Monica Hellesøy’s research while affiliated with Haukeland University Hospital and other places

Targeting Venetoclax Resistance in TP53 -Mutated Acute Myeloid Leukemia

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Inge Jonassen

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely (1) automated annotation of cell populations guided by a reference dataset and (2) systematic utilization of single-cell data for effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good trade-off between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients reported salient interactions between signaling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects.

Article

November 2023

40 Reads

Blood

[...]

Introduction: The combination therapy of Venetoclax (Ven) and Azacitidine (Aza) has shown remarkable improvements in the treatment of acute myeloid leukemia (AML) patients not eligible for chemotherapy. Nevertheless, patients with TP53-mutated AML are more prone to have a refractory disease or develop rapid resistance. A recent study indicated that a combined expression ratio of BCL-2, BCL-XL and MCL-1 proteins in leukemic stem cells predicts clinical response to Ven+Aza (Waclawiczek et al. 2023). Furthermore, our previous work has demonstrated that erythroid and megakaryoblastic leukemia blasts are more dependent on BCL-XL than BCL-2 (Kuusanmäki et al. 2023). Emerging evidence suggests that the erythroid-biased differentiation and gene signature is enhanced in many TP53-mutated patients (Rodriquez-Meira et al. 2022). Here we hypothesized that in TP53 -mutated AML, leukemic blasts correspond more closely to megakaryocyte-erythroid progenitors (MEPs) leading to an unfavorable BCL-2 family expression ratio, which can be associated with poor responses to Ven. Consequently, we aimed to identify novel compounds targeting this specific patient subgroup. Methods: We utilized flow cytometry to analyze the mean fluorescent intensity (MFI) of BCL-2 family proteins (BCL-2, BCL-XL, MCL-1) in the CD34+ blast cells of 16 bone marrow-derived AML samples taken before Ven+Aza therapy ( TP53mut n=8, TP53wt n=8). Ex vivo drug testing was performed with eight compounds and blast-specific drug responses were analyzed after 48 hours with flow cytometry. RNA sequencing was performed on CD34+ or CD117+ cells enriched from 35 pre-Ven+Aza treatment samples. To assess erythroid signature scores, we calculated the mean expression of six different erythroid genes ( GATA1, KLF1, ZFPM1, GATA2, GYPA, TFRC) for each patient, following the approach by Rodriquez-Meira et al. 2022. The expression levels of 19 intracellular proteins, including BCL-2, BCL-XL, MCL-1, were quantified from the corresponding cell population of the samples by mass cytometry (CyTOF). Results: In the comparison of BCL-2 family protein expression and treatment responses, BCL-XL levels were significantly higher (p=0.03) in the treatment-resistant group, while no differences were found in BCL-2 or MCL-1 ( Figure 1A). In the TP53-mutated blasts, a trend of decreased BCL-2 was identified compared to the TP53 wild-typeblasts (p=0.08), but no difference in BCL-XL or MCL-1 levels was noted. However, all three Ven+Aza-refractory TP53-mutants exhibited high BCL-XL expression compared to the treatment-responsive TP53-mutated patients. Next, we assessed whether an erythroid-like phenotype was associated with TP53 mutation status by computing the erythroid gene signature scores from the RNAseq data of enriched blasts. Erythroid scores were increased among TP53-mutated samples (p=0.04), and interestingly, a higher erythroid score was linked to lower BCL-2 protein expression, regardless of TP53 mutation status ( Figure 1B). Finally, we assessed the drug efficacy of eight pre-selected compounds, including BCL-2 family inhibitors. As expected, Ven was ineffective in the TP53-mutated samples. In contrast, the TP53-mutated blasts showed higher ex vivo sensitivity to Navitoclax (BCL-2/BCL-XL inhibitor) (Nav IC50=90nM, Ven IC50>1000nM). Additionally, LCL-161 (IAPs inhibitor) demonstrated notable efficacy in the TP53-mutants and was significantly more effective compared to the wild-type controls (LCL-161 IC50=300nM, Ven IC50>1000nM). Discussion: Here, we show that increased BCL-XL protein expression is associated with clinical resistance to Ven+Aza. Furthermore, the link between erythroid gene signature and BCL-2 expression suggests that especially those AML blasts harboring TP53 mutations might have impaired BCL-2 expression due to a closer resemblance to MEPs. The increased sensitivity of TP53-mutated AML cells to LCL-161 demonstrated in the study, highlights the potential of targeting extrinsic apoptosis pathway via inhibiting the inhibitors of apoptosis proteins (IAPs). Furthermore, co-targeting of BCL-2/BCL-XL might provide alternative therapeutic approach in Ven resistant TP53-mutated patients. These findings are currently being validated with a larger patient cohort.

P409: BCL-2/BCL-XL INHIBITION INDUCES APOPTOSIS AND CIRCUMVENTS VENETOCLAX RESISTANCE IN TP53-MUTATED ACUTE MYELOID LEUKEMIA

August 2023

27 Reads

[...]

Author Correction: Early response evaluation by single cell signaling profiling in acute myeloid leukemia

March 2023

75 Reads

Nature Communications

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Early response evaluation by single cell signaling profiling in acute myeloid leukemia

Characterization of immunophenotype in 32 AML patients by FlowSOM
a t-SNE maps of the 10 FlowSOM identified metaclusters based on surface marker expression across the seven healthy donors and the 32 AML patients at pre-treatment, 4, and 24 h (20,000 cells per plot for visualization). t-SNE maps are annotated with color-coded overlay shown in (c). b Stacked bar chart showing the metacluster relative distribution in each patient and healthy donor, as a percent of the total population in the pre-treatment sample. Patient numbers are colored by patient 5-year survival (blue = alive, orange = deceased, BM = bone marrow, PB = peripheral blood). The ten patients who received lenalidomide treatment in addition to standard induction chemotherapy (“3 + 7”) are annotated by stars. c Heatmap showing the median marker intensity of the 19 surface markers used for clustering in the pre-treatment sample of the 32 AML patients. The total metacluster size among the 32 AML patients is shown as a percent of the total. Source data are provided as a Source Data file.

P-ERK1/2 signaling at 24 h after the start of induction therapy predicts patient 2- and 5-year survival
a t-SNE plot highlighting the position and distribution of metacluster (MC) 9 (red) in the t-SNE plot of all 32 patients at pre-treatment. The other metaclusters are shown in gray. b Heatmap of the arcsinh transformed 90th percentile p-ERK1/2 in MC9 at all timepoints sorted by the 24 h value, divided by median into high and low 24 h-p-ERK1/2 groups. Patient numbers are color coded by 5-year survival (blue = alive, orange = deceased). Complete remission (CR) by bone marrow aspiration at day 17 or before cycle two of induction therapy, European Leukemia Net (ELN) 2017 risk classification, transplantation status, and minimal residual disease (MRD) status after cycle 2 are shown in color-coded bars to the right. c LASSO Cox regression analysis identified p-ERK1/2 at 24 h in MC 9, as significantly associated with patient 2 and 5-year survival. The 32 AML patients were divided into two groups based on the 24 h median value as shown in (b); high and low 24 h-p-ERK1/2 groups, with 16 patients in each group. Kaplan–Meier survival curve of the 5-year overall survival (OS) in the two groups (Log-rank (Mantel–Cox) test p = 0.0015, hazard ratio (log-rank) low/high group 0.2307 and 95% CI of ratio 0.0945 to 0.5625) show the poor survival of the high group. d Line-graph of the arcsinh transformed 90th percentile p-ERK1/2 value at all timepoints in the two groups. Abbreviations Allo allogenous stem cell transplantation, Auto autologous stem cell transplantation, CRi complete remission with incomplete count recovery, nonCR >5% remaining blast in the bone marrow after the first cycle of induction therapy. Source data are provided as a Source Data file.

Validation by manual blast gating confirm p-ERK1/2 and p-p38 prediction of survival
Manual gating of mass cytometry data was performed by bi-axial gating in cytobank on all 32 patients. a Bi-axial gating strategy of patient P11 (PB peripheral blood) and a healthy donor (PB) for gating guidance. AML blast cells were defined as CD66 low and CD45 low, the red arrows pinpoint the exported population. Raw 90th percentile data of p-ERK1/2 and p-p38 from the CD66low, CD45low cell population was exported from cytobank and arcsinh transformed with cofactor 5. b Heatmap shows the arcsinh transformed 90th percentile data for p-ERK 1/2 and c p-p38, sorted by their respective 24 h values. Patient numbers are color-coded by 5-year survival (blue = alive, orange = deceased). d Kaplan–Meier survival curves (n = 32) in 24 h-low and high-pERK1/2 group (n = 16) and 24 h-low and high-pp38 group (n = 16).Groups are defined by the median 24 h value for each marker. Curve comparison p-values are calculated by Log–rank (Mantel–Cox) test (p-ERK1/2; HR low/high group: 0.2252, 95% CI of ratio: 0.09215–0.5502, p-p38; HR low/high group: 0.2437, 95% CI of ratio: 0.09791–0.6064). Source data are provided as a Source Data file.

RNA sequencing shows AP-1 complex induction after the start of induction chemotherapy
a Venn diagram showing that 525 p-ERK1/2 and p-p38 induced genes were identified among our RNAseq data. These genes were used for further RNA sequencing data analysis. b Heatmap of 90th percentile arcsinh transformed p-ERK1/2 mass cytometry data in M9. The high and low groups used for RNAseq data analysis were defined by the 24 hp-ERK1/2 median value in MC9. Patient numbers are color-coded by 5-year survival (blue = alive, orange = deceased). An overview of the patient sample material and the analysis performed is shown to the right. All 32 patients in the study were analyzed by mass cytometry, 15 patients with mass cytometry and proteomics, and 14 patients with mass cytometry, proteomics, and RNAseq. In addition, diagnostic fresh bone marrow or peripheral blood samples from 18 of the 32 patients was screened with an ex vivo drug sensitivity screen and a selective drug sensitivity score (sDSS). was calculated c Hierarchical clustering (Euclidean distance) of the 10 patients with post-treatment samples, based on the gene expression (DESeq normalized counts) of the 62 differentially expressed genes post-treatment identified between the 24 h-p-ERK1/2 high and low groups (only genes with p-values < 0.05 are shown, exact p-values are shown in the source data, two-sided students t-test). The p-values were not adjusted for multiple comparison testing. The heatmap shows z-scored DESeq2 normalized counts. The two timepoints post-chemotherapy (4 and 24 h) for each patient were considered post-treatment replicates. Mutations identified by NGS and diagnostic cytogenetics are shown for each patient in the top panel. d Line plots of particularly relevant genes showing the DESeq2 normalized counts at the three different timepoints for each patient. Each line represents one patient, and numbers for identification are annotated next to the lines. For patients with only pre-treatment samples, the patient number is annotated to the left. Patients in the 24 h p-ERK1/2 low group are colored in gray, and patients in the 24 h p-ERK1/2 high group are in red. Source data are provided as a Source Data file.

January 2023

466 Reads

21 Citations

Nature Communications

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Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics. The molecular mechanisms underlying response to chemotherapy in Acute myeloid leukemia (AML) remain to be explored. Here, the authors perform 36-dimensional mass cytometry in 32 AML patients during intensive chemotherapy and suggest functional signalling analysis for prognosis prediction early after treatment in AML.

Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning

Figure S1: Overview of the deployed gating strategy. Gating strategy to identify hematopoietic cell types from seven healthy donors. In total, ~1.2 million cells were analysed using flowDensity. From them, 582,010 cells were selected to describe expression profiles of B-cells, T-cells (Cytotoxic and Helper), Natural Killer cells, Multi Potent Progenitor (MPP), Hematopoietic Stem Cells (HSC), Granulocytes and Monocyte cells. The selected healthy cells were used for the rest of the analysis to construct the reference of hematopoietic cell types, and to train the single-cell hematopoietic classifier.

Figure S2: Visualization of the selected healthy cell populations via Principal Component Analysis (PCA). a) We use the same PCA space shown in Figure 1a. b-d) Projected cells colored based on the normalised expression of CD38, CD33, CD66b and markers.

Figure S6: Analysis of single-cells from patients with leukemia considering ELN (2017) patient stratification. PCA analysis of 30,000 randomly selected single-cells from patients of ELN Adverse a) and ELN Favorable groups b). The cells have been projected into the PCA space constructed using healthy reference cell types from Figure 1a. For comparisons purposes the PCA space has been colored using normalized expression levels of surface markers CD34, CD66b, CD123. We also show signalling markers pErk, pSTAT1 and pNFkB, to highlight the complexity of analysing single-cell signalling data for cell types. The centroids of the gated healthy cell types from Figure 1a are highlighted for comparisons.

Figure S8: Single-cell classification of cells from leukemia patients allows for cellular abundance comparisons using clinical info. a) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots show the cellular abundance of 45 patients stratified based on ELN (2017) group. b) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots show the cellular abundance of 45 patients stratified based on Remission group. c) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots

Figure S10: Single-cell classification of cells from leukemia patients allows for cellular abundance comparisons using genetic information. a) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots show the cellular abundance of 45 patients stratified based on karyotype status. b) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots show the cellular abundance of 45 patients stratified based on presence/absence of FLT3-IDT mutations. c) Percentages (%) of annotated cells after single-cell classification at time of diagnosis. The boxplots show the cellular abundance of 45 patients stratified

August 2022

158 Reads

Dimitrios Kleftogiannis

[...]

Inge Jonassen

The use of single-cell profiling of phenotypes is suggested to inform about chemoresistance and lack of treatment response in cancer. Mass cytometry by time-of-flight (CyTOF) allows high throughput multiparametric analysis at the single-cell level to perform for in-depth characterisation of heterogeneity in leukemia. However, computational identification of cell populations from CyTOF, and utilisation of single-cell data for biomarker discoveries is challenging. Here, we deployed a machine learning-based framework that enables automatic cell population annotation, and systematic exploration of interactions between signalling proteins in a CyTOF antibody panel. We applied the developed framework to analyse a cohort of 45 leukemia patients. We investigated associations between the cellular composition and clinicopathological and genetic features, and reported salient signalling interactions of Multipotent Progenitor-like leukemia cells that were sufficient to predict short-term survival at time of diagnosis. Our findings confirmed that targeting cell type-specific signalling interactions in leukemia might improve existing patient stratification methods with the potential to inform early about more precise treatment options.

Early response evaluation by single cell signaling profiling in acute myeloid leukemia

February 2022

107 Reads

1 Citation

Oda Fagerholt

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Background: A fundamental hallmark of cancer cells is their ability to sustain proliferative signaling and cell survival, reflected in a cellular chemotherapy response that is poorly understood. We questioned whether chemotherapy modulated phospho-signaling at 4 and 24 h in vivo could provide information about long-term survival in acute myeloid leukemia (AML), and if the signaling response to therapy was more informative than analysis at time of diagnosis. Methods: Peripheral blood was collected from 32 younger AML patients (age 16-74 years), before, 4- and 24 hours after start of induction chemotherapy. Samples were analyzed by 36-dimensional mass cytometry for assessment of alterations in immunophenotypes and intracellular signaling using unsupervised and supervised machine learning approaches. Results were validated by RNA sequencing and mass spectrometry proteomics (Super SILAC). Targeted sequencing was used to characterize patient samples for recurrent AML mutations. Drug sensitivity and resistance testing ex vivo was compared to activation of relevant signal transduction pathways and mutational profile. Findings: 5-year patient survival was accurately predicted in the leukemic cell population at 24 hours after therapy onset by phospho-proteins p-ERK1/2 (T202/Y204) and p-p38 (T180/Y182). RNA sequencing showed induction of MAPK target gene expression and the AP-1 transcription complex in patients with high p-ERK1/2. Super-SILAC proteomics confirmed an increase in the abundance of p38 prime target MAPKAPK2(MK2) 24 hours after start of induction therapy. Ex vivo drug sensitivity testing demonstrated high sensitivity to MEK inhibitors in the patient cells with high p-ERK1/2 measured at diagnosis or 24 hours after start of chemotherapy. Interpretation: Early single cell signaling response to chemotherapy provided precise prognostic information independent of stratification by genetics. We propose that early functional measurement of chemotherapy-potentiated MAPK pathway signaling could identify non-responders to intensive chemotherapy allowing precise treatment adjustment.

Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia

November 2021

197 Reads

121 Citations

Cancer Discovery

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We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275

Bemcentinib (Oral AXL Inhibitor) in Combination with Low-Dose Cytarabine Is Well Tolerated and Efficacious in Older Relapsed AML Patients.Updates from the Ongoing Phase II Trial (NCT02488408) and Preliminary Translational Results Indicating Bemcentinib Elicits Anti-AML Immune Responses

Article

November 2021

50 Reads

5 Citations

Blood

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Background: Relapsed (REL) & refractory (REF) r/r AML pts unsuitable for intensive therapy (IT) due to age or co-morbidities, have limited treatment options. Low-dose cytarabine (LDAC) demonstrated limited survival benefit (mOS ≤6 months), highlighting the significant unmet need for new treatments in this patient population. Bemcentinib (BEM) is an orally bioavailable, highly selective AXL-inhibitor. AXL is a receptor tyrosine kinase conferring poor prognosis, resistance to chemotherapy and decreased antitumor immune response. AXL is overexpressed on leukemic cells, especially in the stem cell compartment, thus representing an important novel target in AML. Aims: The ongoing BGBC003 PhII trial cohorts receiving BEM+LDAC (B+L) include newly diagnosed (ND) and r/r AML pts unfit for IT. Based on the initial efficacy signal observed, the r/r AML patient sub-group was selected for an expansion cohort, to further explore safety and efficacy and to pursue translational biomarker analysis. Here, we present preliminary efficacy data in r/r pts, with a safety overview for all pts treated with B+L. We additionally include translational plasma biomarker and multiomics data from bone marrow mononuclear cells (BMMNC). Methods: Pts received the combination of BEM at the RP2D (200mg PO/d) and LDAC SoC schedule. Efficacy endpoints were objective response (OR) and clinical benefit (OR+unchanged [UC]+stable disease [SD=UC for at least 3 BEM cycles]). Secondary objectives looked at overall survival (OS) and exploratory biomarker analyses. Longitudinal BMMNC samples (n=36) from 15 patients were subjected to scRNA-seq and CiteSeq (Chromium 10x genomics; TotalSeq, Biolegend). For scRNA-seq data analyses, Cell Ranger (v3.1.0) and the Seurat (v.4.0.1) in R (v.4.0) were used. Pts were stratified by best response: CR, CRi, PR for Responders; SD, UC, PD for Non-Responders. Cell type annotations were based on the identified clusters and inferred from the expression of known markers at both RNA and protein level. Results: As of 15 July 2021, the B+L cohorts comprised 27 r/r (20 REL, 7 REF) AML pts. Median prior lines of therapy: 1 [1-8] in REL, 3 [1-4] in REF. Median age: 75.5yrs [66-86] for REL, 75yrs [71-81] for REF. Adverse cytogenetic risk profile: 6/18 (33%) in REL; 2/7 (29%) in REF. 17/20 REL pts were evaluable for efficacy (BM assessment post-baseline). 4/17 (24%) achieved remission (4 CR/CRi) between wk13(C5)-wk19(C7); 4 pts had SD, 6 pts were UC; observed clinical benefit rate was 82%. Late onset responses may reflect immunological mechanism of action targeting AXL + innate immune cells in REL pts and may also contribute to a longer time-on-treatment (ToT) and survival. Median ToT was 36.9 wks for CR/CRi pts; mDOR 33wks [12.0-69.9]; 4 pts remain on treatment. Median OS currently 13.3 months (historical controls suggest 4.5 months mOS in this population) continues to mature. In contrast, REF pts showed no response (0/7), with 4/7 (57%) demonstrating clinical benefit; mToT 12.0wks for benefitting patients and mOS 5.3 months; no pts ongoing on treatment. Overall, the safety of B+L (compared with previously published BEM monotherapy) is comparable with the known safety profile of LDAC. TRAEs of ≥G3 observed in ≥10% of pts were anaemia (21% B+L; 4% BEM), and ECG QT prolonged (12% B+L; 7% BEM). No G5 TRAEs reported. scRNA and multiomic analysis of longitudinal samples reflect differences in the immune compartment, underscoring the clinical impression of an immune-mediated MOA associated with response to BEM. CD8+ effector T-cells of responding patients demonstrated enhanced pro-inflammatory signatures involving TNF-alpha and IFN-gamma as compared with non-responders. Furthermore, increased activation of B plasma cells was observed in correlation with response to BEM confirming that BEM mediates an anti-AML immune response through activation of the two major adaptive immune cell populations. Conclusion: B+L is well tolerated and offers promising survival benefit to older unfit REL AML patients. Translational research including scRNA and multiomics, identified specific activation of CD8+ T cells and B plasma cells associated with response to treatment, indicating that BEM elicits activation of the two major adaptive immune cell populations responsible for anti-AML immune responses. B+L warrants evaluation in a randomized pivotal trial in this population. Disclosures Loges: BerGenBio ASA: Honoraria, Research Funding, Speakers Bureau; BMS: Research Funding; Eli Lilly: Research Funding; Roche Pharma: Research Funding; ADC Therapeutics: Research Funding; BMS: Honoraria, Speakers Bureau; Boehringer Ingelheim: Honoraria, Speakers Bureau; Eli Lilly: Honoraria, Speakers Bureau; Roche Pharma: Honoraria, Speakers Bureau; Medac GmbH and Sanofi Aventis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; AstraZeneca: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Bayer: Honoraria, Speakers Bureau. Heuser: Astellas: Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Pharma AG: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; BergenBio: Research Funding; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolremo: Membership on an entity's Board of Directors or advisory committees. Kapp-Schwoerer: BerGenBio ASA: Research Funding. Lemoli: Celgene: Other: Support for attending meetings and/or travel; Jazz, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie, Daiichi Sankyo, Servier: Honoraria, Speakers Bureau. Ben Batalla: BerGenBio ASA: Research Funding. Hellesøy: BerGenBio ASA: Research Funding. Rayford: BerGenBio ASA: Current Employment. Nautiyal: BerGenBio ASA: Current Employment. Berkman-Gottlieb: BerGenBio ASA: Consultancy, Ended employment in the past 24 months. Micklem: BerGenBio ASA: Current Employment, Current equity holder in publicly-traded company. Gabra: BerGenBio ASA: Current Employment, Current equity holder in publicly-traded company. Gorcea-Carson: BerGenBio ASA: Current Employment. Lorens: BerGenBio ASA: Current equity holder in publicly-traded company, Ended employment in the past 24 months, Patents & Royalties. Fiedler: Servier: Consultancy, Other: support for meeting attendance; Amgen: Consultancy, Other: support for meeting attendance, Patents & Royalties, Research Funding; ARIAD/Incyte: Consultancy; Daiichi Sankyo: Consultancy, Other: support for meeting attendance; Stemline: Consultancy; Abbvie: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Other: support for meeting attendance; Novartis: Consultancy; Pfizer: Consultancy, Research Funding; Celgene: Consultancy; Morphosys: Consultancy. Alvarado: Jazz Pharmaceuticals: Research Funding; BerGenBio: Research Funding; Daiichi-Sankyo: Research Funding; FibroGen: Research Funding; CytomX Therapeutics: Consultancy; Astex Pharmaceuticals: Research Funding; MEI Pharma: Research Funding; Sun Pharma: Consultancy, Research Funding. Gjertsen: BerGenBio: Consultancy; Pfizer Inc.: Consultancy; Alden Cancer Therapy: Current holder of stock options in a privately-held company; KinN Therapeutics: Current holder of stock options in a privately-held company; Novartis: Consultancy.

Sex‐separated overview of genes mutated in more than 2% (≥ 10 patients) of the subsampled Beat AML sample cohort (n = 498), identified by analysing the final curated exome sequencing variant list downloaded at http://www.vizome.org/aml/geneset/. Of note, the result of the FLT3 gene is separated by ITD and non‐ITD FLT3 mutations on separate rows. In all other analyses in this report, FLT3‐ITD annotations include additional samples where FLT3‐ITDs were identified exclusively by conventional methods. * indicates single genes (left panel) and gene classes (right panel) with a significantly different mutation frequency between male and female patients. Statistical significance was evaluated using the Fisher exact test, *P > 0.05.

(A) Heatmap depicting unsupervised clustering of CPM‐log2 values of all transcripts differentially expressed between female and male FLT3‐ITD‐positive individuals. Clustering was done by columns and rows and then manually faceted by FLT3‐ITD status and sex. (B) Pairwise comparison of the 17 transcripts identified as differentially expressed between female and male FLT3‐ITD‐positive samples based on the results of the DGE analysis, excluding samples also significantly different in the FLT3‐wt subgroup. Only statistically significant results are shown. Statistical significance was evaluated using the Wilcoxon rank sum test/Mann–Whitney test. Each dot represents an individual specimen, and the box plot graphically presents the median and the spread. The lower and upper hinges correspond the 25th and 75th percentiles, respectively, and the upper and lower whisker extends to the largest and smaller values (no further than 1.5 times the total interquartile range from the hinges). Only FLT3‐positive samples are presented. The same comparison for FLT3‐ITD‐negative samples is available in Fig. S9. (C) Kaplan–Meier curve comparing the outcome of patients characterized by high (n = 61) and low (n = 159) mRNA expression (log2 CPM) of NETO1. Statistical significance is evaluated using the log‐rank test. P = 0.00058.

(A) Comparison of AUC between FLT3‐ITD‐mutated female and male specimens for the 14 compounds identified to demonstrate significant sex‐related divergence. The same comparison for FLT3‐ITD‐negative samples is available in Fig. S13. Statistical significance (P < 0.05) was evaluated using the Wilcoxon rank sum test/Mann–Whitney test. P‐values for each pairwise comparison are indicated in the plot. Additional statistical results for all compounds included in the analysis as well as sample size for all compared groups are reported in Tables S10 and S11. (B) Heatmap graphically presenting the pairwise correlation between the CPM‐log2 values of the differentially expressed genes and the AUC distribution of compounds identified as differentially potent across female and male FLT3‐ITD‐positive samples. The heatmap represents the correlation coefficient. Only statistically significant correlations are plotted.

(A) Kaplan–Meier curve comparing the outcome of FLT3‐ITD‐mutated (n = 382) and non‐FLT3‐ITD‐mutated (n = 1178) patients across the four cohorts analysed together. (B) Kaplan–Meier curve comparing the outcome of FLT3‐ITD‐mutated and non‐FLT3‐ITD‐mutated patients analysed across the four cohorts but analysed individually for females (FLT3‐ITD negative n = 523; FLT3‐ITD positive n = 198) and males (FLT3‐ITD negative n = 655; FLT3‐ITD positive n = 184). (C) Kaplan–Meier curve comparing the outcome of FLT3‐ITD‐mutated and non‐FLT3‐ITD‐mutated patients across the four cohorts analysed together, separated by age (FLT3‐ITD‐negative patients: > 60 years n = 788; ≥ 60 years n = 390. FLT3‐ITD‐positive patients: > 60 years n = 278; ≥ 60 years n = 104). (D) Kaplan–Meier curve comparing the outcome of FLT3‐ITD‐mutated and non‐FLT3‐ITD‐mutated patients across the four cohorts combined, separated by sex and age (female FLT3‐ITD negative > 60 years n = 379; ≥ 60 years n = 144, female FLT3‐ITD positive > 60 years n = 143; ≥ 60 years n = 55; male FLT3‐ITD negative > 60 years n = 409; ≥ 60 years n = 246, male FLT3‐ITD positive > 60 years n = 135; ≥ 60 years n = 49). Statistical significance is evaluated using the log‐rank test. P‐values are indicated in each individual plot.

Sex disparity in acute myeloid leukaemia with FLT3 internal tandem duplication mutations: implications for prognosis

June 2021

61 Reads

15 Citations

[...]

Incidence, molecular presentation and outcome of acute myeloid leukaemia (AML) is influenced by sex, but little attention has been directed at untangling sex‐related molecular and phenotypic differences between female and male patients. While increased incidence and poor risk is generally associated with a male phenotype, the poor prognostic FLT3 internal tandem duplication (FLT3‐ITD) mutation and co‐mutations with NPM1 and DNMT3A is overrepresented in female AML. Here, we investigated the relationship between sex and FLT3‐ITD mutation status by comparing clinical data, mutational profiles, gene expression and ex vivo drug sensitivity in four cohorts: BeatAML, LAML‐TCGA and two independent HOVON/SAKK cohorts, comprising 1755 AML patients in total. We found prevalent sex‐associated molecular differences. Co‐occurrence of FLT3‐ITD, NPM1 and DNMT3A mutations was overrepresented in females, while males with FLT3‐ITDs were characterised by additional mutations in RNA splicing and epigenetic modifier genes. We observed diverging expression of multiple leukaemia‐associated genes as well as discrepant ex vivo drug responses, suggestive of discrete functional properties. Importantly, significant prognostication was only observed in female FLT3‐ITD mutated AML. Thus, we suggest optimisation of FLT3‐ITD mutation status as a clinical tool in a sex‐adjusted manner, and hypothesize that prognostication, prediction and development of therapeutic strategies in AML could be improved by including sex‐specific considerations.

Early response evaluation by single cell signaling profiling in acute myeloid leukemia

... scRNA-seq analysis also confirmed the indispensable role of the mature and terminal, CD56 bright, and transitional subsets of NK cells in maintaining treatment-free remission in CML patients (115). Finally, a mass cytometry study of 32 AML patients revealed that the reduction of extracellularsignal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in myeloid cells 24 hours after chemotherapy was an important predictor of 5-year overall survival (116). Therefore, it is evident that the development of single-cell sequencing technology provides us with significantly more possibilities and hope for the treatment of leukemia. ...
Reference:
Application and research progress of single cell sequencing technology in leukemia

Citing Article
Full-text available
January 2023

Nature Communications

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Early response evaluation by single cell signaling profiling in acute myeloid leukemia

... A novel feature engineering approach based on DREMI was utilised to score all pairwise interactions between signalling proteins in the antibody panel. As a case study we analysed 45 samples from leukemia patients (22). By applying LASSO regression to DREMI scores computed from single-cell populations, we constructed computational models to predict short term survivors at time of diagnosis. ...
Reference:
Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning

Citing Preprint
File available
February 2022

Oda Fagerholt

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Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia

... However, clinical responses to AXL inhibitors extend beyond AXL-positive tumors. For instance, a cohort of 27 unselected relapsed or refractory AML patients, unsuitable for intensive therapy, treated with Bemcentinib (AXL inhibitor) and low-dose Cytarabine demonstrated promising survival results, with an overall survival (OS) of 13.3 months compared to 4.5 months in historical controls [8]. Additionally, AXL-negative nonsmall cell lung cancer (NSCLC) patients also responded to treatment, although response rate was lower than AXL-positive patients by immunohistochemistry [9]. ...
Reference:
AXL in myeloid malignancies – an elusive target?

Citing Article
November 2021

Blood

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... Finally, multiple reports have demonstrated that drug response in patient-derived tumoroid models, at least in some cases, correlates highly with matched donor response during clinical treatment 29,[48][49][50][51][52][63][64][65][66][67][68] . Advancements in functional precision oncology -where measurable improvements in patient outcome are increasingly being cited -have been hampered by difficulties in standardized approaches for tumoroid culture 9,69-71 . ...
Reference:
Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications

Citing Article
Full-text available
November 2021

Cancer Discovery

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... These differences persist after progression to AML, where female patients more frequently harbor mutations in DNMT3A, FLT3, NPM1, and TP53, while mutations in ASXL1, SRSF2, U2AF1, RUNX1, and KIT are more common in males [8][9][10][11]. Additionally, comutational patterns differ between sexes as the co-occurrence of FLT3-ITD, NPM1, and DNMT3A mutations was reported overrepresented in females, while males with an FLT3-ITD mutation were shown to be characterized by additional mutations in RNA splicing and epigenetic modifier genes [12]. These sex-specific mutational differences may be partly attributed to varying environmental exposures, such as smoking, which has historically been more common in males and is associated with ASXL1 mutations [13]. ...
Reference:
Acute myeloid leukemia: does sex matter?

Sex disparity in acute myeloid leukaemia with FLT3 internal tandem duplication mutations: implications for prognosis

Citing Article
Full-text available
June 2021

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... Inframe amino acid sequence duplications and insertion of an unpredictable length of base pair fragment comprising three to a few hundred base pairs in the region that encodes for the juxtamembrane domain (JMD) are the primary impacts of ITDs on the FLT3 gene's exons 14 and 15 [9][10][11] . The exact reason behind FLT3-ITD's duplicated segment insertion events and in-frame sequence duplication is still undefined 12,13 . Conformational alterations in the JMD structure have the potential to disrupt the kinase activity's auto-inhibitory regulatory mechanism, which in turn stimulates kinase domain activation by constitutively activating the FLT3 receptor dimerization process in a ligand-independent manner 14,15 . ...
Reference:
Evaluation of FLT3-ITD Mutation in Fifty Newly Diagnosed Iraqi Patients with Acute Myeloid Leukemia

FLT3‐ITD mutations in acute myeloid leukaemia – Molecular characteristics, distribution and numerical variation

Citing Article
Full-text available
May 2021

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... Although, MALDI MS does generate intact molecular proteins allowing putative protein identification based on accurate mass, it may be limited by the low cellular abundance of proteins and would benefit from the development of protein specific tags or chemical derivation agents that would improve the ionization efficiency of the proteins of interest (Fülöp et al., 2020). This approach was used for determining the protein expression of the p53 protein in cancer cells by staining the cells with heavy SINGLE-CELL AND SINGLE-ORGANELLE MASS SPECTROMETRY metal isotope labeled antibodies for subsequent detection using mass cytometry (Fagerholt et al., 2020). Combining liquid microjunction extraction techniques to desorption electrospray ionization mass spectrometry (DESI) has led the development of nanospray DESI (nanoDESI) which has demonstrated the label-free detection of proteins in tissue sections (Feider et al., 2016;Griffiths et al., 2017;Hsu et al., 2015). ...
Reference:
Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements

Single Cell Detection of the p53 Protein by Mass Cytometry

Citing Article
Full-text available
December 2020

Cancers

Stein-Erik Gullaksen