP was 63%, and was associated with psychiatric-behavioral disorders (OR 1.64), the number of daily taken medications (OR 1.08) and long-term care discharge (OR 1.91), whereas better functional performance was protective (OR 0.93). Neither the presence nor the number of PIP were associated with rehospitalization or mortality at 6 months. However, insulin sliding scale (OR 4.97) and use of drugs inappropriate in heart failure (OR 4.64) were associated with an increased risk of rehospitalization, whereas prescription of digoxin ≥0.125 mg/daily (OR 1.77) and antipsychotics (OR 1.65) were associated with a higher risk of mortality., Conclusions: Among older hospital-discharged patients, we documented a high prevalence of PIP that was significantly associated with polytherapy, the presence of psychiatric-behavioral disorders and discharge to long-term care facilities. Although the presence and the number of PIP were not associated with adverse outcomes, some specific inappropriate prescriptions were associated with a higher risk of hospital readmission and death. Geriatr Gerontol Int 2018; 18: 561-568., (© 2017 Japan Geriatrics Society.)

Targeted therapy is an effective standard of care in BRAF-mutated malignant melanoma. However, the duration of tumor remission varies unpredictably among patients, and relapse is almost inevitable. Here, we examine the responses of several BRAF-mutated melanoma cell lines (including isogenic subclones) to BRAF inhibitors. We observe complex response dynamics across cell lines, with short-term responses (<100 h) varying from cell line to cell line. In the long term, however, we observe equilibration of all drug-treated populations into a nonquiescent state characterized by a balanced rate of death and division, which we term the "idling" state, and to our knowledge, this state has not been previously reported. Using mathematical modeling, we propose that the observed population-level dynamics are the result of cells transitioning between basins of attraction within a drug-modified phenotypic landscape. Each basin is associated with a drug-induced proliferation rate, a recently introduced metric of an antiproliferative drug effect. The idling population state represents a new dynamic equilibrium in which cells are distributed across the landscape such that the population achieves zero net growth. By fitting our model to experimental drug-response data, we infer the phenotypic landscapes of all considered melanoma cell lines and provide a unifying view of how BRAF-mutated melanomas respond to BRAF inhibition. We hypothesize that the residual disease observed in patients after targeted therapy is composed of a significant number of idling cells. Thus, defining molecular determinants of the phenotypic landscape that idling populations occupy may lead to "targeted landscaping" therapies based on rational modification of the landscape to favor basins with greater drug susceptibility., (Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.)