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BACKGROUND: Evolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear. METHODS: To ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 "metastasis-specific" mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition. RESULTS: The ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable. CONCLUSIONS: Our results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/genética , Mutación , Secuenciación del ExomaRESUMEN
ABSTRACT: Patients with multiple myeloma (MM) treated with B-cell maturation antigen (BCMA)-specific chimeric antigen receptor (CAR) T cells usually relapse with BCMA+ disease, indicative of CAR T-cell suppression. CD200 is an immune checkpoint that is overexpressed on aberrant plasma cells (aPCs) in MM and is an independent negative prognostic factor for survival. However, CD200 is not present on MM cell lines, a potential limitation of current preclinical models. We engineered MM cell lines to express CD200 at levels equivalent to those found on aPCs in MM and show that these are sufficient to suppress clinical-stage CAR T-cells targeting BCMA or the Tn glycoform of mucin 1 (TnMUC1), costimulated by 4-1BB and CD2, respectively. To prevent CD200-mediated suppression of CAR T cells, we compared CRISPR-Cas9-mediated knockout of the CD200 receptor (CD200RKO), to coexpression of versions of the CD200 receptor that were nonsignaling, that is, dominant negative (CD200RDN), or that leveraged the CD200 signal to provide CD28 costimulation (CD200R-CD28 switch). We found that the CD200R-CD28 switch potently enhanced the polyfunctionality of CAR T cells, and improved cytotoxicity, proliferative capacity, CAR T-cell metabolism, and performance in a chronic antigen exposure assay. CD200RDN provided modest benefits, but surprisingly, the CD200RKO was detrimental to CAR T-cell activity, adversely affecting CAR T-cell metabolism. These patterns held up in murine xenograft models of plasmacytoma, and disseminated bone marrow predominant disease. Our findings underscore the importance of CD200-mediated immune suppression in CAR T-cell therapy of MM, and highlight a promising approach to enhance such therapies by leveraging CD200 expression on aPCs to provide costimulation via a CD200R-CD28 switch.
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Inmunoterapia Adoptiva , Mieloma Múltiple , Humanos , Ratones , Animales , Mieloma Múltiple/metabolismo , Antígenos CD28/metabolismo , Linfocitos T , Antígeno de Maduración de Linfocitos B/metabolismo , Recurrencia Local de Neoplasia/metabolismoRESUMEN
Alterations to the natural microbiome are linked to different diseases, and the presence or absence of specific microbes is directly related to disease outcomes. We performed a comprehensive analysis with unique cohorts of the four subtypes of breast cancer (BC) characterized by their microbial signatures, using a pan-pathogen microarray strategy. The signature (includes viruses, bacteria, fungi, and parasites) of each tumor subtype was correlated with clinical data to identify microbes with prognostic potential. The subtypes of BC had specific viromes and microbiomes, with ER+ and TN tumors showing the most and least diverse microbiome, respectively. The specific microbial signatures allowed discrimination between different BC subtypes. Furthermore, we demonstrated correlations between the presence and absence of specific microbes in BC subtypes with the clinical outcomes. This study provides a comprehensive map of the oncobiome of BC subtypes, with insights into disease prognosis that can be critical for precision therapeutic intervention strategies.
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Neoplasias de la Mama/microbiología , Microbiota , Neoplasias de la Mama/parasitología , Neoplasias de la Mama/patología , Neoplasias de la Mama/virología , Femenino , Humanos , Estadificación de Neoplasias , Análisis de Componente Principal , Pronóstico , Receptor ErbB-2/metabolismo , Receptores de Estrógenos/metabolismo , Receptores de Progesterona/metabolismo , Neoplasias de la Mama Triple Negativas/microbiologíaRESUMEN
Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.
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BACKGROUND: The presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement. OBJECTIVE: To investigate quantitative machine-based assessment of the extent and prognostic utility of ICC, especially within individual Gleason grade groups. DESIGN, SETTING, AND PARTICIPANTS: A machine learning approach was developed for ICC segmentation using 70 RP patients and validated in a cohort of 749 patients from four sites whose median year of surgery was 2007 and with median follow-up of 28 mo. ICC was segmented on one representative hematoxylin and eosin RP slide per patient and the fraction of tumor area composed of ICC, the cribriform area index (CAI), was measured. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The association between CAI and BCR was measured in terms of the concordance index (c index) and hazard ratio (HR). RESULTS AND LIMITATIONS: CAI was correlated with BCR (c index 0.62) in the validation set of 411 patients with ICC morphology, especially those with Gleason grade group 2 cancer (n = 192; c index 0.66), and was less prognostic when patients without ICC were included (c index 0.54). A doubling of CAI in the group with ICC morphology was prognostic after controlling for Gleason grade, surgical margin positivity, preoperative prostate-specific antigen level, pathological T stage, and age (HR 1.19, 95% confidence interval 1.03-1.38; p = 0.018). CONCLUSIONS: Automated image analysis and machine learning could provide an objective, quantitative, reproducible, and high-throughput method of quantifying ICC area. The performance of CAI for grade group 2 cancer suggests that for patients with little Gleason 4 pattern, the ICC fraction has a strong prognostic role. PATIENT SUMMARY: Machine-based measurement of a specific cell pattern (cribriform; sieve-like, with lots of spaces) in images of prostate specimens could improve risk stratification for patients with prostate cancer. In the future, this could help in expanding the criteria for active surveillance.
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Próstata , Neoplasias de la Próstata , Eosina Amarillenta-(YS) , Hematoxilina , Humanos , Masculino , Recurrencia Local de Neoplasia/patología , Pronóstico , Próstata/patología , Prostatectomía/métodos , Neoplasias de la Próstata/patología , Neoplasias de la Próstata/cirugíaRESUMEN
Nearly all breast cancer deaths result from metastatic disease. Despite this, the genomic events that drive metastatic recurrence are poorly understood. We performed whole-exome and shallow whole-genome sequencing to identify genes and pathways preferentially mutated or copy-number altered in metastases compared with the paired primary tumors from which they arose. Seven genes were preferentially mutated in metastases - MYLK, PEAK1, SLC2A4RG, EVC2, XIRP2, PALB2, and ESR1 - 5 of which are not significantly mutated in any type of human primary cancer. Four regions were preferentially copy-number altered: loss of STK11 and CDKN2A/B, as well as gain of PTK6 and the membrane-bound progesterone receptor, PAQR8. PAQR8 gain was mutually exclusive with mutations in the nuclear estrogen and progesterone receptors, suggesting a role in treatment resistance. Several pathways were preferentially mutated or altered in metastases, including mTOR, CDK/RB, cAMP/PKA, WNT, HKMT, and focal adhesion. Immunohistochemical analyses revealed that metastases preferentially inactivate pRB, upregulate the mTORC1 and WNT signaling pathways, and exhibit nuclear localization of activated PKA. Our findings identify multiple therapeutic targets in metastatic recurrence that are not significantly mutated in primary cancers, implicate membrane progesterone signaling and nuclear PKA in metastatic recurrence, and provide genomic bases for the efficacy of mTORC1, CDK4/6, and PARP inhibitors in metastatic breast cancer.
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Neoplasias de la Mama , Regulación Neoplásica de la Expresión Génica , Mutación , Proteínas de Neoplasias , Vía de Señalización Wnt , Neoplasias de la Mama/genética , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/patología , Femenino , Humanos , Metástasis de la Neoplasia , Proteínas de Neoplasias/biosíntesis , Proteínas de Neoplasias/genéticaRESUMEN
We have established a microbiome signature for prostate cancer using an array-based metagenomic and capture-sequencing approach. A diverse microbiome signature (viral, bacterial, fungal and parasitic) was observed in the prostate cancer samples compared with benign prostate hyperplasia controls. Hierarchical clustering analysis identified three distinct prostate cancer-specific microbiome signatures. The three signatures correlated with different grades, stages and scores of the cancer. Thus, microbiome signature analysis potentially provides clinical diagnosis and outcome predictions. The array data were validated by PCR and targeted next-generation sequencing (NGS). Specific NGS data suggested that certain viral genomic sequences were inserted into the host somatic chromosomes of the prostate cancer samples. A randomly selected group of these was validated by direct PCR and sequencing. In addition, PCR validation of Helicobacter showed that Helicobacter cagA sequences integrated within specific chromosomes of prostate tumor cells. The viral and Helicobacter integrations are predicted to affect the expression of several cellular genes associated with oncogenic processes.
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Microbiota , Neoplasias de la Próstata/microbiología , Cromosomas Humanos , Análisis por Conglomerados , Helicobacter/aislamiento & purificación , Herpesvirus Humano 8/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Masculino , Hibridación de Ácido Nucleico , Papillomaviridae/genética , Reacción en Cadena de la Polimerasa/métodos , Neoplasias de la Próstata/virología , Reproducibilidad de los Resultados , Integración ViralRESUMEN
Site variation in fixation, staining, and scanning can confound automated tissue based image classifiers for disease characterization. In this study we incorporated stability into four feature selection methods for identifying the most robust and discriminating features for two prostate histopathology classification tasks. We evaluated 242 morphology features from N = 212 prostatectomy specimens from four sites for automated cancer detection and grading. We quantified instability as the rate of significant cross-site feature differences. We mapped feature stability and discriminability using 188 non-cancerous and 210 cancerous regions via 3-fold cross validation, then held one site out, creating independent training and testing sets. In training, one feature set was selected only for discriminability, another for discriminability and stability. We trained a classifier with each feature set, testing on the hold out site. Experiments were repeated with 117 Gleason grade 3 and 112 grade 4 regions. Stability was calculated across non-cancerous regions. Gland shape features yielded the best stability and area under the receiver operating curve (AUC) trade-off while co-occurrence texture features were generally unstable. Our stability-informed method produced a cancer detection AUC of 0.98 ± 0.05 and increased average Gleason grading AUC by 4.38%. Color normalization of the images tended to exacerbate feature instability.
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Prostatectomía , Neoplasias de la Próstata/patología , Neoplasias de la Próstata/cirugía , Área Bajo la Curva , Humanos , Interpretación de Imagen Asistida por Computador , Masculino , Clasificación del TumorRESUMEN
Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.
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Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Neoplasias de la Próstata/clasificación , Neoplasias de la Próstata/patología , Humanos , Masculino , Clasificación del Tumor , Curva ROC , Reproducibilidad de los Resultados , Estudios RetrospectivosRESUMEN
A dysbiotic microbiome can potentially contribute to the pathogenesis of many different diseases including cancer. Breast cancer is the second leading cause of cancer death in women. Thus, we investigated the diversity of the microbiome in the four major types of breast cancer: endocrine receptor (ER) positive, triple positive, Her2 positive and triple negative breast cancers. Using a whole genome and transcriptome amplification and a pan-pathogen microarray (PathoChip) strategy, we detected unique and common viral, bacterial, fungal and parasitic signatures for each of the breast cancer types. These were validated by PCR and Sanger sequencing. Hierarchical cluster analysis of the breast cancer samples, based on their detected microbial signatures, showed distinct patterns for the triple negative and triple positive samples, while the ER positive and Her2 positive samples shared similar microbial signatures. These signatures, unique or common to the different breast cancer types, provide a new line of investigation to gain further insights into prognosis, treatment strategies and clinical outcome, as well as better understanding of the role of the micro-organisms in the development and progression of breast cancer.
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Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (â¼6 million of samples in 24 hours) with far fewer samples (â¼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
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Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/patología , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la ComputaciónRESUMEN
The microbiome is fundamentally one of the most unique organs in the human body. Dysbiosis can result in critical inflammatory responses and result in pathogenesis contributing to neoplastic events. We used a pan-pathogen array technology (PathoChip) coupled with next-generation sequencing to establish microbial signatures unique to human oral and oropharyngeal squamous cell carcinomas (OCSCC/OPSCC). Signatures for DNA and RNA viruses including oncogenic viruses, gram positive and negative bacteria, fungi and parasites were detected. Cluster and topological analyses identified 2 distinct groups of microbial signatures related to OCSCCs/OPSCCs. Results were validated by probe capture next generation sequencing; the data from which also provided a comprehensive map of integration sites and chromosomal hotspots for micro-organism genomic insertions. Identification of these microbial signatures and their integration sites may provide biomarkers for OCSCC/OPSCC diagnosis and prognosis as well as novel avenues for study of their potential role in OCSCCs/OPSCCs.
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Carcinoma de Células Escamosas/etiología , Microbiota , Neoplasias de la Boca/etiología , Neoplasias Orofaríngeas/etiología , Animales , Bacterias/clasificación , Bacterias/genética , Carcinoma de Células Escamosas/epidemiología , Biología Computacional/métodos , Interacciones Huésped-Parásitos , Interacciones Huésped-Patógeno , Humanos , Metagenoma , Metagenómica/métodos , Neoplasias de la Boca/epidemiología , Mutagénesis Insercional , Neoplasias Orofaríngeas/epidemiología , Parásitos/clasificación , Parásitos/genética , Reproducibilidad de los ResultadosRESUMEN
PURPOSE: Breast cancer metastases differ biologically from primary disease; therefore, metastatic biopsies may assist in treatment decision making. Commercial genomic testing of both tumor and circulating tumor DNA have become available clinically, but utility of these tests in breast cancer management remains unclear. METHODS: Patients undergoing a clinically indicated metastatic tumor biopsy were consented to the ongoing METAMORPH registry. Tumor and blood were collected at the time of disease progression before subsequent therapy, and patients were followed for response on subsequent treatment. Tumor testing (n = 53) and concurrent cell-free DNA (n = 32) in a subset of patients was performed using CLIA-approved assays. RESULTS: The proportion of patients with a genomic alteration was lower in tumor than in blood (69 vs. 91%; p = 0.06). After restricting analysis to alterations covered on both platforms, 83% of tumor alterations were detected in blood, while 90% of blood alterations were detected in tumor. Mutational load specific for the panel genes was calculated for both tumor and blood. Time to progression on subsequent treatment was significantly shorter for patients whose tumors had high panel-specific mutational load (HR 0.31, 95% CI 0.12-0.78) or a TP53 mutation (HR 0.35, 95% CI 0.20-0.79), after adjusting for stage at presentation, hormone receptor status, prior treatment type, and number of lines of metastatic treatment. CONCLUSIONS: Treating oncologists must distinguish platform differences from true biological heterogeneity when comparing tumor and cfDNA genomic testing results. Tumor and concurrent cfDNA contribute unique genomic information in metastatic breast cancer patients, providing potentially useful biomarkers for aggressive metastatic disease.
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Neoplasias de la Mama/genética , ADN de Neoplasias/sangre , ADN de Neoplasias/genética , Adulto , Anciano , Neoplasias de la Mama/sangre , Neoplasias de la Mama/patología , Progresión de la Enfermedad , Femenino , Genómica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Persona de Mediana Edad , Mutación , Metástasis de la Neoplasia , PronósticoRESUMEN
With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.
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Neoplasias de la Mama/patología , Carcinoma Ductal de Mama/patología , Interpretación de Imagen Asistida por Computador/métodos , Adulto , Anciano , Algoritmos , Neoplasias de la Mama/diagnóstico por imagen , Carcinoma Ductal de Mama/diagnóstico por imagen , Aprendizaje Profundo , Femenino , Humanos , Persona de Mediana Edad , Invasividad Neoplásica , Carga Tumoral , Adulto JovenRESUMEN
Humans and other mammals are colonized by microbial agents across the kingdom which can represent a unique microbiome pattern. Dysbiosis of the microbiome has been associated with pathology including cancer. We have identified a microbiome signature unique to ovarian cancers, one of the most lethal malignancies of the female reproductive system, primarily because of its asymptomatic nature during the early stages in development. We screened ovarian cancer samples along with matched, and non-matched control samples using our pan-pathogen array (PathoChip), combined with capture-next generation sequencing. The results show a distinct group of viral, bacterial, fungal and parasitic signatures of high significance in ovarian cases. Further analysis shows specific viral integration sites within the host genome of tumor samples, which may contribute to the carcinogenic process. The ovarian cancer microbiome signature provides insights for the development of targeted therapeutics against ovarian cancers.
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Bacterias/genética , Hongos/fisiología , Helmintos/fisiología , Infecciones/genética , Microbiota , Neoplasias Ováricas/genética , Virus/genética , Animales , Carcinogénesis , Aberraciones Cromosómicas , Disbiosis , Femenino , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Infecciones/microbiología , Infecciones/parasitología , Infecciones/virología , Neoplasias Ováricas/microbiología , Neoplasias Ováricas/parasitología , Neoplasias Ováricas/virología , TranscriptomaRESUMEN
Quantitative histomorphometry (QH) is the process of computerized feature extraction from digitized tissue slide images to predict disease presence, behavior, and outcome. Feature stability between sites may be compromised by laboratory-specific variables including dye batch, slice thickness, and the whole slide scanner used. We present two new measures, preparation-induced instability score and latent instability score, to quantify feature instability across and within datasets. In a use case involving prostate cancer, we examined QH features which may detect cancer on whole slide images. Using our method, we found that five feature families (graph, shape, co-occurring gland tensor, sub-graph, and texture) were different between datasets in 19.7% to 48.6% of comparisons while the values expected without site variation were 4.2% to 4.6%. Color normalizing all images to a template did not reduce instability. Scanning the same 34 slides on three scanners demonstrated that Haralick features were most substantively affected by scanner variation, being unstable in 62% of comparisons. We found that unstable feature families performed significantly worse in inter- than intrasite classification. Our results appear to suggest QH features should be evaluated across sites to assess robustness, and class discriminability alone should not represent the benchmark for digital pathology feature selection.
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In applications involving large tissue specimens that have been sectioned into smaller tissue fragments, manual reconstruction of a "pseudo whole-mount" histological section (PWMHS) can facilitate (a) pathological disease annotation, and (b) image registration and correlation with radiological images. We have previously presented a program called HistoStitcher, which allows for more efficient manual reconstruction than general purpose image editing tools (such as Photoshop). However HistoStitcher is still manual and hence can be laborious and subjective, especially when doing large cohort studies. In this work we present AutoStitcher, a novel automated algorithm for reconstructing PWMHSs from digitized tissue fragments. AutoStitcher reconstructs ("stitches") a PWMHS from a set of 4 fragments by optimizing a novel cost function that is domain-inspired to ensure (i) alignment of similar tissue regions, and (ii) contiguity of the prostate boundary. The algorithm achieves computational efficiency by performing reconstruction in a multi-resolution hierarchy. Automated PWMHS reconstruction results (via AutoStitcher) were quantitatively and qualitatively compared to manual reconstructions obtained via HistoStitcher for 113 prostate pathology sections. Distances between corresponding fiducials placed on each of the automated and manual reconstruction results were between 2.7%-3.2%, reflecting their excellent visual similarity.
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Interpretación de Imagen Asistida por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Próstata/diagnóstico por imagen , Intensificación de Imagen Radiográfica/métodos , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética , Masculino , Próstata/patologíaRESUMEN
The mechanisms of metastatic progression from hormonal therapy (HT) are largely unknown in luminal breast cancer. Here we demonstrate the enrichment of CD133(hi)/ER(lo) cancer cells in clinical specimens following neoadjuvant endocrine therapy and in HT refractory metastatic disease. We develop experimental models of metastatic luminal breast cancer and demonstrate that HT can promote the generation of HT-resistant, self-renewing CD133(hi)/ER(lo)/IL6(hi) cancer stem cells (CSCs). HT initially abrogates oxidative phosphorylation (OXPHOS) generating self-renewal-deficient cancer cells, CD133(hi)/ER(lo)/OXPHOS(lo). These cells exit metabolic dormancy via an IL6-driven feed-forward ER(lo)-IL6(hi)-Notch(hi) loop, activating OXPHOS, in the absence of ER activity. The inhibition of IL6R/IL6-Notch pathways switches the self-renewal of CD133(hi) CSCs, from an IL6/Notch-dependent one to an ER-dependent one, through the re-expression of ER. Thus, HT induces an OXPHOS metabolic editing of luminal breast cancers, paradoxically establishing HT-driven self-renewal of dormant CD133(hi)/ER(lo) cells mediating metastatic progression, which is sensitive to dual targeted therapy.
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Antineoplásicos Hormonales , Neoplasias Óseas/genética , Neoplasias de la Mama/genética , Carcinoma Ductal de Mama/genética , Carcinoma Lobular/genética , Autorrenovación de las Células/genética , Resistencia a Antineoplásicos/genética , Regulación Neoplásica de la Expresión Génica , Células Madre Neoplásicas/metabolismo , Antígeno AC133 , Anastrozol , Androstadienos , Animales , Antígenos CD/metabolismo , Neoplasias Óseas/secundario , Neoplasias de la Mama/patología , Carcinoma Ductal de Mama/secundario , Carcinoma Lobular/secundario , Línea Celular Tumoral , Estradiol/análogos & derivados , Femenino , Citometría de Flujo , Fulvestrant , Glicoproteínas/metabolismo , Humanos , Técnicas In Vitro , Interleucina-6/genética , Letrozol , Leuprolida , Células MCF-7 , Ratones Endogámicos NOD , Ratones SCID , Metástasis de la Neoplasia , Trasplante de Neoplasias , Nitrilos , Fosforilación Oxidativa , Péptidos/metabolismo , Reacción en Cadena en Tiempo Real de la Polimerasa , Receptor Notch3 , Receptores de Estrógenos/metabolismo , Receptores Notch/genética , Transducción de Señal/genética , Tamoxifeno , TriazolesRESUMEN
BACKGROUND: To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. METHODS: This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy-seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. RESULTS: A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho = -0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = -0.52). CONCLUSION: Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS.