Lactate Lights up PI3K Inhibitor Resistance in Breast Cancer

The emerging role of lactate in tumor microenvironment and its clinical relevance

In recent years, the significant impact of lactate in the tumor microenvironment has been greatly documented. Acting not only as an energy substance in tumor metabolism, lactate is also an imperative signaling molecule. It plays key roles in metabolic remodeling, protein lactylation, immunosuppression, drug resistance, epigenetics and tumor metastasis, which has a tight relation with cancer patients' poor prognosis. This review illustrates the roles lactate plays in different aspects of tumor progression and drug resistance. From the comprehensive effects that lactate has on tumor metabolism and tumor immunity, the therapeutic targets related to it are expected to bring new hope for cancer therapy.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses

Background

Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients.

Methods

RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the “edgeR” R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses.

Results

We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P < 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs.

Conclusions

We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer

Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

Discovery of Novel Phosphoinositide-3-Kinase α Inhibitors with High Selectivity, Excellent Bioavailability, and Long-Acting Efficacy for Gastric Cancer

Phosphoinositide-3-kinase (PI3K) overexpressed in many tumors is a promising target for cancer therapy. However, due to toxicity from the ubiquitous expression of PI3K in many tissues, the development of PI3K inhibitors with high selectivity and low toxicity has become an urgent need for tumor treatment. Herein, based on the HipHop, we designed and synthesized a series of 6-(4,6-dimorpholino-1,3,5-triazin-2-yl)benzo[d]oxazol-2-amine derivatives as potent, selective, and long-acting PI3Kα inhibitors. Compound 27 was determined with potent PI3Kα inhibitory activity (IC₅₀ = 4.4 nM), which exhibited excellent selectivity for homologous PI3K enzymes and a 370 kinome panel. Meanwhile, 27 featured favorable stability (T_1/2 > 10 h) and high bioavailability (130%). Importantly, compound 27 exerted great antigastric cancer activity in vivo when combined with taxol. Collectively, these characteristics suggested 27 to be a promising PI3K agent for cancer treatment.

Mechanisms of Resistance to PI3K Inhibitors in Cancer: Adaptive Responses, Drug Tolerance and Cellular Plasticity

The phosphatidylinositol-3-kinase (PI3K) pathway plays a central role in the regulation of several signalling cascades which regulate biological processes such as cellular growth, survival, proliferation, motility and angiogenesis. The hyperactivation of this pathway is linked to tumour progression and is one of the most common events in human cancers. Additionally, aberrant activation of the PI3K pathway has been demonstrated to limit the effectiveness of a number of anti-tumour agents paving the way for the development and implementation of PI3K inhibitors in the clinic. However, the overall effectiveness of these compounds has been greatly limited by inadequate target engagement due to reactivation of the pathway by compensatory mechanisms. Herein, we review the common adaptive responses that lead to reactivation of the PI3K pathway, therapy resistance and potential strategies to overcome these mechanisms of resistance. Furthermore, we highlight the potential role in changes in cellular plasticity and PI3K inhibitor resistance.