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Abstract Purpose Delta-like protein 3 (DLL3) is being developed as a predictive biomarker for DLL3-targeting antibody-drug conjugate and other therapies. Given the neuroendocrine features of Merkel cell carcinoma (MCC), we sought to evaluate DLL3 expression and its role in MCC. Experimental Design Formalin-fixed and paraffin-embedded MCC cases were consecutively selected. Immunohistochemistry was performed for DLL3 (SC16.65 antibody) and polyomavirus large T-antigen (sc-136172 antibody). Slides were read out for percentage of positive tumor cells. Cox proportional hazards model was applied to assess the association between DLL3 expression and overall survival (OS). A patient with a DLL3-expressing MCC was treated with rovalpituzumab tesirine (Rova-T) in the “other tumor” cohort of NCT02709889 and assessed for response. Results The median H-score of DLL3 expression of 65 patients included was 60 (interquartile range, 30–100). Fifty-eight cases (89%) had ≥1% tumor cells positive for DLL3 expression with any intensity, of which the median DLL3 expression was 50% (interquartile range, 25%–70%). Thirty-four cases (52%) had ≥50% tumor cells positive for DLL3 expression with any intensity. Higher H-score of DLL3 expression was associated with higher polyomavirus nuclear expression (p = .003) when it was dichotomized to negative versus positive. H-score of DLL3 expression did not predict OS of patients with MCC (p = .4) after being adjusted for common clinicopathological factors. A patient treated with Rova-T for refractory metastatic MCC achieved partial response. Conclusions DLL3 overexpression is very common in MCC by immunohistochemistry. The response to treatment suggests that DLL3 expression may have predictive relevance for DLL3-targeting therapies in MCC. Implications for Practice Delta-like protein 3 (DLL3) is being developed as a predictive biomarker to identify patients for treatment with DLL3-targeting agents. Merkel cell carcinoma (MCC) is an aggressive neuroendocrine carcinoma of the skin. It was found that DLL3 overexpression is very common in MCC by immunohistochemistry and significantly associated with Merkel cell polyomavirus expression. Despite the lack of prognostic significance in this cohort, DLL3 expression may have predictive relevance for DLL3-targeting therapies in MCC. The high levels of DLL3 expression in a subset of MCC may potentially be used to select patients to receive DLL3-targeting therapies.

PURPOSE Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are crude, heterogeneous, incomplete, and largely unstructured, presenting challenges to effective analyses for timely, reliable results. Particularly, research dealing with clinical notes relevant to patient care and outcome is seldom conducted, due to the complexity of data extraction and accurate annotation in the past. RECIST is a set of widely accepted research criteria to evaluate tumor response in patients undergoing antineoplastic therapy. The aim for this study was to identify textual sources for RECIST information in EHRs and to develop a corpus of pharmacotherapy and response entities for development of natural language processing tools. METHODS We focused on pharmacotherapies and patient responses, using 55,120 medical notes (n = 72 types) in Mayo Clinic’s EHRs from 622 randomly selected patients who signed authorization for research. Using the Multidocument Annotation Environment tool, we applied and evaluated predefined keywords, and time interval and note-type filters for identifying RECIST information and established a gold standard data set for patient outcome research. RESULTS Key words reduced clinical notes to 37,406, and using four note types within 12 months postdiagnosis further reduced the number of notes to 5,005 that were manually annotated, which covered 97.9% of all cases (n = 609 of 622). The resulting data set of 609 cases (n = 503 for training and n = 106 for validation purpose), contains 736 fully annotated, deidentified clinical notes, with pharmacotherapies and four response end points: complete response, partial response, stable disease, and progressive disease. This resource is readily expandable to specific drugs, regimens, and most solid tumors. CONCLUSION We have established a gold standard data set to accommodate development of biomedical informatics tools in accelerating research into antineoplastic therapeutic response.

The relationship between selenium and metabolic syndrome (MetS) has been discussed controversially, and limited studies have examined the associations of single nucleotide polymorphisms in selenoproteins genes with MetS. Hence, to examine the associations of plasma selenium concentrations and selenoprotein P rs7579 polymorphism with MetS, a case-control study of 1279 MetS cases and 1279 sex- and age- (±2 years) matched controls was conducted based on the baseline data of the Tongji-Ezhou Cohort study. Plasma selenium concentrations were measured by inductively coupled plasma mass spectrometry. MetS was defined using the definition of the Joint Interim Statement, adjusted for the Chinese population. In addition, the rs7579 polymorphism was genotyped by the Agena MassARRAY System. Plasma selenium concentrations in the MetS group were higher than in the control group (93.88 μg/L (83.17-107.41) vs. 92.66 μg/L (82.36-103.53), P<0.05). Compared with quartile 4 (≥103.53 μg/L), the multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) associated with MetS were 0.79 (0.59-1.06) for quartile 1 (<82.36 μg/L), 0.75 (0.56-1.01) for quartile 2 (82.37-92.66 μg/L), and 0.61 (0.45-0.83) for quartile 3 (92.67-103.52 μg/L). The cubic spline analyses revealed a U-shaped association between plasma selenium and MetS, with the lowest risk at around 93.69 μg/L. Moreover, in cubic spline analyses, plasma selenium showed U-shaped associations with central obesity and high blood pressure, positive associations with hypertriglyceridemia and hyperglycemia, and a negative association with low high-density lipoprotein cholesterol. Additionally, both the GA and GA+AA genotype carriers were associated with increased ORs of MetS comparing with the GG genotype carriers. Our findings suggested a U-shaped association between plasma selenium and MetS and diverse associations between plasma selenium and components of MetS. Furthermore, our study found that the A allele of rs7579 was associated with higher odds of MetS. Further studies are needed to confirm our findings and elucidate the underlying mechanisms.

Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.

TPS7569 Background: Progress in genome technology allows analysis of previously completed trials to identify patients subgroups potentially benefiting from therapy. Enzastaurin is a potent inhibitor of protein kinase C beta (PKC- b) and suppresses the phosphoinositide 3-kinase (PI3K)/AKT pathway. The safety and efficacy of Enzastaurin has been tested in more than 60 clinical trials including 2 major studies in DLBCL: (1) PRELUDE (A phase III maintenance trial of Enzastaurin vs Placebo, N=758) ( Crump, 2016), and (2) S028 (A randomized phase II study of Enzastaurin/R-CHOP vs R-CHOP in frontline intermediate/high-risk DLBCL, N=101) ( Hainsworth, 2016). DNA samples extracted from blood of patients from PRELUDE were retrospectively genotyped using whole genome SNP arrays. From the genome wide screening a novel genetic biomarker, DGM1, was identified showing high correlation with response to Enzastaurin treatment ( Luo, ASH 2018). Importantly, these findings were replicated in the phase II S028 study. In the S028 study the hazard ratio (HR) for OS in high-risk (IPI ≥ 3) DGM1 positive (+) patients who received Enzastaurin/R-CHOP was 0.28 (0.1-0.81) when compared to subjects who received R-CHOP, a benefit favoring Enzastaurin (p=0.018). These data suggest that addition of Enzastaurin to R-CHOP may significantly improve outcome in frontline high-risk DGM1 (+) DLBCL. The ENGINE study was initiated to validate this finding in a prospective study. Methods: Adult patients must have untreated CD20+ DLBCL, IPI ≥ 3. Patients are randomized 1:1 to Enzastaurin/R-CHOP or Placebo/R-CHOP for 6 cycles during combination phase. Each subject’s treatment assignment will be unblinded after response assessment at the end of the combination phase. Subjects randomized to the investigational arm who have a complete or partial response will have the option to continue in the single agent phase to receive Enzastaurin for up to 2 additional years. The study intends to enroll approximately 235 patients with primary endpoint of OS in DGM1 (+) patients. The study is ongoing with 51 sites open in the US and China. As of 22 Jan 2019, 70 patients have been randomized. Clinical trial information: NCT03263026.

503 Background: To correlate clinical features of estrogen receptor positive breast cancer with somatic mutations, massively parallel sequencing (MPS) was applied to tumor and normal DNAs accrued from patients treated with neoadjuvant aromatase inhibitors (AI). Methods: MPS was applied to 77 baseline tumor biopsy samples from the preoperative letrozole trial (JACS 2009: 208, 906) and the Z1031 trial (JCO 2011: 29, 2342) followed by targeted sequencing in another 240 trial samples. Standard statistical approaches were used to compare mutation status and clinical parameters and pathway-based correlation was used to assess interactions between signaling perturbations induced by gene mutations and response to neoadjuvant AI. Results: Eighteen genes were significantly mutated above background. Aside from PIK3CA mutations, the list is dominated by loss-of-function mutations in tumor suppressor genes. Five (RUNX1, CBFP, MYH9, MLL3 and SF3B1) have been previously linked to benign and malignant hematopoietic disorders. Clinical correlation revealed that TP53 mutation was associated with PAM50 LumB status, high-grade histology and high proliferation rates whereas loss-of-function mutations in MAP3K1 associate with PAM50 lumA status, low proliferation rates, and low grade histology. Mutations in GATA3 were associated with greater suppression of proliferation upon AI treatment suggesting mutGATA3 may predict endocrine response. Notably, mutations in MAP3K1 were more common in PIK3CA mutant cases, suggesting cooperation. Pathway analysis demonstrated that rare MAP2K4 mutations produce similar pathway perturbations as MAP3K1 mutation, a logical finding since MAP2K4 is a substrate for MAP3K1. Signaling network patterns driven by lncRNA MALAT1 mutations were associated with multiple poor clinical outcome features. Rare mutations in druggable kinases included two in the kinase domain of HER2. Conclusions: Tumor heterogeneity in luminal-type breast cancer is driven by specific patterns of somatic mutations, however most druggable or potentially prognostic mutations are infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing approaches and large scale investigations.

Abstract Chemotherapy is important for cancer treatment, however, toxicities limit its use. While great strides have been made to ameliorate the acute toxicities induced by chemotherapy, long-term comorbidities including bone loss remain a significant problem. Chemotherapy-driven estrogen loss is postulated to drive bone loss, but significant data suggests the existence of an estrogen-independent mechanism of bone loss. Using clinically relevant mouse models, we showed that senescence and its senescence-associated secretory phenotype (SASP) contribute to chemotherapy-induced bone loss that can be rescued by depleting senescent cells. Chemotherapy-induced SASP could be limited by targeting the p38MAPK-MK2 pathway, which resulted in preservation of bone integrity in chemotherapy-treated mice. These results transform our understanding of chemotherapy-induced bone loss by identifying senescent cells as major drivers of bone loss and the p38MAPK–MK2 axis as a putative therapeutic target that can preserve bone and improve a cancer survivor's quality of life. Significance: Senescence drives chemotherapy-induced bone loss that is rescued by p38MAPK or MK2 inhibitors. These findings may lead to treatments for therapy-induced bone loss, significantly increasing quality of life for cancer survivors.

We clarify that DNA damage triggers autophagy by CHK2-FOXK axis, and misregulation of this pathway leads to chemoresistance.