TP53 abnormalities correlate with immune infiltration and associate with response to flotetuzumab immunotherapy in AML

Abstract

Somatic TP53 mutations and 17p deletions with genomic loss of TP53 occur in 37% to 46% of acute myeloid leukemia (AML) with adverse-risk cytogenetics and correlate with primary induction failure, high risk of relapse, and dismal prognosis. Herein, we aimed to characterize the immune landscape of TP53-mutated AML and determine whether TP53 abnormalities identify a patient subgroup that may benefit from immunotherapy with flotetuzumab, an investigational CD123 × CD3 bispecific dual-affinity retargeting antibody (DART) molecule. The NanoString PanCancer IO360 assay was used to profile 64 diagnostic bone marrow (BM) samples from patients with TP53-mutated (n = 42) and TP53-wild-type (TP53-WT) AML (n = 22) and 45 BM samples from patients who received flotetuzumab for relapsed/refractory (R/R) AML (15 cases with TP53 mutations and/or 17p deletion). The comparison between TP53-mutated and TP53-WT primary BM samples showed higher expression of IFNG, FOXP3, immune checkpoints, markers of immune senescence, and phosphatidylinositol 3-kinase-Akt and NF-κB signaling intermediates in the former cohort and allowed the discovery of a 34-gene immune classifier prognostic for survival in independent validation series. Finally, 7 out of 15 patients (47%) with R/R AML and TP53 abnormalities showed complete responses to flotetuzumab (<5% BM blasts) on the CP-MGD006-01 clinical trial (NCT #02152956) and had significantly higher tumor inflammation signature, FOXP3, CD8, inflammatory chemokine, and PD1 gene expression scores at baseline compared with nonresponders. Patients with TP53 abnormalities who achieved a complete response experienced prolonged survival (median, 10.3 months; range, 3.3-21.3 months). These results encourage further study of flotetuzumab immunotherapy in patients with TP53-mutated AML.

Graphical abstract

Figure 1.

TP53 mutations correlate with an immune-infiltrated TME in TCGA-AML. (A) Heatmap of immune-cell-type–specific scores and biological activity scores in TCGA-AML cases with information on prognostic molecular lesions (n = 118; unsupervised hierarchical clustering; Euclidean distance; complete linkage). ClustVis, an online tool for clustering of multivariate data, was used for data analysis and visualization. The optimal number of clusters was defined through silhouette scoring using an open-source machine learning toolkit (Orange3, version 3.25.0). FLT3-ITD, fms-like tyrosine kinase 3 internal tandem duplication; NPM1, nucleophosmin-1. ELN intermediate cases were further subclassified into molecular low-risk cases (NPM1 mutations without FLT3-ITD) and molecular high-risk cases (NPM1 WT with FLT3-ITD). (B) Fraction of genome altered in TCGA-AML cases with TP53 mutations (n = 14) and other prognostic molecular lesions (n = 104). Bars denote median values. Data were retrieved through cBioPortal and were compared using the Mann-Whitney U test for unpaired determinations. (C) Box plots showing immune signature scores in TCGA-AML cases with TP53 mutations (n = 14) and other prognostic molecular lesions (n = 19 with NPM1 mutations; n = 22 with RUNX1 mutations; n = 48 with FLT3-ITD without NPM1 mutations; n = 15 with CHIP-defining mutations). Data were compared using the Kruskal-Wallis test for unpaired determinations with Bonferroni’s correction for multiple comparisons. (D) TIS, inflammatory chemokine, IFN-γ, and lymphoid signature scores in TCGA cases with TP53 mutations with (n = 9 cases) or without a CK (n = 13 cases) for whom immune gene signature scores could be computed. Data were compared using the Mann-Whitney U test for unpaired determinations. (E) Expression of FOXP3 and immune checkpoints PD-L1 and TIGIT in TCGA-AML cases with TP53 mutations (n = 14) and other prognostic molecular lesions (n = 104). Bars denote median values. Data were compared using the Mann-Whitney U test for unpaired determinations. ND, not determined.

Figure 2.

TP53-related cancer pathways and expression of actionable immune checkpoints in patients with TP53-mutated AML (SAL and Bologna cohorts). (A) TP53 mutations were categorized as missense or no missense (frameshift, splice site and nonsense) using the IARC TP53 database (http://p53.iarc.fr/) and based on prior knowledge.^, (B) Principal-component (PC) analysis of 770 immune genes (IO 360 panel) in patients with TP53-mutated (n = 42) and TP53WT AML (n = 22). Points are colored by TP53 mutational status (mutated, red; WT, blue). ClustVis was used for data analysis and visualization. (C) Heatmap of immune-cell-type–specific and biological activity scores in patients with TP53-mutated and TP53-WT AML (unsupervised hierarchical clustering; Euclidean distance; complete linkage). The number of TP53-mutated cases in each immune cluster (high, intermediate, and low) is indicated. ClustVis, an online tool for clustering of multivariate data, was used for data analysis and visualization. (D) Heatmap of cancer pathway scores in patients with TP53-mutated and TP53-WT AML (unsupervised hierarchical clustering; Euclidean distance; complete linkage). (E) Cancer pathway scores in patients with TP53-mutated and TP53-WT AML. Bars denote median values. Data were compared using the Kruskal-Wallis test for unpaired determinations. (F) Box plots summarizing the expression of negative immune checkpoints and immune genes related to T-cell infiltration, regulatory T cells, and cytolytic activity in patients with TP53-mutated and TP53-WT AML. Bars denote median values. Data were compared using the Mann-Whitney U test for unpaired determinations. NA, not available; PI3K, phosphatidylinositol 3-kinase.

Figure 3.

Identification of a TP53-related immune gene set in patients with TP53-mutated AML (SAL and Bologna cohorts). (A) Volcano plot (R package version 1.4.0) showing DE genes (P threshold = .01; log₂ fold change ≥1.5) between patients with TP53-mutated (n = 42) and TP53-WT AML (n = 22). B) Heatmap displaying the 34 genes with the greatest differential expression between patients with TP53-mutated and TP53-WT AML (P threshold = .01; log₂ fold change ≥1.5). ClustVis, an online tool for clustering of multivariate data, was used for data analysis and visualization. (C) Analysis of functional protein association networks using STRING (https://string-db.org/). Top 10 molecules interacting with DE genes between TP53-mutated and TP53-WT AML are shown together with their predicted mode of action (highest-confidence interaction scores >0.900). Network nodes (query proteins) represent proteins produced by a single protein-coding gene locus. White nodes represent second shells of interactors. Empty and filled nodes indicate proteins of unknown or partially known 3-dimensional structure, respectively. Edges represent protein-protein associations. Line shapes denote predicted modes of action. (D) Correlation between abnormalities of genes in the TP53 immune classifier and prognostic molecular lesions, including TP53 mutations, in TCGA-AML cases. Data were retrieved and analyzed using cBioPortal for Cancer Genomics (http://www.cbioportal.org/) and were compared using the Fisher's exact test. NS, not significant.

Figure 4.

Upregulated genes in the TP53 immune gene classifier correlate with immune infiltration in TCGA-AML cases. (A) Heatmap of immune-cell-type–specific scores and biological activity scores (unsupervised hierarchical clustering; Euclidean distance; complete linkage) in TCGA-AML cases with (n = 93) or without (n = 54) abnormalities in the DE genes (TP53 immune classifier) between patients with TP53-mutated (n = 42) and TP53-WT AML (n = 22). Abnormalities were defined as mRNA upregulation, gene amplification, deep deletion, and missense mutations relative to the gene’s expression distribution in all profiled AML samples. Abnormalities in only 1 gene used in the cBioPortal query (by default, nonsynonymous mutations, fusions, amplifications, and deep deletions) were sufficient to define that particular patient sample as “altered." (B) Expression of IFN-γ signaling molecules, immune checkpoints, and markers of T-cell infiltration in TCGA-AML cases with or without abnormalities in the TP53 classifier genes. Bars denote median values. (C) Abnormalities in the 18 upregulated immune genes in the TP53 classifier in pretreatment BM samples from TCGA-AML cases. Data were retrieved, analyzed, and visualized using cBioPortal. The Kaplan-Meier method was used to generate survival curves, which were compared using a log-rank test.

Figure 5.

Integrated mRNA and protein profiling of AML cells lines with missense and truncating TP53 mutations. (A) Volcano plot (R package version 1.4.0) showing DE mRNA species and proteins between AML cell lines with missense (Kasumi-1 cells; p.R248Q; Broad Institute Cancer Cell Line Encyclopedia) and truncating (splice site) mutations of TP53 (KG-1 cells). (B) Heatmap of the top DE mRNA species and proteins between KG-1 AML and Kasumi-1 AML (unsupervised hierarchical clustering; Euclidean distance; complete linkage). ClustVis, an online tool for clustering of multivariate data, was used for data analysis and visualization. (C) Heatmap of signaling pathway scores in KG-1 and Kasumi-1 cells (unsupervised hierarchical clustering; Euclidean distance; complete linkage). Signature scores were calculated as detailed in "Materials and methods." (D) Kaplan-Meier estimate of survival from diagnosis in TCGA-AML cases with abnormalities in DE genes between KG-1 (n = 34) and Kasumi-1 cells (n = 10). KM curves (median split of signature scores) were generated using GEPIA2, an enhanced Web server for TCGA gene expression profiling and interactive analysis (http://gepia2.cancer-pku.cn/#index). Signature scores are calculated as the mean value of log₂ transcripts per million. GEPIA2 uses the log-rank (Mantel-Cox) test to compare survival distributions.

Figure 6.

Immune landscape and immunotherapy response in patients with relapsed/refractory AML with and without TP53 mutations and/or 17p abnormalities. (A) Heat-map of immune cell type-specific scores and biological activity scores in patients with relapsed/refractory AML treated with flotetuzumab immunotherapy. ClustVis, an online tool for clustering of multivariate data, was used for data analysis and visualization. (B) Waterfall plot depicting changes in BM blasts after cycle 1 of flotetuzumab in patients with TP53 mutations and/or 17p deletion (n = 14; a BM sample was not available in 1 patient who progressed on treatment. (C) Response to flotetuzumab in relation to TP53 abnormalities (χ² test). (D) Swimmer plot showing time on treatment and time to death and/or censoring in relation to clinical response in the 15 patients with TP53 mutations and/or 17p deletion with genomic loss of TP53. Response criteria are described in "Materials and methods." (E) TIS, inflammatory chemokine, Treg cell, CD8, IFN-γ, PD1, PD-L1, and exhaustion mRNA scores in baseline BM samples from patients with TP53 mutations and/or 17p deletion. Complete responses were defined as either CR, CR with partial hematological recovery, CRi, or MLFS. Data were compared using the Mann-Whitney U test for unpaired determinations. CR, complete remission; CRh, complete remission with partial hematopoietic recovery; FU, follow-up; OB, other benefit (>30% decrease of BM blasts relative to baseline); NR, no response; PD, progressive disease; SD, stable disease.