Citations

Abstract Background: Disease relapse is observed in a significant proportion of HER2-positive breast cancer (BC) patients with residual disease (RD) after neoadjuvant treatment, as well as in a subgroup of those achieving pathological complete response (pCR). As the host immune response plays a key role in modulating the activity of anti-HER2 agents, we investigated the association of T- and B-cell receptor (TCR and BCR) repertoires with pCR and event-free survival (EFS) in the NeoALTTO phase 3 trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 patients out of the 455 enrolled in the study. Among those, 166 did not achieve a pCR defined as ypT0/is. Matched RNAseq data from RD samples were available for 43 cases. TCR/BCR repertoires were extracted from RNAseq data using the MiXCR software. TCR and BCR read counts, number of clones, evenness, Shannon entropy, Gini index, length of the complementarity determining region 3, top and second top clone proportion were evaluated. Survival analysis was performed using univariate and multivariate (adjusted for tumor size, nodal status, grade, estrogen receptor [ER] status, age and treatment arm) Cox proportional hazard models, while logistic regressions were used for pCR. False discovery rate (FDR) was obtained using Benjamini & Hochberg method. Results: Baseline TCR top (odds ratio [OR]=0.63 [95% CI 0.46-0.87], FDR=0.021) and second top (OR=0.57 [0.41-0.79], FDR=0.004) clone proportion were significantly associated with a lower probability of achieving pCR in the multivariate analysis. BCR evenness (hazard ratio [HR]=1.5 [1.2-2], FDR=0.015) and Gini index (HR=0.66 [0.52-0.85], FDR=0.015) were significantly associated with EFS in the multivariate analysis. In residual disease, BCR evenness and Gini index showed a similar trend in the EFS univariate analysis, while TCR read counts, number of clones, and entropy were associated with lower HR (P<0.05), although FDR were borderline significant (0.05<FDR<0.1). A model to predict EFS including baseline BCR evenness, ER status, stromal tumor-infiltrating lymphocyte level, and pCR was able to identify 3 groups with good, intermediate and poor prognosis (Kaplan-Meier 5-year EFS rates of 95%, 80% and 58%, respectively). Of note, about 63%, 45% and 12% of the patients achieved a pCR in the three prognostic groups, respectively. Conclusions: In the NeoALTTO trial, the presence of an evenly distributed BCR repertoire was associated with worse EFS. A model integrating baseline immune-related and clinical features was able to identify patients with excellent prognosis despite RD or, conversely, with poor prognosis after pCR. We envision that our model has the potential to allow the personalization of post-operative treatment strategies after both pCR and RD in HER2-positive BC. Further validation of our findings is warranted. Citation Format: Mattia Rediti, David Venet, Françoise Rothé, Tao Qing, Marion Maetens, Ian Bradbury, Miguel A. Izquierdo, Serena Di Cosimo, Florentine Hilbers, Mohammed Bajji, Nadia Harbeck, Michael Untch, David L. Rimm, Stephen Chia, Minetta C. Liu, Cristina Saura, Jens Huober, Paolo Nuciforo, Roberto Salgado, Sherene Loi, Lajos Pusztai, Christos Sotiriou. Predictive and prognostic role of T- and B-cell receptor repertoire in HER2-positive breast cancer: An analysis of the NeoALTTO clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1998.

The coronavirus disease 2019 (COVID-19) pandemic has upended daily life and neurologic care for most patients, including those with Parkinson’s disease and parkinsonism. Disruptions to routine care, high volumes of patient and caregiver calls, and our patients’ risk of infection and complications inspired a proactive COVID-19 outreach program. This program targets patients with advanced Parkinson’s disease and related disorders, specifically those who are homebound, receiving or eligible for palliative care, and/or lacking support networks. We describe the program and practical strategies providers can implement to support wellbeing and successful telehealth uptake during this time of social isolation and gradual reopening.

511 Background: Clinicopathological and molecular features, including estrogen receptor (ER) status and PAM50 subtypes, have shown an association with immunogenicity and tumor-infiltrating lymphocyte (TIL) levels in breast cancer (BC). To investigate the complexity of the immune response in HER2+ BC, we explored the association of T- and B-cell receptor (TCR and BCR) repertoires with clinicopathological characteristics, PAM50 subtypes and outcome in the NeoALTTO phase III trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 out of the 455 patients enrolled. TCR and BCR repertoires were extracted from RNAseq data using the MiXCR software. Repertoire and diversity measures (read counts, number of clones, evenness, Gini index, Shannon entropy, length of the complementarity-determining region 3 [CDR3], top and second top clone proportions) were estimated. PAM50 subtypes were computed from RNAseq data. Univariate and multivariate (adjusted for clinicopathological characteristics, TIL levels dichotomized using the median value of 12.5% and treatment arm) Cox proportional hazard models were used for survival analysis, while logistic regressions were used for pathological complete response (pCR), defined as ypT0/is. All results reported had a false discovery rate (FDR) <0.05. Results: Higher BCR read counts, number of clones and Gini index were significantly associated with ER-negative as well as grade 3 tumors. Among the PAM50 subtypes, HER2-enriched (HER2-E) showed significantly higher BCR read counts, number of clones and Gini index along with lower evenness compared to luminal A and B, as well as higher length of CDR3 than luminal A. Of note, basal-like showed similar BCR diversity measures to HER2-E. No significant differences were noted for TCR diversity measures. In multivariate analyses, neither TCR nor BCR features were associated with pCR, while BCR evenness (HR 1.5; 95%CI 1.1-2.1) and Gini index (HR 0.66; 95%CI 0.5-0.88) were associated with event-free survival. Conclusions: BCR repertoire measures suggest a clonal expansion in HER2-E and basal-like PAM50 subtypes. Furthermore, the implementation of BCR-derived biomarkers can help to identify patients with an improved clinical outcome after neoadjuvant anti-HER2 treatment. Our findings highlight the heterogeneity of the immune response within HER2+ BC and provide support for biomarker-driven treatment strategies including immunotherapy in this BC subtype. Further validation is required. Clinical trial information: NCT00553358 .

167 Background: Previous studies have reported high TILs are a favorable prognostic factor in stage II CC. However, whether the impact of TILs on overall survival (OS) differs among pts who did or did not receive ADJ is still to be determined. We assessed the prognostic value of CD3+ TILs in pts with stage II CC according to whether they received ADJ or not (no-ADJ). Methods: Pts treated with curative surgery for stage II CC (2002-2013) were identified through the Santa Maria alle Scotte Hospital database. CD3+ TILs at the invasive front, center of tumor, and stroma, were determined by immunohistochemistry and manually quantified as the rate of TILs/total tissue areas. High TILs (H-TILs) was defined as > 20%. Pts were classified as high or low TILs (L-TILs) and ADJ or no-ADJ. Cox models were used to assess OS with hazard ratio estimates (95% CI). Results: Of the 678 pts included (356 deaths), 137 (20%) received ADJ while 541 (80%) did not. ADJ comprised fluoropyrimidine +/- oxaliplatin. Median follow-up was 8.5 years. The distributions of the 4 groups were: 16% (L-TIL/ADJ), 64% (L-TIL/no-ADJ), 5% (H-TIL/ADJ), 15% (H-TIL/no-ADJ). Compared to H-TILs/no-ADJ, ADJ pts had a significantly longer OS (P < .0001) regardless of the TILS rate while L-TILs/no ADJ had significantly shorter OS and higher risk of death (HR = 1.41; 95% CI, 1.06-1.88; P < .0001) [See table]. On multivariable analysis, adjusting for perforation, obstruction, T-stage, grade, < 12 lymph nodes resected, lymphovascular and perineural invasion, the adverse prognostic impact of L-TILs (vs H-TILs) in no-ADJ pts was confirmed (HR = 1.36; 95% CI 1.02, 1.82; P = .0373). Conclusions: Low CD3+ TILs rate was independently associated with shorter OS in stage II CC pts who did not receive ADJ, but was not prognostic among pts who had ADJ. These data suggest a potentially different impact of TILs in chemo-treated vs -untreated stage II CC which could affect clinical decision making. [Table: see text]

Abstract Introduction: Mutations localized in the tyrosine kinase domain activation loop of FLT3 (FLT3-TKD), representing point mutations in codon D835/I836 and rarely deletions of codon I836, induce constitutive tyrosine phosphorylation and activation of the receptor tyrosine kinase similarly to FLT3 internal tandem duplication (ITD) mutations. However, the prognostic role of FLT3-TKD in AML, particularly in the presence of NPM1 mutations, is not well established. The phase 3 RATIFY trial [NCT00651261; Stone et al. N Engl J Med. 2017] showed that in combination with standard chemotherapy, midostaurin (PKC412) improved survival outcomes across all 3 FLT3 stratification subgroups (ITD high allelic ratio [≥ 0.7], ITD low allelic ratio [< 0.7], and TKD) vs placebo in patients with newly diagnosed FLT3-mutated AML. Here, we evaluated the prognostic impact of FLT3-TKD and NPM1 mutations in a post hoc analysis from the RATIFY trial. Methods: In RATIFY, newly diagnosed patients with AML 18-60 years old were randomly assigned to receive midostaurin or placebo together with standard induction and consolidation therapy followed by 12 28-day cycles of maintenance therapy with midostaurin or placebo. FLT3-TKD mutation was detected by PCR and capillary electrophoresis at 9 reference laboratories. Patients were categorized as NPM1 mutated (mut) or NPM1 wild-type (WT) using PCR. Efficacy outcomes included complete remission (CR), overall survival (OS), event-free survival (EFS) and disease-free survival (DFS). EFS and DFS analyses were performed considering CR within a 60-day window. P values presented have not been adjusted for multiplicity. Results: Of the total randomized 162 FLT3-TKD patients, 134 with available NPM1 data had consented for exploratory analysis and thus were included in this study (see Table for subgroup distribution). Overall, 47.8% of patients were male, and the median age was 49 years (95% CI, 45.5-51.1 years). The median white blood cell (WBC) count was higher in patients with NPM1-mut than in patients with NPM1-WT (34.1 vs 15.5 × 109/L, P = .0011). CR rates (during the first 60 days) were higher in patients with FLT3-TKD/NPM1-mut vs FLT3-TKD/NPM1-WT (66% vs 53%); however, this was driven by the higher rate of CR in the midostaurin arm (76% NPM1-mut vs 44% NPM1-WT) rather than the placebo arm (53% NPM1-mut vs 60% NPM1-WT). The overall CR rate (regardless of NPM1 genotype) was 64% for midostaurin and 56% for placebo in FLT3-TKD patients. The prognostic effect of the NPM1 mutation concurrent with FLT3-TKD was seen for all endpoints consistently with hazard ratios (HRs) around 0.50 or lower (Figures 1 and 2 and Table). Overall (regardless of treatment) OS, EFS, and DFS estimates at 3 years were 73% vs 52%, 48% vs 25%, and 74% vs 47%, respectively, in patients with FLT3-TKD/NPM1-mut vs FLT3-TKD/NPM1-WT. Whereas the HRs for midostaurin vs placebo were 0.73 for both OS and EFS, the impact of treatment on outcomes varied between the individual NPM1/TKD subgroups and was not consistently observed when endpoints were censored at stem cell transplant (SCT) (Table). It should be noted that the number of patients in each subgroup was small and therefore the HRs with 95% CIs should be interpreted with caution. Multivariate analyses in these FLT3-TKD patients revealed that NPM1 genotype was an independent prognostic factor for OS, EFS and DFS (2-sided P < .05), whereas study drug, age, sex, WBC at baseline and SCT (no/yes) did not reach this level of significance in the Cox model. Conclusions: This post hoc analysis of the FLT3-TKD patient subset in the RATIFY trial showed the high prognostic value of NPM1 mutational status. Whereas midostaurin showed an overall benefit in the FLT3-TKD patients for OS, EFS, CR and DFS, the impact of treatment on outcome varied between the individual NPM1 subgroups within these FLT3-TKD patients and was not consistently observed.Further analyses using additional endpoints and additional multivariate analyses are planned. Support: U10CA180821, U10CA180882, U10CA180820, U10CA180791, U10CA180888, U10CA180863, (CCSRI) #704970, U24CA196171; ClinicalTrials.gov Identifier: NCT00651261 Disclosures Voso: Celgene: Research Funding, Speakers Bureau. Larson:Ariad/Takeda: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding. Heuser:Janssen: Consultancy; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; StemLine Therapeutics: Consultancy; Astellas: Research Funding; BergenBio: Research Funding; Karyopharm: Research Funding; Bayer Pharma AG: Consultancy, Research Funding; Tetralogic: Research Funding; Sunesis: Research Funding; Daiichi Sankyo: Research Funding. Wei:Novartis: Honoraria, Other: Advisory committee, Research Funding, Speakers Bureau; Pfizer: Honoraria, Other: Advisory committee; Amgen: Honoraria, Other: Advisory committee, Research Funding; Abbvie: Honoraria, Other: Advisory board, Research Funding, Speakers Bureau; Servier: Consultancy, Honoraria, Other: Advisory committee, Research Funding; Celgene: Honoraria, Other: Advisory committee, Research Funding. Brandwein:Lundbeck: Consultancy; Celgene: Consultancy; Pfizer: Consultancy; Novartis: Consultancy; Boehringer Ingelheim: Consultancy, Research Funding. de Witte:Novartis: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Honoraria, Research Funding. Medeiros:Celgene: Consultancy, Research Funding; Genentech: Employment. Tallman:Cellerant: Research Funding; Orsenix: Other: Advisory board; BioSight: Other: Advisory board; ADC Therapeutics: Research Funding; AROG: Research Funding; AbbVie: Research Funding; Daiichi-Sankyo: Other: Advisory board. Schlenk:Pfizer: Research Funding, Speakers Bureau. Ganser:Novartis: Membership on an entity's Board of Directors or advisory committees. Cheng:Novartis: Employment. Gathmann:Novartis: Employment. Tiecke:Novartis: Employment. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding. Döhner:AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Celator: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Agios: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Pfizer: Research Funding; Pfizer: Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Stone:Otsuka: Consultancy; Argenx: Other: Data and Safety Monitoring Board; Amgen: Consultancy; Agios: Consultancy, Research Funding; Orsenix: Consultancy; Ono: Consultancy; Novartis: Consultancy, Research Funding; Astellas: Consultancy; Arog: Consultancy, Research Funding; Merck: Consultancy; Cornerstone: Consultancy; Fujifilm: Consultancy; Jazz: Consultancy; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee; Pfizer: Consultancy; Sumitomo: Consultancy; AbbVie: Consultancy.

Abstract Background: Over the last two decades several phenotypic, molecular, and chromosomal markers have been identified that are significantly associated with the prognosis of CLL patients. Therefore, clinicians managing CLL patients would benefit from a simplified prognostic index. Methods: We analyzed prospectively collected data from 337 Binet A CLL patients enrolled in Italian the O-CLL1-GISL protocol with the aim of developing scores capable of predicting treatment free survival (TFS). Factors independently associated with TFS were included in the prognostic indexes. To account for differences in the magnitude of the association between the individual independent factors and TFS, we assigned a weighted risk score to each factor based on ranges of their corresponding hazard ratios (HRs) (i.e., 1 point for HR 1.1-1.9; 2 points for HR 2.0-2.9, etc.). The total risk score was then calculated by the sum of the ratings of each factor on individual basis. Risk groups were identified combining risk categories with a non-statistically different TFS. Results: We developed two scores based on weighted multivariable models: the first included clinical and laboratory parameters [clinical score (c-score)], while the second was based on biological markers [biological score (b-score)] (Table 1). The c-score allowed to predict the TFS of patients through the combination of Rai stage, b2-microglobulin and absolute lymphocyte count (ALC), while the b-score predicted TFS by IGHV mutational status and CD38 expression. The c-score showed a C-statistic of 0.72, while the b-score was 0.67, although cases stratified according to the b-score showed a more specific mRNA/microRNA profile. When the two scores were forced in a multivariate analysis, both showed an independent predictive value on TFS with a similar HR, demonstrating their complementarity. Thus, we attempted to integrate the two scores performing a further multivariate analysis in which all parameters, significantly associated with TFS at univariate analysis, were tested (Table 1). ALC, Rai stage, b2-microglobulin together with IGHV mutational status, resulted independently associated with TFS. We constructed a weighted score [comprehensive score (co-score)], including all the above 4 variables, which allowed the identification of 3 different risk groups with significantly different TFS (Figure 1). The C-statistic of the g-score was 0.75, showing a better concordance than the other two scores. Moreover, its validity was externally validated in a series of 297 newly diagnosed Binet A CLL patients from the Mayo Clinic, USA. Conclusions: Using this multistep process and external validation, we developed a score with high discriminatory power and predictive significance on the individual patient level. Table 1. Univariate and multivariate Cox proportional Hazards Models Variable Univariate analysis Multivariate analysis Clinical model Biological model Comprehensive model HR (95% CI) P HR (95% CI) P score HR (95% CI) P score HR (95% CI) P score Age (years) <60/>60 1.12 (0.73-1.74) 0.59 - - - - - - - - - Sex Male/Female 0.93 (0.6-1.44) 0.93 - - - - - - - - - Rai stage 0/I-II 2.30 (1.47-3.50) <0.0001 2.13 (1.24-3.03) 0.004 0/2 - - - 1.76 (1.11-2.78) 0.015 0/1 ALC (109/L) <10/>10 3.43 (1.99-5.92) <0.0001 2.91 (1.85-5.20) <0.0001 0/2 - - - 2.70 (1.54-4.72) 0.001 0/2 b-2 microglobulin normal/elevated 3.04 (1.96-4.70) <0.0001 2.78 (1.79-4.30) <0.0001 0/2 - - - 2.65 (1.66-4.21) <0.0001 0/2 LDH normal/elevated 1.25 (0.57-2.71) 0.57 - - - - - - - - - CD38 negative/positive 3.22 (2.06-5.02) <0.0001 - - - 2.11 (1.15-3.16) 0.02 0/2 1.40 (0.80-2.42) 0.24 - ZAP-70 negative/positive 2.34 (1.51-3.61) <0.0001 - - - 1.21 (0.70-2.16) 0.485 - 1.0 (0.98-1.01) 0.72 - IGHV mutated/unmutated 3.57 (2.32-5.50) <0.0001 - - - 2.10 (1.12-3.90) 0.019 0/2 2.39 (1.27-4.50) 0.007 0/2 FISH risk low+int/high 2.93 (1.46-5.90) 0.002 - - - 1.65 (0.76-3.34) 0.216 - 1.80 (0.84-3.88) 0.13 - Abbreviations: ALC: absolute lymphocyte count; CI: confidential interval; HR: hazard ratio. Figure 1. TFS according to comprehensive progression risk score. Figure 1. TFS according to comprehensive progression risk score. Disclosures Shanafelt: Cephalon: Research Funding; Glaxo-Smith-Kline: Research Funding; Genentech: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Jannsen: Research Funding; Polyphenon E International: Research Funding; Pharmacyclics: Research Funding. Kay:Genentech: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding; Tolero Pharma: Research Funding; Pharmacyclics: Research Funding.

Abstract Background: The level of immune suppression in an individual breast cancer (BC) patient is relevant for having a major benefit from treatments that act via or are directed to the immune response, such as anti-HER2 agents and immune checkpoint molecules, respectively. The link(s) between the tumor immune microenvironment and treatment responses has been investigated in HER2-positive BC. This study analyzed the association between baseline expression of genes regulating T cell activities (PD1, CTLA4, LAG3, TIM3, PDL1, PDL2, FOXP3, IL10, TGFβ (1-3), FOXP1 and other related genes) and pathologic complete response (pCR) rates in the NeoALTTO trial. Methods: NeoALTTO randomized early-stage HER2-positive BC patients to neoadjuvant trastuzumab (T), lapatinib (L) or T+L for 6 weeks followed by 12 weeks of concurrent paclitaxel. Anthracyclines were given after surgery (FEC). Patients with RNA sequencing data from their baseline tumor samples (N=254 patients out of 455 from the original cohort) were included in the current study. The primary endpoint, pCR, was defined as ypT0/is ypN0. Logistic regression was used for analysis of pCR. Cox regression univariate and multivariate (adjusted for clinicopathological parameters and treatment arms) analyses were performed. Results: In the total cohort analyzed, high PD1 (odds ratio, OR: 1.4, P=0.03), PDL2 (OR: 1.4, P=0.04), CTLA4 (OR: 1.4, P=0.03) and TGFβ3 (OR: 1.4, P=0.02) expression was associated with an increased probability of achieving pCR at the multivariate analysis. ERBB2 (HER2) expression was associated with pCR in the three arms (T: OR: 2.7, P=0.02; L: OR: 2.3, P=0.01; T+L: OR: 5, P&lt;0.001). Additionally in the T+L arm, PAM50 HER2-enriched subtype (OR: 2.9, P=0.03), as well as immune genes PD1 (OR: 2.1, P=0.01), PDL1 (OR: 1.8, P=0.03) and CTLA4 (OR: 2; P=0.01), were also associated with pCR. Conclusions: In the NeoALTTO population examined, PD1, PDL2, CTLA-4 and TGFβ3 expression at diagnosis were all associated with improved pCR rates after anti-HER2 treatment. The effect of these treatments seemed to be dependent on HER2 expression levels, which was particularly relevant in the T+L arm. These observations confirm previous findings that link immune infiltrates to higher pCR rates, demonstrate the clinical significance of the PD-1 pathway and additionally show that CTLA-4 and TGFβ3 could also be important in early stage HER2-positive BC. The association of tumors linked with immune regulatory gene expression with positive responses to neoadjuvant anti-HER2 agents suggests that they act in a way that reinvigorates the antitumor immunity in the face of tumor-mediated suppression. Patients whose tumors highly express these genes may be good candidates for immunotherapy before the start or after the neoadjuvant treatment when residual disease is detected at surgery, although these hypotheses require further confirmation. Citation Format: Cinzia Solinas, Pushpamali De Silva, David Venet, Soizic Garaud, Chunyan Gu-Trantien, Florentine Hilbers, Evandro de Azambuja, Olena Werner, Lorena De la Peña, Amylou Dueck, Serena Di Cosimo, Istvan Lang, Jens Huober, Sherko Küemmel, Carsten Denkert, Roberto Salgado, Christos Sotiriou, Martine Piccart-Gebhart, Debora Fumagalli, Karen Willard-Gallo. Immune regulatory gene expression and clinical outcome in the NeoALTTO trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3132.

Abstract Background Understanding the relationship between clinical and patient-reported outcomes (PROs) will help support clinical care and future clinical trial design of novel therapies for focal segmental glomerulosclerosis (FSGS). Methods FSGS patients ≥8 years of age enrolled in the Nephrotic Syndrome Study Network completed Patient-Reported Outcomes Measurement Information System PRO measures of health-related quality of life (HRQoL) (children: global health, mobility, fatigue, pain interference, depression, anxiety, stress and peer relationships; adults: physical functioning, fatigue, pain interference, sleep impairment, mental health, depression, anxiety and social satisfaction) at baseline and during longitudinal follow-up for a maximum of 5 years. Linear mixed-effects models were used to determine which demographic, clinical and laboratory features were associated with PROs for each of the eight children and eight adults studied. Results There were 45 children and 114 adult FSGS patients enrolled that had at least one PRO assessment and 519 patient visits. Multivariable analyses among children found that edema was associated with global health (−7.6 points, P = 0.02) and mobility (−4.2, P = 0.02), the number of reported symptoms was associated with worse depression (−2.7 per symptom, P = 0.009) and anxiety (−2.3, P = 0.02) and the number of emergency room (ER) visits in the prior 6 months was associated with worse mobility (−2.8 per visit, P &lt; 0.001) and fatigue (−2.4, P = 0.03). Multivariable analyses among adults found the number of reported symptoms was associated with worse function in all eight PROMIS measures and the number of ER visits was associated with worse fatigue, pain interference, sleep impairment, depression, anxiety and social satisfaction. Laboratory markers of disease severity (i.e. proteinuria, estimated glomerular filtration rate and serum albumin) did not predict PRO in multivariable analyses, with the single exception of complete remission and better pain interference scores among children (+9.3, P = 0.03). Conclusions PROs provide important information about HRQoL for persons with FSGS that is not captured solely by the examination of laboratory-based markers of disease. However, it is critical that instruments capture the patient experience and FSGS clinical trials may benefit from a disease-specific instrument more sensitive to within-patient changes.