ALBERT

Description: Introduction: DLBCL has two COO subtypes: Activated B Cell (ABC) and Germinal Center B cell (GCB). Patients with the ABC subtype have a poor prognosis compared to those with GCB, and COO can be predictive for response to some new therapeutic agents. Traditionally, COO subtype has been determined by microarray (ABC, GCB, unclassified), IHC-based algorithms (GCB or non-GCB), or expression-based Nanostring Lymph2Cx (ABC, GCB, unclassified). Some reports have failed to show a prognostic difference between GCB and non-GCB when employing IHC-based algorithms. This has led some to adopt the Lymph2Cx assay as the preferred method to assess COO, but in some cases the tumor content or RNA quality is inadequate to perform this assay. COO subtypes have differing gene mutations, with GCB typically characterized by EZH2 alterations and IGH:BCL2 translocations, while ABC is dominated by NF-KB and BCR signaling alterations such as MYD88 and CD79B short variants. Here we utilized mutational differences in COO subtypes to develop a COO DNA classification (COODC) model to predict COO from DNA-based features on a clinically utilized platform. Methods: Comprehensive genomic profiling (CGP) of DLBCL samples was performed using the DNA component of the FoundationOne® Heme platform; sequencing 465 genes for the GOYA trial (R-CHOP vs G-CHOP; N=499; NCT01287741), MAIN trial (R-CHOP +/- bevacizumab; N=44; NCT00486759), and cases from routine clinical care (FM-clinical; N=597). Gold standard COO classifications were determined in GOYA using the Lymph2Cx assay, and in MAIN using a modified Wright algorithm applied to a custom Nanostring expression panel. COODC was developed using a penalized lasso regression with 25-fold internal cross validation and cutoffs that optimize specificity and sensitivity. Concordance was calculated as the percentage of COODC calls matching the gold standard, excluding samples called unclassified in the gold standard or COODC. Results: We developed a novel DNA-based method to determine COO, which is 89% concordant with Lymph2Cx (Table) COODC was trained on 296 GOYA samples with Lymph2Cx COO calls and validated on 139 held-out samples from the same cohort (GOYA). Additional validation was performed on an independent first-line cohort (MAIN) and confirmed a high concordance (92%). COODC also provides prognostic value, confirming the worse prognosis of ABC (progression-free survival [PFS] hazard ratio [HR] 1.6; 95% confidence interval [CI]: 1.1-2.4; p=0.011) and unclassified (PFS HR 1.9; 95% CI: 1.1-3.2; p=0.021) compared with GCB. This approach also allows investigation into the genomics and underlying features of COO subtypes. Alterations in BCL2, EZH2, and TNFRSF14 were important determinants of the GCB phenotype, consistent with the enrichment of GCB samples among the EZB cluster in Schmitz et al. (N Engl J Med 2018), or C3 in Chapuy et al. (Nat Med 2018). Alterations in MYD88 and CD79B were highly predictive of ABC subtype, as expected. Alterations in NOTCH1, NOTCH2, and BCL6 were all slightly predictive of ABC subtype, despite the mixed phenotype of the BN2, N1 (Schmitz et al. N Engl J Med 2018), and C1 (Chapuy et al. Nat Med 2018) groups. Novel features include decreased average copy number of chromosome 6p, which encodes the HLA locus, in ABC (p=0.0064), and increased frequency of T〉A and T〉G alterations in GCB (p

Description: Introduction: B cell lymphoma/leukemias (BCL) are a diverse set of malignancies. The genomic landscape of many BCL subtypes have been described. However, genomic ancestry has rarely been investigated. We applied SNP-based genomic ancestry prediction to comprehensive genomic profiling (CGP) data to identify significant enrichment of ancestry by subtype. We also explored enrichment of genomic alterations (GAs) by ancestry. Methods: During routine clinical care, 2834 unique patient (pt) samples of BCLs underwent CGP for 406 DNA genes and 265 RNA genes to detect all classes of GAs on the FoundationOne® Heme platform. This dataset was enriched for relapsed/refractory pts as they are more likely to have genomic testing as part of clinical care (referral bias). Each pt was assigned an ancestry of American (AMR), African (AFR), East Asian (EAS), European (EUR), or South Asian (SAS) using a SNP-based machine learning methodology (J. Newberg et al., AACR 2019). AMR was defined using a mix of Hispanic and Latin American populations. Enrichment analyses were performed using Fisher's exact test with FDR correction. Results: We compared the ancestry composition of each BCL subtype to the overall ancestry composition of the rest of the sample set (Fig 1A). Pts of AFR ancestry were overrepresented in plasmablastic lymphoma (PBL) (OR=7.2, P

Description: Introduction: Follicular lymphoma (FL) is a slow growing lymphatic cancer characterized by translocations in and overexpression of BCL-2, which inhibits apoptosis. This pathogenesis may generate neoantigens (NAs) that are recognizable by T-cells as part of a patients' immune surveillance and are unique to specific FL mutations. Tumor mutation burden (TMB), and NA prevalence and prognostic nature, have previously been characterized for diffuse large B-cell lymphoma (DLBCL), identifying that TMB correlated with NA burden (NAB). While TMB did not correlate with outcomes, the presence of a NA, especially BCL2 NA, correlated with outcomes in de novo DLBCL. In addition, the majority of patients were predicted to have ≥1 NA (Paulson, EHA 2019). However, to date, the prevalence of NAs in FL, and their association with clinical outcomes, have not been characterized. The aim of our study was to characterize NA prevalence and evaluate the prognostic value of NA biomarkers, assessed by a targeted, comprehensive genomic profiling (CGP) platform, on progression-free survival (PFS) for patients with de novo FL. Methods: CGP data on 465 genes were available from patients with FL who provided biopsy samples at screening for the Phase III PRIMA trial (NCT00140582; intent-to-treat [ITT] population =1018; patients received rituximab [R] maintenance vs observation after response to initial first-line treatment with R-CVP [cyclophosphamide, vincristine, prednisone], R-CHOP [cyclophosphamide, doxorubicin, vincristine, prednisone] or R-FCM [fludarabine, cyclophosphamide, mitoxantrone]). CGP was used to calculate TMB, HLA type (OptiType) and NA prediction (NetMHCpan). The prevalence of NAs at time of screening was analyzed by the number and proportion of patients. The prognostic value of NAs were evaluated against the Cox Proportional Hazards null model for PFS including terms for treatment, country and response to induction treatment. The Akaike Information Criterion (AIC), a likelihood ratio test (LRT) p-value vs the null when including an additional NA term and its associated hazard ratio (95% CI), were calculated. A visualization of the genetic mutational landscape was generated using dimension reduction methods. Results: In total, 247 of the 1202 enrolled patients were assayed with CGP. Baseline characteristics and survival in the biomarker evaluable population (BEP) were consistent with the ITT population. We calculated a median TMB of 6.7 mutations per megabase for the PRIMA cohorts with a median of two predicted NAs per patient. The majority of patients (83%) were predicted to have ≥1 NA. TMB moderately correlated with NAB (0.42 pearson coefficient), Figure A. TMB was not associated with outcomes (hazard ratio [HR] 95% CI: 0.98 [0.94-1.02]) and similarly the presence of a NA was not associated with PFS (HR [95% CI]: 0.90 [0.56-1.44]). The most prevalent predicted NAs (n [patients]; %) were in BCL2 (54; 21.9%), CREBBP (49; 19.8%), EZH2 (14; 5.7%), KMT2D (13; 5.3%), and CARD11 (10; 4.0%). In a pooled analysis of patients from both arms, only presence of EZH2 NA improved the AIC of the null model (AIC=1135.6) to 1134.7 (LRT p=0.09 better than 0.1), Table. The HR (95% CI) of EZH2 NA in this model was 0.46 (0.17-1.25), indicating better survival for the EZH2 NA-negative group. Within each treatment group, HR (95% CI) for EZH2 was 0.27 (0.07-1.13) in the observation group and 1.05 (0.25-4.44) in the R group, Figure B. Inspecting the tSNE mutations by gene, a neighborhood pattern could be seen between EZH2 and BCL2 connected by KMT2D and flanked by CARD11 and CREBBP, Figure C. Conclusions: We observed that similarly to de novo DLBCL, TMB and NAB were also correlated in patients with de novo FL. While NAB was associated with clinical outcomes in de novo DLBCL, we did not observe these associations in FL. In patients with de novo FL,who discontinued R after initial treatment response, the absence of EZH2 predicted neoantigens were associated with PFS. This is consistent with results on EZH2 mutation status reported by Huet, et al.(Blood Cancer J 2017). A caveat to this hypothesis generating analysis is the small number of PFS events in the EZH2 neoantigen group. These insights may inform future personalized strategies in FL. Disclosures Henneges: Genentech (via Syneos Health): Current Employment; University of Wurzburg: Ended employment in the past 24 months. Jin:F. Hoffmann-La Roche: Current equity holder in publicly-traded company; Foundation Medicine Inc: Current Employment. Venstrom:Foundation Medicine, Inc.: Current Employment; F. Hoffmann-La Roche: Current equity holder in publicly-traded company. Trabucco:F. Hoffmann-La Roche, Bristol-Myers Squibb Co., BioNTech: Current equity holder in publicly-traded company; Bristol-Myers Squibb Co: Current equity holder in publicly-traded company; BioNTech: Current equity holder in publicly-traded company; Loxo Oncology: Divested equity in a private or publicly-traded company in the past 24 months; Foundation Medicine, Inc.: Current Employment; Patent pending with Foundation Medicine and Genentech: Patents & Royalties: Patent pending. Nielsen:F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Penuel:Genentech, Inc./ F. Hoffmann-La Roche: Current Employment; F. Hoffmann-La Roche: Current equity holder in publicly-traded company. Salles:Abbvie, Amgen, Celgene, Gilead, Janssen, Kite, Morphosys, Novartis, Roche, Takeda: Other: Participation to educational events; Abbvie, Autolus, BMS/Celgene, Debiopharm, Genmab, Kite/Gilead, Epizyme, Janssen, Karyopharm, Morphosys, Novartis, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees; Abbvie, Amgen, Celgene, Gilead, Janssen, Kite, Morphosys, Novartis, F. Hoffmann-La Roche, Takeda: Honoraria; Abbvie, Autolus, BMS/Celgene, Debiopharm, Genmab, Kite/Gilead, Epizyme, Janssen, Karyopharm, Morphosys, Novartis, F. Hoffmann-La Roche, Takeda: Consultancy. Paulson:Genentech, Inc.: Current Employment; F. Hoffmann-La Roche: Current equity holder in private company, Current equity holder in publicly-traded company.

Description: Genomic studies performed in cancer patients and tumor-derived cell lines have identified a high frequency of alterations in components of the mammalian switch/sucrose non-fermentable (mSWI/SNF or BAF) chromatin remodeling complex, including its core catalytic subunit, SMARCA4. Cells exhibiting loss of SMARCA4 rely on its paralog, SMARCA2, making SMARCA2 an attractive therapeutic target. Here we report the genomic profiling of solid tumors from 131,668 cancer patients, identifying 9434 patients with one or more SMARCA4 gene alterations. Homozygous SMARCA4 mutations were highly prevalent in certain tumor types, notably non-small cell lung cancer (NSCLC), and associated with reduced survival. The large sample size revealed previously uncharacterized hotspot missense mutations within the SMARCA4 helicase domain. Functional characterization of these mutations demonstrated markedly reduced remodeling activity. Surprisingly, a few SMARCA4 missense variants partially or fully rescued paralog dependency, underscoring that careful selection criteria must be employed to identify patients with inactivating, homozygous SMARCA4 missense mutations who may benefit from SMARCA2-targeted therapy.