92-Gene Molecular Profiling in Identification of Cancer Origin: A Retrospective Study in Chinese Population and Performance within Different Subgroups

BACKGROUND: After cancer diagnosis, therapy for the patient is largely dependent on the tumor origin, especially when a metastatic tumor is being treated. However, cases such as untypical metastasis, poorly differentiated tumors or even a limited number of tumor cells may lead to challenges in identifying the origin. Moreover, approximately 3% to 5% of total solid tumor patients will not have to have their tumor origin identified in their lifetime. The THEROS CancerTYPE ID® is designed for identifying the tumor origin with an objective, rapid and standardized procedure. METHODOLOGY AND PRINCIPAL FINDINGS: This is a blinded retrospective study to evaluate performance of the THEROS CancerTYPE ID® in a Chinese population. In total, 184 formalin-fixed paraffin-embedded (FFPE) samples of 23 tumor origins were collected from the tissue bank of Fudan University Shanghai Cancer Center (FDUSCC). A standard tumor cell enrichment process was used, and the prediction results were compared with reference diagnosis, which was confirmed by two experienced pathologists at FDUSCC. All of the 184 samples were successfully analyzed, and no tumor specimens were excluded because of sample quality issues. In total, 151 samples were correctly predicted. The agreement rate was 82.1%. A Pearson Chi-square test shows that there is no difference between this study and the previous evaluation test performed by bioTheranostics Inc. No statistically significant decrease was observed in either the metastasis group or tumors with high grades. CONCLUSIONS: A comparable result with previous work was obtained. Specifically, specimens with a high probability score (>0.85) have a high chance (agreement rate = 95%) of being correctly predicted. No performance difference was observed between primary and metastatic specimens, and no difference was observed among three tumor grades. The use of laser capture micro-dissection (LCM) makes the THEROS CancerTYPE ID® accessible to almost all of the cancer patients with different tumor statuses

Genome wide in silico SNP-tumor association analysis

BACKGROUND: Carcinogenesis occurs, at least in part, due to the accumulation of mutations in critical genes that control the mechanisms of cell proliferation, differentiation and death. Publicly accessible databases contain millions of expressed sequence tag (EST) and single nucleotide polymorphism (SNP) records, which have the potential to assist in the identification of SNPs overrepresented in tumor tissue. METHODS: An in silico SNP-tumor association study was performed utilizing tissue library and SNP information available in NCBI's dbEST (release 092002) and dbSNP (build 106). RESULTS: A total of 4865 SNPs were identified which were present at higher allele frequencies in tumor compared to normal tissues. A subset of 327 (6.7%) SNPs induce amino acid changes to the protein coding sequences. This approach identified several SNPs which have been previously associated with carcinogenesis, as well as a number of SNPs that now warrant further investigation CONCLUSIONS: This novel in silico approach can assist in prioritization of genes and SNPs in the effort to elucidate the genetic mechanisms underlying the development of cancer

Method validation and preliminary qualification of pharmacodynamic biomarkers employed to evaluate the clinical efficacy of an antisense compound (AEG35156) targeted to the X-linked inhibitor of apoptosis protein XIAP

Data are presented on pharmacodynamic (PD) method validation and preliminary clinical qualification of three PD biomarker assays. M65 Elisa, which quantitates different forms of circulating cytokeratin 18 (CK18) as putative surrogate markers of both apoptotic and nonapoptotic tumour cell death, was shown to be highly reproducible: calibration curve linearity r2=0.996, mean accuracy >91% and mean precision <3%, n=27. Employing recombinant (r) CK18 and caspase cleaved CK18 (CK18 Asp396 neo-epitope) as external standards, kit to kit reproducibly was <6% (n=19). rCK18 was stable in plasma for 4 months at −20°C and −80°C, for 4 weeks at 4°C and had a half-life of 2.3 days at 37°C. Cytokeratin 18 Asp396 NE, the M30 Apoptosense Elisa assay antigen, was stable in plasma for 6 months at −20°C and −80°C, for 3 months at 4°C, while its half-life at 37°C was 3.8 days. Within-day variations in endogenous plasma concentrations of the M30 and M65 antigens were assessed in two predose blood samples collected from a cohort of 15 ovarian cancer patients receiving carboplatin chemotherapy and were shown to be no greater than the variability associated with methods themselves. Between-day fluctuations in circulating levels of the M30 and M65 antigens and in XIAP mRNA levels measured in peripheral blood mononuclear cells by quantitative (q) RT–PCR were evaluated in two predose blood samples collected with a 5- to 7-day gap from 23 patients with advanced cancer enrolled in a phase I trial. The mean variation between the two pretreatment values ranged from 13 to 14 to 25%, respectively, for M65, M30 and qRT–PCR. These data suggest that the M30 and M65 Elisa's and qRT–PCR as PD biomarker assays have favourable performance characteristics for further investigation in clinical trials of anticancer agents which induce tumour apoptosis/necrosis or knockdown of the anti-apoptotic protein XIAP

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p