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.

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.

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.

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.

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.

Proliferative and Invasive Colorectal Tumors in Pet Dogs Provide Unique Insights into Human Colorectal Cancer.

Spontaneous tumors in pet dogs represent a valuable but undercharacterized cancer model. To better use this resource, we performed an initial global comparison between proliferative and invasive colorectal tumors from 20 canine cases, and evaluated their molecular homology to human colorectal cancer (CRC). First, proliferative canine tumors harbor overactivated WNT/ß-catenin pathways and recurrent CTNNB1 (ß-catenin) mutations S45F/P, D32Y and G34E. Invasive canine tumors harbor prominent fibroblast proliferation and overactivated stroma. Both groups have recurrent TP53 mutations. We observed three invasion patterns in canine tumors: collective, crypt-like and epithelial⁻mesenchymal transition (EMT). We detected enriched Helicobacter bilis and Alistipes finegoldii in proliferative and crypt-like tumors, but depleted mucosa-microbes in the EMT tumor. Second, guided by our canine findings, we classified 79% of 478 human colon cancers from The Cancer Genome Atlas into four subtypes: primarily proliferative, or with collective, crypt-like or EMT invasion features. Their molecular characteristics match those of canine tumors. We showed that consensus molecular subtype 4 (mesenchymal) of human CRC should be further divided into EMT and crypt-like subtypes, which differ in TGF-ß activation and mucosa-microbe content. Our canine tumors share the same pathogenic pathway as human CRCs. Dog-human integration identifies three CRC invasion patterns and improves CRC subtyping.

SEG - A Software Program for Finding Somatic Copy Number Alterations in Whole Genome Sequencing Data of Cancer.

As next-generation sequencing technology advances and the cost decreases, whole genome sequencing (WGS) has become the preferred platform for the identification of somatic copy number alteration (CNA) events in cancer genomes. To more effectively decipher these massive sequencing data, we developed a software program named SEG, shortened from the word "segment". SEG utilizes mapped read or fragment density for CNA discovery. To reduce CNA artifacts arisen from sequencing and mapping biases, SEG first normalizes the data by taking the log2-ratio of each tumor density against its matching normal density. SEG then uses dynamic programming to find change-points among a contiguous log2-ratio data series along a chromosome, dividing the chromosome into different segments. SEG finally identifies those segments having CNA. Our analyses with both simulated and real sequencing data indicate that SEG finds more small CNAs than other published software tools.

Autotaxin exacerbates tumor progression by enhancing MEK1 and overriding the function of miR-489-3p.

Upregulated expression of autotaxin, a secreted phospholipase and phosphodiesterase enzyme, appears in malignant disease. The identification of a circulating miRNA signature should distinguish autotaxin-mediated disease and also elucidate unknown molecular mechanisms that rationalize its malignant potential. Using female transgenic 'AT-ATX' mice, whereby human wild-type autotaxin is expressed in liver under the control of the alpha-1 antitrypsin promoter, transgenic animals express augmented autotaxin in circulation and a percentage develop tumors. Serum collected at necropsy had circulating miRNAs analyzed for statistical significance. The ensuing autotaxin-mediated miRNome differentiated between groups: healthy FVB/N mice versus AT-ATX mice with and without tumors. Intriguingly, miR-489-3p was sharply increased in AT-ATX tumor-bearing mice. Tissue analysis showed a correlation between miR-489-3p expression in tumors and surrounding milieu with autotaxin concentration in circulation. Sequence alignment suggested miR-489-3p targets MEK1, which was confirmed through in vitro studies. Exogenously added miR-489-3p, which decreases MEK1 in normal cells, dramatically increased MEK1 expression in cells stably expressing autotaxin. Taken together, this suggests that autotaxin overrides the normal regulatory function of miR-489-3p to inhibit MEK1 via coordinately increased miR-489-3p appearing in serum.

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.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

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.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II - clinical validation and regulatory considerations.

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.

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.

Erratum to: Immune monitoring technology primer.

[This corrects the article DOI: 10.1186/s40425-015-0086-9.].

Immune monitoring technology primer.

BACKGROUND: Recent biotechnological developments have resulted in increasing interest in immunology biomarkers. These biomarkers have potential clinical utility in the near future as predictors of treatment response. Hence, clinical validation of these predictive markers is critical. FINDINGS: The process of clinically validating a predictive biomarker is reviewed. Validation of a predictive biomarker requires quantifying the strength of a statistical interaction between marker and a treatment. Different study designs are considered. CONCLUSIONS: Clinical validation of immunology biomarkers can be demanding both in terms of time and resources, and careful planning and study design are critical.

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.

Cost-effectiveness analysis of community active case finding and household contact investigation for tuberculosis case detection in urban Africa.

INTRODUCTION: Case detection by passive case finding (PCF) strategy alone is inadequate for detecting all tuberculosis (TB) cases in high burden settings especially Sub-Saharan Africa. Alternative case detection strategies such as community Active Case Finding (ACF) and Household Contact Investigations (HCI) are effective but empirical evidence of their cost-effectiveness is sparse. The objective of this study was to determine whether adding ACF or HCI compared with standard PCF alone represent cost-effective alternative TB case detection strategies in urban Africa. METHODS: A static decision modeling framework was used to examine the costs and effectiveness of three TB case detection strategies: PCF alone, PCF+ACF, and PCF+HCI. Probability and cost estimates were obtained from National TB program data, primary studies conducted in Uganda, published literature and expert opinions. The analysis was performed from the societal and provider perspectives over a 1.5 year time-frame. The main effectiveness measure was the number of true TB cases detected and the outcome was incremental cost-effectiveness ratios (ICERs) expressed as cost in 2013 US$ per additional true TB case detected. RESULTS: Compared to PCF alone, the PCF+HCI strategy was cost-effective at US$443.62 per additional TB case detected. However, PCF+ACF was not cost-effective at US$1492.95 per additional TB case detected. Sensitivity analyses showed that PCF+ACF would be cost-effective if the prevalence of chronic cough in the population screened by ACF increased 10-fold from 4% to 40% and if the program costs for ACF were reduced by 50%. CONCLUSIONS: Under our baseline assumptions, the addition of HCI to an existing PCF program presented a more cost-effective strategy than the addition of ACF in the context of an African city. Therefore, implementation of household contact investigations as a part of the recommended TB control strategy should be prioritized.

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.

Statistical design and evaluation of biomarker studies.

We review biostatistical aspects of biomarker studies, including design and analysis issues, covering the range of settings required for translational research-from early exploratory studies through clinical trials.