RESUMO
Around 75% of breast cancer (BC) patients have tumors expressing the predictive biomarker estrogen receptor α (ER) and are offered endocrine therapy. One-third eventually develop endocrine resistance, a majority with retained ER expression. Mutations in the phosphatidylinositol bisphosphate 3-kinase (PI3K) catalytic subunit encoded by PIK3CA is a proposed resistance mechanism and a pharmacological target in the clinical setting. Here we explore the frequency of PIK3CA mutations in endocrine-resistant BC before and during treatment and correlate to clinical features. Patients with ER-positive (ER +), human epidermal growth factor receptor 2 (HER2)-negative primary BC with an ER + relapse within 5 years of ongoing endocrine therapy were retrospectively assessed. Tissue was collected from primary tumors (n = 58), relapse tumors (n = 54), and tumor-free lymph nodes (germline controls, n = 62). Extracted DNA was analyzed through panel sequencing. Somatic mutations were observed in 50% (31/62) of the patients, of which 29% occurred outside hotspot regions. The presence of PIK3CA mutations was significantly associated with nodal involvement and mutations were more frequent in relapse than primary tumors. Our study shows the different PIK3CA mutations in endocrine-resistant BC and their fluctuations during therapy. These results may aid investigations of response prediction, facilitating research deciphering the mechanisms of endocrine resistance.
Assuntos
Neoplasias da Mama , Classe I de Fosfatidilinositol 3-Quinases , Resistencia a Medicamentos Antineoplásicos , Mutação , Humanos , Classe I de Fosfatidilinositol 3-Quinases/genética , Neoplasias da Mama/genética , Neoplasias da Mama/patologia , Feminino , Resistencia a Medicamentos Antineoplásicos/genética , Pessoa de Meia-Idade , Idoso , Adulto , Antineoplásicos Hormonais/uso terapêutico , Antineoplásicos Hormonais/farmacologia , Estudos Retrospectivos , Idoso de 80 Anos ou mais , Receptor alfa de Estrogênio/genética , Receptor alfa de Estrogênio/metabolismo , Recidiva Local de Neoplasia/genética , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismoRESUMO
MOTIVATION: Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity. RESULTS: To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to advance our understanding of genetic alterations and their impact on disease advancement. AVAILABILITY AND IMPLEMENTATION: CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.
Assuntos
Variações do Número de Cópias de DNA , Análise de Célula Única , Análise de Célula Única/métodos , Humanos , Aprendizado Profundo , Software , Transcriptoma/genética , Análise de Sequência de RNA/métodos , Neoplasias/genéticaRESUMO
The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.
Assuntos
Linfócitos B , Receptores de Células Precursoras de Linfócitos B , Receptores de Antígenos de Linfócitos T , Linfócitos T , Humanos , Linfócitos B/metabolismo , Células Clonais/metabolismo , Perfilação da Expressão Gênica/métodos , Receptores de Células Precursoras de Linfócitos B/genética , Receptores de Antígenos de Linfócitos T/genética , Linfócitos T/metabolismoRESUMO
Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.
Assuntos
Neoplasias Ovarianas , Árvores , Feminino , Humanos , Árvores/genética , Transcriptoma , Evolução Clonal , Recidiva Local de Neoplasia , Neoplasias Ovarianas/genética , Células Clonais , Análise de Célula Única/métodosRESUMO
BACKGROUND: Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. Understanding the number of distinct subclones and the evolutionary relationships between them is scientifically and clinically very important and still a challenging problem. RESULTS: In this paper, we present BAMSE (BAyesian Model Selection for tumor Evolution), a new probabilistic method for inferring subclonal history and lineage tree reconstruction of heterogeneous tumor samples. BAMSE uses somatic mutation read counts as input and can leverage multiple tumor samples accurately and efficiently. In the first step, possible clusterings of mutations into subclones are scored and a user defined number are selected for further analysis. In the next step, for each of these candidates, a list of trees describing the evolutionary relationships between the subclones is generated. These trees are sorted by their posterior probability. The posterior probability is calculated using a Bayesian model that integrates prior belief about the number of subclones, the composition of the tumor and the process of subclonal evolution. BAMSE also takes the sequencing error into account. We benchmarked BAMSE against state of the art software using simulated datasets. CONCLUSIONS: In this work we developed a flexible and fast software to reconstruct the history of a tumor's subclonal evolution using somatic mutation read counts across multiple samples. BAMSE software is implemented in Python and is available open source under GNU GLPv3 at https://github.com/HoseinT/BAMSE .
Assuntos
Biologia Computacional/métodos , Neoplasias/classificação , Filogenia , Algoritmos , Teorema de Bayes , Carcinoma de Células Renais/genética , Simulação por Computador , Humanos , Neoplasias Renais/genética , Modelos Biológicos , Mutação/genética , Neoplasias/genética , SoftwareRESUMO
The reconstruction of cancer phylogeny trees and quantifying the evolution of the disease is a challenging task. LICHeE and BAMSE are two computational tools designed and implemented recently for this purpose. They both utilize estimated variant allele fraction of somatic mutations across multiple samples to infer the most likely cancer phylogenies. This unit provides extensive guidelines for installing and running both LICHeE and BAMSE. © 2018 by John Wiley & Sons, Inc.