Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

A novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging.

Soil heavy metal(loid)s contamination caused by rapid urbanization and industrialization seriously affects human health and hinders the global sustainable development goals (SDGs). Currently, there is a lack of comprehensive human health risk assessment (HHRA) studies for multiple land use types at the regional scale. We propose a practical risk assessment framework that integrates empirical Bayesian kriging (EBK), pollution level analyses, and modified HHRA modeling. The concentrations of copper industry-related metals (Cu, Ni, Cd, As, and Hg) in 332 topsoil samples from the south bank of the Yangtze River in Tongling were investigated. Obvious enrichment of Cu, Cd, As, and Hg was detected, and the average concentration of Cu was 5.24 times higher than the background values. The distribution of heavy metal(loid) pollution was typically high in the south and east, and low in the north and west. The mean errors of interpolation for Cu, Ni, and Hg were 0.84, 1.29, and 0, respectively, and the root mean square errors of interpolation for Cd and As were 1.29 and 0.86, respectively. Non-carcinogenic risks of soil heavy metal(loid)s were assessed as acceptable throughout the studied area. The hazard index decreased in the order As (0.448) > Ni (0.0729) > Cd (0.0136) > Hg (9.04 ×10-4) > Cu (6.41 ×10-4). Nevertheless, the carcinogenic risks of Ni, Cd, and As in 70-80% of the administrative units (AUs) were between 10-6 to 10-4, considered an unacceptable level. Exposure through the oral ingestion route accounted for 88.0-99.2% of the total three exposure routes. It is worth noting that four AUs were considered to be the priority control units, and Ni and As were identified as the priority control soil heavy metal(loid)s. This case demonstrates the feasibility and scientific validity of the new EBK-HHRA framework, which confirms that EBK can effectively predict the spatial distribution patterns of soil heavy metal(loid)s and that modified HHRA models are conducive to risk integration at the regional scale. The EBK-HHRA approach is generic and provides substantial support for risk source identification and risk management of soil heavy metal(loid)s contamination at the regional scale.

Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer.

Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. PURPOSE: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). MATERIAL AND METHODS: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. RESULTS: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). CONCLUSION: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Survival benefit after neoadjuvant or adjuvant radiotherapy for stage II-III gastroesophageal junction adenocarcinoma: A large population-based cohort study.

Objective: The standard treatment for stage II-III gastroesophageal junction adenocarcinoma (GEJA) remains controversial, and the role of radiotherapy (RT) in stage II-III GEJA is unclear. Herein, we aimed to evaluate the prognosis of different RT sequences and identify potential candidates to undergo neoadjuvant RT (NART) or adjuvant RT (ART). Materials and methods: In total, we enrolled 3,492 patients with resectable stage II-III GEJA from the Surveillance, Epidemiology, and End Results (SEER) database, subsequently assigned to three categories: T1-2N+, T3-4N-, and T3-4N+. Survival curves were evaluated using the Kaplan-Meier method along with the log-rank test. We compared survival curves for NART, ART, and non-RT in the three categories. To further determine histological types impacting RT-associated survival, we proposed new categories by combining the tumor, node, and metastasis (TNM) stage with Lauren's classification. Results: ART afforded a significant survival benefit in patients with T1-2N+ and T3-4N+ tumors. In addition, NART conferred a survival advantage in patients with T3-4N+ and T3-4 exhibiting the intestinal type. Notably, ART and NART were both valuable in patients with T3-4N+, although no significant differences between treatment regimens were noted. Conclusions: Both NART and ART can prolong the survival of patients with stage II-III GEJA. Nevertheless, the selection of NART or ART requires a concrete analysis based on the patient's condition.

Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution.

PURPOSE: Cell-free DNA (cfDNA) offers a noninvasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers. EXPERIMENTAL DESIGN: Three simulation datasets were generated from 26 patients with cancer to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n = 76) from patients with cancer (n = 35) with six different cancer types were used for performance validation during various treatments. RESULTS: We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fractions ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fractions ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods. CONCLUSIONS: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.

Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis.

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA's comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes.

Neutrophil-To-Lymphocyte Ratio Predicted Long-Term Chemotherapy Benefits In Stage IIIB-IV Non-Squamous Non-Small Cell Lung Cancer Patients Without Sensitive Mutations.

PURPOSE: To investigate the predictive capability of clinical parameters for long-term chemotherapy benefits among stage IIIB-IV non-squamous non-small cell lung cancer (NSCLC) patients without sensitive mutations. PATIENTS AND METHODS: We investigated the clinical features of 206 stage IIIB-IV non-squamous NSCLC patients without sensitive mutations and assessed their predictive value for disease control rate (DCR) at 6 and 12 months post-treatment. RESULTS: Seventy-two patients received docetaxel and platinum-based chemotherapy while 134 received pemetrexed and platinum-based chemotherapy. The 6-month and 12-month DCR were 33 (45.8%) and 6 (8.3%) in the docetaxel group and 69 (51.5%) and 19 (14.2%) in the pemetrexed group, respectively. Univariate Cox regression revealed that age, sex, smoking history, adrenal gland metastasis, stage IV disease, neutrophil-to-lymphocyte ratio (NLR), and serum albumin were associated with unfavorable progression-free survival (PFS). Age, stage IV disease, and NLR were identified as independent predictors of PFS using multivariate analysis. NLR was the only parameter that could predict 3-month and 6-month DCRs. NLR and age were able to predict 12-month DCR, with NLR presenting a larger area under the curve. Kaplan-Meier curves demonstrated that patients with NLR > 2.231 displayed significantly reduced long-term disease control. The group with higher NLR had more male patients, lower ALB levels, and serum sodium levels as well as higher platelet counts. CONCLUSION: NLR was an independent predictor of long-term chemotherapy benefits among non-squamous NSCLC patients without sensitive mutations. Patients with lower NLR were optimal candidates for chemotherapy. Patients with high NLR may receive alternative treatments or be included in clinical trials.

Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network.

T-cell development from hematopoietic progenitors depends on multiple transcription factors, mobilized and modulated by intrathymic Notch signaling. Key aspects of T-cell specification network architecture have been illuminated through recent reports defining roles of transcription factors PU.1, GATA-3, and E2A, their interactions with Notch signaling, and roles of Runx1, TCF-1, and Hes1, providing bases for a comprehensively updated model of the T-cell specification gene regulatory network presented herein. However, the role of lineage commitment factor Bcl11b has been unclear. We use self-organizing maps on 63 RNA-seq datasets from normal and perturbed T-cell development to identify functional targets of Bcl11b during commitment and relate them to other regulomes. We show that both activation and repression target genes can be bound by Bcl11b in vivo, and that Bcl11b effects overlap with E2A-dependent effects. The newly clarified role of Bcl11b distinguishes discrete components of commitment, resolving how innate lymphoid, myeloid, and dendritic, and B-cell fate alternatives are excluded by different mechanisms.

Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity.

Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.

Ring Finger Protein 14 is a new regulator of TCF/ß-catenin-mediated transcription and colon cancer cell survival.

T-cell factor/lymphoid enhancer factor (TCF/LEF) proteins regulate transcription by recruiting ß-catenin and its associated co-regulators. Whether TCF/LEFs also recruit more factors through independent, direct interactions is not well understood. Here we discover Ring Finger Protein 14 (RNF14) as a new binding partner for all TCF/LEF transcription factors. We show that RNF14 positively regulates Wnt signalling in human cancer cells and in an in vivo zebrafish model by binding to target promoters with TCF and stabilizing ß-catenin recruitment. RNF14 depletion experiments demonstrate that it is crucial for colon cancer cell survival. Therefore, we have identified a key interacting factor of TCF/ß-catenin complexes to regulate Wnt gene transcription.

Scc1 sumoylation by Mms21 promotes sister chromatid recombination through counteracting Wapl.

DNA double-strand breaks (DSBs) fuel cancer-driving chromosome translocations. Two related structural maintenance of chromosomes (Smc) complexes, cohesin and Smc5/6, promote DSB repair through sister chromatid homologous recombination (SCR). Here we show that the Smc5/6 subunit Mms21 sumoylates multiple lysines of the cohesin subunit Scc1. Mms21 promotes cohesin-dependent small ubiquitin-like modifier (SUMO) accumulation at laser-induced DNA damage sites in S/G2 human cells. Cells expressing the nonsumoylatable Scc1 mutant (15KR) maintain sister chromatid cohesion during mitosis but are defective in SCR and sensitive to ionizing radiation (IR). Scc1 15KR is recruited to DNA damage sites. Depletion of Wapl, a negative cohesin regulator, rescues SCR defects of Mms21-deficient or Scc1 15KR-expressing cells. Expression of the acetylation-mimicking Smc3 mutant does not bypass the requirement for Mms21 in SCR. We propose that Scc1 sumoylation by Mms21 promotes SCR by antagonizing Wapl at a step after cohesin loading at DSBs and in a way not solely dependent on Smc3 acetylation.

Specific loss of histone H3 lysine 9 trimethylation and HP1gamma/cohesin binding at D4Z4 repeats is associated with facioscapulohumeral dystrophy (FSHD).

Facioscapulohumeral dystrophy (FSHD) is an autosomal dominant muscular dystrophy in which no mutation of pathogenic gene(s) has been identified. Instead, the disease is, in most cases, genetically linked to a contraction in the number of 3.3 kb D4Z4 repeats on chromosome 4q. How contraction of the 4qter D4Z4 repeats causes muscular dystrophy is not understood. In addition, a smaller group of FSHD cases are not associated with D4Z4 repeat contraction (termed "phenotypic" FSHD), and their etiology remains undefined. We carried out chromatin immunoprecipitation analysis using D4Z4-specific PCR primers to examine the D4Z4 chromatin structure in normal and patient cells as well as in small interfering RNA (siRNA)-treated cells. We found that SUV39H1-mediated H3K9 trimethylation at D4Z4 seen in normal cells is lost in FSHD. Furthermore, the loss of this histone modification occurs not only at the contracted 4q D4Z4 allele, but also at the genetically intact D4Z4 alleles on both chromosomes 4q and 10q, providing the first evidence that the genetic change (contraction) of one 4qD4Z4 allele spreads its effect to other genomic regions. Importantly, this epigenetic change was also observed in the phenotypic FSHD cases with no D4Z4 contraction, but not in other types of muscular dystrophies tested. We found that HP1gamma and cohesin are co-recruited to D4Z4 in an H3K9me3-dependent and cell type-specific manner, which is disrupted in FSHD. The results indicate that cohesin plays an active role in HP1 recruitment and is involved in cell type-specific D4Z4 chromatin regulation. Taken together, we identified the loss of both histone H3K9 trimethylation and HP1gamma/cohesin binding at D4Z4 to be a faithful marker for the FSHD phenotype. Based on these results, we propose a new model in which the epigenetic change initiated at 4q D4Z4 spreads its effect to other genomic regions, which compromises muscle-specific gene regulation leading to FSHD pathogenesis.

Akt-mediated eminent expression of c-FLIP and Mcl-1 confers acquired resistance to TRAIL-induced cytotoxicity to lung cancer cells.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a potential anticancer agent due to its selectivity in killing transformed cells. However, TRAIL can also stimulate the proliferation and metastasis of TRAIL-resistant cancer cells. Thus, acquired TRAIL resistance during TRAIL therapy would shift the patient's treatment from beneficial to detrimental. In this study, we focused on the acquired TRAIL resistance mechanism and showed that the elevated expression of the antiapoptotic factor cellular FLICE-like inhibitory protein (c-FLIP) and the prosurvival Bcl-2 family member myeloid cell leukemia-1 (Mcl-1) underlie the main mechanism of this type of TRAIL resistance in lung cancer cells. Chronic exposure to TRAIL resulted in lung cancer cell resistance to TRAIL-induced cytotoxicity, and this resistance was associated with the increase in the cellular levels of c-FLIP(L) and Mcl-1(L). Overexpresssion of c-FLIP(L) suppressed recruitment of caspase-8 to the death-inducing signaling complex, whereas increased Mcl-1(L) expression blunted the mitochondrial apoptosis pathway. The elevation of c-FLIP(L) and Mcl-1(L) expression was due to Akt-mediated stabilization of these proteins in TRAIL-resistant cells. Importantly, suppressing c-FLIP(L) and Mcl-1(L) expression by RNA interference collectively alleviated acquired TRAIL resistance. Taken together, these results identify c-FLIP(L) and Mcl-1(L) as the major determinants of acquired TRAIL resistance and could be molecular targets for improving the therapeutic value of TRAIL against lung cancer.

Inhibitors of the proteasome suppress homologous DNA recombination in mammalian cells.

Proteasome inhibitors are novel antitumor agents against multiple myeloma and other malignancies. Despite the increasing clinical application, the molecular basis of their antitumor effect has been poorly understood due to the involvement of the ubiquitin-proteasome pathway in multiple cellular metabolisms. Here, we show that treatment of cells with proteasome inhibitors has no significant effect on nonhomologous end joining but suppresses homologous recombination (HR), which plays a key role in DNA double-strand break (DSB) repair. In this study, we treat human cells with proteasome inhibitors and show that the inhibition of the proteasome reduces the efficiency of HR-dependent repair of an artificial HR substrate. We further show that inhibition of the proteasome interferes with the activation of Rad51, a key factor for HR, although it does not affect the activation of ATM, gammaH2AX, or Mre11. These data show that the proteasome-mediated destruction is required for the promotion of HR at an early step. We suggest that the defect in HR-mediated DNA repair caused by proteasome inhibitors contributes to antitumor effect, as HR plays an essential role in cellular proliferation. Moreover, because HR plays key roles in the repair of DSBs caused by chemotherapeutic agents such as cisplatin and by radiotherapy, proteasome inhibitors may enhance the efficacy of these treatments through the suppression of HR-mediated DNA repair pathways.

Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes.

A critical step in protein biomarker discovery is the ability to contrast proteomes, a process referred generally as quantitative proteomics. While stable-isotope labeling (e.g., ICAT, 18O- or 15N-labeling, or AQUA) remains the core technology used in mass spectrometry-based proteomic quantification, increasing efforts have been directed to the label-free approach that relies on direct comparison of peptide peak areas between LC-MS runs. This latter approach is attractive to investigators for its simplicity as well as cost effectiveness. In the present study, the reproducibility and linearity of using a label-free approach to highly complex proteomes were evaluated. Various amounts of proteins from different proteomes were subjected to repeated LC-MS analyses using an ion trap or Fourier transform mass spectrometer. Highly reproducible data were obtained between replicated runs, as evidenced by nearly ideal Pearson's correlation coefficients (for ion's peak areas or retention time) and average peak area ratios. In general, more than 50% and nearly 90% of the peptide ion ratios deviated less than 10% and 20%, respectively, from the average in duplicate runs. In addition, the multiplicity ratios of the amounts of proteins used correlated nicely with the observed averaged ratios of peak areas calculated from detected peptides. Furthermore, the removal of abundant proteins from the samples led to an improvement in reproducibility and linearity. A computer program has been written to automate the processing of data sets from experiments with groups of multiple samples for statistical analysis. Algorithms for outlier-resistant mean estimation and for adjusting statistical significance threshold in multiplicity of testing were incorporated to minimize the rate of false positives. The program was applied to quantify changes in proteomes of parental and p53-deficient HCT-116 human cells and found to yield reproducible results. Overall, this study demonstrates an alternative approach that allows global quantification of differentially expressed proteins in complex proteomes. The utility of this method to biomarker discovery is likely to synergize with future improvements in the detecting sensitivity of mass spectrometers.

Interferon-gamma-induced gene expression in CD34 cells: identification of pathologic cytokine-specific signature profiles.

Hematopoietic effects of interferon-gamma (IFN-gamma) may be responsible for certain aspects of the pathology seen in bone marrow failure syndromes, including aplastic anemia (AA), paroxysmal nocturnal hemoglobinuria (PNH), and some forms of myelodysplasia (MDS). Overexpression of and hematopoietic inhibition by IFN-gamma has been observed in all of these conditions. In vitro, IFN-gamma exhibits strong inhibitory effects on hematopoietic progenitor and stem cells. Previously, we have studied the transcriptome of CD34 cells derived from patients with bone marrow failure syndromes and identified characteristic molecular signatures common to some of these conditions. In this report, we have investigated genome-wide expression patterns after exposure of CD34 and bone marrow stroma cells derived from normal bone marrow to IFN-gamma in vitro and have detected profound changes in the transcription profile. Some of these changes were concordant in both stroma and CD34 cells, whereas others were specific to CD34 cells. In general, our results were in agreement with the previously described function of IFN-gamma in CD34 cells involving activation of apoptotic pathways and immune response genes. Comparison between the IFN-gamma transcriptome in normal CD34 cells and changes previously detected in CD34 cells from AA and PNH patients reveals the presence of many similarities that may reflect molecular signature of in vivo IFN-gamma exposure.