Robert E. Schoen’s research while affiliated with University of Pittsburgh and other places

Objective U.S. Multi-Society Task Force colonoscopy surveillance intervals are based solely on adenoma characteristics, without accounting for other risk factors. We investigated whether a risk model including demographic, environmental and genetic risk factors could individualize surveillance intervals under an “equal management of equal risks” framework. Methods Using 14,069 individuals from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial who had a diagnostic colonoscopy following an abnormal flexible sigmoidoscopy, we modeled the risk of colorectal cancer (CRC), considering the diagnostic colonoscopy finding, baseline risk factors (e.g., age and sex), 19 lifestyle and environmental risk factors, and a polygenic risk score (PRS) for CRC. Ten-year absolute cancer risks for each diagnostic colonoscopy finding [advanced adenoma (N=2446), ≥3 non-advanced adenomas (N=483), 1-2 non-advanced adenomas (N=4400) and no adenoma (N=7183)] were used as implicit risk thresholds for recommended surveillance intervals. Results The area-under-the-curve (AUC) for the model including colonoscopy findings, baseline characteristics, and PRS was 0.658. Applying the “equal management of equal risks” framework, 28.2% of individuals with no adenoma and 42.7% of those with 1-2 non-advanced adenomas would be considered high risk and assigned a significantly shorter surveillance interval than currently recommended. Among individuals who developed cancer within 10 years, 52.4% with no adenoma and 48.3% with 1-2 non-advanced adenomas would have been considered high risk and assigned a shorter surveillance interval. Conclusion Using a personalized risk-based model has the potential to identify individuals with no adenoma or 1-2 non-advanced adenomas, who are higher risk and may benefit from shorter surveillance intervals.

Background Imaging biomarkers are emerging as non-invasive predictors of cancer prognosis and clinical outcome. We assessed tumor-specific (“radiomics”) and body composition imaging features (“morphomics”) extracted from baseline pre-treatment CT for prediction of recurrence in patients with stage III colorectal cancer (CRC). Methods Patients with newly diagnosed stage III CRC were enrolled in this prospective observational study. Patients with available preoperative scans were included (N = 101). The tumor, if visible, was manually segmented and first-order radiomics features were extracted with a commercially available software. The morphomics features (reflecting muscle, fat, and bone characteristics) were extracted in a standardized fashion using a proprietary software and the values were adjusted and normalized based on a reference standard. Time to recurrence was the final outcome. Correlation between demographics, clinical features, radiomics, and morphomics features and outcome were assessed using univariate and multivariate tests as well as Kaplan–Meier and log-rank tests. Results Morphomic analysis was performed in all 101 patients. 60 patients had discrete tumors suitable for radiomics analysis. These patients had lower ECOG score (p < 0.05), more muscle mass (p > 0.05), and lower fat density (p > 0.05) compared to the patients in whom radiomics analysis could not be performed. Pathological stage (HR: 2.69; p = 0.03), CEA level after surgery (HR: 1.11 for 1 ng/mL; p < 0.005), bone mineral density (HR: 1.01 for 1 Hounsfield Unit; p < 0.01), and tumor skewness (HR: 0.33 for 1 unit; p < 0.05) had association with recurrence based on both univariate and multivariate analyses. A model using Cox’s regression analyses was able to divide the patients into low-, medium-, and high-risk for recurrence. Conclusions Both radiomics and morphomics features were independently associated with the risk of CRC recurrence and, when combined, each contributed valuable information to explain risk of recurrence. Trial registration Clinical trial.gov NCT02842203. Patient recruitment occurred between 22/07/2016 and 18/03/2020. Graphical abstract

Background: Cell-free DNA blood tests (cf-bDNA) and next-generation stool tests could change colorectal cancer (CRC) screening. Objective: To estimate the clinical and economic impacts of novel CRC screening tests. Design: Cost-effectiveness analysis using MOSAIC (Model of Screening and Surveillance for Colorectal Cancer). Data sources: Published data. Target population: Average-risk persons. Time horizon: Ages 45 to 100 years. Perspective: Health sector. Intervention: Novel versus established CRC screening tests. Outcome measures: Incidence and mortality of CRC, quality-adjusted life-years (QALYs), costs. Results of base-case analysis: For colonoscopy every 10 years, annual fecal immunochemical test (FIT), and triennial next-generation multitarget stool DNA, FIT-RNA, cf-bDNA (Guardant Shield), or cf-bDNA (Freenome), the relative rates (RRs) and 95% uncertainty intervals (UIs) versus no screening for CRC incidence were 0.21 (0.19 to 0.22), 0.29 (0.27 to 0.31), 0.33 (0.32 to 0.36), 0.32 (0.30 to 0.34), 0.58 (0.55 to 0.61) and 0.58 (0.55 to 0.60), respectively; the RRs for CRC death were 0.19 (0.17 to 0.20), 0.25 (0.23 to 0.27), 0.28 (0.27 to 0.30), 0.28 (0.26 to 0.30), 0.44 (0.42 to 0.47), and 0.46 (0.44 to 0.49), respectively. The cf-bDNA test (Shield; list price $1495) cost$89 600 ($74 800 to$102 300) per QALY gained versus no screening; alternatives were less costly and more effective. Results of sensitivity analysis: Incremental costs exceeded incremental benefits when novel test intervals were shortened to 2 or 1 years. The cf-bDNA test matched FIT's impact on CRC mortality at 1.35 (1.30 to 1.40)-fold FIT's uptake rate, assuming equal colonoscopy follow-up. If persons who accept colonoscopy or stool tests shifted to cf-bDNA, CRC deaths increased. This adverse effect was overcome if every 3 such substitutions were counterbalanced by cf-bDNA uptake by 2 or more persons refusing alternatives, assuming equal colonoscopy follow-up. Limitation: Longitudinal test-specific participation patterns are unknown. Conclusion: First-generation cf-bDNA tests may deliver net benefit or harm, depending on the balance between achieving screening in persons who decline alternatives versus substituting cf-bDNA for more effective alternatives. Primary funding source: The Gorrindo Family Fund.

Introduction Self-antigens abnormally expressed on tumors, such as MUC1, have been targeted by therapeutic cancer vaccines. We recently assessed in two clinical trials in a preventative setting whether immunity induced with a MUC1 peptide vaccine could reduce high colon cancer risk in individuals with a history of premalignant colon adenomas. In both trials, there were immune responders and non-responders to the vaccine. Methods Here we used PBMC pre-vaccination and 2 weeks after the first vaccine of responders and non-responders selected from both trials to identify early biomarkers of immune response involved in long-term memory generation and prevention of adenoma recurrence. We performed flow cytometry, phosflow, and differential gene expression analyses on PBMCs collected from MUC1 vaccine responders and non-responders pre-vaccination and two weeks after the first of three vaccine doses. Results MUC1 vaccine responders had higher frequencies of CD4 cells pre-vaccination, increased expression of CD40L on CD8 and CD4 T-cells, and a greater increase in ICOS expression on CD8 T-cells. Differential gene expression analysis revealed that iCOSL, PI3K AKT MTOR, and B-cell signaling pathways are activated early in response to the MUC1 vaccine. We identified six specific transcripts involved in elevated antigen presentation, B-cell activation, and NF-κB1 activation that were directly linked to finding antibody response at week 12. Finally, a model using these transcripts was able to predict non-responders with accuracy. Discussion These findings suggest that individuals who can be predicted to respond to the MUC1 vaccine, and potentially other vaccines, have greater readiness in all immune compartments to present and respond to antigens. Predictive biomarkers of MUC1 vaccine response may lead to more effective vaccines tailored to individuals with high risk for cancer but with varying immune fitness.

Background Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure. Methods Using resources from colorectal cancer consortia including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score including 141 variants associated with CRC risk. Results There was no evidence of multiplicative interaction between environmental factors and genetic risk score. There was additive interaction where, for individuals with high genetic susceptibility, either heavy drinking [RERI = 0.24, 95% confidence interval, CI, (0.13, 0.36)], ever smoking [0.11 (0.05, 0.16)], high BMI [female 0.09 (0.05, 0.13), male 0.10 (0.05, 0.14)], or high red meat intake [highest versus lowest quartile 0.18 (0.09, 0.27)] was associated with excess CRC risk greater than that for individuals with average genetic susceptibility. Conversely, we estimate those with high genetic susceptibility may benefit more from reducing CRC risk with aspirin/NSAID use [-0.16 (-0.20, -0.11)] or higher intake of fruit, fiber, or calcium [highest quartile versus lowest quartile -0.12 (-0.18, -0.050); -0.16 (-0.23, -0.09); -0.11 (-0.18, -0.05), respectively] than those with average genetic susceptibility. Conclusions Additive interaction is important to assess for identifying subgroups who may benefit from intervention. The subgroups identified in this study may help inform precision CRC prevention.

Multiplexed imaging technologies have made it possible to interrogate complex tissue microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning-based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the unsupervised context. Here we present an easy-to-use unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data via leveraging a Bayesian-like framework, and nucleus and cell membrane markers. We show that UNSEG is internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods, allowing it to unambiguously identify the cytoplasmic compartment of a cell, and localize molecules to their correct sub-cellular compartment. We also introduce a perturbed watershed algorithm for stably and automatically segmenting a cluster of cell nuclei into individual nuclei that increases the accuracy of classical watershed. Finally, we demonstrate the efficacy of UNSEG on a high-quality annotated gastrointestinal tissue dataset we have generated, on publicly available datasets, and in a range of practical scenarios.

BRAF V600E mutation is a driver mutation in the serrated pathway to colorectal cancers. BRAF V600E drives tumorigenesis through constitutive downstream extracellular signal-regulated kinase (ERK) activation, but high-intensity ERK activation can also trigger tumor suppression. Whether and how oncogenic ERK signaling can be intrinsically adjusted to a ‘just-right’ level optimal for tumorigenesis remains undetermined. In this study, we found that FAK (Focal adhesion kinase) expression was reduced in BRAF V600E -mutant adenomas/polyps in mice and patients. In Vil1-Cre;BRAF LSL-V600E/+ ; Ptk2 fl/fl mice, Fak deletion maximized BRAF V600E ’s oncogenic activity and increased cecal tumor incidence to 100%. Mechanistically, our results showed that Fak loss, without jeopardizing BRAF V600E -induced ERK pathway transcriptional output, reduced EGFR (epidermal growth factor receptor)-dependent ERK phosphorylation. Reduction in ERK phosphorylation increased the level of Lgr4, promoting intestinal stemness and cecal tumor formation. Our findings show that a ‘just-right’ ERK signaling optimal for BRAF V600E -induced cecal tumor formation can be achieved via Fak loss-mediated downregulation of ERK phosphorylation.

A single arm trial (NCT007773097) and a double-blind, placebo controlled randomized trial ( NCT02134925 ) were conducted in individuals with a history of advanced colonic adenoma to test the safety and immunogenicity of the MUC1 tumor antigen vaccine and its potential to prevent new adenomas. These were the first two trials of a non-viral cancer vaccine administered in the absence of cancer. The vaccine was safe and strongly immunogenic in 43% (NCT007773097) and 25% ( NCT02134925 ) of participants. The lack of response in a significant number of participants suggested, for the first time, that even in a premalignant setting, the immune system may have already been exposed to some level of suppression previously reported only in cancer. Single-cell RNA-sequencing (scRNA-seq) on banked pre-vaccination peripheral blood mononuclear cells (PBMCs) from 16 immune responders and 16 non-responders identified specific cell types, genes, and pathways of a productive vaccine response. Responders had a significantly higher percentage of CD4+ naive T cells pre-vaccination, but a significantly lower percentage of CD8+ T effector memory (TEM) cells and CD16+ monocytes. Differential gene expression (DGE) and transcription factor inference analysis showed a higher level of expression of T cell activation genes, such as Fos and Jun, in CD4+ naive T cells, and pathway analysis showed enriched signaling activity in responders. Furthermore, Bayesian network analysis suggested that these genes were mechanistically connected to response. Our analyses identified several immune mechanisms and candidate biomarkers to be further validated as predictors of immune responses to a preventative cancer vaccine that could facilitate selection of individuals likely to benefit from a vaccine or be used to improve vaccine responses.