Abstract 300: An improved strategy for delivering the mesoionic compound MIH 2.4Bl utilizing reconstituted high density nanoparticles (rHDL) in treating breast cancer

Author(s):  
Dipti Debnath ◽  
R. Max Petty ◽  
Nirupama Sabnis ◽  
Jinmin Zhang ◽  
Andras G. Lacko ◽  
...  
2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 8536-8536
Author(s):  
V. Guillem ◽  
M. Mata ◽  
A. Lluch ◽  
M. Gonzalez ◽  
J. Esteve ◽  
...  

8536 Background: t-AML is a syndrome occurring after exposure to chemo or radiotherapy. Since for similar treatments only some patients ends developing a secondary leukemia, it has been proposed a genetic predisposition associated to this syndrome. Methods: To analyse single nucleotide polymorphisms (SNPs) on genes that could be involved on risk of developing t-AML by means of RFLP and SNP genome screening using high density microarrays .Two groups of individuals were genotyped: Group A, composed by patients that develop t-AML after chemotherapy for breast cancer (BC) and Group B (control), formed by chemotherapy treated BC patients that after a period of more than 10 years have not developed t-AML. We have studied 12 polymorphisms on genes from drug detoxification pathways (NOQ1, GSTP1), DNA repair (XPC[3 ], XRCC1[2 ], NBS1, ERCC5 and XRCC3) and DNA synthesis (MTHFR[2 ]), in which the nucleotide change implies a change in the protein sequence (nA=16, nB=18) . Alternatively, for each patient, more than 10.000 SNPs were genotyped by means of of high density microarrays (Affymetrix) (nA=12, nB=18). The alele frequencies for each SNP between two groups were compared. Results: In RFLP study, we observe two SNPs on MTHFR gene displaying remarkably different allele frequencies between BC patients (Table). In microarray study, we found 12 SNPs with differences of allele frequency higher that 45% between A and B groups, located 6 on chromosome 8. Conclusions: The results suggest that the MHFTR gene is a candidate for being studied by its possible relation with the genetic predisposition to develop t-AML after BC treatment although its functional implication with the disease must still be elucidated. Moreover, data from SNP arrays suggest that the genome regions marked by those 12 SNPs, specially those on chromosome 8, are candidate for being studied by its possible relation with the genetic predisposition to develop t-AML after BC treatment. Financed by FIS G03/008. [Table: see text] No significant financial relationships to disclose.


The Breast ◽  
2015 ◽  
Vol 24 ◽  
pp. S46
Author(s):  
V.G. Flote ◽  
R. Vettukattil ◽  
T. Egeland ◽  
A. Mctiernan ◽  
H. Frydenberg ◽  
...  

2005 ◽  
Vol 7 (S2) ◽  
Author(s):  
A Salas ◽  
A Vega ◽  
M Torres ◽  
I Quintela ◽  
C Phillips ◽  
...  

2014 ◽  
Vol 13s4 ◽  
pp. CIN.S15203
Author(s):  
Ming Li ◽  
Yalu Wen ◽  
Wenjiang Fu

Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.


Epigenetics ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Kristin E Williams ◽  
Douglas L Anderton ◽  
Maxwell P Lee ◽  
Brian T Pentecost ◽  
Kathleen F Arcaro

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