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Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and nonuniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained. Supplementary materials for this article are available online.

An important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA sequencing (RNA-seq) technology. The resulting data allow researchers to search for evidence of gene expression heterosis as one potential molecular mechanism underlying heterosis of agriculturally important traits. The null hypotheses of greatest interest in testing for gene expression heterosis are composite null hypotheses that are difficult to test with standard statistical approaches for RNA-seq analysis. To address these shortcomings, we develop a hierarchical negative binomial model and draw inferences using a computationally tractable empirical Bayes approach to inference. We demonstrate improvements over alternative methods via a simulation study based on a maize experiment and then analyze that maize experiment with our newly proposed methodology.Supplementary materials accompanying this paper appear on-line.

Markov chain Monte Carlo (MCMC) is the predominant tool used in Bayesian parameter estimation for hierarchical models. When the model expands due to an increasing number of hierarchical levels, number of groups at a particular level, or number of observations in each group, a fully Bayesian analysis via MCMC can easily become computationally demanding, even intractable. We illustrate how the steps in an MCMC for hierarchical models are predominantly one of two types: conditionally independent draws or low-dimensional draws based on summary statistics of parameters at higher levels of the hierarchy. Parallel computing can increase efficiency by performing embarrassingly parallel computations for conditionally independent draws and calculating the summary statistics using parallel reductions. During the MCMC algorithm, we record running means and means of squared parameter values to allow convergence diagnosis and posterior inference while avoiding the costly memory transfer bottleneck. We demonstrate the effectiveness of the algorithm on a model motivated by next generation sequencing data, and we release our implementation in R packages fbseq and fbseqCUDA.

In many areas of oncology, we lack sensitive tools to track low-burden disease. Although cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low-disease-burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance. Whole-genome sequencing (WGS) of cfDNA allowed ultra-sensitive detection, capitalizing on the cumulative signal of thousands of somatic mutations observed in solid malignancies, with TF detection sensitivity as low as 10 −5 . The WGS approach enabled dynamic tumor burden tracking and postoperative residual disease detection, associated with adverse outcome. Thus, we present an orthogonal framework for cfDNA cancer monitoring via genome-wide mutational integration, enabling ultra-sensitive detection, overcoming the limitation of cfDNA abundance and empowering treatment optimization in low-disease-burden oncology care. A new approach for whole-genome sequencing of plasma circulating tumor DNA allows for dynamic monitoring of disease burden and ultra-sensitive detection of minimal residual disease.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE SNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGE CNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGE SNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition. Detection of circulating tumor DNA using MRD-EDGE, a machine-learning-guided single-nucleotide variant and copy-number variant detection platform for signal enrichment, enables monitoring of minimal residual disease and immunotherapy response in settings of low tumor burden.

Background Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable. Results The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P . Conclusions ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

BackgroundClearance of circulating tumor DNA (ctDNA) following checkpoint blockade (CB) can precede radiographic response,1 2 though current state of the art ctDNA detection via targeted panels faces limited sensitivity in low burden disease (figure 1). We previously showed that whole genome sequencing (WGS) of plasma can overcome low input of ctDNA to dynamically track low volume malignancy using matched tumor tissue.3 We therefore sought to evaluate ctDNA for tracking early response to checkpoint blockade (CB) in melanoma, and developed a novel classifier that allows us to track disease without matched tumor tissue for expanded applicability in immunotherapy.MethodsTo identify ctDNA sparsely diluted in noncancerous plasma cell free DNA (cfDNA), we developed Phoenix, a deep-learning classifier that uses genomic and epigenomic features to distinguish single nucleotide variants (SNVs) in melanoma from sequencing noise. We evaluated Phoenix on a retrospective cohort of serially sampled plasma from patients with advanced cutaneous melanoma on CB (nivolumab alone or with ipilimumab). Plasma was collected at 0, 3, 6 and 12 weeks after first dose of immunotherapy. ctDNA dynamics were compared to radiographic imaging results at 12 weeks.ResultsWe trained Phoenix on tumor-confirmed SNVs in plasma from a single patient with high tumor mutational burden (TMB) melanoma and cfDNA from age-matched patients without known cancer. Overall ctDNA signal-to-noise enrichment ranged from 100 - 260x in validation patients (n=2) with bulky disease. Phoenix learned key features of melanoma ctDNA including the UV mutational signature and short fragment size (figure 2), and sensitively tracked persistent low burden disease seen on imaging (figure 3). To validate these findings, we expanded our cohort (n= 15) of serially tracked tumors. In our preliminary analysis of 12 patients, Phoenix detected pretreatment ctDNA in 92% of patients at a specificity of 97% (figure 4), compared with only 17% with the benchmark in the field (iChorCNA, a plasma-based WGS liquid biopsy tool; table 1). Phoenix detected a decrease in ctDNA 3 weeks after initiation of CB in 80% of patients (figure 5) with an objective response on imaging. No change in ctDNA was seen in patients who did not respond to treatment.ConclusionsPhoenix successfully identified pretreatment melanoma ctDNA without matched tumor tissue and identified response to CB as early as 3 weeks after treatment. Our ongoing studies aim to optimize this technology for early identification of CB response in clinical practice.Abstract 33 Figure 1WGS of plasma increases sensitivity in low-burden diseaseLikelihood of ctDNA SNV detection is a function of tumor fraction, depth, and breadth (number of candidate sites). Because the limited number of genomic equivalents exhausts depth in targeted sequencing, detection sensitivity is limited by the relatively small number of sites in a clinical panel. In contrast, WGS at modest depth (35x) can detect low tumor fraction by integrating signal from thousands of SNVs across the genome.Abstract 33 Figure 2Phoenix learns key covariates for melanoma ctDNAPhoenix was trained on tumor-confirmed SNVs in plasma from patients with high burden melanoma and cfDNA from age-matched patients without known cancer. We aggregated Phoenix positive (ctDNA, blue) and negative (cfDNA, red) predictions on SNVs from a held out validation melanoma plasma sample. Phoenix ctDNA predictions correctly reflect important melanoma SNV attributes including UV-signature (C>T trinucleotide context, a), low DNase accessibility (b), late replication timing (c), and short fragment length (d).Abstract 33 Figure 3Phoenix sensitively tracks response to nivolumabPlasma samples were collected to monitor treatment response to nivolumab. Treatment monitoring by computed tomography (CT) shows response to therapy but residual disease after 3 months of therapy (a). Phoenix quantifies tumor response, matching radiographic changes, in higher temporal resolution than what is feasible with imaging (b). IchorCNA sensitivity captures initial treatment response dynamics but does not detect residual disease after 3 months of treatment (c). Log z score is calculated from a single plasma sample for each timepoint compared to a panel of control samples (n = 37).Abstract 33 Table 1Characteristics of patients at baseline and ctDNA dynamicsAbstract 33 Figure 4Phoenix detects pre- and intratreatment melanoma ctDNAWe evaluated Phoenix post-filter sample-level detection rate. Phoenix detects ctDNA in 92% of pretreatment melanoma plasma samples (green, n=12) at a specificity of 97% relative to held-out noncancerous controls (blue, n=38). Phoenix detected ctDNA in 84% of postreatment plasma samples (n=38, yellow), indicating full ctDNA clearance in 7/38 samples.Abstract 33 Figure 5ctDNA response to checkpoint blockade after 3 weeksSerial plasma samples were taken from patients on checkpoint blockade (nivolumab alone or with ipilimumab). ctDNA burden was measured as detection rate among post-filter candidate SNVs and compared to a 97% specificity boundary among a panel of healthy controls. Phoenix detects a response to checkpoint blockade, measured as a decrease in ctDNA detection rate, as early as 3 weeks as shown in 3 patients (MSK-38, MSK-40, MSK 42).AcknowledgementsThanks to support from the Conquer Cancer FoundationEthics ApprovalUse of human data in this study was approved by Memorial Sloan Kettering’s IRB, Assurance Number FWA0000499ReferencesZhang Q, Luo J, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov 2020 pp: CD-20-0047. doi:10.1158/2159-8290.CD-20-0047Bratman SV, Yang SYC., Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-0096-53.Zviran A, Schulman RC, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med 2020;26(7):1114–1124. doi:10.1038/s41591-020-0915-3Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun2017;8(1):1324. Published 2017 Nov 6. doi:10.1038/s41467-017-00965-y

Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable. The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known \"simultaneous alignment and folding\" of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P's sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences ([appox. equal to]400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P. ExpaRNA-P's novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition. Competing Interest Statement DAL, AJW, CCK, JB and MS submitted two patent applications. AS receives research funding from AstraZeneca, has served on Advisory Boards for AstraZeneca, Blueprint Medicines, and Jazz Pharmaceuticals, and has been a consultant for Genentech. MAP has received consulting fees from BMS, Merck, Array BioPharma, Novartis, Incyte, NewLink Genetics, Aduro, Eisai, and Pfizer, has received honoraria from BMS and Merck, and has received institutional support from RGenix, Infinity, BMS, Merck, Array BioPharma, Novartis, and AstraZeneca. CLA reports collaborations with C2i Genomics and Natera. MKC has received consulting fees from BMS, Merck, InCyte, Moderna, ImmunoCore, and AstraZeneca and receives institutional support from BMS. ST is funded by Cancer Research UK (grant reference number A29911); the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC10988), the UK Medical Research Council (FC10988), and the Wellcome Trust (FC10988); the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden Hospital and Institute of Cancer Research (grant reference number A109), the Royal Marsden Cancer Charity, The Rosetrees Trust (grant reference number A2204), Ventana Medical Systems Inc (grant reference numbers 10467 and 10530), the National Institute of Health (U01 CA247439) and Melanoma Research Alliance (Award Ref no 686061). ST has received speaking fees from Roche, Astra Zeneca, Novartis and Ipsen. ST has the following patents filed: Indel mutations as a therapeutic target and predictive biomarker PCTGB2018/051892 and PCTGB2018/051893. JDW is a Consultant for Amgen; Apricity; Ascentage Pharma; Arsenal IO; Astellas; AstraZeneca; Bicara Therapeutics; Boehringer Ingelheim; Bristol Myers Squibb; Chugai; Daiichi Sankyo, Dragonfly; Georgiamune; Idera; Imvaq; Kyowa Hakko Kirin; Maverick Therapeutics; Psioxus; Recepta; Tizona; Trieza; Trishula; Sellas; Surface Oncology; Werewolf Therapeutics. JDW receives Grant/Research Support from Bristol Myers Squibb; Sephora. JDW has Equity in Tizona Pharmaceuticals; Imvaq; Beigene; Linneaus, Apricity, Arsenal IO; Georgiamune; Trieza; Maverick; Ascentage. DAL received research support from Illumina, Inc. DAL is a scientific co-founder of C2i Genomics.