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Background We consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate? Results We develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determine the best split proportions and to evaluate commonly used splitting strategies (1/2 training or 2/3 training) under a wide variety of conditions. These methods are based on a decomposition of the MSE into three intuitive component parts. Conclusions By applying these approaches to a number of synthetic and real microarray datasets we show that for linear classifiers the optimal proportion depends on the overall number of samples available and the degree of differential expression between the classes. The optimal proportion was found to depend on the full dataset size (n) and classification accuracy - with higher accuracy and smaller n resulting in more assigned to the training set. The commonly used strategy of allocating 2/3rd of cases for training was close to optimal for reasonable sized datasets ( n ≥ 100) with strong signals (i.e. 85% or greater full dataset accuracy). In general, we recommend use of our nonparametric resampling approach for determing the optimal split. This approach can be applied to any dataset, using any predictor development method, to determine the best split.

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. Genomic studies of canine tumours have been done for individual cancer types or dog breeds. Here the authors analyse canine tumour genomics data across multiple breeds and cancer types, finding that mutational burden is associated with TP53 mutations and that Golden Retrievers are enriched for particular signatures.

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.

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question.Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

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.

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit in response to different immunotherapeutic approaches, the majority of patients do not but are still exposed to the significant drug toxicities. Therefore, a growing need for the development and clinical use of predictive biomarkers exists in the field of cancer immunotherapy. Predictive cancer biomarkers can be used to identify the patients who are or who are not likely to derive benefit from specific therapeutic approaches. In order to be applicable in a clinical setting, predictive biomarkers must be carefully shepherded through a step-wise, highly regulated developmental process. Volume I of this two-volume document focused on the pre-analytical and analytical phases of the biomarker development process, by providing background, examples and “good practice” recommendations. In the current Volume II, the focus is on the clinical validation, validation of clinical utility and regulatory considerations for biomarker development. Together, this two volume series is meant to provide guidance on the entire biomarker development process, with a particular focus on the unique aspects of developing immune-based biomarkers. Specifically, knowledge about the challenges to clinical validation of predictive biomarkers, which has been gained from numerous successes and failures in other contexts, will be reviewed together with statistical methodological issues related to bias and overfitting. The different trial designs used for the clinical validation of biomarkers will also be discussed, as the selection of clinical metrics and endpoints becomes critical to establish the clinical utility of the biomarker during the clinical validation phase of the biomarker development. Finally, the regulatory aspects of submission of biomarker assays to the U.S. Food and Drug Administration as well as regulatory considerations in the European Union will be covered.

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.

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 ( β  = −.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).

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.

In many laboratory assay datasets, missing values due to a limit of detection (LOD) are not uncommon. We observed this issue in our CIVIC-UGAFLUVAC hemagglutination inhibition assay (HAI) dataset. The standard imputation method recodes these values as either equal to the LOD or LOD/2. However, ignoring censoring can lead to falsely significant results in research. In this study, we explored the bias in modeling vaccine HAI titer increase. Moreover, we modified the titer increase modeling within the interval censoring framework to adjust for bias in parameter estimates. Our method provided less biased results compared to the standard imputation method. We anticipate that this study will serve as a case study and instructional guide for future vaccine research.