Citations

Purpose Factors associated with early mortality after surgery and treatment with adjuvant chemotherapy in colon cancer are poorly understood. We aimed to characterize the determinants of early mortality in a large cohort of colon cancer trial participants. Methods A pooled analysis of 37,568 patients in 25 randomized trials of adjuvant systemic therapy was conducted. Multivariable logistic regression models with several definitions of early mortality (30, 60, and 90 days, and 6 months) were constructed, adjusting for clinically and statistically significant variables. A nomogram for 6-month mortality was developed and validated. Results Median age among patients was 61 years, patient demographics included 54% men and 90% White, 29% and 71% had stage II and III disease, respectively, and 79%, 20%, and 1% had an Eastern Cooperative Oncology Group performance status (PS) of 0, 1, and ≥ 2, respectively. Early mortality was low: 0.3% at 30 days, 0.6% at 60 days, 0.8% at 90 days, and 1.4% at 6 months. Of those patients who died by 6 months post–random assignment, 40% had documented disease recurrence prior to death. Early disease recurrence was associated with a markedly increased risk of death during the first 6 months post-treatment (hazard ratio, 82.6; 95%CI, 66.9 to 102.1). In prognostic analyses, advanced age, male sex, poorer PS, increasing ratio of positive to examined lymph nodes, earlier decade of enrollment, and higher tumor stage and grade predicted a greater likelihood of early mortality, whereas treatment received was not strongly predictive. A multivariable model for 6-month mortality showed strong optimism-adjusted discrimination (concordance index, 0.73) and calibration. Conclusion Early mortality was infrequent but more prevalent in patients with advanced age and a PS of ≥ 2, underscoring the need to carefully consider the risk-to-benefit ratio when making treatment decisions in these subgroups.

Abstract Purpose: Responses to therapy with chimeric antigen receptor T cells recognizing CD19 (CART19, CTL019) may vary by histology. Mantle cell lymphoma (MCL) represents a B-cell malignancy that remains incurable despite novel therapies such as the BTK inhibitor ibrutinib, and where data from CTL019 therapy are scant. Using MCL as a model, we sought to build upon the outcomes from CTL019 and from ibrutinib therapy by combining these in a rational manner. Experimental Design: MCL cell lines and primary MCL samples were combined with autologous or normal donor-derived anti-CD19 CAR T cells along with ibrutinib. The effect of the combination was studied in vitro and in mouse xenograft models. Results: MCL cells strongly activated multiple CTL019 effector functions, and MCL killing by CTL019 was further enhanced in the presence of ibrutinib. In a xenograft MCL model, we showed superior disease control in the CTL019- as compared with ibrutinib-treated mice (median survival not reached vs. 95 days, P < 0.005) but most mice receiving CTL019 monotherapy eventually relapsed. Therefore, we added ibrutinib to CTL019 and showed that 80% to 100% of mice in the CTL019 + ibrutinib arm and 0% to 20% of mice in the CTL019 arm, respectively, remained in long-term remission (P < 0.05). Conclusions: Combining CTL019 with ibrutinib represents a rational way to incorporate two of the most recent therapies in MCL. Our findings pave the way to a two-pronged therapeutic strategy in patients with MCL and other types of B-cell lymphoma. Clin Cancer Res; 22(11); 2684–96. ©2016 AACR.

Purpose Fluorouracil plus leucovorin (FU + LV) adjuvant chemotherapy reduced the risk of recurrence and death across all time points in a pooled analysis of 20,898 patients with colon cancer from 18 randomized studies. The impact of oxaliplatin added to FU + LV on the time course of recurrence and survival remains unknown. Patients and Methods A total of 12,233 patients enrolled to the randomized trials C-07, C-08, N0147, MOSAIC (Adjuvant Treatment of Colon Cancer), and XELOXA (Adjuvant XELOX) were pooled to examine the impact of oxaliplatin and tumor-specific factors on the time course of recurrence and death. For each end point, continuous-time risk was modeled over 6 years post treatment in all oxaliplatin-treated patients and patients concurrently randomized to FU + LV with or without oxaliplatin; the latter analyses supported time-dependent treatment comparisons. Results Addition of oxaliplatin significantly reduced the risk of recurrence within the first 14 months post treatment for patients with stage II disease and within the first 4 years for patients with stage III disease. Oxaliplatin also significantly reduced risk of death from 2 to 6 years post treatment for patients with stage III disease, with no differences in timing of outcomes between treatment groups (ie, oxaliplatin did not simply postpone recurrence or death compared with FU + LV alone). Patients with stage II disease receiving oxaliplatin did not exhibit a significant reduction in risk of death in the first 6 years post treatment. Recurrence risk peaked near 14 months for both treatments, and risk of recurrence and death increased with increased tumor and nodal burden. Conclusions These analyses support the addition of oxaliplatin to fluoropyrimidine-based adjuvant therapy in patients with stage III disease and underscore the need for adequate surveillance of patients with colon cancer during the first 3 years after adjuvant therapy.

AbstractBACKGROUNDFor many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope–labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays.CONTENTThe Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials—in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry—is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

Purpose In recent retrospective analyses of early-stage colorectal cancer (CRC), low and high body mass index (BMI) scores were associated with worsened outcomes. Whether BMI is a prognostic or predictive factor in metastatic CRC (mCRC) is unclear. Patients and Methods Individual data from 21,149 patients enrolled onto 25 first-line mCRC trials during 1997 to 2012 were pooled. We assessed both prognostic and predictive effects of BMI on overall survival and progression-free survival, and we accounted for patient and tumor characteristics and therapy type (targeted v nontargeted). Results BMI was prognostic for overall survival (P < .001) and progression-free survival (P < .001), with an L-shaped pattern. That is, risk of progression and/or death was greatest for low BMI; risk decreased as BMI increased to approximately 28 kg/m 2 , and then it plateaued. Relative to obese patients, patients with a BMI of 18.5 kg/m 2 had a 27% increased risk of having a PFS event (95% CI, 20% to 34%) and a 50% increased risk of death (95% CI, 43% to 56%). Low BMI was associated with poorer survival for men than women (interaction P < .001). BMI was not predictive of treatment effect. Conclusion Low BMI is associated with an increased risk of progression and death among the patients enrolled on the mCRC trials, with no increased risk for elevated BMI, in contrast to the adjuvant setting. Possible explanations include negative effects related to cancer cachexia in patients with low BMI, increased drug delivery or selection bias in patients with high BMI, and potential for an interaction between BMI and molecular signaling pathways.

There are clear epidemiological associations between circadian disruption, obesity, and pathogenesis of type 2 diabetes. The mechanisms driving these associations are unclear. In the current study, we hypothesized that continuous exposure to constant light (LL) compromises pancreatic β-cell functional and morphological adaption to diet-induced obesity leading to development of type 2 diabetes. To address this hypothesis, we studied wild type Sprague Dawley as well as Period-1 luciferase reporter transgenic rats (Per1-Luc) for 10 weeks under standard light-dark cycle (LD) or LL with concomitant ad libitum access to either standard chow or 60% high-fat diet (HFD). Exposure to HFD led to a comparable increase in food intake, body weight, and adiposity in both LD- and LL-treated rats. However, LL rats displayed profound loss of behavioral circadian rhythms as well as disrupted pancreatic islet clock function characterized by the impairment in the amplitude and the phase islet clock oscillations. Under LD cycle, HFD did not adversely alter diurnal glycemia, diurnal insulinemia, β-cell secretory function as well as β-cell survival, indicating successful adaptation to increased metabolic demand. In contrast, concomitant exposure to LL and HFD resulted in development of hyperglycemia characterized by loss of diurnal changes in insulin secretion, compromised β-cell function, and induction of β-cell apoptosis. This study suggests that circadian disruption and diet-induced obesity synergize to promote development of β-cell failure, likely mediated as a consequence of impaired islet clock function.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Circadian regulation of glucose homeostasis and insulin secretion has long been appreciated as an important feature of metabolic control in humans. Circadian disruption is becoming increasingly prevalent in today's society and is likely responsible in part for the considerable rise in type 2 diabetes ( T2DM ) and metabolic syndrome worldwide. Thus, understanding molecular mechanisms driving the inter‐relationship between circadian disruption and T2DM is important in context of disease prevention and therapeutics. In this regard, the goal of this article is to highlight the role of the circadian system, and islet circadian clocks in particular, as potential regulators of β‐cell function and survival. To date, studies have shown that islet clocks respond to changes in feeding patterns, and regulate a multitude of critical cellular processes in insulin secreting β‐cells (e.g. insulin exocytosis, mitochondrial function and response to oxidative stress). Subsequently, either genetic or environmental disruption of normal islet clock performance compromises β‐cell function and leads to loss of glycaemic control. Future work is warranted to further unravel the role of circadian clocks in human islet function in health and contributions to pathogenesis of T2DM .

Abstract Objective To review and evaluate available software tools for electronic health record–driven phenotype authoring in order to identify gaps and needs for future development. Materials and Methods Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. Results Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. Discussion Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. Conclusions Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.