A miRNA Panel Predicts Sensitivity of FGFR Inhibitor in Lung Cancer Cell Lines

CRISPR/Cas12a-coupled multiplexed strand displacement amplification for miRNA155 one-tube detection: via a dual-cavity PCR tube

A CRISPR/Cas12a-coupled multiplexed strand displacement amplification (CMSDA) for the detection of miR155 has been developed. Non-specific amplification was avoided by designing a single-stranded DNA template with a hairpin structure. The detection target miR155 was used as a primer to initiate a multiple-strand displacement reaction to produce abundant ssDNA. ssDNA was recognized by the Cas12a/CrRNA binary complex, activating the trans-cleaving activity of Cas12a. The multiple-strand displacement reaction is more efficiently detected compared with a single-strand displacement reaction. The detection range is from 250 pM to 1 nM, and the limit of the detection is 6.5 pM. The proposed method showed a good applicability in complex serum environments, indicating that the method has a broad prospect for disease detection and clinical application. In addition, we designed a dual-cavity PCR tube, which realized one-tube detection of miRNA155 and avoided open-cap contamination.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

Targeting the Fibroblast Growth Factor Receptor (FGFR) Family in Lung Cancer

Lung cancer is the most common cause of cancer-related deaths globally. Genetic alterations, such as amplifications, mutations and translocations in the fibroblast growth factor receptor (FGFR) family have been found in non-small cell lung cancer (NSCLC) where they have a role in cancer initiation and progression. FGFR aberrations have also been identified as key compensatory bypass mechanisms of resistance to targeted therapy against mutant epidermal growth factor receptor (EGFR) and mutant Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) in lung cancer. Targeting FGFR is, therefore, of clinical relevance for this cancer type, and several selective and nonselective FGFR inhibitors have been developed in recent years. Despite promising preclinical data, clinical trials have largely shown low efficacy of these agents in lung cancer patients with FGFR alterations. Preclinical studies have highlighted the emergence of multiple intrinsic and acquired resistance mechanisms to FGFR tyrosine kinase inhibitors, which include on-target FGFR gatekeeper mutations and activation of bypass signalling pathways and alternative receptor tyrosine kinases. Here, we review the landscape of FGFR aberrations in lung cancer and the array of targeted therapies under clinical evaluation. We also discuss the current understanding of the mechanisms of resistance to FGFR-targeting compounds and therapeutic strategies to circumvent resistance. Finally, we highlight our perspectives on the development of new biomarkers for stratification and prediction of FGFR inhibitor response to enable personalisation of treatment in patients with lung cancer.

Metastatic adult pancreatoblastoma: Multimodal treatment and molecular characterization of a very rare disease

BACKGROUND

Pancreatoblastoma is a rare malignancy that occurs predominantly in children. Less than 50 adult cases, including 17 patients with metastatic disease, have been published to date. Recent outcome data from children with advanced-stage disease suggest an intensive multimodal treatment approach; however, little is known about the most beneficial therapy in adults. Molecular characterization of pancreatoblastoma is limited to a small number of pediatric cases and revealed few recurrent genetic events without immediate clinical relevance.

METHODS

Patients were treated between 2013 and 2018 at a high-volume German university cancer center. Molecular analyses included whole genome, exome, transcriptome, and fusion gene panel sequencing. Molecularly guided treatment recommendations were discussed within a dedicated molecular tumor board (MTB) embedded in a precision oncology program (NCT MASTER).

RESULTS

We identified four adult patients with metastatic pancreatoblastoma. In three patients, local approaches were combined with systemic treatment. Oxaliplatin-containing protocols showed an acceptable tumor control as well as an adequate toxicity profile. Overall survival was 15, 17, 18 and 24 months, respectively. Three tumors harbored genetic alterations involving the FGFR pathway that included an oncogenic FGFR2 fusion.

CONCLUSION

Oxaliplatin-containing chemotherapy seems to be a reasonable approach in adult patients with advanced pancreatoblastoma, whereas the benefit of intensified treatment including local ablative techniques or surgical resection remains unclear. Our finding of FGFR alterations in three of four cases indicates a potential role of FGFR signaling in adult pancreatoblastoma whose clinical significance warrants further study.

Deciphering role of FGFR signalling pathway in pancreatic cancer

Recently, fibroblast growth factors are identified to play a vital role in the development and progression of human pancreatic cancer. FGF pathway is critical involved in numerous cellular processes through regulation of its downstream targets, including proliferation, apoptosis, migration, invasion, angiogenesis and metastasis. In this review article, we describe recent advances of FGFR signalling pathway in pancreatic carcinogenesis and progression. Moreover, we highlight the available chemical inhibitors of FGFR pathway for potential treatment of pancreatic cancer. Furthermore, we discuss whether targeting FGFR pathway is a novel therapeutic strategy for pancreatic cancer clinical management.