Mean Survival Time from Right Censored Data

A nonparametric estimate of the mean survival time can be obtained as the area under the Kaplan-Meier estimate of the survival curve. A common modification is to change the largest observation to a death time if it is censored. We conducted a simulation study to assess the behavior of this estimator of the mean survival time in the presence of right censoring. We simulated data from seven distributions: exponential, normal, uniform, lognormal, gamma, log-logistic, and Weibull. This allowed us to compare the results of the estimates to the known true values and to quantify the bias and the variance. Our simulations cover proportions of random censoring from 0% to 90%. The bias of the modified Kaplan-Meier mean estimator increases with the proportion of censoring. The rate of increase varied substantially from distribution to distribution. Distributions with long right tails (log-logistic, log normal, exponential) increased the quickest (i.e., at lower censoring proportions). The other distributions are relatively unbiased until around 60% censoring. The Normal distribution remains unbiased up to 90% censoring. Thus, the behavior of the modified Kaplan-Meier mean estimator depends heavily on the nature of the distribution being estimated. Since we rarely have knowledge of the underlying true distribution, care must be taken when estimating the mean from censored data. With modest censoring, estimates are relatively unbiased, but as censoring increases so does the bias. With 30% or more censoring the bias may be too high. This is in contrast to the Kaplan-Meier estimator of the median which is relatively unbiased

Team-based Classroom Pedagogy Reframed: The Student Perspective

Postsecondary learning environments often utilize team-based pedagogical practices to challenge and support student learning outcomes. This manuscript presents the findings of a qualitative research study that analyzed the viewpoints and perceptions of group or team-based projects among undergraduate business students. Results identified five pro-team thematic perspectives of team learners’ views including better deliverables, increased ideas, improved learning experiences, reduced workload, and collective security. Responses from students who preferred to work autonomously resulted in three themes centered on self-sufficiency, social loafing, and schedule challenges. Two situational student responses were identified regarding how and why faculty should utilize group and team projects in consideration of individual efficiency and assignment objectives and outcomes conflicts. This study concludes with research-based recommendations for teaching, learning, and further research

Characteristics and survival of patients with advanced cancer and p53 mutations.

P53 mutations are associated with invasive tumors in mouse models. We assessed the p53mutations and survival in patients with advanced cancer treated in the Phase I Program. Of 691 tested patients, 273 (39.5%) had p53 mutations. Patients with p53 mutations were older (p<.0001) and had higher numbers of liver metastases (p=.005). P53 mutations were associated with higher numbers of other aberrations; PTEN (p=.0005) and HER2 (p=.003)aberrations were more common in the p53 mutation group. No survival difference was observed between patients with p53 mutations and those with wild-type p53. In patients with wild-type p53 and other aberrations, patients treated with matched-therapy against the additional aberrations had longer survival compared to those treated with non-matched-therapy or those who received no therapy (median survival, 26.0 vs. 11.8 vs. 9.8 months, respectively; p= .0007). Results were confirmed in a multivariate analysis (p= .0002). In the p53 mutation group with additional aberrations, those who received matched-therapy against the additional aberrations had survival similar to those treated with non-matched-therapy or those who received no therapy (p=.15). In conclusion, our results demonstrated resistance to matched-targeted therapy to the other aberrations in patients with p53 mutations and emphasize the need to overcome this resistance

Outcomes of patients with advanced cancer and KRAS mutations in phase I clinical trials.

BackgroundKRAS mutation is common in human cancer. We assessed the clinical factors, including type of KRAS mutation and treatment, of patients with advanced cancer and tumor KRAS mutations and their association with treatment outcomes.MethodsPatients referred to the Phase I Clinic for treatment who underwent testing for KRAS mutations were analyzed.ResultsOf 1,781 patients, 365 (21%) had a KRAS mutation. The G12D mutation was the most common mutation (29%). PIK3CA mutations were found in 24% and 10% of patients with and without KRAS mutations (p<0.0001). Of 223 patients with a KRAS mutation who were evaluable for response, 56 were treated with a MEK inhibitor-containing therapy and 167 with other therapies. The clinical benefit (partial response and stable disease lasting ≥6 months) rates were 23% and 9%, respectively, for the MEK inhibitor versus other therapies (p=0.005). The median progression-free survival (PFS) was 3.3 and 2.2 months, respectively (p=0.09). The respective median overall survival was 8.4 and 7.0 months (p=0.38). Of 66 patients with a KRAS mutation and additional alterations, higher rates of clinical benefit (p=0.04), PFS (p=0.045), and overall survival (p=0.02) were noted in patients treated with MEK inhibitor-containing therapy (n=9) compared to those treated with targeted therapy matched to the additional alterations (n=24) or other therapy (n=33).ConclusionsMEK inhibitors in patients with KRAS-mutated advanced cancer were associated with higher clinical benefit rates compared to other therapies. Therapeutic strategies that include MEK inhibitors or novel agents combined with other targeted therapies or chemotherapy need further investigation

Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems

<p>Abstract</p> <p>Background</p> <p>Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.</p> <p>Results</p> <p>Signature strength had the greatest influence on predictor performance. It was possible to develop almost perfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when the spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for 9 real clinical prediction problems in six different breast cancer data sets.</p> <p>Conclusions</p> <p>We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from ER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical methods efficiently identify highly informative features in gene expression data if such features exist and accurate models can be built with as few as 10 highly informative features. Features can be considered highly informative if at least 2-fold expression difference exists between comparison groups but such features do not appear to be common for many clinically relevant prediction problems in human data sets.</p

Pt/SnO2-based CO-oxidation catalysts for long-life closed-cycle CO2 lasers

Noble-metal/tin-oxide based catalysts such as Pt/SnO2 have been shown to be good catalysts for the efficient oxidation of CO at or near room temperature. These catalysts require a reductive pretreatment and traces of hydrogen or water to exhibit their full activity. Addition of Palladium enhances the activity of these catalysts with about 15 to 20 percent Pt, 4 percent Pd, and the balance SnO2 being an optimum composition. Unfortunately, these catalysts presently exhibit significant decay due in part to CO2 retention, probably as a bicarbonate. Research on minimizing the decay in activity of these catalysts is currently in progress. A proposed mechanism of CO oxidation on Pt/SnO2-based catalysts has been developed and is discussed

Dual EGFR inhibition in combination with anti-VEGF treatment in colorectal cancer.

Preclinical studies demonstrate that epidermal growth factor receptor (EGFR) signals through both kinase-dependent and independent pathways and that combining a small-molecule EGFR inhibitor, EGFR antibody, and/or anti-angiogenic agent is synergistic. We conducted a dose-escalation, phase I study combining erlotinib, cetuximab, and bevacizumab. The subset of patients with metastatic colorectal cancer was analyzed for safety and antitumor activity. Forty-one patients with heavily pretreated metastatic colorectal cancer received treatment on a range of dose levels. The most common treatment-related grade ≥2 adverse events were rash (68%), hypomagnesemia (37%), and fatigue (15%). Thirty of 34 patients (88%) treated at the full FDA-approved doses of all three drugs tolerated treatment without drug-related dose-limiting effects. Eleven patients (27%) achieved stable disease (SD) ≥6 months and three (7%) achieved a partial response (PR) (total SD&gt;6 months/PR= 14 (34%)). Of the 14 patients with SD≥6 months/PR, eight (57%) had received prior sequential bevacizumab and cetuximab, two (5%) had received bevacizumab and cetuximab concurrently, and four (29%) had received prior bevacizumab but not cetuximab or erlotinib (though three had received prior panitumumab). The combination of bevacizumab, cetuximab, and erlotinib was well tolerated and demonstrated antitumor activity in heavily pretreated patients with metastatic colorectal cancer

Establishment of Prognostic Models for Astrocytic and Oligodendroglial Brain Tumors with Standardized Quantification of Marker Gene Expression and Clinical Variables

Background Prognosis models established using multiple molecular markers in cancer along with clinical variables should enable prediction of natural disease progression and residual risk faced by patients. In this study, multivariate Cox proportional hazards analyses were done based on overall survival (OS) of 100 glioblastoma multiformes (GBMs, 92 events), 49 anaplastic astrocytomas (AAs, 33 events), 45 gliomas with oligodendroglial features, including anaplastic oligodendroglioma (AO, 13 events) and oligodendraglioma (O, 9 events). The modeling included two clinical variables (patient age and recurrence at the time of sample collection) and the expression variables of 13 genes selected based on their proven biological and/or prognosis functions in gliomas ( ABCG2, BMI1, MELK, MSI1, PROM1, CDK4, EGFR, MMP2, VEGFA, PAX6, PTEN, RPS9, and IGFBP2 ). Gene expression data was a log-transformed ratio of marker and reference ( ACTB ) mRNA levels quantified using absolute real-time qRT-PCR. Results Age is positively associated with overall grade (4 for GBM, 3 for AA, 2_1 for AO_O), but lacks significant prognostic value in each grade. Recurrence is an unfavorable prognostic factor for AA, but lacks significant prognostic values for GBM and AO_O. Univariate models revealed opposing prognostic effects of ABCG2, MELK, BMI1, PROM1, IGFBP2, PAX6, RPS9 , and MSI1 expressions for astrocytic (GBM and AA) and oligodendroglial tumors (AO_O). Multivariate models revealed independent prognostic values for the expressions of MSI1 (unfavorable) in GBM, CDK4 (unfavorable) and MMP2 (favorable) in AA, while IGFBP2 and MELK (unfavorable) in AO_O. With all 13 genes and 2 clinical variables, the model R 2 was 14.2% ( P = 0.358) for GBM, 45.2% ( P = 0.029) for AA, and 62.2% ( P = 0.008) for AO_O. Conclusion The study signifies the challenge in establishing a significant prognosis model for GBM. Our success in establishing prognosis models for AA and AO_O was largely based on identification of a set of genes with independent prognostic values and application of standardized gene expression quantification to allow formation of a large cohort in analysis

Catalysts for long-life closed-cycle CO2 lasers

Long-life, closed-cycle operation of pulsed CO2 lasers requires catalytic CO-O2 recombination both to remove O2, which is formed by discharge-induced CO2 decomposition, and to regenerate CO2. Platinum metal on a tin (IV) oxide substrate (Pt/SnO2) has been found to be an effective catalyst for such recombination in the desired temperature range of 25 to 100 C. This paper presents a description of ongoing research at NASA-LaRC on Pt/SnO2 catalyzed CO-O2 recombination. Included are studies with rare-isotope gases since rare-isotope CO2 is desirable as a laser gas for enhanced atmospheric transmission. Results presented include: (1) achievement of 98% to 100% conversion of a stoichiometric mixture of CO and O2 to CO2 for 318 hours (greater than 1 x 10 to the 6th power seconds), continuous, at a catalyst temperature of 60 C, and (2) development of a technique verified in a 30-hour test, to prevent isotopic scrambling when CO-18 and O-18(2) are reacted in the presence of a common-isotope Pt/Sn O-16(2) catalyst

Insulin-like growth factor binding protein-3 has dual effects on gastrointestinal stromal tumor cell viability and sensitivity to the anti-tumor effects of imatinib mesylate in vitro

<p>Abstract</p> <p>Background</p> <p>Imatinib mesylate has significantly improved survival and quality of life of patients with gastrointestinal stromal tumors (GISTs). However, the molecular mechanism through which imatinib exerts its anti-tumor effects is not clear. Previously, we found up-regulation of insulin-like growth factor binding protein-3 (IGFBP3) expression in imatinib-responsive GIST cells and tumor samples. Because IGFBP3 regulates cell proliferation and survival and mediates the anti-tumor effects of a number of anti-cancer agents through both IGF-dependent and IGF-independent mechanisms, we hypothesized that IGFBP3 mediates GIST cell response to imatinib. To test this hypothesis, we manipulated IGFBP3 levels in two imatinib-responsive GIST cell lines and observed cell viability after drug treatment.</p> <p>Results</p> <p>In the GIST882 cell line, imatinib treatment induced endogenous IGFBP3 expression, and IGFBP3 down-modulation by neutralization or RNA interference resulted in partial resistance to imatinib. In contrast, IGFBP3 overexpression in GIST-T1, which had no detectable endogenous IGFBP3 expression after imatinib, had no effect on imatinib-induced loss of viability. Furthermore, both the loss of IGFBP3 in GIST882 cells and the overexpression of IGFBP3 in GIST-T1 cells was cytotoxic, demonstrating that IGFBP3 has opposing effects on GIST cell viability.</p> <p>Conclusion</p> <p>This data demonstrates that IGFBP3 has dual, opposing roles in modulating GIST cell viability and response to imatinib <it>in vitro</it>. These preliminary findings suggest that there may be some clinical benefits to IGFBP3 therapy in GIST patients, but further studies are needed to better characterize the functions of IGFBP3 in GIST.</p