pf-00299804 and Skin-Neoplasms

pf-00299804 has been researched along with Skin-Neoplasms* in 2 studies

Trials

1 trial(s) available for pf-00299804 and Skin-Neoplasms

ArticleYear
Efficacy and safety of single-agent pan-human epidermal growth factor receptor (HER) inhibitor dacomitinib in locally advanced unresectable or metastatic skin squamous cell cancer.
    European journal of cancer (Oxford, England : 1990), 2018, Volume: 97

    In recurrent or metastatic (R/M) skin squamous cell cancer (sSCC) not amenable to radiotherapy (RT) or surgery, chemotherapy (CT) has a palliative intent and limited clinical responses. The role of oral pan-HER inhibitor dacomitinib in this setting was investigated within a clinical trial.. Patients with diagnosis of R/M sSCC were treated. Dacomitinib was started at a dose of 30 mg daily (QD) for 15 d, followed by 45 mg QD. Primary end-point was response rate (RR). Tumour samples were analysed through next-generation sequencing using a custom panel targeting 36 genes associated with sSCC.. Forty-two patients (33 men; median age 77 years) were treated. Most (86%) received previous treatments consisting in surgery (86%), RT (50%) and CT (14%). RR was 28% (2% complete response; 26% partial response), disease control rate was 86%. Median progression-free survival and overall survival were 6 and 11 months, respectively. Most patients (93%) experienced at least one adverse event (AE): diarrhoea, skin rash (71% each), fatigue (36%) and mucositis (31%); AEs grade 3-4 occurred in 36% of pts. In 16% of cases, treatment was discontinued because of drug-related toxicity. TP53, NOTCH1/2, KMT2C/D, FAT1 and HER4 were the most frequently mutated genes. BRAF, NRAS and HRAS mutations were more frequent in non-responders, and KMT2C and CASP8 mutations were restricted to this subgroup.. In sSCC, dacomitinib showed activity similar to what was observed with anti-epidermal growth factor receptor agents, and durable clinical benefit was observed. Safety profile was comparable to previous experiences in other cancers. Molecular pt selection could improve therapeutic ratio.

    Topics: Aged; Aged, 80 and over; Biomarkers, Tumor; Carcinoma, Squamous Cell; Female; Follow-Up Studies; Humans; Lymphatic Metastasis; Male; Middle Aged; Prognosis; Quinazolinones; Skin Neoplasms; Survival Rate

2018

Other Studies

1 other study(ies) available for pf-00299804 and Skin-Neoplasms

ArticleYear
Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.
    BMC cancer, 2019, Oct-31, Volume: 19, Issue:1

    Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.. Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.. Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.. Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

    Topics: Cell Line, Tumor; Cell Proliferation; Drug Resistance, Neoplasm; Drug Therapy, Combination; ErbB Receptors; Humans; Inhibitory Concentration 50; Machine Learning; Melanoma; Models, Biological; Mutation; Protein Kinase Inhibitors; Proteomics; Proto-Oncogene Proteins B-raf; Quinazolinones; Skin Neoplasms; Vemurafenib

2019