The Human Patent: What Intellectual Property Rights Does an Individual Have in Their Own Genetic Material, and What Are the Global Biosecurity Implications?

Under United States patent law, the landmark Supreme Court decision in Diamond v. Chakrabarty established the patentability of human-made life forms, except for those encompassing a human organism. The America Invents Act of 2011, and decisions from lower courts such as Moore v. Regents of University of California, reaffirm the unpatentability of human organisms and limit an individual’s rights to their own genetic material. Prior to Association for Molecular Pathology v. Myriad Genetics, Inc, which struck down the patentability of human genes as well, the decision in Diamond led to the proliferation of gene patents and the growth of the global biotechnology market. While the debate over the patenting of human genetic material, and individual rights, rages on, DNA-gathering companies such as 23andMe and Ancestry.com routinely utilize their customer’s genetic material for multi-million dollar pharmaceutical research. This research not only raises ethical concerns, but can also pose a potentially dangerous biosecurity threat based on the many ways this genetic material can be used to target individuals, groups, and nations.

POS0454 COMPARISON OF MBDA SCORE, PATIENT GLOBAL ASSESSMENT AND EVALUATOR GLOBAL ASSESSMENT FOR PREDICTING RISK OF RADIOGRAPHIC PROGRESSION

Background:Busy rheumatologists may assess disease activity and risk for radiographic progression (RP) in RA with informal, qualitative versions of evaluator and/or patient global assessments (EGA and PGA). RA patient care may be improved by having a convenient, objective disease activity measure that predicts risk for RP more accurately than EGA or PGA.Objectives:To compare the abilities of MBDA score, patient global assessment and evaluator global assessment to assess risk for radiographic progression (RP), and to assess the ability of MBDA score to predict RP among patients with concordant or discordant PGA and EGA.Methods:Patients were pooled from two RCTs of patients with recent onset RA treated with conventional and biologic DMARDs (OPERA and SWEFOT, N=386) and from a registry of patients with predominantly established RA and diverse treatments (BRASS, N=380). Pearson correlations were determined between MBDA scores (adjusted for the effects of age, sex and adiposity) (scale 1-100), PGA and EGA (each on a scale of 1-10) at baseline. PGA and EGA were considered discordant when they differed by >2.5. Univariable logistic regression assessed ability to predict RP (change in TSS >5 over 1 year) for MBDA score, PGA and EGA as continuous variables; and for discordance of PGA and EGA as 2-level (concordant vs. discordant) or 3-level (PGA>EGA, concordant, EGA>PGA) categorical variables. Multivariable regression considered the main effect and interaction terms of the MBDA score, as a continuous variable, paired with each other variable, to test the ability of each pair to assess risk of RP. All models included a random effect on cohort. Odds ratios were reported for every 10-unit increase in MBDA score. Frequency of RP was determined in subgroups with MBDA score low (<30), moderate (30-44) or high (>44) for patient groups based on PGA/EGA concordance or discordance.Results:The 766 patients studied were 76% female, 76% positive for RF and/or anti-CCP Ab, with mean age 55 years, DAS28-CRP 4.7, CRP 22 mg/L, CDAI 26, SJC 9.1, PGA 4.4, EGA 3.4, MBDA score 53. No interaction was seen between MBDA score and type of cohort (early vs established RA). PGA and EGA were discordant in 294 of 766 (38%) patients and were weakly to moderately correlated (r=0.38). Among discordant patients, PGA was >EGA in 227 cases and EGA was >PGA in 67 cases. Correlations between MBDA score and PGA or EGA were r=0.41 and r=0.34, respectively. In univariable analyses, MBDA score was a statistically significant predictor of radiographic progression (OR=1.53, p=6.3x10-8) whereas PGA, EGA, 2-level discordance and 3-level discordance were not (p=0.38, 0.47, 0.74, 0.83, respectively). In multivariable analyses, significant interactions were observed between MBDA score and discordance (2-level, p=0.0029; 3-level, p=0.0087). The interaction analysis demonstrated, in PGA/EGA-concordant patients, low risk of radiographic progression when MBDA score was low and elevated risk when it was high (OR=1.33 [1.1, 1.59]). A relationship between MBDA score and RP risk was also demonstrated, with heightened trend, among discordant patients with PGA >EGA (OR=2.04 [1.53, 2.81]) and EGA >PGA (OR=3.43 [1.37, 13.8]) (Figure 1).Conclusion:MBDA score was a significant predictor of radiographic progression, whereas PGA and EGA were not. MBDA score predicted progression whether PGA and EGA were concordant or discordant. These results suggest that MBDA score detects joint-damaging disease activity more accurately than PGA and EGA and it does so whether or not PGA and EGA are in agreement.Disclosure of Interests:Leonard Calabrese Grant/research support from: AbbVie, Bristol-Myers Squibb, Cresecendo, Genentech, Gilead, GlaxoSmithKline, Horizon, Janssen, Novartis, and Sanofi., Michael E. Weinblatt Shareholder of: Canfite, Inmedix, Scipher, and Vorso, Consultant of: AbbVie, Aclaris, Amgen, Bayer, Bristol-Myers Squibb, Crescendo Bioscience, Corrona, EqRX, GSK,Genosco, Gilead, Lilly, Novartis, Pfizer, Roche, Set Point, Grant/research support from: Bristol-Myers Squibb, Myriad Genetics, Inc.,Eli Lilly and Sanofi, Nancy Shadick Consultant of: BMS, Grant/research support from: Lilly, mallinckrodt, BMS, Amgen and Sanofi, Cecilie Heegaard Brahe: None declared, Mikkel Østergaard Consultant of: Abbvie, BMS, Boehringer-Ingelheim, Celgene, Eli-Lilly, Centocor, GSK, Hospira, Janssen, Merck, Novartis, Orion, Pfizer, Regeneron, Roche, Takeda, and UCB, Grant/research support from: AbbVie, BMS, Celgene, Myriad Genetics, Inc., Janssen, and Merck, Merete L. Hetland Speakers bureau: Orion, Grant/research support from: AbbVie, Biogen, BMS, CelltrionRoche, Myriad Genetics, Inc., Eli Lily, MSD, Pfizer, and UCB, Megan Horton Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Autoimmune

Cell cycle progression score and PTEN as prognostic factors for metastasis in intermediate- and high-risk prostate cancer overall, and in those who also received salvage radiotherapy.

247 Background: The cell cycle progression (CCP) score and PTEN have never been evaluated together as prognostic markers for risk of metastasis in a radical prostatectomy (RP) cohort of men with National Comprehensive Cancer Network intermediate- and high-risk prostate cancer (PCa), nor in such patients who also received salvage radiation (SRT) alone or with androgen deprivation (SRT+ADT). This study evaluated CCP score and PTEN in both settings. Methods: Participants were treated with RP at Johns Hopkins from 2007-2015. Paraffin-embedded RP tissue was analyzed blind to clinical outcome at Myriad Genetics, for CCP score using qRT-PCR, and PTEN by immunohistochemistry. For overall evaluation of CCP and PTEN in intermediate- and high-risk men a case-cohort sample was selected. Intermediate- and high-risk men with biochemical recurrence who received SRT or SRT+ADT were also sampled to provide a population at particularly high risk. Metastasis-free survival (MFS) was analyzed with the proportional hazards model, weighted for case-cohort design for the overall analysis, and adjusted for CAPRA-S. The clinical cell-cycle risk (CCR) score, a fixed algorithm combining CCP and CAPRA-S was also analyzed in both contexts. Data were analyzed independently by Johns Hopkins and Myriad Genetics. Results: The case-cohort consisted of 209 men, including 47% with Gleason score >4+3, 48% extra-prostatic extension, and 18% with seminal vesicle or lymph node involvement; 42 (20%) developed metastasis. In univariate analyses CCP, CAPRA-S, and PTEN were all highly significant. In multivariable analysis, only CCP and CAPRA-S retained significance (Table section A). CCR was also strongly prognostic, HR=7.9 (95% CI 4.4, 14.5) per unit change, p<0.00001. SRT (56%) or SRT+ADT (44%) were received by 172 men, of whom 78% had Gleason >4+3, 48% extra-prostatic extension, and 34% seminal vesicle or lymph node involvement; 19 (11%) developed metastases. Again, CCP and CAPRA-S, but not PTEN, were statistically significant (Table section B). CCR was also statistically significant, HR=1.7 (95% CI 1.2, 2.4), p=0.002. Conclusions: This is the first comparison, in a recent cohort of intermediate- and high-risk men, of CCP score and PTEN as risk factors for metastasis, and first evaluation in such men receiving SRT. In both multivariable settings, CCP score, but not PTEN, was significantly associated with MFS, adjusted for CAPRA-S. CCR, a fixed algorithm that combines CCP and CAPRA-S was also significant in both settings. [Table: see text]

Association for Molecular Pathology v. Myriad Genetics: A Critical Reassessment

The Supreme Court’s 2013 decision in Association for Molecular Pathology v. Myriad Genetics is an essential piece of the Court’s recent quartet of patent eligibility decisions, which also includes Bilski v. Kappos, Mayo v. Prometheus, and Alice v. CLS Bank. Each of these decisions has significantly shaped the contours of patent eligibility under Section 101 of the Patent Act in ways that have been both applauded and criticized. The Myriad case, however, was significant beyond its impact on Section 101 jurisprudence. It was seen, and litigated, as a case impacting patient rights, access to healthcare, scientific freedom, and human dignity. In this article, I offer a close textual analysis of the Myriad decision and respond to both its critics and supporters. I then situate Myriad within the larger context of biotechnology patenting, the commercialization of academic research, and the U.S. healthcare system. In this regard, the failure of public institutions and governmental agencies to constrain the private exploitation of publicly-funded innovations contributed as much to the healthcare access disparities highlighted by the case as the overly broad protection afforded by the Patent and Trademark Office to genetic inventions. I conclude with observations about the ways that cases like Myriad exemplify the manner in which the common law evolves, particularly in areas of rapid technological change.

AB1239 THE EFFECT OF INFLUENZA VACCINATION ON THE MULTI-BIOMARKER DISEASE ACTIVITY SCORE AND ITS COMPONENT BIOMARKERS IN HEALTHY SUBJECTS

Background:The multi-biomarker disease activity (MBDA) blood test measures 12 protein biomarkers (IL-6, CRP, SAA, EGF, VEGF, VCAM, MMP-1, MMP-3, leptin, resistin, TNF-RI and YKL40). It uses a validated algorithm to provide a score on a scale of 1-100 for assessing disease activity in patients with rheumatoid arthritis (RA). The MBDA score reflects several molecular aspects of inflammation, including cytokines, acute phase reactants, growth factors, molecular adhesion, metalloproteinases and hormones. Insights gained by understanding how vaccination affects these biomarkers in healthy subjects - in whom the level of inflammation prior to vaccination should be low and stable - may aid the understanding of how vaccination affects patients with RA.Objectives:The goal of this study was to understand how immunization of healthy subjects with the influenza vaccine affects the assessment of inflammation with the MBDA score and its 12 biomarkers.Methods:A 4-strain influenza virus vaccine (Fluarix Quadrivalent, GlaxoSmithKline) was administered intramuscularly to 22 healthy volunteer subjects on October 24, 2018. Serum samples were obtained immediately prior to vaccination (baseline) and 1, 2 and 3 weeks after vaccination. No restrictions were placed on subject activity. Samples were stored at -80oC until measurement of the 12 MBDA biomarkers for determination of the adjusted MBDA score, hereafter called the MBDA score. (Adjustment accounts for the effects of age, sex and adiposity1). MBDA scores (natural scale) and biomarker concentrations (log scale) were modeled using generalized estimating equations (GEE) that account for correlations between measurements from the same subject at multiple timepoints. Significance of MBDA score change or biomarker concentration change over time was determined by a likelihood ratio test of timepoints.Results:Of the 22 healthy subjects receiving the influenza virus vaccine, 14 (63.6%) were female, with mean (SD) age of 40.0 years (8.9). MBDA scores were low (<30), moderate (30-44) or high (>44) for 15 (68%), 6 (27%) and 1 (5%) subjects at baseline, and this distribution was stable over time (Figure 1). Overall, MBDA scores did not change significantly over time (p=0.48, Figure 2). Mean changes in MBDA score (95% CI) from baseline to weeks 1, 2 and 3 were 0.32 (-3.07, 3.71), 0.82 (-3.03, 4.67) and 2.86 (-1.10, 6.82), respectively (Figure 2); the week 3 value becomes 0.95 (-1.78, 3.68) if the week 3 outlier is removed. Among the 66 post-baseline measurements of change in MBDA score (Figure 2), 3 (5%) exceeded the 95% CI for change in MBDA score in this study (i.e., 14). When assessing the entire cohort across all timepoints, EGF was the only biomarker that demonstrated statistically significant change over time (p=5.6 x 10-7). At weeks 1, 2 and 3, the mean relative concentrations of EGF, compared with baseline, were 0.62 (0.52, 0.74), 0.86 (0.70, 1.06) and 0.62 (0.50, 0.76), respectively.Figure 1Figure 2Conclusion:Immunization of 22 healthy subjects with a quadrivalent influenza vaccine did not have a statistically significant effect on MBDA scores during a 3-week observation, and it had minimal effect on the component biomarkers.References:[1]Curtis et al.Rheumatology [Oxford]2018;58:874Disclosure of Interests:Daniel Furst Grant/research support from: AbbVie, Actelion, Amgen, BMS, Corbus Pharmaceuticals, the National Institutes of Health, Novartis, Pfizer, and Roche/Genentech, Consultant of: AbbVie, Actelion, Amgen, BMS, Cytori Therapeutics, Corbus Pharmaceuticals, the National Institutes of Health, Novartis, Pfizer, and Roche/Genentech, Speakers bureau: CMC Connect (McCann Health Company), Lauren Lenz Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Megan Horton Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Michael E. Weinblatt Grant/research support from: BMS, Amgen, Lilly, Crescendo and Sonofi-Regeneron, Consultant of: Horizon Therapeutics, Bristol-Myers Squibb, Amgen, Abbvie, Crescendo, Lilly, Pfizer, Roche, Gilead

FRI0057 A MODEL FOR QUANTIFYING THE EFFECT OF INFLAMMATION ON CARDIOVASCULAR DISEASE RISK PREDICTION IN RA PATIENTS

Background:Patients with rheumatoid arthritis (RA) are at increased risk for cardiovascular disease (CVD)[1]. Quantifying the effect of inflammation on CVD risk is important because rheumatologists can reduce inflammation with effective RA medications. A new score has been developed for predicting the risk for a CVD event (MI, stroke or CV death) in RA patients. It combines serological measures of inflammation (the multi-biomarker disease activity [MBDA] score, a measure of RA disease activity; and three individual biomarkers [TNF-RI, MMP-3 and leptin]), with age and four conventional CVD risk factors (smoking, hypertension, diabetes and history of a high- risk CVD condition)[2]. To gain insight into the potential effect that treating inflammation may have on the CVD risk score, it would be useful to know how the score is affected by the level of inflammation.Objectives:Explore the quantitative contribution of inflammation to CVD risk score in individual RA patients.Methods:To quantify the effect of inflammation on the CVD risk score across a range of MBDA scores, a commercial dataset of 177,486 RA patients with ≥2 MBDA tests between October 2010 and June 2019 was split 2:1 into training and validation datasets. Curves showing variation in the CVD risk score across the spectrum of all possible MBDA scores (1-100) were generated for canonical patient types differing in the number of conventional risk factors (0 to 4) and age (45, 55, 65, 75, 85 years). To generate these curves, the contributions of TNF-RI, MMP-3 and leptin to the CVD risk score were treated in aggregate (denoted the molecular score) and estimated using a linear regression model of the difference in molecular scores vs. the difference in MBDA scores. This model for the molecular score was fit in the training dataset, then in the full dataset, with dataset (training or validation) and the interaction between dataset and change in MBDA score included as additional predictor variables. The method was considered validated if the F-test for the interaction variable was not significant at the 0.05 level.Results:The model for estimating the molecular score from the MBDA scores was validated and shown to fit the data well (Figure 1). The estimated molecular score was applied to the CVD risk score algorithm to generate curves that show how CVD risk score varies with MBDA score for several distinct patient types. These curves demonstrate that the predicted 3-year CVD risk increases continuously and markedly with increasing level of inflammation, as represented by the MBDA score (Figure 2). Age and the number of conventional risk factors also affected the predicted CVD risk, with older patients (Figure 2a) and those with more conventional risk factors (Figure 2b) being at higher risk for a CVD event.Conclusion:The level of CVD risk predicted by a new prognostic test for RA patients depends not only on conventional risk factors, which are relatively time invariant, but also varies greatly due to inflammation, which can potentially be reduced with RA treatment.References:[1]Agca et al (2017).Ann Rheum Dis.76(1):17-28. doi: 10.1136/annrheumdis-2016-209775.[2]Curtis JR, Xie F, Crowson CS et al. (2019) ACR meeting abstract #446Disclosure of Interests:Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

FRI0553 DEVELOPMENT AND VALIDATION OF A BIOMARKER-BASED CARDIOVASCULAR RISK PREDICTION SCORE IN RHEUMATOID ARTHRITIS

Background:Rheumatoid arthritis (RA) patients are at elevated risk for cardiovascular (CV) events, but risk stratification based on CV prediction models is not part of routine rheumatology practice.Objectives:To develop and validate a biomarker-based CV risk prediction model and compare it to alternative risk prediction models.Methods:We constructed a cohort of RA patients - age ≥40 with ≥1 RA diagnosis from a rheumatologist, excluding patients with malignancy, past myocardial infarction (MI) or stroke - by linking Medicare administrative data from 2006-2016 to multi-biomarker disease activity (MBDA) test results obtained as part of routine care. The cohort was split 2:1 to create training and internal validation datasets. The composite CV outcome was MI, stroke or CV death occurring within 3 years. Clinical predictors examined were: age, sex, race, traditional CV risk factors (e.g. diabetes, hypertension, hyperlipidemia, high-risk CV conditions [e.g. angina]), RA-related factors (e.g. glucocorticoid use, MTX, number of prior biologics), adjusted MBDA score1and its 12 biomarkers, log-transformed. Backward elimination was used to remove predictors with p ≥0.05. The resulting CV risk score was compared to four prediction models (age+sex; age+sex+CRP; age+sex+diabetes+hypertension+ smoking+high risk CV [±CRP]) in the validation dataset. We evaluated: 1) incremental improvement in the likelihood ratio test (LRT) statistic, 2) discrimination (AUROC), and 3) goodness-of-fit (predicted vs. observed, based on Kaplan-Meier estimates). Validation analyses were prespecified.Results:30,751 RA patients with 904 CV events were linked to MBDA test results and eligible for analysis. Patient characteristics were mean (SD) age 68.7 (9.5) years; 23.4% age <65; 82% women. Comorbidities included diabetes (39%), hypertension (78%), smoking (24%) and history of high-risk CV condition (37%). RA-related features included use of glucocorticoids (58%), MTX (60%), TNFi (33%) and other biologics (16%). Mean (SD) MBDA score was 41 (14). The final covariates included in the MBDA-based CV risk score were age, diabetes, hypertension, smoking, history of high-risk CV conditions, adjusted MBDA score, leptin, TNFRI and MMP-3. Median (IQR) of the predicted 3-year CV risk was 3.4% (2.1%, 5.6%). Based on extrapolation to 10-year risk, 9.4% of patients would be considered low, 10.2% borderline, 52.2% intermediate, and 28.2% high risk per 2019 ACC/AHA guidelines.Compared to four simpler CV prediction models, significant improvement in the LRT statistic was observed with the addition of the biomarker-based CV risk score (Figure 1). Model fit was good across deciles (Figure 2). The AUROC was 0.70. The MBDA-based model reclassified 28.5% of patients vs. the model based on age+sex+diabetes+hypertension +smoking+high risk CV+CRP.Figure 1.Incremental Improvement of MBDA-based CV Risk Score Compared to Other CV Risk Prediction ModelsFigure 2.MBDA-Based CV Risk Score Calibration for Composite CV Outcome at 3 YearsConclusion:A biomarker-based prediction score incorporating a few clinical risk factors appears to have good accuracy to predict CV risk in RA. Additional validation in independent cohorts will help verify its performance characteristics.References:[1] Curtis et al.,Rheumatology2018;58:874.Disclosure of Interests:Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Cheryl Chin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc.

AB1243 TRAINING AND VALIDATION OF A MULTIVARIATE PREDICTOR OF RISK OF RADIOGRAPHIC PROGRESSION FOR PATIENTS WITH RHEUMATOID ARTHRITIS

Background:The multi-biomarker disease activity (MBDA) score, adjusted for age, sex and adiposity (MBDAadj), has been shown to be better than several conventional disease activity measures for predicting risk for radiographic progression (RP) in patients with rheumatoid arthritis (RA).1Serologic status and other non-disease activity measures are also predictive of RP risk. Combining them with the MBDAadjshould result in a stronger prognostic test for RP than any one measure alone.Objectives:Develop a multivariate model for predicting risk for RP that includes the adjusted MBDA score and other known predictors of RP.Methods:Four RA cohorts were used, two for training (OPERA and BRASS, n=555) and two for validation (SWEFOT and Leiden, n=397). Each pair of cohorts was heterogeneous in disease duration and treatment history. BMI data were not available for one validation cohort, so a BMI surrogate was modeled using forward selection with the two training cohorts and 3 others (CERTAIN, InFoRM, RACER) (N=1411). An RP risk score was then trained using forward selection in a linear mixed-effects regression, considering disease-related and demographic variables as predictors of change in modified total Sharp score over one year (ΔmTSS), with a random effect on cohort. The RP risk score was validated as a predictor of RP with two cutoffs (ΔmTSS >3 and >5) using logistic mixed-effects regression. Odds ratios (OR) and 95% profile likelihood-based confidence intervals (CI) were calculated from the models and significance was assessed by likelihood ratio tests. Risk curves were generated to show probability of RP as a function of the RP risk score.Results:The BMI surrogate included leptin, sex, age and age2and correlated well with BMI (ρ = 0.76). In training, the most significant independent predictors of RP were MBDAadj(p = 0.00020), seropositivity (p = 9.3 x 10-5), BMI surrogate score (p = 0.013) and use of targeted therapy (p = 0.0026). The final model was: RP risk score = 0.024 x MBDAadj+ 0.093 if seropositive – 0.063 x BMI surrogate score – 0.61 if using a targeted therapy. In validation, the OR (95% CI) of the RP risk score for predicting ΔTSS >3 or >5 were 2.2 (1.6, 3.2) (p = 2.6 × 10-6) and 3.1 (2.0, 5.0) (p = 5.7 × 10-8), respectively (Figure 1). The odds of a patient having RP increases by 50% for each 21-unit or 15-unit increase in MBDAadj, for RP defined as ΔTSS >3 or >5, respectively.Figure 1.Conclusion:A multivariate model containing adjusted MBDA score, seropositivity, a BMI surrogate and use of targeted therapy has been trained and validated as a prognostic test for radiographic progression in RA.References:[1]Curtis, et al.Rheumatology [Oxford].2018;58:874Disclosure of Interests:Thomas Huizinga Grant/research support from: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Consultant of: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Michael E. Weinblatt Grant/research support from: BMS, Amgen, Lilly, Crescendo and Sonofi-Regeneron, Consultant of: Horizon Therapeutics, Bristol-Myers Squibb, Amgen, Abbvie, Crescendo, Lilly, Pfizer, Roche, Gilead, Nancy Shadick Grant/research support from: Mallinckrodt, BMS, Lilly, Amgen, Crescendo Biosciences, and Sanofi-Regeneron, Consultant of: BMS, Cecilie Heegaard Brahe: None declared, Mikkel Ǿstergaard Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Merck, and Novartis, Consultant of: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Merete L. Hetland Grant/research support from: BMS, MSD, AbbVie, Roche, Novartis, Biogen and Pfizer, Consultant of: Eli Lilly, Speakers bureau: Orion Pharma, Biogen, Pfizer, CellTrion, Merck and Samsung Bioepis, Saedis Saevarsdottir Employee of: Part-time at deCODE Genetics/Amgen Inc, working on genetic research unrelated to this project, Megan Horton Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

A single-institution and commercial laboratory database analysis of BRIP1-associated cancer risks.

1538 Background: BRIP1/ FANCJ participates in DNA replication and repair via interactions with BRCA1 and possibly MLH1. Previous studies have reported that pathogenic variants (PV) in BRIP1 are associated with an ~2-fold increase in risk for ovarian cancer (OC) and triple-negative breast cancer (TNBC). Although multigene panel testing for hereditary cancer (CA) has identified BRIP1 PV and uncertain variants (VUS) in patients with diverse CAs including breast (BC), colorectal (CRC) and melanoma (Mel), association with these CA types has not been established. Methods: We examined BRIP1 risks in two independent populations: Fox Chase Cancer Center (FCCC) and Myriad Genetics (MGL). At FCCC, pedigrees of BRIP1 PV ( N= 10) and VUS families ( N= 47) were reviewed. The MGL population included patients referred for testing by multigene panel (9/2013-12/2019) ( N= 586,740). Multivariable logistic regression analysis estimated BRIP1 CA risks as odds ratios (ORs) and 95% confidence intervals (CIs). Models were adjusted for age, sex, ancestry, personal CA history (PHX), and family CA history. Results: In the FCCC cohort, BRIP1 PV carriers ( N= 12) reported PHX of early-onset ( < 50) BC, CRC, and bladder CA. BRIP1 VUS were also identified among several patients with striking PHX and negative panel testing: BC < 40 ( N= 3), bilateral BC ( N= 4), TNBC ( N= 2), CRC < 40 ( N= 3), and a patient with 3 CAs < 40 (CRC, BC, and Mel). All FCCC families with a BRIP1 PV and select VUS families ( N= 6) are seen in the Table. In the MGL population, 0.3% (1,678/586,740) carried a BRIP1 PV. Logistic regression analyses found that female BRIP1 PV carriers have significantly increased risk for OC (OR 2.40, 95% CI 1.93-2.98) and TNBC (OR 1.93, 95% CI 1.52-2.46). Data were insufficient for testing risk of bladder or prostate CA. Findings did not support associations of BRIP1 with CRC, melanoma, endometrial, pancreatic or gastric CA. Conclusions: BRIP1 PV and VUS may be identified in patients with diverse CA histories. These results confirm studies showing that BRIP1 PV are associated with an ~2-fold increased risk of OC and TNBC, but do not support increased risks of CRC, melanoma or endometrial CA in BRIP1 PV carriers. [Table: see text]