Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study.

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples.

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such "interaction-based" methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Pancreatic Cancer Cell Fraction Estimation in a DNA Sample.

OBJECTIVE: Pancreatic cancers are characterized by dense stroma. To estimate the degree of interference by coexisting noncancer cells in molecular analyses, we aimed to develop a DNA methylation marker that assesses a cancer cell fraction in DNA samples. METHODS: The microarray data of 22 pancreatic cancer tissues from the The Cancer Genome Atlas database and 9 noncancer tissues were used for genome-wide screening. Thirty-one surgical tumor samples (10 intraductal papillary mucinous neoplasms [IPMNs] and 21 pancreatic cancers), 4 normal, and 26 nontumor samples were used for validation. Gene-specific methylation analysis was conducted by bisulfite pyrosequencing. RESULTS: Genome-wide screening isolated SIM1, MIR129-2, NR1I2, and HOXB-AS4, as specifically methylated in pancreatic cancer cells. Bisulfite pyrosequencing validated that one or more of three genes (SIM1, MIR129-2, and NR1I2) were methylated in 22 (71.0%) tumor samples (8 IPMNs and 14 cancers), and all showed low levels of methylation in 26 (86.7%) normal and nontumor samples. Therefore, the three genes collectively constituted one marker for a pancreatic cancer cell fraction. The cancer cell fraction estimated by the marker was highly correlated with that estimated using the KRAS mutant allele frequency (R = 0.79). CONCLUSION: The DNA methylation marker is useful to estimate the pancreatic cancer cell fraction in DNA samples.

[Cell therapy of chronic heart failure: State of the art].

The treatment of patients with CHF using autologous bone marrow-derived mononuclear cell fraction (or its derivatives) is a promising therapy for this serious and numerous group of patients. The article analyzed international and Russian experience of using the cell therapy in patients with CHF and possibilities for extensive use of the autologous bone marrow-derived mononuclear cell fraction in clinical practice.

Establishment of a DNA methylation marker to evaluate cancer cell fraction in gastric cancer.

BACKGROUND: Tumor samples are unavoidably contaminated with coexisting normal cells. Here, we aimed to establish a DNA methylation marker to estimate the fraction of gastric cancer (GC) cells in any DNA sample by isolating genomic regions specifically methylated in GC cells. METHODS: Genome-wide and gene-specific methylation analyses were conducted with an Infinium HumanMethylation450 BeadChip array and by quantitative methylation-specific PCR, respectively. Purified cancer and noncancer cells were prepared by laser-capture microdissection. TP53 mutation data were obtained from our previous study using next-generation target sequencing. RESULTS: Genome-wide DNA methylation analysis of 12 GC cell lines, 30 GCs, six normal gastric mucosae, one sample of peripheral leukocytes, and four noncancerous gastric mucosae identified OSR2, PPFIA3, and VAV3 as barely methylated in normal cells and highly methylated in cancer cells. Quantitative methylation-specific PCR using 26 independent GCs validated that one or more of them was highly methylated in all of the GCs. Using four pairs of purified cells, we confirmed the three genes were highly methylated (85 % or more) in cancer cells and barely methylated (5 % or less) in noncancer cells. The cancer cell fraction assessed by the panel of the three genes showed good correlation with that assessed by the TP53 mutant allele frequency in 13 GCs (r = 0.77). After correction of the GC cell fraction, unsupervised clustering analysis of the genome-wide DNA methylation profiles yielded clearer clustering. CONCLUSIONS: A DNA methylation marker-namely, the panel of the three genes-is useful to estimate the cancer cell fraction in GCs.

Reconstructing Phylogenetic Relationship in Bladder Cancer: A Methodological Overview.

Bladder cancer (BC) expresses itself as a highly heterogeneous disease both at the histological and molecular level, often occurring as synchronous or metachronous multifocal disease with high risk of recurrence and potential to metastasize. Multiple sequencing studies focusing on both non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) gave insights into the extent of both inter- and intrapatient heterogeneity, but many questions on clonal evolution in BC remain unanswered. In this review article, we provide an overview over the technical and theoretical concepts linked to reconstructing evolutionary trajectories in BC and propose a set of tools and established software for phylogenetic analysis.

A Kinetic Stem Cell Counting Analysis of the Specific Effects of Cell Culture Medium Growth Factors on Adipose-Derived Mesenchymal Stem Cells.

A recently described kinetic stem cell (KSC) counting method was used to investigate the stem-cell-specific effects of commercial growth factor supplements used for expanding stem cells in adipose-tissue-derived mesenchymal cell preparations. The supplements were a proprietary growth factor product, a source of fetal bovine serum, two sources of pooled human sera, and two sources of human platelet lysate. KSC counting analyses were performed to monitor effects on the fraction and viability of stem cells in serial cultures with their respective supplements. Serial cultures supplemented with the proprietary growth factor product or fetal bovine serum showed a similar high degree of maintenance of stem cell fraction with passage. In contrast, cultures supplemented with human sera or human platelet lysate showed rapid declines in stem cell fraction. KSC counting was used to discover the cellular basis for the decreasing stem cell fractions. For human platelet lysate, it was attributable to lower rates of self-renewing symmetric stem cell divisions. For human sera, both low rates of symmetric division and high rates of stem cell death were responsible. These results demonstrate the power of the KSC counting method to provide previously inaccessible information for improving future tissue stem cell biomanufacturing.

Evaluation of the Precision of Kinetic Stem Cell (KSC) Counting for Specific Quantification of Human Mesenchymal Stem Cells in Heterogeneous Tissue Cell Preparations.

Kinetic stem cell (KSC) counting is a recently introduced first technology for quantifying tissue stem cells in vertebrate organ and tissue cell preparations. Previously, effective quantification of the fraction or dosage of tissue stem cells had been largely lacking in stem cell science and medicine. A general method for the quantification of tissue stem cells will accelerate progress in both of these disciplines as well as related industries like drug development. Triplicate samples of human oral alveolar bone cell preparations, which contain mesenchymal stem cells (MSCs), were used to estimate the precision of KSC counting analyses conducted at three independent sites. A high degree of intra-site precision was found, with coefficients of variation for determinations of MSC-specific fractions of 8.9% (p < 0.003), 13% (p < 0.006), and 25% (p < 0.02). The estimates of inter-site precision, 11% (p < 0.0001) and 26% (p < 0.0001), also indicated a high level of precision. Results are also presented to show the ability of KSC counting to define cell subtype-specific kinetics factors responsible for changes in the stem cell fraction during cell culture. The presented findings support the continued development of KSC counting as a new tool for advancing stem cell science and medicine.

Cell function and identity revealed by comparative scRNA-seq analysis in human nasal, bronchial and epididymis epithelia.

The evolutionary relationship of cells within tissues having a similar function but located in different anatomical sites is of considerable biological interest. The development of single-cell RNA sequencing (scRNA-seq) protocols has greatly enhanced opportunities to address this topic. Here we focus on cells in the epithelium which lines two regions of the human respiratory tract and the male genital ducts to delineate the shared, differentiated functions of the different cell populations. Transcriptomic data were used to assess the gene expression profiles of human bronchial, nasal, and epididymal epithelium (HBE, HNE, and HEE). Bulk RNA-seq showed many shared genes expressed in cells from the nasal and bronchial epithelium and highlighted their divergence from the epididymal epithelium. ScRNA-seq in HBE and HNE cells demonstrated overlapping gene expression patterns within basal and secretory cell populations. Moreover, the distribution of cell types was altered in HNE cells derived from donors with cystic fibrosis (CF) when compared to cells from healthy donors. Next, the HBE and HNE datasets were merged and confirmed intersection of cell type gene expression profiles from the two sites. However, secretory and ciliated cells were the most abundant types in the HBE samples, while more basal cells were seen in the HNE populations. We then merged single-cell data from the epididymis to determine if overlapping functions of these cells corresponded to those in the airway. Of note, only the pulmonary ionocytes/epididymis clear cells showed a strongly conserved identity, which was confirmed by imputation in bulk RNA-seq datasets from the same cells.

Clonal Evolution at First Sight: A Combined Visualization of Diverse Diagnostic Methods Improves Understanding of Leukemic Progression.

Patients with myeloid neoplasia are classified by the WHO classification systems. Besides clinical and hematological criteria, cytogenetic and molecular genetic alterations highly impact treatment stratification. In routine diagnostics, a combination of methods is used to decipher different types of genetic variants. Eight patients were comprehensively analyzed using karyotyping, fluorescence in situ hybridization, array-CGH and a custom NGS panel. Clonal evolution was reconstructed manually, integrating all mutational information on single nucleotide variants (SNVs), insertions and deletions (indels), structural variants and copy number variants (CNVs). To allow a correct integration, we differentiate between three scenarios: 1) CNV occurring prior to the SNV/indel, but in the same cells. 2) SNV/indel occurring prior to the CNV, but in the same cells. 3) SNV/indel and CNV existing in parallel, independent of each other. Applying this bioinformatics approach, we reconstructed clonal evolution for all patients. This generalizable approach offers the possibility to integrate various data to analyze identification of driver and passenger mutations as well as possible targets for personalized medicine approaches. Furthermore, this model can be used to identify markers to assess the minimal residual disease.

Changes of Immune Cell Fractions in Patients Treated with Immune Checkpoint Inhibitors.

BACKGROUND: With the development of immunology, immune checkpoint inhibitors (ICIs) have been widely used in various cancer treatments. Although some patients can benefit from ICIs, other patients have no response to ICIs or suffer from hyperprogression. There has been no biomarker for predicting the efficacy of ICIs. Thus, the objective of this study was to find biomarkers for predicting the efficacy of ICIs using peripheral blood. METHODS: Adults patients planned to be treated with ICIs were enrolled in this study. Blood sampling was carried out before and after administration of ICIs. Changes of immune cell fraction were analyzed for each patient. RESULTS: Among 182 patients enrolled, immune cell analysis was performed for 90 patients. The objective response rate was 14.4% (n = 13/90). The median progression-free survival (PFS) was 6.0 months (95% CI: 3.1-8.9 months), and the median overall survival (OS) was 13.9 months (95% CI: 5.6-22.2 months). Significant benefits in ORR and OS were shown for patients with increased NKp46-/CD56+ NK cells (p = 0.033 and p = 0.013, respectively). The PFS tended to be longer in these patients, although the difference was not statistically significant (p = 0.050). CONCLUSION: Changes of immune cell fraction before and after administration of ICIs could be a novel biomarker for predicting the efficacy of immunotherapy.

Analysis of Gum proteins involved in xanthan biosynthesis throughout multiple cell fractions in a "single-tube".

Xanthomonas is a phytopathogenic bacterium and of industrial interest due to its capability to produce xanthan, used as a thickener and emulsifier in the food and non-food industry. Until now, proteome analyses of Xcc lacking a detailed view on the proteins involved in xanthan biosynthesis. The proteins involved in the biosynthesis of this polysaccharide are located near, in or at the cell membrane. This study aims to establish a robust and rapid protocol for a comprehensive proteome analysis of Xcc strains, without the need to isolate different cell fractions. Therefore, a method for the analysis of the whole cell proteome was compared to the isolation of specific fractions regarding the total number of identified proteins, the overlap, and the differences between the approaches. The whole cell proteome analysis with extended peptide separation methods resulted in more than 3254 identified proteins covering 73.1% of the whole proteome. The protocol was used to study xanthan production in a label-free quantification approach. Expression profiles of 8 Gum proteins were compared between the stationary and logarithmic growth phase. Differential expression levels within the operon structure indicate a complex regulatory mechanism for xanthan biosynthesis. Data are available via ProteomeXchange with identifier PXD027261. SIGNIFICANCE: Bacteria are metabolite factories with a wide variety of natural products. Thus, proteome analyses play a crucial role to understand the biological processes within a cell behind the biosynthesis of those metabolites. Proteins involved in the biosynthesis of secreted products are often organised on, in or around the membrane allowing metabolite channelling. Experiments targeting those biosynthesis pathways on protein level often require the analysis of multiple cell fractions like cytosolic, inner, and outer membrane. This is time consuming and demands different protocols. The protocol presented here is a rapid and robust solution to study biosynthetic pathways of biological or biotechnological interest in a single approach on protein level, where gene products are partitioned across multiple cell fractions. The use of a single method also simplifies the comparison of different experiments, for example, production vs. nonproduction conditions.

clevRvis: visualization techniques for clonal evolution.

BACKGROUND: A thorough analysis of clonal evolution commonly requires integration of diverse sources of data (e.g., karyotyping, next-generation sequencing, and clinical information). Subsequent to actual reconstruction of clonal evolution, detailed analysis and interpretation of the results are essential. Often, however, only few tumor samples per patient are available. Thus, information on clonal development and therapy effect may be incomplete. Furthermore, analysis of biallelic events-considered of high relevance with respect to disease course-can commonly only be realized by time-consuming analysis of the raw results and even raw sequencing data. RESULTS: We developed clevRvis, an R/Bioconductor package providing an extensive set of visualization techniques for clonal evolution. In addition to common approaches for visualization, clevRvis offers a unique option for allele-aware representation: plaice plots. Biallelic events may be visualized and inspected at a glance. Analyzing 4 public datasets, we show that plaice plots help to gain new insights into tumor development and investigate hypotheses on disease progression and therapy resistance. In addition to a graphical user interface, automatic phylogeny-aware color coding of the plots, and an approach to explore alternative trees, clevRvis provides 2 algorithms for fully automatic time point interpolation and therapy effect estimation. Analyzing 2 public datasets, we show that both approaches allow for valid approximation of a tumor's development in between measured time points. CONCLUSIONS: clevRvis represents a novel option for user-friendly analysis of clonal evolution, contributing to gaining new insights into tumor development.

Whole exome sequencing reveals the genetic heterogeneity and evolutionary history of primary gliomas and matched recurrences.

Diffuse glioma is a highly heterogeneous central nervous system tumor that is refractory to conventional therapy. Residual glioma cells escape from surgery and chemoradiotherapy, leading to lethal recurrence. Understanding the molecular mechanism of this recurrence process is critical to the development of successful therapies. Here, we analyzed whole-exome sequencing (WES) data of 97 paired primary and recurrent samples from 46 patients with glioma via a uniform pipeline. Clonality and phylogenetic analyses revealed that branching evolution was widespread in the recurrent process of gliomas. Recurrent tumors continued to evolve independently with chemoradiotherapy and harbored multiple recurrence-selected genetic alterations, such as amplification of PPFIBP1, PDE4DIP, and KRAS, deletion of TNFRSF14, DCC, CDKN2A, and MSH6, and mutations in ATRX, ARID1A, KEL, TP53, MSH6, and KMT2B. Meanwhile, truncal variants within partial driver genes were identified among primary and recurrent gliomas, suggesting that they might be ideal therapeutic targets. Intriguingly, the immunogenicity of recurrent gliomas did not increase significantly compared to the primary tumors. Genomic analysis of recurrent gliomas provided an opportunity to identify potentially clinically informative alterations not detected in clinically sampled primary tumors.

Integration of Tumor Heterogeneity for Recurrence Prediction in Patients with Esophageal Squamous Cell Cancer.

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest malignancies in China. The prognostic value of mutations, especially those in minor tumor clones, has not been systematically investigated. We conducted targeted deep sequencing to analyze the mutation status and the cancer cell fraction (CCF) of mutations in 201 ESCC patients. Our analysis showed that the prognostic effect of mutations was relevant to the CCF, and it should be considered in prognosis prediction. EP300 was a promising biomarker for overall survival, impairing prognosis in a CCF dose-dependent manner. We constructed a CCF-based predictor using a smooth clipped absolute deviation Cox model in the training set of 143 patients. The 3-year disease-free survival rates were 6.3% (95% CI: 1.6-23.9%), 29.8% (20.9-42.6%) and 70.5% (56.6-87.7%) in high-, intermediate- and low-risk patients, respectively, in the training set. The prognostic accuracy was verified in a validation set of 58 patients and the TCGA-ESCC cohort. The eight-gene model predicted prognosis independent of clinicopathological factors and the combination of our model and pathological staging markedly improved the prognostic accuracy of pathological staging alone. Our study describes a novel recurrence predictor for ESCC patients and provides a new perspective for the clinical translation of genomic findings.

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution.

The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.

An Approach to Monitor Exocytosis in White Adipocytes.

Exocytosis, the fusion of vesicles with the plasma membrane, can be measured with the patch-clamp technique as increases in membrane capacitance. Here we provide detailed information on how to monitor white adipocyte exocytosis using this method. We describe how to isolate the stromal vascular fraction of cells (SVF) within adipose tissue and how to differentiate SVF and cultured 3T3-L1 cells into adipocytes suitable for patch-clamp studies. We also give detailed protocols of how to record and analyze exocytosis in the differentiated cells.

CopyDetective: Detection threshold-aware copy number variant calling in whole-exome sequencing data.

BACKGROUND: Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing (WES) experiments is challenging. Usually, additional experiments have to be performed. FINDINGS: We developed a novel algorithm for somatic CNV calling in matched WES data called "CopyDetective". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq. CONCLUSIONS: Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds-as realized by CopyDetective-can and should be performed prior to actual variant calling.

Analysis of bronchoalveolar lavage samples collected from 30 patients with drug-induced pneumonitis.

BACKGROUND: Drug-induced pneumonitis is a disease encountered by pulmonologists in the clinical setting. The diagnosis generally considers the patient's clinical course and the results of peripheral blood tests, radiological examinations, and often bronchoscopic examinations. However, few studies have reported the association between radiological patterns such as ground-glass opacity (GGO) or consolidation, and bronchoalveolar lavage fluid (BALF) cell fractions. This study aimed to clarify this association. METHODS: Patients with a Naranjo's score of probable or definite were enrolled, and all 30 patients were categorized under probable. Data such as patient background, blood examination results, radiological findings, and BALF cell fractions were retrospectively collected. The association between BALF cell fractions and other factors such as chest computed tomography (CT) findings was evaluated. RESULTS: The most common radiological finding in patients with lymphocyte-dominant BALF was GGO, with only one patient exhibiting consolidation. However, patients with eosinophil-dominant BALF were more likely to have consolidation; only three cases showed crazy paving and one showed GGO. In addition, patients with a GGO-dominant pattern on CT had an increased lymphocyte fraction of 41.0%; those with a consolidation-dominant pattern showed a relatively high eosinophil fraction of 5.2%; and those with a crazy paving pattern showed elevated eosinophil and neutrophil fractions of 19.1% and 9.9%, respectively. CONCLUSIONS: In this study, a remarkable difference in radiological findings was observed among different BALF patterns.

EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.

Gene expression profiling is nowadays routinely performed on clinically relevant samples (e.g., from tumor specimens). Such measurements are often obtained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor in downstream analyses.Many tools have been developed to estimate the proportion of the different cell types from bulk gene expression data. Here, we provide guidelines and examples on how to use these tools, with a special focus on our recent computational method EPIC (Estimating the Proportions of Immune and Cancer cells). EPIC includes RNA-seq-based gene expression reference profiles from immune cells and other nonmalignant cell types found in tumors. EPIC can additionally manage user-defined gene expression reference profiles. Some unique features of EPIC include the ability to account for an uncharacterized cell type, the introduction of a renormalization step to account for different mRNA content in each cell type, and the use of single-cell RNA-seq data to derive biologically relevant reference gene expression profiles. EPIC is available as a web application ( http://epic.gfellerlab.org ) and as an R-package ( https://github.com/GfellerLab/EPIC ).