Citations

Abstract Purpose: Around 10% to 15% of prostate cancers harbor recurrent aberrations in the Forkhead Box A1 gene, FOXA1, whereby the alteration type and the effect on the forkhead (FKH) domain affect protein function. We developed a FOXA1 classification system to inform clinical management. Experimental Design: A total of 5,014 prostate cancer samples were examined using whole-exome and -transcriptome sequencing from the Caris Life Sciences database. We denoted class 1 FOXA1 alterations as missense and in-frame insertions/deletions with subclasses oriented with respect to the FKH domain. These were in the first part of the FKH domain [class 1A: amino acids (AA) 168–246], within the Wing2 region of FKH (class 1B: AA 247–269), or outside FKH (class 1C: AA 1–167, 270+). Two hotspot missense mutations at R219 were denoted class 2. Class 3 included predicted truncating mutations with subclasses partitioned based on the FKH domain (class 3A: AA 1–269 and class 3B: AA 270+). Class 4 represented FOXA1 amplifications. Real-world overall survival and therapy outcomes were determined from insurance claims. Results: FOXA1 alterations did not influence survival when considered in aggregate but had distinct prognostic effects when stratified by class. In primary prostate samples, class 1A alterations were associated with overall improved survival (HR, 0.57; P = 0.03); a similar trend was seen in metastatic biopsies with class 1B (HR, 0.84; P = 0.09). Conversely, in primary specimens, class 1C exhibited worse survival upon second-generation androgen receptor signaling inhibitor treatment (HR, 1.93; P < 0.001). Class 2 mutations (R219C/S) were enriched in neuroendocrine prostate cancers and were associated with overall poor survival (HR, 2.05; P < 0.001) and worse outcomes to first-line androgen-deprivation therapies (HR, 2.5; P < 0.001). Class 3A alterations indicated improved survival (HR, 0.70; P = 0.01), whereas class 3B alterations portended poor outcomes (HR, 1.50; P < 0.001). Amplifications (class 4) indicated poor outcomes in metastatic samples (HR, 1.48; P = 0.02). Molecularly, different FOXA1 alteration classes harbored distinct mutational and immunologic features as well as unique transcriptional programs. Finally, relative to European Americans, African Americans had increased class 1C alterations, whereas Asian/Pacific Islander patients had increased class 1B alterations. Conclusions: FOXA1 alterations should not be interpreted in aggregate, as different classes are associated with divergent molecular features and clinical outcomes. Our revised classification schema facilitates clinical decision-making for patients with prostate cancer and uncovers important racial differences.

Abstract Objectives Diagnostic reconciliation is the collaborative process between patients and clinicians to create and reconcile evidence-based, feasible, and desirable care plans. However, the specific components of this process remain unclear. The objective of this study was to develop the first comprehensive framework to elucidate the diagnostic reconciliation process. Methods We followed a multi-step and iterative approach to develop the framework, including a focused systematic review of diagnostic conversations, quantitative evaluation of recordings of real-life clinical visits recordings, and stakeholder engagement (e.g., patients, clinicians, researchers). Results We identified 17 potential components to the process of diagnostic reconciliation through literature review and stakeholder engagement. After review of 56 clinical visits and further stakeholder engagement, we developed a final framework including four categories: 1) understanding the need for a test/referral, 2) logistics of test/referral scheduling, 3) test/referral information, and 4) test/referral results. Conclusions The proposed framework lays the foundation for evaluation and improvement of diagnostic conversations in practice. Clinicians can enhance patient-centered diagnosis by co-creating diagnostic plans of care in practice and using the components described in the novel diagnostic reconciliation framework.

Introduction: Clonal hematopoiesis (CH) is a common age-related premalignant condition defined by the presence of an acquired somatic mutation in a gene frequently mutated in myeloid malignancies characterized by a variant allele frequency (VAF) of ≥2%, without fulfillment of other diagnostic criteria for myeloid neoplasm. Incidence of CH increases with age and found in 10%-30% of people > 70 years of age. CH-related mutations confer an increased risk of myeloid neoplasms, cardiovascular diseases, and are linked to inferior survival in solid tumor patients treated with chemo/radiation therapy. Such mutations can act as a driver/amplifier of inflammation, especially through myeloid cells. To distinguish true disease drivers from background genetic events related to aging or environmental factors, we need a deeper understanding of the impact of CH in different tissue microenvironments and under therapeutic pressure for better risk stratification of CH carrier patients. Recent reports suggested high prevalence of CH-related mutations in lymphoma. Current analyses aimed to investigate: i) the prevalence of CH-related mutations in two real-world lymphoma cohorts from Molecular Epidemiology Resource (MER) MER-FL and MER-DLBCL, ii) CH-related mutational landscape in DLBCL and FL, iii) the impact of CH-related mutations on clinical outcomes. Methods: Whole exome sequencing (WES) derived from blood/PBMC from lymphoma patients with paired tumor samples was analyzed to identify CH-related myeloid drivers in 42 previously reported genes. We used Sention-Tnhaplotype2 somatic pipeline with a custom panel of normals from whole blood samples. To avoid tumor contamination, we filtered variants for which VAF in the tumor was higher than that in the germline sample. Other filtering criteria included HIGH/MODERATE impact; read count > 1 in both forward and reverse reads; gnomAD_AF < 0.01. We used the R “survival” package Cox regression implementation for outcome and other statistical analysis. Results: Genomic and clinical data of 507 patients from MER-FL and 383 patients from MER-DLBCL cohorts, with median age of 60 and 64 respectively, were available for analysis. CH-related mutations were present in 17.16% (87/507) in FL and 18.28% (70/383) in DLBCL cohorts with a VAF cut off >2%. The most common CH-related mutations were DNMT3A (5%), CREBBP (2%), TET2 (1%), ASXL1 (1%) and PPM1D (1%) in MER-FL while DNMT3A (5%), TET2 (3%), TP53 (3%), ASXL1 (2%) and PPM1D (1%) in MER-DLBCL cohort. Both the prevalence of CH-related mutations and their average VAF increased with age, consistent with literature. Finally, cox regression analysis suggested that CH-related mutations were associated with significantly inferior overall survival (OS) in both FL and DLBCL cohorts (HR 1.6 p=0.028 and HR 2.3 p=<0.001 respectively); however only in DLBCL this association was significant after age correction (HR 1.8 p=0.008). When separated by individual gene mutations, genes of interest with inferior OS included TP53 (n=4, HR 3.1, p=0.057) in FL and PPM1D (n=3 HR 2.6 p=0.1), TP53 (n=8 HR 2.1 p=0.1) and DNMT3A (n=14, HR=2.0, p=0.048) in DLBCL; however, these estimates did not quite reach statistical significance. Conclusions: Taken together, our exploratory analysis indicates that CH-related mutations associate with worse OS in lymphoma patients, supporting the rationale for evaluation of CH for the risk assessment in lymphoma. Furthermore, DLBCL displays of larger size effect, which may be related to different treatment regimes. Additional studies will be needed to elucidate the interaction between high dose chemotherapy regimes and CH-associated adverse events.

Introduction: Diffuse large B cell lymphoma (DLBCL) patients experience favorable outcomes following frontline immunochemotherapy, however, 30-40% of patients will exhibit primary refractory disease or relapse (rrDLBCL). Recent studies have expanded upon well-defined characteristics of newly diagnosed DLBCL (ndDLBCL) to elucidate genomic landscapes underlying rrDLBCL; yet, high-throughput examination of resistance and relapse mechanisms within rrDLBCL subtypes, including the newly defined dark zone signature (DZsig) addition to cell of origin (COO), have yet to be described. To expand upon the current tumor etiology and define subtype-specific driving mechanisms, we describe a multi-omic analysis leveraging clinical and biologic variables in rrDLBCL. Methods: Tumor biopsies (N=228) collected at the time of relapse were obtained from n=85 participants in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER), n=73 in the Mayo Clinic Biobank, and n=38 from CC-122-DLBCL-001 or n=32 CC-122-ST-001 trials. Tumor samples were analyzed by WES (n=158) or WGS (n=70) and by RNAseq (n=145). Comparative analysis against ndDLBCL MER samples (N=444) utilized WES (n=404) and RNAseq (n=321). Chi Square or Fisher's exact test was used for enrichment analysis of categorical variables, while Wilcoxon or Kruskal-Wallis test was used for continuous variables. Results: rrDLBCL samples were enriched for ABC (46% vs 33% in ndDLBCL, p=0.01), and fewer GCB (27% vs 40%, p=0.006). Amongst rrDLBCL patients, variance in overall survival (from time of original diagnosis; log-rank p=0.001) was observed between COO subtypes, with poor outcomes in patients with DZsig (median 15.0 mo) and GCB (36.5 mo) compared to ABC tumors (74.4 mo). Patients with progression or relapse within 6 months featured a more even COO distribution (ABC - 22%, GCB - 34%, DZsig - 28%) than patients with early (6-24 mo; 51%, 22%, 22%) or late relapses (>24 mo; 62%, 19%, 4%), which were predominantly ABC. Comparing mutation frequency within subtypes, rrDLBCL GCB tumors were enriched for B2M (41% vs 15% in ndDLBCL, p=0.002), EP300 (19% vs 7%, p=0.059), CIITA (19% vs 5%, p=0.02), PRKCB (11% vs 2%, p=0.035), CD79B (11% vs 0%, p=0.004), and CD83 (8% vs 1%, p=0.049) mutations, and CNV events including gains at 8q24.21 (MYC locus; 48% vs 14%, p<0.001) and 18q21.33 (BCL2, TCF4 locus; 36% vs 15%, p=0.021), and losses at 17p13.1 (TP53 locus; 40% vs 19%, p=0.035). rrDLBCL DZsig tumors featured enrichment for BCL6 (22% vs 3%, p=0.025), and SPEN (17% vs 3%, p=0.062) mutations and 2q22.2 losses (LRP1B locus; 20% vs 0%, p=0.019). Despite differential mutation and CNV profiles, gene expression analysis revealed minimal variance within COO subtypes in rrDLBCL and ndDLBCL, however upregulation of lymphoma driving genes including MYBPC2 (LogFC=1.68, FDR<0.001), MYOM2 (LogFC=1.33, FDR=0.007), MYC (LogFC=0.61, FDR=0.04), and CELSR2 (LogFC=0.53, FDR=0.02) were observed in ABC rrDLBCL. Increased frequency of LymphGen classifier A53 was observed in ABC (43% vs 17%, p=0.005) and GCB (40% vs 14%, p=0.009) rrDLBCL. Using CIBERSORTx to infer lymphoma microenvironment (LME) cell type abundance, an LME-depleted class was evident in rrDLBCL tumors, including reduction of CD8+ T cells (effect size (r)=-0.78, p<0.001) and T follicular helper cells (r=-0.48, p=0.012) in GCB rrDLBCL and reduction of T follicular helper cells (r=-0.43, p=0.023) and Tregs (r=-0.32, p=0.065) in DZsig rrDLBCL. Significance: We present an update to the genomic landscape of rrDLBCL, highlighting GCB and DZsig tumors with differential biology and advanced clinical course. Our data supports increased implementation of sequencing efforts in rrDLBCL to more broadly evaluate relapse mechanisms, with optimism that a more detailed understanding of r/r disease may guide trials for targeted treatment strategies. For example, rrDLBCL patients may be strong candidates for MYC pathway targeting agents (BETi), or a Lenalidomide-based regimen (R2 or Tafa-Len), as Lenalidomide has been shown to reprogram the LME via PD-L1 expression in addition to downregulation of MYC and MYC target genes. Or, due to enrichment of variants affecting anti-apoptotic proteins including BCL6 or BCL2 in DZsig and GCB rrDLBCL, a venetoclax-based therapeutic may be considered. Further targeted strategies should be considered as development of a more personalized approach remains an unmet need.

Introduction: GOLCA is a potential first-in-class oral cereblon E3 ligase modulator (CELMoD™) agent for non-Hodgkin lymphoma, purposefully designed to target Ikaros/Aiolos and have favorable pharmacokinetics, resulting in apoptotic and immunomodulatory activity with preferential distribution to lymphoid organs. CC-220-DLBCL-001 (NCT04884035) is an ongoing phase 1b, open-label, multicenter, dose-escalation/expansion trial to assess safety and preliminary 1L GOLCA + R-CHOP efficacy in a-BCL. Although longer follow-up is needed to assess durability of response, MRD negativity (MRD-) is an emerging endpoint in a-BCL. Achieving MRD- on and at end of treatment (EOT) can potentially predict clinical benefit. This study aimed to analyze circulating tumor DNA (ctDNA) kinetics of GOLCA + R-CHOP across the trial population and in defined patient (pt) segments-cell of origin (COO), high baseline ctDNA, and high-risk genomics-and to correlate MRD- with imaging and clinical outcomes. Methods: Eligible pts were ≥ 18 years of age with ECOG performance status ≤2, IPI 0-5 in Part 1 and 2-5 in Part 2, and untreated a-BCL with measurable disease. Pts were treated with GOLCA + R-CHOP for 6 x 21-day (D) cycles (C) or until disease progression/unacceptable toxicity/study withdrawal. Dose escalation phase GOLCA dose levels (DLs) were 0.2 mg D1-7 (DL-1), 0.4 mg D1-7 (DL1), or 0.4 mg D1-10 (DL2). During dose expansion, pts were randomized 1:1 to R-CHOP + GOLCA DL-1 or DL1. The primary endpoint was safety of GOLCA at DL1; efficacy was a secondary endpoint, ctDNA was an exploratory endpoint. ctDNA was analyzed using the PhasED-Seq assay (<10-6 sensitivity) at baseline, C2D1, C3D1, and EOT. MRD- was defined as the absence of detectable ctDNA on-treatment. Pretreatment biopsies were sequenced by whole genome sequencing (WGS) and whole transcriptome sequencing (WTS). Results: At data cutoff (June 3, 2024), the median follow-up was 14.3 months. One-year progression-free survival (PFS) was 85.3% at 0.4 mg and 77.3% at 0.2 mg. Rates of MRD- increased over time but were consistently higher at 0.4 vs 0.2 mg, with values of 44% (14/32) vs 29% (8/28) at C2D1; 63% (17/27) vs 54% (15/28) at C3D1, and 90% (26/29) vs 73% (19/26) at EOT, respectively. Interim MRD- at C3D1 strongly predicted EOT MRD-; all 0.4 mg pts and all but two 0.2 mg pts who were MRD- at C3D1 remained MRD- at EOT. In contrast, fold-changes from baseline ctDNA were similar between 0.4 mg and 0.2 mg. The early molecular response rate (≥102-fold drop from baseline at C2D1) was 81.2% (26/32) at 0.4 mg and 82.1% (23/28) at 0.2 mg. The major molecular response rate (≥102.5-fold drop from baseline at C3D1) was 88.9% (24/27) at 0.4 mg and 85.7% (24/28) at 0.2 mg. Across genetically defined pt segments, EOT MRD- at 0.4 mg was prevalent for both COO subtypes measured by the Hans algorithm: 94% (17/18) among germinal center B-cell (GCB) and 75% (6/8) among non-GCB pts. Of the 29 pts with baseline genomics, 13 were genomically high risk: p53 mutant (VAF >0.5), DZsig and/or A7 subtype. In these pts, EOT MRD- was 80% (4/5) at 0.4 mg but only 20% (1/5) at 0.2 mg. In high baseline ctDNA (>102.5 hGE/mL) pts who were high-risk for R-CHOP failure, EOT MRD- at 0.4 mg was 86.7% (13/15) vs 57.1% (8/14) at 0.2 mg. There was an association between high baseline ctDNA and lower PFS at 0.2 mg (HR, 4.56 [95% CI, 0.53-39.12]), but interestingly the association decreased at 0.4 mg (HR, 2.10 [95% CI, 0.19-23.5]). Combining both arms, MRD- at both C3D1 and EOT trends with improved PFS (C3D1: HR, 0.73 [95% CI, 0.2114-2.523]; EOT: HR, 0.53 [95% CI, 0.1103-2.592]); longer follow-up may be required to demonstrate significance. Conclusions: Interim and EOT levels of MRD- were higher at 0.4 mg vs 0.2 mg GOLCA, with a one-year PFS of 85.3% vs 77.3%, respectively. Although pt numbers were small, the data suggest that 0.4 mg is efficacious in high-risk ctDNA and high-genomic risk pts, independent of COO, and support the 0.4 mg GOLCA + R-CHOP regimen in the randomized phase 3 trial GOLSEEK-1. Interim MRD- may play a role in future adaptive trial designs.

ABSTRACTDiffuse large B‐cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non‐PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high‐risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Background To evaluate the benefit-risk assessment of atogepant and calcitonin gene–related peptide (CGRP) monoclonal antibodies (mAbs) vs. placebo based on the number needed to treat (NNT) and the number needed to harm (NNH) in a blended episodic migraine and chronic migraine (EM + CM) population. Methods The NNT was calculated based on achievement of a ≥ 50% reduction in mean monthly migraine days (MMDs) from baseline across 12 weeks. The NNH was calculated using the proportion of participants reporting a discontinuation due to adverse events (AEs). The primary analysis included data from studies of atogepant, erenumab, galcanezumab, eptinezumab and fremanezumab. Results In the primary analysis, the calculated NNT for atogepant 60 mg vs. placebo was 4.2 (95% credible interval (CrI) = 3.1–6.7), which was the lowest relative to the CGRP mAbs in the blended EM + CM population. Participants who received atogepant 60 mg or fremanezumab showed the most favorable NNH values (−1010 (95% Crl = 44 to ∞ to number needed to benefit 80) for atogepant) resulting from lower rates of discontinuation due to AEs compared with those receiving placebo. Conclusions Atogepant demonstrated a favorable benefit-risk profile, with NNT and NNH values comparable (not statistically significant) with those of CGRP mAbs across all analyses.

SummaryTo assess the relevance of co‐occurring somatic mutations in TP53‐mutated myeloid neoplasms with ≥10% blasts, we pooled 325 individuals from 10 centres. We focused on comparing three published somatic co‐alteration signatures comprising (1) nine MDS‐related genes (‘ICC‐MDSR’), (2) ICC‐MDSR + additional secondary mutations‐related genes (‘Tazi signature’) and (3) EPI6 (comprising six genes). Outcomes examined were 24‐month overall survival (OS24) and front‐line complete response (CR1). The median age was 69 years with 77% receiving front‐line hypomethylating agents (HMA). All three signatures ICC‐MDSR (p = 0.009), Tazi signature (p = 0.001) and EPI6 (p = 0.025) predicted inferior CR1. In the low‐intensity (HMA) subgroup, only Tazi signature (p = 0.026) predicted inferior CR1. In OS24 analysis of the HMA‐treated subgroup (N = 200), only Tazi signature was adverse (hazard ratio, HR = 1.6 [1.1–2.2]; p = 0.011). However, a forward stepwise multivariable age‐adjusted Cox model including all three signatures picked EPI6 as the sole significant adverse predictor in the entire cohort (p = 0.0001) as well as within the HMA‐treated subgroup (p = 0.0071). These data confirm the value of testing co‐occurring somatic alterations even within a high‐grade TP53‐mutated myeloid neoplasm cohort.