Using Bayesian Dynamic Borrowing to Maximize the Use of Existing Data: A Case-Study

Bayesian Dynamic Borrowing (BDB) designs are being increasingly used in clinical drug development. These methods offer a mathematically rigorous and robust approach to increase efficiency and strengthen evidence by integrating existing trial data into a new clinical trial. The regulatory acceptability of BDB is evolving and varies between and within regulatory agencies. This paper describes how BDB can be used to design a new randomised clinical trial including external data to supplement the planned sample size and discusses key considerations related to data re-use and BDB in drug development programs. A case-study illustrating the planning and evaluation of a BDB approach to support registration of a new medicine with the Center for Drug Evaluation in China will be presented. Key steps and considerations for the use of BDB will be discussed and evaluated, including how to decide whether it is appropriate to borrow external data, which external data can be re-used, the weight to put on the external data and how to decide if the new study has successfully demonstrated treatment benefit.

An Interpretable Classification Model Using Gluten-Specific TCR Sequences Shows Diagnostic Potential in Coeliac Disease

Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4+ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4+ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.

Genomic profiling of idiopathic peri-hilar cholangiocarcinoma reveals new targets and mutational pathways

Peri-hilar cholangiocarcinoma (pCCA) is chemorefractory and limited genomic analyses have been undertaken in Western idiopathic disease. We undertook comprehensive genomic analyses of a U.K. idiopathic pCCA cohort to characterize its mutational profile and identify new targets. Whole exome and targeted DNA sequencing was performed on forty-two resected pCCA tumors and normal bile ducts, with Gene Set Enrichment Analysis (GSEA) using one-tailed testing to generate false discovery rates (FDR). 60% of patients harbored one cancer-associated mutation, with two mutations in 20%. High frequency somatic mutations in genes not typically associated with cholangiocarcinoma included mTOR, ABL1 and NOTCH1. We identified non-synonymous mutation (p.Glu38del) in MAP3K9 in ten tumors, associated with increased peri-vascular invasion (Fisher's exact, p < 0.018). Mutation-enriched pathways were primarily immunological, including innate Dectin-2 (FDR 0.001) and adaptive T-cell receptor pathways including PD-1 (FDR 0.007), CD4 phosphorylation (FDR 0.009) and ZAP70 translocation (FDR 0.009), with overlapping HLA genes. We observed cancer-associated mutations in over half of our patients. Many of these mutations are not typically associated with cholangiocarcinoma yet may increase eligibility for contemporary targeted trials. We also identified a targetable MAP3K9 mutation, in addition to oncogenic and immunological pathways hitherto not described in any cholangiocarcinoma subtype.

Increasing the use of perioperative risk scoring in emergency laparotomy: nationwide quality improvement programme

Background

Emergency laparotomy is associated with high morbidity and mortality. The early identification of high-risk patients allows for timely perioperative care and appropriate resource allocation. The aim of this study was to develop a nationwide surgical trainee-led quality improvement (QI) programme to increase the use of perioperative risk scoring in emergency laparotomy.

Methods

The programme was structured using the active implementation framework in 15 state-funded Irish hospitals to guide the staged implementation of perioperative risk scoring. The primary outcome was a recorded preoperative risk score for patients undergoing an emergency laparotomy at each site.

Results

The rate of patients undergoing emergency laparotomy receiving a perioperative risk score increased from 0–11 per cent during the exploratory phase to 35–100 per cent during the full implementation phase. Crucial factors for implementing changes included an experienced central team providing implementation support, collaborator engagement, and effective communication and social relationships.

Conclusions

A trainee-led QI programme increased the use of perioperative risk assessment in patients undergoing emergency laparotomy, with the potential to improve patient outcomes and care delivery.

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.

DECoN: A Detection and Visualization Tool for Exonic Copy Number Variants

Detection of copy number variants from targeted sequencing, including whole-exome sequencing, can be particularly difficult since the break points of the CNV are not always captured. Here we describe DECoN, a software tool which uses changes in read depth to identify CNVs that affect whole exons. It is optimized for clinical use and allows for interactive visualization of CNVs identified.

Mid-treatment Fluorodeoxyglucose Positron Emission Tomography in Human Papillomavirus-related Oropharyngeal Squamous Cell Carcinoma Treated with Primary Radiotherapy: Nodal Metabolic Response Rate can Predict Treatment Outcomes

AIMS

To evaluate whether biomarkers derived from fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) performed prior to (prePET) and during the third week (interim PET; iPET) of radiotherapy can predict treatment outcomes in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPC).

MATERIALS AND METHODS

This retrospective analysis included 46 patients with newly diagnosed OPC treated with definitive (chemo)radiation and all patients had confirmed positive HPV status (HPV+OPC) based on p16 immunohistochemistry. The maximum standardised uptake value (SUV_max), metabolic tumour volume (MTV) and total lesional glycolysis (TLG) of primary, index node (node with the highest TLG) and total lymph nodes and their median percentage (≥50%) reductions in iPET were analysed, and correlated with 5-year Kaplan-Meier and multivariable analyses (smoking, T4, N2b-3 and AJCC stage IV), including local failure-free survival, regional failure-free survival, locoregional failure-free survival (LRFFS), distant metastatic failure-free survival (DMFFS), disease-free survival (DFS) and overall survival.

RESULTS

There was no association of outcomes with prePET parameters observed on multivariate analysis. A complete metabolic response of primary tumour was seen in 13 patients; the negative predictive value for local failure was 100%. More than a 50% reduction in total nodal MTV provided the best predictor of outcomes, including LRFFS (88% versus 47.1%, P = 0.006, hazard ratio = 0.153) and DFS (78.2% versus 41.2%, P = 0.01, hazard ratio = 0.234). More than a 50% reduction in index node TLG was inversely related to DMFFS: a better nodal response was associated with a higher incidence of distant metastatic failure (66.7% versus 100%, P = 0.009, hazard ratio = 3.0).

CONCLUSION

The reduction (≥50%) of volumetric nodal metabolic burden can potentially identify a subgroup of HPV+OPC patients at low risk of locoregional failure but inversely at higher risk of distant metastatic failure and may have a role in individualised adaptive radiotherapy and systemic therapy.

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Use of machine learning to identify a T cell response to SARS-CoV-2

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.

Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status

In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasionally including aggressive T-cell lymphoma. Diagnosis, based primarily on histopathological examination of duodenal biopsies, is confounded by poor concordance between pathologists and minimal histological abnormality if insufficient gluten is consumed. CeD pathogenesis involves both CD4⁺ T-cell-mediated gluten recognition and CD8⁺ and γδ T-cell-mediated inflammation, with a previous study demonstrating a permanent change in γδ T-cell populations in CeD. We leveraged this understanding and explored the diagnostic utility of bulk T-cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD. Genomic DNA extracted from duodenal biopsies underwent sequencing for TCR-δ (TRD) (CeD, n = 11; non-CeD, n = 11) and TCR-γ (TRG) (CeD, n = 33; non-CeD, n = 21). We developed a novel machine learning-based analysis of the TCR repertoire, clustering samples by diagnosis. Leave-one-out cross-validation (LOOCV) was performed to validate the classification algorithm. Using TRD repertoire, 100% (22/22) of duodenal biopsies were correctly classified, with a LOOCV accuracy of 91%. Using TCR-γ (TRG) repertoire, 94.4% (51/54) of duodenal biopsies were correctly classified, with LOOCV of 87%. Duodenal biopsy TRG repertoire analysis permitted accurate classification of biopsies from patients with CeD following a strict gluten-free diet for at least 6 months, who would be misclassified by current tests. This result reflects permanent changes to the duodenal γδ TCR repertoire in CeD, even in the absence of gluten consumption. Our method could complement or replace histopathological diagnosis in CeD and might have particular clinical utility in the diagnostic testing of patients unable to tolerate dietary gluten, and for assessing duodenal biopsies with equivocal features. This approach is generalisable to any TCR/BCR locus and any sequencing platform, with potential to predict diagnosis or prognosis in conditions mediated or modulated by the adaptive immune response. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Demonstrating a Continuous Set of Two-Qubit Gates for Near-Term Quantum Algorithms

Quantum algorithms offer a dramatic speedup for computational problems in material science and chemistry. However, any near-term realizations of these algorithms will need to be optimized to fit within the finite resources offered by existing noisy hardware. Here, taking advantage of the adjustable coupling of gmon qubits, we demonstrate a continuous two-qubit gate set that can provide a threefold reduction in circuit depth as compared to a standard decomposition. We implement two gate families: an imaginary swap-like (iSWAP-like) gate to attain an arbitrary swap angle, θ, and a controlled-phase gate that generates an arbitrary conditional phase, ϕ. Using one of each of these gates, we can perform an arbitrary two-qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic simulation (fSim) gate set. We benchmark the fidelity of the iSWAP-like and controlled-phase gate families as well as 525 other fSim gates spread evenly across the entire fSim(θ,ϕ) parameter space, achieving a purity-limited average two-qubit Pauli error of 3.8×10^{-3} per fSim gate.

Inferring B cell specificity for vaccines using a Bayesian mixture model

Background

Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response.

Results

Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity.

Conclusion

Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.

Diabatic Gates for Frequency-Tunable Superconducting Qubits

We demonstrate diabatic two-qubit gates with Pauli error rates down to 4.3(2)×10^{-3} in as fast as 18 ns using frequency-tunable superconducting qubits. This is achieved by synchronizing the entangling parameters with minima in the leakage channel. The synchronization shows a landscape in gate parameter space that agrees with model predictions and facilitates robust tune-up. We test both iswap-like and cphase gates with cross-entropy benchmarking. The presented approach can be extended to multibody operations as well.

Fluctuations of Energy-Relaxation Times in Superconducting Qubits

Superconducting qubits are an attractive platform for quantum computing since they have demonstrated high-fidelity quantum gates and extensibility to modest system sizes. Nonetheless, an outstanding challenge is stabilizing their energy-relaxation times, which can fluctuate unpredictably in frequency and time. Here, we use qubits as spectral and temporal probes of individual two-level-system defects to provide direct evidence that they are responsible for the largest fluctuations. This research lays the foundation for stabilizing qubit performance through calibration, design, and fabrication.

Spectroscopic signatures of localization with interacting photons in superconducting qubits

Quantized eigenenergies and their associated wave functions provide extensive information for predicting the physics of quantum many-body systems. Using a chain of nine superconducting qubits, we implement a technique for resolving the energy levels of interacting photons. We benchmark this method by capturing the main features of the intricate energy spectrum predicted for two-dimensional electrons in a magnetic field-the Hofstadter butterfly. We introduce disorder to study the statistics of the energy levels of the system as it undergoes the transition from a thermalized to a localized phase. Our work introduces a many-body spectroscopy technique to study quantum phases of matter.

Use of Transnasal Humidified Rapid-Insufflation Ventilatory Exchange for Emergent Surgical Tracheostomy: A Case Report

Transnasal humidified rapid-insufflation ventilatory exchange (THRIVE) is a novel airway technique that utilizes high-flow humidified nasal oxygen. It can extend apnea time and maintain oxygen saturation. Here we report the use of THRIVE in a 35-year-old man who required emergent surgical tracheostomy for a clinically relevant compromised airway secondary to acute supraglottic and glottic pathology. Intravenous sedation resulted in hypoventilation close to apnea. THRIVE maintained oxygen saturation for 40 minutes until transient desaturation developed after complete airway obstruction.

Abstract OT1-06-04: [18F] fluoroestradiol (FES) PET as a predictive measure for endocrine therapy in women with newly diagnosed metastatic breast cancer

Background: The majority of metastatic breast cancer patients have estrogen-receptor positive (ER+) disease. Therapy options include cytotoxic chemotherapy and ER-directed therapy, including endocrine therapy with or without molecularly targeted agents such as CK4/6 inhibitors. ER-targeted therapy is most commonly given first-line due to improved tolerability. However, not all patients will respond to first-line endocrine therapy due to intrinsic endocrine-therapy resistance mechanisms as well as tumor heterogeneity. There are no current methods in standard practice to inform on either of these issues.

F-18 fluorestradiol (FES), an estrogen analogue has been considered the most promising ER imaging agent and is widely studied in breast cancer. FES positron emission tomography (PET) evaluates multiple tumor sites simultaneously and, thus, can inform on tumor heterogeneity of ER expression, and can measure ER binding in primary and metastatic sites (e.g., lymph nodes, lung, bone, and soft tissue). Like tissue ER expression, FES positivity, as measured by standardized uptake value (SUV), has been shown to predict response to endocrine therapy with selective ER modulators or aromatase inhibitors in first-line therapy or salvage settings in small studies. Typically, a significantly higher tumor SUV was noted in responders compared with non-responders. Since FES uptake can provide a better assessment of ER expression across all sites of metastatic disease, FES may provide more expansive information on ER expression.. Furthermore, preliminary studies examining FES-PET in metastatic breast cancer have suggested that baseline FES uptake may predict response to endocrine therapy.

Trial Design: This is a prospective trial for patients about to start first line endocrine therapy for advanced ER+ breast cancer. Participants will undergo an FDG-PET within six weeks of FES-PET. FES-PET and serum hormone level to be completed prior to endocrine therapy. Patients may opt to have a 2nd FES-PET for test-re-test of FES-PET.

Specific Aims: Primary Aim : To assess the relationship between ER expression measured by FES-PET/CT and clinical benefit (response plus stable disease) of newly diagnosed ER+ metastatic breast cancer to endocrine therapy.

Secondary Aim: To assess the correlation between FES uptake in ER+ metastatic breast cancer and tissue ER expression.

Eligibility: Patients must have confirmed ER+ HER2- metastatic breast cancer and planning to receive endocrine therapy with or without CK 4/6 inhibitors. Patient must NOT have a history of &gt;1 line of administered chemotherapy for metastatic disease and may not have received endocrine therapy for advanced disease. Prior endocrine or chemotherapy in the adjuvant setting is allowed.

Methods: Participants will undergo a F-18 fluorodeoxyglucose (FDG)-PET/CT within six weeks of FES-PET/CT. FES-PET/CT will be completed prior to treatment initiation. Patients may opt to have a 2nd FES-PET for test-re-test of FES-PET/CT

Present and Target Accrual: A total of 13 out of 99 patients have been enrolled onto this imaging study.

Citation Format: Linden H, Clark A, Fowler A, Novakova A, Mankoff D, Dehdashti F. [18F] fluoroestradiol (FES) PET as a predictive measure for endocrine therapy in women with newly diagnosed metastatic breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr OT1-06-04.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. 

Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). 

Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. 

Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

Measurement-Induced State Transitions in a Superconducting Qubit: Beyond the Rotating Wave Approximation

Many superconducting qubit systems use the dispersive interaction between the qubit and a coupled harmonic resonator to perform quantum state measurement. Previous works have found that such measurements can induce state transitions in the qubit if the number of photons in the resonator is too high. We investigate these transitions and find that they can push the qubit out of the two-level subspace, and that they show resonant behavior as a function of photon number. We develop a theory for these observations based on level crossings within the Jaynes-Cummings ladder, with transitions mediated by terms in the Hamiltonian that are typically ignored by the rotating wave approximation. We find that the most important of these terms comes from an unexpected broken symmetry in the qubit potential. We confirm the theory by measuring the photon occupation of the resonator when transitions occur while varying the detuning between the qubit and resonator.

Multicentric Castleman's disease in a patient with HIV

Multicentric Castleman's disease (MCD) was originally described in non-HIV patients. It is a rare lymphoproliferative disorder, which is more commonly seen in HIV-positive patients and is associated with human herpes virus-8 (HHV-8). We describe a patient with advanced HIV who responded well to conventional highly active antiretroviral treatment. She was diagnosed with MCD soon after her diagnosis of HIV. She presented with multiple flares of her MCD. The case illustrates the difficulty of differentiating between episodes of septicaemia and a flare of MCD. The patient was treated with various chemotherapy regimens, which included several cycles of liposomal doxyrubicin and etoposide. There is currently no consensus on the treatment of MCD and various therapies are described in the literature, which include chemotherapy. Chemotherapy must be chosen with the immunosuppressive effects of the treatment being considered with caution. Both doxyrubicin and etoposide are well tolerated and successfully controlled the symptoms of MCD in our patient.

B-cell repertoire dynamics after sequential hepatitis B vaccination and evidence for cross-reactive B-cell activation

BACKGROUND

A diverse B-cell repertoire is essential for recognition and response to infectious and vaccine antigens. High-throughput sequencing of B-cell receptor (BCR) genes can now be used to study the B-cell repertoire at great depth and may shed more light on B-cell responses than conventional immunological methods. Here, we use high-throughput BCR sequencing to provide novel insight into B-cell dynamics following a primary course of hepatitis B vaccination.

METHODS

Nine vaccine-naïve participants were administered three doses of hepatitis B vaccine (months 0, 1, and 2 or 7). High-throughput Illumina sequencing of the total BCR repertoire was combined with targeted sequencing of sorted vaccine antigen-enriched B cells to analyze the longitudinal response of both the total and vaccine-specific repertoire after each vaccine. ELISpot was used to determine vaccine-specific cell numbers following each vaccine.

RESULTS

Deconvoluting the vaccine-specific from total BCR repertoire showed that vaccine-specific sequence clusters comprised <0.1 % of total sequence clusters, and had certain stereotypic features. The vaccine-specific BCR sequence clusters were expanded after each of the three vaccine doses, despite no vaccine-specific B cells being detected by ELISpot after the first vaccine dose. These vaccine-specific BCR clusters detected after the first vaccine dose had distinct properties compared to those detected after subsequent doses; they were more mutated, present at low frequency even prior to vaccination, and appeared to be derived from more mature B cells.

CONCLUSIONS

These results demonstrate the high-sensitivity of our vaccine-specific BCR analysis approach and suggest an alternative view of the B-cell response to novel antigens. In the response to the first vaccine dose, many vaccine-specific BCR clusters appeared to largely derive from previously activated cross-reactive B cells that have low affinity for the vaccine antigen, and subsequent doses were required to yield higher affinity B cells.

The Diversity and Molecular Evolution of B-Cell Receptors during Infection

B-cell receptors (BCRs) are membrane-bound immunoglobulins that recognize and bind foreign proteins (antigens). BCRs are formed through random somatic changes of germline DNA, creating a vast repertoire of unique sequences that enable individuals to recognize a diverse range of antigens. After encountering antigen for the first time, BCRs undergo a process of affinity maturation, whereby cycles of rapid somatic mutation and selection lead to improved antigen binding. This constitutes an accelerated evolutionary process that takes place over days or weeks. Next-generation sequencing of the gene regions that determine BCR binding has begun to reveal the diversity and dynamics of BCR repertoires in unprecedented detail. Although this new type of sequence data has the potential to revolutionize our understanding of infection dynamics, quantitative analysis is complicated by the unique biology and high diversity of BCR sequences. Models and concepts from molecular evolution and phylogenetics that have been applied successfully to rapidly evolving pathogen populations are increasingly being adopted to study BCR diversity and divergence within individuals. However, BCR dynamics may violate key assumptions of many standard evolutionary methods, as they do not descend from a single ancestor, and experience biased mutation. Here, we review the application of evolutionary models to BCR repertoires and discuss the issues we believe need be addressed for this interdisciplinary field to flourish.

The epigenetic regulators CBP and p300 facilitate leukemogenesis and represent therapeutic targets in acute myeloid leukemia

Growing evidence links abnormal epigenetic control to the development of hematological malignancies. Accordingly, inhibition of epigenetic regulators is emerging as a promising therapeutic strategy. The acetylation status of lysine residues in histone tails is one of a number of epigenetic post-translational modifications that alter DNA-templated processes, such as transcription, to facilitate malignant transformation. Although histone deacetylases are already being clinically targeted, the role of histone lysine acetyltransferases (KAT) in malignancy is less well characterized. We chose to study this question in the context of acute myeloid leukemia (AML), where, using in vitro and in vivo genetic ablation and knockdown experiments in murine models, we demonstrate a role for the epigenetic regulators CBP and p300 in the induction and maintenance of AML. Furthermore, using selective small molecule inhibitors of their lysine acetyltransferase activity, we validate CBP/p300 as therapeutic targets in vitro across a wide range of human AML subtypes. We proceed to show that growth retardation occurs through the induction of transcriptional changes that induce apoptosis and cell-cycle arrest in leukemia cells and finally demonstrate the efficacy of the KAT inhibitors in decreasing clonogenic growth of primary AML patient samples. Taken together, these data suggest that CBP/p300 are promising therapeutic targets across multiple subtypes in AML.

Prognostic Value of 2-[(18)F] Fluoro-2-deoxy-D-glucose Positron Emission Tomography-Computed Tomography Scan Carried out During and After Radiation Therapy for Head and Neck Cancer Using Visual Therapy Response Interpretation Criteria

AIMS

To evaluate the prognostic utility of 2-[(18)F] fluoro-2-deoxy-d-glucose positron emission tomography-computed tomography (FDG PET-CT) carried out in the third week (iPET) and after completion (pPET) of definitive radiation therapy in patients with mucosal primary head and neck squamous cell carcinoma (MPHNSCC) and to investigate the optimal visual grading criteria for therapy response assessment.

MATERIALS AND METHODS

Sixty-nine consecutive patients with newly diagnosed MPHNSCC treated with radical radiation therapy with or without systemic therapy underwent staging. PET-CT, iPET and pPET were included. All PET-CT images were reviewed by using a visual grading system to assess metabolic response for primary tumour: 0 = similar to adjacent background blood pool activity; 1 = more than background but < mediastinal blood pool; 2 ≥ mediastinal blood pool and < liver; 3 ≥ liver; and 4 ≥ brain. The results were correlated with locoregional recurrence-free survival (LRFS), disease-free survival (DFS) and overall survival, using Kaplan-Meier analysis.

RESULTS

The median follow-up was 28 months (range 6-62), the median age was 61 years (range 39-81) and AJCC 7th edition clinical stage II, III and IV were six, 18 and 45 patients, respectively. The optimal threshold for non-complete metabolic response (non-CMR) was defined as focal uptake ≥ liver (grade 3) for iPET and focal uptake ≥ mediastinum (grade 2) for pPET. The 2 year Kaplan-Meier LRFS, DFS and overall survival estimates for primary CMR and non-CMR in iPET were 89.8% versus 71.5% (P = 0.062), 80.1% versus 65.3% (P = 0.132), 79.1% versus 72.1% (P = 0.328) and in pPET 86.2% versus 44.6% (P = 0.0005), 77.6% versus 41.2% (P = 0.006), 81.2% versus 40.6% (P = 0.01), respectively. The negative predictive value (NPV) for LRFS for patients achieving both primary and nodal CMR in iPET was 100%. No locoregional failure was observed in patients with both primary and nodal iPET CMR (P = 0.038), whereas those with nodal iPET CMR had no regional failure (P = 0.033). However, the positive predictive values (PPV) for LRFS and DFS for iPET and pPET were found to be poor: 30% and 36% for iPET and 35% and 39% for pPET, respectively.

CONCLUSION

Standardised criteria using visual assessment are feasible. The metabolic response using visual assessment with standardised interpretation criteria of iPET and pPET can be useful predictors of tumour control. Dose de-escalation can be considered on the basis of a high NPV for iPET. However, the PPV of iPET is poor, indicating that additional discriminative tools are needed.

Analysis of B Cell Repertoire Dynamics Following Hepatitis B Vaccination in Humans, and Enrichment of Vaccine-specific Antibody Sequences

Generating a diverse B cell immunoglobulin repertoire is essential for protection against infection. The repertoire in humans can now be comprehensively measured by high-throughput sequencing. Using hepatitis B vaccination as a model, we determined how the total immunoglobulin sequence repertoire changes following antigen exposure in humans, and compared this to sequences from vaccine-specific sorted cells. Clonal sequence expansions were seen 7 days after vaccination, which correlated with vaccine-specific plasma cell numbers. These expansions caused an increase in mutation, and a decrease in diversity and complementarity-determining region 3 sequence length in the repertoire. We also saw an increase in sequence convergence between participants 14 and 21 days after vaccination, coinciding with an increase of vaccine-specific memory cells. These features allowed development of a model for in silico enrichment of vaccine-specific sequences from the total repertoire. Identifying antigen-specific sequences from total repertoire data could aid our understanding B cell driven immunity, and be used for disease diagnostics and vaccine evaluation.

BCR repertoire sequencing: different patterns of B-cell activation after two Meningococcal vaccines

Next-generation sequencing was used to investigate the B-cell receptor heavy chain transcript repertoire of different B-cell subsets (naive, marginal zone (MZ), immunoglobulin M (IgM) memory and IgG memory) at baseline, and of plasma cells (PCs) 7 days following administration of serogroup ACWY meningococcal polysaccharide and protein-polysaccharide conjugate vaccines. Baseline B-cell subsets could be distinguished from each other using a small number of repertoire properties (clonality, mutation from germline and complementarity-determining region 3 (CDR3) length) that were conserved between individuals. However, analyzing the CDR3 amino-acid sequence (which is particularly important for antigen binding) of the baseline subsets showed few sequences shared between individuals. In contrast, day 7 PCs demonstrated nearly 10-fold greater sequence sharing between individuals than the baseline subsets, consistent with the PCs being induced by the vaccine antigen and sharing specificity for a more limited range of epitopes. By annotating PC sequences based on IgG subclass usage and mutation, and also comparing them with the sequences of the baseline cell subsets, we were able to identify different signatures after the polysaccharide and conjugate vaccines. PCs produced after conjugate vaccination were predominantly IgG1, and most related to IgG memory cells. In contrast, after polysaccharide vaccination, the PCs were predominantly IgG2, less mutated and were equally likely to be related to MZ, IgM memory or IgG memory cells. High-throughput B-cell repertoire sequencing thus provides a unique insight into patterns of B-cell activation not possible from more conventional measures of immunogenicity.

In-Depth Assessment of Within-Individual and Inter-Individual Variation in the B Cell Receptor Repertoire

High-throughput sequencing of the B cell receptor (BCR) repertoire can provide rapid characterization of the B cell response in a wide variety of applications in health, after vaccination and in infectious, inflammatory and immune-driven disease, and is starting to yield clinical applications. However, the interpretation of repertoire data is compromised by a lack of studies to assess the intra and inter-individual variation in the BCR repertoire over time in healthy individuals. We applied a standardized isotype-specific BCR repertoire deep sequencing protocol to a single highly sampled participant, and then evaluated the method in 9 further participants to comprehensively describe such variation. We assessed total repertoire metrics of mutation, diversity, VJ gene usage and isotype subclass usage as well as tracking specific BCR sequence clusters. There was good assay reproducibility (both in PCR amplification and biological replicates), but we detected striking fluctuations in the repertoire over time that we hypothesize may be due to subclinical immune activation. Repertoire properties were unique for each individual, which could partly be explained by a decrease in IgG2 with age, and genetic differences at the immunoglobulin locus. There was a small repertoire of public clusters (0.5, 0.3, and 1.4% of total IgA, IgG, and IgM clusters, respectively), which was enriched for expanded clusters containing sequences with suspected specificity toward antigens that should have been historically encountered by all participants through prior immunization or infection. We thus provide baseline BCR repertoire information that can be used to inform future study design, and aid in interpretation of results from these studies. Furthermore, our results indicate that BCR repertoire studies could be used to track changes in the public repertoire in and between populations that might relate to population immunity against infectious diseases, and identify the characteristics of inflammatory and immunological diseases.