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BRAF protein, human

BRAF protein is a serine/threonine-protein kinase that plays a key role in the MAPK/ERK signaling pathway, which is involved in cell division, differentiation, and survival.
It acts as an effector of the RAS small GTPase and is frequently mutated in various cancers, particularly melanoma.
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Most cited protocols related to «BRAF protein, human»

1
Comprehensive Genomic Profiling of Prostate Cancer
Flash-frozen needle biopsies and matched normal samples underwent nucleic acid extraction as previously described (5 (link)). Extracted DNA underwent whole-exome library construction and somatic mutation analysis as previously described. BAM files were aligned to the hg19 human genome build. Copy-number aberrations were quantified and reported for each gene as previously described (38 (link), 39 (link)). Amplifications and homozygous deletions for a set of 20 genes previously implicated in prostate cancer (SI Appendix, Table S3) underwent further confirmatory review of segmentation files. Annotation of known or likely oncogenic SNVs was performed using the OncoKB platform (16 (link)).
Transcriptome libraries were prepared as previously described (5 (link)), using polyA+ RNA isolation, or captured using Agilent SureSelect Human All Exon V4 reagents, or in some cases using both polyA and capture methods. Library quality assessment and sequencing were performed as previously described. Paired-end transcriptome-sequencing reads were aligned to the human reference genome (GRCh38) using STAR (40 (link)). Gene expression as fragments per kilobase of exon per million fragments mapped (FPKMs) was determined using featureCounts against protein-coding genes from the Gencode v26 reference. Fusions in ETS genes (ERG, ETV1, ETV4, ETV5, FLI1) and RAF1/BRAF were detected using CODAC (41 (link)) and assessed manually in all cases where RNA-sequencing data were available. In addition, the presence of AR splice variants was quantified as the number of reads across specific splice junctions in splice reads per million (SRPMs) and as the ratio of reads across a specific splice junction to the sum of AR promoter 1 and promoter 2 reads (a surrogate of total AR expression), separately for polyA and capture libraries.
NEPC and AR signaling scores were computed by the Pearson’s correlation coefficient between the log2-transformed FPKM values of each score’s gene list and a reference gene expression vector, as previously described (7 (link), 32 (link)). CCP and RB loss scores were computed by the average (i.e., mean) Z score-transformed expression levels across each score’s gene list, as previously described (42 (link), 43 (link)). A high correlation (R ≥ 0.95, P < 0.001, Pearson’s correlation test) was noted between scores derived from polyA versus capture RNA-sequencing libraries (SI Appendix, Fig. S8), allowing for joint analysis of samples sequenced with either library construction method.
All data from SNV, copy-number, and expression analysis as well as clinical characteristics and outcomes measures (Dataset S1) have been made available in cBioPortal (44 (link)) (www.cbioportal.org), and have been deposited in GitHub, https://github.com/cBioPortal/datahub/tree/master/public/prad_su2c_2019.
Abida W., Cyrta J., Heller G., Prandi D., Armenia J., Coleman I., Cieslik M., Benelli M., Robinson D., Van Allen E.M., Sboner A., Fedrizzi T., Mosquera J.M., Robinson B.D., De Sarkar N., Kunju L.P., Tomlins S., Wu Y.M., Nava Rodrigues D., Loda M., Gopalan A., Reuter V.E., Pritchard C.C., Mateo J., Bianchini D., Miranda S., Carreira S., Rescigno P., Filipenko J., Vinson J., Montgomery R.B., Beltran H., Heath E.I., Scher H.I., Kantoff P.W., Taplin M.E., Schultz N., deBono J.S., Demichelis F., Nelson P.S., Rubin M.A., Chinnaiyan A.M, & Sawyers C.L. (2019). Genomic correlates of clinical outcome in advanced prostate cancer. Proceedings of the National Academy of Sciences of the United States of America, 116(23), 11428-11436.
Publication 2019
BRAF protein, human Diploid Cell DNA Library Exome Exons Freezing Gene Deletion Gene Expression Gene Fusion Gene Products, Protein Genes Genetic Vectors Genome, Human Homo sapiens Homozygote isolation Joints Mutation Needle Biopsies Nucleic Acids Oncogenes Poly A Prostate Cancer Raf1 protein, human RNA, Polyadenylated Transcriptome Trees
2
CRISPR Transcriptional Regulation Screens

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Publication 2014
BRAF protein, human Cell Lines Cells Clone Cells Spleen Focus-Forming Virus
3
Oncogenic Mutation Analysis and Targeted Therapy Matching
To assess clinical actionability of mutations detected by MSK-IMPACT, we annotated sequence mutations, copy number alterations, and rearrangements according to OncoKB, a curated knowledge base of the oncogenic effects and treatment implications of somatic mutations (http://oncokb.org)40 . Mutations were classified in a tumor type-specific manner according to the level of evidence that the mutation is a predictive biomarker of drug response. Briefly, mutations were classified according to whether they are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence exists for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). Tumor samples were annotated according to the highest level of evidence for any mutation identified by MSK-IMPACT.
To determine the rate of enrollment to genomically matched clinical trials, we obtained a list of 850 clinical trials open at MSKCC on which any patient tested by MSK-IMPACT was ever enrolled up to September 2016. After reviewing the enrollment criteria and mechanism of action of each therapy, 197/850 clinical trials were deemed to have a target aberration. A patient was considered to be “matched” if he/she harbored at least one alteration considered to be a target for at least one clinical trial on which they were enrolled. Only patients whose tumors were sequenced during the first 18 months of the MSK-IMPACT sequencing initiative (prior to July 2015) were considered, given that utilization of molecular profiling results and changes to treatment regimens may not occur for many months (or longer) after testing. Of 5,009 patients tested by MSK-IMPACT prior to July 2015, 1,894 (38%) were enrolled on any clinical trial, 811 (16%) were enrolled on a clinical trial with a targeted agent, and 527 (11%) harbored genomic alterations matching the drug target. 72% of all matches occurred after the MSK-IMPACT reports were issued, with the remaining matches based on the results of prior molecular testing.
Clinical responses for patients receiving immunotherapy and targeted BRAF-directed therapy were assessed by detailed chart review. Response was defined as radiographic stable disease or tumor regression at or near 3 months from the initiation of therapy.
Zehir A., Benayed R., Shah R.H., Syed A., Middha S., Kim H.R., Srinivasan P., Gao J., Chakravarty D., Devlin S.M., Hellmann M.D., Barron D.A., Schram A.M., Hameed M., Dogan S., Ross D.S., Hechtman J.F., DeLair D.F., Yao J., Mandelker D.L., Cheng D.T., Chandramohan R., Mohanty A.S., Ptashkin R.N., Jayakumaran G., Prasad M., Syed M.H., Rema A.B., Liu Z.Y., Nafa K., Borsu L., Sadowska J., Casanova J., Bacares R., Kiecka I.J., Razumova A., Son J.B., Stewart L., Baldi T., Mullaney K.A., Al-Ahmadie H., Vakiani E., Abeshouse A.A., Penson A.V., Jonsson P., Camacho N., Chang M.T., Won H.H., Gross B.E., Kundra R., Heins Z.J., Chen H.W., Phillips S., Zhang H., Wang J., Ochoa A., Wills J., Eubank M., Thomas S.B., Gardos S.M., Reales D.N., Galle J., Durany R., Cambria R., Abida W., Cercek A., Feldman D.R., Gounder M.M., Hakimi A.A., Harding J.J., Iyer G., Janjigian Y.Y., Jordan E.J., Kelly C.M., Lowery M.A., Morris L.G., Omuro A.M., Raj N., Razavi P., Shoushtari A.N., Shukla N., Soumerai T.E., Varghese A.M., Yaeger R., Coleman J., Bochner B., Riely G.J., Saltz L.B., Scher H.I., Sabbatini P.J., Robson M.E., Klimstra D.S., Taylor B.S., Baselga J., Schultz N., Hyman D.M., Arcila M.E., Solit D.B., Ladanyi M, & Berger M.F. (2017). Mutational Landscape of Metastatic Cancer Revealed from Prospective Clinical Sequencing of 10,000 Patients. Nature medicine, 23(6), 703-713.
Publication 2017
Biological Markers BRAF protein, human Copy Number Polymorphism Diploid Cell Drug Delivery Systems Drug Kinetics Gene Rearrangement Genome Immunotherapy Mutation Neoplasms Oncogenes Patients Pharmaceutical Preparations Therapeutics Treatment Protocols X-Rays, Diagnostic
4
Oncogenic Mutation Analysis and Targeted Therapy Matching
To assess clinical actionability of mutations detected by MSK-IMPACT, we annotated sequence mutations, copy number alterations, and rearrangements according to OncoKB, a curated knowledge base of the oncogenic effects and treatment implications of somatic mutations (http://oncokb.org)40 . Mutations were classified in a tumor type-specific manner according to the level of evidence that the mutation is a predictive biomarker of drug response. Briefly, mutations were classified according to whether they are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence exists for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). Tumor samples were annotated according to the highest level of evidence for any mutation identified by MSK-IMPACT.
To determine the rate of enrollment to genomically matched clinical trials, we obtained a list of 850 clinical trials open at MSKCC on which any patient tested by MSK-IMPACT was ever enrolled up to September 2016. After reviewing the enrollment criteria and mechanism of action of each therapy, 197/850 clinical trials were deemed to have a target aberration. A patient was considered to be “matched” if he/she harbored at least one alteration considered to be a target for at least one clinical trial on which they were enrolled. Only patients whose tumors were sequenced during the first 18 months of the MSK-IMPACT sequencing initiative (prior to July 2015) were considered, given that utilization of molecular profiling results and changes to treatment regimens may not occur for many months (or longer) after testing. Of 5,009 patients tested by MSK-IMPACT prior to July 2015, 1,894 (38%) were enrolled on any clinical trial, 811 (16%) were enrolled on a clinical trial with a targeted agent, and 527 (11%) harbored genomic alterations matching the drug target. 72% of all matches occurred after the MSK-IMPACT reports were issued, with the remaining matches based on the results of prior molecular testing.
Clinical responses for patients receiving immunotherapy and targeted BRAF-directed therapy were assessed by detailed chart review. Response was defined as radiographic stable disease or tumor regression at or near 3 months from the initiation of therapy.
Zehir A., Benayed R., Shah R.H., Syed A., Middha S., Kim H.R., Srinivasan P., Gao J., Chakravarty D., Devlin S.M., Hellmann M.D., Barron D.A., Schram A.M., Hameed M., Dogan S., Ross D.S., Hechtman J.F., DeLair D.F., Yao J., Mandelker D.L., Cheng D.T., Chandramohan R., Mohanty A.S., Ptashkin R.N., Jayakumaran G., Prasad M., Syed M.H., Rema A.B., Liu Z.Y., Nafa K., Borsu L., Sadowska J., Casanova J., Bacares R., Kiecka I.J., Razumova A., Son J.B., Stewart L., Baldi T., Mullaney K.A., Al-Ahmadie H., Vakiani E., Abeshouse A.A., Penson A.V., Jonsson P., Camacho N., Chang M.T., Won H.H., Gross B.E., Kundra R., Heins Z.J., Chen H.W., Phillips S., Zhang H., Wang J., Ochoa A., Wills J., Eubank M., Thomas S.B., Gardos S.M., Reales D.N., Galle J., Durany R., Cambria R., Abida W., Cercek A., Feldman D.R., Gounder M.M., Hakimi A.A., Harding J.J., Iyer G., Janjigian Y.Y., Jordan E.J., Kelly C.M., Lowery M.A., Morris L.G., Omuro A.M., Raj N., Razavi P., Shoushtari A.N., Shukla N., Soumerai T.E., Varghese A.M., Yaeger R., Coleman J., Bochner B., Riely G.J., Saltz L.B., Scher H.I., Sabbatini P.J., Robson M.E., Klimstra D.S., Taylor B.S., Baselga J., Schultz N., Hyman D.M., Arcila M.E., Solit D.B., Ladanyi M, & Berger M.F. (2017). Mutational Landscape of Metastatic Cancer Revealed from Prospective Clinical Sequencing of 10,000 Patients. Nature medicine, 23(6), 703-713.
Publication 2017
Biological Markers BRAF protein, human Copy Number Polymorphism Diploid Cell Drug Delivery Systems Drug Kinetics Gene Rearrangement Genome Immunotherapy Mutation Neoplasms Oncogenes Patients Pharmaceutical Preparations Therapeutics Treatment Protocols X-Rays, Diagnostic
5
Benchmarking Mutation Effect Predictors
To standardize the procedure of compiling mutations that can be employed for the benchmarking of mutation effect predictors, mutations affecting six bona fide oncogenes (BRAF, KIT, PIK3CA, KRAS, EGFR, and ERRB2), whose mutations preferentially affect kinase domains, six recently described cancer genes (DICER1, ESR1, IDH1, IDH2, MYOD1, and SF3B1), whose mutations do not affect kinase domains, and three bona fide TSGs (TP53, BRCA1, and BRCA2) were retrieved from the TCGA Pan-Cancer dataset by Kandoth et al. [36 (link)] and from studies functionally testing mutations affecting these genes (Additional file 2). In addition, for TSGs, specific databases were employed; for TP53, the IARC database [29 (link),37 ], and for BRCA1 and BRCA2, the Universal Mutation Database (UMD) [28 (link),38 ,39 ]. This mining exercise resulted in the identification of 3,706 mutations, of which 3,591 were SNVs (Table 1, Additional file 2). Given that some mutation effect prediction algorithms (that is, PolyPhen-2, MutationTaster, CanDrA, and Condel) do not process dinucleotide or trinucleotide missense mutations, and to have the same number of mutations successfully analyzed by each predictor, we have only included SNVs for the purpose of creating a mutation dataset to benchmark mutation effect predictors. SNVs were also annotated based on their presence in the COSMIC dataset v68 [26 (link)].
Martelotto L.G., Ng C.K., De Filippo M.R., Zhang Y., Piscuoglio S., Lim R.S., Shen R., Norton L., Reis-Filho J.S, & Weigelt B. (2014). Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biology, 15(10), 484.
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Publication 2014
BRAF protein, human BRCA1 protein, human Cosmic composite resin DICER1 protein, human Dinucleoside Phosphates EGFR protein, human Gene, BRCA2 Gene, Cancer Genes IDH2, human K-ras Genes Malignant Neoplasms Missense Mutation Mutation MYOD1 protein, human Oncogenes Phosphotransferases PIK3CA protein, human TP53 protein, human

Most recents protocols related to «BRAF protein, human»

1
Inhibition of MAPK Activation by Natural Compounds
Not available on PMC !

Example 6

It is well known that BRAF inhibitor PLX4032 can induce paradoxical MAPK activation and cause abnormal cell proliferation in RAS mutation cells. The expression level of MAPK signaling pathway related proteins in PDV cells was examined by western blotting (FIG. 7). PLX4032 at 0.5 μM promoted p-MEK and p-ERK protein expression at 6-24 h treatment. MEK inhibitor, AZD6244 reversed the up-regulation of p-ERK induced by PLX4032, while KWM-EO, LM-EO at 75 μg/mL and L+C at 60 μg/mL significantly abolished the p-MEK and p-ERK protein expression in PLX4032-stimulated PDV cells. KWM-EO, LM-EO and L+C treatment also decreased the expression of MEK but not ERK.

US11872260B2. Mint essential oils inhibit HRASQ61L mutant keratinocyte activity and prevent skin carcinogenesis (2024-01-16). ACADEMIA SINICA [TW]. Inventors: Lie-Fen Shyur [TW], Chih-Ting Chang [TW], Yuhsin Chen [TW].
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Patent 2024
AZD 6244 BRAF protein, human Cell Proliferation Cells MAP Kinase Cascade Mutation OCA2 protein, human PLX4032 Proteins
2
MEK Inhibitor Sensitivity Across Cancer Lines
Not available on PMC !

Example 20

240 cell lines representative of multiple cancer indications with known alterations in the MAPK pathway, including KRAS, NRAS, HRAS, NF1, EGFR, BRAF and CRAF mutations, were seeded overnight in 386-well plates, then treated with a 9-point dose response of exemplary MEK inhibitors (starting dose of 100 nM and 3-fold dilution) for 5 days. Cell viability was determined using a Cell Titer Glo (CTG) assay. Percent inhibition was calculated for all compounds utilizing staurosporine (1000 nM) treatment as a measure of maximal inhibition. IC50 and area under the curve (AUC) values were determined by fitting a variable slope, four parameters curve to the compound concentration to percent inhibition relationship.

Compared to RAS/RAF wild-type cell lines, increased sensitivity to MEK inhibitors, such as I-2, was observed in cell lines with KRAS, NRAS, BRAF Class I and III mutations, as well as CRAF-alterations (both CRAF mutations and fusions). Cell lines with mutations in PIK3CA, PTEN, NF1, EGFR and HRAS showed similar sensitivity to MEK inhibition to RAS/RAF wild-type cell lines.

US11878958B2. MEK inhibitors and uses thereof (2024-01-23). Ikena Oncology, Inc. [US]. Inventors: Alfredo C. Castro [US], Michael J. Burke [US], Thomas A. Wynn [US], Sabine K. Ruppel [US], Sergio L. Santillana Soto [US], Eric Haines [US], Lan Xu [US], Oksana Zavidij [US].
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Patent 2024
Biological Assay BRAF protein, human Cell Lines Cells Cell Survival EGFR protein, human HRAS protein, human Hypersensitivity inhibitors K-ras Genes Malignant Neoplasms Mutation NRAS protein, human PIK3CA protein, human Psychological Inhibition PTEN protein, human Raf1 protein, human Staurosporine Technique, Dilution
3
Retrospective Analysis of Metastatic Colorectal Cancer
Clinical information of patients, including age, body mass index, TNM stage, tumor location, pathological differentiation, lymph node metastasis, nerve invasion, vascular invasion, RAS status, and BRAF status, was retrospectively collected from medical records. Follow-up was carried out via telephone or by returning to the hospital for examination. The last follow-up was on July 12, 2021. Any metastasis with an interval of more than 3 months between the diagnosis of primary tumor (PT) and ovarian metastasis was defined as metachronous; otherwise, it was considered synchronous metastasis. The time from patients receiving first-line antitumor treatment to death from any cause was overall survival (OS), whereas the time of tumor progression, death from any cause, or time to receiving second-line treatment was progression-free survival (PFS). The Ethics Committee of Guangxi Medical University Cancer Hospital approved our study (LW2021078).
Huang Z., Li B., Qin H, & Mo X. (2023). Invasion characteristics and clinical significance of tumor-associated macrophages in gastrointestinal Krukenberg tumors. Frontiers in Oncology, 13, 1006183.
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Publication 2023
Blood Vessel BRAF protein, human Diagnosis Disease Progression Ethics Committees, Clinical Index, Body Mass Lymph Node Metastasis Malignant Neoplasms Neoplasms Neoplasms by Site Nervousness Ovary Patients
4
Investigating Genome Size and TE Silencing Pathways
To test for an association between overall piRNA or KRAB-ZFP pathway activity and genome size, we first compiled male and female gonad RNA-Seq datasets for vertebrates of diverse genome sizes, including P. ornatum (ornate burrowing frog), Gallus gallus (chicken), D. rerio (zebrafish), Xenopus tropicalis (Western clawed frog), A. carolinensis (green anole), Mus musculus (mouse), Geotrypetes seraphini (Gaboon caecilian), Rhinatrema bivittatum (two-lined caecilian), and Caecilia tentaculata (bearded caecilian) spanning genomes sizes from 1.0—5.5 Gb, and P. waltl (the Iberian ribbed newt), A. mexicanum (the Mexican axolotl), C. orientalis (the fire-bellied newt), P. annectens, and P. aethiopicus (African and marbled lungfishes) spanning genome sizes from 20—∼130 Gb (Supplementary Files S8,S9). We performed de novo assemblies using the same pipeline as for R. sibiricus on all obtained datasets.
We identified transcripts of 21 genes receiving a direct annotation of piRNA processing in vertebrates in the Gene Ontology knowledgebase that were present in the majority of our target species: ASZ1, BTBD18 (BTBDI), DDX4, EXD1, FKBP6, GPAT2, HENMT1 (HENMT), MAEL, MOV10l1 (M10L1), PIWIL1, PIWIL2, PIWIL4, PLD6, TDRD1, TDRD5, TDRD6, TDRD7, TDRD9, TDRD12 (TDR12), TDRD15 (TDR15), and TDRKH. In addition, we identified transcripts of 14 genes encoding proteins that create a transcriptionally repressive chromatin environment in response to recruitment by PIWI proteins or KRAB-ZFP proteins, 12 of which received a direct annotation of NuRD complex in the Gene Ontology knowledgebase and 2 of which were taken from the literature: CBX5, CHD3, CHD4, CSNK2A1 (CSK21), DNMT1, GATAD2A (P66A), MBD3, MTA1, MTA2, RBBP4, RBBP7, SALL1, SETDB1 (SETB1), and ZBTB7A (ZBT7A) (Ecco et al., 2017 (link); Wang et al., 2023 (link)). Finally, we identified TRIM28, which bridges this repressive complex to TE-bound KRAB-ZFP proteins in tetrapods, lungfishes, and coelacanths (Ecco et al., 2017 (link)). For comparison, we identified transcripts of 14 protein-coding genes receiving a direct annotation of miRNA processing in vertebrates in the Gene Ontology knowledgebase, which we did not predict to differ in expression based on genome size: ADAR (DSRAD), AGO1, AGO2, AGO3, AGO4, DICER1, NUP155 (NU155), PUM1, PUM2, SNIP1, SPOUT1 (CI114), TARBP2 (TRBP2), TRIM71 (LIN41), and ZC3H7B. Expression levels for each transcript in each individual were measured with Salmon (Patro et al., 2017 (link)) (Supplementary File S10).
As a proxy for overall piRNA silencing activity, for each individual, we calculated the ratio of total piRNA pathway expression (summed TPM of 21 genes) to total miRNA pathway expression (summed TPM of 14 genes). As a proxy for transcriptional repression driven by both the piRNA pathway and KRAB-ZFP binding activity, we calculated the ratio of total transcriptional repression machinery expression (summed TPM of 14 genes) to total miRNA pathway expression. Finally, we calculated the ratio of TRIM28 expression to total miRNA pathway expression for each individual. We also calculated these ratios with a more conservative dataset allowing for no missing genes; this yielded 15 piRNA pathway genes, 9 KRAB-ZFP genes, and 13 miRNA genes. We plotted these ratios to reveal any relationship between TE silencing pathway expression and genome size.
Wang J., Yuan L., Tang J., Liu J., Sun C., Itgen M.W., Chen G., Sessions S.K., Zhang G, & Mueller R.L. (2023). Transposable element and host silencing activity in gigantic genomes. Frontiers in Cell and Developmental Biology, 11, 1124374.
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Publication 2023
Ambystoma mexicanum BRAF protein, human CHD4 protein, human Chickens Chromatin CSNK2A1 protein, human DICER1 protein, human DNMT1 protein, human EIF2C2 protein, human Gene Products, Protein Genes Genome Males methyl-CpG binding domain protein 3, human Mi-2 Nucleosome Remodeling and Deacetylase Complex Mice, House MicroRNAs Mta1 protein, human Mus Negroid Races Newts Ovary Piwi-Interacting RNA Proteins PUM2 protein, human Rana RBBP7 protein, human Repression, Psychology RNA-Seq Salmon SETDB1 protein, human Transcription, Genetic TRIM28 protein, human Vertebrates Xenopus laevis ZBTB7A protein, human ZC3H7B protein, human Zebrafish
5
Melanoma Risk Factors Across Lifespans
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all individual participants included in this study in accordance with The Code of Ethics of the World Medical Association. This review conformed to all recommendations of the Strengthening the Reporting of Observational Studies in Epidemiology Initiative and was approved by Yale University’s Institutional Human Investigation Committee (IRB/HIC) [14 (link)]. Yale University’s Joint Data Analytics Team was utilized to accurately select patients from the electronic medical records per the defined inclusion criteria. A retrospective chart review was performed of all patients who presented to a single high-volume institution with the diagnosis of malignant melanoma from 1984 to 2019. Patients presenting with acral lentiginous, desmoplastic, and mucosal melanomas were excluded due to their innately differential presentation by age.
Patients were classified into three groups according to age at the time of presentation: older (71-100 years), middle (41-70), and younger (20-40). Age greater than 70 years has previously been used as a threshold above which immune function and response to disease decrease. Thus, patients were grouped in discreet age cohorts as the efficacy of immune surveillance likely evolves over an adult's lifetime in a nonlinear manner [2 (link),3 (link)]. When clinically indicated, tumors were sent for genetic analysis utilizing DNA microarray, including NRAS and BRAF gene assessment. Tumor characteristics including thickness, presence of ulceration, and nodal status were compared between groups. Tumor thickness was categorized by the widely-used Breslow scale, in which T1 lesions are 1 mm thick or less, T2 between 1.1 mm and 2 mm, T3 between 2.1 mm and 4 mm, and T4 more than 4 mm. Melanoma-specific survival and recurrence-free survival were assessed while accounting for differential follow-up and death from other causes using Kaplan-Meier analysis with log-rank testing. Rates of NRAS and BRAF mutations between older and younger groups were calculated. Chi-squared tests were used to assess the statistical significance of the differences in rates between groups. Bonferroni multiple-comparison correction was utilized where appropriate.
Sasson D.C., Smetona J.T., Parsaei Y., Papageorge M., Ariyan S., Olino K, & Clune J. (2023). Malignant Melanoma in Older Adults: Different Patient or Different Disease?. Cureus, 15(2), e34742.
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Publication 2023
BRAF protein, human Childbirth Diagnosis DNA Chips Fibrosis Genes Homo sapiens Immune System Processes Immunologic Surveillance Joints Melanoma Microarray Analysis Mucous Membrane Mutation Neoplasms NRAS protein, human Patients Recurrence Ulcer Youth

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Plx4032 by Selleck Chemicals
Sourced in United States, Germany
PLX4032 is a laboratory compound used for research purposes. It functions as a selective inhibitor for the BRAF kinase enzyme. This compound is intended for use in scientific research and analysis, and its specific applications should be determined by the user.
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Trametinib by Selleck Chemicals
Sourced in United States, Germany, United Kingdom, France, China, Italy
Trametinib is a selective inhibitor of mitogen-activated protein kinase kinase (MEK) enzymes 1 and 2. It is a white to almost white crystalline powder that is used in various biomedical research applications.

More about "BRAF protein, human"

BRAF, also known as B-Raf proto-oncogene serine/threonine-protein kinase, is a critical component of the mitogen-activated protein kinase (MAPK) signaling pathway.
This pathway plays a pivotal role in cell division, differentiation, and survival, making it a crucial target in cancer research and treatment.
The BRAF protein acts as an effector of the RAS small GTPase, which is frequently mutated in various cancers, particularly melanoma.
Mutations in the BRAF gene can lead to constitutive activation of the MAPK/ERK pathway, driving uncontrolled cell growth and proliferation.
Researchers studying BRAF can utilize tools like the QIAamp DNA FFPE Tissue Kit and QIAamp DNA Mini Kit to extract high-quality DNA from formalin-fixed, paraffin-embedded (FFPE) samples or cultured cells, respectively.
These kits ensure the preservation of genetic material for downstream analyses, such as Sanger sequencing using the BigDye Terminator v3.1 Cycle Sequencing Kit.
Cell culture techniques, including the use of fetal bovine serum (FBS) and Dulbecco's Modified Eagle Medium (DMEM), are often employed to maintain and propagate cell lines harboring BRAF mutations.
Commonly used BRAF inhibitors, such as Vemurafenib and PLX4032, can be used to study the effects of BRAF inhibition on cellular processes and signaling pathways.
Additionally, the PyroMark Q24 platform can be utilized for sensitive and accurate detection of BRAF mutations, enabling researchers to identify and characterize the genetic alterations in their samples.
The incorporation of Penicillin/streptomycin in cell culture media helps to prevent bacterial contamination, ensuring the integrity of experimental results.
Trametinib, a MEK inhibitor, is often used in combination with BRAF inhibitors to enhance the therapeutic efficacy and overcome resistance mechanisms in BRAF-mutant cancers.
By understanding the intricate dynamics of the BRAF protein and the associated signaling pathways, researchers can develop more effective therapies and improve patient outcomes.