Publications by authors named "Rachel Karchin"

112 Publications

The Genetic Evolution of Treatment-Resistant Cutaneous, Acral, and Uveal Melanomas.

Clin Cancer Res 2021 Mar 15;27(5):1516-1525. Epub 2020 Dec 15.

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.

Purpose: Melanoma is a biologically heterogeneous disease composed of distinct clinicopathologic subtypes that frequently resist treatment. To explore the evolution of treatment resistance and metastasis, we used a combination of temporal and multilesional tumor sampling in conjunction with whole-exome sequencing of 110 tumors collected from 7 patients with cutaneous ( = 3), uveal ( = 2), and acral ( = 2) melanoma subtypes.

Experimental Design: Primary tumors, metastases collected longitudinally, and autopsy tissues were interrogated. All but 1 patient died because of melanoma progression.

Results: For each patient, we generated phylogenies and quantified the extent of genetic diversity among tumors, specifically among putative somatic alterations affecting therapeutic resistance.

Conclusions: In 4 patients who received immunotherapy, we found 1-3 putative acquired and intrinsic resistance mechanisms coexisting in the same patient, including mechanisms that were shared by all tumors within each patient, suggesting that future therapies directed at overcoming intrinsic resistance mechanisms may be broadly effective.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-2984DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925434PMC
March 2021

Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.

Cell Rep Med 2020 Nov 17;1(8):100139. Epub 2020 Nov 17.

The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes.
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http://dx.doi.org/10.1016/j.xcrm.2020.100139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691441PMC
November 2020

Multiregion whole-exome sequencing of intraductal papillary mucinous neoplasms reveals frequent somatic mutations predominantly in low-grade regions.

Gut 2021 May 7;70(5):928-939. Epub 2020 Oct 7.

Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

Objective: Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursor lesions that can progress to invasive pancreatic cancer and are classified as low-grade or high-grade based on the morphology of the neoplastic epithelium. We aimed to compare genetic alterations in low-grade and high-grade regions of the same IPMN in order to identify molecular alterations underlying neoplastic progression.

Design: We performed multiregion whole exome sequencing on tissue samples from 17 IPMNs with both low-grade and high-grade dysplasia (76 IPMN regions, including 49 from low-grade dysplasia and 27 from high-grade dysplasia). We reconstructed the phylogeny for each case, and we assessed mutations in a novel driver gene in an independent cohort of 63 IPMN cyst fluid samples.

Results: Our multiregion whole exome sequencing identified , a previously unreported genetic driver of IPMN tumorigenesis, with hotspot mutations in one of two codons identified in >50% of the analyzed IPMNs. Mutations in were significantly more prevalent in low-grade regions in our sequenced cases. Phylogenetic analyses of whole exome sequencing data demonstrated diverse patterns of IPMN initiation and progression. Hotspot mutations in were also identified in an independent cohort of IPMN cyst fluid samples, again with a significantly higher prevalence in low-grade IPMNs.

Conclusion: Hotspot mutations in occur at high prevalence in IPMNs. Unique among pancreatic driver genes, mutations are enriched in low-grade IPMNs. These data highlight distinct molecular features of low-grade and high-grade dysplasia and suggest diverse pathways to high-grade dysplasia via the IPMN pathway.
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http://dx.doi.org/10.1136/gutjnl-2020-321217DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262510PMC
May 2021

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

Nat Cancer 2020 Jan 13;1(1):99-111. Epub 2020 Jan 13.

The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.
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http://dx.doi.org/10.1038/s43018-019-0008-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514475PMC
January 2020

Genomic characterization of malignant progression in neoplastic pancreatic cysts.

Nat Commun 2020 08 14;11(1):4085. Epub 2020 Aug 14.

Service de Pathologie, AP-HP, Hôpital Cochin, Université Paris Descartes, Paris, France.

Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) are non-invasive neoplasms that are often observed in association with invasive pancreatic cancers, but their origins and evolutionary relationships are poorly understood. In this study, we analyze 148 samples from IPMNs, MCNs, and small associated invasive carcinomas from 18 patients using whole exome or targeted sequencing. Using evolutionary analyses, we establish that both IPMNs and MCNs are direct precursors to pancreatic cancer. Mutations in SMAD4 and TGFBR2 are frequently restricted to invasive carcinoma, while RNF43 alterations are largely in non-invasive lesions. Genomic analyses suggest an average window of over three years between the development of high-grade dysplasia and pancreatic cancer. Taken together, these data establish non-invasive IPMNs and MCNs as origins of invasive pancreatic cancer, identifying potential drivers of invasion, highlighting the complex clonal dynamics prior to malignant transformation, and providing opportunities for early detection and intervention.
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http://dx.doi.org/10.1038/s41467-020-17917-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428044PMC
August 2020

Integrated Informatics Analysis of Cancer-Related Variants.

JCO Clin Cancer Inform 2020 03;4:310-317

The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD.

Purpose: The modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multifactorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, Web dependencies that expose protected data, or limited scalability.

Methods: We present the Open Custom Ranked Analysis of Variants Toolkit (OpenCRAVAT) a new open-source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result, the catalog is being actively developed and growing every month. Resources made available via the store are well suited for analysis of cancer, as well as Mendelian and complex diseases.

Results: OpenCRAVAT offers both command-line utility and dynamic graphical user interface, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants.

Conclusion: OpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding, and noncoding variants.
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http://dx.doi.org/10.1200/CCI.19.00132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113103PMC
March 2020

Assessing aneuploidy with repetitive element sequencing.

Proc Natl Acad Sci U S A 2020 03 19;117(9):4858-4863. Epub 2020 Feb 19.

Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287;

We report a sensitive PCR-based assay called Repetitive Element AneupLoidy Sequencing System (RealSeqS) that can detect aneuploidy in samples containing as little as 3 pg of DNA. Using a single primer pair, we amplified ∼350,000 amplicons distributed throughout the genome. Aneuploidy was detected in 49% of liquid biopsies from a total of 883 nonmetastatic, clinically detected cancers of the colorectum, esophagus, liver, lung, ovary, pancreas, breast, or stomach. Combining aneuploidy with somatic mutation detection and eight standard protein biomarkers yielded a median sensitivity of 80% in these eight cancer types, while only 1% of 812 healthy controls scored positive.
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http://dx.doi.org/10.1073/pnas.1910041117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060727PMC
March 2020

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer Immunol Res 2020 03 23;8(3):396-408. Epub 2019 Dec 23.

Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration ( < 2 × 10), including CD8 T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.
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http://dx.doi.org/10.1158/2326-6066.CIR-19-0464DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056596PMC
March 2020

Characterization of genetic subclonal evolution in pancreatic cancer mouse models.

Nat Commun 2019 11 28;10(1):5435. Epub 2019 Nov 28.

Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.

The KPC mouse model, driven by the Kras and Trp53 transgenes, is well regarded for faithful recapitulation of human pancreatic cancer biology. However, the extent that this model recapitulates the subclonal evolution of this tumor type is unknown. Here we report evidence of continuing subclonal evolution after tumor initiation that largely reflect copy number alterations that target cellular processes of established significance in human pancreatic cancer. The evolutionary trajectories of the mouse tumors show both linear and branching patterns as well as clonal mixing. We propose the KPC model and derivatives have unexplored utility as a functional system to model the mechanisms and modifiers of tumor evolution.
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http://dx.doi.org/10.1038/s41467-019-13100-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6882784PMC
November 2019

A multimodality test to guide the management of patients with a pancreatic cyst.

Sci Transl Med 2019 07;11(501)

Department of Surgery, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland.

Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.
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http://dx.doi.org/10.1126/scitranslmed.aav4772DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859881PMC
July 2019

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

Hum Mutat 2019 09 3;40(9):1530-1545. Epub 2019 Sep 3.

Institute of Medical Technology, University of Tampere, Tampere, Finland.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.
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http://dx.doi.org/10.1002/humu.23868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325732PMC
September 2019

CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers.

Cell Syst 2019 07 12;9(1):9-23.e8. Epub 2019 Jun 12.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University, Baltimore, MD 21204, USA. Electronic address:

Large-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the level of genes to mutations because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here, we present CHASMplus, a computational method that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas (TCGA), CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.
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http://dx.doi.org/10.1016/j.cels.2019.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857794PMC
July 2019

Intraductal Papillary Mucinous Neoplasms Arise From Multiple Independent Clones, Each With Distinct Mutations.

Gastroenterology 2019 10 5;157(4):1123-1137.e22. Epub 2019 Jun 5.

Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, Maryland; Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address:

Background & Aims: Intraductal papillary mucinous neoplasms (IPMNs) are lesions that can progress to invasive pancreatic cancer and constitute an important system for studies of pancreatic tumorigenesis. We performed comprehensive genomic analyses of entire IPMNs to determine the diversity of somatic mutations in genes that promote tumorigenesis.

Methods: We microdissected neoplastic tissues from 6-24 regions each of 20 resected IPMNs, resulting in 227 neoplastic samples that were analyzed by capture-based targeted sequencing. Somatic mutations in genes associated with pancreatic tumorigenesis were assessed across entire IPMN lesions, and the resulting data were supported by evolutionary modeling, whole-exome sequencing, and in situ detection of mutations.

Results: We found a high prevalence of heterogeneity among mutations in IPMNs. Heterogeneity in mutations in KRAS and GNAS was significantly more prevalent in IPMNs with low-grade dysplasia than in IPMNs with high-grade dysplasia (P < .02). Whole-exome sequencing confirmed that IPMNs contained multiple independent clones, each with distinct mutations, as originally indicated by targeted sequencing and evolutionary modeling. We also found evidence for convergent evolution of mutations in RNF43 and TP53, which are acquired during later stages of tumorigenesis.

Conclusions: In an analysis of the heterogeneity of mutations throughout IPMNs, we found that early-stage IPMNs contain multiple independent clones, each with distinct mutations, indicating their polyclonal origin. These findings challenge the model in which pancreatic neoplasms arise from a single clone. Increasing our understanding of the mechanisms of IPMN polyclonality could lead to strategies to identify patients at increased risk for pancreatic cancer.
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http://dx.doi.org/10.1053/j.gastro.2019.06.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6756950PMC
October 2019

Dynamics of Tumor and Immune Responses during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer.

Cancer Res 2019 03 12;79(6):1214-1225. Epub 2018 Dec 12.

The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Despite the initial successes of immunotherapy, there is an urgent clinical need for molecular assays that identify patients more likely to respond. Here, we report that ultrasensitive measures of circulating tumor DNA (ctDNA) and T-cell expansion can be used to assess responses to immune checkpoint blockade in metastatic lung cancer patients ( = 24). Patients with clinical response to therapy had a complete reduction in ctDNA levels after initiation of therapy, whereas nonresponders had no significant changes or an increase in ctDNA levels. Patients with initial response followed by acquired resistance to therapy had an initial drop followed by recrudescence in ctDNA levels. Patients without a molecular response had shorter progression-free and overall survival compared with molecular responders [5.2 vs. 14.5 and 8.4 vs. 18.7 months; HR 5.36; 95% confidence interval (CI), 1.57-18.35; = 0.007 and HR 6.91; 95% CI, 1.37-34.97; = 0.02, respectively], which was detected on average 8.7 weeks earlier and was more predictive of clinical benefit than CT imaging. Expansion of T cells, measured through increases of T-cell receptor productive frequencies, mirrored ctDNA reduction in response to therapy. We validated this approach in an independent cohort of patients with early-stage non-small cell lung cancer ( = 14), where the therapeutic effect was measured by pathologic assessment of residual tumor after anti-PD1 therapy. Consistent with our initial findings, early ctDNA dynamics predicted pathologic response to immune checkpoint blockade. These analyses provide an approach for rapid determination of therapeutic outcomes for patients treated with immune checkpoint inhibitors and have important implications for the development of personalized immune targeted strategies. Rapid and sensitive detection of circulating tumor DNA dynamic changes and T-cell expansion can be used to guide immune targeted therapy for patients with lung cancer..
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http://dx.doi.org/10.1158/0008-5472.CAN-18-1127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6432636PMC
March 2019

Single-cell sequencing defines genetic heterogeneity in pancreatic cancer precursor lesions.

J Pathol 2019 03 16;247(3):347-356. Epub 2019 Jan 16.

Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Intraductal papillary mucinous neoplasms (IPMNs) are precursors to pancreatic cancer; however, little is known about genetic heterogeneity in these lesions. The objective of this study was to characterize genetic heterogeneity in IPMNs at the single-cell level. We isolated single cells from fresh tissue from ten IPMNs, followed by whole genome amplification and targeted next-generation sequencing of pancreatic driver genes. We then determined single-cell genotypes using a novel multi-sample mutation calling algorithm. Our analyses revealed that different mutations in the same driver gene frequently occur in the same IPMN. Two IPMNs had multiple mutations in the initiating driver gene KRAS that occurred in unique tumor clones, suggesting the possibility of polyclonal origin or an unidentified initiating event preceding this critical mutation. Multiple mutations in later-occurring driver genes were also common and were frequently localized to unique tumor clones, raising the possibility of convergent evolution of these genetic events in pancreatic tumorigenesis. Single-cell sequencing of IPMNs demonstrated genetic heterogeneity with respect to early and late occurring driver gene mutations, suggesting a more complex pattern of tumor evolution than previously appreciated in these lesions. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/path.5194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368872PMC
March 2019

Minimal functional driver gene heterogeneity among untreated metastases.

Science 2018 09;361(6406):1033-1037

Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA.

Metastases are responsible for the majority of cancer-related deaths. Although genomic heterogeneity within primary tumors is associated with relapse, heterogeneity among treatment-naïve metastases has not been comprehensively assessed. We analyzed sequencing data for 76 untreated metastases from 20 patients and inferred cancer phylogenies for breast, colorectal, endometrial, gastric, lung, melanoma, pancreatic, and prostate cancers. We found that within individual patients, a large majority of driver gene mutations are common to all metastases. Further analysis revealed that the driver gene mutations that were not shared by all metastases are unlikely to have functional consequences. A mathematical model of tumor evolution and metastasis formation provides an explanation for the observed driver gene homogeneity. Thus, single biopsies capture most of the functionally important mutations in metastases and therefore provide essential information for therapeutic decision-making.
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http://dx.doi.org/10.1126/science.aat7171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329287PMC
September 2018

A machine learning approach for somatic mutation discovery.

Sci Transl Med 2018 09;10(457)

Personal Genome Diagnostics, Baltimore, MD 21224, USA.

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.
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http://dx.doi.org/10.1126/scitranslmed.aar7939DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481619PMC
September 2018

Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics.

J Proteome Res 2018 12 5;17(12):4329-4336. Epub 2018 Sep 5.

Department of Biochemistry, Molecular Biology and Biophysics , University of Minnesota , Minneapolis , Minnesota 55455 , United States.

The Chromosome-centric Human Proteome Project (C-HPP) seeks to comprehensively characterize all protein products coded by the genome, including those expressed sequence variants confirmed via proteogenomics methods. The closely related Biology/Disease-driven Human Proteome Project (B/D-HPP) seeks to understand the biological and pathological associations of expressed protein products, especially those carrying sequence variants that may be drivers of disease. To achieve these objectives, informatics tools are required that interpret potential functional or disease implications of variant protein sequence detected via proteogenomics. Toward this end, we have developed an automated workflow within the Galaxy for Proteomics (Galaxy-P) platform, which leverages the Cancer-Related Analysis of Variants Toolkit (CRAVAT) and makes it interoperable with proteogenomic results. Protein sequence variants confirmed by proteogenomics are assessed for potential structure-function effects as well as associations with cancer using CRAVAT's rich suite of functionalities, including visualization of results directly within the Galaxy user interface. We demonstrate the effectiveness of this workflow on proteogenomic results generated from an MCF7 breast cancer cell line. Our free and open software should enable improved interpretation of the functional and pathological effects of protein sequence variants detected via proteogenomics, acting as a bridge between the C-HPP and B/D-HPP.
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http://dx.doi.org/10.1021/acs.jproteome.8b00404DOI Listing
December 2018

Comprehensive Characterization of Cancer Driver Genes and Mutations.

Cell 2018 04;173(2):371-385.e18

Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672.

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.
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http://dx.doi.org/10.1016/j.cell.2018.02.060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029450PMC
April 2018

Evaluation of liquid from the Papanicolaou test and other liquid biopsies for the detection of endometrial and ovarian cancers.

Sci Transl Med 2018 03;10(433)

Ludwig Center for Cancer Genetics and Therapeutics, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

We report the detection of endometrial and ovarian cancers based on genetic analyses of DNA recovered from the fluids obtained during a routine Papanicolaou (Pap) test. The new test, called PapSEEK, incorporates assays for mutations in 18 genes as well as an assay for aneuploidy. In Pap brush samples from 382 endometrial cancer patients, 81% [95% confidence interval (CI), 77 to 85%] were positive, including 78% of patients with early-stage disease. The sensitivity in 245 ovarian cancer patients was 33% (95% CI, 27 to 39%), including 34% of patients with early-stage disease. In contrast, only 1.4% of 714 women without cancer had positive Pap brush samples (specificity, ~99%). Next, we showed that intrauterine sampling with a Tao brush increased the detection of malignancy over endocervical sampling with a Pap brush: 93% of 123 (95% CI, 87 to 97%) patients with endometrial cancer and 45% of 51 (95% CI, 31 to 60%) patients with ovarian cancer were positive, whereas none of the samples from 125 women without cancer were positive (specificity, 100%). Finally, in 83 ovarian cancer patients in whom plasma was available, circulating tumor DNA was found in 43% of patients (95% CI, 33 to 55%). When plasma and Pap brush samples were both tested, the sensitivity for ovarian cancer increased to 63% (95% CI, 51 to 73%). These results demonstrate the potential of mutation-based diagnostics to detect gynecologic cancers at a stage when they are more likely to be curable.
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http://dx.doi.org/10.1126/scitranslmed.aap8793DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320220PMC
March 2018

Non-invasive detection of urothelial cancer through the analysis of driver gene mutations and aneuploidy.

Elife 2018 03 20;7. Epub 2018 Mar 20.

Masonic Cancer Center, University of Minnesota, Minneapolis, United States.

Current non-invasive approaches for detection of urothelial cancers are suboptimal. We developed a test to detect urothelial neoplasms using DNA recovered from cells shed into urine. UroSEEK incorporates massive parallel sequencing assays for mutations in 11 genes and copy number changes on 39 chromosome arms. In 570 patients at risk for bladder cancer (BC), UroSEEK was positive in 83% of those who developed BC. Combined with cytology, UroSEEK detected 95% of patients who developed BC. Of 56 patients with upper tract urothelial cancer, 75% tested positive by UroSEEK, including 79% of those with non-invasive tumors. UroSEEK detected genetic abnormalities in 68% of urines obtained from BC patients under surveillance who demonstrated clinical evidence of recurrence. The advantages of UroSEEK over cytology were evident in low-grade BCs; UroSEEK detected 67% of cases whereas cytology detected none. These results establish the foundation for a new non-invasive approach for detection of urothelial cancer.
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http://dx.doi.org/10.7554/eLife.32143DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860864PMC
March 2018

Systematic Functional Annotation of Somatic Mutations in Cancer.

Cancer Cell 2018 03;33(3):450-462.e10

Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University, St. Louis, MO 63108, USA.

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.
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http://dx.doi.org/10.1016/j.ccell.2018.01.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5926201PMC
March 2018

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations.

Mol Biol Evol 2018 06;35(6):1507-1519

Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD.

The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure.
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http://dx.doi.org/10.1093/molbev/msy036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5967520PMC
June 2018

IPMNs with co-occurring invasive cancers: neighbours but not always relatives.

Gut 2018 09 2;67(9):1652-1662. Epub 2018 Mar 2.

Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Objective: Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions that can give rise to invasive pancreatic carcinoma. Although approximately 8% of patients with resected pancreatic ductal adenocarcinoma have a co-occurring IPMN, the precise genetic relationship between these two lesions has not been systematically investigated.

Design: We analysed all available patients with co-occurring IPMN and invasive intrapancreatic carcinoma over a 10-year period at a single institution. For each patient, we separately isolated DNA from the carcinoma, adjacent IPMN and distant IPMN and performed targeted next generation sequencing of a panel of pancreatic cancer driver genes. We then used the identified mutations to infer the relatedness of the IPMN and co-occurring invasive carcinoma in each patient.

Results: We analysed co-occurring IPMN and invasive carcinoma from 61 patients with IPMN/ductal adenocarcinoma as well as 13 patients with IPMN/colloid carcinoma and 7 patients with IPMN/carcinoma of the ampullary region. Of the patients with co-occurring IPMN and ductal adenocarcinoma, 51% were likely related. Surprisingly, 18% of co-occurring IPMN and ductal adenocarcinomas were likely independent, suggesting that the carcinoma arose from an independent precursor. By contrast, all colloid carcinomas were likely related to their associated IPMNs. In addition, these analyses showed striking genetic heterogeneity in IPMNs, even with respect to well-characterised driver genes.

Conclusion: This study demonstrates a higher prevalence of likely independent co-occurring IPMN and ductal adenocarcinoma than previously appreciated. These findings have important implications for molecular risk stratification of patients with IPMN.
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http://dx.doi.org/10.1136/gutjnl-2017-315062DOI Listing
September 2018

Detection of aneuploidy in patients with cancer through amplification of long interspersed nucleotide elements (LINEs).

Proc Natl Acad Sci U S A 2018 02 5;115(8):1871-1876. Epub 2018 Feb 5.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287;

Aneuploidy is a feature of most cancer cells, and a myriad of approaches have been developed to detect it in clinical samples. We previously described primers that could be used to amplify ∼38,000 unique long interspersed nucleotide elements (LINEs) from throughout the genome. Here we have developed an approach to evaluate the sequencing data obtained from these amplicons. This approach, called Within-Sample AneupLoidy DetectiOn (WALDO), employs supervised machine learning to detect the small changes in multiple chromosome arms that are often present in cancers. We used WALDO to search for chromosome arm gains and losses in 1,677 tumors and in 1,522 liquid biopsies of blood from cancer patients or normal individuals. Aneuploidy was detected in 95% of cancer biopsies and in 22% of liquid biopsies. Using single-nucleotide polymorphisms within the amplified LINEs, WALDO concomitantly assesses allelic imbalances, microsatellite instability, and sample identification. WALDO can be used on samples containing only a few nanograms of DNA and as little as 1% neoplastic content and has a variety of applications in cancer diagnostics and forensic science.
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http://dx.doi.org/10.1073/pnas.1717846115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828610PMC
February 2018

Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches.

Am J Hum Genet 2018 02 25;102(2):233-248. Epub 2018 Jan 25.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. Electronic address:

Many variants of uncertain significance (VUS) have been identified in BRCA2 through clinical genetic testing. VUS pose a significant clinical challenge because the contribution of these variants to cancer risk has not been determined. We conducted a comprehensive assessment of VUS in the BRCA2 C-terminal DNA binding domain (DBD) by using a validated functional assay of BRCA2 homologous recombination (HR) DNA-repair activity and defined a classifier of variant pathogenicity. Among 139 variants evaluated, 54 had ?99% probability of pathogenicity, and 73 had ?95% probability of neutrality. Functional assay results were compared with predictions of variant pathogenicity from the Align-GVGD protein-sequence-based prediction algorithm, which has been used for variant classification. Relative to the HR assay, Align-GVGD significantly (p < 0.05) over-predicted pathogenic variants. We subsequently combined functional and Align-GVGD prediction results in a Bayesian hierarchical model (VarCall) to estimate the overall probability of pathogenicity for each VUS. In addition, to predict the effects of all other BRCA2 DBD variants and to prioritize variants for functional studies, we used the endoPhenotype-Optimized Sequence Ensemble (ePOSE) algorithm to train classifiers for BRCA2 variants by using data from the HR functional assay. Together, the results show that systematic functional assays in combination with in silico predictors of pathogenicity provide robust tools for clinical annotation of BRCA2 VUS.
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http://dx.doi.org/10.1016/j.ajhg.2017.12.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5985401PMC
February 2018
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