Wudhikarn, Kitsada

Abstract The cumulative impact of baseline comorbidities on outcomes of chimeric antigen receptor T-cell (CAR-T) therapy is not well established. Therefore, we developed and validated a Cellular Therapy Comorbidity Index (CT-CI) to predict outcomes following CD19-directed CAR-T therapy for large B-cell lymphoma (LBCL). Patients aged 18 or older receiving commercial CAR-T therapy for LBCL during 2017 to 2020 were selected from the Center for International Blood and Marrow Transplant Research registry. Patients were randomly assigned to training or validation cohorts. Comorbidities given weighted scores comprised the CT-CI, which was then validated for overall survival (OS) prognostication. A total of 1916 patients from 97 medical centers were included, with a median age of 64 years (19-91 years). About 70% of patients had comorbidities, such as cardiac disease (12%); diabetes (14%); hepatic dysfunction (mild, 8%; moderate to severe, 2%); psychiatric disturbance (18%); and pulmonary dysfunction (moderate, 15%; severe, 12%). The CT-CI was calculated, stratified patients in 3 categories, and was associated with increased mortality. Patients with higher CT-CI scores had worse OS (CT-CI 1: hazard ratio [HR], 1.37 [95% confidence interval [CI], 1.16-1.62; P< .001]; CT-CI 2: HR, 1.49 [95% CI, 1.17-1.89; P = .001]; CT-CI ≥ 3: HR, 2.55 [95% CI, 1.90-3.42; P< .001]). Higher CT-CI scores predicted treatment-related mortality and relapse. There was no correlation between the CT-CI score and CAR-T–related toxicities. The novel CT-CI score stratifies the effect of patient comorbidities on survival after CAR-T therapy and can be used for clinical decision-making and treatment selection in high-risk populations. However, comorbidities and fear of increased toxicity should not preclude patients from this effective therapy.

Abstract Tisagenlecleucel (tisa-cel) is a CD19-directed chimeric antigen receptor T-cell therapy for relapsed/refractory precursor B-cell acute lymphoblastic leukemia (R/R B-ALL). We report infectious complications for 100 days (D100) following tisa-cel therapy in 471 pediatric and young adults (median age 13.8 years) with R/R B-ALL reported from September 2017 to June 2022. By D100, 137 (29%) patients had an infectious event, with an infection density of 0.542 per 100 person-days at risk. D100 cumulative incidences of bacterial, viral, and fungal infections were 14.1%, 11.6%, and 1.3%, corresponding to infection density scores of 0.296, 0.213, and 0.033 per 100 person-days at risk, respectively. In a multivariable analysis, receipt of ≥3 lines of therapy before tisa-cel (hazard ratio [HR], 1.86; 95% confidence interval [CI], 1.13-3.08; P = .015), any-grade cytokine release syndrome (HR, 1.78; 95% CI, 1.17-2.71; P = .007), and lack of neutrophil recovery (HR, 2.63; 95% CI, 1.47-4.69; P = .001) were associated with an increased risk for any infection. Similar associations were observed for bacterial infections, with the addition of younger age as an adverse risk (<6 vs 6-15 years; HR, 2.38; 95% CI, 1.23-4.61; P = .01). Risk factors for viral infections included increasing age (1-year increase; HR, 1.05; 95% CI, 1.01-1.09; P = .016), prior history of any infection (HR, 2.76, 95% CI, 1.40-5.46; P = .004), and prior hematopoietic cell transplant (HR, 2.10; 95% CI, 1.18-3.71; P = .011). D100 infection-related mortality (IRM) rate was low at 0.2% (95% CI, 0.0-0.8). In this multicenter real-world study, we observed a high incidence of infectious complications but a low IRM following tisa-cel for R/R B-ALL.

Abstract Infection is increasingly recognized as a significant cause of morbidity and mortality in patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) receiving CD19 chimeric antigen receptor (CAR) T-cell therapy. The current study analyzed the natural history, risk factors, and outcomes of infection in 3350 patients with R/R LBCL receiving commercial CD19 CAR T-cell therapy (n = 2804 axicabtagene ciloleucel [axi-cel], n = 546 tisagenlecleucel) from December 2017 to June 2022. Infection developed in 834 patients (24.9%) within 100 days after infusion, resulting in an infection density of 0.43 per 100 patient days and a 100-day cumulative incidence of 22%. Bacterial, viral, and fungal infections were recorded in 527 (15.7%), 374 (11.2%), and 108 patients (3.2%), respectively, with corresponding infection densities of 0.23, 0.15, and 0.04 per 100 patient days. After a 24-month median follow-up, 1482 patients (44%) had died, with infection as the primary cause in 173 cases (12%). The 100-day infection-related mortality (IRM) was 1.6% (95% confidence interval, 1.2-2.0). Patients with a Karnofsky performance score of ≤80, infection history before CAR T-cell therapy, axi-cel therapy, severe cytokine release syndrome (grade ≥3), and severe immune effector cell–associated neurotoxicity syndrome (grade ≥3) had increased infection risk. Infections within 100 days were an independent risk factor for inferior overall survival beyond day 100 after CD19 CAR T-cell therapy. In conclusion, study results show a significant incidence of infection and IRM in patients with R/R LBCL treated with CD19 CAR T-cell therapy. Furthermore, results identify patients at a heightened risk of infection, offering insights to guide potential interventions aimed at mitigating infection and improving patient outcomes after CAR T-cell therapy.

Abstract Little is known about risk factors for central nervous system (CNS) relapse in mature T-cell and natural killer cell neoplasms (MTNKNs). We aimed to describe the clinical epidemiology of CNS relapse in patients with MTNKN and developed the CNS relapse In T-cell lymphoma Index (CITI) to predict patients at the highest risk of CNS relapse. We reviewed data from 135 patients with MTNKN and CNS relapse from 19 North American institutions. After exclusion of leukemic and most cutaneous forms of MTNKNs, patients were pooled with non-CNS relapse control patients from a single institution to create a CNS relapse–enriched training set. Using a complete case analysis (n = 182), including 91 with CNS relapse, we applied a least absolute shrinkage and selection operator Cox regression model to select weighted clinicopathologic variables for the CITI score, which we validated in an external cohort from the Swedish Lymphoma Registry (n = 566). CNS relapse was most frequently observed in patients with peripheral T-cell lymphoma, not otherwise specified (25%). Median time to CNS relapse and median overall survival after CNS relapse were 8.0 and 4.7 months, respectively. We calculated unique CITI risk scores for individual training set patients and stratified them into risk terciles. Validation set patients with low-risk (n = 158) and high-risk (n = 188) CITI scores had a 10-year cumulative risk of CNS relapse of 2.2% and 13.4%, respectively (hazard ratio, 5.24; 95% confidence interval, 1.50-18.26; P = .018). We developed an open-access web-based CITI calculator (https://redcap.link/citicalc) to provide an easy tool for clinical practice. The CITI score is a validated model to predict patients with MTNKN at the highest risk of developing CNS relapse.

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.

Background: Autologous CAR-T therapy targeting CD19 is an effective treatment for relapsed or refractory large B-cell lymphoma (LBCL). However, morbidity and mortality related to CAR-T toxicity remain significant concerns. This study aims to evaluate the patterns, risk factors, and implications of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) following CD19-CAR-T therapy in LBCL. Methods: This retrospective analysis includes 1916 LBCL patients treated with CD19-CAR-T cell therapy (axicabtagene-ciloleucel [axi-cel] 75% and tisagenlecleucel 25%) between 2018 and 2020 and reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Patient demographics, baseline characteristics, treatment-related variables, and clinical outcomes were collected. The incidence, severity and timing of onset of CRS and ICANS (according to median time to onset) were analyzed, factors associated with these outcomes and their impact on overall survival as time dependent covariates were done using Cox Regression multivariate analyses. Results: The median age of the patients was 64 years, with a majority having a Karnofsky performance status (KPS) below 90% (61%) and not receiving bridging therapy (67%). CRS was observed in 75.1% of patients, with 9% experiencing severe CRS (grades ≥ 3). Similarly, 43% of patients experienced ICANS, with 20% having severe ICANS (grade ≥ 3). The median time to toxicity was shorter for CRS (4 days) compared to ICANS (7 days). Nearly all the patients who developed ICANS also experienced CRS (98%; ), and the correlation between these two events and respective severities are shown in the Figure 1a. In the multivariate analyses, factors associated with development of CRS were gender (women, Odds ratio [OR] 1.39, 95% confidence interval [CI] 1.10- 1.76, p=0.006), high LDH at time of infusion (OR 1.56; 95% CI 1.22 - 2.00, p=0.002) and product (axi-cel OR 4.60, 95% CI 3.56 -5.81, p<0.001). Factors associated with the development of ICANS included age (> 65 years, OR 1.83; 1.50-2.24, p<0.001), KPS (<80%, OR 2.48; 1.88-3.27, p<0.001), disease status at infusion (treatment resistant disease, OR 1.98; 1.09-3.58, p=0.024; and untreated relapse OR 2.62, 1.35-5.10, p=0.004) and product (axi-cel, OR 5.85; 4.47-7.64, p<0.0001). Factors associated with severe and early onset CRS and ICANS are listed in the table below. The median follow-up of 14.2 months, the estimated 12-month overall survival, progression-free survival (PFS), and treatment-related mortality were 61.6% (95% CI: 59.4-63.9), 42.2% (39.9-44.5), and 4.3% (3.4-5.3), respectively. Overall CRS, early onset CRS and overall ICANS were not associated with higher mortality, however grade >3 CRS (HR 1.79; 1.41-2.28, p<0.001), grade >3 ICANS (hazard ratio [HR] 1.34; 95% CI 1.13-1.59, p=0.002) and early onset ICANS (<7 days, HR 1.23; 1.05-1.44, p=0.01) were associated with higher mortality. The impact of severe CRS on survival are shown in the landmark analysis at 30 days from infusion in the figure 1B. Conclusion: In the largest analysis of CD19-CAR-T cell therapy toxicities to date, we observed a high burden of CRS and ICANS among LBCL patients. The CAR T cell product type was consistently associated with higher incidence, shorter onset and higher severity of both toxicities. High LDH, older age and low performance score also influence the severity and onset of these outcomes. Additionally, patients who developed higher grades of CRS/ICANS and earlier onset ICANS had a higher mortality, stressing the need to develop novel strategies to mitigate the incidence and optimize management of these patients to improve their outcomes.

Abstract Backgrounds: Autologous stem cell transplant (ASCT) has been the standard treatment approach for patients with relapsed or high-risk lymphoma for several decades. Despite the improvement in supportive care, age has remained one of the critical factors that determines transplant eligibility and outcomes. Herein, we explored the transplant-related outcomes of patients with lymphoma aged 70 years or older at our institution and compared to those with younger ages. Methods: In this retrospective single center study, we include all patients with diagnosis of lymphoma who underwent ASCT between January 2000 and February 2021 at Mayo Clinic, Rochester, Minnesota. In addition to data obtained from the transplant database, we abstracted data from electronic medical record. We described relevant clinical characteristics and transplant associated outcomes including engraftment, transplant related mortality, relapse incidence, and survival outcomes. We compared the key transplant related outcomes with patients at the younger age group between 65 and 70 years old. Results: A total of 196 patients with lymphoma aged 70 years or older underwent ASCT between January 2000 and February 2021. The median age of patients at the time of ASCT was 72.4 (70.0-78.7) years with 24 patients (12.2%) being older than 75 years old. Of 196 patients, 76 (38.8%) had Charlson comorbidity index of 3 or higher and 10 (5.1%) had performance status of 2 or higher at the time of ASCT. Table 1 summarizes the relevant baseline clinical characteristics of 196 patients in this cohort including the comparison with younger patients (age 65-70 years old, n=296). The most common transplant indication in this cohort was relapsed diffuse large B cell lymphoma. The majority of patients (91.3%) received peripheral blood graft with a median CD34+ cell dose of 5.14x10 6 cells/kg which was significantly lower than younger patients (P=0.01). The rate of neutrophil and platelet engraftment was 99.0% and 98.8% with corresponding median time to engraftment of 13 days for both cell lineages. The 30- and 100-day non-relapse mortality (NRM) of the entire cohort was 1.5% (95%CI 0-3.2%) and 4.6% (95%CI 1.6-7.5%) not statistically different between 70-75 years old and 75 years or older cohorts. With the median follow-up duration of 48 months, the 2-year event free survival (EFS) and overall survival (OS) was 55.9% (95%CI 49.2-63.5%) and 67.4% (95%CI 61.0-74.5%), respectively. Patients from the 70 years old or older cohort had similar EFS but inferior OS compared to the 65-70 years old cohort. However, there was no difference in EFS and OS between 70-75 and >75 years old cohorts. There was overall an improved trend of 30-day NRM, 2-year EFS and 2-year OS over the 3 different transplant periods (Figures). Although not statistically significant, patients who underwent ASCT between 2000 and 2006 had a trend towards higher 30-day NRM compared to patients who were transplanted at later time points (7.3% vs 0%, P=0.06). The major causes of death included progressive lymphoma, infection, and conditioning regimen related organ toxicities. Conclusion: Outcomes of ASCT in older patients with lymphoma have improved over time with an excellent 30-day NRM (0%) in the past recent years. The major cause of treatment failure after ASCT was progressive disease. With appropriate patient selection, ASCT in older patients is feasible and provides acceptable outcomes comparable to the patients from younger age group. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Abstract Introduction Central nervous system (CNS) relapse (CNSr) in patients with aggressive non-Hodgkin lymphoma (NHL) occurs uncommonly (estimated incidence 5%) but carries a high morbidity and mortality. Studies have identified risk factors for CNSr such as high tumor burden and extranodal (EN) disease. However, most focus on B-cell NHL with minimal data in T-cell lymphomas (TCL), which are significantly less common and more heterogenous. The few small series of CNSr in TCL report a median overall survival (OS) less than 3 months (mo) with incidence ranging from 2.6-9%. To better define CNSr in TCL, we performed a multi-institutional retrospective analysis of TCL patients with CNSr and herein describe clinicopathologic characteristics and treatment of CNSr. Methods We performed a retrospective observational study using data from 9 US academic centers with IRB approval at individual sites. We included adult patients diagnosed with a mature T-cell neoplasm as per the 2016 WHO classification between 1/1/2009-1/1/2019, who were found to have CNSr at any time after initial diagnosis, and collected patient, disease, and treatment characteristics at time of initial diagnosis as well as at CNSr. Patients with a diagnosis of a precursor T-cell malignancy or with CNS disease identified at initial TCL diagnosis (TCLd) and/or prior to first-line systemic treatment were excluded. Results In this analysis, we report the outcomes of 75 patients (male n=45, female n=30). At TCLd, the median age was 59 years (range 20-81), and 61% of patients (n=46) had an IPI score of at least 3, 92% (n=69) had EN involvement with 37% (n=28) involving at least 2 EN sites, and 59% (n=44) had BM involvement. The most common pathologic diagnoses were peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS; 24%, n=18), angioimmunoblastic T-cell lymphoma (AITL; 17%, n=13), adult T-cell leukemia/lymphoma (ATLL; 17%, n=13), and mycosis fungoides (MF; 12%, n=9) (Figure 1A). First-line systemic therapy for TCL included anthracyclines for 72% (n=54). Autologous and allogenic transplants were performed prior to CNSr in 12% (n=9) and 8% (n=6) of patients, respectively. Prior to CNSr, 48% (n=36) had non-CNS relapse. Some form of CNS prophylaxis was used during initial systemic lymphoma therapy in 24% of patients (n=18), predominantly intrathecal methotrexate (IT MTX; n=16). Median time from TCLd to CNSr was 8.5mo, though this was significantly longer in MF (46.8mo [range 17.5-187.7]) versus PTCL, NOS (7.6mo [range 1.1-58.4], P=0.0002), AITL (21.2mo [range 2.0-61.6, P=0.008), and ATLL (7.3mo [range 0.7-46.4], P=0.0005) (Figure 1B). CNSr developed within 6mo of TCLd in 31% of patients (n=23) and within 12mo in 57% (n=43). Symptoms related to CNSr occurred in 71% of patients (n=53). CNSr patterns were 61% leptomeningeal (n=46), 21% parenchymal (n=16), and 17% both (n=13) with no significant survival difference between leptomeningeal or parenchymal disease alone (HR 1.45, 95% CI 0.78-2.70, P=0.28). Concomitant systemic relapse was observed in 59% of patients (n=44). The most common CNS-directed therapy for CNSr was IT MTX (56%; n=42), though multiple different IT and/or systemic regimens were used. Patients received a median of 1 line of CNS-directed treatment (range 0-5). The overall response rate to initial CNS directed treatment was 32% (16% CR, 16% PR). Median follow up after CNSr was 40.7mo. At last follow up, 83% had died (n=62). Median OS after CNSr was 4.6mo (range 0.1-68.7) (Figure 1C). Those with ATLL had the shortest median OS after CNSr (2.7mo) versus 6.3mo in MF (HR 3.69, 95% CI 1.44-9.42, P=0.005), 6.5mo in AITL (HR 2.16, 95% CI 0.92-5.06, P=0.054), and 4.8mo in PTCL, NOS (HR 1.49, 95% CI 0.70-3.19, P=0.27) (Figure 1D). The most common cause of death was progressive lymphoma (77%; n=48). Conclusions This is to our knowledge the largest series of CNSr in TCL to date. Most CNSr occurred within 12mo, though CNSr occurred later in patients with MF. Although the prognosis after CNSr was generally poor, we found that median OS in CNSr was longer than previously reported, perhaps reflecting more effective treatments for CNS and systemic relapse, inclusion of MF, or lead time bias. Further analysis of the impact of different treatment strategies and outcomes in CNSr was limited by the small sample size and heterogeneity within our cohort, and future analyses in a larger cohort should focus on factors associated with outcomes. Figure 1 Figure 1. Disclosures Horwitz: ADC Therapeutics, Affimed, Aileron, Celgene, Daiichi Sankyo, Forty Seven, Inc., Kyowa Hakko Kirin, Millennium /Takeda, Seattle Genetics, Trillium Therapeutics, and Verastem/SecuraBio.: Consultancy, Research Funding; Affimed: Research Funding; Aileron: Research Funding; Acrotech Biopharma, Affimed, ADC Therapeutics, Astex, Merck, Portola Pharma, C4 Therapeutics, Celgene, Janssen, Kura Oncology, Kyowa Hakko Kirin, Myeloid Therapeutics, ONO Pharmaceuticals, Seattle Genetics, Shoreline Biosciences, Inc, Takeda, Trillium Th: Consultancy; Celgene: Research Funding; C4 Therapeutics: Consultancy; Crispr Therapeutics: Research Funding; Daiichi Sankyo: Research Funding; Forty Seven, Inc.: Research Funding; Kura Oncology: Consultancy; Kyowa Hakko Kirin: Consultancy, Research Funding; Millennium/Takeda: Research Funding; Myeloid Therapeutics: Consultancy; ONO Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy, Research Funding; Secura Bio: Consultancy; Shoreline Biosciences, Inc.: Consultancy; Takeda: Consultancy; Trillium Therapeutics: Consultancy, Research Funding; Tubulis: Consultancy; Verastem/Securabio: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Chavez: AstraZeneca: Research Funding; ADC Therapeutics: Consultancy, Research Funding; Merk: Research Funding; MorphoSys, AstraZeneca, BeiGene, Genentech, Kite, a Gilead Company, and Epizyme: Speakers Bureau; MorphoSys, Bayer, Karyopharm, Kite, a Gilead Company, Novartis, Janssen, AbbVie, TeneoBio, and Pfizer: Consultancy; BMS: Speakers Bureau. Sokol: Dren Bio: Membership on an entity's Board of Directors or advisory committees; Kyowa-Kirin: Membership on an entity's Board of Directors or advisory committees. Saeed: Nektar Therapeutics: Consultancy, Other: research investigator; MEI Pharma Inc: Consultancy, Other: investigator; Celgene Corporation: Consultancy, Other: investigator; MorphoSys AG: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb Company: Consultancy; sano-aventis U.S.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceutica Products, LP: Consultancy, Other: investigator; Kite Pharma: Consultancy, Other: investigator; Other-TG therapeutics: Consultancy, Other: investigator; Other-Epizyme, Inc.: Consultancy; Other-Secura Bio, Inc.: Consultancy; Seattle Genetics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mehta-Shah: Kiowa Hakko Kirin: Consultancy; C4 Therapeutics: Consultancy; Verastem: Research Funding; Karyopharm: Consultancy; Ono Pharmaceuticals: Consultancy; Secura Bio: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; AstraZeneca: Research Funding; Bristol Myers Squibb: Research Funding; Celgene: Research Funding; Innate Pharmaceuticals: Research Funding; Roche/Genentech: Research Funding; Corvus Pharmaceuticals: Research Funding. Olszewski: TG Therapeutics: Research Funding; PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding. Allen: Epizyme: Consultancy; MorphoSys: Consultancy; ADC Therapeutics: Consultancy; Secure Bio: Consultancy; Kyowa Kirin: Consultancy. Gerson: Abbvie: Consultancy; Kite: Consultancy; TG Therapeutics: Consultancy; Pharmacyclics: Consultancy. Landsburg: Triphase: Research Funding; Takeda: Research Funding; Curis: Research Funding; ADCT: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Other: DSMB member; Incyte: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schuster: Adaptive Biotechnologies: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Genentech/Roche: Consultancy, Research Funding; Tessa Theraputics: Consultancy; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; BeiGene: Consultancy; Alimera Sciences: Consultancy; Acerta Pharma/AstraZeneca: Consultancy; Abbvie: Consultancy, Research Funding; Nordic Nanovector: Consultancy; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Incyte: Research Funding; TG Theraputics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Svoboda: Atara: Consultancy; Adaptive: Consultancy, Research Funding; Astra Zeneca: Consultancy, Research Funding; Imbrium: Consultancy; Pharmacyclics: Consultancy, Research Funding; Genmab: Consultancy; Merck: Research Funding; Incyte: Research Funding; BMS: Consultancy, Research Funding; TG: Research Funding; Seattle Genetics: Consultancy, Research Funding. Barta: Kyowa Kirin: Honoraria; Acrotech: Honoraria; Daiichi Sankyo: Honoraria; Seagen: Honoraria.

AbstractGermline predisposition syndromes (GPS) result from constitutional aberrations in tumor suppressive and homeostatic genes, increasing risk for neoplasia in affected kindred. In this study, we present clinical and genomic data on 144 Mayo Clinic patients with GPS; 59 evaluated prospectively using an algorithm‐based diagnostic approach in the setting of a dedicated GPS/ inherited bone marrow failure syndrome (IBMFS) clinic. Seventy‐two (50%) patients had IBMFS (telomere biology disorders‐32,Fanconi anemia‐18, Diamond Blackfan Anemia – 11, congenital neutropenia–5, Schwachman‐Diamond Syndrome‐5 and Bloom Syndrome‐1), 27 (19%) had GPS with antecedent thrombocytopenia (RUNX1‐FPD‐15, ANKRD26‐6, ETV6‐2, GATA1‐1, MPL‐3), 28 (19%) had GPS without antecedent thrombocytopenia (GATA2 haploinsufficiency‐16, DDX41‐10, CBL‐1 and CEBPA‐1) and 17 (12%) had general cancer predisposition syndromes (ataxia telangiectasia‐7, heterozygous ATM variants‐3, CHEK2‐2, TP53‐2, CDK2NA‐1, NF1‐1 and Nijmegen Breakage Syndrome‐1). Homozygous and heterozygous ATM pathogenic variants were exclusively associated with lymphoproliferative disorders (LPD), while DDX41 GPS was associated with LPD and myeloid neoplasms. The use of somatic NGS‐testing identified clonal evolution in GPS patients, with ASXL1, RAS pathway genes, SRSF2 and TET2 being most frequently mutated. Fifty‐two (91%) of 59 prospectively identified GPS patients had a change in their management approach, including additional GPS‐related screening in 42 (71%), referral for allogenic HSCT workup and screening of related donors in 16 (27%), medication initiation and selection of specific conditioning regimens in 14 (24%), and genetic counseling with specific intent of fertility preservation and preconceptual counseling in 10 (17%) patients; highlighting the importance of dedicated GPS screening, detection and management programs for patients with hematological neoplasms.