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We disclose novel amphiphilic ruthenium and osmium complexes that auto-assemble into nanomedicines with potent antiproliferative activity by inhibition of mitochondrial respiration. The self-assembling units were rationally designed from the [M(p-cymene)(1,10-phenanthroline)Cl]PF(6) motif (where M is either Ru(II) or Os(II)) with an appended C(16) fatty chain to achieve high cellular activity, nano-assembling and mitochondrial targeting. These amphiphilic complexes block cell proliferation at the sub-micromolar range and are particularly potent towards glioblastoma neurospheres made from patient-derived cancer stem cells. A subcutaneous mouse model using these glioblastoma stem cells highlights one of our C(16) Os(II) nanomedicines as highly successful in vivo. Mechanistically, we show that they act as metabolic poisons, strongly impairing mitochondrial respiration, corroborated by morphological changes and damage to the mitochondria. A genetic strategy based on RNAi gave further insight on the potential involvement of microtubules as part of the induced cell death. In parallel, we examined the structural properties of these new amphiphilic metal-based constructs, their reactivity and mechanism.

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Supplementary Figures 1-2 from Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death

Supplementary Data from Phase II Study of Ponatinib in Advanced Gastrointestinal Stromal Tumors: Efficacy, Safety, and Impact of Liquid Biopsy and Other Biomarkers

Purpose:The purpose of this study is to evaluate ponatinib for advanced gastrointestinal stromal tumors (GIST).Patients and Methods:This single-arm phase II trial enrolled patients with metastatic and/or unresectable GIST with failure of prior tyrosine kinase inhibitor (TKI) treatment into two cohorts based on presence or absence of KIT exon 11 (ex11) primary mutations. Patients initially received ponatinib 45 mg once daily. Following a temporary clinical hold in October 2013, dose reductions were implemented to reduce risk of arterial occlusive events (AOE). Primary endpoint was 16-week clinical benefit rate (CBR) in KIT ex11–positive cohort. KIT mutations in circulating tumor DNA (ctDNA) were assessed.Results:Forty-five patients enrolled (30 KIT ex11–positive and 15 KIT ex11–negative); median follow-up was 14.7 and 13.6 months, respectively, as of August 1, 2016. Sixteen-week CBR was 36% (KIT ex11–positive; primary endpoint) and 20% (KIT ex11–negative). ctDNA analyses (n = 37) demonstrated strong concordance of primary KIT mutations between plasma and tumor. At least two secondary mutations were detected in 35% of patients overall and 54% of KIT ex11–positive patients. Changes from baseline in mutated ctDNA levels were consistent with clinical activity. Ponatinib was ineffective in patients with KIT exon 9 primary mutations. Resistance was associated with emergence of V654A. AOEs and venous thromboembolic events occurred in three and two patients, respectively. Six patients died; two deaths (pneumonia and pulmonary embolism) were considered possibly ponatinib-related.Conclusions:Ponatinib demonstrated activity in advanced GIST, particularly in KIT ex11–positive disease. ctDNA analysis confirmed heterogeneous resistance mutations in TKI-pretreated advanced GIST. Safety was consistent with previous studies.

Multitarget compounds that act on a diverse set of regulatory pathways are emerging as a therapeutic approach for a variety of cancers. Toward a more specified use of this approach, we hypothesize that the desired efficacy can be recreated in terms of a particular combination of relatively more specific (i.e., ostensibly single target) compounds. We test this hypothesis for the geldanamycin analogue 17-Allylamino-17-demethoxygeldanamycin (17AAG) in hepatocellular carcinoma cells, measuring critical phosphorylation levels that indicate the kinase pathway effects correlating with apoptotic responsiveness of the Hep3B cell line in contrast to the apoptotic resistance of the Huh7 cell line. A principal components analysis (PCA) constructed from time course measurements of seven phosphoprotein signaling levels identified modulation of the AKT, IκB kinase, and signal transducer and activator of transcription 3 pathways by 17AAG treatment as most important for distinguishing these cell-specific death responses. The analysis correctly suggested from 17AAG-induced effects on these phosphoprotein levels that the FOCUS cell line would show apoptotic responsiveness similarly to Hep3B. The PCA also guided the inhibition of three critical pathways and rendered Huh7 cells responsive to 17AAG. Strikingly, in all three hepatocellular carcinoma lines, the three-inhibitor combination alone exhibited similar or greater efficacy to 17AAG. We conclude that (a) the PCA captures and clusters the multipathway phosphoprotein time courses with respect to their 17AAG-induced apoptotic responsiveness and (b) we can recreate, in a more specified manner, the cellular responses of a prospective multitarget cancer therapeutic. [Mol Cancer Ther 2009;8(8):2183–92]

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The Bcl-2 family encompasses a diverse set of apoptotic regulators that are dynamically activated in response to various cell-intrinsic and -extrinsic stimuli. An extensive variety of cell culture experiments have identified effects of growth factors, cytokines, and drugs on Bcl-2 family functions, but in vivo studies have tended to focus on the role of one or two particular members in development and organ homeostasis. Thus, the ability of physiologically relevant contexts to modulate canonical dependencies that are likely to be more complex has yet to be investigated systematically. In this study, we report findings derived from a pool-based shRNA assay that systematically and comprehensively interrogated the functional dependence of leukemia and lymphoma cells upon various Bcl-2 family members across many diverse in vitro and in vivo settings. This approach permitted us to report the first in vivo loss of function screen for modifiers of the response to a front-line chemotherapeutic agent. Notably, our results reveal an unexpected role for the extrinsic death pathway as a tissue-specific modifier of therapeutic response. In particular, our findings show that particular tissue sites of tumor dissemination play critical roles in demarcating the nature and extent of cancer cell vulnerabilities and mechanisms of chemoresistance. Cancer Res; 71(17); 5850–8. ©2011 AACR.

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Purpose: The purpose of this study is to evaluate ponatinib for advanced gastrointestinal stromal tumors (GIST). Patients and Methods: This single-arm phase II trial enrolled patients with metastatic and/or unresectable GIST with failure of prior tyrosine kinase inhibitor (TKI) treatment into two cohorts based on presence or absence of KIT exon 11 (ex11) primary mutations. Patients initially received ponatinib 45 mg once daily. Following a temporary clinical hold in October 2013, dose reductions were implemented to reduce risk of arterial occlusive events (AOE). Primary endpoint was 16-week clinical benefit rate (CBR) in KIT ex11–positive cohort. KIT mutations in circulating tumor DNA (ctDNA) were assessed. Results: Forty-five patients enrolled (30 KIT ex11–positive and 15 KIT ex11–negative); median follow-up was 14.7 and 13.6 months, respectively, as of August 1, 2016. Sixteen-week CBR was 36% (KIT ex11–positive; primary endpoint) and 20% (KIT ex11–negative). ctDNA analyses (n = 37) demonstrated strong concordance of primary KIT mutations between plasma and tumor. At least two secondary mutations were detected in 35% of patients overall and 54% of KIT ex11–positive patients. Changes from baseline in mutated ctDNA levels were consistent with clinical activity. Ponatinib was ineffective in patients with KIT exon 9 primary mutations. Resistance was associated with emergence of V654A. AOEs and venous thromboembolic events occurred in three and two patients, respectively. Six patients died; two deaths (pneumonia and pulmonary embolism) were considered possibly ponatinib-related. Conclusions: Ponatinib demonstrated activity in advanced GIST, particularly in KIT ex11–positive disease. ctDNA analysis confirmed heterogeneous resistance mutations in TKI-pretreated advanced GIST. Safety was consistent with previous studies.

Metastatic cancer has a poor prognosis, because it is broadly disseminated and associated with both intrinsic and acquired drug resistance. Critical unmet needs in effectively killing drug resistant cancer cells include overcoming the drug desensitization characteristics of some metastatic cancers/lesions, and tailoring therapeutic regimens to both the tumor microenvironment and the genetic profiles of the resident cancer cells. Bioengineers and materials scientists are developing technologies to determine how metastatic sites exclude therapies, and how extracellular factors (including cells, proteins, metabolites, extracellular matrix, and abiotic factors) at metastatic sites significantly affect drug pharmacodynamics. Two looming challenges are determining which feature, or combination of features, from the tumor microenvironment drive drug resistance, and what the relative impact is of extracellular signals vs. intrinsic cell genetics in determining drug response. Sophisticated systems biology tools that can de-convolve a crowded network of signals and responses, as well as controllable microenvironments capable of providing discrete and tunable extracellular cues can help us begin to interrogate the high dimensional interactions governing drug resistance in patients.

BackgroundAdult and pediatric tumors display stark differences in their mutation spectra and chromosome alterations. Here, we attempted to identify common and unique gene dependencies and their associated biomarkers among adult and pediatric tumor isolates using functional genetic lethal screens and computational modeling.MethodsWe performed CRISRP-Cas9 lethality screens in two adult glioblastoma (GBM) tumor isolates and five pediatric brain tumor isolates representing atypical teratoid rhabdoid tumors (ATRT), diffuse intrinsic pontine glioma, GBM, and medulloblastoma. We then integrated the screen results with machine learning-based gene-dependency models generated from data from >900 cancer cell lines.ResultsWe found that >50% of candidate dependencies of 280 identified were shared between adult GBM tumors and individual pediatric tumor isolates. 68% of screen hits were found as nodes in our network models, along with shared and tumor-specific predictors of gene dependencies. We investigated network predictors associated with ADAR, EFR3A, FGFR1 (pediatric-specific), and SMARCC2 (ATRT-specific) gene dependency among our tumor isolates.ConclusionsThe results suggest that, despite harboring disparate genomic signatures, adult and pediatric tumor isolates share a preponderance of genetic dependences. Further, combining data from primary brain tumor lethality screens with large cancer cell line datasets produced valuable insights into biomarkers of gene dependency, even for rare cancers.Importance of the StudyOur results demonstrate that large cancer cell lines data sets can be computationally mined to identify known and novel gene dependency relationships in adult and pediatric human brain tumor isolates. Gene dependency networks and lethality screen results represent a key resource for neuro-oncology and cancer research communities. We also highlight some of the challenges and limitations of this approach.

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.

Ruthenium (Ru) and osmium (Os) complexes are of sustained interest in cancer research and may be alternative to platinum-based therapy. We detail here three new series of ruthenium and osmium complexes, supported by physico-chemical characterizations, including time-dependent density functional theory, a combined experimental and computational study on the aquation reactions and the nature of the metal-arene bond. Cytotoxic profiles were then evaluated on several cancer cell lines although with limited success. Further investigations were, however, performed on the most active series using a genetic approach based on RNA interference and highlighted a potential multi-target mechanism of action through topoisomerase II, mitotic spindle, HDAC and DNMT inhibition., info:eu-repo/semantics/published

Small molecules can affect many cellular processes. The disambiguation of these effects to identify the causative mechanisms of cell death is extremely challenging. This challenge impacts both clinical development and the interpretation of chemical genetic experiments. CX-5461 was developed as a selective RNA polymerase I inhibitor, but recent evidence suggests that it may cause DNA damage and induce G-quadraplex formation. Here we use three complimentary data mining modalities alongside biochemical and cell biological assays to show that CX-5461 exerts its primary cytotoxic activity through topoisomerase II poisoning. We then show that acquired resistance to CX-5461 in previously sensitive lymphoma cells confers collateral resistance to the topoisomerase II poison doxorubicin. Doxorubicin is already a frontline chemotherapy in a variety of hematopoietic malignancies, and CX-5461 is being tested in relapse/refractory hematopoietic tumors. Our data suggest that the mechanism of cell death induced by CX-5461 is critical for rational clinical development in these patients. Moreover, CX-5461 usage as a specific chemical genetic probe of RNA polymerase I function is challenging to interpret. Our multimodal data-driven approach is a useful way to detangle the intended and unintended mechanisms of drug action across diverse essential cellular processes.

Structured AbstractIntroductionModern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that known microenvironmental resistance mechanisms should not be able to compete for outgrowth with genetic resistance within a tumor, and yet evidence of microenvironmental resistance is often observed in the clinic. Quantitatively understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse.Materials and MethodsWe employed spatial agent-based models to explore regimes where spatial constraints enable microenvironmental resistance to significantly compete with genetically resistant subclones. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts (CAFs) in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates.Results and DiscussionOur simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic.ConclusionOur model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful reopenings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mixing was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.3% (RI), 62.5% (MA), and 98.9% (PA). We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown. Attack rate estimates through August 31 2020 are 6.2% (95% CI: 5.7% – 6.8%) of the total population infected for Rhode Island, 6.7% (95% CI: 5.4% – 7.6%) in Massachusetts, and 2.7% (95% CI: 2.5% – 3.1%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations and the close matches between the states’ reported COVID-19 deaths and excess deaths. Data in Pennsylvania may have been underreported for both non-hospitalized and hospitalized patients, casting substantial uncertainty on estimates of attack rate and infection fatality rate.

In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated.To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut.This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021.COVID-19-positive test result reported to state department of health.The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated.At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive.These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

BackgroundAdvances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data.ResultsWe have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer.ConclusionsCanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available athttps://github.com/GilbertLabUCSF/CanDI.

Summary Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment., Graphical abstract, Highlights • A method to test rare genomic findings for their relative conditional selection • ALKATI is not as mutually exclusive with BRAF or NRAS as they are with each other • ALKATI is not likely to be sufficient for cellular transformation or growth in vitro • Expressing activated oncogenic ALK in BRAFV600E melanoma cells is cytotoxic, Biological sciences; Systems biology; Cancer systems biology

Mutant evolution in fragmented populations has been studied extensively in evolutionary biology. With an increased focus on evolutionary dynamics in medical research, quantification of mutant load in fragmented populations with varying levels of migration has become especially important. Examples of fragmented populations are hematopoietic stem cell niches in the bone marrow where cells can re-circulate between niches through the blood, or colonic crypts where movement of cells across different crypts is not thought to be common. Here we use a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, the experiments confirmed that while the mean number of mutants is not influenced by migration, the probability distribution is, which manifested itself in a change in the skewedness of the distribution of the mutant numbers in the demes. In the case of disadvantageous mutants, we investigated the phenomenon of the increase in the expected number of mutants compared to that of the selection-mutation balance. In a single deme, this increase is observed when the deme size is lower than the critical size,Nc. In a fragmented system that consists of connected demes with a probability of migration, the increase in mutant numbers above the selection-mutation balance can be maintained in small (N < Nc) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially withN/Nc. These findings are relevant in the context of the complex and poorly understood processes that may lead to changes in the clonal composition in tissues and tumors.

Targeted therapies that are rationally designed to inhibit driver oncogenes generally offer more durable responses for cancer patients compared to standard chemotherapies. However, in virtually all cases, the development of drug resistance inevitably subverts eradication attempts. In tyrosine kinases inhibitors (TKIs), resistance is often addressed with next-generation TKIs or combination therapies. Appropriate treatment strategies are selected based on the nature of resistance outgrowth. However, intra- and intertumoral heterogeneity makes it impossible to predict a priori which resistance variants will drive relapse in a given patient, and thus what therapeutic strategy will be optimal. Here, we propose a resistance-agnostic treatment approach that seeks to leverage evolutionary principles, rather than combat them. We designed and demonstrated the capability of a gene therapy, which we term “dual-switch selection gene drives”, that can be used to engineer cancer cells in order to guide the tumor’s evolutionary trajectory towards eradication. The dual-switch gene drive system is a genetic circuit composed of two genes that can be delivered to tumor cells on a single vector in situ. The “switch one” gene is a drug target analog that acts as an inducible, transient resistance marker, enabling rapid selection of modified cells. The “switch two” gene is the therapeutic payload of the system. Its role is to kill tumor cells by activating an inert small molecule. Importantly, the activity of the therapeutic gene is diffuse, and this bystander effect is maximized by hitchhiking off the inducible resistance gene. We developed stochastic dynamic models of tumor evolution to predict parameter regimes where eradication is possible. With these design criteria, we constructed and evaluated our system. First, we generated a panel of optimized “switch one” genes co-opting the kinase domains for a range of TKI targets, including EGFR, ALK, and RET. We demonstrated that these synthetic genes could induce a conditional resistance phenotype and be used to select for modified cells in a mixed population. Next, we evaluated the suicide gene cytosine deaminase (CD), which converts the inert prodrug 5-FC into the nucleoside analog 5-FU. Cell viability assays for mixed populations of wild-type and CD-expressing cells confirmed a strong bystander effect in this system. Finally, we assembled a single vector harboring both switches. In a pooled experiment, gene drive cells were able to drive the mixed population to eradication, even when pre-existing resistance to the TKI was introduced. Our dual-switch design demonstrates the potential to target pre-existing resistant subpopulations, without foreknowledge of the exact nature of resistance. Given that drug resistance, and thus treatment failure, is inevitable in most cancers, our treatment strategy enables us to decide the terms of relapse. Rather than combat drug resistance, we can leverage it to amplify a therapeutic gene’s effect and ultimately collapse a complex tumor population. Citation Format: Scott M. Leighow, Haider Inam, Justin R. Pritchard. Design and construction of evolutionary-guided "selection gene drive" therapy [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR014.

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR014) of the Conference Proceedings. Citation Format: Scott M. Leighow, Haider Inam, Justin R. Pritchard. Design and construction of evolutionary-guided "selection gene drive" therapy [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B007.

Discovery of a new HDAC6 inhibitor reveals role for HDAC6 in the regulation of glycolytic metabolism., Triple-negative breast cancer (TNBC) is a subtype of breast cancer without a targeted form of therapy. Unfortunately, up to 70% of patients with TNBC develop resistance to treatment. A known contributor to chemoresistance is dysfunctional mitochondrial apoptosis signaling. We set up a phenotypic small-molecule screen to reveal vulnerabilities in TNBC cells that were independent of mitochondrial apoptosis. Using a functional genetic approach, we identified that a “hit” compound, BAS-2, had a potentially similar mechanism of action to histone deacetylase inhibitors (HDAC). An in vitro HDAC inhibitor assay confirmed that the compound selectively inhibited HDAC6. Using state-of-the-art acetylome mass spectrometry, we identified glycolytic substrates of HDAC6 in TNBC cells. We confirmed that inhibition or knockout of HDAC6 reduced glycolytic metabolism both in vitro and in vivo. Through a series of unbiased screening approaches, we have identified a previously unidentified role for HDAC6 in regulating glycolytic metabolism.

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021. Supplementary Information The online version contains supplementary material available at (10.1186/s12916-021-02038-w).

There are thousands of ubiquitously expressed mammalian genes, yet a genetic knockout can be lethal to one cell, and harmless to another. This context specificity confounds our understanding of genetics and cell biology. 2 large collections of pooled CRISPR screens offer an exciting opportunity to explore cell specificity. One explanation, synthetic lethality, occurs when a single “private” mutation creates a unique genetic dependency. However, by fitting thousands of machine learning models across millions of omic and CRISPR features, we discovered a “public” genetic architecture that is common across cell lines and explains more context specificity than synthetic lethality. This common architecture is built on CRISPR loss-of-function phenotypes that are surprisingly predictive of other loss-of-function phenotypes. Using these insights and inspired by the in silico lossy compression of images, we use machine learning to identify small “lossy compression” sets of in vitro CRISPR constructs where reduced measurements produce genome-scale loss-of-function predictions.

Mutations of ABL1 are the dominant mechanism of relapse in Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph + ALL). We performed highly accurate Duplex Sequencing of exons 4–10 of ABL1 on bone marrow or peripheral blood samples from 63 adult patients with previously untreated Ph + ALL who received induction with intensive chemotherapy plus a BCR-ABL1 TKI. We identified ABL1 mutations prior to BCR-ABL1 TKI exposure in 78% of patients. However, these mutations were generally present at extremely low levels (median variant allelic frequency 0.008% [range, 0.004%–3.71%] and did not clonally expand and lead to relapse in any patient, even when the pretreatment mutation was known to confer resistance to the TKI received. In relapse samples harboring a TKI-resistant ABL1 mutation, the corresponding mutation could not be detected pretreatment, despite validated sequencing sensitivity of Duplex Sequencing down to 0.005%. In samples under the selective pressure of ongoing TKI therapy, we detected low-level, emerging resistance mutations up to 5 months prior to relapse. These findings suggest that pretreatment ABL1 mutation assessment should not guide upfront TKI selection in Ph + ALL, although serial testing while on TKI therapy may allow for early detection of clinically actionable resistant clones.

Genomic studies are revolutionizing clinical oncology, but bridging the lab and the bedside requires the ability to efficiently interrogate rare genetic lesions in unexpected pathological settings using preclinical models. Oncogenes can exhibit intrinsic drug resistance to targeted therapy in different cells of origin, adding complexity to clinical interpretations of genomic findings. Here, we capitalize on the flexibility of engineered cell systems to rapidly profile known multi-kinase inhibitors that harbor rearranged during transfection (RET) kinase activity across multiple RET fusions. Identifying ponatinib as the most potent RET inhibitor tested, we used ponatinib to gauge therapeutic responsiveness in RET fusion-positive patient-derived xenograft (PDX) models. Using a genomics guided outlier approach, we identified 4 RET fusion PDX models with 3 different fusion partners (KIF5B, CCDC6, and NCOA4) in both non-small cell lung cancer and colorectal cancer. By comparing ponatinib activity in RET fusion-positive and RET fusion-negative PDX models alongside a standard of care chemotherapeutic agent, we show that RET fusions in colorectal tumors are therapeutically responsive to RET inhibition. Finally, we suggest that coupling engineered cell systems and genomics guided PDX model selection provides a rapid workflow to triage rare genomics findings.

SummaryTheoretical treatments of evolutionary dynamics tend to model the probability that a single “resistant” species will arise in a population. However, experimental studies have identified a diversity of mutations that can lead to genetic resistance. By quantitatively predicting mutations that occur across an entire drug target during treatment, we identify and bridge a fundamental gap in drug resistance theory: that nucleotide/codon substitution biases can dictate which resistant variants arise in the clinic. We find that the likeliest mutation can beat the most resistant mutation. This creates a new paradigm in drug resistance that we term“survival of the likeliest”. We use epidemiological evidence in leukemia, isogenic experiments, stochastic dynamics, and large-scale simulations to support this theory. In addition, this work has strong implications for drug design because not all resistance liabilities are created equal. In pathogenic populations that exhibit survival of the likeliest, exploiting the least likely evolutionary path can minimize resistance across a population during widespread drug use, even when a vulnerability-free molecule or combination cannot be made.Data and Code Availabilityhttps://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/

Introduction: GIST is the most common sarcoma of the GI tract, with most cases driven by activating mutations in KIT Ex11 or Ex9. Prognosis is poor for patients whose tumors progress following first-line (1L) imatinib treatment, despite availability of 3 approved drugs (sunitinib [2L], regorafenib [3L], ripretinib [4L]). In most patients, resistance is driven by secondary mutations in KIT, with nearly all occurring in either the ATP binding pocket (Ex13/14) or activation loop (Ex17/18). No approved drug has potent activity against both classes of resistance mutations and most patients appear to have multiple resistance mutations. There remains a need for a pan-mutant KIT inhibitor, which has potent activity against all classes of mutations. Experimental Procedures: Drug activity was determined by assessing effects on tumor cell lines and BaF3 cells expressing mutant forms of KIT. Mice were dosed orally once (THE-630 and sunitinib) or twice (ripretinib) daily at doses that did not exceed the MTD. Results: THE-630 potently (IC50 ~3 nM) inhibited KIT-driven viability and downstream signaling in GIST-T1 cells, which contain an activating deletion in Ex11 (Ex11Del). Similar results were observed in engineered BaF3 cells containing Ex11Del or an activating insertion in Ex9 (both IC50s ≤3 nM), whereas potency of imatinib, regorafenib, and ripretinib, was reduced by ≥5-fold against the Ex9 variant. Consistent with available clinical data, in engineered BaF3 cells containing a primary Ex11Del mutation and a resistance mutation, none of the 4 approved drugs had potent activity against both classes of resistance mutations. In contrast, THE-630 potently inhibited ATP binding pocket mutants (V654A and T670I) and activation loop mutants (D816G/H, D820A/G, N822K, Y823D, A829P), with IC50s all below 25 nM. THE-630 also had potent activity against V654A and D816H in the context of an Ex9 primary mutation (IC50s 10 and 33 nM). In vivo, THE-630 strongly inhibited tumor growth (by 86%) in a model containing the most common ATP binding pocket mutant (V654A) as a secondary mutation, while ripretinib achieved only modest inhibition (26%). THE-630 was also highly active in models expressing secondary activation loop mutants, regressing tumors containing N822K by 88% (compared to 25% tumor growth inhibition by sunitinib) and regressing tumors containing D820A by 59% (compared to 1% tumor regression by ripretinib). Conclusions: We have identified a next-generation pan-KIT inhibitor that has potent activity against all major classes of activating and resistance mutations observed in KIT-mutant GIST patients. A phase 1 clinical trial of THE-630 is planned to begin in 2021. Citation Format: Victor M. Rivera, Wei-Sheng Huang, Mengrou Lu, Justin R. Pritchard, David Dalgarno, William C. Shakespeare. Preclinical characterization of THE-630, a next-generation inhibitor for KIT-mutant gastrointestinal stromal tumors (GIST) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1292.

Purpose: Sequential treatment with targeted therapies can result in complex combinations of resistance mutations in drug targets. This mutational complexity has spurred the development of pan-target inhibitors, i.e., therapies for which no single target mutation can cause resistance. Because the propensity for on- versus off-target resistance varies across cancer types, a deeper understanding of the mutational burden in drug targets could rationalize treatment outcomes and prioritize pan-target inhibitors for indications where on-target mutations are most likely. Experimental Design: To measure and model the mutational landscape of a drug target at high resolution, we integrated single-molecule Duplex Sequencing of the ABL1 gene in Philadelphia-positive (Ph+) leukemias with computational simulations. Results: A combination of drug target mutational burden and tumor-initiating cell fraction is sufficient to predict that most patients with chronic myeloid leukemia are unlikely to harbor ABL1 resistance mutations at the time of diagnosis, rationalizing the exceptional success of targeted therapy in this setting. In contrast, our analysis predicts that many patients with Ph+ acute lymphoblastic leukemia (Ph+ ALL) harbor multiple preexisting resistant cells with single mutants. The emergence of compound mutations can be traced to initial use of an ABL1 inhibitor that is susceptible to resistance from single point mutations. Conclusions: These results argue that early use of therapies that achieve pan-inhibition of ABL1 resistance mutants might improve outcomes in Ph+ ALL. Our findings show how a deep understanding of the mutational burden in drug targets can be quantitatively coupled to phenotypic heterogeneity to rationalize clinical phenomena. Clin Cancer Res; 24(21); 5321–34. ©2018 AACR.

International audience; Mycobacterium abscessus, a rapidly growing mycobacterium (RGM) and an opportunistic human pathogen, is responsible for a wide spectrum of clinical manifestations ranging from pulmonary to skin and soft tissue infections. This intracellular organism can resist the bactericidal defense mechanisms of amoebae and macrophages, an ability that has not been observed in other RGM. M. abscessus can up-regulate several virulence factors during transient infection of amoebae, thereby becoming more virulent in subsequent respiratory infections in mice. Here, we sought to identify the M. abscessus genes required for replication within amoebae. To this end, we constructed and screened a transposon (Tn) insertion library of an M. abscessus subspecies massiliense clinical isolate for attenuated clones. This approach identified five genes within the ESX-4 locus, which in M. abscessus encodes an ESX-4 type VII secretion system that exceptionally also includes the ESX conserved EccE component. To confirm the screening results and to get further insight into the contribution of ESX-4 to M. abscessus growth and survival in amoebae and macrophages, we generated a deletion mutant of eccB4 that encodes a core structural element of ESX-4. This mutant was less efficient at blocking phagosomal acidification than its parental strain. Importantly, and in contrast to the wild-type strain, it also failed to damage phagosomes and showed reduced signs of phagosome-to-cytosol contact, as demonstrated by a combination of cellular and immunological assays. This study attributes an unexpected and genuine biological role to the underexplored mycobacterial ESX-4 system and its substrates.

The development of new drug regimens that allow rapid, sterilizing treatment of tuberculosis has been limited by the complexity and time required for genetic manipulations in Mycobacterium tuberculosis. CRISPR interference (CRISPRi) promises to be a robust, easily engineered and scalable platform for regulated gene silencing. However, in M. tuberculosis, the existing Streptococcus pyogenes Cas9-based CRISPRi system is of limited utility because of relatively poor knockdown efficiency and proteotoxicity. To address these limitations, we screened eleven diverse Cas9 orthologues and identified four that are broadly functional for targeted gene knockdown in mycobacteria. The most efficacious of these proteins, the CRISPR1 Cas9 from Streptococcus thermophilus (dCas9Sth1), typically achieves 20- to 100-fold knockdown of endogenous gene expression with minimal proteotoxicity. In contrast to other CRISPRi systems, dCas9Sth1-mediated gene knockdown is robust when targeted far from the transcriptional start site, thereby allowing high-resolution dissection of gene function in the context of bacterial operons. We demonstrate the utility of this system by addressing persistent controversies regarding drug synergies in the mycobacterial folate biosynthesis pathway. We anticipate that the dCas9Sth1 CRISPRi system will have broad utility for functional genomics, genetic interaction mapping and drug-target profiling in M. tuberculosis.

SUMMARY Rationally designing drugs that last longer in the face of biological evolution is a critical objective of drug discovery. However, this goal is thwarted by the diversity and stochasticity of evolutionary trajectories that drive uncertainty in the clinic. Although biophysical models can qualitatively predict whether a mutation causes resistance, they cannot quantitatively predict the relative abundance of resistance mutations in patient populations. We present stochastic, first-principle models that are parameterized on a large in vitro dataset and that accurately predict the epidemiological abundance of resistance mutations across multiple leukemia clinical trials. The ability to forecast resistance variants requires an understanding of their underlying mutation biases. Beyond leukemia, a meta-analysis across prostate cancer, breast cancer, and gastrointestinal stromal tumors suggests that resistance evolution in the adjuvant setting is influenced by mutational bias. Our analysis establishes a principle for rational drug design: when evolution favors the most probable mutant, so should drug design., Graphical Abstract, In Brief Drug resistance is often addressed through next-generation drug design, but evolutionary diversity complicates these efforts. Here, Leighow et al. demonstrate that multi-scale models can quantitatively predict mutant frequency. We find that when heterogeneity is limited, analysis requires an understanding of substitution likelihood. We show that these models can inform evolutionarily optimized drug design.

available in PMC 2014 April 04, The current clinical application of combination chemotherapy is guided by a historically successful set of practices that were developed by basic and clinical researchers 50–60 years ago. Thus, in order to understand how emerging approaches to drug development might aid the creation of new therapeutic combinations, it is critical to understand the defining principles underlying classic combination therapy and the original experimental rationales behind them. One such principle is that the use of combination therapies with independent mechanisms of action can minimize the evolution of drug resistance. Another is that in order to kill sufficient cancer cells to cure a patient, multiple drugs must be delivered at their maximum tolerated dose – a condition that allows for enhanced cancer cell killing with manageable toxicity. In light of these models, we aim to explore recent genomic evidence underlying the mechanisms of resistance to the combination regimens constructed on these principles. Interestingly, we find that emerging genomic evidence contradicts some of the rationales of early practitioners in developing commonly used drug regimens. However, we also find that the addition of recent targeted therapies has yet to change the current principles underlying the construction of anti-cancer combinatorial regimens, nor have they made substantial inroads into the treatment of most cancers. We suggest that emerging systems/network biology approaches have an immense opportunity to impact the rational development of successful drug regimens. Specifically, by examining drug combinations in multivariate ways, next generation combination therapies can be constructed with a clear understanding of how mechanisms of resistance to multi-drug regimens differ from single agent resistance., Massachusetts Institute of Technology (Eisen and Chang Career Development Associate Professor of Biology), National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), #U54-CA112967-06), National Institutes of Health (U.S.) (NIH RO1-CA128803-04)

The Bcl-2 family encompasses a diverse set of apoptotic regulators that are dynamically activated in response to various cell-intrinsic and -extrinsic stimuli. An extensive variety of cell culture experiments have identified effects of growth factors, cytokines, and drugs on Bcl-2 family functions, but in vivo studies have tended to focus on the role of one or two particular members in development and organ homeostasis. Thus, the ability of physiologically relevant contexts to modulate canonical dependencies that are likely to be more complex has yet to be investigated systematically. In this study, we report findings derived from a pool-based shRNA assay that systematically and comprehensively interrogated the functional dependence of leukemia and lymphoma cells upon various Bcl-2 family members across many diverse in vitro and in vivo settings. This approach permitted us to report the first in vivo loss of function screen for modifiers of the response to a front-line chemotherapeutic agent. Notably, our results reveal an unexpected role for the extrinsic death pathway as a tissue-specific modifier of therapeutic response. In particular, our findings show that particular tissue sites of tumor dissemination play critical roles in demarcating the nature and extent of cancer cell vulnerabilities and mechanisms of chemoresistance. Cancer Res; 71(17); 5850–8. ©2011 AACR., National Institutes of Health (U.S.) (NIH RO1 CA128803), National Cancer Institute (U.S.) (Integrated Cancer Biology Program grant NCI 1-U54-CA112967), David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship), Massachusetts Institute of Technology. Dept. of Biology (training grant)

Tumor necrosis factor-α (TNF) can stimulate multiple disparate hepatocyte responses—proliferation, survival, or apoptosis—depending on the cellular context. TNF binds and activates the receptor TNFR, leading to downstream activation of the c-Jun N-terminal kinase (JNK), inhibitor of nuclear factor-κB (IκB) kinase (IKK)–nuclear factor (NF)-κB, and p38 signaling pathways and caspase cascade.1 The modulation of these and other intracellular signaling pathways by concomitant synergistic and antagonistic cytokine stimuli, viruses, and/or pharmacological treatments determine specific cell responses to TNF.2–4 In the partial hepatectomy (PHx) model of liver regeneration, normally quiescent hepatocytes are stimulated to proliferate in a process regulated by multiple redundant signaling pathways and molecules.5 Following PHx, Kupffer cells are activated and secrete TNF and interleukin (IL)-6. These cytokines stimulate the transcription of a set of “immediate early” genes and a G0–G1 cell cycle progression in hepatocytes.6,7 TNF signaling primes hepatocytes for DNA replication through subsequent stimulation by hepatocyte growth factor and epidermal growth factor receptor (EGFR) ligands.5,7–9 In primary hepatocyte cultures, TNF not only potentiates growth factor-stimulated proliferation, but acts as a mitogen itself6,10 through the induced release of autocrine transforming growth factor-α (TGF-α) and its activation of serine-threonine protein kinase B (PKB)/Akt and extracellular signal-regulated kinase (ERK).11,12 The IL-1 receptor (IL-1R) agonists IL-1α and IL-1β antagonize hepatocyte proliferation when produced by nonparenchymal cells in vivo during liver regeneration and when added exogenously to mitogenic factors in vitro13,14 Hepatocytes are resistant to apoptosis stimulated by TNF alone as it activates both proapoptotic and anti-apoptotic signaling pathways.1,4 Consequently, pharmacologic or genetic interference with antiapoptotic signaling is commonly used to examine TNF-induced hepatocyte apoptosis.4 In diseased and/or virus-infected hepatocytes, TNF signaling contributes to apoptotic and necrotic cell death.4,15 Recently, we have shown that infection with a replication-deficient adenoviral vector (Adv) potently sensitizes human epithelial cell lines, including the C3A hepatocarcinoma cell line, to TNF-induced apoptosis via both proapoptotic and antiapoptotic signaling pathways.3 Adenoviral gene therapy vectors targeting the liver and other organs are often compromised due to hepatocyte death induced by both the viral vector itself and cytokines of the innate immune response such as TNF and IL-1β.16 Therefore, Adv infection might provide a physiologically relevant environment to potentiate TNF-induced apoptosis in primary hepatocytes and could lead to insights in liver adenoviral gene therapy. Hepatocyte death responses to TNF and other inflammatory cytokines can be antagonized by many of the same growth factors that stimulate hepatocyte proliferation8,17 or by naturally occurring inhibitors of cytokine signaling such as IL-1 receptor antagonist (IL-1ra).18 Whereas many of the factors, such as TNF, that affect hepatocytes during injury or stress arise primarily from exogenous sources, hepatocytes themselves secrete growth factors and cytokines that act in autocrine fashion to enhance or oppose exogenous stimuli.8,9,11,12,17,19 Recently, we have demonstrated that the response of human epithelial cell lines to TNF involves release of TGF-α, IL-1α, and IL-1ra, which provide conflicting and interlinked autocrine feedback signals governing apoptotic responses to TNF.2,20 Hepatocytes express TGF-α, IL-1α, IL-1β, and IL-1ra and their receptors but it is unknown whether these ligands operate via interlinked autocrine circuits to modulate hepatocyte proliferation and apoptosis responses to TNF.9,12,21,22 Here we show that rat hepatocyte proliferation and apoptosis responses to TNF are both mediated by an inducible, coupled, and self-antagonizing TGF-α–IL-1α/β–IL-1ra autocrine cascade. The net effect of this coupled autocrine cascade is pro-proliferative as induced by TNF alone but proapoptotic when induced by TNF in Adv-infected hepatocytes. Moreover, elucidation of this self-antagonizing autocrine cascade is a useful paradigm that helps rationalize the diverse landscape of hepatocyte phenotypic responses to TNF and TGF-α costimulation and their induction of autocrine IL-1α/β signaling.