Targeted immunotherapy for HER2-low breast cancer with 17p loss.

The clinical challenge for treating HER2 (human epidermal growth factor receptor 2)-low breast cancer is the paucity of actionable drug targets. HER2-targeted therapy often has poor clinical efficacy for this disease due to the low level of HER2 protein on the cancer cell surface. We analyzed breast cancer genomics in the search for potential drug targets. Heterozygous loss of chromosome 17p is one of the most frequent genomic events in breast cancer, and 17p loss involves a massive deletion of genes including the tumor suppressor TP53 Our analyses revealed that 17p loss leads to global gene expression changes and reduced tumor infiltration and cytotoxicity of T cells, resulting in immune evasion during breast tumor progression. The 17p deletion region also includes POLR2A, a gene encoding the catalytic subunit of RNA polymerase II that is essential for cell survival. Therefore, breast cancer cells with heterozygous loss of 17p are extremely sensitive to the inhibition of POLR2A via a specific small-molecule inhibitor, α-amanitin. Here, we demonstrate that α-amanitin-conjugated trastuzumab (T-Ama) potentiated the HER2-targeted therapy and exhibited superior efficacy in treating HER2-low breast cancer with 17p loss. Moreover, treatment with T-Ama induced immunogenic cell death in breast cancer cells and, thereby, delivered greater efficacy in combination with immune checkpoint blockade therapy in preclinical HER2-low breast cancer models. Collectively, 17p loss not only drives breast tumorigenesis but also confers therapeutic vulnerabilities that may be used to develop targeted precision immunotherapy.

MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation.

Immune evasion is a pivotal event in tumor progression. To eliminate human cancer cells, current immune checkpoint therapy is set to boost CD8+ T cell-mediated cytotoxicity. However, this action is eventually dependent on the efficient recognition of tumor-specific antigens via T cell receptors. One primary mechanism by which tumor cells evade immune surveillance is to downregulate their antigen presentation. Little progress has been made toward harnessing potential therapeutic targets for enhancing antigen presentation on the tumor cell. Here, we identified MAL2 as a key player that determines the turnover of the antigen-loaded MHC-I complex and reduces the antigen presentation on tumor cells. MAL2 promotes the endocytosis of tumor antigens via direct interaction with the MHC-I complex and endosome-associated RAB proteins. In preclinical models, depletion of MAL2 in breast tumor cells profoundly enhanced the cytotoxicity of tumor-infiltrating CD8+ T cells and suppressed breast tumor growth, suggesting that MAL2 is a potential therapeutic target for breast cancer immunotherapy.

Metabolic interventions: A new insight into the cancer immunotherapy.

Metabolic reprogramming confers cancer cells plasticity and viability under harsh conditions. Such active alterations lead to cell metabolic dependency, which can be exploited as an attractive target in development of effective antitumor therapies. Similar to cancer cells, activated T cells also execute global metabolic reprogramming for their proliferation and effector functions when recruited to the tumor microenvironment (TME). However, the high metabolic activity of rapidly proliferating cancer cells can compete for nutrients with immune cells in the TME, and consequently, suppressing their anti-tumor functions. Thus, therapeutic strategies could aim to restore T cell metabolism and anti-tumor responses in the TME by targeting the metabolic dependence of cancer cells. In this review, we highlight current research progress on metabolic reprogramming and the interplay between cancer cells and immune cells. We also discuss potential therapeutic intervention strategies for targeting metabolic pathways to improve cancer immunotherapy efficacy.

eIF3a Regulation of NHEJ Repair Protein Synthesis and Cellular Response to Ionizing Radiation.

Translation initiation in protein synthesis regulated by eukaryotic initiation factors (eIFs) is a crucial step in controlling gene expression. eIF3a has been shown to regulate protein synthesis and cellular response to treatments by anticancer agents including cisplatin by regulating nucleotide excision repair. In this study, we tested the hypothesis that eIF3a regulates the synthesis of proteins important for the repair of double-strand DNA breaks induced by ionizing radiation (IR). We found that eIF3a upregulation sensitized cellular response to IR while its downregulation caused resistance to IR. eIF3a increases IR-induced DNA damages and decreases non-homologous end joining (NHEJ) activity by suppressing the synthesis of NHEJ repair proteins. Furthermore, analysis of existing patient database shows that eIF3a expression associates with better overall survival of breast, gastric, lung, and ovarian cancer patients. These findings together suggest that eIF3a plays an important role in cellular response to DNA-damaging treatments by regulating the synthesis of DNA repair proteins and, thus, eIIF3a likely contributes to the outcome of cancer patients treated with DNA-damaging strategies including IR.

Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Recently, rather than predicting categorical variables (e.g., stages and subtypes) as in cancer diagnosis, several prognosis prediction models basing on patients' survival information have been adopted to estimate the clinical outcome of cancer patients. However, most existing studies treat the diagnosis and prognosis tasks separately. In fact, the diagnosis information (e.g., TNM Stages) indicates the extent of the disease severity that is highly correlated with the patients' survival. While the diagnosis is largely made based on histopathological images, recent studies have also demonstrated that integrative analysis of histopathological images and genomic data can hold great promise for improving the diagnosis and prognosis of cancers. However, direct combination of these two types of data may bring redundant features that will negatively affect the prediction performance. Therefore, it is necessary to select informative features from the derived multi-modal data. Based on the above considerations, we propose a multi-task multi-modal feature selection method for joint diagnosis and prognosis of cancers. Specifically, we make use of the task relationship learning framework to automatically discover the relationships between the diagnosis and prognosis tasks, through which we can identify important image and genomics features for both tasks. In addition, we add a regularization term to ensure that the correlation within the multi-modal data can be captured. We evaluate our method on three cancer datasets from The Cancer Genome Atlas project, and the experimental results verify that our method can achieve better performance on both diagnosis and prognosis tasks than the related methods.

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

BACKGROUND: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. METHODS: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. RESULT: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. CONCLUSION: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Corrigendum: eIF3a Regulation of NHEJ Repair Protein Synthesis and Cellular Response to Ionizing Radiation.

[This corrects the article DOI: 10.3389/fcell.2020.00753.].

Thiodiketopiperazines from the marine-derived fungus Phoma sp. OUCMDZ-1847.

Three new thiodiketopiperazines, named phomazines A-C (1-3), along with 10 known analogues (4-13), were isolated from the fermentation broth of an endophytic fungus, Phoma sp. OUCMDZ-1847, associated with the mangrove plant Kandelia candel. The structures including the absolute configurations of the new compounds were unambiguously elucidated by spectroscopic, X-ray crystallographic, and Mosher's methods along with quantum ECD and (13)C NMR calculations. Compounds 2, 4, 5, 11, and 12 showed cytotoxicities against the HL-60, HCT-116, K562, MGC-803, and A549 cell lines with IC50 values in the range 0.05 to 8.5 µM.