A comparative evaluation of gemtuzumab ozogamicin + daunorubicin-cytarabine and other treatments for newly diagnosed acute myeloid leukemia

Clinical Benefits and Safety of Gemtuzumab Ozogamicin in Treating Acute Myeloid Leukemia in Various Subgroups: An Updated Systematic Review, Meta-Analysis, and Network Meta-Analysis

Background

Previous trials demonstrated evidence involving the total effects of gemtuzumab ozogamicin (GO), an anti-CD33 humanized antibody, on treating acute myeloid leukemia (AML). In this updated systematic review, meta-analysis, and network meta-analysis (NMA), we aimed to comprehensively explore the clinical benefits and safety of GO in various subtypes of AML.

Methods

PubMed, Embase, Cochrane, and Chinese databases were filtered to search randomized controlled trials (RCTs) and retrospective cohort studies that compared clinical efficiency and toxicity of GO with non-GO groups in AML. Random-effects models were used to calculate pooled effect sizes and 95% confidence intervals (CIs). Relative risk (RR) was used for estimating complete remission (CR), early death, and toxicity. Hazard risk (HR) was accomplished to evaluate survival.

Results

Fifteen RCTs and 15 retrospective cohort studies were identified (GO: 4,768; Control: 6,466). GO tended to improve CR (RR 0.95, p = 0.084), followed by significantly improved survival (overall survival: HR 0.86, p = 0.003; event-free survival: HR 0.86, p = 0.015; relapse-free survival: HR 0.83, p = 0.001; cumulative incidence of relapse: HR 0.82, p < 0.001). GO benefits of CR and survival were evident in favorable- and intermediate-risk karyotypes (p ≤ 0.023). GO advantages were also associated with nucleophosmin 1 mutations (p ≤ 0.04), wild-type FMS-like tyrosine kinase 3 internal tandem duplication gene (p ≤ 0.03), age of <70 years (p < 0.05), de novo AML (p ≤ 0.017), and CD33(+) (p ≤ 0.021). Both adding GO into induction therapy (p ≤ 0.011) and a lower (<6 mg/m2) dose of GO (p ≤ 0.03) enhanced survival. Prognosis of combined regimens with GO was heterogeneous in both meta-analysis and NMA, with several binding strategies showing improved prognosis. Additionally, GO was related to increased risk of early death at a higher dose (≥6 mg/m2) (RR 2.01, p = 0.005), hepatic-related adverse effects (RR 1.29, p = 0.02), and a tendency of higher risk for hepatic veno-occlusive disease or sinusoidal obstruction syndrome (RR 1.56, p = 0.072).

Conclusions

These data indicated therapeutic benefits and safety of GO in AML, especially in some subtypes, for which further head-to-head RCTs are warranted.

Systematic Review Registration

[PROSPERO: https://www.crd.york.ac.uk/prospero/], identifier [CRD42020158540].

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption

Background

In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency.

Methods

The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets.

Results

Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated.

Conclusions

Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01290-1.

Cancer biomarkers for targeted therapy

Tumor-associated antigens (TAA) or cancer biomarkers are major targets for cancer therapies. Antibody- based agents targeting the cancer biomarkers include monoclonal antibodies (MoAbs), radiolabeled MoAbs, bispecific T cell engagers, and antibody-drug conjugates. Antibodies targeting CD19, CD20, CD22, CD30, CD33, CD38, CD79B and SLAMF7 are in clinical applications for hematological malignancies. CD123, CLL-1, B cell maturation antigen, and CD138 are targets for cancer immunotherapeutic agents, including the chimeric antigen receptor - engineered T cells. Immune checkpoint inhibitors (ICIs) against PD-1, PD-L1, and CTLA-4 have led to the revolution of cancer immunotherapy. More ICIs targeting IDO, LAG3, TIM-3, TIGIT, SIGLECs, VISTA and CD47 are being explored. Small molecule inhibitors (SMIs) against tyrosine kinase oncoproteins such as BCR-ABL, JAK2, Bruton tyrosine kinase, FLT3, EGFR, ALK, HER2, VEGFR, FGFR, MEK, and MET have fundamentally changed the landscape of cancer therapy. SMIs against BCL-2, IDHs, BRAF, PI3 kinase, mTOR, PARP, and CDKs have become the mainstay in the treatment of a variety of cancer types. To reduce and avoid off-tumor toxicities, cancer-specific TAAs such as CD33 are being manufactured through systems biology approach. Search for novel biomarkers and new designs as well as delivery methods of targeted agents are fueling the next wave of advances in cancer therapy.

Gemtuzumab ozogamicin and novel antibody-drug conjugates in clinical trials for acute myeloid leukemia

Targeted agents are increasingly used for the therapy of acute myeloid leukemia (AML). Gemtuzumab ozogamicin (GO) is the first antibody-drug conjugate (ADC) approved for induction therapy of AML. When used in fractionated doses, GO combined with the conventional cytarabine/anthracycline-based induction chemotherapy significantly improves the outcome of previously untreated AML patients. Single-agent GO is effective and safe for AML patient ineligible for intensive chemotherapy. Multiple combination regimens incorporating GO have also been recommended as potential alternative options. In addition, several novel ADCs targeting CD33, CD123 and CLL-1 are currently undergoing preclinical or early clinical investigations. In this review, we summarized the efficacy and limitations of GO as well as novel ADCs for adult AML patients.