Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer.

BACKGROUND: Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. METHODS: A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). RESULTS: The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054). CONCLUSION: We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Microstructural and thermodynamic characterization of wormlike micelles formed by polydisperse ionic surfactant solutions.

For industrial applications of self-assembled wormlike micelles, measurement and characterization of a micellar material's microstructure and rheology are paramount for the development and deployment of new high-performing and cost-effective formulations. Within this workflow, there are significant bottlenecks associated with experimental delays and a lack of transferability of results from one chemistry to another. In this work, we outline a process to predict microscopic and thermodynamic characteristics of wormlike micelles directly from rheological data by combining a more robust and efficient fitting algorithm with a recently published constitutive model called the Toy Shuffling model [J. D. Peterson and M. E. Cates, J. Rheol. 64, 1465-1496 (2020) and J. D. Peterson and M. E. Cates, J. Rheol. 65, 633-662 (2021)]. To support this work, linear rheology measurements were taken for 143 samples comprising a common base formulation of commercial sodium lauryl ether sulfate, cocamidopropyl betaine, and salt (NaCl). The steady state zero shear viscosity evident in linear rheology was measured in duplicate via direct steady and oscillatory shear experiments. Fitting the collected data to the model, we found trends in the microstructural and thermodynamic characteristics that agree with molecular dynamics simulations. These trends validate our new perspective on the parameters that inform the study of the relationship between chemical formulation and rheology. This work, when implemented at scale, can potentially be used to inform and test strategies for predicting self-assembled micellar structures based on chemical formulation.

Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform.

PURPOSE: Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. METHODS: We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. RESULTS: A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. CONCLUSION: A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.

Thyroidectomy practice in pediatric population: a national perspective.

PURPOSE: To examine presentations and outcomes of pediatric patients underoing thyroidectomy. MATERIALS AND METHODS: A retrospective cross-sectional analysis of the Nationwide Readmissions Database, 2010-2014, was performed. Study population included pediatric (<18 years) inpatients undergoing thyroidectomy. RESULTS: A total of 361 patients were included. Mean age was 13.5 ± 0.2 years, and 79.8% were female. Thyroid diseases included: (i) 19.0% thyroid cancer, (ii) 5.4% Multiple Endocrine Neoplasia type II, (iii) 33.6% toxic nodular disease, and (iv) 42.0% non-toxic benign disease. Total thyroidectomy was performed in 67.7% of the patients, and 3.2% of the patients who had initial lobectomy were readmitted within 3 months for completion thyroidectomy. Postoperative complications were reported in 14.2% of the sample, and hypocalcemia was the most common complication (98.2%). Risk of hypocalcemia was significantly higher in patients who had thyroid cancer (risk = 20.9%, p = 0.011) or toxic thyroid diseases (risk = 19.8%, p = 0.033). Of the study population, 25.6% were managed exclusively in children's hospitals. Management in children's hospitals was not associated with improved outcomes or shorter hospital stay; however, it was associated with a significantly higher cost of health services [US $19,4575.0 ± 195.49 vs. US $13,788.00 ± 238.51, p < 0.001]. CONCLUSIONS: This study reports a national perspective on thyroidectomy in the pediatric population. Most thyroid surgeries performed in the pediatric population are performed for benign conditions. Most pediatric thyroidectomies are performed at low-volume centers. Surgeries performed in children's hospitals are significantly higher in cost without any associated improvement in outcomes or length of hospital stay.

A Pan-RNase Inhibitor Enabling CRISPR-mRNA Platforms for Engineering of Primary Human Monocytes.

Monocytes and their downstream effectors are critical components of the innate immune system. Monocytes are equipped with chemokine receptors, allowing them to migrate to various tissues, where they can differentiate into macrophage and dendritic cell subsets and participate in tissue homeostasis, infection, autoimmune disease, and cancer. Enabling genome engineering in monocytes and their effector cells will facilitate a myriad of applications for basic and translational research. Here, we demonstrate that CRISPR-Cas9 RNPs can be used for efficient gene knockout in primary human monocytes. In addition, we demonstrate that intracellular RNases are likely responsible for poor and heterogenous mRNA expression as incorporation of pan-RNase inhibitor allows efficient genome engineering following mRNA-based delivery of Cas9 and base editor enzymes. Moreover, we demonstrate that CRISPR-Cas9 combined with an rAAV vector DNA donor template mediates site-specific insertion and expression of a transgene in primary human monocytes. Finally, we demonstrate that SIRPa knock-out monocyte-derived macrophages have enhanced activity against cancer cells, highlighting the potential for application in cellular immunotherapies.

An in-silico human cell model reveals the influence of spatial organization on RNA splicing.

Spatial organization is a characteristic of all cells, achieved in eukaryotic cells by utilizing both membrane-bound and membrane-less organelles. One of the key processes in eukaryotes is RNA splicing, which readies mRNA for translation. This complex and highly dynamical chemical process involves assembly and disassembly of many molecules in multiple cellular compartments and their transport among compartments. Our goal is to model the effect of spatial organization of membrane-less organelles (specifically nuclear speckles) and of organelle heterogeneity on splicing particle biogenesis in mammalian cells. Based on multiple sources of complementary experimental data, we constructed a spatial model of a HeLa cell to capture intracellular crowding effects. We then developed chemical reaction networks to describe the formation of RNA splicing machinery complexes and splicing processes within nuclear speckles (specific type of non-membrane-bound organelles). We incorporated these networks into our spatially-resolved human cell model and performed stochastic simulations for up to 15 minutes of biological time, the longest thus far for a eukaryotic cell. We find that an increase (decrease) in the number of nuclear pore complexes increases (decreases) the number of assembled splicing particles; and that compartmentalization is critical for the yield of correctly-assembled particles. We also show that a slight increase of splicing particle localization into nuclear speckles leads to a disproportionate enhancement of mRNA splicing and a reduction in the noise of generated mRNA. Our model also predicts that the distance between genes and speckles has a considerable effect on the mRNA production rate, with genes located closer to speckles producing mRNA at higher levels, emphasizing the importance of genome organization around speckles. The HeLa cell model, including organelles and sub-compartments, provides a flexible foundation to study other cellular processes that are strongly modulated by spatiotemporal heterogeneity.

Sialographic analysis of parotid ductal abnormalities associated with Sjogren's syndrome.

OBJECTIVES: To analyze the location and degree of parotid ductal abnormalities associated with Sjogren's syndrome (SS) and to correlate findings with the duration of the disease. To develop a classification system based on contemporary sialography techniques and employ the system to grade findings on sialograms. To assess the role for therapeutic intervention in patients with SS. METHODS: Retrospective chart review of a consecutive series of 337 sialograms done by the senior investigator over a 10-year period identified 26 sialograms in patients who met the criteria for SS as defined by the American-European Consensus Group (2002). A classification system was developed to grade the degree of ductal abnormalities identified on the sialograms. Individual, initial blinded review of these sialograms was performed by two head and neck radiologists to identify and grade abnormalities. Radiographic findings were correlated with patient history including symptom duration. RESULTS: All patients with SS had stenoses within the ductal system. About 73.1% of patients had stenoses in each branch of the ductal system (primary, secondary, and tertiary ducts). In 19% of patients, the main duct was of normal caliber despite the presence of stenosis in the more proximal ducts (secondary and tertiary ducts). Peripheral (proximal) duct dilation was characterized among those affected in patterns classified as destructive (34.6%), cavitary (26.9%), globular (11.5%), or punctate (11.5%). A statistically significant positive correlation (p = .0360) was identified between symptom duration and degree of main ductal stenosis. CONCLUSION: Sialography may be useful to objectively assess the degree of parotid ductal damage in SS employing a newly proposed classification system. This assessment may assist clinicians in tailoring management to selectively include ductal dilation.

Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells.

Recent advances in whole-genome sequencing have brought the vision of personal genomics and genomic medicine closer to reality. However, current methods lack clinical accuracy and the ability to describe the context (haplotypes) in which genome variants co-occur in a cost-effective manner. Here we describe a low-cost DNA sequencing and haplotyping process, long fragment read (LFR) technology, which is similar to sequencing long single DNA molecules without cloning or separation of metaphase chromosomes. In this study, ten LFR libraries were made using only ∼100 picograms of human DNA per sample. Up to 97% of the heterozygous single nucleotide variants were assembled into long haplotype contigs. Removal of false positive single nucleotide variants not phased by multiple LFR haplotypes resulted in a final genome error rate of 1 in 10 megabases. Cost-effective and accurate genome sequencing and haplotyping from 10-20 human cells, as demonstrated here, will enable comprehensive genetic studies and diverse clinical applications.

Effects of DNA replication on mRNA noise.

There are several sources of fluctuations in gene expression. Here we study the effects of time-dependent DNA replication, itself a tightly controlled process, on noise in mRNA levels. Stochastic simulations of constitutive and regulated gene expression are used to analyze the time-averaged mean and variation in each case. The simulations demonstrate that to capture mRNA distributions correctly, chromosome replication must be realistically modeled. Slow relaxation of mRNA from the low copy number steady state before gene replication to the high steady state after replication is set by the transcript's half-life and contributes significantly to the shape of the mRNA distribution. Consequently both the intrinsic kinetics and the gene location play an important role in accounting for the mRNA average and variance. Exact analytic expressions for moments of the mRNA distributions that depend on the DNA copy number, gene location, cell doubling time, and the rates of transcription and degradation are derived for the case of constitutive expression and subsequently extended to provide approximate corrections for regulated expression and RNA polymerase variability. Comparisons of the simulated models and analytical expressions to experimentally measured mRNA distributions show that they better capture the physics of the system than previous theories.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function.

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans.

BACKGROUND: While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen. RESULTS: We use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates. CONCLUSIONS: This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism's metabolism than previously thought.

Ribosome biogenesis in replicating cells: Integration of experiment and theory.

Ribosomes-the primary macromolecular machines responsible for translating the genetic code into proteins-are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume [Earnest et al., Biophys J 2015, 109, 1117-1135] to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells' lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 min doubling time) E. coli cells, replication was initiated 42 min into the cell cycle and completed after an additional 42 min. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of nonribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 735-751, 2016.

Towards a computational model of a methane producing archaeum.

Progress towards a complete model of the methanogenic archaeum Methanosarcina acetivorans is reported. We characterized size distribution of the cells using differential interference contrast microscopy, finding them to be ellipsoidal with mean length and width of 2.9 µ m and 2.3 µ m, respectively, when grown on methanol and 30% smaller when grown on acetate. We used the single molecule pull down (SiMPull) technique to measure average copy number of the Mcr complex and ribosomes. A kinetic model for the methanogenesis pathways based on biochemical studies and recent metabolic reconstructions for several related methanogens is presented. In this model, 26 reactions in the methanogenesis pathways are coupled to a cell mass production reaction that updates enzyme concentrations. RNA expression data (RNA-seq) measured for cell cultures grown on acetate and methanol is used to estimate relative protein production per mole of ATP consumed. The model captures the experimentally observed methane production rates for cells growing on methanol and is most sensitive to the number of methyl-coenzyme-M reductase (Mcr) and methyl-tetrahydromethanopterin:coenzyme-M methyltransferase (Mtr) proteins. A draft transcriptional regulation network based on known interactions is proposed which we intend to integrate with the kinetic model to allow dynamic regulation.

Development and testing of a versatile genome editing application reporter (V-GEAR) system.

CRISPR-Cas9 and novel cas fusion proteins leveraging specific DNA targeting ability combined with deaminases or reverse transcriptases have revolutionized genome editing. However, their efficacy heavily relies upon protein variants, targeting single guide RNAs, and surrounding DNA sequence context within the targeted loci. This necessitates the need for efficient and rapid screening methods to evaluate these editing reagents and designs. Existing plasmid-based reporters lack flexibility, being fixed to specific DNA sequences, hindering direct comparisons between various editing approaches. To address this, we developed the versatile genome editing application reporter (V-GEAR) system. V-GEAR comprises genes detectable after desired editing via base editing, prime editing, or homology-directed repair within relevant genomic contexts. It employs a detectable synthetic cell surface protein (RQR8) followed by a customizable target sequence resembling genomic regions of interest. These genes allow for reliable identification of corrective editing and cell enrichment. We validated the V-GEAR system with base editors, prime editors, and Cas9-mediated homology-directed repair. Furthermore, the V-GEAR system offers versatility by allowing transient screening or stable integration at the AAVS1 safe harbor loci, rapidly achieved through immunomagnetic isolation. This innovative system enables direct comparisons among editing technologies, accelerating the development and testing of genome editing approaches.

Engineering Memory T Cells as a platform for Long-Term Enzyme Replacement Therapy in Lysosomal Storage Disorders.

Enzymopathy disorders are the result of missing or defective enzymes. Amongst these enzymopathies, mucopolysaccharidosis type I, is a rare genetic lysosomal storage disorder caused by mutations in the gene encoding alpha-L-iduronidase (IDUA), ultimately causes toxic build-up of glycosaminoglycans (GAGs). There is currently no cure and standard treatments provide insufficient relief to the skeletal structure and central nervous system (CNS). Human memory T cells (Tm) migrate throughout the body's tissues and can persist for years, making them an attractive approach for cellular-based, systemic enzyme replacement therapy. Here, we tested genetically engineered, IDUA-expressing Tm as a cellular therapy in an immunodeficient mouse model of MPS I. Our results demonstrate that a single dose of engineered Tm leads to detectable IDUA enzyme levels in the blood for up to 22 weeks and reduced urinary GAG excretion. Furthermore, engineered Tm take up residence in nearly all tested tissues, producing IDUA and leading to metabolic correction of GAG levels in the heart, lung, liver, spleen, kidney, bone marrow, and the CNS. Our study indicates that genetically engineered Tm holds great promise as a platform for cellular-based enzyme replacement therapy for the treatment of mucopolysaccharidosis type I and potentially many other enzymopathies and protein deficiencies.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a 'virtual biopsy'.

Patterned polymer films via reactive silane infusion-induced wrinkling.

A method for simultaneously patterning and functionalizing thin poly(2-hydroxyethyl methacrylate) films through a reactive silane infusion based wrinkling is developed. Wrinkled patterns with tunable wavelengths on submicrometer size are easily produced over large area surfaces and can express a wide variety of chemical functional groups on the surface. The characteristic wavelength of wrinkling scales linearly with initial film thickness, in agreement with a gradationally swollen film model. Results from X-ray photoelectron spectroscopy confirm that the wrinkled film is composed of two layers: a gradient cross-linked top layer and a uniform un-cross-linked bottom layer. The surface chemical properties of wrinkles can be easily tuned by infusion of different functional silanes. Hierarchical wrinkled patterns with micro/nano structure can be achieved by combining wrinkling with other simple lithography methods. Wrinkled nanopatterns can be used as a mold to transfer the topology to a variety of other materials using nanoimprint lithography.

Next generation immuno-oncology tumor profiling using a rapid, non-invasive, computational biophysics biomarker in early-stage breast cancer.

Background: Immuno-oncology (IO) therapies targeting the PD-1/PD-L1 axis, such as immune checkpoint inhibitor (ICI) antibodies, have emerged as promising treatments for early-stage breast cancer (ESBC). Despite immunotherapy's clinical significance, the number of benefiting patients remains small, and the therapy can prompt severe immune-related events. Current pathologic and transcriptomic predictions of IO response are limited in terms of accuracy and rely on single-site biopsies, which cannot fully account for tumor heterogeneity. In addition, transcriptomic analyses are costly and time-consuming. We therefore constructed a computational biomarker coupling biophysical simulations and artificial intelligence-based tissue segmentation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRIs), enabling IO response prediction across the entire tumor. Methods: By analyzing both single-cell and whole-tissue RNA-seq data from non-IO-treated ESBC patients, we associated gene expression levels of the PD-1/PD-L1 axis with local tumor biology. PD-L1 expression was then linked to biophysical features derived from DCE-MRIs to generate spatially- and temporally-resolved atlases (virtual tumors) of tumor biology, as well as the TumorIO biomarker of IO response. We quantified TumorIO within patient virtual tumors (n = 63) using integrative modeling to train and develop a corresponding TumorIO Score. Results: We validated the TumorIO biomarker and TumorIO Score in a small, independent cohort of IO-treated patients (n = 17) and correctly predicted pathologic complete response (pCR) in 15/17 individuals (88.2% accuracy), comprising 10/12 in triple negative breast cancer (TNBC) and 5/5 in HR+/HER2- tumors. We applied the TumorIO Score in a virtual clinical trial (n = 292) simulating ICI administration in an IO-naïve cohort that underwent standard chemotherapy. Using this approach, we predicted pCR rates of 67.1% for TNBC and 17.9% for HR+/HER2- tumors with addition of IO therapy; comparing favorably to empiric pCR rates derived from published trials utilizing ICI in both cancer subtypes. Conclusion: The TumorIO biomarker and TumorIO Score represent a next generation approach using integrative biophysical analysis to assess cancer responsiveness to immunotherapy. This computational biomarker performs as well as PD-L1 transcript levels in identifying a patient's likelihood of pCR following anti-PD-1 IO therapy. The TumorIO biomarker allows for rapid IO profiling of tumors and may confer high clinical decision impact to further enable personalized oncologic care.

High-throughput, high-accuracy array-based resequencing.

Although genomewide association studies have successfully identified associations of many common single-nucleotide polymorphisms (SNPs) with common diseases, the SNPs implicated so far account for only a small proportion of the genetic variability of tested diseases. It has been suggested that common diseases may often be caused by rare alleles missed by genomewide association studies. To identify these rare alleles we need high-throughput, high-accuracy resequencing technologies. Although array-based genotyping has allowed genomewide association studies of common SNPs in tens of thousands of samples, array-based resequencing has been limited for 2 main reasons: the lack of a fully multiplexed pipeline for high-throughput sample processing, and failure to achieve sufficient performance. We have recently solved both of these problems and created a fully multiplexed high-throughput pipeline that results in high-quality data. The pipeline consists of target amplification from genomic DNA, followed by allele enrichment to generate pools of purified variant (or nonvariant) DNA and ends with interrogation of purified DNA on resequencing arrays. We have used this pipeline to resequence approximately 5 Mb of DNA (on 3 arrays) corresponding to the exons of 1,500 genes in >473 samples; in total >2,350 Mb were sequenced. In the context of this large-scale study we obtained a false positive rate of approximately 1 in 500,000 bp and a false negative rate of approximately 10%.

Evaluation and management of paediatric vertigo.

PURPOSE OF REVIEW: This review summarizes the most current information on cause, evaluation and treatment of dizziness in children. RECENT FINDINGS: There has been an increased understanding of the multifactorial cause of dizziness in the paediatric population. Quantitative vestibular testing is increasingly used and valuable as a diagnostic adjunct. Vestibular rehabilitation, migraine hygiene, psychological therapies, pharmaceuticals and/or surgery can be used as well tolerated and effective treatments for vertigo in children and adolescents when tailored to cause. SUMMARY: Paediatric vertigo can be effectively evaluated through careful history taking and physical examination along with adjunctive tests, such as vestibular testing and audiometry, when appropriate. Options for treatment of vestibular disorders in children and adolescents have greatly expanded in recent years allowing for the effective management of nearly all cases of paediatric vertigo, though a multimodal and/or multidisciplinary approach is often needed.