BackgroundThe incidence of liver disease is increasing in USA. Animal models had shown glutathione species in plasma reflects liver glutathione state and it could be a surrogate for the detection of hepatocellular carcinoma (HCC).MethodsThe present study aimed to translate methods to the human and to explore the role of glutathione/metabolic prints in the progression of liver dysfunction and in the detection of HCC. Treated plasma from healthy subjects (n = 20), patients with liver disease (ESLD, n = 99) and patients after transplantation (LTx, n = 7) were analyzed by GC- or LC/MS. Glutathione labeling profile was measured by isotopomer analyzes of 2H2O enriched plasma. Principal Component Analyzes (PCA) were used to determined metabolic prints.ResultsThere was a significant difference in glutathione/metabolic profiles from patients with ESLD vs healthy subjects and patients after LTx. Similar significant differences were noted on patients with ESLD when stratified by the MELD score. PCA analyses showed myristic acid, citric acid, succinic acid, l-methionine, d-threitol, fumaric acid, pipecolic acid, isoleucine, hydroxy-butyrate and glycolic, steraric and hexanoic acids were discriminative metabolites for ESLD-HCC+ vs ESLD-HCC− subject status.ConclusionsGlutathione species and metabolic prints defined liver disease severity and may serve as surrogate for the detection of HCC in patients with established cirrhosis.
BackgroundGWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.ResultsApplication of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.ConclusionsOur experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.ReviewersThis article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.
357 Background: Even with the advent of newer systemic therapies; long term survival in advanced PDAC is dismal. Hence there is an urgent need to use technologies such as NGS to identify potential therapeutic targets. Methods: A retrospective analysis was performed of all advanced PDAC patients (pts) evaluated with NGS through Foundation One (Foundation Medicine Inc., MA). DNA was extracted from biopsy specimens and sequencing was performed of 315 cancer-related genes plus select introns. Statistical analysis including Kaplan Meier survival analysis along with the log-rank test was used to compare the differences between groups. Results: 53pts were analyzed between Nov 2012 - Sep 2014; 25 (47%) were females; median age was 59 yrs (range: 33-77). 178 genomic alterations (GAs) were identified in 52 pts (average 3.43 GAs/pt). The GAs included mutations 140 (78%), amplifications 25 (14%), loss/deletions 13 (7%) and 1 pt had no GAs. Most frequent GAs and associated survival are listed in Table 1. There was no statistically significant difference in median overall survival (mOS) between the groups with or without KRAS (22.1 vs. 21 months; p=0.95) and with or without TP53 (21 vs. 23.4 months; p=0.76). 6/52 pts (12%) who had received at least 3 prior lines of therapy (range 3-4) and had an ECOG performance status <2; were treated off label with trametinib. The median PFS on the last therapy prior was 5 months (range 1.6-7). On trametinib, the median PFS was 1.9 months (range 1.1-7.1) with mOS of 5.1 months. Conclusions: Our data demonstrate that there are a range of GAs that are eligible for investigational targeted therapies. Secondly, the choice of agents (eg. Trametinib - targets downstream RAS effectors ) based on individual genomic profile can be considered when an appropriate clinical trial is not available or inaccessible even in heavily pretreated patients with a good performance status. Tumor profiling should be utilized in drug development to personalize treatment options in patients with advanced PDAC. [Table: see text]
Background: Neoadjuvant chemotherapy is used to help downstage cancer, and is widely used in locally-advanced breast cancer. Studies of Ki67 proliferation index in breast cancer have been fairly extensively evaluated. Comparison of core and surgical specimens in breast cancers without exposure to chemo- or radiotherapy revealed technical variation of up to 20% in the Ki67 index score. In non-small cell lung cancer (NSCLC), however, little is known about the association of rate of change of Ki67 after neoadjuvant chemotherapy +/- radiotherapy with radiographic response and clinical outcomes. We surveyed NSCLC from patients treated with neoadjuvant chemotherapy +/- radiotherapy. Methods: NSCLC patients treated with neoadjuvant chemotherapy were identified from 3 Hungarian hospitals and 1 community hospital in the United States. Matched pre-chemotherapy and post-surgical resection formalin-fixed, paraffin-embedded (FFPE) tumor specimens were collected and the Ki67 index was scored under an IRB exemption. We set an absolute difference of 20% between pre-chemotherapy and post-resection Ki67 scores as “meaningful” to avoid possible technical variation reported in the literature (1). Radiographic response to neoadjuvant chemotherapy by RECIST 1.0 criteria was also measured. Fisher's exact test was used to measure the relationship between gender, histology, and type of chemo with “meaningful” Ki67 index. Logistic regression was used to test the relationship between Ki67 index decline rate and outcome subgroup (response/no response). Decline rate was defined as a ratio of decrease of Ki67 to its level at baseline. For univariate analysis, Kaplan-Meier method was used to estimate the survival probability and the log rank test was used to assess the difference in survival between groups. For multivariate analysis, the Cox proportional hazards regression was used. P-values were adjusted for multiple comparison. Results: 63 matched cases were identified. 46 cases were analyzable for pre-chemotherapy and post-operative Ki67, and chemotherapy regimen; 40 cases also had response criteria by RECIST (Table I). Of the 46 cases, the median patient age was 59 years (range 40-77), 24 were men, and 30 of 34 had a smoking history. There were 24 adenocarcinomas and 22 squamous cell carcinomas. Stages I, II, III, and IV were 2, 9, 31, and 4; respectively. All but two patients received a platinum-doublet, with 24 containing gemcitabine. 5 patients also received neoadjuvant radiotherapy. 22 are deceased as of last follow-up. Median overall survival (OS) is 28.5 months (range 7.4-107.7 months). The mean Ki67 index scores were 35% (range 1-100%) pre-chemotherapy and 32% (0-100%) post-resection (see representative example in Figure 1). 9 patients (19.6%) had a paradoxical “meaningful” increase in Ki67 index after neoadjuvant chemotherapy. Of the 40 patients with RECIST response data, there was 1 complete response, 34 partial responses, 4 stable diseases, and 1 disease progression. There were no statistically significant differences between gender, histology subtype, or type of platinum doublet administered associated with this paradoxical increase. There was no statistically significant difference in Ki67 decline rate between responders and non-responders. Additionally, there was no statistically significant association by univariate or multivariate analysis with gender, histology, chemo type (gemcitabine vs. other), paradoxical Ki67 increase, or RECIST response and OS. Conclusion: In this cohort of patients, there was a paradoxical “meaningful” increase in Ki67 index after neoadjuvant chemotherapy in ~20% of patients, without a clear association between histology or platinum doublet administered. For patients receiving neoadjuvant chemotherapy, the Ki67 index decline rate was not associated with radiographic response or OS. Approximately 1/5 of NSCLC may have selection of tumor cells for a higher proliferative index when undergoing neoadjuvant platinum-based chemotherapy, though this does not appear to impact OS. Citation Format: Glen J. Weiss, Judit Moldvay, Balazs Dome, Katalin Fabian, Eszter Podmaniczky, Judit Papay, Marton Gyulai, Jozsef Furak, Ildiko Szirtes, Jizhou Ai, Ryan McCabe, Janine LoBello, Balazs Hegedus. Ki67 proliferation index score paradoxical increase after neoadjuvant therapy in resected NSCLC. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr B29.
Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.
Background. Current standard of care for HR+HER2+ breast cancer >1cm per NCCN guidelines is chemotherapy with either trastuzumab(T) or trastuzumab and pertuzumab(TP) with anti-HER2 therapy continued for 1 year and anti-hormonal therapy for 5-10 years. Patients with HR+HER2+ breast cancer benefit less from chemotherapy than those with HR-HER2+ breast cancer. Recent studies show that pCR rates with aromatase inhibitor and dual-HER2 blockade for 3-6 months duration range between 21%- 33% with the highest pCR seen with 6 months. No clinical trial to date has looked at targeted therapy without chemotherapy for HR+HER2+ breast cancer up to 1 year. Methods. NEOADAPT is an IRB-approved phase II prospective single cohort study for patients with stage I-II HR+HER2+ breast cancer. Eligible pts received neoadjuvant aromatase inhibitor (and GnRH analogue or oophorectomy for premenopausal patients) and TP with q3w exams and q3mo breast MRI. Treatment may continue up to 1 year if ongoing response or radiographic CR (rCR) was observed. Surgery was allowed as soon as 3 months after the first MRI showing rCR provided the follow up MRI did not show further improvement. Primary endpoint was pCR rate defined as absence of invasive disease in the breast and lymph nodes. A Fleming two stage design was implemented with stopping rules with the null hypothesis being that the pCR rate is <20% versus the alternative hypothesis where the pCR rate is >40%. Secondary objective included median duration of treatment and exploratory analysis of correlation with Mammaprint Blueprint(MB) testing. 9 patients were enrolled before the study was prematurely closed. 3 patients who refused chemotherapy when the study was closed and were treated with the NEOADAPT protocol and fit the same inclusion/exclusion criteria are also included in this report. Results. Median age 55 (41-67); BMI 26.0 (22.2-37.6); T 2.75cm (1.3-4.8). 7 were premenopausal. 7 had disease that was HER2+ by FISH ratio >2.0 only (2+ IHC). All 12 patients had MB test done: 8 high risk luminal, 3 high risk HER2, and 1 low risk HER2. 5/12 patients achieved rCR, with median duration of treatment to achieve rCR in those 5 pts being 6 months (3-9). 11/12 patients have completed definitive surgery, with the outlier being a patient who refuses surgery. 4/11 patients achieved pCR all of whom received 12 months of targeted treatment before surgery. All 4 pCR patients had rCR before surgery. Of the other 3/7 pts who had rCR and not pCR, 2 had rCR at 3 months and both had surgery at 6 mo with 1.7cm and 1.5mm of residual disease seen and 1 had rCR at 6mo completed 1 yr of prescribed treatment but refused surgery. 2/12 patients were taken off study before surgery and switched to a standard of care chemotherapy regimen due to progression but remain in NED post-surgery to date. No patients to date have had local recurrence or metastatic disease. Discussion. These results from a prematurely closed study suggest as confirmed in other recent studies, that a chemotherapy-sparing endocrine and targeted neoadjuvant approach for HR+HER2+ breast cancer seems to be a feasible and reasonable option particularly for those desiring a clinically informed de-escalation to their treatment plan. Clinical trial: NCT02689921 Citation Format: Eugene R Ahn, Ricardo H Alvarez, Damien Hansra, Anjanette Sorensen, Jizhou Ai, Maurie Markman. Neoadjuvant aromatase inhibitor and pertuzumab and trastuzumab (NEOADAPT) up to one year for HR+HER2+ breast cancer: Results from a prematurely closed de-escalation study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-18-30.
This study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011–2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: −1 to −365 days and −180 to −365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011–2013, 2011–2015, and 2011–2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011–2017 data from −1 to −365 and −180 to −365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.
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