Statistical methods for tissue array images - algorithmic scoring and co-training

Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology

Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classification and semantic segmentation. However, due to texture bias in models trained on real-world images, transfer learning for histopathology applications might result in underperforming models, which necessitates the need for using unlabeled histopathology data and self-supervised methods to discover domain-specific characteristics. Here, we tested the premise that histopathology-specific pretrained models provide better initializations for pathology vision tasks, i.e., gland and cell segmentation. In this study, we compare the performance of gland and cell segmentation tasks with domain-specific and non-domain-specific pretrained weights. Moreover, we investigate the data size at which domain-specific pretraining produces a statistically significant difference in performance. In addition, we investigated whether domain-specific initialization improves the effectiveness of out-of-domain testing on distinct datasets but the same task. The results indicate that performance gain using domain-specific pretraining depends on both the task and the size of the training dataset. In instances with limited dataset sizes, a significant improvement in gland segmentation performance was also observed, whereas models trained on cell segmentation datasets exhibit no improvement

Centrosome loss results in an unstable genome and malignant prostate tumors

Localized, nonindolent prostate cancer (PCa) is characterized by large-scale genomic rearrangements, aneuploidy, chromothripsis, and other forms of chromosomal instability (CIN), yet how this occurs remains unclear. A well-established mechanism of CIN is the overproduction of centrosomes, which promotes tumorigenesis in various mouse models. Therefore, we developed a single-cell assay for quantifying centrosomes in human prostate tissue. Surprisingly, centrosome loss-which has not been described in human cancer-was associated with PCa progression. By chemically or genetically inducing centrosome loss in nontumorigenic prostate epithelial cells, mitotic errors ensued, producing aneuploid, and multinucleated cells. Strikingly, transient or chronic centrosome loss transformed prostate epithelial cells, which produced highly proliferative and poorly differentiated malignant tumors in mice. Our findings suggest that centrosome loss could create a cellular crisis with oncogenic potential in prostate epithelial cells.6 month embargo; published online: 2 September 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Trading in your spindles for blebs: the amoeboid tumor cell phenotype in prostate cancer

Prostate cancer (PCa) remains a principal cause of mortality in developed countries. Because no clinical interventions overcome resistance to androgen ablation therapy, management of castration resistance and metastatic disease remains largely untreatable. Metastasis is a multistep process in which tumor cells lose cell-cell contacts, egress from the primary tumor, intravasate, survive shear stress within the vasculature and extravasate into tissues to colonize ectopic sites. Tumor cells reestablish migratory behaviors employed during nonneoplastic processes such as embryonic development, leukocyte trafficking and wound healing. While mesenchymal motility is an established paradigm of dissemination, an alternate, ‘amoeboid’ phenotype is increasingly appreciated as relevant to human cancer. Here we discuss characteristics and pathways underlying the phenotype, and highlight our findings that the cytoskeletal regulator DIAPH3 governs the mesenchymal-amoeboid transition. We also describe our identification of a new class of tumor-derived microvesicles, large oncosomes, produced by amoeboid cells and with potential clinical utility in prostate and other cancers

Chromosomal instability in untreated primary prostate cancer as an indicator of metastatic potential.

BackgroundMetastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome.MethodsPNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121).ResultsThe CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene "metastasis" signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression.ConclusionsMeasuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis

A method of quantifying centrosomes at the single-cell level in human normal and cancer tissue

Centrosome abnormalities are emerging hallmarks of cancer. The overproduction of centrosomes (known as centrosome amplification) has been reported in a variety of cancers and is currently being explored as a promising target for therapy. However, to understand different types of centrosome abnormalities and their impact on centrosome function during tumor progression, as well as to identify tumor subtypes that would respond to the targeting of a centrosome abnormality, a reliable method for accurately quantifying centrosomes in human tissue samples is needed. Here, we established a method of quantifying centrosomes at a single-cell level in different types of human tissue samples. We tested multiple anti-centriole and pericentriolar-material antibodies to identify bona fide centrosomes and multiplexed these with cell border markers to identify individual cells within the tissue. High-resolution microscopy was used to generate multiple Z-section images, allowing us to acquire whole cell volumes in which to scan for centrosomes. The normal cells within the tissue serve as internal positive controls. Our method provides a simple, accurate way to distinguish alterations in centrosome numbers at the level of single cells.National Cancer Institute (NCI) [P30CA23074]; Department of Defense Prostate Cancer Research Program [W81XWH-14-2-0182, W81XWH-14-2-0183, W81XWH-14-2-0185, W81XWH-14-2-0186, W81XWH-15-2-0062]; National Institutes of Health (NIH) [R01GM110166, R01GM126035]; NIH-NCI [RO1CA159406]; Tim and Diane Bowden Cancer Biology Research FellowshipThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

PARP-1 regulates DNA repair factor availability.

PARP-1 holds major functions on chromatin, DNA damage repair and transcriptional regulation, both of which are relevant in the context of cancer. Here, unbiased transcriptional profiling revealed the downstream transcriptional profile of PARP-1 enzymatic activity. Further investigation of the PARP-1-regulated transcriptome and secondary strategies for assessing PARP-1 activity in patient tissues revealed that PARP-1 activity was unexpectedly enriched as a function of disease progression and was associated with poor outcome independent of DNA double-strand breaks, suggesting that enhanced PARP-1 activity may promote aggressive phenotypes. Mechanistic investigation revealed that active PARP-1 served to enhance E2F1 transcription factor activity, and specifically promoted E2F1-mediated induction of DNA repair factors involved in homologous recombination (HR). Conversely, PARP-1 inhibition reduced HR factor availability and thus acted to induce or enhance BRCA-ness . These observations bring new understanding of PARP-1 function in cancer and have significant ramifications on predicting PARP-1 inhibitor function in the clinical setting

DNA Methylation Profiles of Ovarian Epithelial Carcinoma Tumors and Cell Lines

BACKGROUND:Epithelial ovarian carcinoma is a significant cause of cancer mortality in women worldwide and in the United States. Epithelial ovarian cancer comprises several histological subtypes, each with distinct clinical and molecular characteristics. The natural history of this heterogeneous disease, including the cell types of origin, is poorly understood. This study applied recently developed methods for high-throughput DNA methylation profiling to characterize ovarian cancer cell lines and tumors, including representatives of three major histologies. METHODOLOGY/PRINCIPAL FINDINGS:We obtained DNA methylation profiles of 1,505 CpG sites (808 genes) in 27 primary epithelial ovarian tumors and 15 ovarian cancer cell lines. We found that the DNA methylation profiles of ovarian cancer cell lines were markedly different from those of primary ovarian tumors. Aggregate DNA methylation levels of the assayed CpG sites tended to be higher in ovarian cancer cell lines relative to ovarian tumors. Within the primary tumors, those of the same histological type were more alike in their methylation profiles than those of different subtypes. Supervised analyses identified 90 CpG sites (68 genes) that exhibited 'subtype-specific' DNA methylation patterns (FDR<1%) among the tumors. In ovarian cancer cell lines, we estimated that for at least 27% of analyzed autosomal CpG sites, increases in methylation were accompanied by decreases in transcription of the associated gene. SIGNIFICANCE:The significant difference in DNA methylation profiles between ovarian cancer cell lines and tumors underscores the need to be cautious in using cell lines as tumor models for molecular studies of ovarian cancer and other cancers. Similarly, the distinct methylation profiles of the different histological types of ovarian tumors reinforces the need to treat the different histologies of ovarian cancer as different diseases, both clinically and in biomarker studies. These data provide a useful resource for future studies, including those of potential tumor progenitor cells, which may help illuminate the etiology and natural history of these cancers

Racial differences in prostate inflammation: Results from the REDUCE study

Prostate cancer (PC) risk differs between races, and we previously showed prostate inflammation in benign prostate tissue was linked with a lower future PC risk. However, whether prostate tissue inflammation varies by race is unknown. We analyzed baseline acute and chronic prostate inflammation by race in REDUCE, a 4-year, multicenter, placebo-controlled study where all men had a negative prostate biopsy prior to enrollment. We included 7,982 men with standardized central pathology review to determine the presence or absence of chronic or acute inflammation in baseline prostate biopsy tissue. Logistic regression was used to compare prostate inflammation by race, adjusting for confounders. Of 7,982 men, 7,271 were white (91.1%), 180 (2.3%) black, 131 (1.6%) Asian, 319 (4.0%) Hispanic and 81 (1%) unknown. A total of 78% had chronic and 15% had acute inflammation. On multivariable analysis relative to white men, black men were less likely (OR = 0.65, 95%CI: 0.41-1.03