Human basal-like breast cancer is represented by one of the two mammary tumor subtypes in dogs.

BACKGROUND: About 20% of breast cancers in humans are basal-like, a subtype that is often triple-negative and difficult to treat. An effective translational model for basal-like breast cancer is currently lacking and urgently needed. To determine whether spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog-human molecular homology at the subtype level. METHODS: We subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We identified feature genes for human BLBC and luminal A subtypes via machine learning and used these genes to repeat canine-alone and cross-species tumor classifications. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog-human subtype comparison. RESULTS: Our independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 and feature gene classifications, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched in histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER-) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER-PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon-γ response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors and with the expression of T cell exhaustion markers (e.g., PDCD1) in ER-PR+ canine tumors. CONCLUSIONS: We identify a canine mammary tumor subtype that molecularly resembles human BLBC overall and thus could serve as a vital translational model of this devastating breast cancer subtype. Our study also sheds light on the dog-human difference in the mammary tumor histology and the hormonal cycle.

Evaluating biomarkers for treatment selection from reproducibility studies.

We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and standard biomarkers are both measured on a set of patient samples, or adopting replicated measures of the error-contaminated standard biomarkers in the original study. This approach is easier to conduct and much less expensive than studies that require new samples from patients randomized to the intervention. In addition, it makes it possible to perform the estimation of the clinical performance quickly, since there will be no requirement to wait for events to occur as would be the case with prospective validation. The treatment selection is assessed via a working model, but the proposed estimator of the mean restricted lifetime is valid even if the working model is misspecified. The proposed approach is assessed through simulation studies and applied to a cancer study.

Development and internal validation of risk scores to diagnose infectious mononucleosis among college students.

BACKGROUND: Individual symptoms and signs of infectious mononucleosis (IM) are of limited value for diagnosis. OBJECTIVE: To develop and validate risk scores based on signs and symptoms with and without haematologic parameters for the diagnosis of IM. DESIGN AND SETTING: Data were extracted from electronic health records of a university health centre and were divided into derivation (9/1/2015-10/31/2017) and a prospective temporal internal validation (11/1/2017-1/31/2019) cohort. METHOD: Independent predictors for the diagnosis of IM were identified in univariate analysis using the derivation cohort. Logistic regression models were used to develop 2 risk scores: 1 with only symptoms and signs (IM-NoLab) and 1 adding haematologic parameters (IM-Lab). Point scores were created based on the regression coefficients, and patients were grouped into risk groups. Primary outcomes were area under the receiver operating characteristic curve (AUROCC) and classification accuracy. RESULTS: The IM-NoLab model had 4 predictors and identified a low-risk group (7.9% with IM) and a high-risk group (22.2%) in the validation cohort. The AUROCC was 0.75 in the derivation cohort and 0.69 in the validation cohort. The IM-Lab model had 3 predictors and identified a low-risk group (3.6%), a moderate-risk group (12.5%), and a high-risk group (87.6%). The AUROCC was 0.97 in the derivation cohort and 0.93 in the validation cohort. CONCLUSION: We derived and internally validated the IM-NoLab and IM-Lab risk scores. The IM-Lab score in particular had very good discrimination and have the potential to reduce the need for diagnostic testing for IM.

Sample size methods for evaluation of predictive biomarkers.

Treatment selection biomarkers are those that can be useful in guiding choice of therapy. Just as new therapies require evaluation in appropriately designed clinical trials to determine their benefit, therapy selection biomarkers require evaluation in appropriately designed studies. These studies may be prospective clinical trials or retrospective studies based on specimens stored from a completed clinical trial. Ideally, patient treatment assignments should be randomized, and consideration should be given to an appropriate sample size-either for prospective planning of a new study or access to a sufficient number of stored specimens. Here, we develop a novel sample size method for estimation of a confidence interval of specified average width, for an intuitively appealing previously proposed parameter that reflects the expected benefit of using biomarker-guided therapy relative to a standard-of-care therapy. The estimation approach combines Monte Carlo and regression to result in a procedure that performs well over a range of scenarios. Although derived under a specific Cox proportional hazards regression model, robustness to model violations is demonstrated by evaluation under accelerated failure time and cure models. The sample size method produces adequate or conservative sample size estimates under a range of scenarios. Computer code in R and C++, and applications for Mac and Windows are made available for implementation of the sample size estimation procedure. The method is applied to a real data setting and results discussed.

Defining adequate contact for transmission of Mycobacterium tuberculosis in an African urban environment.

BACKGROUND: The risk of infection from respiratory pathogens increases according to the contact rate between the infectious case and susceptible contact, but the definition of adequate contact for transmission is not standard. In this study we aimed to identify factors that can explain the level of contact between tuberculosis cases and their social networks in an African urban environment. METHODS: This was a cross-sectional study conducted in Kampala, Uganda from 2013 to 2017. We carried out an exploratory factor analysis (EFA) in social network data from tuberculosis cases and their contacts. We evaluated the factorability of the data to EFA using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO). We used principal axis factoring with oblique rotation to extract and rotate the factors, then we calculated factor scores for each using the weighted sum scores method. We assessed construct validity of the factors by associating the factors with other variables related to social mixing. RESULTS: Tuberculosis cases (N = 120) listed their encounters with 1154 members of their social networks. Two factors were identified, the first named "Setting" captured 61% of the variance whereas the second, named 'Relationship' captured 21%. Median scores for the setting and relationship factors were 10.2 (IQR 7.0, 13.6) and 7.7 (IQR 6.4, 10.1) respectively. Setting and Relationship scores varied according to the age, gender, and nature of the relationship among tuberculosis cases and their contacts. Family members had a higher median setting score (13.8, IQR 11.6, 15.7) than non-family members (7.2, IQR 6.2, 9.4). The median relationship score in family members (9.9, IQR 7.6, 11.5) was also higher than in non-family members (6.9, IQR 5.6, 8.1). For both factors, household contacts had higher scores than extra-household contacts (p < .0001). Contacts of male cases had a lower setting score as opposed to contacts of female cases. In contrast, contacts of male and female cases had similar relationship scores. CONCLUSIONS: In this large cross-sectional study from an urban African setting, we identified two factors that can assess adequate contact between tuberculosis cases and their social network members. These findings also confirm the complexity and heterogeneity of social mixing.

Urban-rural disparities in treatment outcomes among recurrent TB cases in Southern Province, Zambia.

BACKGROUND: At least 13-20% of all Tuberculosis (TB) cases are recurrent TB. Recurrent TB has critical public health importance because recurrent TB patients have high risk of Multi-Drug Resistant TB (MDR-TB). It is critical to understand variations in the prevalence and treatment outcomes of recurrent TB between different geographical settings. The objective of our study was to estimate the prevalence of recurrent TB among TB cases and compare risk of unfavorable treatment outcomes between rural and urban settings. METHODS: In a retrospective cohort study conducted in southern province of Zambia, we used mixed effects logistic regression to asses associations between explanatory and outcome variables. Primary outcome was all-cause mortality and exposure was setting (rural/urban). Data was abstracted from the facility TB registers. RESULTS: Overall 3566 recurrent TB cases were diagnosed among 25,533 TB patients. The prevalence of recurrent TB was 15.3% (95% CI: 14.8 15.9) in urban and 11.3% (95% CI: 10.7 12.0) in rural areas. Death occurred in 197 (5.5%), 103 (2.9%) were lost to follow-up, and 113 (3.2%) failed treatment. Rural settings had 70% higher risk of death (adjusted OR: 1.7; 95% CI: 1.2 2.7). Risk of lost to follow-up was twice higher in rural than urban (adjusted OR: 2.0 95% CI: 1.3 3.0). Compared to HIV-uninfected, HIV-infected individuals on Antiretroviral Treatment (ART) were 70% more likely to die (adjusted OR: 1.7; 95% CI: 1.2 3.1). CONCLUSION: Recurrent TB prevalence was generally high in both urban and rural settings. The risk of mortality and lost to follow-up was higher among rural patients. We recommend a well-organized Directly Observed Therapy strategy adapted to setting where heightened TB control activities are focused on areas with poor treatment outcomes.

Canine spontaneous head and neck squamous cell carcinomas represent their human counterparts at the molecular level.

Spontaneous canine head and neck squamous cell carcinoma (HNSCC) represents an excellent model of human HNSCC but is greatly understudied. To better understand and utilize this valuable resource, we performed a pilot study that represents its first genome-wide characterization by investigating 12 canine HNSCC cases, of which 9 are oral, via high density array comparative genomic hybridization and RNA-seq. The analyses reveal that these canine cancers recapitulate many molecular features of human HNSCC. These include analogous genomic copy number abnormality landscapes and sequence mutation patterns, recurrent alteration of known HNSCC genes and pathways (e.g., cell cycle, PI3K/AKT signaling), and comparably extensive heterogeneity. Amplification or overexpression of protein kinase genes, matrix metalloproteinase genes, and epithelial-mesenchymal transition genes TWIST1 and SNAI1 are also prominent in these canine tumors. This pilot study, along with a rapidly growing body of literature on canine cancer, reemphasizes the potential value of spontaneous canine cancers in HNSCC basic and translational research.

The need for a network to establish and validate predictive biomarkers in cancer immunotherapy.

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, the entire medical oncology field has been revolutionized by the introduction of immune checkpoints inhibitors. Despite success in a variety of malignancies, responses typically only occur in a small percentage of patients for any given histology or treatment regimen. There are also concerns that immunotherapies are associated with immune-related toxicity as well as high costs. As such, identifying biomarkers to determine which patients are likely to derive clinical benefit from which immunotherapy and/or be susceptible to adverse side effects is a compelling clinical and social need. In addition, with several new immunotherapy agents in different phases of development, and approved therapeutics being tested in combination with a variety of different standard of care treatments, there is a requirement to stratify patients and select the most appropriate population in which to assess clinical efficacy. The opportunity to design parallel biomarkers studies that are integrated within key randomized clinical trials could be the ideal solution. Sample collection (fresh and/or archival tissue, PBMC, serum, plasma, stool, etc.) at specific points of treatment is important for evaluating possible biomarkers and studying the mechanisms of responsiveness, resistance, toxicity and relapse. This white paper proposes the creation of a network to facilitate the sharing and coordinating of samples from clinical trials to enable more in-depth analyses of correlative biomarkers than is currently possible and to assess the feasibilities, logistics, and collated interests. We propose a high standard of sample collection and storage as well as exchange of samples and knowledge through collaboration, and envisage how this could move forward using banked samples from completed studies together with prospective planning for ongoing and future clinical trials.

Differential Toxicities of Nickel Salts to the Nematode Caenorhabditis elegans.

This study focused on assessing whether nickel (Ni) toxicity to the nematode Caenorhabditis elegans was affected by the molecular structure of the Ni salt used. Nematodes were exposed to seven Ni salts [Ni sulfate hexahydrate (NiSO4·6H2O), Ni chloride hexahydrate (NiCl2·6H2O), Ni acetate tetrahydrate (Ni(OCOCH3)2·4H2O), Ni nitrate hexahydrate (N2NiO6·6H2O), anhydrous Ni iodide (NiI2), Ni sulfamate hydrate (Ni(SO3NH2)2·H2O), and Ni fluoride tetrahydrate (NiF2·4H2O)] in an aquatic medium for 24 h, and lethality curves were generated and analyzed. Ni fluoride, Ni iodide, and Ni chloride were most toxic to C. elegans, followed by Ni nitrate, Ni sulfamate, Ni acetate, and Ni sulfate. The LC50 values of the halogen-containing salts were statistically different from the corresponding value of the least toxic salt, Ni sulfate. This finding is consistent with the expected high bioavailability of free Ni ions in halide solutions. We recommend that the halide salts be used in future Ni testing involving aquatic invertebrates.

Sample size requirements for training high-dimensional risk predictors.

A common objective of biomarker studies is to develop a predictor of patient survival outcome. Determining the number of samples required to train a predictor from survival data is important for designing such studies. Existing sample size methods for training studies use parametric models for the high-dimensional data and cannot handle a right-censored dependent variable. We present a new training sample size method that is non-parametric with respect to the high-dimensional vectors, and is developed for a right-censored response. The method can be applied to any prediction algorithm that satisfies a set of conditions. The sample size is chosen so that the expected performance of the predictor is within a user-defined tolerance of optimal. The central method is based on a pilot dataset. To quantify uncertainty, a method to construct a confidence interval for the tolerance is developed. Adequacy of the size of the pilot dataset is discussed. An alternative model-based version of our method for estimating the tolerance when no adequate pilot dataset is available is presented. The model-based method requires a covariance matrix be specified, but we show that the identity covariance matrix provides adequate sample size when the user specifies three key quantities. Application of the sample size method to two microarray datasets is discussed.

Covariance adjustment for batch effect in gene expression data.

Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.

Comparison of confidence interval methods for an intra-class correlation coefficient (ICC).

BACKGROUND: The intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies. METHODS: This study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods. RESULTS: We show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited. CONCLUSIONS: Decision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

Human basal-like breast cancer is represented by one of the two mammary tumor subtypes in dogs.

Background: About 20% of breast cancers in humans are basal-like, a subtype that is often triple negative and difficult to treat. An effective translational model for basal-like breast cancer (BLBC) is currently lacking and urgently needed. To determine if spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog-human molecular homology at the subtype level. Methods: We subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog-human subtype comparison. Results: Our independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 classification, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER-) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER-PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon-γ response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors, and with the expression of T cell exhaustion markers (e.g., PDCD1 ) in ER-PR+ canine tumors. Conclusions: We identify a canine mammary tumor subtype that molecularly resembles human BLBC overall, and thus could serve as a vital spontaneous animal model of this devastating breast cancer subtype. Our study also sheds light on the dog-human difference in the mammary tumor histology and the hormonal cycle.

Lower confidence bounds for prediction accuracy in high dimensions via AROHIL Monte Carlo.

MOTIVATION: Implementation and development of statistical methods for high-dimensional data often require high-dimensional Monte Carlo simulations. Simulations are used to assess performance, evaluate robustness, and in some cases for implementation of algorithms. But simulation in high dimensions is often very complex, cumbersome and slow. As a result, performance evaluations are often limited, robustness minimally investigated and dissemination impeded by implementation challenges. This article presents a method for converting complex, slow high-dimensional Monte Carlo simulations into simpler, faster lower dimensional simulations. RESULTS: We implement the method by converting a previous Monte Carlo algorithm into this novel Monte Carlo, which we call AROHIL Monte Carlo. AROHIL Monte Carlo is shown to exactly or closely match pure Monte Carlo results in a number of examples. It is shown that computing time can be reduced by several orders of magnitude. The confidence bound method implemented using AROHIL outperforms the pure Monte Carlo method. Finally, the utility of the method is shown by application to a number of real microarray datasets.

Rearrangement of CRLF2 is associated with mutation of JAK kinases, alteration of IKZF1, Hispanic/Latino ethnicity, and a poor outcome in pediatric B-progenitor acute lymphoblastic leukemia.

Gene expression profiling of 207 uniformly treated children with high-risk B-progenitor acute lymphoblastic leukemia revealed 29 of 207 cases (14%) with markedly elevated expression of CRLF2 (cytokine receptor-like factor 2). Each of the 29 cases harbored a genomic rearrangement of CRLF2: 18 of 29 (62%) had a translocation of the immunoglobulin heavy chain gene IGH@ on 14q32 to CRLF2 in the pseudoautosomal region 1 of Xp22.3/Yp11.3, whereas 10 (34%) cases had a 320-kb interstitial deletion centromeric of CRLF2, resulting in a P2RY8-CRLF2 fusion. One case had both IGH@-CRLF2 and P2RY8-CRLF2, and another had a novel CRLF2 rearrangement. Only 2 of 29 cases were Down syndrome. CRLF2 rearrangements were significantly associated with activating mutations of JAK1 or JAK2, deletion or mutation of IKZF1, and Hispanic/Latino ethnicity (Fisher exact test, P < .001 for each). Within this cohort, patients with CRLF2 rearrangements had extremely poor treatment outcomes compared with those without CRLF2 rearrangements (35.3% vs 71.3% relapse-free survival at 4 years; P < .001). Together, these observations suggest that activation of CRLF2 expression, mutation of JAK kinases, and alterations of IKZF1 cooperate to promote B-cell leukemogenesis and identify these pathways as important therapeutic targets in this disease.

Identification of novel cluster groups in pediatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome.

To resolve the genetic heterogeneity within pediatric high-risk B-precursor acute lymphoblastic leukemia (ALL), a clinically defined poor-risk group with few known recurring cytogenetic abnormalities, we performed gene expression profiling in a cohort of 207 uniformly treated children with high-risk ALL. Expression profiles were correlated with genome-wide DNA copy number abnormalities and clinical and outcome features. Unsupervised clustering of gene expression profiling data revealed 8 unique cluster groups within these high-risk ALL patients, 2 of which were associated with known chromosomal translocations (t(1;19)(TCF3-PBX1) or MLL), and 6 of which lacked any previously known cytogenetic lesion. One unique cluster was characterized by high expression of distinct outlier genes AGAP1, CCNJ, CHST2/7, CLEC12A/B, and PTPRM; ERG DNA deletions; and 4-year relapse-free survival of 94.7% ± 5.1%, compared with 63.5% ± 3.7% for the cohort (P = .01). A second cluster, characterized by high expression of BMPR1B, CRLF2, GPR110, and MUC4; frequent deletion of EBF1, IKZF1, RAG1-2, and IL3RA-CSF2RA; JAK mutations and CRLF2 rearrangements (P < .0001); and Hispanic ethnicity (P < .001) had a very poor 4-year relapse-free survival (21.0% ± 9.5%; P < .001). These studies reveal striking clinical and genetic heterogeneity in high-risk ALL and point to novel genes that may serve as new targets for diagnosis, risk classification, and therapy.

Access and Utilization of Asthma Medications Among Patients Who Receive Care in Federally Qualified Health Centers.

OBJECTIVES: To describe access to and use of prescription asthma medications, and to assess factors associated with asthma exacerbation, healthcare utilization, and health status among asthma patients treated at Federally Qualified Health Centers. METHODS: This is a retrospective cross-sectional study. We analyzed data from the 2014 National Health Center Patient Survey. This data is publicly available from the Health Resources and Services Administration. Data was collected from patients receiving face-to-face care from health centers funded under Section 330 of the Public Health Service Act. Data from patients was collected between October 8, 2014, and April 17, 2015. We included adult participants who reported having a diagnosis of asthma and confirmed that they still have asthma. Association between explanatory variables (access to prescription medications and use of asthma controller medications) and outcome variables (asthma exacerbations, asthma hospitalizations or emergency department visits, and self-rated health) was assessed using multivariable regression analyses while adjusting for demographics. RESULTS: A total of 919 participants with asthma were included. Approximately 32% of the participants experienced delays in getting prescription medications, 26% were unable to get them, 60% experienced an asthma exacerbation last year, 48% rated their health as fair/poor, and 19% visited a hospital or an emergency department last year. Multivariable results showed that participants who were currently taking controller medications were more likely to have experienced an asthma exacerbation (OR = 4.02; 95% CI 1.91 to 8.45; P < .01), or visited a hospital or an emergency department (OR = 3.07; 95% CI 1.39 to 6.73; P < .01) in the last year compared with those who had never taken controller medications. Experiencing difficulties in accessing asthma medications was associated with lower self-rated health (ß = -.51; 95% CI -0.94 to -0.08; P = .02). CONCLUSIONS: Future interventions should seek to improve asthma patient care and health outcomes using innovative strategies that act at multiple levels of the healthcare system (eg, individual, interpersonal, community levels).

Characterization of the Proportion of Clustered Tuberculosis Cases in Guatemala: Insights from a Molecular Epidemiology Study, 2010-2014.

There is little information about the amount of recent tuberculosis transmission in low-income settings. Genetic clustering can help identify ongoing transmission events. A retrospective observational study was performed on Mycobacterium tuberculosis isolates from persons living with HIV (PLHIV) and HIV-seronegative participants who submitted samples to a referral tuberculosis laboratory in Guatemala City, Guatemala from 2010 to 2014. Genotyping results were classified according to the international spoligotyping database, SITVIT2. Spoligotype patterns were categorized as clustered or nonclustered depending on their genotype. The proportion of clustering and the index of recent transmission index (RTIn-1) were estimated. In the RTIn-1 method, clustered cases represent recent transmission, whereas nonclustered cases represent reactivation of older tuberculosis infections. As a secondary aim, the potential risk factors associated with clustering in isolates from the subset of participants living with HIV were explored. From 2010 to 2014, a total of 479 study participants were confirmed as culture-positive tuberculosis cases. Among the 400 available isolates, 71 spoligotype patterns were identified. Overall, the most frequent spoligotyping families were Latin American-Mediterranean (LAM) (39%), followed by T (22%) and Haarlem (14%). Out of the 400 isolates, 365 were grouped in 36 clusters (range of cluster size: 2-92). Thus, the proportion of clustering was 91% and the RTIn-1 was 82%. Among PLHIV, pulmonary tuberculosis was associated with clustering (OR = 4.3, 95% CI 1.0-17.7). Our findings suggest high levels of ongoing transmission of M. tuberculosis in Guatemala as revealed by the high proportion of isolates falling into genomic clusters.

Canine tumor mutational burden is correlated with TP53 mutation across tumor types and breeds.

Spontaneous canine cancers are valuable but relatively understudied and underutilized models. To enhance their usage, we reanalyze whole exome and genome sequencing data published for 684 cases of >7 common tumor types and >35 breeds, with rigorous quality control and breed validation. Our results indicate that canine tumor alteration landscape is tumor type-dependent, but likely breed-independent. Each tumor type harbors major pathway alterations also found in its human counterpart (e.g., PI3K in mammary tumor and p53 in osteosarcoma). Mammary tumor and glioma have lower tumor mutational burden (TMB) (median < 0.5 mutations per Mb), whereas oral melanoma, osteosarcoma and hemangiosarcoma have higher TMB (median ≥ 1 mutations per Mb). Across tumor types and breeds, TMB is associated with mutation of TP53 but not PIK3CA, the most mutated genes. Golden Retrievers harbor a TMB-associated and osteosarcoma-enriched mutation signature. Here, we provide a snapshot of canine mutations across major tumor types and breeds.

Time to blood, respiratory and urine culture positivity in the intensive care unit: Implications for de-escalation.

OBJECTIVES: Concern for late detection of bacterial pathogens is a barrier to early de-escalation efforts. The purpose of this study was to assess blood, respiratory and urine culture results at 72 h to test the hypothesis that early negative culture results have a clinically meaningful negative predictive value. METHODS: We retrospectively reviewed all patients admitted to the medical intensive care unit between March 2012 and July 2018 with blood cultures obtained. Blood, respiratory and urine culture results were assessed for time to positivity, defined as the time between culture collection and preliminary species identification. The primary outcome was the negative predictive value of negative blood culture results at 72 h. Secondary outcomes included sensitivity, specificity, positive predictive value and negative predictive value of blood, respiratory and urine culture results. RESULTS: The analysis included 1567 blood, 514 respiratory and 1059 urine cultures. Of the blood, respiratory and urine cultures ultimately positive, 90.3%, 76.2% and 90.4% were positive at 72 h. The negative predictive value of negative 72-h blood, respiratory and urine cultures were 0.99, 0.82 and 0.97, respectively. Antibiotic de-escalation had good specificity, positive predictive value and negative predictive value for finalized negative cultures. CONCLUSION: Negative blood and urine culture results at 72 h had a high negative predictive value. These findings have important ramifications for antimicrobial stewardship efforts and support protocolized re-evaluation of empiric antibiotic therapy at 72 h. Caution should be used in patients with clinically suspected pneumonia, since negative respiratory culture results at 72 h were weakly predictive of finalized negative cultures.