SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution.
Christopher A. Miller,Brian S. White,Nathan D. Dees,Malachi Griffith,John S. Welch,Obi L. Griffith,Ravi Vij,Michael H. Tomasson,Timothy A. Graubert,Matthew J. Walter,Matthew J. Ellis,William Schierding,John F. DiPersio,Timothy J. Ley,Elaine R. Mardis,Richard K. Wilson,Li Ding +16 more
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SciClone is a computational method that is used to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample, and can track tumor evolution and identify the spatial origins of cells resisting therapy.Abstract:
The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.read more
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
SF-010-4 Distant metastasis occurs late during the genetic evolution of pancreatic cancer
TL;DR: A quantitative analysis of the timing of the genetic evolution of pancreatic cancer was performed, indicating at least a decade between the occurrence of the initiating mutation and the birth of the parental, non-metastatic founder cell.
Journal ArticleDOI
Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future.
TL;DR: It is suggested that bold approaches to drug development, harnessing the adaptive properties of the immune-microenvironment while limiting those of the tumor, combined with advances in clinical trial-design, will improve patient outcome.
Journal ArticleDOI
Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance
Whijae Roh,Pei Ling Chen,Alexandre Reuben,Christine N. Spencer,Peter A. Prieto,John P. Miller,Vancheswaran Gopalakrishnan,Feng Wang,Zachary A. Cooper,Sangeetha M. Reddy,Curtis Gumbs,Latasha Little,Qing Chang,Wei Shen Chen,Khalida Wani,Mariana Petaccia de Macedo,Eveline Chen,Jacob Austin-Breneman,Hong Jiang,Jason Roszik,Michael T. Tetzlaff,Michael A. Davies,Jeffrey E. Gershenwald,Hussein Abdul-Hassan Tawbi,Alexander J. Lazar,Patrick Hwu,Wen-Jen Hwu,Adi Diab,Isabella C. Glitza,Sapna Pradyuman Patel,Scott E. Woodman,Rodabe N. Amaria,Victor G. Prieto,Jianhua Hu,Padmanee Sharma,James P. Allison,Lynda Chin,Jianhua Zhang,Jennifer A. Wargo,P. Andrew Futreal +39 more
TL;DR: Deep molecular profiling of melanoma patients treated with sequential checkpoint blockade demonstrated that a more clonal T cell repertoire was predictive of response to PD-1 but not CTLA-4 blockade, suggesting the potential utility of a combinatorial biomarker to optimize patient care with checkpoint blockade therapy.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Comprehensive molecular portraits of human breast tumours
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TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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