Access control models describe frameworks that dictate how subjects (e.g. users) access resources. In the Role-Based Access Control (RBAC) model access to resources is based on the role the user holds within the organization. RBAC is a rigid model where access control decisions have only two output options: Grant or Deny. Break The Glass (BTG) policies on the other hand are flexible and allow users to break or override the access controls in a controlled and justifiable manner. The main objective of this paper is to integrate BTG within the NIST/ANSI RBAC model in a transparent and secure way so that it can be adopted generically in any domain where unanticipated or emergency situations may occur. The new proposed model, called BTG-RBAC, provides a third decision option BTG, which grants authorized users permission to break the glass rather than be denied access. This can easily be implemented in any application without major changes to either the application code or the RBAC authorization infrastructure, apart from the decision engine. Finally, in order to validate the model, we discuss how the BTG-RBAC model is being introduced within a Portuguese healthcare institution where the legislation requires that genetic information must be accessed by a restricted group of healthcare professionals. These professionals, advised by the ethical committee, have required and asked for the implementation of the BTG concept in order to comply with the said legislation.

The Electronic Medical Record (EMR) integrates heterogeneous information within a Healthcare Institution stressing the need for security and access control. The Biostatistics and Medical Informatics Department from Porto Faculty of Medicine has recently implemented a Virtual EMR (VEMR) in order to integrate patient information and clinical reports within a university hospital. With more than 500 medical doctors using the system on a daily basis, an access control policy and model were implemented. However, the healthcare environment has unanticipated situations (i.e. emergency situations) where access to information is essential. Most traditional policies do not allow for overriding. A policy that allows for "Break-The-Glass (BTG)" was implemented in order to override access control whilst providing for non-repudiation mechanisms for its usage. The policy was easily integrated within the model confirming its modularity and the fact that user intervention in defining security procedures is crucial to its successful implementation and use.

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Background: Bendamustine and rituximab (BR) has been a preferred regimen for frontline therapy of patients (pts) with advanced stage follicular lymphoma (FL) since randomized trials demonstrated both favorable efficacy and toxicity profiles (Rummel et al 2013, Flinn et al 2014). However, the incidence of transformation and outcomes of pts with early progression within 24 months (POD24) after BR remain poorly documented. Since 2013, BR has been the recommended frontline therapy for all pts with advanced stage, symptomatic FL in British Columbia (BC). We report this population-based analysis evaluating outcomes following the introduction of BR, including the incidence of transformation and POD24, compared to a historical cohort of pts treated with frontline RCVP. Methods: The BC Lymphoid Cancer Database was used to identify all FL pts treated with frontline BR prior to April 1st 2018. A period of observation prior to systemic therapy was permitted, but pts were excluded if they received prior radiation or single-agent rituximab. All pts had pathologically confirmed FL grades 1-3A and symptomatic advanced stage disease (Ann-Arbor I/II if too bulky/not amenable to radiation or stage III-IV). Pts were excluded if they were HIV positive or had documented discordant/composite lymphoma. Event-free survival (EFS), overall survival (OS), and time-to-transformation (TTTF) were calculated from the date of initiation of systemic therapy. Early progression (POD24) was defined as relapse or progression, death from lymphoma or treatment toxicity within 24 months of initiation of systemic therapy. Outcomes were compared with a historical cohort of pts treated with frontline RCVP, which was the recommended induction prior to BR. All pts were eligible to receive rituximab maintenance, which is standard of care for responding pts post-induction therapy in BC. Results: A total of 296 BR-treated pts were identified with a median age of 61 years (range 24-86) and baseline characteristics as outlined in Table 1. Only 34 (11%) had been previously observed and 239 (81%) received rituximab maintenance. A historical cohort of 347 RCVP-treated pts was identified, with comparable clinical characteristics but longer duration of follow-up (median 8.4y, range 0.6-12.6). With a median follow-up for living pts of 2.8y (range 0.2-7.6), estimates for 2-y EFS and OS were 85% (95% CI 80-89%) and 92% (95% CI 88-95%), respectively, for BR-treated pts. As expected, use of BR was associated with an improvement in EFS compared with RCVP (2-y EFS 76% [95% CI 71-80%], p=0.001), but no difference in OS with current follow-up. A total of 28 (9%) transformations have occurred in BR-treated pts, 68% of which were documented histologically. Only elevated LDH was associated with increased risk of transformation (p<0.001). Compared with RCVP-treated pts, the incidence of transformation over time appears similar with current follow-up (Figure 1). Post-transformation outcome in BR-treated pts was poor, with 2-y OS 39% (95% CI 18-59). Early progression (POD24) has occurred in 35/296 (12%) of BR-treated pts. The majority of these, 27 (77%), had transformed lymphoma. Five POD24 pts (14%) died of lymphoma or treatment toxicity without documented transformation and 3 (9%) had relapse with FL and are still alive at last follow up. By comparison, POD24 occurred in 77/347 (22%) of RCVP-treated pts: comprising 31 (40%) transformed lymphoma, 27 (35%) died of lymphoma or treatment toxicity without documented transformation and 19 (25%) relapsed with FL and are still alive at last follow up. Outcome in BR-treated pts with POD24 was poor, with post-progression 2y OS 38% (95% CI 20-55%) compared to non-POD24 BR-treated patients (Figure 2). Conclusion: This population-based analysis demonstrates that in the absence of transformation or POD24, pts with advanced stage FL have excellent outcomes after frontline BR. The use of BR has not changed the rate of transformation compared with that seen after frontline RCVP, with limited follow-up. The occurrence of early progression (POD24) may be decreasing following the introduction of BR, but the majority of POD24 pts now have transformed lymphoma. As a consequence, only a small proportion of POD24 pts following BR have FL-only relapse that may be considered for novel approaches specific for FL. A greater impact on outcome for POD24 pts after BR will require early prediction and improved treatment of transformed lymphoma. Disclosures Freeman: Seattle Genetics: Honoraria; Abbvie: Honoraria. Scott:NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Janssen: Research Funding; Celgene: Consultancy, Honoraria; Roche: Research Funding. Connors:Merck: Research Funding; Janssen: Research Funding; Bristol Myers-Squibb: Research Funding; Cephalon: Research Funding; NanoString Technologies: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Bayer Healthcare: Research Funding; Genentech: Research Funding; Lilly: Research Funding; Seattle Genetics: Honoraria, Research Funding; F Hoffmann-La Roche: Research Funding; Roche Canada: Research Funding; Takeda: Research Funding; Amgen: Research Funding. Sehn:Karyopharm: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Background: Although R-CHOP has significantly improved outcome in diffuse large B-cell lymphoma (DLBCL), 40% of patients still experience relapsed/refractory disease. Further investigation into the genomic architecture of DLBCL is needed to determine the biological correlates that underlie treatment failure. Recent studies using next-generation sequencing strategies have described the landscape of recurrent mutations in DLBCL. However, with the exception of TP53 and FOXO1, little is known about the clinical relevance of recurrent mutations and importantly, the interactions of these genetic alterations in DLBCL. Moreover, an integrated analysis of copy number alterations and recurrent mutations annotated across cell-of-origin (COO) distinctions for a large cohort of DLBCL cases who have received uniform therapy is lacking. The present study examined the frequency and clinical impact of recurrent genetic aberrations of DLBCL using high-resolution technologies in a large population-based DLBCL cohort. Methods: We analyzed 348 newly diagnosed DLBCL cases that were uniformly treated with R-CHOP at the BC Cancer Agency (Vancouver) with available DNA extracted from fresh frozen biopsy material (tumor content >30%). Matched germ line DNA was available for 67 patients. Comprehensive clinical annotation was available through the BCCA Lymphoid Cancer Database. Targeted re-sequencing of the coding exons of 56 genes was performed using a Truseq Custom Amplicon assay. Gene selection was based on mutational frequencies that have been previously described in DLBCL mutational landscape publications. High-resolution copy number analyses were performed using Affymetrix SNP 6.0 arrays. Tissue microarrays were constructed using duplicate 0.6mm cores from 332 cases, and breakapart FISH assays for MYC, BCL2 and BCL6 and IHC staining for MYC, BCL2 and cell of origin proteins were performed. COO classification was available in 331 cases, according to gene expression profiling by the Lymph2Cx assay using the NanoString platform (Scott, Blood 2014;123) in 299 patients as well as Hans algorithm (Hans, Blood 2004;103) in 32 cases with low tumor content. 194 cases were assigned to GCB subtype, 107 cases, ABC/non-GCB and 30 were unclassifiable. Results: In the mutation analysis, we identified 2,757 SNVs and 245 small indels. The mean depth of coverage was 634. Recurrent mutation frequencies varied between 0 and 58, with a mean of 8.25 per case. 98% of cases harbored at least one mutation and 95% of cases multiple mutations. 10 mutated genes were detected significantly more frequently in the GCB subtype including CREBBP, GNA13, EZH2, TNFRSF14, IRF8,STAT3, BCL2, SGK1, MEF2B and CD83, and 4 mutated genes, MYD88, CD79B, PRDM1 and PIM1, in the ABC subtype. In the copy number analysis, 45 significant amplification peaks and 57 deletion peaks were revealed by the GISTIC algorithm. As previously reported, 9p21.3, including CDKN2A,were more frequently detected in the ABC subtypes. With a median follow up of 6.5 years for living patients, the 5 y disease specific survival (DSS) and time to progression (TTP) of all patients were 72% and 64%, respectively. The clinical cohort was representative of registry data from BC based on a comparison of patient characteristics and survival outcomes with 1,194 control DLBCL R-CHOP patients. The ABC subtype was significantly associated with an inferior DSS and TTP (both p<0.0001). In univariate analyses we identified several gene mutations and copy number aberrations significantly associated with survival in all patients. Of these genes, MYD88 and TP53 mutations were associated with significantly inferior TTP in the ABC subtype (p=0.04) and GCB subtype (p=0.002), respectively, while TMEM30A, CREBBP, PIM1 and BTG1 mutations were associated with prognosis in DLBCL. Our analyses confirm the poor prognosis conferred by TP53 mutations in DLBCL and, importantly, identified several novel genetic alterations associated with survival stratified by COO distinctions. Conclusions: Our approach using next generation sequencing and high resolution SNP array provides an accurate estimation of frequency and clinical significance of recurrent genetic alterations of DLBCL in a uniformly R-CHOP-treated large population-based cohort of patients. Disclosures No relevant conflicts of interest to declare.

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Background: LAG3 is one of the immune check point receptors that are expressed on activated cytotoxic T-cells and regulatory T cells. Physiologically, T-cell proliferation and memory T-cell differentiation is negatively regulated by LAG3-MHC interaction. In cancer tissues, T-cells that are chronically exposed to tumor antigens might upregulate LAG3 and receive inhibitory stimuli to enter an exhaustion state limiting anti-tumor immune responses. Currently, clinical trials using double blockade of LAG3/PD1 are active in several solid tumours, but there are only a small number of clinical trials using LAG3 monoclonal antibodies in lymphoma. Recently, we published a characteristic LAG3+ T-cell population as a mediator of immune suppression in classical Hodgkin lymphoma (Aoki & Chong et al. Cancer Discovery 2020). However, the abundance and variability of LAG3 positive T-cell populations across a spectrum of B-cell lymphoma has not been well studied and it remains an open question if LAG3 expression is associated with treatment outcome under standard-of-care conditions. Methods: We performed a LAG3 immunohistochemical (IHC) screen in a large cohort of B-cell Non-Hodgkin lymphoma (diffuse large B-cell lymphoma (DLBCL); N=341, follicular lymphoma (FL); N=198 (grade 1-3A), transformed FL to aggressive lymphoma (tFL); N=120, mantle cell lymphoma (MCL); N=179, primary mediastinal large B-cell lymphoma (PMBCL); N=61) and classical Hodgkin lymphoma (HL; N=459) to assess LAG3 expression in the tumor microenvironment (TME). Moreover, we characterized LAG3+ T-cell populations using multi-color immmunohistochemistry (IHC) (LAG3, PD1, CD4, CD8, FOXP3, CD20) in various lymphoma subtypes. Clinical parameters including treatment outcome were correlated with the abundance of LAG3+ T-cell populations in the TME. Results: On average, HL (7%) and PMBCL (6%) showed higher LAG3+ cellular frequency than the other B-cell lymphoma subtypes studied (DLBCL and FL: 2%, MCL: 0.8%). Comparing the frequency of LAG3+ cells according to MHC class I/II status, DLBCL showed a significant correlation with MHC class I status, and LAG3 expression correlated with MHC class II status in HL. Next, we performed multi-color IHC to describe subtype-specific expression patterns of LAG3 in T cell subsets. LAG3+PD1- T-cells were predominantly found in HL and PMBCL with only rare LAG3+PD1+ cells in HL. The majority of LAG3+ T-cells co-expressed CD4 in HL, in contrast to CD8 in PMBCL. DLBCL showed a mixed population pattern with a 1:1 ratio of LAG3+PD1- and LAG3+PD1+ T-cells. In FL, the majority of LAG3+ T-cells were CD4+PD1+, suggesting a more exhausted TME phenotype in FL than in other lymphoma subtypes. Cellular distance analysis showed that LAG3+CD4+ T-cells were in close vicinity to CD20+ lymphoma cells in FL, while in DLBCL and PMBCL, the nearest neighbors of malignant cells were LAG3+CD8+. Triple-positive LAG3+PD1+CD8+ T-cells significantly correlated with high infiltrating M2 macrophage (Pearson's correlation test, P &lt; 0.001) content and the ABC cell-of-origin subtype (Pearson's correlation test, P = 0.002) in DLBCL. The abundance of LAG3+CD8+PD1- cells correlated with a high FLIPI score (Pearson's correlation test, P = 0.033), disease specific survival (HR = 2.8, 95% CI = 1.3-5.9, P = 0.006), time to progression (HR = 2.8, 95% CI = 1.6-5.0, P = 0.001) and transformation (HR = 4.0, 95%CI = 1.7-9.6, P = 0.002) in FL treated with R-CVP (N = 135). Assessing LAG3 expression by single color IHC in FL (cut-off at 5%), patients with LAG3-positive samples showed significantly higher FL transformation rates (P = 0.023) and tFL samples showed higher abundance of LAG3+ cells than the corresponding primary pretreatment FL samples (primary FL: 1.5±1.7% vs. tFL: 4.2±3.8%, t-test, P = 0.01). The increased transformation risk was validated in an independent FL cohort treated with R-CHOP/CVP (N=97, HR = 6.2, 95% CI = 2.8-13.9, P &lt; 0.001). Conclusion: The highest abundance of LAG3+ T-cells in the TME was found in HL and its related entity PMBCL. The differential outcome correlations and co-expression patterns in LAG3+ T cells across B-cell lymphoma subtypes indicate heterogeneity in TME composition and related pathogenic mechanisms. Our results suggest that LAG3 expression patterns will be important in the interpretation of ongoing studies and highlight populations that may benefit from LAG3 checkpoint inhibition. Disclosures Sehn: AstraZeneca: Consultancy, Honoraria; Genentech, Inc.: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Chugai: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; Verastem Oncology: Consultancy, Honoraria; Teva: Consultancy, Honoraria, Research Funding; Servier: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Acerta: Consultancy, Honoraria. Savage:Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy; BeiGene: Other: Steering Committee; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria. Scott:Celgene: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Janssen: Consultancy, Research Funding. Steidl:Bayer: Consultancy; Juno Therapeutics: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy; Curis Inc: Consultancy.