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Sub-Clonal Analysis of Tumor Grade and Treatment-Resistant Pancreatic Neuroendocrine Tumors (pNET) at Multiple Disease Time Points.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sub-Clonal Analysis of Tumor Grade and Treatment-Resistant Pancreatic Neuroendocrine Tumors (pNET) at Multiple Disease Time Points./
作者:	Bahadur, Nadia S.P.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	29 p.
附註:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997554
ISBN:	9798617066175

Sub-Clonal Analysis of Tumor Grade and Treatment-Resistant Pancreatic Neuroendocrine Tumors (pNET) at Multiple Disease Time Points.
Bahadur, Nadia S.P.

Sub-Clonal Analysis of Tumor Grade and Treatment-Resistant Pancreatic Neuroendocrine Tumors (pNET) at Multiple Disease Time Points. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 29 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--Icahn School of Medicine at Mount Sinai, 2020.

This item must not be sold to any third party vendors.

Background/Introduction: Pancreatic neuroendocrine tumors (NETs) are considered a rare group of cancers that commonly develop in the gastrointestinal tract or the pancreas but, arise anywhere throughout the body. The progression of pancreatic neuroendocrine tumors often occur in liver metastases that often become unresectable in patients. There are several treatments options for unresectable pNETs. Standard first-line treatment is with somatostatin analog (SSA), which is the secretion of the hormone somatostatin that cause antiproliferative effects on tumor growth. If SSA therapy does not successfully inhibit metastatic tumors and the neuroendocrine tumors evolve to sub clonally more aggressive forms other treatments are conducted are trans- arterial embolization that is performing ischemia on the tumors to cause cell death, trans- arterial radioembolization (TARE) and systemic chemotherapy performed with cytotoxic alkylation agents (AA) later elaborated in this article. However, with these several treatments, pNETs can have an initial resistance or develop resistance to the many treatment types through sub-clonal evolution overtime to combat the treatment. There are no guidelines that iterate genetic sequencing of treatment-resistant pancreatic neuroendocrine tumors (pNET).The tumor grade level which is the histological level of abnormality due to mutations of a cell correlates with the survival rate of pNET patients. The lower the amount of mutational abnormalities the higher the patient's survival rate. The WHO organization utilizes measuring the Ki67 level of protein in order to determine the level of tumor grade (Farrell 2014). Tumor grade, typically determined by immunohistochemical proliferation stain (Ki67), is the most important prognostic factor for pNETs. pNETs are categorized as grade 1 (Ki67<2%), grade 2 (2%<Ki6720%). It would be fundamental to compare tumor grade of each sample with a sub-clonal tumor phylogenetic ancestry tree to further understand their relation. The progression of tumors can be either linear, originating from low to high tumor grade, or multi-clonal by branching into a low or high tumor grade (figure 3) . It would be significant to evaluate the sub-clonal evolution and their population structures of pNET to understand the mutational occurrences of treatment-resistant tumors. With the understanding of mutations that generate due to resistant treatment we can develop further therapies that combat these mutational changes and therefore, possibly change the survival rate of patients with pNET.Aim 1: Using a computational tool called treeomics to create a phylogenetic tree of whole exome sequencing (WES) of multi-region and various timepoint samples of pNETs to determine either a linear or multi-clonal evolution of pNET tumors.Aim 2: To visualize the change over time of tumor grade by looking at Ki67 protein levels at various time points after diagnosis in order to determine linear or multi-clonal divergence of tumor grade.Aim 3: Using a bayesian statistical model called pyclone to infer the clonal population structures of variant cellular allele frequencies of various timepoints of tumor patient samples.Methods: Patients with pNET have previously undergone standard-of-care core needle biopsies or resection of tumor tissue. Patients were also consented under 06-107 bio-banking protocol which stipulates that tissue can be utilized for other research analyses. The resected or core needle biopsy tissue was processed, micro dissected, DNA extracted and then the whole exome sequence (WES) was generated by the Center of Molecular Oncology laboratory (CMO) at Memorial Sloan Kettering Cancer Center under IRB protocol 06-107 bio-baking study. CMO performed WES on tumor and matched normal blood specimens as a control and each patient had a target coverage of 150X on an Illumina HiSeq system. The level of Ki67 IHC was extracted from pathology reports in order to determine the level of tumor grade of pNET at various timepoints. Once the WES of the variant allele frequency was obtained, it was inputted into a statistical modeling python software called pyclone that is executed through terminal command line. Pyclone assess cellular frequency patterns using bayesian clustering methods. Furthermore, phylogeny inference software called Treeomics will be used to infer the sub-clonal phylogeny of tumor metastases.Results: We retrospectively identified patients in our institution that have undergone multiple biopsy and/or surgical resections of pNETs over multiple time points in their treatments. We observed fluctuating tumor grades over time of the tumor sub-clones (Figure 2). Throughout time in figure 2 patient 2, 6 and 9 had a slow progression sub-clonal evolution from low to high grade. Whereas patients 4, 5 and 3 demonstrated markedly rapid progression from grade 1 to within one year (figure 2). A third subset of patients interestingly demonstrated a decrease from high to low tumor grade over time at later metastasis time points in figure 2.To better understand the evolutionary relationship WES was performed on a single patient that progressed with tumor metastasis through several treatments including SSA, TAE, and AA over the course of a 10-year period. In Figure 1 metastasis sample 8 is a grade 3 tumor and metastasis sample 9 was a grade 9 tumor level demonstrating an evolution from high to low tumor grade. After metastasis 8 this patient undergone treatment and therefore, this high to low change in tumor grade may be due to the success of treatment on pNETs. Also, in figure 1 the first time point tissue samples of metastasis 1,3 and 4 (sample 2 WES was not utilized) showed evolutionary similarity and all were grade 1. In prior literature the mutation of the DAXX gene is associated with pNET progression and also seen in figure 1 were two different DAXX mutations that arose in metastasis 9 and in the primary tumor site.Sub-clonal population structures were created using cellular variant frequencies using pyclone for 1 patient with 10 sample timepoints and 9 patients that had up to 3 patient sample timepoints. Patient 1 with various timepoint samples had lower cellular frequencies at metastasis 6 but, became larger at metastasis 8 and 9. The cellular frequencies demonstrated by the 9 patients demonstrated no large population difference in sub-clonal variant frequency per patient. Conclusion: The differences in evolution can be seen in figure 1 as tumors that have a tumor grade of 1 are the primary beginning metastasis sample that later evolved to grade 2 and 3 at later timepoints. It would be essential to verify this fluctuating evolution of tumor grade in relation of patient treatment to understand specific mutations with respect to treatment. The patient seen in figure 1 subsequently progressed and was treated with SSA, TAE, and AA over the course of 9 years. Patient was on started on sunitinib chemotherapy after metastasis 8 was biopsied. This metastasis disappeared on follow-up. The sunitinib treatment may account for the high to low tumor grade transition found for metastasis 8 to metastasis 9 as it had a positive effect on treating the cancer. Sub-clonal frequencies created with pyclone demonstrate slightly fluctuating mutational cellular frequency changes per sample timepoint.

ISBN: 9798617066175Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Pancreatic neuroendocrine tumors

Sub-Clonal Analysis of Tumor Grade and Treatment-Resistant Pancreatic Neuroendocrine Tumors (pNET) at Multiple Disease Time Points.
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520 $a Background/Introduction: Pancreatic neuroendocrine tumors (NETs) are considered a rare group of cancers that commonly develop in the gastrointestinal tract or the pancreas but, arise anywhere throughout the body. The progression of pancreatic neuroendocrine tumors often occur in liver metastases that often become unresectable in patients. There are several treatments options for unresectable pNETs. Standard first-line treatment is with somatostatin analog (SSA), which is the secretion of the hormone somatostatin that cause antiproliferative effects on tumor growth. If SSA therapy does not successfully inhibit metastatic tumors and the neuroendocrine tumors evolve to sub clonally more aggressive forms other treatments are conducted are trans- arterial embolization that is performing ischemia on the tumors to cause cell death, trans- arterial radioembolization (TARE) and systemic chemotherapy performed with cytotoxic alkylation agents (AA) later elaborated in this article. However, with these several treatments, pNETs can have an initial resistance or develop resistance to the many treatment types through sub-clonal evolution overtime to combat the treatment. There are no guidelines that iterate genetic sequencing of treatment-resistant pancreatic neuroendocrine tumors (pNET).The tumor grade level which is the histological level of abnormality due to mutations of a cell correlates with the survival rate of pNET patients. The lower the amount of mutational abnormalities the higher the patient's survival rate. The WHO organization utilizes measuring the Ki67 level of protein in order to determine the level of tumor grade (Farrell 2014). Tumor grade, typically determined by immunohistochemical proliferation stain (Ki67), is the most important prognostic factor for pNETs. pNETs are categorized as grade 1 (Ki67<2%), grade 2 (2%<Ki6720%). It would be fundamental to compare tumor grade of each sample with a sub-clonal tumor phylogenetic ancestry tree to further understand their relation. The progression of tumors can be either linear, originating from low to high tumor grade, or multi-clonal by branching into a low or high tumor grade (figure 3) . It would be significant to evaluate the sub-clonal evolution and their population structures of pNET to understand the mutational occurrences of treatment-resistant tumors. With the understanding of mutations that generate due to resistant treatment we can develop further therapies that combat these mutational changes and therefore, possibly change the survival rate of patients with pNET.Aim 1: Using a computational tool called treeomics to create a phylogenetic tree of whole exome sequencing (WES) of multi-region and various timepoint samples of pNETs to determine either a linear or multi-clonal evolution of pNET tumors.Aim 2: To visualize the change over time of tumor grade by looking at Ki67 protein levels at various time points after diagnosis in order to determine linear or multi-clonal divergence of tumor grade.Aim 3: Using a bayesian statistical model called pyclone to infer the clonal population structures of variant cellular allele frequencies of various timepoints of tumor patient samples.Methods: Patients with pNET have previously undergone standard-of-care core needle biopsies or resection of tumor tissue. Patients were also consented under 06-107 bio-banking protocol which stipulates that tissue can be utilized for other research analyses. The resected or core needle biopsy tissue was processed, micro dissected, DNA extracted and then the whole exome sequence (WES) was generated by the Center of Molecular Oncology laboratory (CMO) at Memorial Sloan Kettering Cancer Center under IRB protocol 06-107 bio-baking study. CMO performed WES on tumor and matched normal blood specimens as a control and each patient had a target coverage of 150X on an Illumina HiSeq system. The level of Ki67 IHC was extracted from pathology reports in order to determine the level of tumor grade of pNET at various timepoints. Once the WES of the variant allele frequency was obtained, it was inputted into a statistical modeling python software called pyclone that is executed through terminal command line. Pyclone assess cellular frequency patterns using bayesian clustering methods. Furthermore, phylogeny inference software called Treeomics will be used to infer the sub-clonal phylogeny of tumor metastases.Results: We retrospectively identified patients in our institution that have undergone multiple biopsy and/or surgical resections of pNETs over multiple time points in their treatments. We observed fluctuating tumor grades over time of the tumor sub-clones (Figure 2). Throughout time in figure 2 patient 2, 6 and 9 had a slow progression sub-clonal evolution from low to high grade. Whereas patients 4, 5 and 3 demonstrated markedly rapid progression from grade 1 to within one year (figure 2). A third subset of patients interestingly demonstrated a decrease from high to low tumor grade over time at later metastasis time points in figure 2.To better understand the evolutionary relationship WES was performed on a single patient that progressed with tumor metastasis through several treatments including SSA, TAE, and AA over the course of a 10-year period. In Figure 1 metastasis sample 8 is a grade 3 tumor and metastasis sample 9 was a grade 9 tumor level demonstrating an evolution from high to low tumor grade. After metastasis 8 this patient undergone treatment and therefore, this high to low change in tumor grade may be due to the success of treatment on pNETs. Also, in figure 1 the first time point tissue samples of metastasis 1,3 and 4 (sample 2 WES was not utilized) showed evolutionary similarity and all were grade 1. In prior literature the mutation of the DAXX gene is associated with pNET progression and also seen in figure 1 were two different DAXX mutations that arose in metastasis 9 and in the primary tumor site.Sub-clonal population structures were created using cellular variant frequencies using pyclone for 1 patient with 10 sample timepoints and 9 patients that had up to 3 patient sample timepoints. Patient 1 with various timepoint samples had lower cellular frequencies at metastasis 6 but, became larger at metastasis 8 and 9. The cellular frequencies demonstrated by the 9 patients demonstrated no large population difference in sub-clonal variant frequency per patient. Conclusion: The differences in evolution can be seen in figure 1 as tumors that have a tumor grade of 1 are the primary beginning metastasis sample that later evolved to grade 2 and 3 at later timepoints. It would be essential to verify this fluctuating evolution of tumor grade in relation of patient treatment to understand specific mutations with respect to treatment. The patient seen in figure 1 subsequently progressed and was treated with SSA, TAE, and AA over the course of 9 years. Patient was on started on sunitinib chemotherapy after metastasis 8 was biopsied. This metastasis disappeared on follow-up. The sunitinib treatment may account for the high to low tumor grade transition found for metastasis 8 to metastasis 9 as it had a positive effect on treating the cancer. Sub-clonal frequencies created with pyclone demonstrate slightly fluctuating mutational cellular frequency changes per sample timepoint.
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