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Introduction The majority of clotting factor IX (FIX) resides extravascularly, in the subendothelial basement membrane, where it is important for haemostasis. Aim We summarize preclinical studies demonstrating extravascular FIX and its role in haemostasis and discuss clinical observations supporting this. We compare the in vivo binding of BeneFIX® and the extended half‐life FIX, Alprolix®, to extravascular type IV collagen (Col4). Methods Three mouse models of haemophilia were used: the FIX knockout as the CRM− model and two knock‐in mice, representing a CRM+ model of a commonly occurring patient mutation (FIXR333Q) or a mutation that binds poorly to Col4 (FIXK5A). The murine saphenous vein bleeding model was used to assess haemostatic competency. Clinical publications were reviewed for relevance to extravascular FIX. Results CRM status affects recovery and prophylactic efficacy. Prophylactic protection decreases ~5X faster in CRM+ animals. Extravascular haemostasis can explain unexpected breakthrough bleeding in patients treated with some EHL‐FIX therapeutics. In mice, both Alprolix® and BeneFIX® bind Col4 with similar affinities (Kd~20–40 nM) and show dose‐dependent recoveries. As expected, the concentration of binding sites in the mouse calculated for Alprolix® (574 nM) was greater than for BeneFIX® (405 nM), due to Alprolix® binding to both Col4 and the endothelial cell neonatal Fc receptor. Conclusion Preclinical and clinical results support the interpretation that FIX plays a role in haemostasis from its extravascular location. We believe that knowing the CRM status of haemophilia B patients is important for optimizing prophylactic dosing with less trial and error, thereby decreasing clinical morbidity.

AimsWe sought to use PCR followed by high-resolution melting analysis to develop a single closed-tube screening panel to screen for Lynch syndrome. This comprises tests for microsatellite instability (MSI), MLH1 methylation promoter and BRAF mutation.MethodsFor MSI testing, five mononucleotide markers (BAT25, BAT26, BCAT25, MYB, EWSR1) were developed. In addition, primers were designed to interrogate Region C of the MLH1 promoter for methylation (using bisulphite-modified DNA) and to test for mutations in codon 600 of BRAF. Two separate cohorts from Nottingham (n=99, 46 with MSI, 53 being microsatellite stable (MSS)) and Edinburgh (n=88, 45 MSI, 43 MSS) were tested.ResultsAll the cases (n=187) were blind tested for MSI and all were correctly characterised by our panel. The MLH1 promoter and BRAF were tested only in the Nottingham cohort. Successful blinded analysis was performed on the MLH1 promoter in 97 cases. All MSS cases showed a pattern of non-methylation while 41/44 cases with MSI showed full methylation. The three cases with MSI and a non-methylated pattern had aberrations in MSH2 and MSH6 expression. BRAF mutation was detected in 61% of MSI cases and 11% of MSS cases.Finally, 12 cases were blind screened by using the whole panel as a single test. Of these, five were identified as MSS, four as MSI/non-LS and three as MSI/possible LS. These results were concordant with the previous data.ConclusionWe describe the Nottingham Lynch Syndrome Test (N_LyST). This is a quick, simple and cheap method for screening for Lynch syndrome.

Background Human activated factor VII (hFVIIa), which is used in hemophilia treatment, binds to the endothelial protein C (PC) receptor (EPCR) with unclear hemostatic consequences. Interestingly, mice lack the activated FVII (FVIIa)-EPCR interaction. Therefore, to investigate the hemostatic consequences of this interaction in hemophilia, we previously engineered a mouse FVIIa (mFVIIa) molecule that bound mouse EPCR (mEPCR) by using three substitutions from mouse PC (mPC), i.e. Leu4→Phe, Leu8→Met, and Trp9→Arg. The resulting molecule, mFVIIa-FMR, modeled the EPCR-binding properties of hFVIIa and showed enhanced hemostatic capacity in hemophilic mice versus mFVIIa. These data implied a role of EPCR in the action of hFVIIa in hemophilia treatment. However, the substitutions in mFVIIa-FMR only broadly defined the sequence determinants for its mEPCR interaction and enhanced function in vivo. Objectives To determine the individual contributions of mPC Phe4, Met8 and Arg9 to the in vitro/in vivo properties of mFVIIa-FMR. Methods The mEPCR-binding properties of single amino acid variants of mFVIIa or mPC at position 4, 8 or 9 were investigated. Results and conclusions Phe4 in mFVIIa or mPC was solely critical for interaction with mEPCR. In hemophilic mice, administration of mFVIIa harboring a Phe4 resulted in a 1.9-2.5-fold increased hemostatic capacity versus mFVIIa that was EPCR binding-dependent. This recapitulated previous observations made with triple-mutant mFVIIa-FMR. As Leu8 is crucial for hFVIIa-EPCR binding, we describe the sequence divergence of this interaction in mice, now allowing its further characterization in vivo. We also illustrate that modulation of the EPCR-FVIIa interaction may lead to improved FVIIa therapeutics.

e19297 Background: Obtaining clinical outcomes for analysis has historically been a critical barrier to cancer genomics research. EHRs could constitute an important data source to bridge this gap, but EHRs rarely capture structured outcomes such as cancer progression. Novel, robust methods are needed to capture clinically relevant outcomes from EHRs. Methods: Among patients with lung adenocarcinoma whose tumors were sequenced via the Dana Farber Cancer Institute/Brigham and Women’s PROFILE study from 2013-2018, imaging reports following first palliative-intent systemic therapy were annotated using natural language processing (NLP) models trained to capture cancer progression according to the structured “PRISSMM” framework. NLP-based cancer progression and imaging report frequency were jointly modeled using inverse-intensity weighted generalized estimated equations, censored at six months, to explore associations between alterations in lung cancer biomarkers (ALK, EGFR, ROS1, BRAF, KRAS, SMARCA4) and progression. Among patients with KRAS mutations who received immunotherapy, we also analyzed the association between STK11 mutations and progression. The novel outcome generated by the model – imaging report-based progression (iPROG) – corresponded to the difference in the mean log odds of progression per inverse-intensity weighted report associated with a given biomarker; it was reported as adjusted mean probability and in exponentiated form as an odds ratio (OR). Results: Among 690 patients with lung adenocarcinoma, associations between tumor mutations and the iPROG outcome are listed in the Table. Conclusions: A deep NLP model applied to EHR data can capture a novel cancer progression outcome, which is associated with known prognostic markers in lung cancer. Application of this method to large “real world” datasets, with attention to interactions between treatment and genomics, could speed biomarker discovery. [Table: see text]