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Table 5.2: Genes identified through refined average probe gain. The list of 8 genes from the Bhattacharjee NSCLC dataset, as down selected using the refined average probe gain criteria. probeset_id Gene_id chromosome gain avg probe gain 1794_at CCND3 6 0.687 0.605 31775_at SFTPD 10 0.743 0.652 32052_at KRT121P 11 0.861 0.697 33109_f_at SOX2 3 0.800 0.628 36617_at ID1 20 0.701 0.694 36629_at TSC22D3 X 0.610 0.607 37926_at KLF5 13 0.683 0.631 40786_at PPP2R5C 14 0.685 0.678 The 8 ProbeSets identified by the refined selection of ProbeSets with sufficient average probe gain to pass the double selection criterion complement the underlying biological story developed above. ID1 is a member of the basic helix-loop-helix transcription factors which regulate a range of cellular functions, including cell cycle progression/proliferation [44, 45]. ID1 regulation has been linked to several pathways, including matrix metalloproteinases [45] and bone morphogenetic proteins [46]. It has been demonstrated that CAV1 binds ID1 through the HLH domain and this interaction was essential for the activation of AkT activation, promoting cancer cell invasion in prostate cancer [45]. Additionally, ID1 has been demonstrated to be essential for the expression of cyclin D1, a facilitator of cell phase transitions [44]. While the cyclin D3 ProbeSet (1794_at) demonstrated a gradient transition of expression levels between normals, squamous, and adenocarcinoma samples, the highest mean expression was in squamous cell carcinomas. Cyclin D3 plays a pivotol role in the phase transition between G1 to S stages [47]. The adenocarcinoma samples similarly expressed significant down regulation of the ID1 mRNA transcript, suggesting that these samples are residing in the growth stages. 128
Kruppel-like factor 5 (KLF5) has also been implicated in carcinogenesis pathways. In particular, it activates in response to DNA damage to induce apoptosis or DNA repair, through the p53 dependent pathways [48]. Increased KLF5 expression accelerates G2/M phase transitions by activating cyclin B1 [49]. Similarly, PPP2R5C also participates in p53 activation after the induction of DNA damage, acting as a mitotic checkpoint to suppress cancer growth [50, 51]. DNA damage activates ATM (ataxia telangiectasia mutated), which phosphorylates p53 at Ser15. The phosphorylation promotes the p53 and PPP2R5C interaction, thereby causing the dephosphorylation of p53-Thr55 and cell cycle arrest [50, 51]. Both KLF5 and PPP2R5C have elevated levels of mRNA transcript in the squamous cells, which supports the hypothesis that the squamous carcinomas have undergone DNA damage. Finally the TSC22D3, or GILZ, ProbeSet demonstrates significant up-regulation in the normal samples. This gene regulates a number of cell functions, including cell proliferation, and has been identified has a tumor suppressor gene. Conclusion Cancer is a complex disease that can affect nearly every pattern of expression of a cells genes, depending on the stage. This obscures important mechanisms that discriminate the types of disease and perhaps possible points of control. We have shown that by application of the BaFL cleansing routine, followed by straightforward machine learning methods for down-selection criteria and clustering parameters, even very simple classification methods reveal a distinct picture of tumorogenesis for the Bhattacharjee NSCLC samples. While the complexity of the disease cannot be overstated, this picture is much simpler and amenable to straightforward assays for confirmation. For example, a significant proportion of the identified genes have been documented to have alternative transcripting events, oncogenic genomic amplification, 129
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An Adenocarcinoma Case Study of the
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DEDICATION The work presented in th
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TABLE OF CONTENTS LIST OF TABLES ..
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Conclusion.........................
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LIST OF FIGURES Figure Page Figure
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violate the linear correlation rela
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Chapter 1: An Introduction to Micro
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previously stated, rigorous reagent
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Factors influencing Signal Interpre
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likelihood that no such structural
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hybridization and multiple dye stra
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classical reaction equations, modif
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prevent the hybridization of probes
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platforms are used to deposit them
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and chip layout [64]. In particular
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whether analyses based upon individ
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The process of determining a candid
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implemented in order to demonstrate
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affecting probe-target duplex forma
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It has long been known that the var
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probes can thus be reincorporated i
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(Carr, ms in review). This ProbeFAT
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have specific attributes which can
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elow saturation. The investigator m
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a. A filter based on how many probe
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means. The remaining sets possess s
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for the 24 samples in the Lu, et al
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Table 2.1: Probe Numbers per filter
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the effect can be. Both the type of
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described above. In Figure 2.3, the
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various data processing stages are
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position of the probe on the transc
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Figure 2.5: BaFL consistency. Demon
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Figure 2.6: Probe-Transcript region
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Table 2.3: ProbeSet behavior predic
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cleansing process. This is demonstr
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Table 2.4: ProbeSet behavior of pro
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A Priori Prediction We demonstrated
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Conclusion We have presented a comp
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mind that they are most aware of th
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Probe Cleansing Methods The BaFL pr
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are significant assume such a data
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cleansing process and were assessed
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sample selection, with replacement,
Page 89 and 90: Figure 3.1: P value distributions.
Page 91 and 92: Figure 3.2: Down selection models-
Page 93 and 94: cleansing methods were used to gene
Page 95 and 96: Discussion A striking result from t
Page 97 and 98: Figure 3.6: Concordance summary. Th
Page 99 and 100: whole model analysis without permut
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Page 103 and 104: though in Chapter 2 we showed that
Page 105 and 106: Although a list of 325 candidate ge
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Page 109 and 110: Figure 4.1: Non-traditional PCA ana
Page 111 and 112: for the RMA and dCHIP interpretatio
Page 113 and 114: was one of them: it is counted as p
Page 115 and 116: Figure 4.5: Kaplan-Meyer survival c
Page 117 and 118: Figure 4.7: Low grade tumor surviva
Page 119 and 120: The candidate gene list values were
Page 121 and 122: ejecting the null hypothesis [8], a
Page 123 and 124: Figure 4.9: GO connectivity of cand
Page 125 and 126: under linear and non-linear dimensi
Page 127 and 128: sample cleansing process and these
Page 129 and 130: averaged to give a final approximat
Page 131 and 132: 119 Table 5.1: : NSCLC candidate ge
Page 133 and 134: Figure 5.1: NSCLC ProbeSet gain sel
Page 135 and 136: Figure 5.3: NSCLC refined average p
Page 137 and 138: statistical criterion, but it appea
Page 139: lacking p53 mutations [30]. While,
Page 143 and 144: Appendix A # This is the main drive
Page 145 and 146: def Driver(usr, pswd, db, logfile,
Page 147 and 148: def DriverXH(usr, pswd, db, logfile
Page 149 and 150: fp.write('\tTable '), fp.write(msk[
Page 151 and 152: #determine outliers lowrs1=nonzero(
Page 153 and 154: tmp="update sample_mask set exclude
Page 155 and 156: fp.write('\n'+msk[ptr[j]]+' exclude
Page 157 and 158: cur, conn=UpdateWorkReg(cur, conn,
Page 159 and 160: Appendix E # The result of permutin
Page 161 and 162: BIBLIOGRAPHY 149
Page 163 and 164: 13. Bowtell DD: Options available--
Page 165 and 166: method addressing dye, intensity-de
Page 167 and 168: 73. Alter O, Brown PO, Botstein D:
Page 169 and 170: 11. Kumari S, Verma LK, Weller JW:
Page 171 and 172: 38. Michael Stonebraker LAR, Michae
Page 173 and 174: 64. Cropp CS, Lidereau R, Leone A,
Page 175 and 176: 15. Irizarry RA: affy. In.: Biocond
Page 177 and 178: Chapter 4: 1. Minna JD, Roth JA, Ga
Page 179 and 180: 30. Delaval B, Ferrand A, Conte N,
Page 181 and 182: 27. Bai J, Cederbaum AI: Catalase p
Page 183 and 184: 51. Shouse GP, Cai X, Liu X: Serine
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