Mukherjee, Sudipto

Abstract Introduction: Despite advent of multiple tyrosine kinase inhibitors (TKIs) and newer treatment methods for AML and ALL, the outcomes of patients (pts) with chronic myeloid leukemia in blast phase (CML-BP) remain poor. Consensus guidelines on treatment of CML-BP are scarce. A recent study from the European leukemia network blast phase registry on 240 pts reported marked heterogeneity in treatment patterns and lack of an accepted standard. Hence, we undertook this study through the H Jean Khoury Cure CML Consortium (HJKC3) to describe the treatment patterns and outcomes of pts with CML-BP in the US. Methods: This is a multi-institution, retrospective study. IRB approval was obtained at each institution and data sharing agreements were executed. All pts diagnosed with CML-BP based on modified MD Anderson Criteria (≥30% blasts in the blood or bone marrow or extramedullary (EM) disease) from 2010 to 2025 were included. Categorical variables were compared using Chi-square test. Overall survival (OS) was calculated from time of diagnosis of CML BP to death from any cause. Event free survival (EFS) was calculated from date of best response to event (death or relapse). Results: Of the 283 pts with CML-BP, 96 (33.9%) were female and 187 (66.1%) were males. At the time of diagnosis, 183 (64.6%) had CML-CP, 21 (7.4%) pts had CML-AP and 79 (27.9%) pts had de-novo CML-BP. For the 204 pts that were initially diagnosed with CML-CP or CML-AP, the median time to progression to CML-BP was 22.5 months (1-207 months). Median age at diagnosis of CML-BP was 50 years (range 16-86 years). EM disease was present in 78 (27.6%) pts (38 myeloid sarcoma, 30 central nervous system and 10 skin). Blasts were of myeloid phenotype in 168 (59.4%), lymphoid in 103 (36.4%) and mixed in 9 (3.2%) pts. The bcr-abl protein was p210 in 229 (80.9%), p190 in 8 (2.8%) and p230 in 2 (0.7%) pts. ABL kinase domain mutation was seen in 42.1% (80/190) pts, with T315I being the most common (n=32), followed by E255K (n=16). Next generation sequencing was performed in 117 pts. The most common mutations were ASXL1 (n=27), RUNX1 (n=14), BCOR (n=4), DNMT3A (n=4) and BCORL1 (n=3). Of the 283 pts, 269 underwent CML-BP directed therapy. 63 (23.4%) pts were treated with TKI monotherapy, 184 (68.4%) pts were treated with a combination of chemotherapy (chemo) and TKI and 22 (8.2%) pts were treated with chemo alone. Response rate after CML-BP directed therapy was 75.3% (n=213). Best response was return to CML-CP in 49 pts, CHR with PCyR in 18 pts, CCyR in 31 pts, MMR in 31 pts, MR4 in 18 pts, MR4.5 in 19 pts and MR5 in 47 pts. Rate of response with 2G and ponatinib was significantly better than imatinib (85.3% and 77.3% vs 63.6%; p=0.04). 149 (55.4%) pts proceeded to alloSCT. Of the 213 pts that responded, 59 (27.7%) relapsed. At a median follow-up of 47 months, the median OS for all pts was 19.7 months (95% CI: 16.3-40.2 months), with 5-year OS rate of 38% (95% CI: 32-45%). Pts with myeloid phenotype had a significantly lower OS compared to lymphoid and mixed phenotype (Median OS 14.9 vs 45.9 and 31.1 months respectively; p=0.02). OS of pts that were treated with a combination of TKI and chemo was significantly longer compared to pts treated with TKI or chemo alone (52.9 vs 16.1 or 7.5 months; p<0.0001). Dasatinib (n=99; 34.9%) and ponatinib (n=97; 34.3%) were the most used TKIs followed by imatinib (n=23; 8.1%), nilotinib (n=21; 7.4%), bosutinib (n=8; 2.8%) and asciminib (n=3; 1.1%). Pts treated with ponatinib had a numerically longer OS compared to 2G-TKI and imatinib, however this was not statistically significant (40.1 vs 30.6 and 20.3 months; p=0.52). EFS for pts treated with ponatinib was significantly longer compared to 2G-TKI or imatinib (25.9 vs 17.6 and 15.9 months; p=0.03). In a landmark analysis, pts that underwent alloSCT within 6 months of diagnosis had a significantly longer OS compared to those who did not (69.3 vs 16.0 months; p=0.006). Conclusion: CML-BP continues to have poor prognosis. Treatment is heterogenous involving various combinations of TKI and chemo. In our analysis, treatment with 2G-TKI or ponatinib led to better responses. CML-BP with myeloid phenotype had worse OS. Treatment with ponatinib was associated with a numerically longer OS and significantly longer EFS compared to 2G-TKI and imatinib. In addition, treatment with chemo in combination with TKI and alloSCT led to improved OS.

Abstract Castleman disease (CD) describes a group of rare lymphoproliferative disorders that exhibit a wide range of symptomatology and degree of lymphadenopathy, particularly across the 2 forms of CD with unknown etiology, unicentric CD (UCD), and human herpesvirus-8–negative/idiopathic multicentric CD (iMCD). Whereas UCD cases typically present with localized lymphadenopathy and mild symptoms, iMCD involves multicentric lymphadenopathy and cytokine storm–driven symptoms with 3 recognized clinical phenotypes. Increasingly, there are anecdotal reports of cases that do not fit into this framework, but these cases have not been systematically described. Herein, we use the ACCELERATE natural history registry to characterize the spectrum of CD based on disease features, symptomatology, and severity. Our results characterize a cohort of 179 CD cases, which were reviewed and confirmed by an expert panel of clinicians and hematopathologists. We show that patients with CD present on a continuous spectrum of clinical phenotypes, and we describe oligocentric CD (OligoCD), an intermediate phenotype that does not fit the criteria for UCD or iMCD. These cases tend to have “oligocentric” lymphadenopathy (median [interquartile range] regions of lymphadenopathy, 3.0 [2.0-4.0]) in a regional pattern and exhibit a mild clinical phenotype that is more similar to UCD than iMCD. We also show that patients with OligoCD are inconsistently categorized as UCD vs iMCD, highlighting the need for this characterization. Future data collected through ACCELERATE may further elucidate the natural history and risk profile of these patients.

Introduction: Despite advent of multiple tyrosine kinase inhibitors (TKIs) and newer treatment methods for both AML and ALL, the outcomes of patients (pts) with chronic myeloid leukemia in blast phase (CML-BP) remain poor. Consensus guidelines on treatment of CML-BP are scarce. A recent study from the European leukemia network (ELN) blast phase registry reported outcomes of 240 CML-BP pts. They also reported marked heterogeneity in treatment patterns and lack of an accepted standard. Hence, we are undertaking this study through the H Jean Khoury Cure CML Consortium (HJKC3) to study the treatment patterns and outcomes of pts with CML-BP in the US. Methods: This is a multi-institution, retrospective study. IRB approval was obtained at each institution separately and data sharing agreements were executed. All pts diagnosed with CML-BP based on modified MD Anderson Criteria (≥30% blasts in the blood or bone marrow or extramedullary (EM) disease) from 2010 to 2024 were included. Categorical variables were compared using the Chi-square test. Overall survival (OS) was calculated from the time of diagnosis to death from any cause. Survival probabilities were computed using the Kaplan-Meier method and compared with the log-rank test. Results: Of the 72 pts with CML-BP included in this analysis, 29 (40.3%) were female and 43 (59.7%) were males. At the time of diagnosis, 36 (50%) had CML-CP (chronic myeloid leukemia in chronic phase), 11 (15.3%) pts were diagnosed with CML-AP (accelerated phase CML) and 25 (34.7%) pts with de-novo CML-BP. For the 47 pts that were initially diagnosed with CML-CP or CML-AP, the median time to progression to CML-BP was 26 months (1-207 months). Median age at diagnosis of CML-BP was 54 years (range 21-86 years). EM disease was present in 17 (23.6%) pts (7 myeloid sarcoma, 5 central nervous system and 5 skin) with 3 pts presenting with EM disease only. Blasts were of myeloid phenotype in 41 (56.9%), lymphoid in 29 (40.3%) and mixed in 2 (2.8%) pts. The bcr-abl protein was p210 in 59 (81.9%), p190 in 5 (6.9%) and p230 in 1 (1.4%) pt. Four pts had prior allogeneic stem cell transplant (alloSCT) for CML-CP. Of the pts who had an ABL kinase domain mutation tested, 42.9% (18/42) pts were found to have an ABL kinase domain mutation. The most common TKD mutation was T315I (n=6). Next generation sequencing was performed in 32 pts. The most common mutations were ASXL1 (n=10), RUNX1 (n=4), BCOR (n=2), BCORL1 (n=2), DNMT3A (n=2) and 1 each in CSF3R, KMT2A, EZH2, NF1 and TP53. Of the 72 pts, 68 underwent CML-BP directed therapy. While only 9 (13.2%) pts were treated with TKI alone (4 dasatinib, 2 imatinib, 2 ponatinib and 1 asciminib), 54 (79.4%) pts were treated with a combination of chemotherapy with TKI (24 dasatinib, 13 ponatinib, 9 imatinib, 6 nilotinib, 1 bosutinib and 1 asciminib), and 5 (7.4%) pts were treated with chemotherapy alone. The response rate after CML-BP directed therapy was 72.1% (n=49). Best response was return to CML-CP in 17 pts, CHR with PCyR in 1 pt, CCyR in 11 pts, MMR in 5 pts, MR4 in 4 pts, MR4.5 in 5 pts and MR5 in 6 pts. Rate of response with third and second generation TKI was significantly better than first generation TKI (80% and 77.1% vs 36.3%; p=0.02287). 30 (44.1%) pts proceeded to alloSCT. Of the 49 pts that responded, 16 (32.6%) relapsed. The median time to relapse was 5 months (range 1-46 months). At a median follow up of 57 months, the median OS for all pts was 18 months. There was no difference in OS based on blast phenotype (14 months for myeloid, 21 for lymphoid and 7.5 for mixed; p=0.1185), initial diagnosis (38 months CP-CML, 13 for CML-AP and 16 for de-novo BP pts; p=0.6380), or TKI (13 months for first generation, 20 for second and not reached for third generation TKI; p=0.1729). The OS of pts that were treated with a combination of TKI and chemotherapy was significantly longer compared to pts treated with TKI or chemo alone (28 vs 13 or 4.5 months; p=0.0003). Of the 32 pts that responded, OS for pts that underwent alloSCT was significantly longer compared to pts that did not (not reached vs 14 months; p= 0.0022). Conclusion: CML-BP continues to have a dismal prognosis. Treatment is heterogenous involving various combinations of TKI and chemotherapy. In our analysis, treatment with second or third generation TKI led to better responses. In addition, treatment with chemotherapy in combination with TKI and alloSCT led to improved OS. Updated results from further multi-institution analysis will be presented at the meeting.

Hematopoietic growth factors, including erythrocyte stimulating agents (ESAs), granulocyte colony-stimulating factors, and thrombopoietin mimetics, can mitigate anemia, neutropenia, and thrombocytopenia resulting from chemotherapy for the treatment of cancer. In the context of pandemic SARS-CoV-2 infection, patients with cancer have been identified as a group at high risk of morbidity and mortality from this infection. Our subcommittee of the NCCN Hematopoietic Growth Factors Panel convened a voluntary group to review the potential value of expanded use of such growth factors in the current high-risk environment. Although recommendations are available on the NCCN website in the COVID-19 Resources Section (https://www.nccn.org/covid-19/), these suggestions are provided without substantial context or reference. Herein we review the rationale and data underlying the suggested alterations to the use of hematopoietic growth factors for patients with cancer in the COVID-19 era.

AbstractIdiopathic multicentric Castleman disease (iMCD) is a rare immunologic disorder characterized by systemic inflammation, multicentric lymphadenopathy, and organ dysfunction. Enlarged lymph nodes demonstrate a spectrum of characteristic but variable histopathologic features historically categorized into hyaline vascular (HV) (or hypervascular [HyperV] more recently), plasmacytic, or “mixed.” Though the etiology is unknown, a pro‐inflammatory cytokine storm, often involving interleukin‐6 (IL‐6), contributes to pathogenesis. Anti‐IL‐6 therapy with siltuximab is the only FDA‐ or EMA‐approved treatment based on efficacy and safety in multiple studies. Importantly, no patients considered to have HV histopathology achieved the primary endpoint in the Phase II study. NCCN currently recommends siltuximab first‐line for iMCD, except for patients considered to have HV histopathology. We investigated whether histopathologic subtype should guide siltuximab treatment decisions. Secondary analyses of clinical trial and real‐world data revealed similar clinical benefit across histopathologic subtypes. Notably, only 18 of 79 patients in the Phase II study were consistently classified into histopathologic subtype by three independent review panels, demonstrating limited reliability to guide treatment decisions. Real‐world data further demonstrate siltuximab's effectiveness in patients considered to have HV (or HyperV). Though histopathology is a critical component for diagnosis, there is insufficient evidence to guide treatment based solely on lymph node histopathologic subtype.

Abstract Background: Older adults with acute myeloid leukemia (AML) represent a vulnerable population in whom disease-based and clinical risk factors, patient goals, prognosis, and practitioner- and patient-perceived treatment risks and benefits influence treatment recommendations. Objective: These evidence-based guidelines of the American Society of Hematology (ASH) are intended to support patients, clinicians, and other health care professionals in their decisions about management of AML in older adults. Methods: ASH formed a multidisciplinary guideline panel that included specialists in myeloid leukemia, geriatric oncology, patient-reported outcomes and decision-making, frailty, epidemiology, and methodology, as well as patients. The McMaster Grading of Recommendations Assessment, Development and Evaluation (GRADE) Centre supported the guideline-development process, including performing systematic evidence reviews (up to 24 May 2019). The panel prioritized clinical questions and outcomes according to their importance to patients, as judged by the panel. The panel used the GRADE approach, including GRADE’s Evidence-to-Decision frameworks, to assess evidence and make recommendations, which were subject to public comment. Results: The panel agreed on 6 critical questions in managing older adults with AML, mirroring real-time practitioner-patient conversations: the decision to pursue antileukemic treatment vs best supportive management, the intensity of therapy, the role and duration of postremission therapy, combination vs monotherapy for induction and beyond, duration of less-intensive therapy, and the role of transfusion support for patients no longer receiving antileukemic therapy. Conclusions: Treatment is recommended over best supportive management. More-intensive therapy is recommended over less-intensive therapy when deemed tolerable. However, these recommendations are guided by the principle that throughout a patient’s disease course, optimal care involves ongoing discussions between clinicians and patients, continuously addressing goals of care and the relative risk-benefit balance of treatment.

Management of febrile neutropenia (FN) is an integral part of supportive care for patients undergoing cancer treatment. The NCCN Guidelines for Hematopoietic Growth Factors provide suggestions for appropriate evaluation, risk determination, prophylaxis, and management of FN. These NCCN Guidelines are intended to guide clinicians in the appropriate use of growth factors for select patients undergoing treatment of nonmyeloid malignancies. These NCCN Guidelines Insights highlight important updates to the NCCN Guidelines regarding the incorporation of newly FDA-approved granulocyte-colony stimulating factor biosimilars for the prevention and treatment of FN.

Abstract Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

Abstract Introduction Population level data about the survival outcomes of patients with hematologic malignancies occurring as second cancers is limited. We undertook one of the largest analyses of second hematologic malignancies (SHM) using the Surveillance, Epidemiology & End Results (SEER) registry to identify factors associated with survival. Methods SEER 9 &13 registries were queried to identify adult patients (pts>18 years) diagnosed with SHM that comprised of Acute Myeloid Leukemia (AML), Acute Lymphoid Leukemia (ALL), Myelodysplastic Syndromes (MDS), Chronic Myeloid Leukemia (CML) & Other Acute Leukemias (OAL, biphenotypic leukemias, myeloid sarcomas & leukemias NOS) > 1 year (yr) after diagnosis of a primary unrelated cancer, using ICD-O-3 codes over a 40 yr period (1973-2012). Data on demographics, follow up, primary tumor & survival were abstracted. Univariate & multivariate analyses were performed to identify factors influencing survival of SHM & compared with hematologic malignancies occurring as first cancers. Hazard ratios (HR) with 95% confidence intervals (CIs) are reported & significance was set at p< 0.01. Results SHM cases included 4665 AML, 3569 MDS, 1140 OAL, 1076 CML & 536 ALL pts. Median age at diagnosis of SHM was 73 yrs (range, 18-100) for AML, 79 yrs (18-100) for MDS, 74 yrs(20-99) for OAL, 74 yrs(18-98) for CML & 70 yrs(19-97) for ALL. Pts>60 yrs comprised 79.2% of AML, 92% of MDS, 88.7% of OAL, 82.9% of CML & 75.2% of ALL cases. Gender distribution was similar across all categories, with females comprising 48.5% of AML, 42.2% of MDS, 45.3% of OAL, 42.8% of CML & 50.2% of ALL cases. More than 85% of pts in each group were Caucasians. While the majority of pts had one primary unrelated cancer (82-89%), 14% of AML pts, 17.8% of MDS pts, 13.8% of OAL, 15.3% of CML pts& 10.6% of ALL pts had >1 primary unrelated cancer. Most common primary sites were prostate (19.1-29.6%), breast (16.1-21.9%) & colorectal (9.3-14.5%). Median survival (range) was as follows: AML 0.2 yrs (0-28.2); MDS 1.5 yrs (0-14); OAL 0.2 yrs (0-29.1); CML 2.5 yrs (0-27.1); & ALL 0.7 yrs (0-28.8). Median latency periods (range) for secondary cancers were as follows: AML 5.2 yrs (1.0-24.2); MDS 7.6 yrs (1.0-35.5); OAL 5.7 yrs (1.0-=34.3); CML 5.2 yrs (1.0-31.6); & ALL 6.2 yrs (1.0-35.5). In univariable analyses, age <60 yrs predicted for better survival (p<0.0001) for all SHM, as did diagnosis in recent yrs (2001-2012) (p< 0.0001 for AML, OAL, CML & ALL). Effect of time period on survival for MDS was not done, as it became formally reportable in 2001. Having >2 primaries was associated with an unfavorable outcome for subsequent AML (HR=1.15; 1.08-1.23; p < 0.0001), MDS (HR=1.13; 1.05-1.22: p=0.001) & CML (HR=1.30; 1.13-1.48: p=0.0002). In multivariable analyses, advancing age was an independent adverse prognostic factor for all secondary leukemias & MDS, both as a continuous variable & when categorized as <60 yrs vs. >60 yrs (p<0.001). Race & gender did not impact survival. Using breast cancer as a referent primary, AML pts had worse outcomes following colorectal (HR=1.31; 1.14-1.5; p=0.0002) or lung (HR=1.46; 1.22-1.74; p< 0.0001) primaries, while MDS pts fared worse with prior lung (HR=1.61; 1.22-2.12; p=0.0007) or Non-Hodgkin lymphoma (HR=1.58; 1.3-1.92: p< 0.0001). Of all 2ndCML cases, those after colorectal cancer had worst survival (HR=1.54; 1.12-2.13; p=0.008). Among SHM pts, MDS pts had better survival than those with AML (HR=0.42; 0.4-0.44: p< 0.0001), as did pts with CML (HR=0.37; 0.35-0.41: p< 0.0001) & ALL (HR=0.64; 0.58-0.70; p< 0.0001). Out OAL had similar outcomes compared to AML (HR=0.99; 0.92-1.05; p=0.66). Pts with CML fared better than MDS (HR=0.47; 0.41-0.54; p< 0.0001) & OAL (HR=0.37; 0.34-0.41: p< 0.0001). Pts with ALL had worse survival than CML (HR=1.8; 1.6-2.03: p< 0.0001) & MDS (HR=1.28; 1.11-1.48: p=0.0005), but better than OAL (HR=0.65; 0.58-0.73: p< 0.0001). (Figure 1) Patients with all SHM, except CML, had worse outcomes compared to those with primary hematologic cancers (Figure 2). Conclusion The prolonged latencies for SHM in this study suggest that they are likely therapy-related. Our findings show that type & number of primary cancers influence survival of subsequent leukemias & MDS. SHM have worse survival compared to those with primary hematologic malignancies except CML where non-separation of survival curves suggest that CML as SHM does not behave like a therapy-related neoplasm. Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: The incidence of thyroid cancers has been rising in the United States primarily due to the increased detection of well differentiated thyroid cancers (WDTC). Radioactive iodine (RAI), which is frequently used to treat WDTC, can be considered a circulating radiation emitter with the potential for mutagenic effects on hematopoietic stem cells. With a growing number of WDTC patients (pts) surviving long term following RAI therapy, establishing the risk of developing a myelodysplastic syndrome (MDS) in this cohort has important clinical implications. Methods: A novel R program, SEERaBomb (Radiat Environ Biophys. 2014;53(1):55-63) was used to query all 18 Surveillance Epidemiology and End Results (SEER) registries to identify WDTC cases treated with RAI and MDS as second cancers. SEERaBomb allows merging of all 18 SEER registries - a function not permitted by the SEER*Stat MP-SIR that limits researchers to use to one of two datasets (SEER 9 or SEER 13), thereby significantly increasing the capture of MDS cases (Figure 1). WDTC includes papillary and follicular thyroid histologies. WDTC survivor person-years at risk for developing MDS was based on age at diagnosis of WDTC, survival time, and age at diagnosis of MDS. Relative risks (RR) for developing MDS were then based on observed/expected cases between the cohorts receiving surgery alone (control), surgery combined with RAI as well as those receiving external beam radiation therapy (EBRT) and surgery with or without (+/-) RAI. Mean radiation exposure to bone marrow was compared between RAI and prostate cancer (as example) treated with other radiation modalities - brachytherapy (seeds), EBRT (3D, IMRT and VMRT). Results: In total 132,157 WDTC patients were identified from 1973-2011, of whom 69,975 (53%) received surgery alone, 59,015 (45%) underwent surgery + RAI and 3167 (2%) underwent EBRT [combined with surgery+/- adjuvant RAI]. Mean age at the time of WDTC diagnosis was: 49.4 years (yrs, range, 2-105) in the surgery group; 46.7 yrs (2-99) in the surgery + RAI group and 55.1 yrs (10-101) in the EBRT group. Median person years of follow up after WDTC diagnosis was: 6.1 yrs (2.5-11.6) in pts undergoing thyroidectomy; 5.4 yrs (2.4-9.7) in the surgery + RAI group and 5.9 yrs (1.7-11) in the EBRT group. A total of 55 patients developed MDS; 24 had surgery alone, 27 received surgery + RAI and 4 had EBRT. Median time to development of MDS after WDTM diagnosis (25-75 interquartile range) was: 6.4 yrs (4-15) in pts receiving surgery alone; 4 yrs (1.7-6.7) in the surgery + RAI group and 3.6 yrs (2.4-7.9) in the EBRT group. MDS cases were classified as RA (n=11), RARS (n=2), RCMD (n=3), RAEB (n=9), MDS with 5q deletion syndrome (n=1), and MDS-U (n=29). Compared to patients definitively treated with surgery, those who additionally received RAI (+/- EBRT) had a statistically significantly increased risk of developing MDS within the first two years of exposure (RR=1.9; 95% CI, 0.43-5.61 vs. RR=5.8; 95% CI, 2.84-12.22). Beyond 2 years, the risk for MDS drops to baseline rates, and a trend observed beyond 12 years, which did not reach statistical significance (Figure 2). Conclusion: WDTC pts undergoing RAI treatment appear to have an increased risk of developing MDS within the first two years of exposure to RAI. It is difficult to determine whether this early excess risk is true risk or an effect of ascertainment bias (RAI pts followed more closely), as the kinetics of RAI indicate a lower magnitude of bone marrow exposure compared to other radiation modalities (Figure 3). Considering the long latency of MDS seen in atomic bomb cohorts, relative young age of WDTC pts, current trend towards overdiagnosis and overtreatment of WDTCÕs, MDS rates are likely to continue to rise. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees.