Yousefi, Kasra

Abstract Background: Real world data (RWD) is one of the key components to our understanding of the treatment trajectory of patients with multiple myeloma (MM) in clinical practice. However, treatment responses recorded in patients' medical records are not often standardized. We have developed an algorithm to derive real-world response (dR) for MM patients based on the International Myeloma Working Group (IMWG) criteria to overcome issues with missing values (Xu et al. Pharmacoepidemiol Drug Saf 2021). In this study, we evaluate its association with overall survival (OS) in a RWD cohort. Methods: This study included patients diagnosed with MM between Jan 1, 2011 and Jan 31, 2021 from the US-based Flatiron Health electronic health record (EHR)-derived de-identified database. We derived overall response (partial response [PR] or better, e.g. > 50% reduction of serum M-protein from baseline) to 1L treatment in newly diagnosed MM patients based on the IMWG response criteria, taking into account missing data. Associations between dR and OS were evaluated at individual- and treatment-level. At individual-level, associations were assessed using landmark analyses at approximating 3/4/5 cycles of treatment and at 6 months. Patients who died before the landmark were excluded from the analysis. At treatment-level, we stratified the cohort by year of treatment initiation (every two years from 2011-2021). Within each stratum, treatment groups were compared to estimate the hazard ratios (HRs) of OS and odds ratios (ORs) of dR, both adjusted for potential confounding factors (age, ECOG status, cytogenetic risk groups [high vs standard], time between diagnosis and first-line [1L] start date). The association between HRs and ORs was assessed by coefficient of determination (R 2) from a stratum-size weighted linear regression model, where values close to 1 imply a strong correlation and 0 indicates no association. Only significant HRs and ORs (P < 0.05) in the multivariable analysis were used in the analysis. Results: Of 6806 patients in the Flatiron Health MM database, 70% (n=4830) had serum myeloma (M) protein measured by protein electrophoresis within 30 days before the start of 1L treatment, 27% (n=1838) had urine M protein and 57.8% (n=3935) had serum free light chains (FLCs). Patients with at least one serum M protein or FLC measurement were eligible for study entry (n=5609). During 1L treatment, 4727 patients (46% female, mean ± standard deviation age 67.8 ± 10.4 years) had valid laboratory test results for dR assessment (for each patient we required a baseline measurement plus at least one additional measurement after treatment start of the same lab type), of which 71.7% (n=3387) had PR or better. The majority of the patients (n=2188, 46.3%) received a proteasome inhibitor (PI)+steroid+immunomodulatory drug (IMiD) treatment regimen, followed by PI+chemo+steroid (n=639, 13.5%), PI+steroid (n=621, 13.1%), and steroid+IMiD (n=607, 12.8%). Other treatment groups had fewer than 100 patients and were not considered in the treatment-level analysis (Table 1). At the individual-level analysis, dR was significantly associated with OS at all landmarks (HRs 0.80 to 0.81, P < 0.001, Table 2). At treatment-level, the association between dR and OS was R 2=0.67 (P < 0.001, Figure 1). Subgroup analyses were conducted for each pair of treatment groups; however, only PI+IMiD+steroid vs PI+steroid had sufficient sample sizes, which showed R 2 of 0.82 (P = 0.02). Conclusions: In the absence of recorded response data in a patient's EHR, laboratory assessments can successfully be applied using our algorithm to determine, based on part of the IMWG response criteria, the response status for patients with MM. In the RWD cohort, patients who had PR or better to 1L treatment demonstrated longer survival than non-responders. A moderate association was observed at the treatment-level between dR at 1L and OS. However, these associations might be specific to certain treatment groups and the underlying mode of action of the drugs. Further analysis will be needed to validate these results. Figure 1 Figure 1. Disclosures Xu: F. Hoffmann-La Roche AG: Current Employment. Tyanova: F. Hoffmann-La Roche: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; AbbVie: Other: Received travel sponsorship ; F. Hoffmann-La Roche Ltd.: Current Employment; NHS: Ended employment in the past 24 months; Sanofi: Consultancy; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Roche: Current equity holder in publicly-traded company; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Celgene: Honoraria, Other: Received travel sponsorship ; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag Ltd: Honoraria, Speakers Bureau. Yousefi: Roche: Current Employment, Current equity holder in publicly-traded company. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Inflammation is linked to prostate cancer progression and is mediated by NF-κB. Tristetraprolin is a key node of NF-κB activation and we investigated its biological and prognostic role in lethal prostate cancer. Methods: In vitro assays assessed the function of tristetraprolin and the association between low mRNA tristetraprolin levels and lethal prostate cancer (metastatic disease or death) was assessed across independent prostatectomy cohorts: (i) nested case-control studies from Health Professionals Follow-up Study and Physicians’ Health Study, and (ii) prostatectomy samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and Memorial Sloan Kettering Cancer Center. Tristetraprolin expression levels in prostatectomy samples from patients with localized disease and biopsies of metastatic castration–resistant prostate cancer (mCRPC) were assessed in a Cornell University cohort. Results: In vitro tristetraprolin expression was inversely associated with NF-κB–controlled genes, proliferation, and enzalutamide sensitivity. Men with localized prostate cancer and lower quartile of tumor tristetraprolin expression had a significant, nearly two-fold higher risk of lethal prostate cancer after adjusting for known clinical and histologic prognostic features (age, RP Gleason score, T-stage). Tristetraprolin expression was also significantly lower in mCRPC compared with localized prostate cancer. Conclusions: Lower levels of tristetraprolin in human prostate cancer prostatectomy tissue are associated with more aggressive prostate cancer and may serve as an actionable prognostic and predictive biomarker. Impact: There is a clear need for improved biomarkers to identify patients with localized prostate cancer in need of treatment intensification, such as adjuvant testosterone suppression, or treatment de-intensification, such as active surveillance. Tristetraprolin levels may serve as informative biomarkers in localized prostate cancer.

5062 Background: Inflammation has been linked to prostate cancer (PCa) progression which may be mediated by the transcription factor nuclear factor kappa B (NFκB). Using a bioinformatic screen focused on NFκB pathway activation in lethal PCa, we identified the tumor suppressor ZFP36 as a key node of the NFκB network. We also showed in vitro that ZFP36 expression was inversely associated with both NFκB-controlled gene levels and proliferation and sensitivity to enzalutamide. We examined the role of ZFP36 and PCa progression in patient cohorts. Methods: The association between low mRNA expression of ZFP36 (levels in the lower quartile) and lethal PCa (defined as metastatic disease or death) was assessed with logistic regression among men with localized PCa in two independent cohorts treated with radical prostatectomy (RP). The discovery cohort was RP samples from Health Professional Follow-up Study and Physicians Health Study and the validation cohort was RP samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and MSKCC. In a third cohort from Cornell University, ZFP36 expression levels were assessed in RP samples from patients with localized PCa and biopsies of metastatic castration resistant prostate cancer (mCRPC). Results: Table 1 shows men with localized PCa and lower quartile of tumor ZFP36 expression had a nearly 2-fold higher risk of lethal PCa after adjusting for known clinical and histological prognostic features (age, RP Gleason score, T-stage); an association confirmed in the validation cohort. ZFP36 gene expression was also significantly lower in mCRPC (n=53) compared with a localized RP cohort (n=68) (p-value <0.0001). Conclusions: In humans lower ZFP36 in RP specimens is associated with clinically significant risk of lethal PCa after treatment with curative intent and lower ZFP36levels are found in metastatic tissue. [Table: see text]

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions.

Abstract Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT. Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT–treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling–related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis. Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035). Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908–16. ©2018 AACR.

125 Background: Men with lymph node involvement (LNI) at prostatectomy (RP) are at high risk of dying from prostate cancer. However, survival following RP is highly variable, with some men apparently cured. Nomograms developed for men with LNI have been based on series where all or the vast majority of men received adjuvant treatment. Because administration of adjuvant treatment is not universal, even for LNI, we evaluated whether the Decipher genomic classifier (GC) can improve upon clinical models to predict metastasis within 5 years (MET5) and prostate cancer specific mortality within 10 years (PCSM10) in a cohort of LNI patients, the majority of whom did not receive adjuvant treatment. Methods: 141 patients from 4 institutions (Johns Hopkins, Mayo Clinic, Leuven, MD Anderson) had LNI at RP, had adequate tissue and clinical data for analysis of MET5, and 86 were analyzed for PCSM10. 43% of men received adjuvant therapy. RP tumor tissue was analyzed by Affymetrix Human Exon 1.0 ST GeneChip; the GC was calculated based on 22 genes in the previously trained and validated algorithm. Logistic regression evaluated whether the GC, dichotomized as high risk (GC score > 0.6) vs low-intermediate risk (≤0.6), improved prediction of MET5 and PCSM10 beyond that achieved with established clinical prognostic factors. Results: 62 men (43%) developed MET5, and 35 (41%) developed PCSM10. For both MET5 and PCSM10 CAPRA-S, number of positive lymph nodes, and age were significant; adjuvant therapy was not significant. GC was a significant independent strong prognostic factor when added to the clinical model for prediction of MET5, odds ratio = 4.04 (95% CI: 1.48, 11.02), p = 0.006, and prediction of PCSM10, odds ratio = 6.71 (95% CI: 2.01, 22.38), p = 0.002. Addition of GC to the clinical model improved the AUC from 0.77 to 0.79 for MET5, and from 0.65 to 0.74 for PCSM10. Conclusions: The Decipher GC significantly improves upon clinical variables to predict metastasis and prostate cancer specific mortality in men at high risk due to LNI. This is the first study to show a genomic classifier predicts outcomes in men with LNI; validation is needed to determine if Decipher can improve treatment decisions in men with LNI.