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Purpose: The discovery of MAPK/ERK pathway mutations has led to the approval of MEK-inhibitor (MEKi) cobimetinib in histiocytic neoplasms. However, the pivotal clinical trial that led to the approval of cobimetinib included only 4 patients with Langerhans cell histiocytosis (LCH). Therefore, there is a need to define outcomes with MEKi therapy in patients with LCH. We undertook a multi-center collaboration through the Adult LCH Working Group for the Histiocyte Society to address this gap in knowledge. Methods: Adults with LCH who received MEKi therapy were included from five institutions. Objective response rate (ORR) was evaluated using PET/CT criteria, and CT/MRI if PET scan was not available; response categories were categorized into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Progression-free (PFS) and overall survival (OS) were calculated from the time of targeted therapy initiation. Risk organ disease was defined as liver, spleen, or bone marrow involvement. Results: 25 adults with LCH were included in this study; median age at MEKi initiation 43y (range, 18-85). Most common systems involved at diagnosis were bone (52%), pituitary (44%), and lung (36%). Risk organ involvement was observed in 7 (28%, 6 liver, 1 liver and spleen) and 2 (8%) patients had intra-axial CNS involvement. Median lines of prior therapy received were 2 (range: 1-5), and 4 (16%) patients received MEKi in first-line. MAPK/ERK pathway status was assessed via target capture next generation sequencing (NGS) in 22 (96%) cases. Amongst those who underwent NGS, alterations identified included: BRAF V600E (2/22, 9%), BRAF deletions (11/22 50%), MAP2K1 or MEK mutations (4/22, 18%), and MAPK1 or ERK2 (n=1, 5%). Overall, 5 patients also received BRAF inhibitors (4 patients as first therapy prior to MEKi and 1 concomitantly due to BRAF V600E positive melanoma). Specific MEKi agents utilized included cobimetinib (n=13) and trametinib (n=12). Among the response evaluable patients (n=23), ORR to MEKi treatment was 78% (9 CR, 9 PR, 4 SD, 1 PD). Among the 7 patients with liver involvement, responses were seen in 2 (29%) cases. ORR did not significantly differ according to mutation status among those with NGS data (78% MAPK mutated vs. 100% unmutated; p=1.0). In this subcohort, all of the 4 cases that did not respond to MEKi had liver involvement; 3 had BRAF deletions (2 SD and 1 PD), and one had MAPK1 mutation (SD). Median follow-up duration after MEKi initiation in the entire cohort was 18 months (95% CI 6 - 30). The median PFS and OS were not reached; 2-year PFS and OS were 78.1% and 79.9%, respectively (Figure 1A). The median PFS was much shorter in those with liver involvement (7 months [95% CI 0 - 14]) compared with those without liver disease (not reached [95% CI 19-not reached] P=0.047; Figure 1B). PFS did not differ significantly between MAPK pathway mutated and unmutated patients (p=0.34) Adverse events (AEs) were reported in 36% (n=9) and most commonly included rash (n=5) and fatigue (n=3). Four patients experienced grade 3-4 AEs including rash, fatigue, and thrombocytopenia. Common causes of MEKi discontinuation included AEs (n=3), progression (n=2), and drug holiday (n=2). At last follow-up, 5 patients died and 18 remained on MEKi. Death was attributable to LCH (n=3), concomitant malignancy (n=1), or unknown causes (n=1). Conclusions: In our multi-institutional study of adults with LCH, we found MEKi therapy to be highly efficacious. However, liver involvement correlated with a lack of response and worse PFS, while mutational status did not. Future studies are needed to evaluate optimum treatment for high-risk subgroups of LCH like liver disease.

Introduction: Risk stratification approaches using clinical criteria or biomarkers in acute graft-versus-host disease (GVHD) have traditionally been implemented at diagnosis or initiation systemic treatment with corticosteroids. For patients who require second-line treatment, the significance of biomarkers at salvage therapy and the relationship between biomarker risk and choice of therapy are unknown. Methods: We studied adult patients who received second-line treatment for acute GVHD between January 2016 and December 2021 in the Mount Sinai Acute GVHD Consortium (MAGIC) database, which collects clinical data and longitudinal serum samples from allogeneic HCT recipients using a rigorous PRoBE study design. Exclusion criteria included relapse of underlying malignant disease prior to second-line therapy, missing response data, or missing serum sample. Serum samples collected from 7 days prior to 3 days after initiation of second-line therapy met criteria for biomarker analysis. The MAGIC algorithm probability (MAP) was calculated as a single value based on concentrations of ST2 and REG3α and was stratified at a threshold of 0.290 to define high MAP and low MAP groups. The primary objective evaluated the association between MAP and survival for patients receiving second-line therapy for acute GVHD. The key secondary objective was to investigate the MAP and choice of second-line systemic agent on Day 28 overall response rate (ORR) and survival. Results: In total, 316 adult patients received second-line treatment for acute GVHD; 149 patients (47%) were excluded for disease relapse (n=27), missing response data (n=48), or missing sample (n=74).Of the 167 evaluable patients, most had steroid-refractory acute GVHD (n=146, 87%), rather than steroid-dependent (n=8, 5%) or steroid-sensitive (n=14, 8%) disease. 62 patients received ruxolitinib while 102 received other systemic agents (best available therapy: BAT). Baseline clinical characteristics of the study population according to treatment choice were similar, except for donor type (Rux: MRD 15%, MUD 74%, haplo 11%; BAT: MRD 30%, MUD 67%, haplo 3%; P=0.014) and median days from corticosteroids to second-line treatment (Rux: 18, BAT: 12; P=0.02).In total, 81 patients had high MAPs and 86 patients had low MAPs, with similar distribution in each treatment cohort (P=0.53). For the entire cohort, the median follow-up for survivors was 22 months (range, 1.5-27).The MAP separated patients into 2 groups (high vs low) with significantly different mortality (NRM at 2 years, 76% vs 29%, P<.0001) and survival (OS at 2 years, 12% vs 63%, P<.0001). Patients who received ruxolitinib as second-line therapy had lower mortality (NRM at 2 years, 35% vs 60%, P<.002) and improved survival (OS at 2 years, 51% vs 32%, P<.008), compared to those who received other systemic agents. However, the outcomes of patients with high MAP were poor, regardless of second-line treatment choice ( Figure 1A). The MAP (low vs high), time from corticosteroids to second-line treatment (≥14 days vs < 14 days), GVHD grade at second-line (0-2 vs 3-4), and choice of second-line therapy (Rux vs BAT) were all significant (P<0.05) predictors of lower NRM and higher OS in multivariate analysis. The MAP also separated patients into 2 groups with significantly different D28 ORR (low: 49%, high: 30%, P<.0001). In agreement with prospective trials, ruxolitinib resulted in higher D28 response rates compared to other systemic therapies (ORR 54.9% vs 30.5%, P=0.005), which was observed in both low MAP (ORR, 64.5% vs 38.5%, P=0.017) and high MAP cohorts (ORR, 42.9% vs 22.6%, P=0.06) ( Figure 1B). The MAP (low vs high), GVHD grade at second line (0-2 vs 3-4), and choice of second-line therapy (Rux vs BAT) were significant predictors (P<0.05) of higher D28 ORR in multivariate analysis. Conclusion: The MAP measured at the initiation of second-line systemic treatment for acute GVHD predicts NRM and OS, as well as treatment response. The outcomes of patients with high MAP are poor, regardless of second-line treatment choice, and the higher CR rate in low MAP patients may drive higher survival rates. Incorporation of MAP into clinical trials in this setting warrants investigation. The results also support the current use of ruxolitinib as second-line treatment for acute GVHD, particularly in patients with low MAP.

Background: Anaplastic large cell lymphomas (ALCLs) are CD30-positive T-cell lymphomas that share pathologic features but differ in clinical presentation, outcome, and molecular features. The World Health Organization (WHO) and International Consensus Classification (ICC) classify ALCLs by presence or absence of ALK rearrangements (R) and clinical presentation (systemic, cutaneous [c], or breast implant-associated [BIA]). ICC, but not WHO, recognizes DUSP22-R as defining a new genetic subtype of ALK- ALCL. The classifications otherwise do not reflect additional molecular heterogeneity in genetics (e.g., TP63-R) or therapeutic vulnerabilities (e.g., JAK-STAT3 pathway activation). Methods: ALCLs (N=689) underwent expert consensus review (WHO/ICC) through the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). All cases also underwent genetic subtyping (ALK, DUSP22, TP63, and triple-negative [TN]) using fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC), as well as IHC for phospho-STAT3 Tyr705 (pSTAT3). RNAseq was performed and evaluable in 393 cases; the remaining cases had insufficient tissue, tumor content, RNA yield or quality, and/or sequencing data quality. Sequenced and non-sequenced sub-cohorts had similar demographics and subtype distribution. Results: Unsupervised gene expression profiling (GEP) identified 2 main molecular types of ALCL. Type I ALCLs predominantly included ALK+ ALCLs, BIA-ALCLs, and a subset of TN ALCLs (designated TN-I), whereas Type II ALCLs predominantly included ALCLs with DUSP22-R, TP63-R, or both (double-hit; DH), and the remaining TN ALCLs (TN-II). Type I ALCLs were strongly associated with pSTAT3 expression (74.2±26.4% positive malignant cells vs 9.9±22.7% for Type II; P<0.0001). An independently derived pSTAT3 staining threshold of 30% assigned Type I vs II with 91% accuracy. A third cluster of ALCLs, mostly cALCL, showed an epithelial GEP signature rather than a lymphoma signature; these cases were assigned to Types I or II based on pSTAT3 IHC. Distinct sub-signatures were identified for ALK+, DUSP22-R, TP63-R, and BIA ALCL (Fig. 1), but not ALK- ALCL or cALCL. Gene set enrichment analysis showed Type I ALCLs to be enriched for JAK-STAT3 signaling genes (normalized enrichment score [NES], 2.22; FDR<0.0001) and related pathways, such as TNFα-NFκB signaling (NES, 2.21; FDR<0.0001). In contrast, Type II ALCLs were enriched for cell cycle, DNA repair, epigenetic, and metabolic pathway genes, but not for major tyrosine kinase-mediated signaling pathway genes. Enriched epigenetic pathways included chromatin modifying enzymes (NES, -1.86; FDR=0.002) and histone methylation (NES, -1.71; FDR=0.002). EZH2 was the most overexpressed gene in Type II ALCLs (fold-change, 7.74; FDR=8.84×10 -306). At the protein level, EZH2 IHC H-scores were 275±45 in Type II and 168±70 in Type I ALCLs (P<0.0001); H3K27me3 H-scores were 170±81 and 76±63, respectively (P<0.001). The top metabolic gene set involved cholesterol biosynthesis (NES, -1.88; FDR=0.002). Overall survival (OS) data were available in 257 systemic ALCL patients (145 sequenced and 112 non-sequenced; cALCL and BIA-ALCL were excluded). Non-sequenced TN ALCLs were assigned to TN-I or TN-II using pSTAT3 IHC. The results supported earlier data indicating favorable prognosis of DUSP22-R ALCL (5 y OS, 95%) and ALK+ ALCL (87%), intermediate prognosis of TN ALCL (TN-I, 52% and TN-II, 38%; P=NS), and poor prognosis of TP63-R/DH ALCL (0%)(P<0.0001; Fig. 2). Conclusions: Two overarching molecular types of ALCL exist, predominantly associated with the presence (Type I) or absence (Type II) of the JAK-STAT3 signaling program. Distinct GEP signatures exist for ALK+ ALCL and BIA-ALCL (predominantly Type I), and DUSP22-R ALCL and TP63-R ALCL (predominantly Type II). TN ALCLs lacking ALK-R, DUSP22-R, and TP63-R can be stratified into TN-I and TN-II subtypes. pSTAT3 IHC has >90% accuracy as a surrogate for GEP-based subtyping. ALK- ALCL and cALCL cluster by molecular subtype rather than by defining GEP signatures. Type II ALCLs are enriched for targetable epigenetic and metabolic pathways, including EZH2/histone methylation and cholesterol biosynthesis. This molecular classification is diagnostically, prognostically, and potentially therapeutically relevant, and can be applied using FISH and IHC in routine practice and in the clinical trial setting.

Purpose: The understanding of cardiac-involved ECD (ECD-CV) and how this affects outcomes and pre-existing diseases remains limited. We aimed to determine the association, burden, and severity of pre-existing and de novo cardiovascular (CV) comorbidities with ECD-CV compared to patients (pts) without cardiac-involved ECD (ECD-noCV). Methods: Records of pts with ECD diagnosed from Jan-1990 to Dec-2021, consecutively seen at Mayo Clinic, AZ, FL, and MN were reviewed. Cardiac involvement was diagnosed by imaging. CV comorbidities were captured before and after ECD diagnosis. CTCAE v3.0 based grading system was used to assess CV comorbidities which graded the conditions as mild (grade 1), moderate (grade 2), severe (grade 3), and life-threatening or disabling (grade 4). Chi-square test was used to determine if having ECD-CV worsens pre-existing CV comorbidities in pts with ECD. As per the established methods, the cumulative CV disease burden was defined after considering 8 CV morbidities that are known to have direct structural and functional implications on the heart, including hypertension (HTN), coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), atrial fibrillation (afib), pericardial effusion, valvular disease, and conduction disorders. A composite CV morbidity grade was assigned based on the highest grade in one of the 8 CV comorbidities (Oeffinger, NEJM, 2006). Lastly, a multivariant analysis was conducted using the presence of BRAFV600E mutation, smoking, age ≥ 60 years (yr), conventional vs targeted therapy, and ECD-CV in pts with a composite grade of 4. Results: Among 106 ECD pts, 38% (n=40) had cardiac involvement. The median age at diagnosis of ECD was 57 yr (range: 49-67); 61% were males. The median follow-up was 5 yr (95% CI: 3.5-6.1), and 4.1 yr (95% CI: 3.2-6.5) for ECD-CV and ECD-noCV pts, respectively p=0.92. The median time of CV diagnoses for the entire cohort before and after ECD diagnosis was 4.8 yr (range: 0.06-42.5) and 3.4 yr (range: 0.05-18.7), respectively. BRAFV600E mutation was present in 65% (n=26) of ECD-CV vs 42% (n=28) of ECD-noCV, p=0.02. Eight pts with ECD-CV died; one from sudden cardiac death, while 9 ECD-noCV pts died, p=0.39. Smoking was present in 43% (n=18) of ECD-CV pts compared to 24% with ECD-noCV (n=16), p=0.05. In comparison to ECD-noCV pts, increased prevalence of pre-existing HTN [59% (n=23) vs 35% (n=23), p=0.016] and MI [10% (n=4) vs 2% (n=1), p=0.046] was seen in ECD-CV. There was no difference in hyperlipidemia, diabetes mellitus, CAD, CHF, valvular disease, afib, and conduction disorders among the two cohorts. After ECD diagnosis, de novo diagnosis of hyperlipidemia [67% (n=26) vs 38% (n=25); p=0.004] and CHF [35% (n=14) vs 7% (n=5); p=<0.001] was seen more frequently in ECD-CV pts compared to ECD-noCV. Following the diagnosis of ECD, the severity of pre-existing conditions did not change in pts with or without ECD-CV. ECD-CV pts had a greater cumulative CV disease burden as the total number of CV morbidities was greater in ECD-CV pts both before and after ECD diagnosis (Table 1). Additionally, pts with ECD-CV had greater severity of CV morbidity (composite grade ≥4) than ECD-noCV before (35% v 3%; p=<0.001) and after (42.5% vs 7.6%; p=<0.001) ECD diagnosis. The composite grade of 4 before ECD diagnosis was most attributable to pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (50% in pts with ECD-CV vs 100% in pts with ECD-noCV); after ECD diagnosis pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (41% in pts with ECD-CV vs 100% in pts with ECD-noCV). After considering BRAFV600E mutation, age ≥60, smoking, ECD-CV, and first-line therapy (conventional vs targeted therapy), ECD-CV was the only factor independently associated with a composite grade of ≥4, before (OR 25.6, 95% CI: 4.8-137) and after (OR 9.7, 95% CI: 3.0-30.8) ECD diagnosis. Nonetheless, smoking, age >60, and ECD-CV were independent factors associated when considered a composite grade of ≥3, before and after ECD diagnosis. Conclusion: ECD-CV did not prognosticate pts and did not worsen the pre-existing CV conditions compared to pts with ECD-noCV. However, ECD-CV pts had a higher cumulative CV disease burden and a higher grade of pre-existing and de novo CV morbidities compared to ECD-noCV. The presence of cardiac involvement in ECD was independently associated with a severe cumulative CV disease burden grade.

[Background] The current standard first-line treatment for acute graft-versus-host disease (GVHD), systemic high-dose steroids, induces clinical responses in a majority of patients. Flares of GVHD after initial improvement often occur during tapering or discontinuation of immunosuppression. There is no consensus regarding the definition of GVHD flare and little has been published regarding its natural history. [Methods] We retrospectively evaluated clinical data and blood samples from 968 allogeneic hematopoietic cell transplantation (HCT) recipients from 23 Mount Sinai Acute GVHD International Consortium (MAGIC) transplant centers who achieved complete response (CR) or very good partial response (VGPR) within 4 weeks of acute GVHD treatment between 2014 and 2021. VGPR was defined as the complete resolution of acute GVHD manifestations except residual stage 1 skin disease. Flares were defined as a recurrence of acute GVHD after achievement of CR/VGPR if (1) patients had an increased symptom severity of at least 1 organ stage; (2) received intensified treatment (increase in steroid dose ≥ 0.25 mg/kg or initiation of additional systemic immunosuppression); and (3) had no prior history of primary disease relapse or donor lymphocyte infusion preceding the flare. Serum samples obtained at the time of CR/VGPR and at the time of flares were analyzed for ST2 and REG3α concentrations that generated MAGIC algorithm probabilities (MAPs) resulting in previously validated Ann Arbor (AA) scores (1, 2, and 3). [Results] The median recipient age at HCT was 55 years (range: 0 to 79); 18.6% of patients had grades III-IV acute GVHD before CR/VGPR. The median maximum daily dose of corticosteroids before CR/VGPR was 1 mg/kg methylprednisolone equivalent (range, 0.1 to 3.2 mg/kg). Flares developed within 6 months following CR/VGPR in 210/968 (21.7%) patients with a median onset of 28 days (range: 2 to 448). Symptom severity at the onset of flare symptoms varied widely (grade I: 22%; grade II: 38%; grade III-IV: 41%) and the development of a flare was associated with a five-fold increase in NRM (hazard ratio [HR], 4.84 [95% CI, 3.19-7.36], P < 0.001) when considered as a time-dependent covariate in a multivariate regression model. The large majority of non-relapse deaths after flares (54/71, 76%) were due to acute GVHD or complications from its treatment. The AA scores at the time of first CR/VGPR successfully stratified patients for risk of six-month NRM (AA1: 5%, AA2: 11%, AA3: 34%, P < 0.001) (Figure A). Increasing AA score at CR/VGPR was also associated with significantly increased risk of GVHD flares (Figure B). Not only did the risk of flare increase with each rising AA score, but the number of patients with severe (grade III/IV) symptoms at flare onset also increased (AA1: 34%, AA2: 43%, AA3: 54%). We next generated a multivariate regression model of risk factors for the development of flares. High AA scores at the time of CR/VGPR (AA2: HR, 1.81 [95% CI, 1.32-2.48], P = 0.001; AA3: HR, 3.14 [95% CI, 1.98-4.98], P < 0.001), HCT from HLA mismatched unrelated donor (HR, 1.74 [95% CI, 1.00-3.02], P = 0.049), and interestingly, rapid achievement of CR/VGPR from the time of initial treatment (≤14 days) (HR, 1.84 [95% CI, 1.21-2.80], P = 0.004) were all identified as significant risk factors. Meanwhile, no other transplant, GVHD, or treatment characteristics associated with the development of flares including maximum GVHD severity, maximum steroid dose, and use of additional immunosuppressive agents other than steroids before CR/VGPR. AA scores measured at the onset of flare symptoms in 98 patients also predicted six-month NRM (AA1, 6%; AA2, 19%; AA3 42%, P = 0.01). [Conclusion] Despite advances in treatment, flares of GVHD symptoms following excellent (CR/VGPR) responses still occur in over one fifth of patients in current practice and are associated with a five-fold increase in six-month NRM. AA scores from serum biomarkers predict outcomes despite the absence of symptoms, suggesting that MAGIC biomarkers detect subclinical damage to GI crypts that can drive later recrudescence of clinical GVHD. Measurement of the MAP at the time of response to GVHD treatment may thus help to risk-stratify tapering strategies of immunosuppressive therapy.

The overall response rate (ORR) 28 days (D28) after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). ORR combines complete response (CR) and partial response (PR) because CR and PR have very similar non relapse mortality (NRM). D28 ORR has some disadvantages: first, physicians usually decide to modify immunosuppression much earlier than D28, and second, NRM does not always correlate well with ORR at D28, particularly for patients who present with intermediate risk or low risk disease. MAGIC serum biomarkers at day 14 (D14) of GVHD treatment have been shown to correlate better with NRM than clinical symptom response (Srinagesh et al, Blood Advances, 2019). We therefore hypothesized that a combination of clinical symptom severity and biomarkers at D14 of treatment would predict NRM at least as well and possibly better than clinical response at D28. We studied 1144 patients from 23 MAGIC sites who were systemically treated with at least 0.25 mg/kg/day of systemic steroids (prednisone equivalents) and had clinical data and serum samples available in the MAGIC database and biorepository. We divided the patients into a training set (n=764) and a validation set (n=380). We used 12-month NRM from the time of acute GVHD onset as the primary outcome of interest. We first trained a recursive partitioning algorithm in the training set to create a response model (MAGIC D14 clinical response) according to overall GVHD grade at both onset and D14 that predicted 12-month NRM and then applied it to the validation set. Patients that either progressed to grade III/IV GVHD, continued having grade III/IV GVHD, or only improved to grade II at D14 from grade III/IV at D0 had high NRM and were classified as non-responders. Patients with grade I/II GVHD at day 0 who had grade 0-II GVHD at day 14 and patients who improved to grade 0/I at day 14 from grade III/IV at day 0 had low NRM and were classified as responders. Although counterintuitive, responders included patients whose GVHD remained at grade II because the 12 month NRM of this group was only 20%. The new clinical model predicted NRM as well as the D28 standard response model in the validation set. When we integrated the D14 MAP biomarker score (low: ≤ 0.290 vs high: >0.290) with the new D14 clinical response (D14 MAGIC integrated response), we observed three response groups of integrated response with strikingly different 12 month NRM (8%, 35%, 76%, p<0.001), in contrast to the D28 standard response (Figure 1A, 1B). D14 complete response required MAGIC clinical response with low MAP; D14 partial response required either MAGIC clinical response with high MAP or MAGIC clinical non-response with low MAP; and D14 non-response required MAGIC clinical non-response with high MAP. The D14 response groups also displayed significant differences in 12 month OS (82%, 58%, 14% respectively, p<0.001) We then compared the D14 integrated response with the D28 standard response for 12 month NRM prediction. This D14 integrated response model was superior for time-dependent AUC (0.78 vs 0.71, P=0.03), sensitivity (0.71 vs 0.54), positive predictive value (0.52 vs 0.48) and negative predictive value (0.92 vs 0.89), with minimal loss in specificity (0.85 vs 0.87) in the prediction of 12-month NRM compared to the D28 standard response model. Using decision curve analysis, the D14 integrated response displayed higher net benefit (correct identification of patients that will experience NRM by 12 months) over the whole range of threshold probabilities/preferences for changing immunosuppression when 12-month NRM was used as the outcome. We conclude first, that the definition of response that uses onset and D14 grades is as accurate as D28 ORR model; and second, that a model using clinical grades and biomarkers on D14 more accurately predicts long term NRM than the standard D28 definition and may be useful as a clinical trial endpoint.

Introduction: In recent years, increasing evidence suggests an important role of tumor-infiltrating B lymphocytes (TIL-Bs) in cancer immunity. In various types of solid tumors, high density of TIL-Bs and presence of mature tertiary lymphoid structures consistently correlate with better prognosis and response to immune checkpoint blockade therapies. Diffuse large B-cell lymphoma (DLBCL) is a type of aggressive lymphoma arising from mature B cells. TIL-Bs have been identified in DLBCL based on single-cell gene-expression data in several previous studies, but their prognostic significance in DLBCL is unknown. In this study, we determined the clinical significance of TIL-B abundance and investigated the potential underlying mechanisms. Patients and Methods: This abstract includes a discovery study in which we distinguished TIL-Bs from malignant B-cells by immunoglobulin gene (IG) clonotype analysis and a validation study using flow cytometry. In the discovery study, a molecularly well-characterized DLBCL cohort assembled by the International DLBCL R-CHOP Consortium Program was analyzed. DNA samples from DLBCL patients were sequenced by high-throughput ultra-deep sequencing assays using multiplexed forward and reverse primers. Based on the IG heavy chain (IGH) sequencing results, malignant B-cell and TIL-B VDJ clonotypes were identified for 138 patients, and the frequencies and numbers of TIL-B clonotypes in individual sequence repertoires were calculated. In 79 DLBCL cases, a DJ-only sequence was predominant and no productive VDJ sequences met the criteria for diagnostic sequences. Total frequencies of productive VDJ sequences in the sequence repertoires were then used to access high and low TIL-B frequencies. TIL-B frequencies were also evaluated by IG light chain clonotypes for these 217 patients and additional 52 patients. The TIL-B frequencies were correlated with patient survival, gene-expression profiling (GEP) data GSE31312, and frequencies of DLBCL-infiltrating immune cells quantified by fluorescent multiplex immunohistochemistry (mIHC). To validate the prognostic significance of TIL-B frequencies, we assembled a validation cohort of 94 DLBCL patients treated at Duke University Medical Center. TIL-Bs in tissues were quantified by flow cytometry immunophenotyping. Ratios of TIL-Bs to abnormal B cells were assessed for prognostic effects. Results: First, we found thathigh frequencies of TIL-Bs and clonotypic diversity were associated with significantly favorable prognostic effects in 138 DLBCL patients with their malignant B-cell IGH-VDJ clonotypes identified (Figure A). The prognostic effects of TIL-Bs outweighed those of the germinal center B-cell-like/activated B-cell-like classification (Figure B). Second, patients with high frequencies of TIL-Bs compared with those without had significant upregulation of immune response genes by GEP (Figure C), higher CD4 T cell infiltration quantified by fluorescent mIHC (Figure D), and enrichment of EcoTyper-defined specific cell states, including significantly increased frequencies of B cell state S2 (pre-memory), CD4 T cell state S3 (naïve), follicular helper T cell state S3, and endothelial cell state S3, whereas lower frequencies of B cell state S5 (plasmablast) and follicular helper T cell state S1. Third, high TIL-B abundance in 79 ‘DJ-only’ clonotype cases and additional 52 cases assessed by light chain clonotype analysis showed similar favorable prognostic effects and associations with upregulated immune gene expression and CD4 T cell infiltration. Fourth, the identified GEP signatures, prominent with genes involved in T-dependent B cell activation and T cell activation and differentiation, showed significant prognostic effects in two GEO datasets. Fifth, TIL-B abundance showed significant prognostic effects in DLBCL cases infiltrated with T-cells in both the discovery study (Figure E) and validation study (Figure F, flow cytometry cohort). Conclusions: TIL-B frequencies and clonotypic diversity have remarkable prognostic and biological effects in DLBCL, which has significant implications for developing novel therapeutic strategies. Our study identified a new prognostic biomarker in DLBCL and an important player in anti-lymphoma immune responses, providing a new point of view to analyze the tumor biology in aggressive B-cell lymphoma and precisely identify prognostic determinants.

Background: cHL is one of the most curable forms of childhood cancer, but patients (pts) who have a SER to initial chemo are at high risk of relapse. The approaches currently used to treat pts with SER, such as dose intensification and radiotherapy (RT), can cause long-term toxicity.An unmet need remains to optimize treatment in pts with cHL and SER while minimizing long-term toxicity. The open-label, phase 2 KEYNOTE-667 study (NCT03407144) is being conducted to evaluate the efficacy and safety of pembro plus chemo in pediatric pts with cHL and SER to front-line chemo. Early analysis of pts in KEYNOTE-667 with high-risk cHL showed that pembro plus cyclophosphamide, vincristine, prednisone/prednisolone, and dacarbazine (COPDAC-28) consolidation had manageable safety and promising antitumor activity, with 66% of pts being spared RT. We present updated results for pts with high-risk cHL and SER. Methods: Pts eligible for the high-risk group were 3-25 y old and had newly diagnosed stage IIEB, IIIEA, IIIEB, IIIB, IVA, or IVB cHL, measurable disease per investigator assessment, and a Lansky Play-Performance Scale ≥50 (<16 y old) or a Karnofsky score ≥50 (≥16 y old). All pts received 2 cycles of induction therapy with vincristine, etoposide, prednisone/prednisolone, and doxorubicin (OEPA). After induction, response was assessed by PET/MRI/CT (early response assessment). Pts with rapid early response received nonstudy therapy. Pts with SER (Deauville score, 4 or 5) received consolidation with pembro 2 mg/kg up to 200 mg IV Q3W (3-17 y) or 200 mg IV Q3W (18-25 y) plus 4 cycles of COPDAC-28. Response was then assessed by PET/CT/MRI (late response assessment [LRA]). Pts with PET positivity at LRA (Deauville score, 4 or 5) received involved-site RT (28.8 Gy) to late PET-positive residua plus maintenance pembro Q3W for up to 17 cycles; pts with PET negativity received maintenance pembro Q3W for up to 17 cycles without RT. The primary end point was ORR by BICR per Cheson 2007 IWG criteria in pts with SER, determined at any time after starting pembro and before start of new anticancer therapy. Secondary end points included PET negativity after consolidation, EFS, OS, and safety. Results: This analysis included 62 pts with high-risk cHL and SER to frontline OEPA. The median time from enrollment to data cutoff (March 1, 2023) was 16.8 mo (range, 2.3-36.4). At the data cutoff, 34 pts (55%) had completed consolidation and maintenance therapy, 23 (37%) were ongoing on treatment, and 5 (8%) had discontinued because of adverse events (AEs; n = 1), progressive disease (n = 2), or withdrawal by pt (n = 2). Median age was 14 y (range, 5-22), 30 pts (48%) were male, 29 (47%) had bulky disease, and 41 (66%) had Ann Arbor stage IV disease. Pts had received a median of 17 administrations of pembro (range, 1-17); the median time on pembro was 11.0 mo (range, <0.1-11.8). Of 62 pts who received consolidation with pembro plus COPDAC-28, 53 (85%) had a LRA (9 are still in consolidation and have not reached the LRA time point), of whom 35 (66%) were PET negative by BICR (37 [70%] PET negative by investigator review). AEs regardless of causality that occurred during consolidation and maintenance were reported in 54 pts (87%), most commonly (≥15%) headache (n = 11; 18%), nausea (n = 11; 18%), cough (n = 10; 16%), COVID-19 (n = 10; 16%), and pyrexia (n = 10; 16%). Grade 3/4 AEs occurred in 21 pts (34%). 13 pts (21%) had a serious AE. No pts died because of AEs. Treatment-related AEs occurred in 38 pts (61%). Grade 3/4 treatment-related AEs occurred in 10 pts (16%). One pt (2%) discontinued treatment because of a treatment-related AE (grade 3 AST increased). Pembro-related AEs occurred in 23 (37%) pts; most commonly (≥5%) hypothyroidism (n = 4; 6%), nausea (n = 3; 5%), and ALT increased (n = 3; 5%). Three pts (5%) experienced grade 3/4 pembro-related AEs. One serious AE was related to pembro (abdominal pain). Six pts (10%) experienced immune-mediated AEs (hypothyroidism, n = 4 [2 grade 1, 2 grade 2]; colitis, n = 1 [grade 2]; pneumonitis, n = 1 [grade 2]). Conclusion: In this updated analysis, consolidation with pembro plus COPDAC-28 continued to have manageable safety and promising antitumor activity in pediatric pts with high-risk cHL and SER to front-line OEPA. Of pts with a LRA, 66% had a PET-negative response and were spared RT. These findings support the results of prior analyses and suggest that adding pembro to COPDAC-28 consolidation may augment responses in the high-risk cHL population.

Background: DNA mismatch repair (MMR) deficiency (dMMR), which leads to genomic and microsatellite instability, is a biomarker that predicts response to immunotherapies and has variable prognostic effects for chemotherapies in solid tumors. The dMMR can be detected using gene sequencing or immunohistochemistry for four essential MMR proteins: MSH6, MSH2, MLH1, and PMS2. Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma. Previous studies have suggested the role of MMR gene variants in DLBCL lymphomagenesis, yet the prognostic role of dMMR in DLBCL has not been well studied. Methods: We performed targeted next-generation sequencing and immunohistochemistry for MSH6, MSH2, MLH1, and PMS2 in a large cohort of DLBCL patients treated with standard chemoimmunotherapy. Gene expression profiling (GEP) was performed using the Affymetrix GeneChip Human Genome HG-U133 Plus 2.0 microarray (data in GSE31312). Fluorescent multiplex immunohistochemistry (mIHC) was performed using MultiOmyx multiplexing immunofluorescence staining protocols and antibodies against 13 immune markers. We investigated the frequencies of dMRR and MMR protein expression and correlated the mutation status and expression levels with patient survival and DLBCL biology, including the number of mutated genes, GEP data, previously analyzed DLBCL biomarkers, and immune cell immunophenotypes. Results: MMR gene mutations and loss of MMR protein expression were infrequent in DLBCL (Figure A) and did not show a significant prognostic impact. MMR proteins were commonly expressed in DLBCL samples and the germinal centers of reactive tonsil controls (Figure B), with higher mean and median percentages of tumor cells expressing MSH6 and MLH1 proteins than in the solid tumor samples used for comparison. High expression of MMR proteins was associated with Ki-67, MYC, and p53 overexpression as determined by immunohistochemistry. GEP analysis identified significantly upregulated genes involved in the mitotic cell cycle and DNA metabolism and prominent downregulated immune gene signatures in DLBCL with high MMR protein expression. Fluorescent mIHC confirmed the associations with decreased T cell abundance in MSH6/MSH2/MLH1/PMS2 highly expressing DLBCL (Figure C) independent of p53 and MYC expression status, whereas MSH6/ MLH1 mutations were associated with increased T cell frequencies. High versus low expression of MSH6, MLH1, and PMS2 showed significant unfavorable prognostic effects in DLBCL when the optimal cutoffs were used for high expression. However, when the median percentages were used as cutoffs, the prognostic effects were not significant; nonetheless, MSH6 and PMS2 high expression showed significant adverse prognostic effects in the MYC¯ DLBCL subset. Interestingly, when we used Ki-67, MYC + or BCL2 + percentages in DLBCL assessed by immunohistochemistry to subtract MMR protein percentages and then correlated the differences to survival, we found significant favorable prognostic impact by higher percentage expression differences between MMR proteins and BCL2, or by intermediately higher differences between MMR proteins and MYC. For MSH2/MLH1/PMS2, the higher percentages compared with MYC/BCL2 showed no significant correlations with T cell infiltration levels, whereas the higher differences between MSH6 and MYC/BCL2 were associated with significantly lower T cell frequencies (Figure D). Conclusions: This study revealed that high expression of MMR proteins is a common feature of DLBCL associated with lower tumor-infiltrating T cells, MYC and p53 overexpression, and context-dependent prognostic effects. In contrast, dMMR and loss of MMR proteins are infrequent and have no significant prognostic effects in DLBCL treated with standard chemoimmunotherapy. These results may have implications for understanding DLBCL biology and the low efficacy of PD-1 blockade immunotherapy in DLBCL.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).