Introduction: Liquid biopsy has been reported to be useful in predicting residual disease in patients with diffuse large B-cell lymphoma (DLBCL). Most of the studies focused on quantifying the level ...of circulating lymphoma-specific DNA. We explored the clinical relevance of the specific mutated genes in predicting progression in patients with DLBCL.
Method: Peripheral blood samples were collected from patients with DLBCL based on their visit to clinic without other specific selection. Median age of patients is 69 (range 28-91), with 51% of the patients being male. These patients were treated on multiple protocols including R-CHOP, R-EPOCH, Magrath, HCVAD, CAR-T (#2 patients), and others. cfDNA was extracted and sequenced by next generation sequencing using 177 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth is increased to more than 2000X after removing duplicates. Low level mutations are confirmed by inspecting BAM file.
Results: A total of 86 sample from 61 patients were collected post clinical remission at different time points (median 28 weeks, range: 1-994 weeks). Of these samples, 56 (65%) from 46 patients (75%) were positive. However, 6 of these samples from 4 patients had germline mutations or mutations in TET2, ASXL1, or DNMT3A that are consistent with CHIP (clonal hematopoiesis of indeterminate potential). The remaining 50 positive samples from 42 patients had 8 repeats on the same patients collected at different time points. Comparing the 19 negative patients with the 42 positive patients post-remission, patients with residual molecular disease were significantly older than patients without residual disease (P=0.01). However, there was no significant difference between the two groups in gender, ethnic background, LDH, cell of origin classification, or TP53 positivity by IHC. Patients with residual disease showed tendency for short progression-free survival (P=0.08). Focusing on patients with specific mutations detected in the cfDNA showed that 14 (23%) patients had mutations either in TP53 or MYD88. There was no significant difference in age between these two groups or any of the other clinical variables. However, patients with TP53/MYD88 mutations had significantly shorter survival (P=0.04).
Conclusion: Post-remission residual disease as measured by circulating cfDNA is an independent predictor of potential relapse in patients with DLBCL. However, presence of it is important to determine the aggressiveness of the residual circulating clone. Residual circulating lymphoma DNA with TP53 or MYD88 mutations is a strong predictor of earlier relapse.
Display omitted
Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Goy: Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Honoraria, Other; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria; Acerta: Consultancy, Research Funding; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Michael J Hennessey Associates INC: Consultancy; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medscape: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Genentech/Hoffman la Roche: Research Funding; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; OncLive Peer Exchange: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xcenda: Consultancy, Honoraria; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy; LLC(Targeted Oncology): Consultancy; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Xcenda: Consultancy; Hoffman la Roche: Consultancy; Incyte: Honoraria; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Phamacyclics: Research Funding; Constellation: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment.
12134
Background: Psychosocial risk factors such as anxiety and depression can affect immune responses and cancer-related outcomes. Accordingly, we investigated whether an existing anxiety or ...depression diagnosis influenced immune checkpoint inhibitor (ICI) safety or efficacy. Methods: In our multicenter retrospective study across all cancers, we identified 913 patients who began anti-PD-1 or anti-PD-L1 monotherapy PD(L)-1 between 2011-2018 with follow up until January 2021. Depression and anxiety disorder diagnosis at the time of treatment initiation were abstracted from medical charts. Immune-related adverse events (irAEs) were graded according to CTCAE v4.03. Overall survival (OS) and time to treatment failure (TTF) were recorded. Multivariable logistic regression models and proportional hazard models were used to test relationships between anxiety (or depression) diagnosis and irAEs (any grade vs. none), OS, and TTF, respectively, while adjusting for race, gender, age, and other relevant clinical variables associated with each outcome (p < 0.1) in the univariate analysis (e.g., performance status, autoimmune disease, and number of metastatic sites). Results: At the time of treatment initiation, 11% (n = 102), 12% (n = 106), and 4% (n = 41) of patients in the overall PD(L)-1 ICI cohort had an anxiety diagnosis, depression diagnosis, or both, respectively. The overall incidence of any grade irAEs was 32% (n = 295), including 44% (n = 45) and 37% (n = 39) among those with anxiety and depression history, respectively. Patients with an anxiety diagnosis had a greater likelihood of experiencing an irAE Odds ratio (OR) = 1.80; 95% CI = 1.16 to 2.80, p = 0.009. There was not a statistically-significant relationship between depression diagnosis and irAEs (univariable OR = 1.25; 95% CI = 0.82 to 1.91, p = 0.295). In the subset of patients with lung cancer (n = 443) receiving PD(L)-1 ICI, the rates of any grade irAEs, anxiety, and depression diagnosis were 32% (n = 143), 10% (n = 44), and 12% (n = 52), respectively. In multivariable analysis, anxiety diagnosis was associated with better OS (HR = 0.61; 95% CI = 0.39 to 0.97; p = 0.038) and TTF (HR = 0.68; 95% CI = 0.47 to 0.98; p = 0.038). The link between anxiety diagnosis and irAEs was weaker in this subsample (OR = 1.46; 95% CI = 0.74 to 2.87, p = 0.274). No significant associations between depression diagnosis and any grade irAEs (OR = 0.83; 95% CI = 0.44 to 1.57, p = 0.573), OS (HR = 1.06; 95% CI = 0.73 to 1.55, p = 0.755), or TTF (HR = 1.13; 95% CI = 0.83 to 1.54, p = 0.433) were observed. Conclusions: Our results suggest that baseline psychosocial risk factors, especially anxiety disorders, may influence ICI efficacy and safety. Prospective studies are warranted to better understand the relationship between psychosocial risk factors and ICI outcomes. The study also suggests a possible role for targeted supportive care in influencing cancer-related outcomes for those with psychosocial risk factors.
TPS2080
Background: CYNK-001 is a CD56+CD3- enriched, off-the-shelf, allogeneic natural killer (NK) cell product expanded from placental CD34 cells. CYNK-001 exhibits in vitro cytotoxicity against ...patient-derived GBM cell lines and secretes cytolytic cytokines during co-culture with cancer cells. CYNK-001 administered via the intracranial (IC) route exhibited in vivo antitumor activity in a U-87MG orthotopic mouse model. Methods: A Phase I/IIa clinical trial is enrolling IDH1 wild-type GBM patients at first or second recurrence with contrast-enhancing measurable disease (per RANO criteria) who are candidate for surgical resection. Screening MRI scans for inclusion are performed within 14 days prior to Day -5 lymphodepletion with Cyclophosphamide 900mg/m
2
and fludarabine 30mg/m
2
plus mesna. Using a standard 3+3 dose escalation schema, patients will receive the first cycle of CYNK-001 intravenously (IV) at an initial dose of 2.4 x10
9
cells on Days 1, 8 and 15 after lymphodepletion. Cell supportive IL-2 at 6M IU administered SQ on Days 1, 3, 5, 8, 10, 12, and 15 within 3 hours prior to CYNK-001 IV infusion where applicable. Cycle 2 begins with surgical resection on Day 22 in which CYNK-001 is administered directly into the tumor cavity wall at an initial dose of 100 x10
6
NK cells and an Ommaya catheter placement. Subsequent CYNK-001 IC administrations via the Ommaya are on days 29 and 36 with 6M IU IL-2 SQ. DLT is evaluated for all dosing cohorts from day 1 to 7 days post last dose of cycle 2. Once a maximum tolerated dose is identified, a safety lead-in cohort with an additional 3 cycles of CYNK-001 IC will be administered prior to initiating the Phase IIa portion of the study. Endpoints: The primary endpoint is dose-limiting toxicity for the Phase I analysis and 6-month progression free survival post tumor resection for the Phase IIa component. Post-resected tumor tissue will be characterized for effector immune cell function and immune suppression with assessments directed at CYNK-001 tumor distribution using methodology developed at Celularity Inc. Approximately 66 patients are planned for this Phase I/IIa study. Approximately 66 patients are planned for this Phase I/IIa study. Clinical trial information: NCT05218408.
3018
Background: Diagnosis and classification of tumors is becoming increasingly dependent on biological and molecular biomarkers. RNA expression profiling using next generation sequencing (NGS) ...provides information on various biological and molecular changes in the cancer and in the microenvironment. We explored the potential of using targeted transcriptome and artificial intelligence (AI) in the differential diagnosis and classification of various hematologic and solid tumors. Methods: RNA from hematologic neoplasms (N = 2606) and solid tumors (N = 2038) as well as normal bone marrow and lymph node control (N = 806) were sequenced by NGS using a targeted 1408-gene panel. The hematologic neoplasms included 20 different subtypes. Solid tumors included 24 different subtypes. Machine learning is used for comparing two classes at a time. Geometric Mean Naïve Bayesian (GMNB) classifier is used to provide differential diagnosis across 45 diagnostic entities with assigned ranking. Results: Machine learning showed high accuracy in distinguishing between two diagnoses with AUC varied between 1 (Sarcoma vs GIST) and 0.841 (MDS vs normal control) (examples in Table). For differential diagnosis between all 45 different diagnoses, we used 3045 samples for training the GMNB algorithm and 1415 samples for testing. Correct first choice diagnosis was obtained in 100% of ALL, 88% of AML, 85% of DLBCL, 82% of colorectal cancer, 88% of lung cancer, 72% of CLL, and 72% of follicular lymphoma. The algorithm had difficulty in typically overlapping diagnoses and diagnosed as first choice 19% of MDS, 46% of normal, and 12% of MPN. Diagnosis improved significantly when second choice was considered. Conclusions: Targeted RNA profiling with proper AI can provide highly useful tools for the pathologic diagnosis and classification of various cancers. Additional information such as mutation profile and clinical information can improve these algorithms, reduce subjectivity, and minimize errors in pathologic diagnoses. Table: see text
3047
Background: Expressed RNA can capture mutations, changes in expression levels due to methylation, and provide information on cell of origin, growth, and proliferation status. We developed an ...approach to isolate fragmented RNA from peripheral blood plasma and explored its potential to be used in liquid biopsy. Methods: Peripheral blood cfRNA was extracted from patients with neoplasms in B-cell (#105), T-cell (#16), Myeloid (#73), and from solid tumors (#44), Normal individuals (#51), and reactive post-transplant (#137). RNA was sequenced using a 1459-gene panel. Expression profile was generated using Cufflinks. Results: cfRNA levels of various solid tumor biomarkers (CA-125, CA-15-3, CEA 8, Keratin19, Keratin6A...) were significantly higher (P < 0.0001) in samples from solid tumors as compared with normal control. Similarly, cfRNA lymphoid markers (CD19, CD22, CD79A, and CD79B...) and cfRNA myeloid markers (CD33, CD14, CD117, CD56...) were all higher in B-cell lymphoid neoplasms and myeloid neoplasms, respectively (P < 0.0001), as compared with control. In evaluating the host immune system, cfRNA CD4:CD8B and CD3D:CD19 ratios in normal controls were as expected (median: 5.92 and 6.87, respectively) and were significantly lower in solid tumors (median 3.40 and 2.23, respectively, P < 0.0002). Solid tumor cfRNA showed CTLA4:CD8B ratio significantly higher in tumors than in normal (median 0.74 vs 0.19, P = 0.0001), while there was no difference in cfRNA PD-L1:CD8B ratio (median 1.45 vs 1.77, P = 0.96). Similar distinct patterns are noted for various cytokine and chemokines. cfRNA was highly predictive of diagnosis (AUC > 0.98) of solid tumors, B-cell lymphoid neoplasms, T-cell lymphoid neoplasms, and myeloid neoplasms as compared with normal control. When a specific neoplastic disease was considered against all cases including control and other neoplasms, the AUC varied between 0.77 and 0.949. Conclusions: This data shows that liquid biopsy using targeted sequencing of cfRNA in patients with various types of cancer provides comprehensive and reliable information on the neoplastic disease as well as the host. Table: see text
3048
Background: Expressed RNA can capture mutations, gene fusions, and biomarker profiles. In principle, each abnormal cell has one copy of mutated gene, but numerous copies of mutated RNA. ...Cell-free RNA (cfRNA) is not used due to the assumption that it is degraded. Next Generation Sequencing (NGS) by design is particularly adaptable for fragmented DNA and RNA. We developed an approach to isolate cell-free total nucleic acid (cfTNA) and cell-free RNA (cfRNA) from peripheral blood. Using targeted sequencing, we explored the potential of this approach to detect mutations, fusion mRNA, and copy number variation (CNV) in solid tumors and hematologic neoplasms. Methods: Peripheral blood cfTNA and cfRNA were extracted from B-cell lymphoid neoplasms (#105), T-cell neoplasms (#16), Myeloid neoplasms (#73), solid tumors (#44), and Normal individuals (#51), and sequenced using a targeted panel of 1459 genes. Results: Numbers of mutations detected in solid tumors and hematologic neoplasms were significantly (P > 0.0001) higher in cfRNA (No. = 1229) than in cfTNA (No. = 1004). Overall variant allele frequency (VAF) was significantly higher in cfRNA than in cfTNA (P < 0.0001). However, numerous mutations detected by RNA were not detected by cfTNA and vice versa. In general, nonsense mutations were more likely to be detected by cfTNA than by cfRNA and at higher VAF. Low-level mutations (VAF < 10%) were more likely to be detected by cfRNA than by cfTNA. For example, 136 mutations in TP53 gene were detected using cfRNA and only 70 mutations were detected in cfTNA. KRAS mutations were also higher in cfRNA (#33) as compared with cfTNA (#21). In contrast, when most of the mutations were nonsense, as in ASXL1 gene, more mutations were detected by cfTNA (24 vs 23). When mutations were detected in both cfRNA and cfTNA, mutation load (level of mutant copies) was overall slightly higher in cfTNA (P = 0.06), likely due to higher degradation of RNA, but varied significantly dependent on the type of mutated gene and type of mutation. cfRNA was reliable in detecting fusion transcripts in solid tumors and in hematologic neoplasms (SLC34A2-ROS1, DDX5-BCL6, ETV6-RUNX1, RUNX1T1-RUNX1, PML-RARA, RUNX1-ZFPM2, DEK-NUP214, EP300-ZNF384) irrespective of the breakpoint or partner gene. The cfTNA detected various CNVs expected by cytogenetic analysis when tumor fraction was adequate (VAF > 10%). Conclusions: This data demonstrates that using cfRNA and cfTNA provides complementary comprehensive information for evaluating mutations, fusion genes, and CNV. This approach increased sensitivity and reliability of liquid biopsy. Furthermore, the cfRNA provides critical information on relative expression of various genes that can be used as biomarkers in characterizing the neoplastic process (see ASCO abstract, Liquid Biopsy Based on Cell-Free RNA and Biomarkers profiling of hematologic and solid tumors).
Introduction: Acute graft-vs.-host disease (aGVHD) remains a major diagnostic and clinical problem in patients after allogenic hematopoietic stem cell transplant (HSCT). Finding biomarkers that play ...a role in aGVHD not only helps in predicting and diagnosing aGVHD, but might help in developing prophylaxis and therapeutic approaches. Using Next Generation Sequencing (NGS) and targeted RNA sequencing along with a machine learning approach to predict, we investigated the potential of discovering new biomarkers that can predict aGVHD.
Methods: RNA extracted from bone marrow aspiration samples collected around day 90 post HSCT from 46 patients were sequenced using 1408 targeted genes. cDNA was first generated, then adapters were ligated. The coding regions of the expressed genes were captured from this library using sequence-specific probes to create the final library. Sequencing was performed using an Illumina NextSeq 550 platform. Ten million reads per sample in a single run were required. Read length was 2 × 150 bp. Expression profile was generated using Cufflinks. A machine learning system is developed to predict the GVHD cases and to discover the relevant genes. A subset of genes relevant to GVHD is automatically selected for the classification system, based on a k-fold cross-validation procedure (with k=10). For an individual gene, a Naïve Bayesian classifier was constructed on the training of k-1 subsets and tested on the other testing subset. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict GVHD. The processes of gene selection and GVHD classification are applied iteratively to obtain an optimal classification system and a subset of genes relevant to GVHD.
Results: The analyzed bone marrow samples included patients transplanted for aplastic anemia (#1), acute lymphoblastic leukemia (#9), acute myeloid leukemia (#16), mixed phenotype acute leukemia (#1), myelodysplastic syndrome (#10), chronic myelomonocytic leukemia (#5), and myeloproliferative neoplasm (#4). Of the 46 patients, 30 (65%) had a diagnosis of aGVHD (grade 2-4). The GMNB modified Bayesian model selected 7 genes as top classifiers. These top classifier genes included Class II Major Histocompatibility gene (CIITA), B-cell markers genes (CD19 and CD22), early T-cell related gene (TCL1A), hematopoietic-specific transcription factor (IKZF3), a gene involved in protein-protein interaction, and a gene involved in DNA helicase nucleotide excision repair (ERCC3). When these 7 genes were used in GMNB-modified classifier with 10-fold cross validation to predict aGVHD, the model classified 28 of the 30 positive cases accurately and 14 of the 16 negative cases accurately. The sensitivity was 93% (95% CI, 76%-99%). The specificity was 87.5% (95% CI: 60%-97%). The positive predictive value (PPV) was 93% (95% CI: 76%-99%) and the negative predictive value (NPV) was 87.5% (95% CI: 60%-98%).
Conclusion: While most biomarker discovery has been focused on inflammatory cytokines, chemokines, and their receptors, our data suggest that hematopoietic proliferation and transcription regulators in bone marrow might provide important information for the diagnosis and prediction of aGVHD. This data suggests that biomarkers related to B-cell, T-cell, and MHC play a role in aGVHD at the bone marrow level. These findings also suggest that targeting these biomarkers in the bone marrow might be a realistic approach for prophylaxis and treatment that needs to be explored. Although further validation is needed, this study suggests that targeted RNA sequencing by NGS combined with machine learning algorithm can be a practical and cost-effective approach for the diagnosis and prediction of aGVHD.
Display omitted
Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Goy: Rosewell Park: Consultancy; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Acerta: Consultancy, Research Funding; Genentech/Hoffman la Roche: Research Funding; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xcenda: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; OncLive Peer Exchange: Honoraria; Xcenda: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Infinity/Verastem: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Honoraria, Other; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Hoffman la Roche: Consultancy; Michael J Hennessey Associates INC: Consultancy; LLC(Targeted Oncology): Consultancy; Medscape: Consultancy; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria; Constellation: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Phamacyclics: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Rowley: ReAlta Life Sciences: Consultancy.
Introduction: Using next generation sequencing (NGS) in monitoring residual disease in patients with myeloid neoplasms is complicated by the significant heterogeneity in these diseases and the ...frequent presence of CHIP (clonal hematopoiesis of indeterminate potential) in patients with hematologic neoplasms on which these neoplasms arise. This is particularly relevant post hematopoietic stem cell transplant (HSCT). We explored the ability of using plasma cell-free DNA (cfDNA) in monitoring patients after HSCT and evaluated the potential of using liquid biopsy as a replacement for bone marrow biopsy.
Method: cfDNA was isolated from 204 peripheral blood samples obtained from 75 patients, collected at various time points ranging from 27 days to 650 days (median 178 days) post-transplant. DNA from 102 bone marrow (BM) samples was extracted and sequenced using the same panel and approach as cfDNA. Diagnoses included 30 acute myeloid leukemia (AML), 2 chronic myelogenous leukemia (CML), 5 chronic myelomonocytic leukemia (CMML), 4 lymphoma, 10 myelodysplastic syndrome (MDS), 2 multiple myeloma (MM), 9 myeloproliferative neoplasm (MPN), 1 aplastic anemia, and 11 acute lymphoblastic leukemia. cfDNA was sequenced by NGS using 177 gene panel on Illumina platform. Single primer extension (SPE) approach with UMI was used. Sequencing depth was increased to more than 2000X after removing duplicates. Low-level mutations were confirmed by inspecting BAM file.
Results: 156 cfDNA samples (76%) tested negative and 48 samples from 30 different patients were positive. The negative samples were collected from 28 days to 650 days post-transplant (median 277 days). The positive samples were collected from 27 days to 650 days post-transplant (median 188 days). One of these positive patients was in full clinical relapse at the time of testing. No negative patient who remained negative had clinical relapse. Five patients converted from negative to positive and 12 from positive to negative with subsequent testing. Three from the converted to positive patients developed clinical relapse. Patients who were positive without clinical relapse had median variant allele frequency (VAF) of 0.85% (range: 0.01-13.25) and typically one mutated gene. The mutated genes in this group were: JAK2, IDH2, ASXL1, TET2, DNMT3A, ASXL1, PTPN11, SF3B1, MPL, CEBPA1. Patients who had clinical relapse (#4) had median VAF of 16.33% (0.4%-57.63%) with multiple mutated genes. The mutated genes in this group were: TP53, FLT3, ASXL1, CEBPA, EZH1, NRAS, SETBP1, TET2. To evaluate relevance to BM testing, we compared BM samples with cfDNA samples collected within 120 days of each other. This showed 17 pairs with concordant negative results, 10 with concordant positive results, 5 pairs with positive by cfDNA but negative by BM cells, and one pair with positive by BM but negative by cfDNA. This BM positive sample was performed at 78 days after the cfDNA sample and showed mutation in DNMT3A gene at VAF of 0.63%. Four of the 5 pairs with positive cfDNA but negative BM were collected approximately 3 months after bone marrow and the 5th case was one month prior to BM sample.
Conclusion: These data suggest that monitoring residual disease after HSCT using cfDNA and NGS is a reliable approach and may replace the need of bone marrow biopsy. However, low-level mutations should not be used as the sole criterion for determining relapse. Variant allele frequency and the mutated gene should be considered in evaluating actionable findings.
Pecora: Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees; Genetic testing cooperative: Other: equity investor. Rowley: ReAlta Life Sciences: Consultancy.
Background: AlloHCT is a potentially curative treatment for patients with acute myeloid leukemia (AML) and other myeloid malignancies (MM), however has a 40% relapse rate. The 2-year post-relapse ...survival rate is less than 20%; sustainable remissions are rare. Multiple strategies to mitigate relapse have been employed with variable degrees of success. An as yet untested approach involves manipulation of the T-cell milieu in the post-alloHCT setting using CI. Preclinical animal models of tumors have shown that blockade of PD-1 by monoclonal antibodies(mAbs) can enhance the anti-tumor immune response and result in tumor rejection, suggesting that host mechanisms limit the antitumor response. A murine model of an anti-PD-1 mAb given at the time of transplant showed that PD-1/PD-L1 interactions decrease acute GVHD, but increase chronic GVHD suggesting that PD-1 pathway modulation may provide unique opportunities for stimulating immune regulation post-alloHCT. Use of ipilimumab (I) to treat post-transplant MM resulted in a complete response rate of 42% and decreased the Treg/Tconv cell ratio, consistent with enhancement of the graft-versus-tumor effect. The CPIT-001 Trial demonstrated the feasibility and safety of combined CI with I and nivolumab (N) as consolidation following autologous stem cell transplantation (ASCT) for high-risk hematological malignancies as well as a 67% PFS rate at 18 months post-ASCT, a significant improvement as compared with historical data.
Study Design: The study employs a 3x3 design with intrapatient dose escalation. Patients will alternately be assigned to receive either N or I as a single agent. If the safety endpoint is met for each group, enrollment to the combination CI group will open. See table 1 for dosing. For all Groups, intrapatient dose escalation will be utilized. If the patient tolerates 28 days of treatment, he/she will be escalated to the next dose level and the next patient will be enrolled. Enrollment must occur within 60 days prior to HCT conditioning to allow for collection of baseline samples. Patients will start CI therapy 60 to 100 days after stem cell infusion when acute GvHD is adequately controlled on a prednisone dose of 20 mg daily or less, organ function, and peripheral counts are adequate. Key inclusion criteria at study start up included patient age 75 years or less with high risk AML or MDS undergoing alloHCT with a matched-related or 10/10 unrelated donor who were receiving non-myeloablative conditioning.
Bone marrow aspiration will be performed for response evaluation approximately every 3 months post CI initiation. The primary objective is to assess the safety of CI in this patient population. Secondary objectives include efficacy, assessment of blood immune reconstitution, phenotype and TCR repertoire by sequencing, assessment of tumor site immune phenotype, TCR repertoire and PD-L1/2 expression both prior to alloHCT conditioning and at relapse. The tertiary objective is to identify specific intestinal microbial strains associated with improved outcomes in alloHCT patients treated with CI. Microbial composition in stool samples of patients will be analyzed at screening, at engraftment, at various time points post-transplant, and at time of relapse, as it occurs.
Study Experience Thus Far: The study opened to accrual in May of 2017. Four patients have signed informed consent. Two patients have been treated with CI. One patient withdrew consent prior to treatment and a second patient is currently status post alloHCT, but has not yet reached day 60. One patient received 2 doses of N and the second patient received 1 dose of I.
Accrual to trial has been slower than anticipated. A detailed analysis of the patient screening log was completed. It was determined that 139 patients had been screened for trial. Major reasons for being ineligible included a diagnosis of ALL (34 patients; 24%), use of a haploidentical donor (30 patients; 22%), use of a full dose conditioning regimen (14 patients; 10%), diagnosis of CML (12 pateints; 10%), age greater than 75 (12 patients; 9%), diagnosis of CMML (12 patients; 9%). Following this analysis, an amendment was filed to include patients receiving haplo identical transplant and/or myeloablative conditioning, and to expand diagnosis to include all MM including CMML, CML blast crisis, and myelofibrosis.
Display omitted
Koprivnikar:Amgen: Speakers Bureau; Pfizer: Honoraria; Abbvie: Speakers Bureau; Novartis: Speakers Bureau. McCloskey:Jazz: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Takeda: Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Abbvie: Speakers Bureau; Novartis: Speakers Bureau. Rowley:Fate Therapeutics: Consultancy; Allergan: Equity Ownership.
Nivolumab - immunomodulation Ipilimumab - immunomodulation
PDF
