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Background: The optimal treatment strategy for managing Acute Myeloid Leukemia (AML) and the use of reliable and predictive biomarkers to guide selection of cytotoxic chemotherapy regimens among patients with diverse genomic profiles remain unmet needs in the clinic. The combination of MEC [mitoxantrone (MIT), etoposide (VP16), and cytarabine (ARA-C)] is a commonly used regimen for relapsed or refractory AML patients. Unfortunately, many patients do not respond to MEC, and which of the three drug agents matters most for each individual patient is not known. Predictors of response are needed urgently. Methods: The Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The aberration and copy number variations from individual cases served as input into the CBM. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Biosimulations were conducted on the Cellworks Biosimulation Platform by measuring the effect of chemotherapy on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drugs was conducted by mapping the interaction of various drug combinations with the patient's genomic and pathway alterations based on signaling pathway mechanisms and their phenotypic consequences. The Cellworks Biosimulation Platform identified unique chromosomal signatures that permit a stratification of patients that are most likely to respond to MIT, VP16, or ARA-C as well as their combinations. 65 AML patients were selected for this study largely based on genomic data published in TCGA and PubMed: ARA-C [N=12, 7 responders (R) & 5 non-responders (NR)]ARA-C + MIT [N=30, 29 R & 1 NR]ARA-C + MIT + VP16 [N=23, 12 R & 11 NR] Results: Of the12 patients treated with ARA-C alone, 5 were predicted to be NR and 7 were predicted to be R. Of the 5 NR, 4 had 5q del which resulted in loss of APC, CSNK1A1 and SLC22A4 (nucleoside carrier) forming the non-response biomarkers for ARA-C. Notably, the biosimulation predicted lenalidomide to be beneficial for these patients. Out of 7 R, 4 patients also had 5q del, but were predicted to be R because of co-occurring aberrations involving CLSPN del, DHODH del, MSH2 del, EP300 del, CREBBP del, MSH6 Del, and RRM2 del. These genes were exclusively present in ARA-C responders. Of 53 patients who received ARA-C + VP16 + MIT or ARA-C + MIT, 41 patients were predicted to be R and 12 patients were predicted to be NR. The genomic aberrations predicted by biosimulation to be associated with response to this regimen include: NPM1-mut, TET2-mut, IDH1-mut, IDH2-mut, RAD17-del, NRAS-mut (Table 1). Notably, CBM predicted 19 of the 41 R had no genomic biomarkers of response to VP16 or MIT, suggesting these patients might have benefited equally from ARA-C alone with less toxicity and cost. Finally, 11/65 patients were predicted NR to MEC treatment. In the biosimulation, treatment failure was associated with high aberration frequencies of KMT2C-mut/del, FLT3 mut, TWIST1 del, LIMK1 del, SNAI2 amp, FNTA amp, and KAT6A amp. Of note, these genomic markers suggested a likelihood of benefit from other therapies, including vincristine, JQ1 and rigosertib. Conclusions: The Cellworks Biosimulation Platform identified novel polygenic biomarkers of response that can be employed to determine the optimal therapy for relapsed AML patients. Biosimulation permits avoidance of cytotoxic drugs with little chance of efficacy and reveals vulnerabilities in each patient's cancer that can be exploited to improve disease control. In AML, biosimulation promises to improve intensive therapy regimens by tailoring chemotherapy to optimize disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Caris Life Sciences Inc.: Consultancy; Exact sciences Inc.: Consultancy; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Chauhan: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Mandal: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance. Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells. Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients. Figure 1 Figure 1. Disclosures Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Background: Although some genomic biomarkers have been integrated into therapeutic decision-making for the management of AML, the complete remission and cure rates have significant margin for improvement. Except for a few targeted therapies, genomic assessments offer limited guidance on treatment. Nevertheless, comprehensive molecular profiling of AML discloses a complex and heterogeneous disease network that impacts the efficacy of individual chemotherapeutics differently in individual patients. The Cellworks Computational Omics Biology Model (CBM) was developed using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The Cellworks Biosimulation Platform uses the CBM to model each patient's unique cancer and predict personalized responses to standard AML drugs, identify novel drug combinations for treatment-refractory patients and optimize treatment selection to improve outcomes. Methods: A prospectively designed study involving observational data from 416 de novo AML patients was used to test the hypothesis that biosimulation using the Cellworks Biosimulation Platform predicts clinical response to individual drugs and estimates likelihood of response and survival better than physician prescribed treatment (PPT) alone. Cytogenetic and molecular data obtained from clinical trials including AMLSG 07-04, Beat AML, TCGA and PubMed publications was used to create personalized in silico models of each patient's AML and generate a Singula™ biosimulation report with a Therapy Response Index (TRI) to determine the efficacy of specific chemotherapeutic agents. The impact of specific AML agents on each patient's disease network was biosimulated to determine a treatment efficacy score by estimating the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was mapped to each patient's genome and biological consequences determined response. Multivariate logistic regression models for clinical response and likelihood ratio tests were used to assess the contribution of the Cellworks Biosimulation Platform beyond PPT. Similarly, multivariate Cox proportional hazards models were used to test the hypothesis that the Cellworks Biosimulation Platform is predictive of overall survival (OS) and provides predictive information beyond PPT alone. Scoring quantifies the benefit of each drug used to treat each patient's AML. Kaplan-Meier curves, associated log rank tests, and median OS are provided for patients predicted by predefined low and high treatment benefit groups. Results: The TRI Score, scaled from 0 to 100, predicted complete response (CR) (likelihood ratio χ 12 = 52.54, p < 0.0001). Specific leukemia therapies generated a variable likelihood of benefit for individual patients. Notably, Cellworks biosimulation was able to predict treatment benefit or failure better than PPT alone (likelihood ratio χ 12 = 14.86, p < 0.0001). The use of therapy biosimulation to select therapy is estimated to increase the odds of CR by 19% per every 25 units of the TRI Score. TRI was also a significant predictor of OS (likelihood ratio χ 12 = 80.41, p < 0.0001) and provides predictive information above and beyond PPT alone (likelihood ratio χ 12 = 58.70, p < 0.0001 ). Inclusion of the Cellworks Biosimulation Platform is estimated to reduce the hazard ratio for death above and beyond PPT alone by 16% per every 25 units of the TRI Score. Furthermore, predictiveness curves suggest that approximately 25% of de novo AML patients had low probabilities of CR resulting in lower OS and could benefit substantially from inclusion of drugs and combinations identified by biosimulation into frontline management. Conclusions: By predicting the impact of aberrations and copy number alterations on drug response, the Cellworks Biosimulation Platform can improve treatment outcomes for AML patients. The Cellworks TRI predicts response and OS beyond PPT alone, and the Cellworks Biosimulation Platform provides individualized, networked-based alternate treatment options for patients predicted to be non-responders to standard care. Disclosures Howard: Sanofi: Consultancy, Other: Speaker fees; Cellworks Group Inc.: Consultancy; Servier: Consultancy. Watson: Cellworks Group Inc.: Consultancy, Other: Advisor; CellMax Life: Consultancy, Other: Advisor; AlloVir: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAi Health: Consultancy, Membership on an entity's Board of Directors or advisory committees. Castro: Cellworks Group Inc.: Current Employment; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau; Exact sciences Inc.: Consultancy; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Kapoor: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Rajagopalan: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Roy: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Raju: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Agios: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings.

Background: DNA methyltransferase inhibition (DNMTi) with hypomethylating agents (HMA), azacitidine (AZA) or decitabine (DAC), remains the mainstay of therapy for most high-risk Myelodysplastic syndrome (MDS) patients. However, only 40-50% of MDS patients achieve clinical improvement with DNMTi. Previously, combinations of HMA and histone deacetylase (HDAC) inhibitors have been explored in MDS with varying clinical outcomes. However, the heterogeneity of genomic aberrations in MDS portend widely divergent responses from HDAC inhibition, implying that a predictive clinical decision support tool could select patients most likely to benefit from this combination. We explored the molecular basis of observed clinical response in a group of patients treated with DAC and Valproic-Acid (VPA). Method: 16 MDS patients with known clinical responses to DAC + VPA were selected for study from the Cellworks patient repository. The aberration and copy number variations from individual cases served as input into the Computational Omics Biology Model, a computational multi-omic biology software model largely created using literature sourced from PubMed, to generate a patient-specific protein network map. Disease biomarkers unique to each patient were identified within these maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a patient-specific disease model. Biosimulations were then conducted on each patient-specific disease model to measure the effect of DAC + VPA according to a cell growth score. This score was comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: In the biosimulation, VPA is a relatively weak HDAC inhibitor, but it also inhibits GSK3B and in turn increases beta-catenin (CTNNB1) levels. Additionally, monosomy 7 associated with loss of CAV1, HIPK2, and TRRAP also causes high CTNNB1, thereby further contributing to drug resistance. Biosimulation correctly identified that 7 of 8 patients with these genomic findings were clinical non-responders (NR) to VPA, indicating that CTNNB1 status is likely to predict treatment failure from the VPA + HMA combination in this disease. Notably, CTNNB1 levels have been reported to foster an immune-evasive tumor microenvironment resistant to CTL activation. By contrast, high levels of c-MYC predict response to VPA + HMA combination. VPA inhibits MYC transcription and thereby reduces MYC-induced downregulation of p21 through CKS1B. Additionally MYC is a transcriptional regulator of DNMT1 which is degraded after hyperacetylation induced by HDAC3 inhibition suggesting that VPA also enhances DNMT1 turnover. One patient analyzed had trisomy 8 resulting in c-MYC over-expression and responded to HMA + VPA. Additionally, other aberrations enhancing c-MYC transcription such as copy number variant (CNV) loss of MXI1, HHEX, FBXW7, SMAD7 or CNV gain of BRD4, BCL7B led to high clinical response to the combination (Table 1). By comparison to the CTNNB1-driven subset, the impact of VPA on CTNNB1 in the MYC-dominant disease network did not negate the benefit of VPA for these patients. Additionally, the inhibition of GSK3B by VPA leading to diminished FBXW7 and less ubiquitin-mediated turnover of c-MYC was not sufficient to overcome the inhibition of MYC transcription and HDAC3i-mediated turnover. Immune activation has become a recognized mechanism of responsiveness to HMA. However, among patients with upregulated CTNNB1, VPA is likely to further decrease response to treatment. By contrast, among MYC-driven cancers that are typically immune-evasive, VPA appears to be a vital mechanism of overcoming MYC-driven immune evasion. Conclusion: Signaling pathway consequences related to CTNNB1 and c-MYC upregulation predict response to DAC + VPA. Although HMA plus HDAC inhibition can be generally beneficial for MDS, variable mechanisms of action among various HDAC inhibitors and unique patient disease characteristics should be considered for optimal treatment selection. Finally, CTNNB1 emerged from the Cellworks biosimulations as a therapeutically relevant target in MDS that determines whether VPA synergizes or antagonizes the effect of other agents in this challenging subtype of MDS. Figure 1 Figure 1. Disclosures Castro: Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy; Cellworks Group Inc.: Current Employment; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Bugworks: Consultancy. Kumar: Cellworks Group Inc.: Current Employment. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Kumar: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Amara: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Raveendaran: Cellworks Group Inc.: Current Employment. Veedu: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

The success of differentiation therapy is limited to acute promyelocytic leukemia (APL), and approaches to overcome the differentiation block in non-M3 AML have been unsuccessful. Nuclear hormone receptors (NHR) belong to ligand-inducible transcription factors that govern many cellular functions like differentiation, metabolism, and development. Retinoic Acid Receptor Alpha (RXRA) is a class of NHR that, when activated by all-trans retinoic acid (ATRA), successfully alleviates differentiation block in APL. To identify the NHRs/cofactors that could mediate or prevent differentiation in AML, we examined the differentially expressed NHRs and cofactors between ATRA sensitive (ATs) (NB4 and HL60) vs. ATRA resistant (ATr) AML cell lines (KG1a, Hel, K562, MV4-11, and OCI-AML3). Nuclear Receptor Interacting Protein 1 (NRIP1), a corepressor known to prevent transactivation of ligand-activated NHRs preferentially, was one of the top upregulated targets in the ATr cell lines (3.5 fold increase in RNA expression, figure 1a ). Immunoblot analysis also showed a significant increase in NRIP1 protein expression in the ATr than ATs cell lines (Figure 1b). Further, probing for NRIP1 expression in the publicly available TCGA and MILE AML study cohorts showed decreased NRIP1 expression in the APL cohort compared to other AML subtypes. Methylation profile from CCLE database of the NRIP1 promoter in AML cell lines showed ATs cell lines to be highly methylated compared to the ATr cell lines, suggesting the involvement of NRIP1 in mediating differentiation block in non-M3 AML (Figure 1c). To further dissect the role of NRIP1 in mediating this differentiation block, we carried out experiments in the AML cell line KG1a (having primitive blast features, high expression of NRIP1, and unresponsive to ATRA). Using CRISPR-cas9, we developed an NRIP1 knock-out (KO) cell line (Figure 1d). NRIP1 KO cell line showed a significant reduction in proliferation rate (Doubling time 26.2 vs. 36.5Hrs p This study suggests a potential mechanism of differentiation inhibition mediated by corepressor NRIP1 in AML cells unresponsive to retinoic acids. Further in-depth analyses of molecular pathways governed by NRIP1 during ligand activation of NHRs are warranted to design differentiation therapies for AML. Figure 1 Figure 1. Disclosures Mathews: Christian Medical College: Patents & Royalties: US 2020/0345770 A1 - Pub.Date Nov.5, 2020; AML: Other: Co-Inventor.

Solid organ transplantation (SOT) treats over 1500 children/year in the US. While short-term outcomes have improved, long-term outcomes still remain poor. With the rare exception of monozygotic twins as donors, recipients require lifelong immunosuppression with its accompanying toxicities, the risk of nonadherence, and chronic rejection. Further, up to half of recipients require a second transplant. We have pioneered translational approaches that abrogate rejection and the need for post-transplant immunosuppression. Previous attempts in adult patients utilizing allogeneic hematopoietic stem cell transplantation (HSCT) to eliminate the need for post-SOT immunosuppression in non-histocompatible kidney transplant (KT) recipients have failed(Lowsky R, BMT 2019). The establishment of mixed lymphoid chimerism has permitted a reduction in the number of immunosuppressive drugs needed, but not their discontinuation, whereas complete donor engraftment after HSCT has resulted in fatal graft-versus-host disease in some patients (Leventhal JR, Eur J Pediatr 2000). The use of HSCT to eliminate the need for immunosuppression following pediatric KT has not been evaluated. We have pioneered sequential haploidentical HSCT followed by KT in three patients with Schimke immuno-osseous dysplasia (SIOD) to abrogate kidney rejection and the need for post-transplant immunosuppression. SIOD is a monogenic disorder with progressive nephropathy correctable by KT and a T-cell immunodeficiency curable by HSCT. All three patients received αβT-cell/CD19 B-cell depleted HSCT (αβhaplo-HSCT) from a parent prior to a KT from the same parental donor. The pre-HSCT reduced intensity preparative regimen consisted of fludarabine (a starting dose of 1 mg/kg x 4 days, which was adjusted based on AUC), total body irradiation (TBI) 200 cGy, cyclophosphamide 1200 mg/m 2, anti-thymocyte globulin (ATG) thymoglobulin® 7.5 mg/kg, and rituximab 200 mg/m 2. Immunosuppressive drugs were not prophylactically administered after HSCT. After confirmation of full donor lymphoid chimerism post-HSCT, the patients received a living donor KT from their parental HSCT donor. Post-KT immunosuppression included intraoperative methylprednisolone and post-operative low-dose oral prednisone (0.5 mg/kg/day with taper) and tacrolimus (target serum level of 3-5 ng/ml) to reduce potential reperfusion inflammation. All immunosuppressive drugs were tapered off by Day +30 post-KT. To demonstrate functional tolerance after KT, Mixed Lymphocyte Cultures (MLC) were performed between peripheral blood mononuclear cells (PBMC) and patient/parent-derived irradiated EBV transformed lymphoblastoid cells lines (EBV-LCL) as stimulators (Figure 1). PBMC were isolated from SIOD patients (n=3) by Ficoll Hypaque separation and were labelled with Invitrogen™ CellTrace Violet Proliferation Kit (CTV). EBV-LCL were established from SIOD patients, parents and healthy donors. The assay was performed in round bottom microtiter plates with 100,000 labelled PBMC as responder cells (R) and 50,000 irradiated (30gy) EBV-LCL in RPMI medium with 10% fetal calf serum. Cells were harvested on Day 6, and T-cell proliferation determined by CTV expression of CD3+ T cells using a Novocyte Penteon flow cytometer (Agilent). Donor-derived T cells isolated from the peripheral blood of all three recipients > 1-year post-HSCT/KT, did not respond to the donor cells, while they did proliferate to the non-donor parental and control cells (Fig. 1). These results demonstrate that post-HSCT/KT the circulating donor-derived T cells are functionally tolerant to the transplanted kidney and, therefore, are unable to mediate graft rejection even in the absence of immune suppression. We have achieved two goals: first, ablating the recipient immune system before HSCT we safely established a new donor-derived immune system - that could also, if required, cure a patient's underlying disease- and, second, functional tolerance to the transplanted kidney. At 12 to 24 months after KT, all patients have full donor lymphoid and myeloid chimerism, normal renal function without immunosuppression, MLC demonstrated tolerance towards donor cells, and normal proliferative responses to non-donor parental cells. These findings suggest that the combination of αβhaplo-HSCT and SOT could be extended to other solid transplantation like liver and small intestine. Figure 1 Figure 1. Disclosures Bertaina: Cellevolve Bio: Membership on an entity's Board of Directors or advisory committees; Neovii: Membership on an entity's Board of Directors or advisory committees; AdicetBio: Membership on an entity's Board of Directors or advisory committees. Shah: OrchardTherapeutics: Membership on an entity's Board of Directors or advisory committees, Other: Dr. Shah currently serves on the medical advisory board for Orchard Therapeutics . Parkman: Jasper Biotech: Consultancy.

Background: Chimeric antigen receptor T-cell (CAR-T) therapy is an effective strategy for patients (pts) with B-cell non-Hodgkin lymphomas (B-NHLs). However, long-term remissions are only observed in ~40% of pts with large cell lymphomas (LCL) treated with FDA-approved CD19-targeted products and there is currently no approved CAR-T for other histologies other than mantle cell lymphoma (MCL). CD20 is a proven therapeutic target for B-NHL, supported by previously approved naked and radiolabeled anti-CD20 antibodies and promising results from bispecific antibodies. CD20-targeted CAR-T is another potential adoptive immunotherapy option that could be utilized in combination or in sequence with CD19 CAR-T. We present interim results of our ongoing phase I/II clinical trial investigating safety and efficacy CD20 CAR-T for high-risk B-NHLs (NCT03277729). Methods: MB-106 is a fully human third-generation CD20-targeted CAR-T with both 4-1BB and CD28 costimulatory domains. We use a continuous reassessment method dose escalation design to find the maximally tolerated dose. Lymphodepletion (LD) consists of cyclophosphamide (Cy) ± fludarabine (Flu). CAR-T cells are administered at one of 4 dose levels (DL): DL1: 3.3 x 105, DL2: 1 x106, DL3: 3.3x106, DL4: 1x107 CAR T cells/kg. CAR-T is infused in the outpatient setting except for the first pts of each dose cohort (overnight observation). Pts with CD20+ B-NHL are eligible, including but not limited to LCL, follicular lymphoma (FL)/indolent histologies and MCL. Prior treatment with CD19 CAR-T is permitted. Treatment response is assessed on day 28 using 2014 Lugano criteria. Cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) were graded per 2014 Lee criteria and the ASTCT consensus grading, respectively. Results: Between 2018-2020, 12 pts underwent leukapheresis (LA) and 11 (92%) were treated on the protocol. The first 7 pts (3 FL, 3 MCL, 1 hairy cell variant) were treated using the original cell manufacturing method with separate culturing of CD4+ and CD8+ cells ("original process"). First 4 of those 7 pts (2 treated at DL1 and 2 at DL2) only received Cy for LD and the other 3 (all at DL1) were treated after LD with Cy-Flu. There was no evidence of ICANS in those 7 pts and only 1 pt developed CRS (grade 3 - unexplained elevated alkaline phosphatase (ALP) in the setting of fever). Responses at 4 weeks for pts treated with the "original process" included stable disease (SD) in 4 and progressive disease (PD) in 3 pts. Given the challenges in meeting the protocol requirements for CD4+ and CD8+ specific doses, poor CAR-T expansion, and lack of clinical responses, enrollment was placed on a hold and cell manufacturing process underwent a major revision which included changing to a combined culture of CD4+ and CD8+ cells, among other modifications ("modified process"). Since December 2019 enrollment was reinitiated at DL1 under the "modified process." Four pts were treated after LD with Cy-Flu at DL1 (2 FL) and dose level 2 (1 FL and 1 MCL) (details in Table 1). Target cell doses were achieved in all 4 patients and treatment was tolerated with no occurrence of CRS (any grade) or ICANS (any grade). Responses were observed at both dose levels and included complete remissions (CRs) in 2 FL pts (1 treated at DL1 and 1 at DL2), partial remission (PR) in a MCL pt treated at DL2 and PD in a FL pt who was treated at DL1. Robust CAR-T expansion was seen in 3 of the 4 patients, with peak blood levels of CAR+ T cells of 161, 5.5, and 401 CAR+ cells/µl, corresponding to 80%, 23%, and 72% of all CD8+ cells. Combining all 11 pts treated under the "original" and "modified" processes, no dose-limiting toxicity (DLT) was observed. Grade ≥ 3 toxicities included: anemia (n=4, 36%), lymphopenia (n=3,27%), febrile neutropenia (n=2, 18%), hypertension (n=1, 9%) , hypotension (n=1, 9%), thromboembolic event (n=1, 9%), neutropenia (n=1, 9%), elevated ALP (n=1, 9%), pneumonia (n=1, 9%), bacteremia (n=1, 9%), hyperglycemia (n=1, 9%), pleural effusion (n=1, 9%) and generalized pain (n=1, 9%). Conclusion: Early results of our ongoing study of CD20 CAR-T for high-risk NHLs suggests an extremely favorable safety profile (no CRS or ICANS of any grades) with the modified manufacturing process. There is also evidence of clinical activity even at low dose levels with observed CRs at DLs 1 and 2. Enrollment at higher DLs is currently ongoing and the data will be updated at the time of presentation. Disclosures Shadman: Abbvie, Genentech, Astra Zeneca, Sound Biologics , Pharmacyclics, Verastem, ADC therapeutics, Beigene, Cellectar, BMS, Morphosys and Atara Biotherapeutics: Consultancy; Mustang Bio, Celgene, Pharmacyclics, Gilead, Genentech, Abbvie, TG therapeutics, Beigene, Astra Zeneca, Sunesis, Beigene: Research Funding. Yeung:OBI;Pfizer: Research Funding; Adaptive Biotechnology;Eli Lilly;Merck: Consultancy. Ramachandran:Mustang Bio: Current Employment. Graf:TG Therapeutics: Research Funding; BeiGene: Research Funding; MorphoSys: Consultancy; Acerta Pharma: Research Funding. Gopal:IgM bio, BMS, merck: Research Funding; Seattle Genetics; Janssen; IMab Bio; TG Therapeutics; Astra Zeneca; Merck; Gilead; ADC Therapeutics; Nurix; TG therapeutics, Cellectar; Actinium: Consultancy; imab bio, takeda,astrazeneca,gilead: Research Funding; Seattle Genetics; Janssen; Takeda; IgM Bio; IMab Bio; BMS; Astra Zeneca; Merck; Gilead: Research Funding. Gauthier:JMP, Eusapharma, Multerra Bio: Honoraria. Cassaday:Pfizer: Honoraria, Research Funding; Seattle Genetics: Current Employment, Current equity holder in publicly-traded company; Vanda Pharmaceuticals: Research Funding; Kite/Gilead: Consultancy, Research Funding; Merck: Research Funding; Amgen: Consultancy, Research Funding. Kiem:Magenta Therapeutics, CSL,Homology Medicines, Vor Biopharma , Enochian, Umoja, Rocket Pharma: Consultancy. Turtle:Novartis: Consultancy; Myeloid Therapeutics: Current equity holder in private company, Honoraria, Membership on an entity's Board of Directors or advisory committees; Allogene: Consultancy; PACT Pharma: Consultancy; Arsenal Bio: Current equity holder in private company, Honoraria, Membership on an entity's Board of Directors or advisory committees; Caribou Biosciences: Current equity holder in private company, Honoraria, Membership on an entity's Board of Directors or advisory committees; Eureka Therapeutics: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Precision Biosciences: Current equity holder in publicly-traded company, Honoraria, Membership on an entity's Board of Directors or advisory committees; Physician Education Resource: Consultancy; AstraZeneca: Consultancy, Research Funding; Nektar Therapeutics: Consultancy, Research Funding; Juno/BMS: Patents & Royalties, Research Funding; Century Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; T-CURX: Membership on an entity's Board of Directors or advisory committees; Humanigen: Consultancy; Kite/Gilead: Consultancy. Maloney:Pharmacyclics: Consultancy, Honoraria; Juno Therapeutics: Consultancy, Honoraria, Patents & Royalties: Patents are pending, but not issued, licensed, no royalties, no licensees., Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Gilead Sciences: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Bioline Rx: Consultancy, Honoraria; A2 Biotherapeutics: Consultancy, Current equity holder in publicly-traded company, Honoraria; Genentech: Consultancy, Honoraria. Till:Mustang: Patents & Royalties, Research Funding.

Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value < 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR > 100, p-value < 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

Background: Acute promyelocytic leukemia (APL) is a biologically and clinically distinct subtype of acute myeloid leukemia (AML) with unique molecular pathogenesis, clinical manifestations, and treatment. APL is cytogenetically characterized by a balanced translocation t(15;17) (q24;q21), which involves the retinoic acid receptor alpha (RARA) gene on chromosome 17 and the promyelocytic leukemia (PML) gene on chromosome 15 that results in a PML-RARA fusion gene (PMID: 30575821). The PML-RARA fusion gene is the most critical event involved in the pathogenesis of APL, reported in 99% of APL patients (PMID: 32182684). The fusion confers a selective sensitivity to the targeted drugs, arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA), with response rates over 90% (PMID: 31635329). However, the mechanism of resistance in the minority of non-responders is not well understood. This study used the Cellworks Omics Biology Model (CBM) to predict response to the combination of ATO-ATRA in patients harboring the PML-RARA fusion and identify mechanisms of resistance. Methods: Outcomes of 30 APL patients treated with ATRA or ATRA plus ATO were compared with outcomes predicted by CBM (Table 1). Genomic data from 6 publications (Table 2) derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV) and/or karyotype data were used. All data was anonymized, de-identified and exempt from IRB review. The available genomic data for each profile was entered into the CBM which generates a patient-specific disease protein network model using PubMed and other online resources. The CBM predicts the patient-specific biomarker and phenotype response of a personalized diseased cell to drug agents, radiation and cell signaling. Disease biomarkers that are unique to each patient were identified within the protein network models. ATO and ATRA were simulated on all 30 patient cases. The treatment impact was assessed by quantitatively measuring the drug's effect on a cell growth score which is a composite of the quantified values for cell proliferation, survival, and apoptosis, along with the simulated impact on each patient-specific disease biomarker score. Each patient-specific model was also digitally screened to identify response to ATO and ATRA. Results: The CBM correctly predicted the response to ATO and ATRA in 28 of 30 cases. The overall prediction accuracy was 93% with a PPV of 100%, NPV of 60%, sensitivity of 93%, and specificity of 100%. In 2 of 30 patients who did not respond to ATO and ATRA, the CBM identified clinically relevant deletions to EZH2, KMT2E, and HIPK2 genes. All three genes are located on chromosome 7 and these non-responders had monosomy 7. Conclusions: The Cellworks Omics Biology Model predicted response to ATO and ATRA in APL patients harboring PML-RARA fusions. Predicting non-response to ATO and ATRA in patients with PML-RARA fusion up-front could prevent ineffective treatment, avoid unnecessary adverse events and reduce treatment costs. Additionally, computational modeling can identify new mechanisms of resistance and suggest alternative regimens for non-responding patients by targeting the patient-specific disease biomarkers unique to each. Disclosures Howard: Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Tyagi:Cellworks Research India Private Limited: Current Employment. Kumari:Cellworks Research India Private Limited: Current Employment. Prasad:Cellworks Research India Private Limited: Current Employment. Mitra:Cellworks Research India Private Limited: Current Employment. Lala:Cellworks Research India Private Limited: Current Employment. Azam:Cellworks Research India Private Limited: Current Employment. Gupta:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment.

The LEOPARD trial evaluated lenalidomide and alternate day prednisolone (RAP) as post ASCT maintenance in newly diagnosed transplant eligible MM patients (TE NDMM). 60 patients were recruited. Estimated median potential follow-up was 44 months (IQR 26m - 52m). Median PFS from time of commencing RAP was 38.3m (95% CI, 25.8 to 54.8); median OS was not reached (71.4% of patients were alive at 36 months). Here we present the findings from correlative immune studies of this trial. Aims: To undertake mass cytometry (CyTOF) based immune profiling in patients with TE NDMM treated with RAP maintenance post ASCT. Methods: The LEOPARD trial was a phase II, multi centre, open label, single arm study of RAP maintenance after a single melphalan conditioned (200mg/m2) ASCT as part of up-front therapy. Patients were restaged at D+42 ASCT, and if eligible, were commenced on RAP maintenance (LEN 10mg daily increasing to 15mg daily after 8 weeks and alternate day prednisolone 50mg) within 8 weeks of D+0 of the ASCT. Therapy continued until toxicity/progression. CyTOF was performed in sequential samples in two selected groups of patients: long runners (LR, n=7), defined as those with PFS > 36 months (median) and early relapsers (ER, n=8), defined as those who progressed/died before reaching the lower quartile of PFS. [All patients had peripheral blood collected at baseline (pre-ASCT), 6w post-ASCT and weeks 4, 8, 12, 20, 28 and 40 of RAP]. Cells were barcoded using the Cell-ID 20-Plex Pd barcoding kit (Fluidigm) followed by staining with sub-set/function defining antibodies (targeting myeloid, B, T and NK cells: CD45, CD3, CD19, CD5, CD1c, CD226. CD8, CD11c, CD16, CD127, CD138, CD123, NKG2A, TIGIT, TIM3, CD45RA. CD274, CD27, CD197, CD28, Ki67, CD66b, CD183, KLRG1, CD43, NKG2D, CD38, CD278/ICOS, CD25, HLA-DR, CD4, CD57, GramB, PD-1, CD14, CD56, CD11b, Tbet, CD33). Samples were acquired on the Helios instrument. Supervised analysis was performed to determine differences in canonical immune cell populations. Unsupervised analysis was then performed: data were clustered in the VORTEX package. Significant differences in cluster frequency were assessed by Mann-Whitney test for statistical significance. Cluster phenotypes were determined and validated via multiple visualisation approaches. Results: Median age was 56yrs for LR versus 63yrs for ER. Median PFS for LR was 46.3m (38.4 - 51.5m) versus 10.2 m (2.1 - 21.3m) for ER. Supervised analysis was performed on all samples, dichotomized into baseline and last time point sampled for each patient. At baseline, Ki67+CD8+ T cells, ICOS+CD8+ T cells, HLA-DR+CD4+ T cells and CD11c+ myeloid cells were enriched in LR compared to ER. At the last timepoint sampled, Ki67+CD8+ T cells and ICOS+CD8+ T cells were again enriched in LR compared to ER. Conversely, B-reg-like cells (CD19+CD5+CD43-) were enriched in ER compared to LR at the last timepoint sampled. Unsupervised analysis was performed on all samples (all timepoints were pooled). Five clusters were significantly enriched in LR compared to ER. Four of these clusters represented activated/cytotoxic NK cells: CD56 dim, CD16-, NKG2A(CD159a)+, NKG2D(CD314)+, Granzyme B+ and CD38+, and additional expression of CD57 on one cluster; one cluster represented a mature myeloid population, with high expression of HLA-DR, CD11b and CD11c and low expression of CD33. One cluster was significantly enriched in ER compared to LR, representing activated neutrophils, with high expression of CD66b, CD11b and CD16. The clusters that were enriched were then assessed longitudinally over all time points. There was no difference in the kinetics of these populations between groups. Conclusions Significant differences in both T-cell and NK cell populations were demonstrable at baseline in LR versus ER patients. Subsequently, durable responses to post-ASCT lenalidomide maintenance were associated with a cytotoxic, controlled immune response whereas early relapse was characterised by a more uncontrolled inflammatory response and the emergence of B-reg-like cells prior to relapse. We conclude that immune profiling at baseline and after initiation of therapy may help to predict a more sustained response to lenalidomide maintenance enabling pre-emptive tailored treatment decisions. Disclosures Kalff: Roche: Honoraria; Janssen: Honoraria; Amgen: Honoraria; CSL: Honoraria; Celgene: Honoraria. Young:Bristol Meyers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Thakurta:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Oxford University: Other: visiting professor. Oppermann:Bristol Meyers Squibb: Research Funding. Guo:Bristol Meyers Squibb: Research Funding. Reynolds:Novartis AG: Current equity holder in publicly-traded company. Spencer:AbbVie, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Consultancy; AbbVie, Amgen, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Honoraria; Amgen, Celgene, Haemalogix, Janssen, Servier and Takeda: Research Funding; Celgene, Janssen and Takeda: Speakers Bureau.