Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma

Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs.

SIGNIFICANCE

The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.

Abstract 5407: A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5407.

Analysis of germline-driven ancestry-associated gene expression in cancers

Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort.

For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).

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Protocol for obtaining controlled access TCGA datasets

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Protocols for quality control analysis and genotype imputation of TCGA germline data

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Statistical analysis for determining ancestry-associated SNPs

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Determination of ancestry-associated germline genetic variation driving mRNA expression

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract 1202: A repository of PDX histology images for exploring spatial heterogeneity and cancer dynamics

Patient-derived xenografts (PDXs) recapitulate intratumoral spatial heterogeneity and simulate a tumor microenvironment in which human immune and stromal cells in the PDX are replaced over passages by murine cells partially lacking immune function. Histological imaging enables exploring the spatial heterogeneity and dynamics of cancer, stromal, and immune cell interactions as correlates of tumor stage and therapeutic response over passages. We created a repository of curated, haematoxylin and eosin (H&E) images as a community resource for addressing these questions.

Images were generated at five sites within the NCI’s PDX Development and Trial Centers Research Network (PDXNet) and the NCI Patient-Derived Models Repository. Over 900 images, including 739 from PDXs and 190 from paired patients, are hosted on the Seven Bridges Genomics Cancer Genomics Cloud. They represent 42 cancer subtypes, including breast cancer (n=134), colon adenocarcinoma (COAD; n=94), pancreatic cancer (n=87), lung adenocarcinoma (LUAD; n=80), melanoma (n=71), and squamous cell lung cancer (LUSC; n=65). Paired human/PDX images are available for each of these cancers. Human and/or PDX images generated following patient treatment are available for 37 of the subtypes. Most images are from early passages (P0: 158; P1: 292; P2: 152; P3: 69; >P3: 55). Annotations include sex, age, race, ethnicity, and, for most images, pathological assessment of tissue-level percent cancer, stromal, and necrotic cell content (n=639) and tumor stage (n=650). RNA and exome sequencing data are available for 99 and 228 images, respectively, matched at the patient or sample level.

Quality control was performed using HistoQC. Cells were segmented and labeled as neoplastic, necrotic, immune, stromal, or other using Hover-Net and predictions of total neoplastic cell area correlated with whole-slide pathological assessment of cancer cell percentage (COAD: r=0.51; LUSC: r=0.59). HD-Staining, another classification approach, was applied to a subset of images and our clinical annotations will facilitate validation of this and related methods. Features of 512 x 512 pixel tiles were computed using the Inception V3 convolutional neural network pre-trained on ImageNet. Unsupervised clustering of these features demonstrate inter-patient heterogeneity within pathologist-annotated tumor regions. A classifier developed using pathologist-annotated cancer, stromal, and necrotic regions and trained on the features in LUSC images (n=10 images) achieved a cross-validation accuracy of 96% for cancer tiles across (n=5) LUAD images. Accuracy was lower for stromal classification (90%), likely reflecting current limitations of our small, but growing, labeled training set.

Our repository of clinically-annotated PDX H&E images should aid the community in studying spatial heterogeneity and in training deep learning-based image analysis methods.

Citation Format: Brian S. White, Xingyi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Akshat M. Savaliya, Lacey E. Dobrolecki, John D. Landua, Matthew H. Bailey, Maihi Fujita, Kurt W. Evans, Bingliang Fang, Junya Fujimoto, Maria Gabriela Raso, Shidan Wang, Guanghua Xiao, Yang Xie, Sherri R. Davies, Ryan C. Fields, R Jay Mashl, Jacqueline L. Mudd, Yeqing Chen, Min Xiao, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet Consortium, Yvonne A. Evrard, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Michael T. Lewis, Ramaswamy Govindan, Li Ding, Shunqiang Li, Meenhard Herlyn, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. A repository of PDX histology images for exploring spatial heterogeneity and cancer dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1202.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Ancestry-specific predisposing germline variants in cancer

BACKGROUND

Distinct prevalence of inherited genetic predisposition may partially explain the difference of cancer risks across ancestries. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis of diverse populations.

METHODS

We conducted analyses using germline and somatic sequencing data generated by The Cancer Genome Atlas. Collapsing pathogenic and likely pathogenic variants to cancer predisposition genes (CPG), we analyzed the association between CPGs and cancer types within ancestral groups. We also identified the predisposition-associated two-hit events and gene expression effects in tumors.

RESULTS

Genetic ancestry analysis classified the cohort of 9899 cancer cases into individuals of primarily European (N = 8184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N = 48, 0.5%), Native/Latin American (N = 41, 0.4%), and admixed (N = 11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) previously identified in Europeans, along with a known association of BRCA2 in ovarian serous cystadenocarcinoma (OR = 8.5 [95% CI, 1.5-47.4]; FDR = 0.045). In the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR = 12.8 [95% CI, 1.8-90.8]; FDR = 0.038). Rare variant burden analysis further identified 7 suggestive associations in African ancestry individuals previously described in European ancestry, including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Most predisposing variants were found exclusively in one ancestry in the TCGA and gnomAD datasets. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic-specific expression and low gene expression of their respective affected genes, and FH splice-site variant carriers showed mis-splicing of FH.

CONCLUSIONS

While several CPGs are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Studies using larger cohorts of diverse ancestries are required to pinpoint ancestry-specific genetic predisposition and inform genetic screening strategies.

Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer

We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

CharGer: clinical Characterization of Germline variants

SUMMARY

CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User-designed pathogenicity criteria can be incorporated into CharGer's flexible framework, thereby allowing users to create a customized classification protocol.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at https://github.com/ding-lab/CharGer and is distributed under the GNU GPL-v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix-based operating systems.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Database of evidence for precision oncology portal

Summary

A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three-dimensional protein structures, putative druggable sites that cluster with known druggable mutations.

Availability and implementation

http://dinglab.wustl.edu/depo.

Dynamic host immune response in virus-associated cancers

Viruses drive carcinogenesis in human cancers through diverse mechanisms that have not been fully elucidated but include promoting immune escape. Here we investigated associations between virus-positivity and immune pathway alteration for 2009 tumors across six virus-related cancer types. Analysis revealed that for 3 of 72 human papillomavirus (HPV)-positive head and neck squamous cell carcinoma (HNSC) the HPV genome integrated in immune checkpoint genes PD-L1 or PD-L2, driving elevated expression in the corresponding gene. In addition to the previously described upregulation of the PD-1 immunosuppressive pathway in Epstein-Barr virus (EBV)-positive stomach tumors, we also observed upregulation of the PD-1 pathway in cytomegalovirus (CMV)-positive tumors. Furthermore, we found signatures of T-cell and B-cell response in HPV-positive HNSC and EBV-positive stomach tumors and HPV-positive HNSC patients were associated with better survival when T-cell signals were detected. Our work reveals that viral infection may recruit immune effector cells, and upregulate PD-1 and CTLA-4 immunosuppressive pathways.

Pathogenic Germline Variants in 10,389 Adult Cancers

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Abstract 5359: Regulatory germline variants in 10,389 adult cancers

Previous studies of rare germline variants in cancer has largely been limited to the coding regions of known predisposition genes. The TCGA PanCanAtlas Germline Working Group is analyzing germline predisposing variants of 10,389 cases in 33 cancer types. We deployed more than 121,000 virtual machines running for over 600,000 hours on the ISB Cancer Genome Cloud to conduct massively parallel variant calling and analyses, and the resulting data are shared with scientists across institutions worldwide. Carriers of the functional regulatory variants add on to the 8.9% of cases carrying known pathogenic variants. Burden analyses reveal enrichment of rare variants in the 3'UTR region of NHP2 and POLH. Further, we observed variants aggregating in conserved regions of selected microRNA families that are also affected by somatic mutations, including mir-17 and mir-29. We nominate regulatory variants by using GWAVA and FunSeq2 corroborated with their enrichment in cancer. The prioritized variants are then further evaluated by further co-occurrence of two-hit events and expression changes in their respective tumor samples. Finally, we examine ancestries, familial history and age at onset for carriers of these variants. Overall, we aim to discover and establish the role of regulatory germline variants in oncogenesis.

Citation Format: Kuan-lin Huang, Amila Weerasinghe, Yige Wu, Wen-wei Liang, R. Jay Mashl, Sheila Reynolds, Kathleen E. Houlahan, Ninad Oak, The Cancer Genome Atlas, Alexander J. Lazar, Michael C. Wendel, Ekta Khurana, Sharon Plon, Feng Chen, Mark Gerstein, Ilya Shmulevich, Li Ding. Regulatory germline variants in 10,389 adult cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5359.

Abstract 3424: Genomic alterations in clonal hematopoiesis

Cancer development is an incremental process. Mutations that initiate clonal hematopoiesis often arise in individuals many years before disease symptoms are apparent. We discovered blood somatic mutations in 8,612 germline blood samples in The Cancer Genome Atlas, drawing fundamental distinctions between these events and germline variants. In a preliminary scan of 5,949 samples, 13,345 blood-specific mutations were identified, preferentially distributed across 26 genes, including ones frequently mutated in hematologic malignancies (e.g., DNMT3A, ASXL1, TET2, JAK2, IDH2, SF3B1, MLL3, and KDM6A) and genes rarely mutated in primary hematologic tumors, including EGFR, PIK3CA, NOTCH2, and PPM1D. Further, we utilized both exome sequencing and SNP array data to conduct the first large-scale survey of blood somatic copy number variation, discovering potentially oncogenic events such as JAK2 amplification. Finally, elderly individuals and smokers have increased likelihoods of undergoing clonal expansion. Our analysis of genomic alterations reveals the origins of hematologic malignancies and can facilitate advancement in early detection and prevention.

Citation Format: Kuan-Lin Huang, Mingchao Xie, Yige Wu, Reyka Jayasinghe, Rajees Varghese, R. Jay Mashl, Song Cao, Matthew Wyczalkowski, Wen-wei Liang, Michael C. Wendel, Ryan Fields, Michael D. McLellan, Daniel C. Link, Feng Chen, Li Ding. Genomic alterations in clonal hematopoiesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3424.

GenomeVIP: a cloud platform for genomic variant discovery and interpretation

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.

Simulation of charge transport in ion channels and nanopores with anisotropic permittivity

Ion channels are part of nature's solution for regulating biological environments. Every ion channel consists of a chain of amino acids carrying a strong and sharply varying permanent charge, folded in such a way that it creates a nanoscopic aqueous pore spanning the otherwise mostly impermeable membranes of biological cells. These naturally occurring proteins are particularly interesting to device engineers seeking to understand how such nanoscale systems realize device-like functions. Availability of high-resolution structural information from X-ray crystallography, as well as large-scale computational resources, makes it possible to conduct realistic ion channel simulations. In general, a hierarchy of simulation methodologies is needed to study different aspects of a biological system like ion channels. Biology Monte Carlo (BioMOCA), a three-dimensional coarse-grained particle ion channel simulator, offers a powerful and general approach to study ion channel permeation. BioMOCA is based on the Boltzmann Transport Monte Carlo (BTMC) and Particle-Particle-Particle-Mesh (P(3)M) methodologies developed at the University of Illinois at Urbana-Champaign. In this paper we briefly discuss the various approaches to simulating ion flow in channel systems that are currently being pursued by the biophysics and engineering communities, and present the effect of having anisotropic dielectric constants on ion flow through a number of nanopores with different effective diameters.

End-point targeted molecular dynamics: large-scale conformational changes in potassium channels

Large-scale conformational changes in proteins that happen often on biological time scales may be relatively rare events on the molecular dynamics time scale. We have implemented an approach to targeted molecular dynamics called end-point targeted molecular dynamics that transforms proteins between two specified conformational states through the use of nonharmonic "soft" restraints. A key feature of the method is that the protein is free to discover its own conformational pathway through the plethora of possible intermediate states. The method is applied to the Shaker K(v)1.2 potassium channel in implicit solvent. The rate of cycling between the open and closed states was varied to explore how slow the cycling rate needed to be to ensure that microscopic reversibility along the transition pathways was well approximated. Results specific to the K(+) channel include: 1), a variation in backbone torsion angles of residues near the Pro-Val-Pro motif in the inner helix during both opening and closing; 2), the identification of possible occlusion sites in the closed channel located among Pro-Val-Pro residues and downstream; 3), a difference in the opening and closing pathways of the channel; and 4), evidence of a transient intermediate structural substate. The results also show that likely intermediate conformations during the opening-closing process can be generated in computationally tractable simulation times.

Sphingomyelin-cholesterol domains in phospholipid membranes: atomistic simulation

We have carried out an atomic-level molecular dynamics simulation of a system of nanoscopic size containing a domain of 18:0 sphingomyelin and cholesterol embedded in a fully hydrated dioleylposphatidylcholine (DOPC) bilayer. To analyze the interaction between the domain and the surrounding phospholipid, we calculate order parameters and area per molecule as a function of molecule type and proximity to the domain. We propose an algorithm based on Voronoi tessellation for the calculation of the area per molecule of various constituents in this ternary mixture. The calculated areas per sphingomyelin and cholesterol are in agreement with previous simulations. The simulation reveals that the presence of the liquid-ordered domain changes the packing properties of DOPC bilayer at a distance as large as approximately 8 nm. We calculate electron density profiles and also calculate the difference in the thickness between the domain and the surrounding DOPC bilayer. The calculated difference in thickness is consistent with data obtained in atomic force microscopy experiments.

Structure of sphingomyelin bilayers: a simulation study

We have carried out a molecular dynamics simulation of a hydrated 18:0 sphingomyelin lipid bilayer. The bilayer contained 1600 sphingomyelin (SM) molecules, and 50,592 water molecules. After construction and initial equilibration, the simulation was run for 3.8 ns at a constant temperature of 50 degrees C and a constant pressure of 1 atm. We present properties of the bilayer calculated from the simulation, and compare with experimental data and with properties of dipalmitoyl phosphatidylcholine (DPPC) bilayers. The SM bilayers are significantly more ordered and compact than DPPC bilayers at the same temperature. SM bilayers also exhibit significant intramolecular hydrogen bonding between phosphate ester oxygen and hydroxyl hydrogen atoms. This results in a decreased hydration in the polar region of the SM bilayer compared with DPPC. Since our simulation system is very large we have calculated the power spectrum of bilayer undulation and peristaltic modes, and we compare these data with similar calculations for DPPC bilayers. We find that the SM bilayer has significantly larger bending modulus and area compressibility compared to DPPC.

Cholesterol-induced modifications in lipid bilayers: a simulation study

We present analysis of new configurational bias Monte Carlo and molecular dynamics simulation data for bilayers of dipalmitoyl phosphatidyl choline and cholesterol for dipalmitoyl phosphatidyl choline:cholesterol ratios of 24:1, 47:3, 11.5:1, 8:1, 7:1, 4:1, 3:1, 2:1, and 1:1, using long molecular dynamics runs and interspersed configurational bias Monte Carlo to ensure equilibration and enhance sampling. In all cases with cholesterol concentrations above 12.5% the area per molecule of the heterogeneous membrane varied linearly with cholesterol fraction. By extrapolation to pure cholesterol, we find the cross-sectional area of cholesterol in these mixtures is approximately 22.3 A(2). From the slope of the area/molecule relationship, we also find that the phospholipid in these mixtures is in a liquid ordered state with an average cross-sectional area per lipid of 50.7 A(2), slightly above the molecular area of a pure phospholipid gel. For lower concentrations of cholesterol, the molecular area rises above the straight line, indicating the "melting" of at least some of the phospholipid into a fluid state. Analysis of the lateral distribution of cholesterol molecules in the leaflets reveals peaks in radial distributions of cholesterols at multiples of approximately 5 A. These peaks grow in size as the simulation progresses, suggesting a tendency for small subunits of one lipid plus one cholesterol, hydrogen bonded together, to act as one composite particle, and perhaps to aggregate with other composites. Our results are consistent with experimentally observed effects of cholesterol, including the condensation effect of cholesterol in phospholipid monolayers and the tendency of cholesterol-rich domains to form in cholesterol-lipid bilayers. We are continuing to analyze this tendency on longer timescales and for larger bilayer patches.

Molecular simulation of dioleoylphosphatidylcholine lipid bilayers at differing levels of hydration

The structure and dynamics of the lipid and water components of dioleoylphosphatidylcholine bilayers at various levels of hydration were studied using molecular dynamics (MD) simulations. Equilibration of these systems proceeded by use of a hybrid MD and configurational-bias Monte Carlo technique using one atmosphere of pressure normal to the membrane and a set point for the lateral area derived from experimental Bragg spacings, combined with experimentally derived specific volumes for each of the system components. Membrane surface tensions were observed to be of the order of tens of dyn/cm. The transbilayer molecular fragment peak positions at low hydration were found to agree with experimental neutron and x-ray scattering profiles and previously published simulations. For hydration levels of 5.4, 11.4, and 16 waters/lipid, molecular fragment distributions and order parameters for the headgroup, lipid chains, and water were quantified. Spin-lattice relaxation rates and lateral self-diffusion coefficients of water agreed well with results from experimental nuclear magnetic resonance studies. Relaxation rates of the choline segments and chemical shift anisotropies for the phosphate and carbonyls were computed. Headgroup orientation, as measured by the P-N vector, showed enhanced alignment with the membrane surface at low hydration. The sign of the membrane dipole potential reversed at low hydration, with the membrane interior negative relative to the interlamellar region. Calculation of the number of water molecules in the headgroup hydration shell, as a function of hydration level, supports the hypothesis that the break point in the curve of Bragg spacing versus hydration level near 12 waters/lipid, observed experimentally by Hristova and White (1988. Biophys. J. 74:2419-2433), marks the completion of the first hydration shell.

Hierarchical approach to predicting permeation in ion channels

A hierarchical computational strategy combining molecular modeling, electrostatics calculations, molecular dynamics, and Brownian dynamics simulations is developed and implemented to compute electrophysiologically measurable properties of the KcsA potassium channel. Models for a series of channels with different pore sizes are developed from the known x-ray structure, using insights into the gating conformational changes as suggested by a variety of published experiments. Information on the pH dependence of the channel gating is incorporated into the calculation of potential profiles for K(+) ions inside the channel, which are then combined with K(+) ion mobilities inside the channel, as computed by molecular dynamics simulations, to provide inputs into Brownian dynamics simulations for computing ion fluxes. The open model structure has a conductance of approximately 110 pS under symmetric 250 mM K(+) conditions, in reasonable agreement with experiments for the largest conducting substate. The dimensions of this channel are consistent with electrophysiologically determined size dependence of quaternary ammonium ion blocking from the intracellular end of this channel as well as with direct structural evidence that tetrabutylammonium ions can enter into the interior cavity of the channel. Realistic values of Ussing flux ratio exponents, distribution of ions within the channel, and shapes of the current-voltage and current-concentration curves are obtained. The Brownian dynamics calculations suggest passage of ions through the selectivity filter proceeds by a "knock-off" mechanism involving three ions, as has been previously inferred from functional and structural studies of barium ion blocking. These results suggest that the present calculations capture the essential nature of K(+) ion permeation in the KcsA channel and provide a proof-of-concept for the integrated microscopic/mesoscopic multitiered approach for predicting ion channel function from structure, which can be applied to other channel structures.

Spontaneous-curvature theory of clathrin-coated membranes

Clathrin-coated membranes are precursors to coated vesicles in the receptor-mediated endocytic pathway. In this paper we present a physical model for the first steps of the transformation of a clathrin-coated membrane into a coated vesicle. The theory is based on in vitro cytoplasmic acidification experiments of Heuser (J. Cell Biol. 108:401-411) that suggest the transformation proceeds by changes in the chemical environment of the clathrin lattice, wherein the chemical environment determines the amount of intrinsic, or spontaneous, curvature of the network. We show that a necessary step of the transformation, formation of free pentagons in the clathrin network, can proceed via dislocation unbinding, driven by changes in the spontaneous curvature. Dislocation unbinding is shown to favor formation of coated vesicles that are quite small compared to those predicted by the current continuum theories, which do not include the topology of the clathrin lattice.