Genomic profiling of plasmablastic lymphoma using array comparative genomic hybridization (aCGH): revealing significant overlapping genomic lesions with diffuse large B-cell lymphoma

<p>Abstract</p> <p>Background</p> <p>Plasmablastic lymphoma (PL) is a subtype of diffuse large B-cell lymphoma (DLBCL). Studies have suggested that tumors with PL morphology represent a group of neoplasms with clinopathologic characteristics corresponding to different entities including extramedullary plasmablastic tumors associated with plasma cell myeloma (PCM). The goal of the current study was to evaluate the genetic similarities and differences among PL, DLBCL (AIDS-related and non AIDS-related) and PCM using array-based comparative genomic hybridization.</p> <p>Results</p> <p>Examination of genomic data in PL revealed that the most frequent segmental gain (> 40%) include: 1p36.11-1p36.33, 1p34.1-1p36.13, 1q21.1-1q23.1, 7q11.2-7q11.23, 11q12-11q13.2 and 22q12.2-22q13.3. This correlated with segmental gains occurring in high frequency in DLBCL (AIDS-related and non AIDS-related) cases. There were some segmental gains and some segmental loss that occurred in PL but not in the other types of lymphoma suggesting that these foci may contain genes responsible for the differentiation of this lymphoma. Additionally, some segmental gains and some segmental loss occurred only in PL and AIDS associated DLBCL suggesting that these foci may be associated with HIV infection. Furthermore, some segmental gains and some segmental loss occurred only in PL and PCM suggesting that these lesions may be related to plasmacytic differentiation.</p> <p>Conclusion</p> <p>To the best of our knowledge, the current study represents the first genomic exploration of PL. The genomic aberration pattern of PL appears to be more similar to that of DLBCL (AIDS-related or non AIDS-related) than to PCM. Our findings suggest that PL may remain best classified as a subtype of DLBCL at least at the genome level.</p

Prevalence of variations in melanoma susceptibility genes among Slovenian melanoma families

<p>Abstract</p> <p>Background</p> <p>Two high-risk genes have been implicated in the development of CM (cutaneous melanoma). Germline mutations of the CDKN2A gene are found in < 25% of melanoma-prone families and there are only seven families with mutation of the <it>CDK4 </it>gene reported to date. Beside those high penetrance genes, certain allelic variants of the <it>MC1R </it>gene modify the risk of developing the disease.</p> <p>The aims of our study were: to determine the prevalence of germline <it>CDKN2A </it>mutations and variants in members of families with familial CM and in patients with multiple primary CM; to search for possible <it>CDK4 </it>mutations, and to determine the frequency of variations in the <it>MC1R </it>gene.</p> <p>Methods</p> <p>From January 2001 until January 2007, 64 individuals were included in the study. The group included 28 patients and 7 healthy relatives belonging to 25 families, 26 patients with multiple primary tumors and 3 children with CM. Additionally 54 healthy individuals were included as a control group. Mutations and variants of the melanoma susceptibility genes were identified by direct sequencing.</p> <p>Results</p> <p>Seven families with CDKN2A mutations were discovered (7/25 or 28.0%). The L94Q mutation found in one family had not been previously reported in other populations. The D84N variant, with possible biological impact, was discovered in the case of patient without family history but with multiple primary CM. Only one mutation carrier was found in the control group. Further analysis revealed that c.540C>T heterozygous carriers were more common in the group of CM patients and their healthy relatives (11/64 vs. 2/54). One p14ARF variant was discovered in the control group and no mutations of the <it>CDK4 </it>gene were found.</p> <p>Most frequently found variants of the <it>MC1R </it>gene were T314T, V60L, V92M, R151C, R160W and R163Q with frequencies slightly higher in the group of patients and their relatives than in the group of controls, but the difference was statistically insignificant.</p> <p>Conclusion</p> <p>The present study has shown high prevalence of p16INK4A mutations in Slovenian population of familial melanoma patients (37%) and an absence of p14ARF or <it>CDK4 </it>mutations.</p

A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer

<p>Abstract</p> <p>Background</p> <p>The distinction between primary and secondary ovarian tumors may be challenging for pathologists. The purpose of the present work was to develop genomic and transcriptomic tools to further refine the pathological diagnosis of ovarian tumors after a previous history of breast cancer.</p> <p>Methods</p> <p>Sixteen paired breast-ovary tumors from patients with a former diagnosis of breast cancer were collected. The genomic profiles of paired tumors were analyzed using the Affymetrix GeneChip^®Mapping 50 K Xba Array or Genome-Wide Human SNP Array 6.0 (for one pair), and the data were normalized with ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) algorithm or Partek Genomic Suite, respectively. The transcriptome of paired samples was analyzed using Affymetrix GeneChip^®Human Genome U133 Plus 2.0 Arrays, and the data were normalized with gc-Robust Multi-array Average (gcRMA) algorithm. A hierarchical clustering of these samples was performed, combined with a dataset of well-identified primary and secondary ovarian tumors.</p> <p>Results</p> <p>In 12 of the 16 paired tumors analyzed, the comparison of genomic profiles confirmed the pathological diagnosis of primary ovarian tumor (n = 5) or metastasis of breast cancer (n = 7). Among four cases with uncertain pathological diagnosis, genomic profiles were clearly distinct between the ovarian and breast tumors in two pairs, thus indicating primary ovarian carcinomas, and showed common patterns in the two others, indicating metastases from breast cancer. In all pairs, the result of the transcriptomic analysis was concordant with that of the genomic analysis.</p> <p>Conclusions</p> <p>In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors. Transcriptomic analysis may be used when primary breast tissue specimen is not available.</p

Immunohistochemical Profile for Unknown Primary Adenocarcinoma

BACKGROUND: Development of tailored treatment based on immunohistochemical profiles (IPs) of tumors for cancers of unknown primary is needed. METHODOLOGY/PRINCIPAL FINDINGS: We developed an algorithm based on primary known adenocarcinoma for testing sensitivity and specificity. Formalin-fixed paraffin-embedded tissue samples from 71 patients of unfavorable subsets of unknown primary adenocarcinoma were obtained. We examined 15 molecular markers using the algorithm incorporating these IPs and classified the tumours into 9 subsets based on the primary tumour site. The sensitivity and specificity of this algorithm were 80.3% and 97.6%, respectively. Apparent primary sites were lung in 17 patients, digestive organs in 13, gynecological organs in 9, prostate in 7, liver or kidney in 6, breast in 4, urothelial organ in 2, biliary tract and pancreatic profile in none, and unclassified in 13. The response rate to chemotherapy was highest for the gynecological IPs. Patients with gynecological or lung cancer IPs had longer median progression-free survival than those with others: 11.2 months for gynecological IPs (p<0.001) and 6.8 months for lung IPs (p = 0.05). Lung, digestive, prostate, and gynecological profiles were associated with significantly longer median survival time than the other profiles. Multivariate analysis confirmed that the IPs were independent prognostic factors for survival. CONCLUSIONS/SIGNIFICANCE: The IPs identified in this study can be used to further stratify patient prognosis for unfavorable subsets of unknown primary adenocarcinoma

Methodological Deficits in Diagnostic Research Using ‘-Omics’ Technologies: Evaluation of the QUADOMICS Tool and Quality of Recently Published Studies

Background: QUADOMICS is an adaptation of QUADAS (a quality assessment tool for use in systematic reviews of diagnostic accuracy studies), which takes into account the particular challenges presented by '-omics' based technologies. Our primary objective was to evaluate the applicability and consistency of QUADOMICS. Subsequently we evaluated and describe the methodological quality of a sample of recently published studies using the tool. Methodology/Principal Findings: 45'-omics'- based diagnostic studies were identified by systematic search of Pubmed using suitable MeSH terms (>Genomics>, >Sensitivity and specificity>, >Diagnosis>). Three investigators independently assessed the quality of the articles using QUADOMICS and met to compare observations and generate a consensus. Consistency and applicability was assessed by comparing each reviewer's original rating with the consensus. Methodological quality was described using the consensus rating. Agreement was above 80% for all three reviewers. Four items presented difficulties with application, mostly due to the lack of a clearly defined gold standard. Methodological quality of our sample was poor; studies met roughly half of the applied criteria (mean ± sd, 54.7±18.4°%). Few studies were carried out in a population that mirrored the clinical situation in which the test would be used in practice, (6, 13.3%);none described patient recruitment sufficiently; and less than half described clinical and physiological factors that might influence the biomarker profile (20, 44.4%). Conclusions: The QUADOMICS tool can consistently be applied to diagnostic '-omics' studies presently published in biomedical journals. A substantial proportion of reports in this research field fail to address design issues that are fundamental to make inferences relevant for patient care. © 2010 Parker et al.This work was supported by the Spanish Agency for Health Technology Assessment, Exp PI06/90311, Instituto de Salud Carlos III and CIBER en Epidemiología y Salud Pública (CIBERESP) in SpainPeer Reviewe

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers