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Mamun, A. A.

Publications and source records attributed to Mamun, A. A..

2 recordsLinked to original sources

Acquired secondary HER2 mutations enhance HER2/MAPK signaling and promote resistance to HER2 kinase inhibition in HER2-mutant breast cancer

HER2 mutations drive the growth of a subset of breast cancers and are targeted with HER2 tyrosine kinase inhibitors (TKIs) such as neratinib. However, acquired resistance is common and limits the durability of clinical responses. Most HER2-mutant breast cancers progressing on neratinib-based therapy acquire secondary mutations in HER2. Apart from the HER2T798I gatekeeper mutation, whether these secondary HER2 mutations are causal to neratinib resistance is not known. We show herein that secondary acquired HER2T862A and HER2L755S mutations promote resistance to HER2 TKIs via enhanced HER2 activation and impaired neratinib binding. While cells expressing each acquired HER2 mutation alone were sensitive to neratinib, expression of acquired double mutations enhanced HER2 signaling and reduced neratinib sensitivity in 2D and 3D assays. Computational structural modeling suggested that secondary HER2 mutations stabilize the HER2 active state and reduce neratinib binding affinity. Cells expressing double HER2 mutations exhibited resistance to most HER2 TKIs but retained sensitivity to mobocertinib and poziotinib. Double-mutant cells showed enhanced MEK/ERK signaling which was blocked by combined inhibition of HER2 and MEK, providing a potential treatment strategy to overcome resistance to HER2 TKIs in HER2-mutant breast cancer.

cancer biology↗

Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder

The tumor cell population in cancer tissue has distinct molecular characteristics and exhibits different phenotypes, thus, resulting in different subpopulations. This phenomenon is known as Intratumor Heterogeneity (ITH), a major contributor to drug resistance, poor prognosis, etc. Therefore, quantifying the levels of ITH in cancer patients is essential, and many algorithms do so in different ways, using different types of omics data. DEPTH (Deviating gene Expression Profiling Tumor Heterogeneity) is the latest algorithm that uses transcriptomic data to evaluate the ITH score. It shows promising performance, has strong similarity with six other algorithms and has an advantage over two algorithms that uses the same type of data (tITH, sITH). However, it has a major drawback since it uses expression values of all the genes ([~]20K genes) in quantifying ITH levels. We hypothesize that a subset of key genes is sufficient to quantify the ITH level. To prove our hypothesis, we developed a deep learning-based computational framework using unsupervised Concrete Autoencoder (CAE) to select a set of cancer-specific key genes that can be used to evaluate the ITH score. For the experiment, we used gene expression profile data of tumor cohorts of breast, kidney, and lung cancer from the TCGA repository. Using multi-run CAE, we selected three sets of key genes, each set related to breast, kidney, and lung tumor cohorts. For the three cancers stated and three molecular subtypes of lung cancer, we calculated the ITH level using all genes and key genes selected by CAE and performed a side-by-side comparison. We could reach similar conclusions for survival and prognostic outcomes based on ITH scores derived from all genes and the sets of key genes. Additionally, for subtypes of lung cancer, the comparative distribution of ITH scores derived from all and key genes remains similar. Based on these observations, it can be stated that a subset of key genes, instead of all genes, is sufficient for ITH quantification. Our results also showed that many key genes are prognostically significant, which can be used as possible therapeutic targets.

bioinformatics↗