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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

PARP inhibition causes premature loss of cohesion in cancer cells

Poly(ADP-ribose) polymerases (PARPs) regulate various aspects of cellular function including mitotic progression. Although PARP inhibitors have been undergoing various clinical trials and the PARP1/2 inhibitor olaparib was approved as monotherapy for BRCA-mutated ovarian cancer, their mode of action in killing tumour cells is not fully understood. We investigated the effect of PARP inhibition on mitosis in cancerous (cervical, ovary, breast and osteosarcoma) and non-cancerous cells by live-cell imaging. The clinically relevant inhibitor olaparib induced strong perturbations in mitosis, including problems with chromosome alignment at the metaphase plate, anaphase delay, and premature loss of cohesion (cohesion fatigue) after a prolonged metaphase arrest, resulting in sister chromatid scattering. PARP1 and PARP2 depletion suppressed the phenotype while PARP2 overexpression enhanced it, suggesting that olaparib-bound PARP1 and PARP2 rather than the lack of catalytic activity causes this phenotype. Olaparib-induced mitotic chromatid scattering was observed in various cancer cell lines with increased protein levels of PARP1 and PARP2, but not in non-cancer or cancer cell lines that expressed lower levels of PARP1 or PARP2. Interestingly, the sister chromatid scattering phenotype occurred only when olaparib was added during the S-phase preceding mitosis, suggesting that PARP1 and PARP2 entrapment at replication forks impairs sister chromatid cohesion. Clinically relevant DNA-damaging agents that impair replication progression such as topoisomerase inhibitors and cisplatin were also found to induce sister chromatid scattering and metaphase plate alignment problems, suggesting that these mitotic phenotypes are a common outcome of replication perturbation.

cell biology↗

bioRxiv2016

Tissue specificity of in vitro drug sensitivity

Research in oncology traditionally focuses on specific tissue type from which the cancer develops. However, advances in high-throughput molecular profiling technologies have enabled the comprehensive characterization of molecular aberrations in multiple cancer types. It was hoped that these large-scale datasets would provide the foundation for a paradigm shift in oncology which would see tumors being classified by their molecular profiles rather than tissue types, but tumors with similar genomic aberrations may respond differently to targeted therapies depending on their tissue of origin. There is therefore a need to reassess the potential association between pharmacological response and tissue of origin for therapeutic drugs, and to test how these associations translate from preclinical to clinical settings.\n\nIn this paper, we investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in clinical trial descriptions. Our meta-analysis of the four largest in vitro drug screening datasets indicates that tissue of origin is strongly associated with drug response. We identify novel tissue-drug associations, which may present exciting new avenues for drug repurposing. One caveat is that the vast majority of the significant associations found in preclinical settings do not concur with clinical observations. Accordingly, our results call for more testing to find the root cause of the discrepancies between preclinical and clinical observations.

bioinformatics↗

bioRxiv2017

Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations

The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a root mean square error in binding free energy changes of [Formula] kcal/mol (95% confidence interval) and correctly classifying mutations as resistant or susceptible with [Formula] accuracy. Since these calculations are fast on modern GPUs, this benchmark establishes the potential for physical modeling to collaboratively support the rapid assessment and anticipation of the potential for patient mutations to affect drug potency in clinical applications.

cancer biology↗

bioRxiv2018

Neurofilament light as a blood biomarker for neurodegeneration in Down syndrome

INTRODUCTIONDown syndrome (DS) may be considered a genetic form of Alzheimers disease (AD) due to universal development of AD neuropathology, but diagnosis and treatment trials are hampered by a lack of reliable blood biomarkers. A potential biomarker is neurofilament light (NF-L), due to its association with axonal damage in neurodegenerative conditions.\n\nMETHODSWe measured blood NF-L concentration in 100 adults with DS using Simoa NF-light(R) assays, and examined relationships with age, and cross-sectional and longitudinal dementia diagnosis.\n\nRESULTSNF-L levels increased with age (Spearmans rho = 0.789, p<0.001), with a steep increase after age 40, and were predictive of dementia status (p=0.022 adjusting for age, sex, and APOE4) but showed no relationship with longstanding epilepsy or premorbid ability. Baseline NF-L levels were associated with longitudinal dementia status.\n\nDISCUSSIONNF-L is a biomarker for neurodegeneration in DS, with potential for use in future clinical trials to prevent or delay dementia.\n\nResearch in contextO_ST_ABSSystematic reviewC_ST_ABSThe authors reviewed the literature using PubMed searches supplemented with our knowledge of pending papers in this research area. While blood NF-L has been associated with clinical features of progression in a number of neurodegenerative conditions, we have not identified any reports of NF-L associated with cognitive decline in DS, a genetic form of AD.\n\nInterpretationOur findings demonstrate the potential utility of NF-L as a blood biomarker of neurodegeneration in DS, a population that may not be able to tolerate more invasive procedures such as neuroimaging and lumbar punctures to track progression.\n\nFuture directionsThe association between NF-L and other markers of longitudinal AD progression should be explored further in future work.

molecular biology↗

bioRxiv2018

Better than maximum likelihood estimation of model-based and model-free learning style

Multiple decision making systems work together to shape the final choices in human behavior. Habitual and goal-directed systems are the two most important systems that are studied in the reinforcement learning (RL) literature by model-free and model-based learning methods. Human behavior resembles the weighted combination of these systems and such a combination is modeled by weighted summation of actions value from the model based and model free systems. Extraction of this weighted parameter, which is important for many applications and computational modeling, has been mostly based on the maximum likelihood or maximum a posteriori methods. We show these methods bring many challenges and their respective extracted values are less reliable especially in the proximity of extremes values. We propose that using a free format learning method (k-nearest neighbor) which uses more information besides the fitted values e.g. global information like stay probability instead of trial by trial information can ameliorate the estimation error. The proposed method is examined by simulation and results show the advantage of the proposed method. In addition, investigation of the human behavior data from previous researchers proved the proposed method to result in more statistically robust results in predicting other behavioral indices such as the number of gaze directions toward each target. In brief, the proposed method increases the reliability of the estimated parameters and enhances the applicability of reinforcement learning paradigms in clinical trials.

neuroscience↗

bioRxiv2016

Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.

Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.\n\nTo inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients.\n\nWe outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.

Cancer Biology↗

bioRxiv2017

Combining CRISPR/Cas9 mutagenesis and a small-molecule inhibitor to probe the function of MELK in cancer

The Maternal Embryonic Leucine Zipper Kinase (MELK) has been identified as a promising therapeutic target in multiple cancer types. MELK over-expression is associated with aggressive disease, and MELK has been implicated in numerous cancer-related processes, including chemotherapy resistance, stem cell renewal, and tumor growth. On the basis of these findings, a MELK inhibitor is currently being tested in several clinical trials. Here, we report that cancer cell lines harboring CRISPR/Cas9-induced null mutations in MELK exhibit wild-type growth in vitro, under environmental stress, in the presence of multiple chemotherapy agents, and in vivo. By combining our MELK-knockout clones with a recently-described, highly-specific MELK inhibitor, we further demonstrate that the acute inhibition of MELK results in no specific anti-proliferative phenotype. Analysis of gene expression data from cohorts of cancer patients identifies MELK expression as a correlate of tumor mitotic activity, explaining its association with poor clinical prognosis. In total, our results demonstrate the power of CRISPR/Cas9-based genetic approaches to investigate cancer drug targets, and call into question the rationale for treating patients with anti-MELK monotherapies.

cancer biology↗

bioRxiv2016

Real-time predictions of reservoir size and rebound time during antiretroviral therapy interruption trials for HIV

Monitoring the efficacy of novel reservoir-reducing treatments for HIV is challenging. The limited ability to sample and quantify latent infection means that supervised antiretroviral therapy (ART) interruption studies are generally required. Here we introduce a set of mathematical and statistical modeling tools to aid in the design and interpretation of ART-interruption trials. We show how the likely size of the remaining reservoir can be updated in real-time as patients continue off treatment, by combining the output of laboratory assays with insights from models of reservoir dynamics and rebound. We design an optimal schedule for viral load sampling during interruption, whereby the frequency of follow-up can be decreased as patients continue off ART without rebound. While this scheme can minimize costs when the chance of rebound between visits is low, we find that the reservoir will be almost completely reseeded before rebound is detected unless sampling occurs at least every two weeks and the most sensitive viral load assays are used. We use simulated data to predict the clinical trial size needed to estimate treatment effects in the face of highly variable patient outcomes and imperfect reservoir assays. Our findings suggest that large numbers of patients - between 40 and 150 - will be necessary to reliably estimate the reservoir-reducing potential of a new therapy and to compare this across interventions. As an example, we apply these methods to the two \"Boston patients\", recipients of allogeneic hematopoietic stem cell transplants who experienced large reductions in latent infection and underwent ART-interruption. We argue that the timing of viral rebound was not particularly surprising given the information available before treatment cessation. Additionally, we show how other clinical data can be used to estimate the relative contribution that remaining HIV+ cells in the recipient versus newly infected cells from the donor made to the residual reservoir that eventually caused rebound. Together, these tools will aid HIV researchers in the evaluating new potentially-curative strategies that target the latent reservoir.

Microbiology↗

bioRxiv2017

The impact of tumor receptor heterogeneity on the response to anti-angiogenic cancer treatment

Multiple promoters and inhibitors mediate angiogenesis, the formation of new blood vessels, and these factors represent potential targets for impeding vessel growth in tumors. Vascular endothelial growth factor (VEGF) is a potent angiogenic factor targeted in anti-angiogenic cancer therapies. In addition, thrombospondin-1 (TSP1) is a major endogenous inhibitor of angiogenesis, and TSP1 mimetics are being developed as an alternative type of anti-angiogenic agent. The combination of bevacizumab, an anti-VEGF agent, and ABT-510, a TSP1 mimetic, has been tested in clinical trials to treat advanced solid tumors. However, the patients responses are highly variable and show disappointing outcomes. To obtain mechanistic insight into the effects of this combination anti-angiogenic therapy, we have constructed a novel whole-body systems biology model including the VEGF and TSP1 reaction networks. Using this molecular-detailed model, we investigated how the combination anti-angiogenic therapy changes the amounts of pro-angiogenic and anti-angiogenic complexes in cancer patients. We particularly focus on answering the question of how the effect of the combination therapy is influenced by tumor receptor expression, one aspect of patient-to-patient variability. Overall, this model complements the clinical administration of combination anti-angiogenic therapy, highlights the role of tumor receptor variability in the heterogeneous responses to anti-angiogenic therapy, and identifies the tumor receptor profiles that correlate with a high likelihood of a positive response to the combination therapy. Our model provides novel understanding of the VEGF-TSP1 balance in cancer patients at the systems-level and could be further used to optimize combination anti-angiogenic therapy.

systems biology↗

bioRxiv2016

Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared

Joint analysis of data from multiple studies in collaborative efforts strengthens scientific evidence, with the gold standard approach being the pooling of individual participant data (IPD). However, sharing IPD often has legal, ethical, and logistic constraints for sensitive or high-dimensional data, such as in clinical trials, observational studies, and large-scale omics studies. Therefore, meta-analysis of study-level effect estimates is routinely done, but this compromises on statistical power, accuracy, and flexibility. Here we propose a novel meta-analytical approach, named partial derivatives meta-analysis, that is mathematically equivalent to using IPD, yet only requires the sharing of aggregate data. It not only yields identical results as pooled IPD analyses, but also allows post-hoc adjustments for covariates and stratification without the need for site-specific re-analysis. Thus, in case that IPD cannot be shared, partial derivatives meta-analysis still produces gold standard results, which can be used to better inform guidelines and policies on clinical practice.

Bioinformatics↗

bioRxiv2018

Single Session Real-time fMRI Neurofeedback has a Lasting Impact on Cognitive Behavioral Therapy Strategies

To benefit from cognitive behavioral therapy (CBT), individuals must not only learn new skills but also strategically implement them outside the session. Here, we tested a novel technique for personalizing CBT skills and facilitating their generalization to daily life. We hypothesized that showing participants the impact of specific CBT strategies on their own brain function using real-time functional magnetic imaging (rt-fMRI) neurofeedback would increase their metacognitive awareness, help them identify effective strategies, and motivate real-world use. In a within-subjects design, participants who had completed a clinical trial of a standardized course of CBT created a personal repertoire of negative autobiographical stimuli and mood regulation strategies. From each participants repertoire, a set of experimental and control strategies were identified; only experimental strategies were practiced in the scanner. During the rt-fMRI neurofeedback session, participants used negative stimuli and strategies from their repertoire to manipulate activation in the anterior cingulate cortex, a region implicated in emotional distress. The primary outcome measures were changes in participant ratings of strategy difficulty, efficacy, and frequency of use. As predicted, ratings for unscanned control strategies were stable across observations, whereas ratings for experimental strategies changed after neurofeedback. At follow-up one month after the session, efficacy and frequency ratings for scanned strategies were predicted by neurofeedback during the rt-fMRI session. These results suggest that rt-fMRI neurofeedback created a salient and durable learning experience for patients, extending beyond the clinic to guide and motivate CBT skill use weeks later. This metacognitive approach to neurofeedback offers a promising model for increasing clinical benefits from cognitive-behavioral therapy by personalizing skills and facilitating generalization.

neuroscience↗

bioRxiv2017

Non-invasive imaging platform reveals a potential tumourigenicity hazard of systemically administered cells

BackgroundCell-based regenerative medicine therapies are now frequently tested in clinical trials. In many conditions, cell therapies are administered systemically, but there is little understanding of their fate, and adverse events are often under-reported. Currently, it is only possible to assess safety and fate of cell therapies in preclinical studies, specifically by monitoring animals longitudinally using multimodal imaging approaches. Here, using a suite of in vivo imaging modalities to explore the fate of a range of human and murine cells, we investigate how route of administration, cell type and host immune status affect the fate of administered cells.\n\nMethodsWe applied a unique imaging toolkit combining bioluminescence, optoacoustic and magnetic resonance imaging modalities to assess the safety of different human and murine cell types by following their biodistribution and persistence in mice following administration into the venous or arterial system. Results: Longitudinal imaging analyses (i) suggested that the intra-arterial route may be more hazardous than intravenous administration for certain cell types; (ii) revealed that the potential of a mouse mesenchymal stem/stromal cell (MSC) line to form tumours, depended on administration route and mouse strain; and (iii) indicated that clinically tested human umbilical cord (hUC)-derived MSCs can transiently and unexpectedly proliferate when administered intravenously to mice.\n\nConclusionsIn order to perform an adequate safety assessment of potential cell-based therapies, a thorough understanding of cell biodistribution and fate post administration is required. The non-invasive imaging toolbox used here can expose not only the general organ distribution of these therapies, but also a detailed view of their presence within different organs and, importantly, tumourigenic potential. Our observation that the hUC-MSCs but not the human bone marrow (hBM)-derived MSCs persisted for a period in some animals, suggests that therapies with these cells should proceed with caution.

cell biology↗

bioRxiv2017

Brain and psychological determinants of placebo pill response in chronic pain patients

Placebo response is universally observed in randomized controlled trials (RCTs), yet these effects are commonly dismissed as consequences of uncontrollable confounds. In this prospective neuroimaging-based RCT performed in chronic back pain patients, we demonstrate that the intensity, but not quality, of pain is diminished with placebo pill ingestion. The response to placebo pills depended on brain: subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of the dorsolateral prefrontal cortex (DLPFC) with the periaqueductal grey (PAG), the rostral anterior cingulate cortex (rACC), and the precentral gyrus (PreCG); and psychological factors. All features were present before exposure to the pill; most remained stable across treatment and washout periods, although specific functional coupling between DLPFC and PAG dissipated with repeated exposure. These brain properties and specific psychological factors, such as interoceptive awareness and openness, were also predictive of the magnitude of response (continuous variable). We used machine learning in a fully cross-validated procedure and demonstrated that psychological factors were sufficient for classifying and predicting response magnitude; and response magnitude could also be predicted from a functional network (nodes mainly located in the limbic community, the DLPFC, the orbitofrontal cortex, and the temporo-parietal junction); the combined model explained 36% of the variance. Together, our results demonstrate that placebo pill analgesia observed in clinical trials depends on a combination of brain properties and specific psychological factors.

neuroscience↗

bioRxiv2018

Pan-cancer machine learning predictors of tissue of origin and molecular subtype

BackgroundIt is estimated by the American Cancer Society that approximately 5% of all metastatic tumors have no defined primary site (tissue) of origin and are classified as cancers of unknown primary (CUPs). The current standard of care for CUP patients depends on immunohistochemistry (IHC) based approaches to identify the primary site. The addition of post-mortem evaluation to IHC based tests helps to reveal the identity of the primary site for only 25% of the CUPs, emphasizing the acute need for better methods of determination of the site of origin. CUP patients are therefore given generic chemotherapeutic agents resulting in poor prognosis. When the tissue of origin is known, patients can be given site specific therapy with significant improvement in clinical outcome. Similarly, identifying the primary site of origin of metastatic cancer is of great importance for designing treatment.\n\nIdentification of the primary site of origin is an import first step but may not be sufficient information for optimal treatment of the patient. Recent studies, primarily from The Cancer Genome Atlas (TCGA) project, and others, have revealed molecular subtypes in several cancer types with distinct clinical outcome. The molecular subtype captures the fundamental mechanisms driving the cancer and provides information that is essential for the optimal treatment of a cancer. Thus, along with primary site of origin, molecular subtype of a tumor is emerging as a criterion for personalized medicine and patient entry into clinical trials.\n\nHowever, there is no comprehensive toolset available for precise identification of tissue of origin or molecular subtype for precision medicine and translational research.\n\nMethods and FindingsWe posited that metastatic tumors will harbor the gene expression profiles of the primary site of origin of the cancer. Therefore, we decided to learn the molecular characteristics of the primary tumors using the large number of cancer genome profiles available from the TCGA project. Our predictors were trained for 33 cancer types and for the 11 cancers where there are established molecular subtypes. We estimated the accuracy of several machine learning models using cross-validation methods. The extensive testing using independent test sets revealed that the predictors had a median sensitivity and specificity of 97.2% and 99.9% respectively without losing classification of any tumor. Subtype classifiers achieved median sensitivity of 87.7% and specificity of 94.5% via cross validation and presented median sensitivity of 79.6% and specificity of 94.6% in two external datasets of 1,999 total samples. Importantly, these external data shows that our classifiers can robustly predict the primary site of origin from external microarray data, metastatic cancer data, and patient-derived xenograft (PDX) data.\n\nConclusionWe have demonstrated the utility of gene expression profiles to solve the important clinical challenge of identifying the primary site of origin and the molecular subtype of cancers based on machine learning algorithms. We show, for the first time to our knowledge, that our pan-cancer classifiers can predict multiple cancers primary site of origin from metastatic samples. The predictors will be made available as open source software, freely available for academic non-commercial use.

cancer biology↗

bioRxiv2018

Anti-V2 Antibody Deficiency in Individuals Infected With HIV-1

The positive correlation of high levels of plasma anti-V2 antibodies (Abs) with protective immunity in the Phase III anti-HIV RV144 vaccine trial generated interest in the induction of these Abs for HIV vaccine development. We analyzed plasma samples from 79 chronically infected Cameroonian individuals for Ab reactivity against three V1V2 fusion proteins and five cyclic V2 peptides and found that HIV-1 infection induces different levels of anti-V2 Abs. While the majority of plasma samples reacted strongly with one or more V2 antigens, 10% (8) of the samples were nonreactive. Deficiency of anti-V2 Abs was consistently found in longitudinal plasma samples tested over 8 to 54 months of HIV infection. There was a strong correlation between binding activities of plasma anti-V2 Abs and anti-gp120 and anti-gp41 Abs, suggesting that deficiency of V2 Abs could be related, in part, to a limited ability to elicit strong Ab responses. Analysis of gp120 sequences revealed that the V2 region of viruses from donors with V2-deficient versus V2-reactive Abs displayed a tendency toward longer length, more glycans, and lower isoelectric point and charge. No differences between these two patient groups were noted in the same parameters measured in the V1 region. These data suggest that immunogens containing a shorter V2 region with fewer glycosylation sites and higher electrostatic charges would be beneficial for induction of anti-V2 Abs, but the ability to mount a strong general Ab response to HIV-1 appears to be a dominant factor.\n\nIMPORTANCEThe results of the RV144 vaccine clinical trial showed a correlation between plasma Abs against a V1V2 fusion protein and a decreased risk of acquiring HIV-1 infection. This turned the focus of some HIV vaccine design to the induction of elevated levels of anti-V2 Abs to increase vaccine efficacy. In plasma samples from Cameroonian individuals infected with HIV-1, we observed broad variations in levels of anti-V2 Abs, and 8 of the 79 plasma samples tested displayed substantial deficiency of V2 Abs. Sequence analysis of the V2 region from plasma viruses and multivariate analyses of V2 characteristics showed a significant difference in several features between V2-deficient and V2-reactive plasma Abs. These results suggest that HIV vaccine immunogens containing a V2 region with shorter length, fewer glycosylation sites, and higher electrostatic charges may be beneficial for induction of a higher level of anti-V2 Abs and thus contribute to HIV vaccine efficacy.

immunology↗

bioRxiv2018

The immune checkpoint kick start: Optimization of neoadjuvant combination therapy using game theory

An upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 ER+breast cancer combines an aromatase inhibitor and a PD-L1 checkpoint inhibitor, and aims to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We develop a mathematical model of the essential components of the PEPI score in order to identify successful combination therapy regimens that minimize both tumor burden and metastatic potential, based on time-dependent trade-offs in the system. We consider two molecular traits, CCR7 and PD-L1 which correlate with treatment response and increased metastatic risk. We use a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded into an ecological model of tumor population growth dynamics. The resulting model predicts both evolutionary and ecological dynamics that track with changes in the PEPI score. We consider various treatment regimens based on combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was found to be a 1 month kick start of the immune checkpoint inhibitor followed by five months of continuous combination therapy. Relative to a protocol with both therapeutics given together from the start, this delayed regimen results in transient sub-optimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final five months of therapy. The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

cancer biology↗

bioRxiv2018

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

The high failure rate of therapeutics showing promise in mouse disease models to translate to patients is a pressing challenge in biomedical science. However, mouse models are a useful tool for evaluating mechanisms of disease and prioritizing novel therapeutic agents for clinical trials. Though retrospective studies have examined the fidelity of mouse models of inflammatory disease to their respective human in vivo conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for prospective inference of disease-associated human in vivo differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human in vivo biology from mouse model datasets. We found that a semi-supervised artificial neural network identified significantly more true human in vivo associations than interpreting mouse experiments directly (95% CI on F-score for mouse experiments [0.090, 0.175], neural network [0.278, 0.375], p = 0.00013). Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches combining mouse and human data for biological inference provides the most accurate assessment of human in vivo disease and therapeutic mechanisms. The task of translating insights from model systems to human disease contexts may therefore be better accomplished by the use of systems modeling driven approaches.\n\nAuthor SummaryComparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. Here, we address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse experiment datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and pathways as if the model system experiment had been run in the human cohort. A semi-supervised version of a feed forward artificial neural network was the most efficacious model for translating experimentally derived mouse molecule-phenotype associations to the human in vivo disease context. We find that computational generalization of signaling insights from mouse to human contexts substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

systems biology↗

bioRxiv2014

High Bacterial Load Predicts Poor Outcomes in Patients with Idiopathic Pulmonary Fibrosis

BackgroundRepetitive alveolar damage and aberrant repair may be important in the development of the fatal condition Idiopathic Pulmonary Fibrosis (IPF). The role played by microorganisms in this cycle is unknown.\n\nMethodsWe consecutively enrolled patients diagnosed with IPF according to international criteria together with healthy smokers, non-smokers and subjects with moderate Chronic Obstructive Pulmonary Disease (COPD) as controls. Subjects underwent bronchoalveolar lavage (BAL) from which genomic DNA was isolated. The V3-V5 region of the bacterial 16S rRNA gene was amplified, allowing quantification of bacterial load and identification of communities by 16S rRNA qPCR and pyrosequencing.\n\nResultsOur 65 IPF patients had 3.9x109 copies of the 16S rRNA gene per ml of BAL, two-fold more than the 1.8x109 copies in 44 sex- and smoking-matched controls (P<0.0001). Baseline BAL bacterial burden predicted Forced Vital Capacity (FVC) decline (P=0.02). Patients in the highest tertile of bacterial burden were at a higher risk of mortality compared to subjects in the lowest tertile (hazard ratio 4.59 (95% CI, 1.05-20); P=0.04).\n\nSequencing yielded 912,883 high quality reads from all subjects. Operational Taxonomic Units (OTUs) representing Haemophilus, Streptococcus, Neisseria and Veillonella were 1.5 to 3.5 fold more abundant in cases than controls (P<0.05). Regression analyses indicated that these specific OTUs as well as bacterial burden associated independently with IPF.\n\nConclusionsIPF is characterised by an increased bacterial burden in BAL that predicts decline in lung function and death. Clinical trials of antimicrobial therapy may determine if microbial burden is causal or not in IPF progression.

Microbiology↗