A Pilot Phase II Study of Erlotinib for the Treatment of Patients with Relapsed/Refractory Acute Myeloid Leukemia

Dalle, Iman Abou; Cortes, Jorge; Pinnamaneni, Pramod; Lamothe, Betty; Duque, Adolfo Diaz; Randhawa, Jasleen K.; Pemmaraju, Naveen; Jabbour, Elias; Ferrajoli, Alessandra; Wierda, William G.; Estrov, Zeev; Konopleva, Marina; Ravandi, Farhad; Alvarado, Yesid; Borthakur, Gautam; Gandhi, Varsha; Kantarjian, Hagop M.

doi:10.1159/000490092

Cited by 15 publications

(22 citation statements)

References 28 publications

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“…An inverse correlation between EREG expression and methylation was also observed in non-small cell lung cancers, head and neck cancers, and acute myeloid leukemias by examination of the TCGA data set [7]. These tumors are also indications for anti-EGFR therapies [23-26]. Recently, EREG expression as a predictive functional marker of sensitivity to anti-EGFR treatment was also reported in basal-like head and neck squamous cell carcinoma [27].…”

Section: Discussionmentioning

confidence: 92%

High EREG Expression Is Predictive of Better Outcomes in Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy

et al. 2020

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Background/Aim: A great proportion of patients with rectal cancer initially present with locally advanced disease and can potentially benefit from neoadjuvant concurrent chemoradiotherapy (CCRT) for downstaging before surgery. However, risk and clinical outcome stratification remain a great challenge. We aimed to find the potential biomarker to predict the effect of neoadjuvant CCRT on rectal cancer. Methods: We identified epiregulin (EREG) as the most significant predictive marker for neoadjuvant CCRT response from the published rectal cancer transcriptome data set GSE35452. We collected 172 biopsy specimens from rectal cancer patients who received neoadjuvant CCRT followed by radical proctectomy, performed EREG immunohistochemistry, and analyzed the H-scores. We further examined the correlations between the expression level of EREG and clinicopathological features, tumor regression grade, and survival, including disease-specific survival (DSS), locoregional recurrence-free survival (LRFS), and metastasis-free survival (MeFS). Results: High EREG expression was significantly related to early pretreatment (pre-Tx) and posttreatment (post-Tx) tumor status (T1, T2, p = 0.047 and p < 0.001), pre-Tx and post-Tx negative nodal status (N0, p < 0.001 and p = 0.004), less vascular and perineurial invasion (p = 0.015 and p = 0.023), and higher tumor regression grade (p < 0.001). In the survival analysis, high EREG expression was significantly associated with better DSS (p < 0.0001), LRFS (p = 0.0004), and MeFS (p < 0.0001). In the multivariate analysis, high EREG expression remained prognostically significant for better DSS (p = 0.003; hazard ratio: 5.599). Conclusion: These data suggest that EREG is a potential predictive marker and therapeutic target in rectal cancer patients receiving neoadjuvant CCRT.

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Section: Discussionmentioning

confidence: 92%

High EREG Expression Is Predictive of Better Outcomes in Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy

et al. 2020

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“…In addition, another explanation for cross-tissue generalization can be the presence of drivers shared across cancer types. Different studies have identified pan-cancer driver genes across solid and non-solid tissues such as TP53, ERBB2, EGFR, or EML4-ALK (Mano 2008;Zapata et al 2017;Abou Dalle et al 2018;Bailey et al 2018;Joshi et al 2020). Interestingly, ERBB2 and EGFR are known to be associated with Erlotinib (one of the drugs we studied) that demonstrated cross-tissue generalization to non-solid tissues.…”

Section: Discussionmentioning

confidence: 94%

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Sharifi-Noghabi

Harjandi

Zolotareva

et al. 2021

Preprint

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Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid this assumption, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients and therefore, may guide precision oncology more accurately.

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“…However, no evidence of direct binding by which these molecules induce phenotypic alterations in AML has been revealed. While multiple clinical trials using erlotinib or afatinib in AML have yielded con icting, but mostly negative clinical results 21,22,23,24 . Effective use of EGFRi in the treatment of AML patients has been stymied due to the absence of a method identifying the patients that might optimally bene t from this approach.…”

Section: Introductionmentioning

confidence: 99%

CD34 is a target for covalent EGFR inhibitors to eliminate stem/progenitor cells in acute and chronic myeloid leukemia

Yang

et al. 2021

Preprint

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The effect of epidermal growth factor receptor (EGFR) inhibitors on acute myeloid leukemia (AML) was discovered over one decade ago. However, clinical trials of EGFR inhibitors in AML have yielded controversial outcomes. Leukemia cells lack EGFR expression, and the mechanism by which EGFR inhibitors affect leukemia cell growth is unknown, obscuring the precise subset of AML patients that might be targeted by these compounds. Since myeloid leukemia arises from malignant stem/progenitors, here we evaluated the effect of EGFR inhibitors on primary leukemia stem/progenitors that expressed the stem cell marker CD34 which were sorted from leukemia patients. EGFR inhibitors induced significant apoptosis of primary CD34+ but not CD34− cells derived from AML and chronic myeloid leukemia (CML) patients both in vitro and in patient-derived xenotransplantation model. Using two EGFR inhibitors osimertinib and afatinib, we demonstrated binding and covalent adducts of the inhibitors with the cysteine(C) 199 residue of the CD34 protein, which downregulated phosphorylation of tyrosine 329(Y329) of CD34, leading to the dissociation of CD34 from tyrosine kinase Src and thereafter the inhibition of STAT3 phosphorylation. Most importantly, administration of osimertinib yielded clinical responses in two CD34-high AML patients identified by quantitative proteomics with reduced levels of Y329 phosphorylation of CD34 after treatment. Collectively, these findings delineate a novel molecular pathway whereby EGFR inhibitors kill leukemia and reveal that the CD34 antigen is a targetable signaling molecule that mediates cell survival signals via connecting to Src-STAT3 pathway.

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A Pilot Phase II Study of Erlotinib for the Treatment of Patients with Relapsed/Refractory Acute Myeloid Leukemia

Cited by 15 publications

References 28 publications

High EREG Expression Is Predictive of Better Outcomes in Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy

High EREG Expression Is Predictive of Better Outcomes in Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

CD34 is a target for covalent EGFR inhibitors to eliminate stem/progenitor cells in acute and chronic myeloid leukemia

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