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Pandya D, Tomita S, Rhenals MP, Swierczek S, Reid K, Camacho-Vanegas O, Camacho C, Engelman K, Polukort S, RoseFigura J, Chuang L, Andikyan V, Cohen S, Fiedler P, Sieber S, Shih IM, Billaud JN, Sebra R, Reva B, Dottino P, Martignetti JA. Mutations in cancer-relevant genes are ubiquitous in histologically normal endometrial tissue. Gynecol Oncol 2024; 185:194-201. [PMID: 38452634 DOI: 10.1016/j.ygyno.2024.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
OBJECTIVE Endometrial cancer (EndoCA) is the most common gynecologic cancer and incidence and mortality rate continue to increase. Despite well-characterized knowledge of EndoCA-defining mutations, no effective diagnostic or screening tests exist. To lay the foundation for testing development, our study focused on defining the prevalence of somatic mutations present in non-cancerous uterine tissue. METHODS We obtained ≥8 uterine samplings, including separate endometrial and myometrial layers, from each of 22 women undergoing hysterectomy for non-cancer conditions. We ultra-deep sequenced (>2000× coverage) samples using a 125 cancer-relevant gene panel. RESULTS All women harbored complex mutation patterns. In total, 308 somatic mutations were identified with mutant allele frequencies ranging up to 96.0%. These encompassed 56 unique mutations from 24 genes. The majority of samples possessed predicted functional cancer mutations but curiously no growth advantage over non-functional mutations was detected. Functional mutations were enriched with increasing patient age (p = 0.045) and BMI (p = 0.0007) and in endometrial versus myometrial layers (68% vs 39%, p = 0.0002). Finally, while the somatic mutation landscape shared similar mutation prevalence in key TCGA-defined EndoCA genes, notably PIK3CA, significant differences were identified, including NOTCH1 (77% vs 10%), PTEN (9% vs 61%), TP53 (0% vs 37%) and CTNNB1 (0% vs 26%). CONCLUSIONS An important caveat for future liquid biopsy/DNA-based cancer diagnostics is the repertoire of shared and distinct mutation profiles between histologically unremarkable and EndoCA tissues. The lack of selection pressure between functional and non-functional mutations in histologically unremarkable uterine tissue may offer a glimpse into an unrecognized EndoCA protective mechanism.
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Affiliation(s)
- Deep Pandya
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | - Shannon Tomita
- Departments of Obstetrics/Gynecology & Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Maria Padron Rhenals
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Sabina Swierczek
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America; Department of Obstetrics, Gynecology and Reproductive Sciences, Larner College of Medicine, University of Vermont, Burlington, VT, United States of America
| | - Katherine Reid
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Olga Camacho-Vanegas
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Catalina Camacho
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Kelsey Engelman
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Stephanie Polukort
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | | | - Linus Chuang
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | - Vaagn Andikyan
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | - Samantha Cohen
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Paul Fiedler
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | - Steven Sieber
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America
| | - Ie-Ming Shih
- Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States of America
| | - Jean-Noël Billaud
- QIAGEN Bioinformatics, 1001 Marshall Street, Redwood City, CA 94063, United States of America
| | - Robert Sebra
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Boris Reva
- Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America
| | - Peter Dottino
- Departments of Obstetrics/Gynecology & Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America; MDDx Inc., Tarrytown, NY 10591., United States of America
| | - John A Martignetti
- The Rudy L. Ruggles Biomedical Research Institute, Nuvance Health, Danbury, CT 06902, United States of America; Departments of Obstetrics/Gynecology & Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America; Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America; MDDx Inc., Tarrytown, NY 10591., United States of America.
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Nussinov R, Tsai CJ, Jang H. Why Are Some Driver Mutations Rare? Trends Pharmacol Sci 2019; 40:919-929. [PMID: 31699406 DOI: 10.1016/j.tips.2019.10.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/13/2022]
Abstract
Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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3
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Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol 2019; 15:e1006658. [PMID: 30921324 PMCID: PMC6438456 DOI: 10.1371/journal.pcbi.1006658] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.
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4
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Wang H, Bender A, Wang P, Karakose E, Inabnet WB, Libutti SK, Arnold A, Lambertini L, Stang M, Chen H, Kasai Y, Mahajan M, Kinoshita Y, Fernandez-Ranvier G, Becker TC, Takane KK, Walker LA, Saul S, Chen R, Scott DK, Ferrer J, Antipin Y, Donovan M, Uzilov AV, Reva B, Schadt EE, Losic B, Argmann C, Stewart AF. Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas. Nat Commun 2017; 8:767. [PMID: 28974674 PMCID: PMC5626682 DOI: 10.1038/s41467-017-00992-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 08/10/2017] [Indexed: 12/19/2022] Open
Abstract
Although diabetes results in part from a deficiency of normal pancreatic beta cells, inducing human beta cells to regenerate is difficult. Reasoning that insulinomas hold the “genomic recipe” for beta cell expansion, we surveyed 38 human insulinomas to obtain insights into therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize the genomic and molecular landscape of insulinomas relative to normal beta cells. Here, we show at the pathway level that the majority of the insulinomas display mutations, copy number variants and/or dysregulation of epigenetic modifying genes, most prominently in the polycomb and trithorax families. Importantly, these processes are coupled to co-expression network modules associated with cell proliferation, revealing candidates for inducing beta cell regeneration. Validation of key computational predictions supports the concept that understanding the molecular complexity of insulinoma may be a valuable approach to diabetes drug discovery. Diabetes results in part from a deficiency of functional pancreatic beta cells. Here, the authors study the genomic and epigenetic landscapes of human insulinomas to gain insight into possible pathways for therapeutic beta cell regeneration, highlighting epigenetic genes and pathways.
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Affiliation(s)
- Huan Wang
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,The Graduate School, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Aaron Bender
- The Graduate School, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Peng Wang
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Esra Karakose
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - William B Inabnet
- The Department of Surgery, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Steven K Libutti
- The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Andrew Arnold
- Center for Molecular Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Luca Lambertini
- The Departments of Environmental Medicine and Public Health and Obstetrics, Gynecology, and Reproductive Sciences, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Micheal Stang
- The Department of Surgery, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Herbert Chen
- The Department of Surgery, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Yumi Kasai
- The New York Genome Center, New York, NY, 10013, USA
| | - Milind Mahajan
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yayoi Kinoshita
- The Department of Pathology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Thomas C Becker
- The Sarah W. Stedman Center for Nutrition and Metabolism, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Karen K Takane
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Laura A Walker
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shira Saul
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Rong Chen
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Donald K Scott
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jorge Ferrer
- The Department of Genetics in Medicine, Imperial College, London, W12 0NN, UK
| | - Yevgeniy Antipin
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Michael Donovan
- The Department of Pathology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew V Uzilov
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Boris Reva
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric E Schadt
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Bojan Losic
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Carmen Argmann
- The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew F Stewart
- The Diabetes Obesity and Metabolism Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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5
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Nair N, Camacho-Vanegas O, Rykunov D, Dashkoff M, Camacho SC, Schumacher CA, Irish JC, Harkins TT, Freeman E, Garcia I, Pereira E, Kendall S, Belfer R, Kalir T, Sebra R, Reva B, Dottino P, Martignetti JA. Genomic Analysis of Uterine Lavage Fluid Detects Early Endometrial Cancers and Reveals a Prevalent Landscape of Driver Mutations in Women without Histopathologic Evidence of Cancer: A Prospective Cross-Sectional Study. PLoS Med 2016; 13:e1002206. [PMID: 28027320 PMCID: PMC5189938 DOI: 10.1371/journal.pmed.1002206] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 11/18/2016] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Endometrial cancer is the most common gynecologic malignancy, and its incidence and associated mortality are increasing. Despite the immediate need to detect these cancers at an earlier stage, there is no effective screening methodology or protocol for endometrial cancer. The comprehensive, genomics-based analysis of endometrial cancer by The Cancer Genome Atlas (TCGA) revealed many of the molecular defects that define this cancer. Based on these cancer genome results, and in a prospective study, we hypothesized that the use of ultra-deep, targeted gene sequencing could detect somatic mutations in uterine lavage fluid obtained from women undergoing hysteroscopy as a means of molecular screening and diagnosis. METHODS AND FINDINGS Uterine lavage and paired blood samples were collected and analyzed from 107 consecutive patients who were undergoing hysteroscopy and curettage for diagnostic evaluation from this single-institution study. The lavage fluid was separated into cellular and acellular fractions by centrifugation. Cellular and cell-free DNA (cfDNA) were isolated from each lavage. Two targeted next-generation sequencing (NGS) gene panels, one composed of 56 genes and the other of 12 genes, were used for ultra-deep sequencing. To rule out potential NGS-based errors, orthogonal mutation validation was performed using digital PCR and Sanger sequencing. Seven patients were diagnosed with endometrial cancer based on classic histopathologic analysis. Six of these patients had stage IA cancer, and one of these cancers was only detectable as a microscopic focus within a polyp. All seven patients were found to have significant cancer-associated gene mutations in both cell pellet and cfDNA fractions. In the four patients in whom adequate tumor sample was available, all tumor mutations above a specific allele fraction were present in the uterine lavage DNA samples. Mutations originally only detected in lavage fluid fractions were later confirmed to be present in tumor but at allele fractions significantly less than 1%. Of the remaining 95 patients diagnosed with benign or non-cancer pathology, 44 had no significant cancer mutations detected. Intriguingly, 51 patients without histopathologic evidence of cancer had relatively high allele fraction (1.0%-30.4%), cancer-associated mutations. Participants with detected driver and potential driver mutations were significantly older (mean age mutated = 57.96, 95% confidence interval [CI]: 3.30-∞, mean age no mutations = 50.35; p-value = 0.002; Benjamini-Hochberg [BH] adjusted p-value = 0.015) and more likely to be post-menopausal (p-value = 0.004; BH-adjusted p-value = 0.015) than those without these mutations. No associations were detected between mutation status and race/ethnicity, body mass index, diabetes, parity, and smoking status. Long-term follow-up was not presently available in this prospective study for those women without histopathologic evidence of cancer. CONCLUSIONS Using ultra-deep NGS, we identified somatic mutations in DNA extracted both from cell pellets and a never previously reported cfDNA fraction from the uterine lavage. Using our targeted sequencing approach, endometrial driver mutations were identified in all seven women who received a cancer diagnosis based on classic histopathology of tissue curettage obtained at the time of hysteroscopy. In addition, relatively high allele fraction driver mutations were identified in the lavage fluid of approximately half of the women without a cancer diagnosis. Increasing age and post-menopausal status were associated with the presence of these cancer-associated mutations, suggesting the prevalent existence of a premalignant landscape in women without clinical evidence of cancer. Given that a uterine lavage can be easily and quickly performed even outside of the operating room and in a physician's office-based setting, our findings suggest the future possibility of this approach for screening women for the earliest stages of endometrial cancer. However, our findings suggest that further insight into development of cancer or its interruption are needed before translation to the clinic.
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Affiliation(s)
- Navya Nair
- Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Olga Camacho-Vanegas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Matthew Dashkoff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Sandra Catalina Camacho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | | | | | | | - Elijah Freeman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Isaac Garcia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Elena Pereira
- Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Sviatoslav Kendall
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Rachel Belfer
- Jefferson School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Tamara Kalir
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Peter Dottino
- Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - John A. Martignetti
- Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Laboratory for Translational Research, Western Connecticut Health Network, Danbury, Connecticut, United States of America
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6
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Prasad V, Fojo T, Brada M. Precision oncology: origins, optimism, and potential. Lancet Oncol 2016; 17:e81-e86. [PMID: 26868357 DOI: 10.1016/s1470-2045(15)00620-8] [Citation(s) in RCA: 144] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 12/18/2015] [Accepted: 12/20/2015] [Indexed: 01/04/2023]
Abstract
Imatinib, the first and arguably the best targeted therapy, became the springboard for developing drugs aimed at molecular targets deemed crucial to tumours. As this development unfolded, a revolution in the speed and cost of genetic sequencing occurred. The result--an armamentarium of drugs and an array of molecular targets--set the stage for precision oncology, a hypothesis that cancer treatment could be markedly improved if therapies were guided by a tumour's genomic alterations. Drawing lessons from the biological basis of cancer and recent empirical investigations, we take a more measured view of precision oncology's promise. Ultimately, the promise is not our concern, but the threshold at which we declare success. We review reports of precision oncology alongside those of precision diagnostics and novel radiotherapy approaches. Although confirmatory evidence is scarce, these interventions have been widely endorsed. We conclude that the current path will probably not be successful or, at a minimum, will have to undergo substantive adjustments before it can be successful. For the sake of patients with cancer, we hope one form of precision oncology will deliver on its promise. However, until confirmatory studies are completed, precision oncology remains unproven, and as such, a hypothesis in need of rigorous testing.
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Affiliation(s)
- Vinay Prasad
- Division of Hematology Oncology, Knight Cancer Institute, Department of Public Health and Preventive Medicine, and Center for Health Care Ethics, Oregon Health and Science University, Portland, OR, USA
| | - Tito Fojo
- Columbia University and James J Peters Veterans Affairs Medical Center, New York, NY, USA.
| | - Michael Brada
- Department of Molecular and Clinical Cancer Medicine & Department of Radiation Oncology University of Liverpool Clatterbridge Cancer Centre NHS Foundation Trust Bebington, UK
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Kehr B, Melsted P, Halldórsson BV. PopIns: population-scale detection of novel sequence insertions. Bioinformatics 2015; 32:961-7. [PMID: 25926346 DOI: 10.1093/bioinformatics/btv273] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 04/22/2015] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. Although the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions. RESULTS We have developed the program PopIns, which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this article, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a dataset of 305 Icelanders demonstrate the practicality of the new approach. AVAILABILITY AND IMPLEMENTATION The source code of PopIns is available from http://github.com/bkehr/popins CONTACT birte.kehr@decode.is SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Birte Kehr
- deCODE genetics/Amgen, Reykjavík, Iceland
| | - Páll Melsted
- deCODE genetics/Amgen, Reykjavík, Iceland, Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland and
| | - Bjarni V Halldórsson
- deCODE genetics/Amgen, Reykjavík, Iceland, Institute of Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland
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8
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Wheler JJ, Parker BA, Lee JJ, Atkins JT, Janku F, Tsimberidou AM, Zinner R, Subbiah V, Fu S, Schwab R, Moulder S, Valero V, Schwaederle M, Yelensky R, Miller VA, Stephens MPJ, Meric-Bernstam F, Kurzrock R. Unique molecular signatures as a hallmark of patients with metastatic breast cancer: implications for current treatment paradigms. Oncotarget 2015; 5:2349-54. [PMID: 24811890 PMCID: PMC4058010 DOI: 10.18632/oncotarget.1946] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Our analysis of the tumors of 57 women with metastatic breast cancer with next generation sequencing (NGS) demonstrates that each patient's tumor is unique in its molecular fingerprint. We observed 216 somatic aberrations in 70 different genes, including 131 distinct aberrations. The most common gene alterations (in order of decreasing frequency) included: TP53, PIK3CA, CCND1, MYC, HER2 (ERBB2), MCL1, PTEN, FGFR1, GATA3, NF1, PIK3R1, BRCA2, EGFR, IRS2, CDH1, CDKN2A, FGF19, FGF3 and FGF4. Aberrations included mutations (46%), amplifications (45%), deletions (5%), splices (2%), truncations (1%), fusions (0.5%) and rearrangements (0.5%), with multiple distinct variants within the same gene. Many of these aberrations represent druggable targets, either through direct pathway inhibition or through an associated pathway (via ‘crosstalk’). The ‘molecular individuality’ of these tumors suggests that a customized strategy, using an “N-of-One” model of precision medicine, may represent an optimal approach for the treatment of patients with advanced tumors.
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Affiliation(s)
- Jennifer J Wheler
- Department of Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX
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Boegel S, Löwer M, Bukur T, Sahin U, Castle JC. A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines. Oncoimmunology 2014; 3:e954893. [PMID: 25960936 PMCID: PMC4355981 DOI: 10.4161/21624011.2014.954893] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Accepted: 07/15/2014] [Indexed: 01/14/2023] Open
Abstract
Cancer cell lines are a tremendous resource for cancer biology and therapy development. These multipurpose tools are commonly used to examine the genetic origin of cancers, to identify potential novel tumor targets, such as tumor antigens for vaccine devel-opment, and utilized to screen potential therapies in preclinical studies. Mutations, gene expression, and drug sensitivity have been determined for many cell lines using next-generation sequencing (NGS). However, the human leukocyte antigen (HLA) type and HLA expression of tumor cell lines, characterizations necessary for the development of cancer vaccines, have remained largely incomplete and, such information, when available, has been distributed in many publications. Here, we determine the 4-digit HLA type and HLA expression of 167 cancer and 10 non-cancer cell lines from publically available RNA-Seq data. We use standard NGS RNA-Seq short reads from "whole transcriptome" sequencing, map reads to known HLA types, and statistically determine HLA type, heterozygosity, and expression. First, we present previously unreported HLA Class I and II genotypes. Second, we determine HLA expression levels in each cancer cell line, providing insights into HLA downregulation and loss in cancer. Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination. Fourth, we integrate the cancer cell-line specific HLA types and HLA expression with available cell-line specific mutation information and existing HLA binding prediction algorithms to make a catalog of predicted antigenic mutations in each cell line. The compilation of our results are a fundamental resource for all researchers selecting specific cancer cell lines based on the HLA type and HLA expression, as well as for the development of immunotherapeutic tools for novel cancer treatment modalities.
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Key Words
- BRENDA, BRaunschweig ENzyme Database
- CCLE, Cancer Cell Line Encyclopedia
- COSMIC, Catalog of Somatic Mutations in Cancer
- DLBCL, diffuse large B-cell lymphoma
- HLA expression
- HLA type
- HLA, Human Leukocyte Antigen
- IEDB, Immune Epitope Database
- NGS, Next Generation Sequencing
- RNA-Seq
- RNA-Seq, RNA Sequencing
- RPKM, reads per kilobase of exon model per million mapped reads
- SNV, single nucleotide variation
- SRA, Sequence Read Archive
- cancer cell lines
- immunotherapy
- neoepitopes
- nsSNV, non synonymous SNV
- somatic mutations
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Affiliation(s)
- Sebastian Boegel
- TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany ; University Medical Center of the Johannes Gutenberg-University Mainz ; Mainz, Germany
| | - Martin Löwer
- TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany
| | - Thomas Bukur
- TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany ; University Medical Center of the Johannes Gutenberg-University Mainz ; Mainz, Germany
| | - Ugur Sahin
- TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany ; University Medical Center of the Johannes Gutenberg-University Mainz ; Mainz, Germany ; BioNTech AG; Kupferbergterrasse ; Mainz, Germany
| | - John C Castle
- TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany
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Schramek D, Sendoel A, Segal JP, Beronja S, Heller E, Oristian D, Reva B, Fuchs E. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 2014; 343:309-13. [PMID: 24436421 DOI: 10.1126/science.1248627] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Mining modern genomics for cancer therapies is predicated on weeding out "bystander" alterations (nonconsequential mutations) and identifying "driver" mutations responsible for tumorigenesis and/or metastasis. We used a direct in vivo RNA interference (RNAi) strategy to screen for genes that upon repression predispose mice to squamous cell carcinomas (SCCs). Seven of our top hits-including Myh9, which encodes nonmuscle myosin IIa-have not been linked to tumor development, yet tissue-specific Myh9 RNAi and Myh9 knockout trigger invasive SCC formation on tumor-susceptible backgrounds. In human and mouse keratinocytes, myosin IIa's function is manifested not only in conventional actin-related processes but also in regulating posttranscriptional p53 stabilization. Myosin IIa is diminished in human SCCs with poor survival, which suggests that in vivo RNAi technology might be useful for identifying potent but low-penetrance tumor suppressors.
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Affiliation(s)
- Daniel Schramek
- Howard Hughes Medical Institute, Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY 10065, USA
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Bromberg Y, Capriotti E. Thoughts from SNP-SIG 2012: future challenges in the annotation of genetic variations. BMC Genomics 2013; 14 Suppl 3:S1. [PMID: 23819751 PMCID: PMC3665538 DOI: 10.1186/1471-2164-14-s3-s1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.
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