Search Results Heading

MBRLSearchResults

mbrl.module.common.modules.added.book.to.shelf
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
    Done
    Filters
    Reset
  • Discipline
      Discipline
      Clear All
      Discipline
  • Is Peer Reviewed
      Is Peer Reviewed
      Clear All
      Is Peer Reviewed
  • Item Type
      Item Type
      Clear All
      Item Type
  • Subject
      Subject
      Clear All
      Subject
  • Year
      Year
      Clear All
      From:
      -
      To:
  • More Filters
      More Filters
      Clear All
      More Filters
      Source
    • Language
169 result(s) for "Farrell, Alex"
Sort by:
How the concavity of reproduction/survival trade-offs impacts the evolution of life history strategies
Previous works using different mathematical techniques, however, show that the concavity of the trade-off relationship can alter the expected life history strategies. Thus we developed a model and found that the concavity of the reproduction-survival curve can still have a large impact on life history strategies in an ecological model with Darwinian evolution.
Mutational analysis of microsatellite-stable gastrointestinal cancer with high tumour mutational burden: a retrospective cohort study
Genomic signatures contributing to high tumour mutational burden (TMB-H) independent from mismatch-repair deficiency (dMMR) or microsatellite instability-high (MSI-H) status are not well studied. We aimed to characterise molecular features of microsatellite stable (MSS) TMB-H gastrointestinal tumours. Molecular alterations of 48 606 gastrointestinal tumours from Caris Life Sciences (CARIS) identified with next-generation sequencing were compared among MSS–TMB-H, dMMR/MSI-H, and MSS–TMB-low (L) tumours, using χ2 or Fisher's exact tests. Antitumour immune response within the tumour environment was predicted by analysing the infiltration of immune cells and immune signatures using The Cancer Genome Atlas database. The Kaplan-Meier method and the log-rank test were used to evaluate the impact of gene alterations on the efficacy of immune checkpoint inhibitors in MSS gastrointestinal cancers from the CARIS database, a Memorial Sloan Kettering Cancer Center cohort, and a Peking University Cancer Hospital cohort. MSS–TMB-H was observed in 1600 (3·29%) of 48 606 tumours, dMMR/MSI-H in 2272 (4·67%), and MSS–TMB-L in 44 734 (92·03%). Gene mutations in SMAD2, MTOR, NFE2L2, RB1, KEAP1, TERT, and RASA1 might impair antitumour immune response despite TMB-H, while mutations in 16 other genes (CDC73, CTNNA1, ERBB4, EZH2, JAK2, MAP2K1, MAP2K4, PIK3R1, POLE, PPP2R1A, PPP2R2A, PTPN11, RAF1, RUNX1, STAG2, and XPO1) were related to TMB-H with enhanced antitumour immune response independent of dMMR/MSI-H, constructing a predictive model (modified TMB [mTMB]) for immune checkpoint inhibitor efficacy. Patients with any mutation in the mTMB gene signature, in comparison with patients with mTMB wildtype tumours, showed a superior survival benefit from immune checkpoint inhibitors in MSS gastrointestinal cancers in the CARIS cohort (n=95, median overall survival 18·77 months [95% CI 17·30–20·23] vs 7·03 months [5·73–8·34]; hazard ratio 0·55 [95% CI 0·31–0·99], p=0·044). In addition, copy number amplification in chromosome 11q13 (eg, CCND1, FGF genes) was more prevalent in MSS–TMB-H tumours than in the dMMR/MSI-H or MSS–TMB-L subgroups. Not all mutations related to TMB-H can enhance antitumour immune response. More composite biomarkers should be investigated (eg, mTMB signature) to tailor treatment with immune checkpoint inhibitors. Our data also provide novel insights for the combination of immune checkpoint inhibitors and drugs targeting cyclin D1 or FGFs. US National Cancer Institute, Gloria Borges WunderGlo Foundation, Dhont Family Foundation, Gene Gregg Pancreas Research Fund, San Pedro Peninsula Cancer Guild, Daniel Butler Research Fund, Victoria and Philip Wilson Research Fund, Fong Research Project, Ming Hsieh Research Fund, Shanghai Sailing Program, China National Postdoctoral Program for Innovative Talents, China Postdoctoral Science Foundation, National Natural Science Foundation of China.
Within-host infectious disease models accommodating cellular coinfection, with an application to influenza
Abstract Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.
Treatment outcome in an SI model with evolutionary resistance: a Darwinian model for the evolution of resistance
We consider a Darwinian (evolutionary game theoretic) version of a standard susceptible-infectious SI model in which the resistance of the disease causing pathogen to a treatment that prevents death to infected individuals is subject to evolutionary adaptation. We determine the existence and stability of all equilibria, both disease-free and endemic, and use the results to determine conditions under which the treatment will succeed or fail. Of particular interest are conditions under which a successful treatment in the absence of resistance adaptation (i.e. one that leads to a stable disease-free equilibrium) will succeed or fail when pathogen resistance is adaptive. These conditions are determined by the relative breadths of treatment effectiveness and infection transmission rate distributions as functions of pathogen resistance.
Do fatal infectious diseases eradicate host species?
In simple SI epidemic and endemic models, three classes of incidence functions are identified for their potential to be associated with host extinction: weakly upper density-dependent incidences are never associated with host extinction. Power incidences that depend on the number of susceptibles and infectives by powers strictly between 0 and 1 are associated with initial-constellation-dependent host extinction for all parameter values. Homogeneous incidences, of which frequency-dependent incidence is a very particular case, and power incidences are associated with global host extinction for certain parameter constellations and with host survival for others. Laboratory infection experiments with salamander larvae are equally well fitted by power incidences and certain upper density-dependent incidences such as the negative binomial incidence and do not rule out homogeneous incidences such as an asymmetric frequency-dependent incidence either.
Times from Infection to Disease-Induced Death and their Influence on Final Population Sizes After Epidemic Outbreaks
For epidemic models, it is shown that fatal infectious diseases cannot drive the host population into extinction if the incidence function is upper density-dependent. This finding holds even if a latency period is included and the time from infection to disease-induced death has an arbitrary length distribution. However, if the incidence function is also lower density-dependent, very infectious diseases can lead to a drastic decline of the host population. Further, the final population size after an epidemic outbreak can possibly be substantially affected by the infection-age distribution of the initial infectives if the life expectations of infected individuals are an unbounded function of infection age (time since infection). This is the case for lognormal distributions, which fit data from infection experiments involving tiger salamander larvae and ranavirus better than gamma distributions and Weibull distributions.
Characterization and impact of non‐canonical WNT signaling on outcomes of urothelial carcinoma
Background Non‐canonical WNT family (WNT5A pathway) signaling via WNT5A through ROR1 and its partner, ROR2, or Frizzled2 (FZD2) is linked to processes driving tumorigenesis and therapy resistance. We utilized a large dataset of urothelial carcinoma (UC) tumors to characterize non‐canonical WNT signaling through WNT5A, ROR1, ROR2, or FZD2 expression. Methods NextGen Sequencing of DNA (592 genes or WES)/RNA (WTS) was performed for 4125 UC tumors submitted to Caris Life Sciences. High and low expression of WNT5A, ROR1, ROR2, and FZD2 was defined as ≥ top and
559 Molecular characterization of Merkel cell carcinoma and association with Merkel cell polyomavirus
BackgroundMerkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine cancer with rapid progression and mortality rates of 33–46%.1 The majority of MCC is caused by Merkel Cell Polyomavirus (MCPyV) with the remainder induced by UV-mediated damage.1 2 Regardless of virus positivity or tumor mutational burden (TMB), immune checkpoint inhibitors (ICIs) are first line treatment for MCC1 with half of patients not responding or developing resistance. Few options exist for those refractory to immunotherapy.3 There is a need to identify the MCC-specific factors driving resistance and to identify alternate molecular targets.Methods205 MCC tumors were analyzed using next-generation sequencing (592, NextSeq; WES, NovaSeq) and WTS (NovaSeq) (Caris Life Sciences, Phoenix, AZ). TMB was measured by totaling somatic mutations per tumor (TMB-H: >10 mutations/MB). MCPy viral (MCPyV) status was determined for 68 WES profiled cases using a cut-off of 1000 reads after concordance testing with IHC. Immune cell infiltrates were estimated by Quantiseq. Significance was determined using Chi-square and Mann-Whitney U tests and adjusted for multiple comparisons (q-value <0.05).ResultsThe majority (89.3%) of MCPyV-negative MCC tumors were TMB-high (>10 mutations/Mb), with 96.4% having mutations in TP53 and 80.8% in RB1. Other gene mutations included NOTCH1 (37%), KMT2C (28.6%), TERT (17.9%), FAT1 (14.3%), and PIK3CA (14.3%). In contrast, MCPyV-positive tumors were frequently TMB-low (100%) and rarely harbored mutations in TP53 and RB1 (10.3% and 2.6%, respectively). Immune checkpoint gene (CD80, CD86, CD274, PD1, PD1L, and CTLA4) expression was similar between MCPyV-positive and -negative tumors. Estimated NK cell infiltration was significantly higher in MCPyV-negative tumors. MCPyV-negative MCC also had significantly higher expression of a MAPK pathway activation signature (MPAS).ConclusionsMCPyV-positive and -negative MCC represent two classes of molecularly distinct tumors and can be differentiated based on their TMB and mutational profile. The significantly increased NK cell infiltration seen in MCPyV-negative MCC represents a potential therapeutic pathway with the efficacy of NK cell-stimulating agents currently under investigation in the Quilt-3.063 trial.4 MPAS up-regulation in MCPyV-negative MCC suggests that MAPK inhibitors could be used as an alternative to ICIs, which is supported by preclinical data.3 5 MCPyV-negative and -positive MCC are distinct tumor subtypes whose molecular and immune cell profiles warrant further investigation to optimize use of current ICIs and identify therapeutic targets.ReferencesPark SY, Doolittle-Amieva C, Moshiri Y, Akaike T, Parvathaneni U, Bhatia S, Zaba LC, Nghiem P. How we treat Merkel cell carcinoma: within and beyond current guidelines. Future Oncol. 2021 Apr;17(11):1363−1377.Knepper TC, Montesion M, Russell JS, Sokol ES, Frampton GM, Miller VA, Albacker LA, McLeod HL, Eroglu Z, Khushalani NI, Sondak VK, Messina JL, Schell MJ, DeCaprio JA, Tsai KY, Brohl AS. The Genomic Landscape of Merkel Cell Carcinoma and Clinicogenomic Biomarkers of Response to Immune Checkpoint Inhibitor Therapy. Clin Cancer Res. 2019 Oct 1;25(19):5961−5971.Fang B, Kannan A, Zhao S, Nguyen QH, Ejadi S, Yamamoto M, Barreto JC, Zhao H, Gao L. Inhibition of PI3K by copanlisib exerts potent antitumor effects on Merkel cell carcinoma cell lines and mouse xenografts. Sci Rep. 2020 Jun 1;10(1):8867.ImmunityBio,Inc. QUILT-3.063: A Study of N-803, haNK and Avelumab in Patients With Merkel Cell Carcinoma That Has Progressed After Checkpoint Therapy. ClinicalTrials.gov identifier: NCT03853317. Updated June 18, 2023. Accessed June 27, 2023.Temblador A, Topalis D, Andrei G, Snoeck R. Synergistic targeting of the PI3K/mTOR and MAPK/ERK pathways in Merkel cell carcinoma. Tumour Virus Res. 2022 Dec;14:200244.Ethics ApprovalThis study was conducted in accordance with guidelines of the Declaration of Helsinki, Belmont report, and U.S. Common rule utilizing retrospective, deidentified clinical data in keeping with 45 CFR 46.101(b)(4). Therefore, this study is considered Institutional Review Board (IRB) exempt and no consent was necessary from the subjects.
1509 Characterizing somatostatin receptor 2 (SSTR2) expression and the immune landscape of olfactory neuroblastoma (ONB), sinonasal neuroendocrine carcinoma (SNEC), and sinonasal undifferentiated carcinoma (SNUC)
BackgroundONB, SNEC and SNUC are rare sinonasal neoplasms demonstrating varying neuroendocrine characteristics, with limited systemic treatment options. ONB expresses SSTR2, diagnostically and therapeutically actionable; SNUC is heterogeneous and a diagnosis of exclusion. We examined the gene expression of SSTR2 and the immune landscape in ONB, SNUC and SNECs.MethodsONB (N = 26), SNUC (N = 25), and SNEC (N = 7) tumors (all histologies per referring clinician, not internally validated) were tested at Caris Life Sciences (Phoenix, AZ) with NextGen Sequencing of DNA (592-gene or whole exome [WES]) and RNA (whole transcriptome). Tumors were defined as HPV 16/18+ using WES and Epstein Barr Virus+ (EBV+) using WES and EBER ISH. SNUC was further stratified into EBV+/EBV- cohorts. PD-L1 expression (22C3; Positive (+): TPS ≥1%) was assessed by IHC. A combination of IHC, NGS, and fragment analysis was used to assess deficient mismatch repair status and microsatellite instability (dMMR/MSI). RNA-Seq data were analyzed for a transcriptomic signature predictive of immunotherapy response (T-cell inflamed score) and immune cell fractions were estimated using quanTIseq. Mann-Whitney U, Fisher’s Exact and χ2 tests were applied as appropriate with p-values adjusted for multiple comparisons (p < 0.05).ResultsAll ONB and SNEC were EBV-, while 60% (15/25) of SNUC were EBV+. Only 10% (1/10) of EBV- SNUCs were HPV16+ with all other tumors being HPV 16/18-. The median expression of SSTR2 was highest in ONB followed by EBV+ SNUC, EBV- SNUC and SNEC (27.9, 8.4, 3.1, 3.1 (transcripts/million [TPM])) (figure 1, asterisk indicates p < 0.05). 18.75% (3/16) of ONB, 10% (1/10) of EBV- SNUC, and 100% (13/13) of EBV+ SNUC were PD-L1+ (p < 0.05, no data for SNEC). T cell-inflamed tumors were significantly more prevalent in EBV+ SNUC (67%) compared to EBV- SNUC (49%), ONB (9%) and SNEC (0%, p < 0.001). EBV+ SNUC had significantly higher median estimates (%) of immune cell infiltrates, including B cells (15.1, 8.2, 7.1 and 8.8), CD8+ T cells (5.2, 0.2, 0.5, 0.0), M1 macrophages (6.0, 1.3, 0.4, 0.6), and Tregs (7.2, 1.0, 0.0, 0.0) compared to EBV- SNUC, ONB and SNEC (all p < 0.05)ConclusionsIn this real-world dataset, the highest expression of SSTR2 was found in ONB and EBV+ SNUC, suggesting a role for SSTR2-directed strategies. Additionally, all EBV+ SNUCs were PD-+, immunogenic, and predominantly T-cell inflamed suggesting potential for immunotherapeutic strategies. For SNUC, validation of these results in additional cohorts is important.ConsentThis study was conducted in accordance with the guidelines of the Declaration of Helsinki, Belmont Report, and US Common Rule. In keeping with 45 CFR 46.101 (b), this study was performed utilizing retrospective, deidentified clinical data. Therefore, this study was deemed Institutional Review Board exempt, and no patient consent was necessary from the subjects.Abstract 1509 Figure 1SSTR2 expression across rare sinonasal malignancies