Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab.

PURPOSE: Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of response to immunotherapy that provides patient-specific prediction of outcome using the initial change in tumor size/burden from baseline to the first follow-up visit on standard imaging scans. METHODS: We applied the model to 600 patients with advanced solid tumors who received durvalumab in Study 1108, a phase I/II trial, and compared outcome prediction performance versus size-based criteria with RECIST version 1.1 best overall response (BOR), baseline circulating tumor (ct)DNA level, and other clinical/pathologic predictors of immunotherapy response. RESULTS: In multiple solid tumors, the mathematical parameter representing net tumor growth rate at the first on-treatment computed tomography (CT) scan assessed around 6 weeks after starting durvalumab (α1) had a concordance index to predict overall survival (OS) of 0.66-0.77 on multivariate analyses. This measurement of early tumor dynamics significantly improved multivariate OS models that included standard RECIST v1.1 criteria, baseline ctDNA levels, and other clinical/pathologic factors in predicting OS. Furthermore, α1 was assessed consistently at the first on-treatment CT scan, whereas all traditional RECIST BOR groups were confirmed only after this time. CONCLUSION: These results support further exploring α1 as an integral biomarker of response to immunotherapy. This biomarker may be predictive of further benefit and can be assessed before RECIST response groups can be assigned, potentially providing an opportunity to personalize oncologic management.

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lung cancer.

Although immunotherapy with PD-(L)1 blockade is routine for lung cancer, little is known about acquired resistance. Among 1,201 patients with non-small cell lung cancer (NSCLC) treated with PD-(L)1 blockade, acquired resistance is common, occurring in >60% of initial responders. Acquired resistance shows differential expression of inflammation and interferon (IFN) signaling. Relapsed tumors can be separated by upregulated or stable expression of IFNγ response genes. Upregulation of IFNγ response genes is associated with putative routes of resistance characterized by signatures of persistent IFN signaling, immune dysfunction, and mutations in antigen presentation genes which can be recapitulated in multiple murine models of acquired resistance to PD-(L)1 blockade after in vitro IFNγ treatment. Acquired resistance to PD-(L)1 blockade in NSCLC is associated with an ongoing, but altered IFN response. The persistently inflamed, rather than excluded or deserted, tumor microenvironment of acquired resistance may inform therapeutic strategies to effectively reprogram and reverse acquired resistance.

A Randomized Phase II Study of MEDI0680 in Combination with Durvalumab versus Nivolumab Monotherapy in Patients with Advanced or Metastatic Clear-cell Renal Cell Carcinoma.

PURPOSE: MEDI0680 is a humanized anti-programmed cell death-1 (PD-1) antibody, and durvalumab is an anti-PD-L1 antibody. Combining treatment using these antibodies may improve efficacy versus blockade of PD-1 alone. This phase II study evaluated antitumor activity and safety of MEDI0680 plus durvalumab versus nivolumab monotherapy in immunotherapy-naïve patients with advanced clear-cell renal cell carcinoma who received at least one prior line of antiangiogenic therapy. PATIENTS AND METHODS: Patients received either MEDI0680 (20 mg/kg) with durvalumab (750 mg) or nivolumab (240 mg), all intravenous, every 2 weeks. The primary endpoint was investigator-assessed objective response rate (ORR). Secondary endpoints included best overall response, progression-free survival (PFS), safety, overall survival (OS), and immunogenicity. Exploratory endpoints included changes in circulating tumor DNA (ctDNA), baseline tumor mutational burden, and tumor-infiltrated immune cell profiles. RESULTS: Sixty-three patients were randomized (combination, n = 42; nivolumab, n = 21). ORR was 16.7% [7/42; 95% confidence interval (CI), 7.0-31.4] with combination treatment and 23.8% (5/21; 95% CI, 8.2-47.2) with nivolumab. Median PFS was 3.6 months in both arms; median OS was not reached in either arm. Because of adverse events, 23.8% of patients discontinued MEDI0680 and durvalumab and 14.3% of patients discontinued nivolumab. In the combination arm, reduction in ctDNA fraction was associated with longer PFS. ctDNA mutational analysis did not demonstrate an association with response in either arm. Tumor-infiltrated immune profiles showed an association between immune cell activation and response in the combination arm. CONCLUSIONS: MEDI0680 combined with durvalumab was safe and tolerable; however, it did not improve efficacy versus nivolumab monotherapy.

Design and Efficacy of a Monovalent Bispecific PD-1/CTLA4 Antibody That Enhances CTLA4 Blockade on PD-1+ Activated T Cells.

The clinical benefit of PD-1 blockade can be improved by combination with CTLA4 inhibition but is commensurate with significant immune-related adverse events suboptimally limiting the doses of anti-CTLA4 mAb that can be used. MEDI5752 is a monovalent bispecific antibody designed to suppress the PD-1 pathway and provide modulated CTLA4 inhibition favoring enhanced blockade on PD-1+ activated T cells. We show that MEDI5752 preferentially saturates CTLA4 on PD-1+ T cells versus PD-1- T cells, reducing the dose required to elicit IL2 secretion. Unlike conventional PD-1/CTLA4 mAbs, MEDI5752 leads to the rapid internalization and degradation of PD-1. Moreover, we show that MEDI5752 preferentially localizes and accumulates in tumors providing enhanced activity when compared with a combination of mAbs targeting PD-1 and CTLA4 in vivo. Following treatment with MEDI5752, robust partial responses were observed in two patients with advanced solid tumors. MEDI5752 represents a novel immunotherapy engineered to preferentially inhibit CTLA4 on PD-1+ T cells. SIGNIFICANCE: The unique characteristics of MEDI5752 represent a novel immunotherapy engineered to direct CTLA4 inhibition to PD-1+ T cells with the potential for differentiated activity when compared with current conventional mAb combination strategies targeting PD-1 and CTLA4. This molecule therefore represents a step forward in the rational design of cancer immunotherapy.See related commentary by Burton and Tawbi, p. 1008.This article is highlighted in the In This Issue feature, p. 995.

Anti-PD-1 monoclonal antibody MEDI0680 in a phase I study of patients with advanced solid malignancies.

BACKGROUND: The safety, efficacy, pharmacokinetics, and pharmacodynamics of the anti-programmed cell death-1 antibody MEDI0680 were evaluated in a phase I, multicenter, dose-escalation study in advanced solid malignancies. METHODS: MEDI0680 was administered intravenously once every 2 weeks (Q2W) or once every 3 weeks at 0.1, 0.5, 2.5, 10 or 20 mg/kg. Two cohorts received 20 mg/kg once a week for 2 or 4 weeks, then 20 mg/kg Q2W. All were treated for 12 months or until progression. The primary endpoint was safety. Secondary endpoints were efficacy and pharmacokinetics. Exploratory endpoints included pharmacodynamics. RESULTS: Fifty-eight patients were treated. Median age was 62.5 years and 81% were male. Most had kidney cancer (n = 36) or melanoma (n = 9). There were no dose-limiting toxicities. Treatment-related adverse events occurred in 83% and were grade ≥ 3 in 21%. Objective clinical responses occurred in 8/58 patients (14%): 5 with kidney cancer, including 1 with a complete response, and 3 with melanoma. The relationship between dose and serum levels was predictable and linear, with apparent receptor saturation at 10 mg/kg Q2W and all 20 mg/kg cohorts. CONCLUSIONS: MEDI0680 induced peripheral T-cell proliferation and increased plasma IFNγ and associated chemokines regardless of clinical response. CD8+ T-cell tumor infiltration and tumoral gene expression of IFNG, CD8A, CXCL9, and granzyme K (GZMK) were also increased following MEDI0680 administration. TRIAL REGISTRATION: NCT02013804 ; date of registration December 12, 2013.

Correction to: 33rd Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2018).

After publication of this supplement [1, 2], it was brought to our attention that due to an error authors were missing in the following abstracts. This has now been included in this correction.

Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements.

Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS. We extend this approach to characterize convergent downstream candidate mechanisms of distinct intergenic SNPs across distinct diseases within the same clinical classification. We construct a multipartite network consisting of 467 diseases organized in 15 classes, 2,358 disease-associated SNPs, 6,301 SNPassociated mRNAs by eQTL, and mRNA annotations to 4,538 Gene Ontology mechanisms. Functional similarity between two SNPs (similar SNP pairs) is imputed using a nested information theoretic distance model for which p-values are assigned by conservative scale-free permutation of network edges without replacement (node degrees constant). At FDR≤5%, we prioritized 3,870 intergenic SNP pairs associated, among which 755 are associated with distinct diseases sharing the same disease class, implicating 167 intergenic SNPs, 14 classes, 230 mRNAs, and 134 GO terms. Co-classified SNP pairs were more likely to be prioritized as compared to those of distinct classes confirming a noncoding genetic underpinning to clinical classification (odds ratio ∼3.8; p≤10-25). The prioritized pairs were also enriched in regions bound to the same/interacting transcription factors and/or interacting in long-range chromatin interactions suggestive of epistasis (odds ratio ∼ 2,500; p≤10-25). This prioritized network implicates complex epistasis between intergenic polymorphisms of co-classified diseases and offers a roadmap for a novel therapeutic paradigm: repositioning medications that target proteins within downstream mechanisms of intergenic disease-associated SNPs. Supplementary information and software: http://lussiergroup.org/publications/disease_class.

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

OBJECTIVE: To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. MATERIALS AND METHODS: Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1-pathways." The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. RESULTS: Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). DISCUSSION: Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. CONCLUSION: The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

BACKGROUND: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. RESULTS: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. CONCLUSION: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

53BP1 Contributes to Igh Locus Chromatin Topology during Class Switch Recombination.

In B lymphocytes, Ig class switch recombination (CSR) is induced by activation-induced cytidine deaminase, which initiates a cascade of events leading to DNA double-strand break formation in switch (S) regions. Resolution of DNA double-strand breaks proceeds through formation of S-S synaptic complexes. S-S synapsis is mediated by a chromatin loop that spans the C region domain of the Igh locus. S-S junctions are joined via a nonhomologous end joining DNA repair process. CSR occurs via an intrachromosomal looping out and deletion mechanism that is 53BP1 dependent. However, the mechanism by which 53BP1 facilitates deletional CSR and inhibits inversional switching events remains unknown. We report a novel architectural role for 53BP1 in Igh chromatin looping in mouse B cells. Long-range interactions between the Eµ and 3'Eα enhancers are significantly diminished in the absence of 53BP1. In contrast, germline transcript promoter:3'Eα looping interactions are unaffected by 53BP1 deficiency. Furthermore, 53BP1 chromatin occupancy at sites in the Igh locus is B cell specific, is correlated with histone H4 lysine 20 marks, and is subject to chromatin spreading. Thus, 53BP1 is required for three-dimensional organization of the Igh locus and provides a plausible explanation for the link with 53BP1 enforcement of deletional CSR.

kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects.

MOTIVATION: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). METHODS: We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. RESULTS: In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01). CONCLUSION: Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions.

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterize when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modeling of 2 million pairs of disease-associated SNPs drawn from genome wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter-intra and inter-intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritized SNP pairs with overlapping mRNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritized SNP pairs in independent studies of Alzheimer's disease (entropy p=0.046), bladder cancer (entropy p=0.039), and rheumatoid arthritis (PheWAS case-control p<10-4). Using ENCODE datasets, we further statistically validated that the biological mechanisms shared within prioritized SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a "roadmap" of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.

MOTIVATION: As 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples. RESULTS: In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcriptome dynamics through the analysis of aggregated cell-cell statistical distances within biomolecular pathways. Cell-cell statistical distances are calculated from pathway mRNA fold changes between two cells. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably with Gene Set Enrichment Analysis and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS). CCS quantifies the effect size and significance of differentially expressed pathways for a single cell of interest. Improved rose plots of differentially expressed pathways in each cell highlight the utility of CCS for therapeutic decision-making. AVAILABILITY AND IMPLEMENTATION: http://www.lussierlab.org/publications/CCS/ CONTACT: yves@email.arizona.edu or piegorsch@math.arizona.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs.

The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as naïve SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p<0.001) and theoretic Mann-Whitney U test (p<10(-30)) statistics indicate that mRNAs corresponding to complex disease SNPs via eQTL associations are likely to be regulated by a larger number of SNPs than expected. We name this novel property mRNA hubness in eQTL networks, and further term mRNAs with high hubness as master integrators. mRNA master integrators receive and coordinate the perturbation signals from large numbers of polymorphisms and respond to the personal genetic architecture integratively. This genetic signal integration contrasts with the mechanism underlying some Mendelian diseases, where a genetic polymorphism affecting a single protein hub produces a divergent signal that affects a large number of downstream proteins. Indeed, we verify that this property is independent of the hubness in protein networks for which these mRNAs are transcribed. Our findings provide novel insights into the pleiotropy of mRNAs targeted by complex disease polymorphisms and the architecture of the information flow between the genetic polymorphisms and transcriptomes of complex diseases.

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.

MOTIVATION: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). RESULTS: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences). CONCLUSION: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient's transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the 'interpretable 'omics' of single subjects (e.g. personalome). AVAILABILITY AND IMPLEMENTATION: http://www.lussierlab.net/publications/N-of-1-pathways.

Camelid Ig V genes reveal significant human homology not seen in therapeutic target genes, providing for a powerful therapeutic antibody platform.

Camelid immunoglobulin variable (IGV) regions were found homologous to their human counterparts; however, the germline V repertoires of camelid heavy and light chains are still incomplete and their therapeutic potential is only beginning to be appreciated. We therefore leveraged the publicly available HTG and WGS databases of Lama pacos and Camelus ferus to retrieve the germline repertoire of V genes using human IGV genes as reference. In addition, we amplified IGKV and IGLV genes to uncover the V germline repertoire of Lama glama and sequenced BAC clones covering part of the Lama pacos IGK and IGL loci. Our in silico analysis showed that camelid counterparts of all human IGKV and IGLV families and most IGHV families could be identified, based on canonical structure and sequence homology. Interestingly, this sequence homology seemed largely restricted to the Ig V genes and was far less apparent in other genes: 6 therapeutically relevant target genes differed significantly from their human orthologs. This contributed to efficient immunization of llamas with the human proteins CD70, MET, interleukin (IL)-1ß and IL-6, resulting in large panels of functional antibodies. The in silico predicted human-homologous canonical folds of camelid-derived antibodies were confirmed by X-ray crystallography solving the structure of 2 selected camelid anti-CD70 and anti-MET antibodies. These antibodies showed identical fold combinations as found in the corresponding human germline V families, yielding binding site structures closely similar to those occurring in human antibodies. In conclusion, our results indicate that active immunization of camelids can be a powerful therapeutic antibody platform.

Constraints contributed by chromatin looping limit recombination targeting during Ig class switch recombination.

Engagement of promoters with distal elements in long-range looping interactions has been implicated in regulation of Ig class switch recombination (CSR). The principles determining the spatial and regulatory relationships among Igh transcriptional elements remain poorly defined. We examined the chromosome conformation of C region (CH) loci that are targeted for CSR in a cytokine-dependent fashion in mature B lymphocytes. Germline transcription (GLT) of the γ1 and ε CH loci is controlled by two transcription factors, IL-4-inducible STAT6 and LPS-activated NF-κB. We showed that although STAT6 deficiency triggered loss of GLT, deletion of NF-κB p50 abolished both GLT and γ1 locus:enhancer looping. Thus, chromatin looping between CH loci and Igh enhancers is independent of GLT production and STAT6, whereas the establishment and maintenance of these chromatin contacts requires NF-κB p50. Comparative analysis of the endogenous γ1 locus and a knock-in heterologous promoter in mice identified the promoter per se as the interactive looping element and showed that transcription elongation is dispensable for promoter/enhancer interactions. Interposition of the LPS-responsive heterologous promoter between the LPS-inducible γ3 and γ2b loci altered GLT expression and essentially abolished direct IgG2b switching while maintaining a sequential µâ†’γ3→γ2b format. Our study provides evidence that promoter/enhancer looping interactions can introduce negative constraints on distal promoters and affect their ability to engage in germline transcription and determine CSR targeting.

'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

BACKGROUND: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. METHOD: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. RESULTS: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). CONCLUSIONS: The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. SOFTWARE: http://lussierlab.org/publications/N-of-1-pathways.

Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study.

BACKGROUND: Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge. METHODS: "N-of-1-pathways" translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology. RESULTS: In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10-5), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples. CONCLUSIONS: N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.

In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment.

BACKGROUND: The current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists. METHOD: We developed a customized dual-species array, H&M array, and performed cross-species and species-specific hybridization measurements. We validated a new methodology for establishing the stroma vs cancer ratio using transcriptomic data. RESULTS: In the biological validation of the H&M array, cross-species hybridization of human and mouse probes was significantly reduced (4.5 and 9.4 fold reduction, respectively; p < 2x10-16 for both, Mann-Whitney test). We confirmed the capability of the H&M array to determine the stromal to cancer cells ratio based on the estimation of cellularity index of mouse/human mRNA content in vitro. This new metrics enable to investigate more efficiently the stroma-cancer cell interactions (e.g. cellularity) bypassing labour intensive requirement and biases of laser capture microdissection. CONCLUSION: These results provide the initial evidence of improved and cost-efficient analytics for the investigation of cancer cell microenvironment, using species-specificity arrays specifically designed for xenografts models.