HER2 + breast cancers evade anti-HER2 therapy via a switch in driver pathway

Inhibition of HER2 in HER2-amplified breast cancer has been remarkably successful clinically, as demonstrated by the efficacy of HER-kinase inhibitors and HER2-antibody treatments. Whilst resistance to HER2 inhibition is common in the metastatic setting, the specific programs downstream of HER2 driving resistance are not established. Through genomic profiling of 733 HER2-amplified breast cancers, we identify enrichment of somatic alterations that promote MEK/ERK signaling in metastatic tumors with shortened progression-free survival on anti-HER2 therapy. These mutations, including NF1 loss and ERBB2 activating mutations, are sufficient to mediate resistance to FDA-approved HER2 kinase inhibitors including tucatinib and neratinib. Moreover, resistant tumors lose AKT dependence while undergoing a dramatic sensitization to MEK/ERK inhibition. Mechanistically, this driver pathway switch is a result of MEK-dependent activation of CDK2 kinase. These results establish genetic activation of MAPK as a recurrent mechanism of anti-HER2 therapy resistance that may be effectively combated with MEK/ERK inhibitors.

Dicer-to-Argonaute switch controls biogenesis of oncogenic miRNA

miRNAs are post-transcriptional regulators of gene expression, controlling biological processes from development to pathogenesis. We asked whether the reshaped functional miRNA landscape in cancers is driven by altered transcription of its precursors, or altered biogenesis and maturation of miRNAs. Integrated analysis of genomic and transcriptomic data in 9,111 samples across 10 cancer types and healthy tissues revealed a recurrent genomic switch from DICER-dependent to non-canonical Argonaute-mediated, DICER-independent, miRNA biogenesis. Experimental validation in AGO2-amplified clinical samples and cancer cell lines confirmed that canonical miRNAs can undergo maturation in a DICER-independent manner, and that elevated Argonaute levels promote selective maturation of the oncogenic miR-106b/25 cluster as shown by the altered ratio of mature miRNA to immature pri-miRNA levels. The preferential maturation of these oncogenic miRNAs, whose processing bypasses DICER1, promotes cancer progression and predicts poor prognosis. This highlights the evolution of non-canonical AGO2-dependent oncomiR processing as a novel driver pathway in cancer.

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways

Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene–pathway and gene–miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

The coding and non-coding transcriptional landscape of subependymal giant cell astrocytomas

Tuberous sclerosis complex (TSC) is an autosomal dominantly inherited neurocutaneous disorder caused by inactivating mutations in TSC1 or TSC2, key regulators of the mechanistic target of rapamycin complex 1 (mTORC1) pathway. In the CNS, TSC is characterized by cortical tubers, subependymal nodules and subependymal giant cell astrocytomas (SEGAs). SEGAs may lead to impaired circulation of CSF resulting in hydrocephalus and raised intracranial pressure in patients with TSC. Currently, surgical resection and mTORC1 inhibitors are the recommended treatment options for patients with SEGA. In the present study, high-throughput RNA-sequencing (SEGAs n = 19, periventricular control n = 8) was used in combination with computational approaches to unravel the complexity of SEGA development. We identified 9400 mRNAs and 94 microRNAs differentially expressed in SEGAs compared to control tissue. The SEGA transcriptome profile was enriched for the mitogen-activated protein kinase (MAPK) pathway, a major regulator of cell proliferation and survival. Analysis at the protein level confirmed that extracellular signal-regulated kinase (ERK) is activated in SEGAs. Subsequently, the inhibition of ERK independently of mTORC1 blockade decreased efficiently the proliferation of primary patient-derived SEGA cultures. Furthermore, we found that LAMTOR1, LAMTOR2, LAMTOR3, LAMTOR4 and LAMTOR5 were overexpressed at both gene and protein levels in SEGA compared to control tissue. Taken together LAMTOR1–5 can form a complex, known as the ‘Ragulator’ complex, which is known to activate both mTORC1 and MAPK/ERK pathways. Overall, this study shows that the MAPK/ERK pathway could be used as a target for treatment independent of, or in combination with mTORC1 inhibitors for TSC patients. Moreover, our study provides initial evidence of a possible link between the constitutive activated mTORC1 pathway and a secondary driver pathway of tumour growth.

FGF19–FGFR4 Signaling in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common type of cancer, with an increasing mortality rate. Aberrant expression of fibroblast growth factor 19–fibroblast growth factor receptor 4 (FGF19–FGFR4) is reported to be an oncogenic-driver pathway for HCC patients. Thus, the FGF19–FGFR4 signaling pathway is a promising target for the treatment of HCC. Several pan-FGFR (1–4) and FGFR4-specific inhibitors are in different phases of clinical trials. In this review, we summarize the information, recent developments, binding modes, selectivity, and clinical trial phases of different available FGFR4/pan-FGF inhibitors. We also discuss future perspectives and highlight the points that should be addressed to improve the efficacy of these inhibitors.