Real-world Experience With Neurotrophic Tyrosine Receptor Kinase Fusion-positive Tumors and Tropomyosin Receptor Kinase Inhibitors in Veterans.

PURPOSE: Neurotrophic tyrosine receptor kinase 1-3 (NTRK1-3) gene fusions are found in a broad range of tumor types. Clinical trials demonstrated high response rates to tropomyosin receptor kinase (TRK) inhibitors in NTRK fusion-positive cancers, but few reports have described real-world experience with these targeted agents. We evaluated the prevalence of NTRK fusions and the outcomes with TRK inhibitor therapy in a real-world population of patients in the Veterans Health Administration. METHODS: Patients with NTRK fusions or rearrangements were identified from the Veterans Affairs (VA) National Precision Oncology Program (NPOP), and patients who were prescribed TRK inhibitors were identified from the Corporate Data Warehouse. Baseline data and clinical outcomes were obtained by retrospective review of medical records. RESULTS: A total of 33 patients with NTRK fusions or rearrangements were identified, including 25 patients comprising 0.12% of all patients with solid tumors sequenced through VA NPOP. Twelve patients with NTRK fusions or rearrangements were treated with TRK inhibitors, none of whom had objective responses. Eight patients experienced toxicities leading to drug interruption, dose reduction, or discontinuation. CONCLUSION: In this retrospective study of VA patients, NTRK fusions and rearrangements were less common than in previous studies, and objective responses to TRK inhibitors were not observed. Real-world experience with TRK inhibitors differs markedly from clinical trial findings, possibly due to differences in patient demographics, tumor types, and sequencing methods. Our findings highlight the need to study TRK inhibitors in the real-world setting and in populations underrepresented in clinical trials.

Molecular-Guided Off-Label Targeted Therapy in a Large-Scale Precision Oncology Program.

PURPOSE: Increasing utilization of comprehensive genomic profiling (CGP) and a growing number of targeted agents (TAs) have led to substantial improvements in outcomes among patients with cancer with actionable mutations. We sought to evaluate real-world experience with off-label TAs among Veterans who underwent CGP. METHODS: The National Precision Oncology Program database and VA Corporate Data Warehouse were queried to identify patients who underwent CGP between February 2019 and December 2021 and were prescribed 1 of 73 TAs for malignancy. OncoKB annotations were used to select patients who received off-label TAs based upon CGP results. Chart abstraction was performed to review response, toxicities, and time to progression. RESULTS: Of 18,686 patients who underwent CGP, 2,107 (11%) were prescribed a TA and 169 (0.9%) were prescribed a total of 183 regimens containing off-label TAs for variants in 31 genes. Median age was 68 years and 83% had prior systemic therapy, with 28% receiving three or more lines. Frequency of off-label TA prescriptions was highest for patients undergoing CGP for thyroid (8.6%) and breast (7.6%) cancers. Most patients harbored alterations in BRCA1/BRCA2/ATM (22.5%), ERBB2 (19.5%), and BRAF (19.5%). Among the 160 regimens prescribed > 4 weeks, 43 (27%) led to response. Median progression-free survival and overall survival were 5.3 (4.2-6.5) and 9.7 (7.5-11.9) months, respectively. Patients with OncoKB level 2/3A/3B annotations had longer median progression-free survival (5.8 [4.5-7] months v 3.7 [1.6-7.7] months; hazard ratio, 0.45; 95% CI, 0.24 to 0.82; P = .01) compared with those receiving level 4 treatments. CONCLUSION: Although administration of off-label TAs is infrequent after CGP, more than one quarter of treatment regimens led to response. TAs associated with level 4 annotations lead to worse outcomes than TAs bearing higher levels of evidence.

Analysis of actionable genetic alterations in lung carcinoma from the VA National Precision Oncology Program.

Lung cancer is the leading cause of cancer mortality in men and women. Genomic sequencing of non-small cell lung cancer (NSCLC) is critical for the optimal treatment of NSCLC. In this study we sought to describe the frequencies of highly actionable driver mutations in lung adenocarcinoma (LUAD), squamous cell (LUSQ) and other NSCLC histologies (LUOT) in Veterans tested through the VA's National Precision Oncology Program (NPOP) and compare these frequencies to other published datasets from highly specialized academic cancer centers. The NPOP cohort included 3,376 unique Veterans with a diagnosis of lung carcinoma tested between February 2019 and January 2021 including 1892 with LUAD, 940 with LUSQ, and 549 with LUOT. Among patients with LUAD, 27.5% had highly actionable genetic variants. The frequency of targetable mutations was as follows: ALK rearrangement 0.8%, BRAF V600E 2.1%, EGFR exon 20 insertion mutation 0.48%, EGFR sensitizing mutations 6.6%, ERBB2 small variants 1.2%, KRAS G12C 14.0%, MET exon 14 skipping mutation 1.5%, NTRK rearrangement 0.1%, RET rearrangement 0.4%, and ROS1 rearrangement 0.3%. The frequency of EGFR mutations, RET rearrangement, MET exon 14 and ERBB2 small variants frequencies were significantly lower in NPOP compared to other published reports while MET amplification was more common in NPOP. Combined rates of highly actionable genetic variants were 2.7% and 13.4% in LUSQ and LUOT, respectively. In this study, 27.5% of Veterans with lung adenocarcinoma have actionable genetic alterations eligible for FDA approved targeted therapies, a frequency only slightly lower than other published datasets despite higher smoking rates in Veterans. Genomic sequencing should be performed in all Veterans with advanced LUAD and LUOT.

Association of STS database variables with repair durability in ischemic mitral regurgitation using machine learning.

BACKGROUND: Machine learning (ML) can identify nonintuitive clinical variable combinations that predict clinical outcomes. To assess the potential predictive contribution of standardized Society of Thoracic Surgeons (STS) Database clinical variables, we used ML to detect their association with repair durability in ischemic mitral regurgitation (IMR) patients in a single institution study. METHODS: STS Database variables (n = 53) served as predictors of repair durability in ML modeling of 224 patients who underwent surgical revascularization and mitral valve repair for IMR. Follow-up mortality and echocardiography data allowed 1-year outcome analysis in 173 patients. Supervised ML analyses were performed using recurrence (≥3+ IMR) or death versus nonrecurrence (<3+ IMR) as the binary outcome classification. RESULTS: We tested standard ML and deep learning algorithms, including support vector machines, logistic regression, and deep neural networks. Following training, final models were utilized to predict class labels for the patients in the test set, producing receiver operating characteristic (ROC) curves. The three models produced similar area under the curve (AUC), and predicted class labels with promising accuracy (AUC = 0.72-0.75). CONCLUSIONS: Readily-available STS Database variables have potential to play a significant role in the development of ML models to direct durable surgical therapy in IMR patients.

Women and ethnoracial minorities with poor cardiovascular health measures associated with a higher risk of developing mood disorder.

BACKGROUND: Mood disorders (MDS) are a type of mental health illness that effects millions of people in the United States. Early prediction of MDS can give providers greater opportunity to treat these disorders. We hypothesized that longitudinal cardiovascular health (CVH) measurements would be informative for MDS prediction. METHODS: To test this hypothesis, the American Heart Association's Guideline Advantage (TGA) dataset was used, which contained longitudinal EHR from 70 outpatient clinics. The statistical analysis and machine learning models were employed to identify the associations of the MDS and the longitudinal CVH metrics and other confounding factors. RESULTS: Patients diagnosed with MDS consistently had a higher proportion of poor CVH compared to patients without MDS, with the largest difference between groups for Body mass index (BMI) and Smoking. Race and gender were associated with status of CVH metrics. Approximate 46% female patients with MDS had a poor hemoglobin A1C compared to 44% of those without MDS; 62% of those with MDS had poor BMI compared to 47% of those without MDS; 59% of those with MDS had poor blood pressure (BP) compared to 43% of those without MDS; and 43% of those with MDS were current smokers compared to 17% of those without MDS. CONCLUSIONS: Women and ethnoracial minorities with poor cardiovascular health measures were associated with a higher risk of development of MDS, which indicated the high utility for using routine medical records data collected in care to improve detection and treatment for MDS among patients with poor CVH.

Using electronic health records and claims data to identify high-risk patients likely to benefit from palliative care.

OBJECTIVES: Palliative care has been demonstrated to have positive effects for patients, families, health care providers, and health systems. Early identification of patients who are likely to benefit from palliative care would increase opportunities to provide these services to those most in need. This study predicted all-cause mortality of patients as a surrogate for patients who could benefit from palliative care. STUDY DESIGN: Claims and electronic health record (EHR) data for 59,639 patients from a large integrated health care system were utilized. METHODS: A deep learning algorithm-a long short-term memory (LSTM) model-was compared with other machine learning models: deep neural networks, random forest, and logistic regression. We conducted prediction analyses using combined claims data and EHR data, only claims data, and only EHR data, respectively. In each case, the data were randomly split into training (80%), validation (10%), and testing (10%) data sets. The models with different hyperparameters were trained using the training data, and the model with the best performance on the validation data was selected as the final model. The testing data were used to provide an unbiased performance evaluation of the final model. RESULTS: In all modeling scenarios, LSTM models outperformed the other 3 models, and using combined claims and EHR data yielded the best performance. CONCLUSIONS: LSTM models can effectively predict mortality by using a combination of EHR data and administrative claims data. The model could be used as a promising clinical tool to aid clinicians in early identification of appropriate patients for palliative care consultations.

Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data. METHODS: The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks - to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric. RESULTS: The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures. CONCLUSIONS: We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.

Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain.

The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal: anterior, posterolateral and (2) mid: posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.

Salvianolic acid B protects against sepsis-induced liver injury via activation of SIRT1/PGC-1α signaling.

Liver injury occurs frequently during sepsis, which leads to high mortality and morbidity. A previous study has suggested that salvianolic acid B (SalB) is protective against sepsis-induced lung injury. However, whether SalB is able to protect against sepsis-induced liver injury remains unclear. The present study aimed to investigate the effects of SalB on sepsis-induced liver injury and its potential underlying mechanisms. Sepsis was induced in mice using a cecal ligation and puncture (CLP) method. The mice were treated with SalB (30 mg/kg intraperitoneally) at 0.5, 2 and 8 h after CLP induction. Pathological alterations of the liver were assessed using hematoxylin and eosin staining. The serum levels of alanine transaminase (ALT), aspartate aminotransferase (AST), tumor necrosis factor (TNF)-α and interleukin (IL)-6 were measured. The hepatic mRNA levels of TNF-α, IL-6, Bax and Bcl-2 were also detected. The results suggested that treatment with SalB ameliorated sepsis-induced liver injury in the mice, as supported by the mitigated pathologic changes and lowered serum aminotransferase levels. SalB also decreased the levels of the inflammatory cytokines TNF-α and IL-6 in the serum and the liver of the CLP model mice. In addition, SalB significantly downregulated Bax expression and upregulated Bcl-2 expression, and upregulated the expression levels of SIRT1 and PGC-1α. However, when sirtuin 1 (SIRT1) small interfering RNA was co-administered with SalB, the protective effects of SalB were attenuated and the expression levels of SIRT1 and PGC-1α were reduced. In summary, these results indicate that SalB mitigates sepsis-induced liver injury via reduction of the inflammatory response and hepatic apoptosis, and the underlying mechanism may be associated with the activation of SIRT1/PGC-1α signaling.

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage.

BACKGROUND: Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better understand risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients. METHODS: We used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups. RESULTS: Model performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings. CONCLUSION: Deep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers).

Coronary heart disease and mortality following a breast cancer diagnosis.

BACKGROUND: Coronary heart disease (CHD) is a leading cause of morbidity and mortality for breast cancer survivors, yet the joint effect of adverse cardiovascular health (CVH) and cardiotoxic cancer treatments on post-treatment CHD and death has not been quantified. METHODS: We conducted statistical and machine learning approaches to evaluate 10-year risk of these outcomes among 1934 women diagnosed with breast cancer during 2006 and 2007. Overall CVH scores were classified as poor, intermediate, or ideal for 5 factors, smoking, body mass index, blood pressure, glucose/hemoglobin A1c, and cholesterol from clinical data within 5 years prior to the breast cancer diagnosis. The receipt of potentially cardiotoxic breast cancer treatments was indicated if the patient received anthracyclines or hormone therapies. We modeled the outcomes of post-cancer diagnosis CHD and death, respectively. RESULTS: Results of these approaches indicated that the joint effect of poor CVH and receipt of cardiotoxic treatments on CHD (75.9%) and death (39.5%) was significantly higher than their independent effects [poor CVH (55.9%) and cardiotoxic treatments (43.6%) for CHD, and poor CVH (29.4%) and cardiotoxic treatments (35.8%) for death]. CONCLUSIONS: Better CVH appears to be protective against the development of CHD even among women who had received potentially cardiotoxic treatments. This study determined the extent to which attainment of ideal CVH is important not only for CHD and mortality outcomes among women diagnosed with breast cancer.

Effects of iron deficiency and exogenous sucrose on the intermediates of chlorophyll biosynthesis in Malus halliana.

Malus halliana is an iron (Fe)-efficient apple rootstock growing in calcareous soil that shows obvious 'greenness' traits during Fe deficiency. Recent studies have shown that exogenous sugars can be involved in abiotic stress. To identify the key regulatory steps of chlorophyll (Chl) biosynthesis in M. halliana under Fe deficiency and to verify whether exogenous sucrose (Suc) is involved in Fe deficiency stress, we determined the contents of the Chl precursor and the expression of several Chl biosynthetic genes in M. halliana. The results showed that Fe deficiency caused a significant increase in the contents of protoporphyrin IX (Proto IX), Mg-protoporphyrin IX (Mg-Proto IX) and protochlorophyllide (Pchlide) in M. halliana compared to the Fe-sensitive rootstock Malus hupehensis. Quantitative real-time PCR (RT-qPCR) also showed that the expression of protoporphyrinogen oxidase (PPOX), which synthesizes Proto IX, was upregulated in M. halliana and downregulated in M. hupehensis under Fe deficiency. Exogenous Suc application prominently enhanced the contents of porphobilinogen (PBG) and the subsequent precursor, whereas it decreased the level of δ-aminolaevulinic acid (ALA), suggesting that the transformation from ALA to PBG was catalyzed in M. halliana. Additionally, the transcript level of δ-aminolevulinate acid dehydratase (ALAD) was noticeably upregulated after exogenous Suc treatment. This result, combined with the precursor contents, indicated that Suc accelerated the steps of Chl biosynthesis by modulating the ALAD gene. Therefore, we conclude that PPOX is the key regulatory gene of M. halliana in response to Fe deficiency. Exogenous Suc enhances M. halliana tolerance to Fe deficiency stress by regulating Chl biosynthesis.

Transcriptome analysis in Malus halliana roots in response to iron deficiency reveals insight into sugar regulation.

Iron (Fe) deficiency is a frequent nutritional problem limiting apple production in calcareous soils. The utilization of rootstock that is resistant to Fe deficiency is an effective way to solve this problem. Malus halliana is an Fe deficiency-tolerant rootstock; however, few molecular studies have been conducted on M. halliana. In the present work, a transcriptome analysis was combined with qRT-PCR and sugar measurements to investigate Fe deficiency responses in M. halliana roots at 0 h (T1), 12 h (T2) and 72 h (T3) after Fe deficiency stress. Total of 2473, 661, and 776 differentially expressed genes (DEGs) were identified in the pairs of T2 vs. T1, T3 vs. T1, and T3 vs. T2, respectively. Several DEGs were enriched in the photosynthesis, glycolysis and gluconeogenesis, tyrosine metabolism and fatty acid degradation pathways. The glycolysis and photosynthesis pathways were upregulated under Fe deficiency. In this experiment, sucrose accumulated in Fe-deficient roots and leaves. However, the glucose content significantly decreased in the roots, while the fructose content significantly decreased in the leaves. Additionally, 15 genes related to glycolysis and sugar synthesis and sugar transport were selected to validate the accuracy of the transcriptome data by qRT-PCR. Overall, these results indicated that sugar synthesis and metabolism in the roots were affected by Fe deficiency. Sugar regulation is a way by which M. halliana responds to Fe deficiency stress.

Transcriptional and physiological analyses of short-term Iron deficiency response in apple seedlings provide insight into the regulation involved in photosynthesis.

BACKGROUND: Iron (Fe) is an essential micronutrient for plants. Utilization of Fe deficiency-tolerant rootstock is an effective strategy to prevent Fe deficiency problems in fruit trees production. Malus halliana is an apple rootstock that is resistant to Fe deficiency; however, few molecular studies have been conducted on M. halliana. RESULTS: To evaluate short-term molecular response of M. halliana leaves under Fe deficiency condition, RNA sequencing (RNA-Seq) analyses were conducted at 0 (T1), 0.5 (T2) and 3 d (T3) after Fe-deficiency stress, and the timepoints were determined with a preliminary physiological experiment. In all, 6907, 5328, and 3593 differentially expressed genes (DEGs) were identified in pairs of T2 vs. T1, T3 vs. T1, and T3 vs. T2. Several of the enriched DEGs were related to heme binding, Fe ion binding, thylakoid membranes, photosystem II, photosynthesis-antenna protein, porphyrin and chlorophyll metabolism and carotenoid biosynthesis under Fe deficiency, which suggests that Fe deficiency mainly affects the photosynthesis of M. halliana. Additionally, we found that Fe deficiency induced significant down-regulation in genes involved in photosynthesis at T2 when seedlings were treated with Fe-deficient solution for 0.5 d, indicating that there was a rapid response of M. halliana to Fe deficiency. A strong up-regulation of photosynthesis genes was detected at T3, which suggested that M. halliana was able to recover photosynthesis after prolonged Fe starvation. A similar expression pattern was found in pigment regulation, including genes for coding chlorophyllide a oxygenase (CAO), ß-carotene hydroxylase (ß-OHase), zeaxanthin epoxidase (ZEP) and 9-cis-epoxycarotenoid dioxygenase (NCED). Our results suggest that pigment regulation plays an important role in the Fe deficiency response. In addition, we verified sixteen genes related to photosynthesis-antenna protein, porphyrin and chlorophyll metabolism and carotenoid biosynthesis pathways using quantitative real-time PCR (qRT-PCR) to ensure the accuracy of transcriptome data. Photosynthetic parameters, Chl fluorescence parameters and the activity of Chlase were also determined. CONCLUSIONS: This study broadly characterizes a molecular mechanism in which pigment and photosynthesis-related regulations play indispensable roles in the response of M. halliana to short-term Fe deficiency and provides a basis for future analyses of the key genes involved in the tolerance of Fe deficiency.

[Compound huangdai tablet as induction therapy for 193 patients with acute promyelocytic leukemia].

OBJECTIVE: To report the results of curative and adverse effects of compound huangdai tablet (CHDT) as induction therapy for 193 patients with acute promyelocytic leukemia (APL). METHODS: CHDT was administered 1.25 g orally three times a day after meal for three days, then the dosage was gradually increased to 7.5 g/d. RESULTS: One hundred and ninety-three patients achieved complete remission (CR), 78.8% of whom in 30 to 60 days with an average time of 44.3 d. No serious infection, bleeding or DIC occurred during the treatment course. The major adverse effects were gastrointestinal symptoms. There was no change in lanine transaminase, urea, creatinine or electrocardiographic QTc interval in 110 APL patients observed before and after the treatment. CONCLUSION: CHDT therapy is a modality of higher CR rate, good safety and tolerance without bone marrow suppression for APL patients.

[Effects of inactivated rabbit serum containing compound realgar and natural indigo tablet on cell line NB4].

OBJECTIVE: To explore the effects of inactivated rabbit serum containing compound realgar and natural indigo tablet (CRNIT) on cell line NB(4). METHODS: The experimental rabbits were taken as the provider of the animal serum, and the serum was inactivated before the experiment. The serum was divided into two groups based on whether the rabbits were given CRNIT. The concentration of arsenic in the rabbit's serum was detected by AFS-230a double path atom fluorescence photometer. The inhibition rates and apoptosis rates were regarded as the observational indexes. RESULTS: The concentration of arsenic in the inactivated rabbit serum containing and not containing the drug were (0.010 0+/-0.001 0) mg/L and (0.110 0+/-0.006 4) mg/L respectively, and the difference had statistical significance (P<0.01). The two groups of serum all had inhibitory effect on the growth of NB(4) cells depending on the drug concentration and effect time. And there were significant differences among the groups. The two groups of serum all induced the apoptosis of NB(4) with positive relations with the concentration and effect time. And there were significant differences among the groups. CONCLUSIONS: The rabbit serum containing CRNIT can obviously restrain the growth of NB(4) cells and the inhibitory effect depends on the concentration and effect time. And the rabbit serum containing CRNIT can also induce the apoptosis of NB(4) cell line and the apoptosis rates depend on the concentration and effect time.

[Study on Realgar inducing apoptosis in T lymphocytic cell line CEM].

OBJECTIVE: To study the Realgar induced T lymphocytic leukemia cell line CEM apoptosis in vitro. METHODS: CEM cells were incubated with Realgar. Cell proliferation inhibition was determined by MTT. Cell cycle, apoptosis, Apo2.7 and Fas were measured by cytometer. RESULTS: Realgar inhibited the proliferation of CEM cell line. The cells treated with Realgar showed a Sub-G(0)/G(1) apoptotic peak in DNA distribution histogram, increment of Apo2.7 protein expression, and arrested cells in G(2)/M phase, but ineffectiveness on Fas expression. CONCLUSION: The Realgar can induce CEM cell apoptosis in vitro.