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While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

This study describes exposure to repetitive head impacts (RHI) by player position and activity during a collegiate football season, and investigates the relationship between RHI and acute (i.e., daily and weekly) and short-term (i.e., pre- to post-season) changes in balance, reaction time, symptoms, and cognition. We recorded RHI exposure in twenty Division I collegiate American football players during a single season using the Riddell InSite system. Participants sustained 4,586 impacts (4.20% high impact, i.e., >63 g; 95.79% low impact, i.e., 20–63 g). Greatest exposure to RHI was observed in running backs and defensive ends during games, and tight ends and defensive ends during practices. Running plays and team drills placed players at greatest risk for exposure during practice. Cumulative RHI exposure across the season was associated with short-term declines in reaction time (p = 0.045), but not balance or cognition. Acute decline in balance was associated with the number of impacts sustained in the past week (p 0.05). Acute increase in total symptom score was also associated with the number of impacts sustained in the past week (p 0.05). Reaction time did not decline based on impact exposure in the past 24 hours or week. This study identifies activities and positions that may put players at risk for RHI exposure, and demonstrates that RHI sustained during the course of typical American football play by non-concussed individuals may result in small changes in balance, reaction time, and symptoms, but not cognition.

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

Motivation Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. Results This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. Availability and implementation PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

Rationale: Clinical interest in combining targeted radionuclide therapies (TRT) with immunotherapies is growing. External beam radiation therapy (EBRT) activates a type 1 interferon (IFN1) response mediated via stimulator of interferon genes (STING), and this is critical to its therapeutic interaction with immune checkpoint blockade. However, little is known about the time course of IFN1 activation after EBRT or whether this may be induced by decay of a TRT source. Methods: We examined the IFN1 response and expression of immune susceptibility markers in B78 and B16 melanomas and MOC2 head and neck cancer murine models using qPCR and western blot. For TRT, we used 90Y chelated to NM600, an alkylphosphocholine analog that exhibits selective uptake and retention in tumor cells including B78 and MOC2. Results: We observed significant IFN1 activation in all cell lines, with peak activation in B78, B16, and MOC2 cell lines occurring 7, 7, and 1 days, respectively, following RT for all doses. This effect was STING-dependent. Select IFN response genes remained upregulated at 14 days following RT. IFN1 activation following STING agonist treatment in vitro was identical to RT suggesting time course differences between cell lines were mediated by STING pathway kinetics and not DNA damage susceptibility. In vivo delivery of EBRT and TRT to B78 and MOC2 tumors resulted in a comparable time course and magnitude of IFN1 activation. In the MOC2 model, the combination of 90Y-NM600 and dual checkpoint blockade therapy reduced tumor growth and prolonged survival compared to single agent therapy and cumulative dose equivalent combination EBRT and dual checkpoint blockade therapy. Conclusions: We report the time course of the STING-dependent IFN1 response following radiation in multiple murine tumor models. We show the potential of TRT to stimulate IFN1 activation that is comparable to that observed with EBRT and this may be critical to the therapeutic integration of TRT with immunotherapies.

Background The One Health concept promotes the enhancement of human, animal and ecosystem health through multi-sectorial governance support and policies to combat health security threats. In Vietnam, antimicrobial resistance (AMR) in animal and human health settings poses a significant threat, but one that could be minimised by adopting a One Health approach to AMR surveillance. To advance understanding of the willingness and abilities of the human and animal health sectors to undertake investigations of AMR with a One Health approach, we explored the perceptions and experiences of those tasked with investigating AMR in Vietnam, and the benefits a multi-sectorial approach offers. Methods This study used qualitative methodology to provide key informants’ perspectives from the animal and human health sectors. Two scenarios of food-borne AMR bacteria found within the pork value chain were used as case studies to investigate challenges and opportunities for improving collaboration across different stakeholders and to understand benefits offered by a One Health approach surveillance system. Fifteen semi-structured interviews with 11 participants from the animal and six from the human health sectors at the central level in Hanoi and the provincial level in Thai Nguyen were conducted. Results Eight themes emerged from the transcripts of the interviews. From the participants perspectives on the benefits of a One Health approach: (1) Communication and multi-sectorial collaboration; (2) Building comprehensive knowledge; (3) Improving likelihood of success. Five themes emerged from participants views of the challenges to investigate AMR: (4) Diagnostic capacity; (5) Availability and access to antibiotics (6) Tracing ability within the Vietnamese food chain; (7) Personal benefits and (8) Managing the system. Conclusion The findings of this study suggest that there is potential to strengthen multi-sectorial collaboration between the animal and human health sectors by building upon existing informal networks. Based on these results, we recommend an inclusive approach to multi-sectorial communication supported by government network activities to facilitate partnerships and create cross-disciplinary awareness and participation. The themes relating to diagnostic capacity show that both sectors are facing challenges to undertake investigations in AMR. Our results indicate that the need to strengthen the animal health sector is more pronounced.

The coronavirus SARS-CoV-2 is the cause of the ongoing COVID-19 pandemic. Most SARS-CoV-2 infections are mild or even asymptomatic. However, a small fraction of infected individuals develops severe, life-threatening disease, which is caused by an uncontrolled immune response resulting in hyperinflammation. However, the factors predisposing individuals to severe disease remain poorly understood. Here, we show that levels of CD47, which is known to mediate immune escape in cancer and virus-infected cells, are elevated in SARS-CoV-2-infected Caco-2 cells, Calu-3 cells, and air−liquid interface cultures of primary human bronchial epithelial cells. Moreover, SARS-CoV-2 infection increases SIRPalpha levels, the binding partner of CD47, on primary human monocytes. Systematic literature searches further indicated that known risk factors such as older age and diabetes are associated with increased CD47 levels. High CD47 levels contribute to vascular disease, vasoconstriction, and hypertension, conditions that may predispose SARS-CoV-2-infected individuals to COVID-19-related complications such as pulmonary hypertension, lung fibrosis, myocardial injury, stroke, and acute kidney injury. Hence, age-related and virus-induced CD47 expression is a candidate mechanism potentially contributing to severe COVID-19, as well as a therapeutic target, which may be addressed by antibodies and small molecules. Further research will be needed to investigate the potential involvement of CD47 and SIRPalpha in COVID-19 pathology. Our data should encourage other research groups to consider the potential relevance of the CD47/ SIRPalpha axis in their COVID-19 research.

Simple Summary Osteosarcoma is a rare type of cancer with poor prognoses. However, to the best of our knowledge, there are no mathematical models that study the impact of chemotherapy treatments on the osteosarcoma microenvironment. In this study, we developed a data driven mathematical model to analyze the dynamics of the important players in three groups of osteosarcoma tumors with distinct immune patterns in the presence of the most common chemotherapy drugs. The results indicate that the treatments’ start times and optimal dosages depend on the unique growth rate of the tumor, which implies the necessity of personalized medicine. Furthermore, the developed model can be extended by others to build models that can recommend individual-specific optimal dosages. Abstract Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances of dendritic cells and HMGB1 increase when drugs are given and decrease when drugs are absent. Populations of helper T cells, cytotoxic cells, and IFN-γ grow, and populations of cancer cells and other immune cells shrink during treatment. According to the model, the MAP regimen does a good job at killing cancer, and is more effective than doxorubicin and cisplatin combined or methotrexate alone. The results also indicate that it is important to consider the tumor’s unique growth rate when deciding the treatment details, as fast growing tumors need early treatment start times and high dosages.

Molecular and cellular effects of radiotherapy on tumor microenvironment (TME) can help prime and propagate antitumor immunity. We hypothesized that delivering radiation to all tumor sites could augment response to immunotherapies. We tested an approach to enhance response to immune checkpoint inhibitors (ICIs) by using targeted radionuclide therapy (TRT) to deliver radiation semiselectively to tumors. NM600, an alkylphosphocholine analog that preferentially accumulates in most tumor types, chelates a radioisotope and semiselectively delivers it to the TME for therapeutic or diagnostic applications. Using serial 86Y-NM600 positron emission tomography (PET) imaging, we estimated the dosimetry of 90Y-NM600 in immunologically cold syngeneic murine models that do not respond to ICIs alone. We observed strong therapeutic efficacy and reported optimal dose (2.5 to 5 gray) and sequence for 90Y-NM600 in combination with ICIs. After combined treatment, 45 to 66% of mice exhibited complete response and tumor-specific T cell memory, compared to 0% with 90Y-NM600 or ICI alone. This required expression of STING in tumor cells. Combined TRT and ICI activated production of proinflammatory cytokines in the TME, promoted tumor infiltration by and clonal expansion of CD8+ T cells, and reduced metastases. In mice bearing multiple tumors, combining TRT with moderate-dose (12 gray) external beam radiotherapy (EBRT) targeting a single tumor augmented response to ICIs compared to combination of ICIs with either TRT or EBRT alone. The safety of TRT was confirmed in a companion canine study. Low-dose TRT represents a translatable approach to promote response to ICIs for many tumor types, regardless of location.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R 2 will be artificially inflated to the extent that it is highly improbable that an R 2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality., To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R 2 will be artificially inflated to the extent that it is highly improbable that an R 2 value below .85 will be obtained no matter the true model accuracy.

Brain metastases develop in over 60% of advanced melanoma patients and negatively impact quality of life and prognosis. In a murine melanoma model, we previously showed that an in situ vaccination (ISV) regimen, combining radiation treatment and intratumoral (IT) injection of immunocytokine (IC: anti-GD2 antibody fused to IL2), along with the immune checkpoint inhibitor + anti-CTLA-4, robustly eliminates peripheral flank tumors but only has modest effects on co-occurring intracranial tumors. In this study, we investigated the ability of low-dose radiation to the brain to potentiate anti-tumor immunity against a brain tumor when combined with ISV + anti-CTLA-4. B78 (GD2(+), immunologically “cold”) melanoma tumor cells were implanted into the flank and the right striatum of the brain in C57BL/6 mice. Flank tumors (50–150 mm(3)) were treated following a previously optimized ISV regimen [radiation (12 Gy × 1, treatment day 1), IT-IC (50 lg daily, treatment days 6–10), and + anti-CTLA-4 (100 lg, treatment days 3, 6, 9)]. Mice that additionally received whole-brain radiation treatment (WBRT, 4 Gy × 1) on day 15 demonstrated significantly increased survival compared to animals that received ISV + anti-CTLA-4 alone, WBRT alone or no treatment (control) (P < 0.001, log-rank test). Timing of WBRT was critical, as WBRT administration on day 1 did not significantly enhance survival compared to ISV + anti-CTLA-4, suggesting that the effect of WBRT on survival might be mediated through immune modulation and not just direct tumor cell cytotoxicity. Modest increases in T cells (CD8(+) and CD4(+)) and monocytes/macrophages (F4/80(+)) but no changes in FOXP3(+) regulatory T cells (Tregs), were observed in brain melanoma tumors with addition of WBRT (on day 15) to ISV + anti-CTLA-4. Cytokine multiplex immunoassay revealed distinct changes in both intracranial melanoma and contralateral normal brain with addition of WBRT (day 15) to ISV + anti-CTLA-4, with notable significant changes in pro-inflammatory (e.g., IFNγ, TNFα and LIX/CXCL5) and suppressive (e.g., IL10, IL13) cytokines as well as chemokines (e.g., IP-10/CXCL10 and MIG/CXCL9). We tested the ability of the alkylphosphocholine analog, NM600, to deliver immunomodulatory radiation to melanoma brain tumors as a targeted radionuclide therapy (TRT). Yttrium-86 ((86)Y) chelated to NM600 was delivered intravenously by tail vein to mice harboring flank and brain melanoma tumors, and PET imaging demonstrated specific accumulation up to 72 h at each tumor site (~12:1 brain tumor/brain and ~8:1 flank tumor/muscle). When NM600 was chelated to therapeutic β-particle-emitting (90)Y and administered on treatment day 13, T-cell infiltration and cytokine profiles were altered in melanoma brain tumor, like that observed for WBRT. Overall, our results demonstrate that addition of low-dose radiation, timed appropriately with ISV administration to tumors outside the brain, significantly increases survival in animals co-harboring melanoma brain tumors. This observation has potentially important translational implications as a treatment strategy for increasing the response of tumors in the brain to systemically administered immunotherapies.

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the UK Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.

The coronavirus SARS-CoV-2 is the cause of the ongoing COVID-19 pandemic. Most SARS-CoV-2 infections are mild or even asymptomatic. However, a small fraction of infected individuals develops severe, life-threatening disease, which is caused by an uncontrolled immune response resulting in hyperinflammation. Antiviral interventions are only effective prior to the onset of hyperinflammation. Hence, biomarkers are needed for the early identification and treatment of high-risk patients. Here, we show in a range of model systems and data from post mortem samples that SARS-CoV-2 infection results in increased levels of CD47, which is known to mediate immune escape in cancer and virus-infected cells. Systematic literature searches also indicated that known risk factors such as older age and diabetes are associated with increased CD47 levels. High CD47 levels contribute to vascular disease, vasoconstriction, and hypertension, conditions which may predispose SARS-CoV-2-infected individuals to COVID-19-related complications such as pulmonary hypertension, lung fibrosis, myocardial injury, stroke, and acute kidney injury. Hence, CD47 is a candidate biomarker for severe COVID-19. Further research will have to show whether CD47 is a reliable diagnostic marker for the early identification of COVID-19 patients requiring antiviral therapy.

OBJECTIVENeurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations.METHODSUsing electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity.RESULTSAmong the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%-7.8%,pFDRpFDRINTERPRETATIONUsing an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.

The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly used with nearest-neighbor methods, and the derivations provide the starting point for the following novel results. We use extreme value theory to derive the mean and variance for metrics that are normalized by the range of each attribute (difference of max and min). We derive analytical formulas for a new metric for genetic variants, which are categorical variables that occur in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and the transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance distribution properties. This metric is applicable to correlation-based predictors derived from time-series data. The analytical means and variances are in strong agreement with simulation results. We also use simulations to explore the sensitivity of the expected means and variances in the presence of correlation and interactions in the data. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies, including gene expression, GWAS, and fMRI data.

Pork and pork products are important staple food in the diet of Vietnamese consumers. The safety of pork, including biological contamination, is a concern to several public authorities and value chain actors. This cross-sectional study aimed to identify Salmonella and total bacterial count (TBC) contamination of cut pork sold in different outlets, and determine the potential factors leading to contamination. A total of 671 pork samples were collected from different retail channels in three provinces in Northern Vietnam. Hygiene conditions and practices at pork vending premises were also observed and recorded. Data analysis used descriptive statistics and regression analysis. Overall, Salmonella prevalence in retailed pork was 58.1%. Salmonella contamination in pork from traditional retail, modern retail and food services were 60.5%, 50.9% and 80.5%, respectively. Eighty percent and 68% of fresh pork in canteen and street food was contaminated with Salmonella. Only a small proportion of a subset of the pork samples (6.2%) tested met the Vietnamese standard requirement for TBC contamination. Average concentration of TBC in fresh pork in traditional retail, modern retail and food services were 6.51 (SD: 0.64), 6.38 (0.65), and 6.96 (0.85) LogCFU/g, respectively. Transport time, use of the same tools for pork and other types of meat, storage temperature, and environment hygiene are important factors that might affect microbial contamination. The findings underline the high level of microbial contamination, which requires practical interventions to improve food safety hygiene practices and behavior of pork retailers.

respecifies allowable python versions for pypi to include 3.7.0 through 3.7.9

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients, the size of the tumor is sensitive to the parameters related to macrophages such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.

In head and neck squamous cell carcinoma (HNSCC) tumors that over-expresses huEGFR, the anti-EGFR antibody, cetuximab, antagonizes tumor cell viability and sensitizes to radiation therapy. However, the immunologic interactions between cetuximab and radiation therapy are not well understood. We transduced two syngeneic murine HNSCC tumor cell lines to express human EGFR (MOC1- and MOC2-huEGFR) in order to facilitate evaluation of the immunologic interactions between radiation and cetuximab. Cetuximab was capable of inducing antibody-dependent cellular cytotoxicity (ADCC) in MOC1- and MOC2-huEGFR cells but showed no effect on the viability or radiosensitivity of these tumor cells, which also express muEGFR that is not targeted by cetuximab. Radiation enhanced the susceptibility of MOC1- and MOC2-huEGFR to ADCC, eliciting a type I interferon response and increasing expression of NKG2D ligands on these tumor cells. Co-culture of splenocytes with cetuximab and MOC2-huEGFR cells resulted in increased expression of IFNγ in not only NK cells but also in CD8+ T cells, and this was dependent upon splenocyte expression of FcγR. In MOC2-huEGFR tumors, combining radiation and cetuximab induced tumor growth delay that required NK cells, EGFR expression, and FcγR on host immune cells. Combination of radiation and cetuximab increased tumor infiltration with NK and CD8+ T cells but not regulatory T cells. Expression of PD-L1 was increased in MOC2-huEGFR tumors following treatment with radiation and cetuximab. Delivering anti-PD-L1 antibody with radiation and cetuximab improved survival and resulted in durable tumor regression in some mice. Notably, these cured mice showed evidence of an adaptive memory response that was not specifically directed against huEGFR. These findings suggest an opportunity to improve the treatment of HNSCC by combining radiation and cetuximab to engage an innate anti-tumor immune response that may prime an effective adaptive immune response when combined with immune checkpoint blockade. It is possible that this approach could be extended to any immunologically cold tumor that does not respond to immune checkpoint blockade alone and for which a tumor-specific antibody exists or could be developed.

Background Antibodies can play an important role in innate and adaptive immune responses against cancer. Using a high-density peptide array, we assessed potential protein-targets for antibodies detected in mice cured of melanoma through a combined immunotherapy regimen. Our goal was to determine the linear peptide sequences recognized by anti-tumor antibodies produced in mice cured of melanoma following immunotherapy. Methods Mice with GD2-expressing syngeneic B78 melanoma were treated with a combination immunotherapy (local radiation therapy + intratumoral anti-GD2 mAb linked to IL2) capable of inducing an ‘in situ vaccine’ effect (ISV), enabling mice to be cured of their tumors with long-term immune memory.1 Naive and immune sera were collected from these mice. Using flow cytometry, immune sera showed strong antibody-binding against B16 (parental cell line of B78 without GD2 expression). These sera were then used on a Nimble Therapeutics’ peptide-array (either whole proteome or a curated list of ~650 proteins) to determine specific antibody-binding sites, and data were analyzed using a dynamic programming method that scans adjacent peptides to determine whether a peptide is bound by antibodies. Proteins were selected if peptides were bound using immune sera but not bound with the sera from naive or non-responding tumor-bearing mice. Results Multiple proteins were selectively identified by immune sera that were not detected by sera from naive or non-responding tumor-bearing mice. When focusing on the whole mouse proteome data, thousands of peptides were targeted by 2 or more mice and exhibited strong antibody binding only by immune sera. We also identified a few proteins that elicited an immune response in the naive mouse sera that showed a significantly stronger signal in the immune sera of the same mice indicating that the cancer and/or the received therapy strengthened the immune response to these proteins. Conclusions We are able to detect selective antibody binding to immune sera. However, we are continuing to refine our analytical methods and are further investigating the identified proteins. These peptides may potentially serve as targets for antibody-based or cellular therapies. In addition, we are examining whether some of the identified tumor-specific endogenous antibodies might be used as biomarkers to predict response to our ISV regimen and potentially other immunotherapy treatments. Reference Morris ZS, et al. In Situ Tumor Vaccination by Combining Local Radiation and Tumor-Specific Antibody or Immunocytokine Treatments. Can Res. 2016; 76:3929–3941

Delivering a keynote talk at a conference organized by a scientific society or being named as a fellow by such a society indicates that a scientist is held in high regard by their colleagues. To explore if the distribution of such indicators of esteem in the field of bioinformatics reflects the composition of this field, we compared the gender, name origin, and country of affiliation of 412 honorees from the "International Society for Computational Biology" (75 fellows and 337 keynote speakers) with over 170,000 last authorships on computational biology papers between 1993 and 2019. The proportion of honors bestowed on women was similar to that of the field's overall last authorship rate. However, names of East Asian origin have been persistently underrepresented among honorees. Moreover, there were roughly twice as many honors bestowed on scientists with an affiliation in the United States as expected based on literature authorship. A record of this paper's transparent peer review process is included in the supplemental information.

Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.

Summarytreeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods.AvailabilityThe treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration.Contactttle@pennmedicine.upenn.edu

BackgroundImmune checkpoint inhibition (ICI) alone is not efficacious for a large number of patients with melanoma brain metastases. We previously established an in situ vaccination (ISV) regimen combining radiation and immunocytokine to enhance response to ICIs. Here, we tested whether ISV inhibits the development of brain metastases in a murine melanoma model.MethodsB78 (GD2+) melanoma ‘primary’ tumors were engrafted on the right flank of C57BL/6 mice. After 3–4 weeks, primary tumors were treated with ISV (radiation (12 Gy, day 1), α-GD2 immunocytokine (hu14.18-IL2, days 6–10)) and ICI (α-CTLA-4, days 3, 6, 9). Complete response (CR) was defined as no residual tumor observed at treatment day 90. Mice with CR were tested for immune memory by re-engraftment with B78 in the left flank and then the brain. To test ISV efficacy against metastases, tumors were also engrafted in the left flank and brain of previously untreated mice. Tumors were analyzed by quantitative reverse transcription-PCR, immunohistochemistry, flow cytometry and multiplex cytokine assay.ResultsISV+α-CTLA-4 resulted in immune memory and rejection of B78 engraftment in the brain in 11 of 12 mice. When B78 was engrafted in brain prior to treatment, ISV+α-CTLA-4 increased survival compared with ICI alone. ISV+α-CTLA-4 eradicated left flank tumors but did not elicit CR at brain sites when tumor cells were engrafted in brain prior to ISV. ISV+α-CTLA-4 increased CD8+ and CD4+ T cells in flank and brain tumors compared with untreated mice. Among ISV + α-CTLA-4 treated mice, left flank tumors showed increased CD8+ infiltration and CD8+:FOXP3+ ratio compared with brain tumors. Flank and brain tumors showed minimal differences in expression of immune checkpoint receptors/ligands or Mhc-1. Cytokine productions were similar in left flank and brain tumors in untreated mice. Following ISV+α-CTLA-4, production of immune-stimulatory cytokines was greater in left flank compared with brain tumor grafts.ConclusionISV augmented response to ICIs in murine melanoma at brain and extracranial tumor sites. Although baseline tumor-immune microenvironments were similar at brain and extracranial tumor sites, response to ISV+α-CTLA-4 was divergent with reduced infiltration and activation of immune cells in brain tumors. Additional therapies may be needed for effective antitumor immune response against melanoma brain metastases.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap”. Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap’s dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap (MBAG) is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.HighlightsThe brain age gap is an out-of-sample residual, and as such varies as a function of age.A recently proposed modification of the brain age gap, designed to mitigate the dependence on age, results in inflated model accuracy statistics if used incorrectly.Given these limitations, we suggest that new methods should be developed to quantify deviation from normal developmental and aging trajectories.