Distribution and pathogenicity of nsSNPs in receptor tyrosine kinases (RTKs) in non-small cell lung cancer (NSCLC) patients (pts).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20618-e20618
Author(s):  
Ari M. Vanderwalde ◽  
Matthew K Stein ◽  
Lindsay Kaye Morris ◽  
Srishti Sareen ◽  
Saradasri Karri ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tyrosine Kinases ◽  
In Silico ◽  
Transmembrane Domain ◽  
Stage Iv ◽  
In Silico Analysis ◽  
Large Cell ◽  
Kinase Domain ◽  
Tyrosine Kinase Domain ◽  
Silico Analysis

e20618 Background: Non-synonymous SNPs (nsSNPs) in RTKs can alter kinase activity and are not exclusive to the tyrosine kinase domain (TKD). In NSCLC, EGFR lesions were previously identified using TKD-limited tests; however, next-generation sequencing (NGS) enables the entire protein sequence of many RTKs to be interrogated. Methods: We analyzed all nsSNPs in 28 RTKs in lung cancer pts who received tumor profiling with Caris NGS from 2013-2015. Mutations were classified by location including the TKD, extracellular domain (ECD), transmembrane domain (TM), juxtamembrane domain (JM), and carboxy-terminal (CT) regions. nsSNPs underwent in silico analysis using PolyPhen-2 (Harvard) to predict pathogenicity. Results: 167 pts (156 NSCLC, 11 SC) were identified with a median age 65 (range 26-85); 51% male; 65% white, 31% black; 77% ≥20 pack-years (py), 11% non-smokers; 52% samples tested were metastases. NSCLC pts were 63% adenocarcinoma, 22%, squamous, 8% large-cell; 81% stage IV, 14% III; 17 were EGFR+, 6 BRAF+, 3 HER2+, 3 ROS1 rearranged and 1 MET exon 14. A total 300 nsSNPs (286 NSCLC, 14 SC) were found in 28 RTKs, excluding EGFR. 123/156 NSCLC pts (79%) and 9/11 SC (82%) had ≥1 RTK lesion with median 2 (range 0-8); 143/300 (48%) nsSNPs were predicted-damaging (pnsSNP) by in silico and 89 pts (53%) had ≥1 pnsSNP (median 1; range 0-5). 28/28 RTKs had ≥3 mutations, with median 11 (range 3-23), and 26/28 contained ≥1 pnsSNP (median 5; range 0-14). RTKs in NSCLC with the most frequent nsSNPs were EPHA3 (14/23 variants were pnsSNP), EPHA5 (11/17), EPHB1 (10/11), RET (9/11), ERBB4 (8/12), ALK (7/16), NTRK3 (7/15), ROS1 (6/22) and FLT1 (6/15). 6/14 lesions in SC pts were pnsSNPs in ERBB3, ERBB4, FGFR1, FLT1, RET and ROS1. nsSNPs were found along RTKs: 57% were ECD (72/172 pnsSNP), 26% TKD (47/77), 10% CT (14/29), 6% JM (8/18) and 1% TM (2/4). 6/6 SC pnsSNPs were ECD. 67% BRAF+ and ROS1-rearranged, 59% EGFR+, 33% HER2+ and 0/1 MET exon 14 pts had ≥1 pnsSNP. Conclusions: Nearly 80% NSCLC and SC pts had ≥1 nsSNP in 28 RTKs, excluding EGFR, with 48% pnsSNPs by in silico analysis. As > 70% nsSNPs were extra-TKD lesions, further characterization is needed to identify kinase-effecting variants and their potential clinical significance.

Expanding the search for significant EGFR mutations in NSCLC outside of the tyrosine kinase domain (TKD) with next-generation sequencing (NGS).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20506-e20506
Author(s):  
Matthew K Stein ◽  
Lindsay Kaye Morris ◽  
Jennifer Sullivan ◽  
Moon Jung Fenton ◽  
Ari M. Vanderwalde ◽  
...  
Keyword(s):  
Tyrosine Kinase ◽  
In Silico ◽  
Kinase Inhibitors ◽  
Stage Iv ◽  
In Silico Analysis ◽  
Large Cell ◽  
Kinase Domain ◽  
Egfr Mutations ◽  
Tyrosine Kinase Domain ◽  
Carboxy Terminal

e20506 Background: While conventional organization of EGFR mutations in NSCLC includes classic lesions sensitive to tyrosine kinase inhibitors (TKI) and variants localized to TKD in exons 18-21, NGS raises the prospect of identifying clinically relevant variants in extra-TKD regulatory regions. Methods: Patients (pts) with lung cancer who received tumor profiling with NGS from 2013-2015 via Caris were identified. EGFR mutations were arranged based upon their known distribution relative to the TKD. In silico analysis was performed with PolyPhen-2 (Harvard) to predict nsSNPs’ pathogenicity. Results: 259 pts (248 NSCLC, 11 SC) had median age 65 years (26-85); 50% female; 64% white, 34% black; 73% with ≥20 pack-years (py), 12% non-smokers; 53% of samples were metastases. 65% NSCLC were adenocarcinoma (A), 21% squamous (S), 8% large-cell; 87% stage IV, 12% III. 44 EGFR variants were seen in 40 pts (15%; 39 NSCLC, 1 SC). While 32 pts had TKD lesions demonstrable through standard testing, 8 had extra-TKD mutations (8/44), of which 5 were extracellular domain (ECD), 1 juxtamembrane (JM) and 2 carboxy terminal (CT). Aside from pathogenic ECD mutation G598V, 5/7 extra-TKD nsSNPs were predicted-damaging (pnsSNP) with in silico (Table 1). 7/7 extra-TKD nsSNP+ pts smoked (6/7 ≥20 py) and all 6 NSCLC pts were stage IV; 50% A, 17% S; 83% male. The pt with JM R675Q had erlotinib, 150 mg daily, added following progression of stage IV NSCLC on carboplatin and paclitaxel and had a partial response for 4 months. No other pt received EGFR-directed therapy. Conclusions: 2% NSCLC cases in our cohort had EGFR pnsSNPs located outside of the TKD, representing >18% of all EGFR mutations. Extra-TKD variants should be characterized collaboratively to determine TKI sensitivity and additional therapeutic targets. [Table: see text]

Distribution and pathogenicity of nsSNPs in receptor tyrosine kinases (RTKs) in patients with colorectal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15012-e15012
Author(s):  
Matthew K Stein ◽  
Saradasri Karri ◽  
Lindsay Kaye Morris ◽  
Srishti Sareen ◽  
Kruti Patel ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Tyrosine Kinases ◽  
Transmembrane Domain ◽  
Hot Spot ◽  
In Silico Analysis ◽  
Kinase Domain ◽  
Tyrosine Kinase Domain ◽  
Nucleotide Polymorphisms ◽  
Significant Difference ◽  
Carboxy Terminal

e15012 Background: Non-synonymous single nucleotide polymorphisms (nsSNPs) occur along the entire sequence of RTKs and can promote oncogenic activity. As prior “hot-spot” testing was limited to the tyrosine kinase domain (TKD), next-generation sequencing (NGS) allows the discovery of novel extra-TKD variants. Methods: We analyzed all nsSNPs in 29 RTKs of colon cancer patients (pts) who received tumor profiling (2013-2015) with Caris NGS. Mutations were classified by location including the TKD, extracellular domain (ECD), transmembrane domain (TM), juxtamembrane domain (JM) and carboxy-terminal (CT) regions. nsSNPs underwent in silico analysis using PolyPhen-2 (Harvard) to predict if damaging (pnsSNP). Results: 110 pts were identified with a median age of 58 years (range 37-86); 55% male; 57% white, 41% black. 51 were KRAS+, 12 BRAF+, 5 NRAS+ and 5 were microsatellite unstable (MSI-H); 67 were left-sided, 31 right-sided, 10 transverse and 2 unknown. A total of 171 nsSNPs and 7 pathogenic mutations (Pmut) were detected: ERBB2 (ECD S310F, TKD V777L and TKD V842I), ERBB3 (ECD A232V and TKD Q809R), FGFR2 (ECD S252L) and RET (TKD L790F). 83/110 (76%) pts had ≥1 RTK mutation (median 1; range 0-9). 72/171 (42%) variants were pnsSNPs and found in 50 (45%) pts; 14% of pts had ≥2. All 29 RTKs had nsSNPs with median 6 (range 2-12); 24/29 RTKs had a Pmut or pnsSNP (median 2; range 0-8). RTKs with the most nsSNPs were EPHA5 (8/10 were pnsSNPs), PDGFA (7/8), ALK (6/8), ERBB4 (5/8), NTRK3 (5/6), cKIT (4/9), ROS1 (3/12), PDGFRB (3/6) and FGFR1 (3/6). nsSNPs were distributed across all RTK domains: 50% were ECD (30/86 pnsSNPs), 27% TKD (28/46), 13% CT (7/22), 5% JM (6/9) and 5% TM (1/8). No significant difference was seen between pnsSNP incidence and sidedness or KRAS/BRAF/NRAS status. In MSI-H pts, 13/22 variants were pnsSNPs (median 2; 1-5); 4/5 MSI-H were right-sided (Fisher’s exact p < 0.01). Conclusions: > 70% colon cancer pts had ≥1 mutation in 29 RTKs with > 70% outside the TKD, and > 40% pnsSNPs. MSI-H had a higher incidence of pnsSNPs; further study is warranted to determine their significance and actionability.

Localization of non-receptor tyrosine kinase (nRTK) variants in solid tumor patients using next-generation sequencing (NGS).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1536-1536
Author(s):  
Srishti Sareen ◽  
Matthew Stein ◽  
Lindsay Kaye Morris ◽  
Saradasri Karri ◽  
Kruti Patel ◽  
...  
Keyword(s):  
Tyrosine Kinase ◽  
In Silico ◽  
Predictive Biomarkers ◽  
In Silico Analysis ◽  
Kinase Domain ◽  
Tyrosine Kinase Domain ◽  
Tumor Patients ◽  
Next Generation Sequencing Ngs ◽  
Silico Analysis ◽  
Generation Sequencing

1536 Background: Non-synonymous SNPs (nsSNPs) in nRTKs may serve as oncologic targets and predictive biomarkers, with significant lesions described in various nRTK regions including the tyrosine kinase domain (TKD). NGS allows the entire coding sequence to be evaluated, facilitating the identification of novel lesions. Methods: We searched all nsSNPs in 14 nRTKs in the tumors of patients (pts) at our institution that received NGS with Caris from 2013-2015 with a diagnosis of advanced breast, colon or lung cancer. Substitutions were classified as either within or extra-TKD; in the case of JAK1-3, pseudokinase domain lesions were also identified. In order to predict the pathogenicity of nsSNPs, in silico analysis with PolyPhen-2 (Harvard) was completed. Results: 356 pts (79 breast, 110 colon and 165 lung (156 NSCLC, 11 small cell)) were identified with a median age of 61 years (range 26-86); 58% female; 62% white, 35% black. 245 variants were found, with 200 nsSNPs and 45 known pathologic mutations (Pmut); Pmut were PIK3CA (21 breast, 13 colon, 5 NSCLC) and AKT1 (6 breast). 169/356 (47%) pts had ≥1 nRTK lesion (0-8). 52/200 (26%) nsSNPs were predicted-damaging (pnsSNPs) with in silico analysis among 49 pts (6 breast, 13 colon and 30 NSCLC). pnsSNPs were found in 14/14 nRTKs with median 3 (1-10). The most frequently mutated nRTKs in breast were SRC (2/2 variants were pnsSNPs) and ABL2 (1/5); in colon ABL1 (5/10), JAK3 (3/27) and CDK12 (2/8); and in NSCLC JAK3 (6/20), BTK (5/8), ABL1 (3/12), JAK2 (3/11), CDK12 (3/9) and JAK1 (3/3). Of 180 nsSNPs with in silico results, 68% were extra-TKD (29/122 variants were pnsSNPs), 23% within the TKD (13/42) and 9% in pseudokinase domains of JAK1-3 (10/16). Notably, 8/10 pseudokinase domain pnsSNPs were in NSCLC pts (3 JAK1, 2 JAK2 and 3 JAK3). Conclusions: > 13% solid tumors held an nRTK nsSNP that was predicted-damaging by in silico analysis, with 69% of these mutations occurring outside of the TKD-proper. Further work is needed to determine how these pnsSNPs affect function and if they are clinically actionable.

scholarly journals Highlighting the Role of Cdk6 Associated MicroRNAs in Cancer Treatment Using In Silico Approaches

2020 ◽  
pp. 1-14
Author(s):  
Sidra Batool ◽  
Muhammad Sibte Hasan Mahmood ◽  
Tiyyaba Furqan ◽  
Sidra Batool
Keyword(s):  
Breast Cancer ◽  
Protein Kinase ◽  
In Silico ◽  
Drug Target ◽  
In Silico Analysis ◽  
Kinase Domain ◽  
Protein Kinase Domain ◽  
Over Expression ◽  
Silico Analysis

MicroRNAs (miRNAs) are small non-coding RNA’s that controls the regulation of a gene. Due to the over expression or under expression of miRNAs it leads to cause tumor or any other type of cancers such as, melanoma, lymphoma, cardiovascular issue, breast cancer etc. So, miRNAs can be used as a drug target for cancer therapy. This study aimed to check binding cavities of microRNA's involved in regulation of CDK6 protein. There are 23 different families of miRNAs that are involved in regulation of CDK6. Each family has one or more miRNAs. All these miRNAs are involved in the up regulation or downregulation of a gene, which lead to different type of cancers. All miRNAs of each family docked with mRNA CDK6 protein. After performing in silico analysis of binding interactions of mRNA with miRNAs the results were further refined by their comparison with information regarding their energies, interaction of the mRNA and miRNAs. The results show that all miRNAs lie in Protein Kinase domain, but the residues that lie is different within the families and across the families.

Identification of Tripeptides against Tyrosine Kinase Domain of EGFR for Lung Cancer Cell Inhibition by In silico and In vitro Studies

2021 ◽  
Author(s):  
Duangnapa Kiriwan ◽  
Supaphorn Seetaha ◽  
Nattanan Jiwacharoenchai ◽  
Lueacha Tabtimmai ◽  
Sérgio F. Sousa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tyrosine Kinase ◽  
Cancer Cell ◽  
In Silico ◽  
Kinase Domain ◽  
Tyrosine Kinase Domain ◽  
Lung Cancer Cell ◽  
Cancer Cell Inhibition ◽  
Cell Inhibition
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