scholarly journals Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations

2018 ◽  
Vol 20 (6) ◽  
pp. 2306-2315 ◽  
Author(s):  
Meng Zou ◽  
Rui Jin ◽  
Kin Fai Au
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Somatic Mutations ◽  
Population Level ◽  
Superior Performance ◽  
Tumor Evolution ◽  
Single Nucleotide Variants ◽  
Long Reads ◽  
Gene Tp53

Abstract The intra-tumor heterogeneity is associated with cancer progression and therapeutic resistance, such as in breast cancer. While the existing methods for studying tumor heterogeneity only analyze variant allele frequency (VAF), the genotype of variant is also informative for inferring subclones, which can be detected by long reads or paired-end reads. We developed GenoClone to integrate VAF with the genotype of variant innovatively, so it showed superior performance of inferring the number of subclones, estimating the fractions of subclones and identifying somatic single-nucleotide variants composition of subclones. When GenoClone was applied to 389 TCGA breast cancer samples, it revealed extensive intra-tumor heterogeneity. We further found that a few somatic mutations were relevant to the late stage of tumor evolution, including the ones at the oncogene PIK3CA and the tumor suppress gene TP53. Moreover, 52 subclones that were identified from 167 samples shared high similarity of somatic mutations, which were clustered into three groups with the sizes of 24, 14 and 14. It is helpful for understanding the development of breast cancer in certain subgroups of people and the drug development for population level. Furthermore, GenoClone also identified the tumor heterogeneity in different aliquots of the same samples. The implementation of GenoClone is available at http://www.healthcare.uiowa.edu/labs/au/GenoClone/.

scholarly journals Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

2018 ◽  
Author(s):  
An-Shun Tai ◽  
Chien-Hua Peng ◽  
Shih-Chi Peng ◽  
Wen-Ping Hsieh
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Tumor Evolution ◽  
Depth Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Copy Number Aberrations

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

scholarly journals Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

2019 ◽  
Author(s):  
Runpu Chen ◽  
Steve Goodison ◽  
Yijun Sun
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Evolutionary Model ◽  
Metastatic Tumor ◽  
Cancer Diagnostics ◽  
Tumor Evolution ◽  
Paired Data ◽  
Cancer Subtypes ◽  
Cancer Data ◽  
Progression Model

AbstractThe interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression, and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics and targeted therapeutics.

scholarly journals Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

2016 ◽  
Author(s):  
Nao Hiranuma ◽  
Jie Liu ◽  
Chaozhong Song ◽  
Jacob Goldsmith ◽  
Michael Dorschner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Breast Cancers ◽  
Single Nucleotide Variants ◽  
Primary Tumors ◽  
Cancer Subtypes ◽  
Multiple Biopsies ◽  
Cancer Genome Atlas

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

scholarly journals Reconstructing mutational lineages in breast cancer by multi-patient-targeted single cell DNA sequencing

2021 ◽  
Author(s):  
Nicholas Navin ◽  
Jake Leighton ◽  
Min Hu ◽  
Emi Sei ◽  
Funda Meric-Bernstam
Keyword(s):  
Breast Cancer ◽  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Somatic Mutations ◽  
Single Cells ◽  
Tumor Evolution ◽  
Sequencing Method ◽  
Genomic Regions ◽  
Microfluidics Platform

Single cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells, however most genomic regions characterized in single cells are non-informative. To overcome this issue, we developed a Multi-Patient-Targeted (MPT) scDNA-seq sequencing method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 TNBC patients, which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From this data, we reconstructed mutational lineages and identified early mutational and copy number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggests that MPT can overcome technical obstacles for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

scholarly journals Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
David Brown ◽  
Dominiek Smeets ◽  
Borbála Székely ◽  
Denis Larsimont ◽  
A. Marcell Szász ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Copy Number ◽  
Somatic Mutations ◽  
Primary Tumour ◽  
Distant Metastases ◽  
Molecular Techniques ◽  
Metastatic Progression ◽  
Number Of Patients ◽  
Cancer Dissemination

Abstract Several studies using genome-wide molecular techniques have reported various degrees of genetic heterogeneity between primary tumours and their distant metastases. However, it has been difficult to discern patterns of dissemination owing to the limited number of patients and available metastases. Here, we use phylogenetic techniques on data generated using whole-exome sequencing and copy number profiling of primary and multiple-matched metastatic tumours from ten autopsied patients to infer the evolutionary history of breast cancer progression. We observed two modes of disease progression. In some patients, all distant metastases cluster on a branch separate from their primary lesion. Clonal frequency analyses of somatic mutations show that the metastases have a monoclonal origin and descend from a common ‘metastatic precursor’. Alternatively, multiple metastatic lesions are seeded from different clones present within the primary tumour. We further show that a metastasis can be horizontally cross-seeded. These findings provide insights into breast cancer dissemination.

scholarly journals CRISPR–Cas9/long-read sequencing approach to identify cryptic mutations in BRCA1 and other tumour suppressor genes

2020 ◽  
pp. jmedgenet-2020-107320 ◽  
Author(s):  
Tom Walsh ◽  
Silvia Casadei ◽  
Katherine M Munson ◽  
Mary Eng ◽  
Jessica B Mandell ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bilateral Breast Cancer ◽  
Tumour Suppressor ◽  
Tumour Suppressor Genes ◽  
Single Nucleotide Variants ◽  
Suppressor Genes ◽  
Small Indels ◽  
Long Reads ◽  
Long Read ◽  
Genomic Regions

AbstractCurrent clinical approaches for mutation discovery are based on short sequence reads (100–300 bp) of exons and flanking splice sites targeted by multigene panels or whole exomes. Short-read sequencing is highly accurate for detection of single nucleotide variants, small indels and simple copy number differences but is of limited use for identifying complex insertions and deletions and other structural rearrangements. We used CRISPR-Cas9 to excise complete BRCA1 and BRCA2 genomic regions from lymphoblast cells of patients with breast cancer, then sequenced these regions with long reads (>10 000 bp) to fully characterise all non-coding regions for structural variation. In a family severely affected with early-onset bilateral breast cancer and with negative (normal) results by gene panel and exome sequencing, we identified an intronic SINE-VNTR-Alu retrotransposon insertion that led to the creation of a pseudoexon in the BRCA1 message and introduced a premature truncation. This combination of CRISPR–Cas9 excision and long-read sequencing reveals a class of complex, damaging and otherwise cryptic mutations that may be particularly frequent in tumour suppressor genes replete with intronic repeats.

scholarly journals Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 761
Author(s):  
Aline P. Becker ◽  
Blake E. Sells ◽  
S. Jaharul Haque ◽  
Arnab Chakravarti
Keyword(s):  
Tumor Heterogeneity ◽  
Population Level ◽  
Response To Treatment ◽  
Tumor Evolution ◽  
Cellular Functions ◽  
Molecular Alterations ◽  
Cancer Tumor ◽  
Technological Developments ◽  
Time Point

One of the main reasons for the aggressive behavior of glioblastoma (GBM) is its intrinsic intra-tumor heterogeneity, characterized by the presence of clonal and subclonal differentiated tumor cell populations, glioma stem cells, and components of the tumor microenvironment, which affect multiple hallmark cellular functions in cancer. “Tumor Heterogeneity” usually encompasses both inter-tumor heterogeneity (population-level differences); and intra-tumor heterogeneity (differences within individual tumors). Tumor heterogeneity may be assessed in a single time point (spatial heterogeneity) or along the clinical evolution of GBM (longitudinal heterogeneity). Molecular methods may detect clonal and subclonal alterations to describe tumor evolution, even when samples from multiple areas are collected in the same time point (spatial-temporal heterogeneity). In GBM, although the inter-tumor mutational landscape is relatively homogeneous, intra-tumor heterogeneity is a striking feature of this tumor. In this review, we will address briefly the inter-tumor heterogeneity of the CNS tumors that yielded the current glioma classification. Next, we will take a deeper dive in the intra-tumor heterogeneity of GBMs, which directly affects prognosis and response to treatment. Our approach aims to follow technological developments, allowing for characterization of intra-tumor heterogeneity, beginning with differences on histomorphology of GBM and ending with molecular alterations observed at single-cell level.

scholarly journals Intra-tumor heterogeneity defines treatment-resistant HER2+ breast tumors

2018 ◽  
Author(s):  
Inga H. Rye ◽  
Anne Trinh ◽  
Anna Sætersdal ◽  
Daniel Nebdal ◽  
Ole Christian Lingjærde ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protein Expression ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Breast Tumors ◽  
Gene Copy ◽  
Tumor Evolution ◽  
Her2 Breast Cancer ◽  
The Impact

AbstractTargeted therapy for patients with HER2 positive (HER2+) breast cancer has improved the overall survival, but many patients still suffer relapse and death of the disease. Intra-tumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level.In this study, we explored the clinical impact of intra-tumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13,000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well.We found that intra-tumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome.This work shows the impact of intra-tumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies.

scholarly journals Cancer mutational signatures representation by large-scale context embedding

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i309-i316 ◽  
Author(s):  
Yang Zhang ◽  
Yunxuan Xiao ◽  
Muyu Yang ◽  
Jian Ma
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Somatic Mutations ◽  
Genome Structure ◽  
Superior Performance ◽  
Supplementary Information ◽  
Patient Specific ◽  
Cancer Subtypes ◽  
Cancer Heterogeneity ◽  
And Function

Abstract Motivation The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analysing the complex mutational patterns. Results Here, we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context. Our method is motivated by the observation that the mutation rate at megabase scale and the local mutational patterns jointly contribute to distinguishing cancer subtypes, both of which can be simultaneously captured by MutSpace. Simulation evaluations show that MutSpace can effectively characterize mutational features from known patient subgroups and achieve superior performance compared with previous methods. As a proof-of-principle, we apply MutSpace to 560 breast cancer patient samples and demonstrate that our method achieves high accuracy in subtype identification. In addition, the learned embeddings from MutSpace reflect intrinsic patterns of breast cancer subtypes and other features of genome structure and function. MutSpace is a promising new framework to better understand cancer heterogeneity based on somatic mutations. Availability and implementation Source code of MutSpace can be accessed at: https://github.com/ma-compbio/MutSpace. Supplementary information Supplementary data are available at Bioinformatics online.

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