This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.
Background Quantitative computed tomography (CT) assessment of visceral adiposity may be superior to body mass index (BMI) as a predictor of surgical morbidity. We sought to examine the association of CT measures of obesity and BMI with short-term post-operative outcomes in colon cancer patients. Methods In this retrospective study, 110 patients treated with colectomy for Stage I–III colon cancer were classified as obese or non-obese by pre-operative CT-based measures of adiposity or BMI. [Obese: BMI≥30kg/m2, visceral fat area (VFA) to subcutaneous fat area ratio (V/S) ≥0.4 and VFA>100cm2)]. Post-operative morbidity and mortality rates were compared. Results Obese patients, by V/S and VFA but not BMI, were more likely to be male and have pre-existing hypertension and diabetes. The overall complication rate was 25.5% and there were no mortalities. Obese patients by VFA (with a trend for VS but not BMI) were more likely to develop postoperative complications as compared to patients classified as non-obese; VFA (30.5% vs.10.7%, p= 0.03), VS (29.2% vs. 9.5%, p=0.05) and BMI (32.4% vs. 21.9%, p=0.23). Conclusions Elevated visceral obesity quantified by CT is associated with the presence of key metabolic comorbidities and increased post-operative morbidity and may be superior to BMI for risk stratification.
Introduction: Broad-based institutional biobanking protocols are crucial for advancing personalized medicine in cancer research. However, in-person consenting is resource intensive and interrupts clinic flow. Ethical standards support online consenting for biobanking protocols, which has the potential to reach more participants for enrollment. Herein, we discuss challenges in implementing broad-based online patient consenting at the Moffitt Cancer Center (MCC) for our institutional biobanking protocol, Total Cancer Care (TCC), and subsequent steps to better integrate this process with clinical care on-boarding. We hypothesized that implementation of online patient consenting would broaden patient enrollment into TCC and the redesigned process would increase the number of online patient consents by at least 5-fold. Methods: TCC started as the institutional biobanking protocol in 2006. TCC online consent was implemented in Feb. 2013 (Phase 1) and redesigned in Oct. 2018 (Phase 2). Phase 1 was developed with the guidance of ethicists and patient advisors, and consisted of (a) communication via email, (b) education online with text, video, and FAQs, (c) review and initialing of the Informed Consent form (ICF), and (d) typed name signature on ICF. The Phase 2 redesign created a more streamlined process that was integrated into the MCC New Patient To-Do list, a clinical on-boarding platform. The revised version used interactive material, a shorter animated video, and an electronically signed ICF. We compared the number of online patient consents in Phase 1 and 2 and have preliminary data on patient acceptance of Phase 2. Results: Since 2006, 108,898 MCC patients enrolled into TCC. 342 patients enrolled online in Phase I between 2013–2018 (5/mo). In the initial 6 weeks of Phase 2, there was a 13-fold increase in online consenting (n=98, 65/mo) compared to Phase 1. According to web analytics, TCC pages in Phase 2 were viewed 991 times compared to 432 views over 6 weeks in Phase 1. In general, patient characteristics were similar between those who enrolled online and those who enrolled in person during the same timeframe, with a slightly higher percentage of online vs. in-person enrollees being female (57% vs. 53%, respectively). In a small survey of new patients who viewed the clinical on-boarding platform in Phase 2, 76% reviewed the TCC material without issue whereas 20% did not review the TCC material. Conclusions: While online consenting has tremendous potential, we had very low uptake of patient education and consenting in our initial design (Phase 1). Challenges included a lack of visibility within the patient portal and a dependency on email as the primary mode of directing patients to online consenting. Alternatively, we have seen initial success with our second phase of online consenting that was directly integrated with patient on-boarding. Initial success highlights the potential of this modality to increase patient enrollment into biobanking studies. Further evaluation of this approach is ongoing. Citation Format: Erin M. Siegel, Kyle P. Hawkins, Lynne Hildreth, Timothy Grose, David Stringfellow, Amanda Bloomer, Shelley Tworoger, Dana Rollison, Scott Gilbert, Thomas A. Sellers. Process improvement in online consenting for the Moffitt Cancer Center Total Cancer Care biobanking protocol [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A32.
Colorectal cancer remains the 2nd leading cause of cancer deaths in the United States. This suggests that traditional prognostic factors are not optimally refined for predicting survival outcomes and guiding therapeutic decisions for some patients. Mounting evidence supports that quantifying the strength and diversity of host immune responses in the tumor microenvironment may improve prognostication and clinical decision-making; however, standard pathological assessment of T cell infiltration is time-consuming and difficult to standardize for clinical utility. The goal of this study was to develop a molecular classifier associated with CRC prognosis based on the expression of 770 immune-related genes measured on the Nanostring (NS) nCounter PanCancer Immune Profiling Panel. This panel includes markers of immune cell types, common cancer antigens, and diverse categories of immune responses (e.g. T cell function, cytokines). We also aimed to assess the validity of combining gene expression data derived from different tissue types (FFPE, fresh frozen) and mRNA profiling platforms (NS, Rosetta/Merck human RSTA Custom Affymetrix 2.0 microarray). FFPE (N=24) and fresh frozen tumor tissues (N=28) from 50 primary stage II colon cancers from the Moffitt Cancer Center Total Cancer Care cohort were profiled using the NS platform, and microarray data were generated on frozen tissues from all patients. Geometric mean-normalized NS data of FFPE and frozen tumor tissues were merged by the ComBat algorithm that adjusted for different RNA source types. 634 (87%) genes in the NS dataset had expression values that positively correlated with those of the microarray data. A 2-way hierarchical cluster analysis of these genes in NS data revealed two clusters of patients with non-overlapping overall survival (OS) curves, but no statistically significant difference due mainly to a lack of events (Log-rank P=0.12; 5-year OS probability=91.3% vs 74.1% for cluster 1 (N=23) and cluster 2 (N=27)). To examine cross-platform predictability of the 2 clusters, a 5-gene classifier was trained on NS data using penalized logistic regression. Applying this classifier to microarray data on the same patient set (N=49) significantly discriminated the clusters (AUC=0.8, P<0.01). Functional annotation of the 5 genes (CD27, CD37, ITGAL, KLRG1 and LAG3) revealed enrichment for T cell receptor signaling, hematopoietic cell lineage, and natural killer cell mediated cytotoxicity (FDR P<0.001). This pilot study provides early evidence that an immune gene expression panel may capture the prognostic value of intratumoral host immune responses. It also supports the feasibility of combining different RNA sources and expression platforms. Expanded future studies that pool across sample types and publicly-available expression datasets are needed to validate the 9-gene classifier from this study and examine its broader prognostic impact. Citation Format: Youngchul Kim, Hannah J. Hoehn, Yunyun Chen, Mollie E. Barnard, Amanda Bloomer, Sean Yoder, Domenico Coppola, Stephanie L. Schmit. Prognostic gene expression signatures of immune responses in the colon cancer microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4217.
Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.
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