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Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.
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Replicación del ADN/genética , Genoma Humano , Secuenciación de Nucleótidos de Alto Rendimiento , Análisis de la Célula Individual , Aneuploidia , Animales , Ciclo Celular/genética , Línea Celular Tumoral , Forma de la Célula , Supervivencia Celular , Cromosomas Humanos/genética , Células Clonales , Elementos Transponibles de ADN/genética , Diploidia , Femenino , Genotipo , Humanos , Masculino , Ratones , Mutación/genética , Filogenia , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.
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Linfocitos Infiltrantes de Tumor/inmunología , Neoplasias Ováricas/patología , Adulto , Anciano , Anciano de 80 o más Años , Antígenos de Neoplasias/genética , Antígenos de Neoplasias/metabolismo , Proteína BRCA1/genética , Proteína BRCA1/metabolismo , Proteína BRCA2/genética , Proteína BRCA2/metabolismo , Antígenos CD8/metabolismo , Análisis por Conglomerados , Femenino , Antígenos HLA/genética , Antígenos HLA/metabolismo , Humanos , Pérdida de Heterocigocidad , Linfocitos Infiltrantes de Tumor/citología , Linfocitos Infiltrantes de Tumor/metabolismo , Persona de Mediana Edad , Clasificación del Tumor , Neoplasias Ováricas/clasificación , Neoplasias Ováricas/inmunología , Polimorfismo de Nucleótido Simple , Receptores de Antígenos de Linfocitos T/genética , Receptores de Antígenos de Linfocitos T/metabolismo , Secuenciación Completa del Genoma , Adulto JovenRESUMEN
Chromosomal instability (CIN) and epigenetic alterations are characteristics of advanced and metastatic cancers1-4, but whether they are mechanistically linked is unknown. Here we show that missegregation of mitotic chromosomes, their sequestration in micronuclei5,6 and subsequent rupture of the micronuclear envelope7 profoundly disrupt normal histone post-translational modifications (PTMs), a phenomenon conserved across humans and mice, as well as in cancer and non-transformed cells. Some of the changes in histone PTMs occur because of the rupture of the micronuclear envelope, whereas others are inherited from mitotic abnormalities before the micronucleus is formed. Using orthogonal approaches, we demonstrate that micronuclei exhibit extensive differences in chromatin accessibility, with a strong positional bias between promoters and distal or intergenic regions, in line with observed redistributions of histone PTMs. Inducing CIN causes widespread epigenetic dysregulation, and chromosomes that transit in micronuclei experience heritable abnormalities in their accessibility long after they have been reincorporated into the primary nucleus. Thus, as well as altering genomic copy number, CIN promotes epigenetic reprogramming and heterogeneity in cancer.
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Inestabilidad Cromosómica , Segregación Cromosómica , Cromosomas , Epigénesis Genética , Micronúcleos con Defecto Cromosómico , Neoplasias , Animales , Humanos , Ratones , Cromatina/genética , Inestabilidad Cromosómica/genética , Cromosomas/genética , Cromosomas/metabolismo , Histonas/química , Histonas/metabolismo , Neoplasias/genética , Neoplasias/patología , Mitosis , Variaciones en el Número de Copia de ADN , Procesamiento Proteico-PostraduccionalRESUMEN
Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3-6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical 'free fitness' framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.
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Carcinogénesis , Evolución Molecular , Neoplasias Pulmonares , Mutación , Carcinogénesis/genética , Carcinogénesis/inmunología , Conjuntos de Datos como Asunto , Genes p53 , Aptitud Genética , Genómica , Voluntarios Sanos , Humanos , Inmunoterapia , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/terapia , Mutación/genética , Mutación Missense , Reproducibilidad de los ResultadosRESUMEN
Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.
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Variaciones en el Número de Copia de ADN , Resistencia a Antineoplásicos , Neoplasias de la Mama Triple Negativas/genética , Animales , Línea Celular Tumoral , Cisplatino/farmacología , Células Clonales/patología , Femenino , Aptitud Genética , Humanos , Ratones , Modelos Estadísticos , Trasplante de Neoplasias , Proteína p53 Supresora de Tumor/genética , Secuenciación Completa del GenomaRESUMEN
Lymphocyte-rich classic Hodgkin lymphoma (LR-CHL) is a rare subtype of Hodgkin lymphoma. Recent technical advances have allowed for the characterization of specific cross-talk mechanisms between malignant Hodgkin Reed-Sternberg (HRS) cells and different normal immune cells in the tumor microenvironment (TME) of CHL. However, the TME of LR-CHL has not yet been characterized at single-cell resolution. Here, using single-cell RNA sequencing (scRNA-seq), we examined the immune cell profile of 8 cell suspension samples of LR-CHL in comparison to 20 samples of the mixed cellularity (MC, 9 cases) and nodular sclerosis (NS, 11 cases) subtypes of CHL, as well as 5 reactive lymph node controls. We also performed multicolor immunofluorescence (MC-IF) on tissue microarrays from the same patients and an independent validation cohort of 31 pretreatment LR-CHL samples. ScRNA-seq analysis identified a unique CD4+ helper T cell subset in LR-CHL characterized by high expression of Chemokine C-X-C motif ligand 13 (CXCL13) and PD-1. PD-1+CXCL13+ T cells were significantly enriched in LR-CHL compared to other CHL subtypes, and spatial analyses revealed that in 46% of the LR-CHL cases these cells formed rosettes surrounding HRS cells. MC-IF analysis revealed CXCR5+ normal B cells in close proximity to CXCL13+ T cells at significantly higher levels in LR-CHL. Moreover, the abundance of PD-1+CXCL13+ T cells in the TME was significantly associated with shorter progression-free survival in LR-CHL (P = 0.032). Taken together, our findings strongly suggest the pathogenic importance of the CXCL13/CXCR5 axis and PD-1+CXCL13+ T cells as a treatment target in LR-CHL.
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Linfocitos B/metabolismo , Quimiocina CXCL13/metabolismo , Enfermedad de Hodgkin/patología , Receptores CXCR5/metabolismo , Linfocitos T Colaboradores-Inductores/metabolismo , Antígeno B7-H1/metabolismo , Técnica del Anticuerpo Fluorescente , Humanos , Ganglios Linfáticos/citología , Receptor de Muerte Celular Programada 1/metabolismo , RNA-Seq , Células de Reed-Sternberg/patología , Análisis de la Célula Individual , Microambiente Tumoral/inmunologíaRESUMEN
Primary mediastinal large B-cell lymphoma (PMBL) is a type of aggressive B-cell lymphoma that typically affects young adults, characterized by presence of a bulky anterior mediastinal mass. Lymphomas with gene expression features of PMBL have been described in nonmediastinal sites, raising questions about how these tumors should be classified. Here, we investigated whether these nonmediastinal lymphomas are indeed PMBLs or instead represent a distinct group within diffuse large B-cell lymphoma (DLBCL). From a cohort of 325 de novo DLBCL cases, we identified tumors from patients without evidence of anterior mediastinal involvement that expressed a PMBL expression signature (nm-PMBLsig+; n = 16; 5%). A majority of these tumors expressed MAL and CD23, proteins typically observed in bona fide PMBL (bf-PMBL). Evaluation of clinical features of nm-PMBLsig+ cases revealed close associations with DLBCL, and a majority displayed a germinal center B cell-like cell of origin (GCB). In contrast to patients with bf-PMBL, patients with nm-PMBLsig+ presented at an older age and did not show pleural disease, and bone/bone marrow involvement was observed in 3 cases. However, although clinically distinct from bf-PMBL, nm-PMBLsig+ tumors resembled bf-PMBL at the molecular level, with upregulation of immune response, JAK-STAT, and NF-κB signatures. Mutational analysis revealed frequent somatic gene mutations in SOCS1, IL4R, ITPKB, and STAT6, as well as CD83 and BIRC3, with the latter genes significantly more frequently affected than in GCB DLBCL or bf-PMBL. Our data establish nm-PMBLsig+ lymphomas as a group within DLBCL with distinct phenotypic and genetic features. These findings may have implications for gene expression- and mutation-based subtyping of aggressive B-cell lymphomas and related targeted therapies.
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Perfilación de la Expresión Génica , Regulación Leucémica de la Expresión Génica , Linfoma de Células B Grandes Difuso/genética , Neoplasias del Mediastino/genética , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Linfocitos B/inmunología , Variaciones en el Número de Copia de ADN/genética , Análisis Mutacional de ADN , Femenino , Células HEK293 , Humanos , Evasión Inmune , Inmunofenotipificación , Quinasas Janus/metabolismo , Linfoma de Células B Grandes Difuso/patología , Linfoma no Hodgkin/genética , Linfoma no Hodgkin/patología , Masculino , Neoplasias del Mediastino/patología , Persona de Mediana Edad , Mutación/genética , Receptores de Interleucina-4/genética , Factores de Transcripción STAT/metabolismo , Hipermutación Somática de Inmunoglobulina/genética , Adulto JovenRESUMEN
OBJECTIVES: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data. METHODS: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation. RESULTS: Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001. CONCLUSIONS: Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities. KEY POINTS: ⢠A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. ⢠An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. ⢠Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.
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Neoplasias Encefálicas , Procesamiento de Imagen Asistido por Computador , Adulto , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Estudios Retrospectivos , Imagen por Resonancia Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Encéfalo/diagnóstico por imagenRESUMEN
Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.
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Inteligencia Artificial , Redes Neurales de la Computación , Encéfalo , Humanos , Imagen por Resonancia Magnética , Estudios RetrospectivosRESUMEN
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
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Perfilación de la Expresión Génica , Linfoma Folicular/patología , Probabilidad , Análisis de Secuencia de ARN/métodos , Análisis de la Célula Individual/métodos , Microambiente Tumoral , Humanos , Linfoma Folicular/inmunologíaRESUMEN
We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.
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Metilación de ADN , Análisis de la Célula Individual , Análisis por Conglomerados , Islas de CpG , Humanos , Probabilidad , Análisis de Secuencia de ADN/métodosRESUMEN
Endometrial carcinoma, the most common gynaecological cancer, develops from endometrial epithelium which is composed of secretory and ciliated cells. Pathologic classification is unreliable and there is a need for prognostic tools. We used single cell sequencing to study organoid model systems derived from normal endometrial endometrium to discover novel markers specific for endometrial ciliated or secretory cells. A marker of secretory cells (MPST) and several markers of ciliated cells (FAM92B, WDR16, and DYDC2) were validated by immunohistochemistry on organoids and tissue sections. We performed single cell sequencing on endometrial and ovarian tumours and found both secretory-like and ciliated-like tumour cells. We found that ciliated cell markers (DYDC2, CTH, FOXJ1, and p73) and the secretory cell marker MPST were expressed in endometrial tumours and positively correlated with disease-specific and overall survival of endometrial cancer patients. These findings suggest that expression of differentiation markers in tumours correlates with less aggressive disease, as would be expected for tumours that retain differentiation capacity, albeit cryptic in the case of ciliated cells. These markers could be used to improve the risk stratification of endometrial cancer patients, thereby improving their management. We further assessed whether consideration of MPST expression could refine the ProMiSE molecular classification system for endometrial tumours. We found that higher expression levels of MPST could be used to refine stratification of three of the four ProMiSE molecular subgroups, and that any level of MPST expression was able to significantly refine risk stratification of the copy number high subgroup which has the worst prognosis. Taken together, this shows that single cell sequencing of putative cells of origin has the potential to uncover novel biomarkers that could be used to guide management of cancers. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Biomarcadores de Tumor/análisis , Carcinoma Endometrioide/patología , Neoplasias Endometriales/patología , Análisis de Secuencia de ARN/métodos , Diferenciación Celular , Femenino , Humanos , Organoides , TranscriptomaRESUMEN
Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution, underpinning important emergent features such as drug resistance and metastasis. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.
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Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Células Clonales/metabolismo , Células Clonales/patología , Genoma Humano/genética , Análisis de la Célula Individual , Ensayos Antitumor por Modelo de Xenoinjerto , Animales , Neoplasias de la Mama/secundario , Análisis Mutacional de ADN , Genómica , Genotipo , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Ratones , Trasplante de Neoplasias , Factores de Tiempo , Trasplante Heterólogo , Ensayos Antitumor por Modelo de Xenoinjerto/métodosRESUMEN
BACKGROUND & AIMS: Most pancreatic ductal adenocarcinomas (PDACs) express an activated form of KRAS, become hypoxic and dysplastic, and are refractory to chemo and radiation therapies. To survive in the hypoxic environment, PDAC cells upregulate enzymes and transporters involved in pH regulation, including the extracellular facing carbonic anhydrase 9 (CA9). We evaluated the effect of blocking CA9, in combination with administration of gemcitabine, in mouse models of pancreatic cancer. METHODS: We knocked down expression of KRAS in human (PK-8 and PK-1) PDAC cells with small hairpin RNAs. Human and mouse (KrasG12D/Pdx1-Cre/Tp53/RosaYFP) PDAC cells were incubated with inhibitors of MEK (trametinib) or extracellular signal-regulated kinase (ERK), and some cells were cultured under hypoxic conditions. We measured levels and stability of the hypoxia-inducible factor 1 subunit alpha (HIF1A), endothelial PAS domain 1 protein (EPAS1, also called HIF2A), CA9, solute carrier family 16 member 4 (SLC16A4, also called MCT4), and SLC2A1 (also called GLUT1) by immunoblot analyses. We analyzed intracellular pH (pHi) and extracellular metabolic flux. We knocked down expression of CA9 in PDAC cells, or inhibited CA9 with SLC-0111, incubated them with gemcitabine, and assessed pHi, metabolic flux, and cytotoxicity under normoxic and hypoxic conditions. Cells were also injected into either immune-compromised or immune-competent mice and growth of xenograft tumors was assessed. Tumor fragments derived from patients with PDAC were surgically ligated to the pancreas of mice and the growth of tumors was assessed. We performed tissue microarray analyses of 205 human PDAC samples to measure levels of CA9 and associated expression of genes that regulate hypoxia with outcomes of patients using the Cancer Genome Atlas database. RESULTS: Under hypoxic conditions, PDAC cells had increased levels of HIF1A and HIF2A, upregulated expression of CA9, and activated glycolysis. Knockdown of KRAS in PDAC cells, or incubation with trametinib, reduced the posttranscriptional stabilization of HIF1A and HIF2A, upregulation of CA9, pHi, and glycolysis in response to hypoxia. CA9 was expressed by 66% of PDAC samples analyzed; high expression of genes associated with metabolic adaptation to hypoxia, including CA9, correlated with significantly reduced survival times of patients. Knockdown or pharmacologic inhibition of CA9 in PDAC cells significantly reduced pHi in cells under hypoxic conditions, decreased gemcitabine-induced glycolysis, and increased their sensitivity to gemcitabine. PDAC cells with knockdown of CA9 formed smaller xenograft tumors in mice, and injection of gemcitabine inhibited tumor growth and significantly increased survival times of mice. In mice with xenograft tumors grown from human PDAC cells, oral administration of SLC-0111 and injection of gemcitabine increased intratumor acidosis and increased cell death. These tumors, and tumors grown from PDAC patient-derived tumor fragments, grew more slowly than xenograft tumors in mice given control agents, resulting in longer survival times. In KrasG12D/Pdx1-Cre/Tp53/RosaYFP genetically modified mice, oral administration of SLC-0111 and injection of gemcitabine reduced numbers of B cells in tumors. CONCLUSIONS: In response to hypoxia, PDAC cells that express activated KRAS increase expression of CA9, via stabilization of HIF1A and HIF2A, to regulate pH and glycolysis. Disruption of this pathway slows growth of PDAC xenograft tumors in mice and might be developed for treatment of pancreatic cancer.
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Antígenos de Neoplasias/metabolismo , Anhidrasa Carbónica IX/metabolismo , Carcinoma Ductal Pancreático/enzimología , Neoplasias Pancreáticas/enzimología , Proteínas Proto-Oncogénicas p21(ras)/genética , Microambiente Tumoral , Animales , Antígenos de Neoplasias/genética , Antimetabolitos Antineoplásicos/farmacología , Protocolos de Quimioterapia Combinada Antineoplásica/farmacología , Factores de Transcripción con Motivo Hélice-Asa-Hélice Básico/metabolismo , Anhidrasa Carbónica IX/antagonistas & inhibidores , Anhidrasa Carbónica IX/genética , Inhibidores de Anhidrasa Carbónica/farmacología , Carcinoma Ductal Pancreático/tratamiento farmacológico , Carcinoma Ductal Pancreático/genética , Carcinoma Ductal Pancreático/patología , Hipoxia de la Célula , Línea Celular Tumoral , Proliferación Celular/efectos de los fármacos , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacología , Femenino , Regulación Neoplásica de la Expresión Génica , Predisposición Genética a la Enfermedad , Glucólisis/efectos de los fármacos , Humanos , Concentración de Iones de Hidrógeno , Subunidad alfa del Factor 1 Inducible por Hipoxia/metabolismo , Masculino , Ratones Endogámicos C57BL , Ratones Endogámicos NOD , Ratones SCID , Neoplasias Pancreáticas/tratamiento farmacológico , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/patología , Fenotipo , Compuestos de Fenilurea/farmacología , Transducción de Señal , Sulfonamidas/farmacología , Carga Tumoral/efectos de los fármacos , Ensayos Antitumor por Modelo de Xenoinjerto , GemcitabinaRESUMEN
Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated 'bulk-equivalent' genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.
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Genómica/métodos , Análisis de la Célula Individual/métodos , Animales , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Línea Celular Tumoral , Femenino , Biblioteca de Genes , Humanos , Dispositivos Laboratorio en un Chip , Ratones SCID , Filogenia , Análisis de la Célula Individual/instrumentación , Ensayos Antitumor por Modelo de XenoinjertoRESUMEN
Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.
Asunto(s)
Biología Computacional/métodos , Neoplasias/genética , Análisis de Secuencia de ADN/métodos , Variación Genética/genética , Genoma , Humanos , Aprendizaje Automático , Modelos Estadísticos , Mutación , Mutación Puntual/genética , Pronóstico , Transcriptoma/genéticaRESUMEN
Single-cell DNA sequencing has great potential to reveal the clonal genotypes and population structure of human cancers. However, single-cell data suffer from missing values and biased allelic counts as well as false genotype measurements owing to the sequencing of multiple cells. We describe the Single Cell Genotyper (https://bitbucket.org/aroth85/scg), an open-source software based on a statistical model coupled with a mean-field variational inference method, which can be used to address these problems and robustly infer clonal genotypes.
Asunto(s)
Cistadenocarcinoma Seroso/genética , Leucemia/genética , Glándulas Mamarias Humanas/metabolismo , Neoplasias Ováricas/genética , Análisis de la Célula Individual/métodos , Programas Informáticos , Células Clonales , Femenino , Genoma Humano , Genotipo , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Modelos Estadísticos , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
The original version of this Article omitted the author Hannah van Meurs from the Department of Gynecology, Center for Gynecologic Oncology Amsterdam, Academic Medical Center, 1100 DD Amsterdam, The Netherlands. This has been corrected in both the PDF and HTML versions of the article.
RESUMEN
The clinical significance of MYC and BCL2 genetic alterations in diffuse large B-cell lymphoma (DLBCL), apart from translocations, has not been comprehensively investigated using high-resolution genetic assays. In this study, we profiled MYC and BCL2 genetic alterations using next-generation sequencing and high-resolution SNP array in 347 de novo DLBCL cases treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) at the British Columbia Cancer Agency. Cell-of-origin (COO) subtype was determined by Lymph2Cx digital gene expression profiling. We showed that the incidence of MYC/BCL2 genetic alterations and their clinical significance were largely dependent on COO subtypes. It is noteworthy that the presence of BCL2 gain/amplification is significantly associated with poor outcome in activated B-cell-like and BCL2 translocation with poor outcome in germinal center B-cell subtypes, respectively. Both have prognostic significance independent of MYC/BCL2 dual expression and the International Prognostic Index (IPI). Furthermore, the combination of BCL2 genetic alterations with IPI identifies markedly worse prognostic groups within individual COO subtypes. Thus, high-resolution genomic assays identify extremely poor prognostic groups within each COO subtype on the basis of BCL2 genetic status in this large, uniformly R-CHOP-treated population-based cohort of DLBCL. These results suggest COO subtype-specific biomarkers based on BCL2 genetic alterations can be used to risk-stratify patients with DLBCL treated with immunochemotherapy.