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Molecular Characterisation and Prognostic Biomarker Discovery in Human Non-Small Cell Lung Cancer
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Non-small cell lung cancer (NSCLC) constitutes a clinically, histologically, and genetically heterogeneous disease entity that represents a major cause of cancer-related death. Early-stage patients, who undergo surgery with curative intent, experience high recurrence rates and the effect of adjuvant treatment is modest. Prognostic biomarkers would be of particular relevance to guide intensified treatment depending on expected outcome and moreover often infer a biological role in tumourigenesis.

This thesis presents a translational study approach to establish a well-characterised NSCLC frozen-tissue cohort and to obtain a profile of each specimen with regard to genome-wide copy number alterations, global gene expression levels and somatic mutations in selected cancer-related genes. Furthermore, the generation of a formalin-fixed, paraffin-embedded tissue microarray enabled validation of findings on the protein level using immunohistochemistry. The comprehensive molecular characterisation, combined with data on clinical parameters, enabled the analysis of biomarkers linked to disease outcome. In Paper I, single nucleotide polymorphism arrays were applied to assess copy number alterations in NSCLC and associations with overall survival in adenocarcinoma and squamous cell carcinoma were described. In Paper II, we evaluated expression levels of selected stromal proteins in NSCLC using immunohistochemistry and the adhesion molecule CD99 was identified as an outcome-related biomarker in two independent cohorts. Paper III presents a strategy for prognostic biomarker discovery based on gene expression profiling, meta-analysis, and validation of protein expression on tissue microarrays, and suggests the putative tumour suppressor CADM1 as a candidate biomarker. In Paper IV, we propose a prognostic role for tumour-infiltrating IGKC-expressing plasma cells in the local tumour microenvironment, indicating an involvement of the humoral immune response in anti-tumor activity. In Paper V, we combined next-generation deep sequencing with statistical analysis of the TP53 database to define novel parameters for database curation.

In summary, this thesis exemplifies the benefits of a translational study approach, based on a comprehensive tumour characterisation, and describes molecular markers associated with clinical outcome in NSCLC.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. , p. 68
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 817
Keyword [en]
non-small cell lung cancer, biomarker, prognosis, microarray, copy number aberration
National Category
Other Basic Medicine Cancer and Oncology
Research subject
Medical Science
Identifiers
URN: urn:nbn:se:uu:diva-181912ISBN: 978-91-554-8482-8 (print)OAI: oai:DiVA.org:uu-181912DiVA, id: diva2:558097
Public defence
2012-11-16, Rudbecksalen, Rudbecklaboratoriet, Dag Hammarskjölds väg 20, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2012-10-26 Created: 2012-10-01 Last updated: 2018-01-12Bibliographically approved
List of papers
1. Gene Copy Number Aberrations Are Associated with Survival in Histologic Subgroups of Non-small Cell Lung Cancer
Open this publication in new window or tab >>Gene Copy Number Aberrations Are Associated with Survival in Histologic Subgroups of Non-small Cell Lung Cancer
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2011 (English)In: Journal of Thoracic Oncology, ISSN 1556-0864, E-ISSN 1556-1380, Vol. 6, no 11, p. 1833-1840Article in journal (Refereed) Published
Abstract [en]

Introduction:

Non-small cell lung cancer (NSCLC) is characterized by a multitude of genetic aberrations with unknown clinical impact. In this study, we aimed to identify gene copy number changes that correlate with clinical outcome in NSCLC. To maximize the chance to identify clinically relevant events, we applied a strategy involving two prognostically extreme patient groups.

Methods:

Short-term (<20 month; n = 53) and long-term survivors (>58 month; n = 47) were selected from a clinically well-characterized NSCLC patient cohort with available fresh frozen tumor specimens. The samples were analyzed using high-resolution single-nucleotide polymorphism array technology to assess gene copy number variations and array-based gene expression profiling. The molecular data were combined with information on clinical parameters.

Results:

Genetic aberrations were strongly associated with tumor histology. In adenocarcinoma (n = 50), gene copy number gains on chromosome 8q21-q24.3 (177 genes) were more frequent in long-term than in short-term survivors. In squamous cell carcinoma (n = 28), gains on chromosome 14q23.1-24.3 (133 genes) were associated with shorter survival, whereas losses in a neighboring region, 14q31.1-32.33 (110 genes), correlated with favorable outcome. In accordance with copy number gains and losses, messenger RNA expression levels of corresponding genes were increased or decreased, respectively.

Conclusion:

Comprehensive tumor profiling permits the integration of genomic, histologic, and clinical data. We identified gene copy number gains and losses, with corresponding changes in messenger RNA levels that were associated with prognosis in adenocarcinoma and squamous cell carcinoma of the lung.

Keyword
Lung cancer, Prognosis, SNP array, CGH, Gene expression profile
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-162455 (URN)10.1097/JTO.0b013e3182295917 (DOI)000296700400008 ()
Available from: 2011-12-01 Created: 2011-11-30 Last updated: 2018-02-01Bibliographically approved
2. CD99 is a novel prognostic stromal marker in non-small cell lung cancer
Open this publication in new window or tab >>CD99 is a novel prognostic stromal marker in non-small cell lung cancer
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2012 (English)In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 131, no 10, p. 2264-2273Article in journal (Refereed) Published
Abstract [en]

The complex interaction between cancer cells and the microenvironment plays an essential role in all stages of tumourigenesis. Despite the significance of this interplay, alterations in protein composition underlying tumour-stroma interactions are largely unknown. The aim of this study was to identify stromal proteins with clinical relevance in non-small cell lung cancer (NSCLC). A list encompassing 203 stromal candidate genes was compiled based on gene expression array data and available literature. The protein expression of these genes in human NSCLC was screened using the Human Protein Atlas. Twelve proteins were selected that showed a differential stromal staining pattern (BGN, CD99, DCN, EMILIN1, FBN1, PDGFRB, PDLIM5, POSTN, SPARC, TAGLN, TNC, VCAN). The corresponding antibodies were applied on tissue microarrays, including 190 NSCLC samples, and stromal staining was correlated with clinical parameters. Higher stromal expression of CD99 was associated with better prognosis in the univariate (p=0.037) and multivariate (p=0.039) analysis. The association was independent from the proportion of tumour stroma, the fraction of inflammatory cells, and clinical and pathological parameters like stage, performance status and tumour histology. The prognostic impact of stromal CD99 protein expression was confirmed in an independent cohort of 240 NSCLC patients (p=0.008). Furthermore, double-staining confocal fluorescence microscopy showed that CD99 was expressed in stromal lymphocytes as well as in cancer associated fibroblasts. Based on a comprehensive screening strategy the membrane protein CD99 was identified as a novel stromal factor with clinical relevance. The results support the concept that stromal properties have an important impact on tumour progression.

National Category
Clinical Laboratory Medicine
Research subject
Pathology
Identifiers
urn:nbn:se:uu:diva-170991 (URN)10.1002/ijc.27518 (DOI)000309185300007 ()22392539 (PubMedID)
Note

Karolina Edlund and Cecilia Lindskog are shared first authors.

Available from: 2012-03-14 Created: 2012-03-14 Last updated: 2018-02-01
3. Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis and tissue microarray validation
Open this publication in new window or tab >>Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis and tissue microarray validation
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2013 (English)In: Clinical Cancer Research, ISSN 1078-0432, E-ISSN 1557-3265, Vol. 19, no 1, p. 194-204Article in journal (Refereed) Published
Abstract [en]

Background:

Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap

Methods:

Here we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data was used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level p<0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860).

RESULTS:

The meta-analysis revealed 14 genes that were significantly associated with survival (p<0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from two independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup.

CONCLUSIONS:

Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics.

National Category
Clinical Laboratory Medicine
Research subject
Pathology
Identifiers
urn:nbn:se:uu:diva-183399 (URN)10.1158/1078-0432.CCR-12-1139 (DOI)000313051100021 ()23032747 (PubMedID)
Available from: 2012-10-25 Created: 2012-10-25 Last updated: 2018-02-01Bibliographically approved
4. The prognostic relevance of tumour-infiltrating plasma cells and immunoglobulin kappa C indicates an important role of the humoral immune response in non-small cell lung cancer
Open this publication in new window or tab >>The prognostic relevance of tumour-infiltrating plasma cells and immunoglobulin kappa C indicates an important role of the humoral immune response in non-small cell lung cancer
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2013 (English)In: Cancer Letters, ISSN 0304-3835, E-ISSN 1872-7980, Vol. 333, no 2, p. 222-228Article in journal (Refereed) Published
Abstract [en]

A prognostic impact of immunoglobulin kappa C (IGKC) expression has been described in cancer. We analysed the influence of B-cell and plasma cell markers, as well as IGKC expression, in non-small lung cancer (NSCLC) using immunohistochemistry on a tissue microarray. IGKC protein expression was independently associated with longer survival, with particular impact in the adenocarcinoma subgroup. Moreover, a correlation was seen with CD138+ cells, but not with CD20. CD138 expression revealed a comparable association with survival. In conclusion, IGKC expression in stroma–infiltrating plasma cells is a prognostic marker in NSCLC, supporting emerging treatment concepts that exploit the humoral immune response.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-181908 (URN)10.1016/j.canlet.2013.01.036 (DOI)000318838700011 ()
Available from: 2012-10-01 Created: 2012-10-01 Last updated: 2018-02-01Bibliographically approved
5. Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors
Open this publication in new window or tab >>Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors
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2012 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 109, no 24, p. 9551-9556Article in journal (Refereed) Published
Abstract [en]

Cancer mutation databases are expected to play central roles in personalized medicine by providing targets for drug development and biomarkers to tailor treatments to each patient. The accuracy of reported mutations is a critical issue that is commonly overlooked, which leads to mutation databases that include a sizable number of spurious mutations, either sequencing errors or passenger mutations. Here we report an analysis of the latest version of the TP53 mutation database, including 34,453 mutations. By using several data-driven methods on multiple independent quality criteria, we obtained a quality score for each report contributing to the database. This score can now be used to filter for high-confidence mutations and reports within the database. Sequencing the entire TP53 gene from various types of cancer using next-generation sequencing with ultradeep coverage validated our approach for curation. In summary, 9.7% of all collected studies, mostly comprising numerous tumors with multiple infrequent TP53 mutations, should be excluded when analyzing TP53 mutations. Thus, by combining statistical and experimental analyses, we provide a curated mutation database for TP53 mutations and a framework for mutation database analysis.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-175985 (URN)10.1073/pnas.1200019109 (DOI)000305511300070 ()22628563 (PubMedID)
Available from: 2012-06-14 Created: 2012-06-14 Last updated: 2018-02-01Bibliographically approved

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