Hans Brunnström

A pathology atlas of the human cancer transcriptome.

Modeling the cancer transcriptome Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth. Science, this issue p. eaan2507 A systems biology approach should allow genome-wide exploration of the effect of individual proteins on cancer clinical outcomes. INTRODUCTION Cancer is a leading cause of death worldwide, and there is great need to define the molecular mechanisms driving the development and progression of individual tumors. The Hallmarks of Cancer has provided a framework for a deeper molecular understanding of cancer, and the focus so far has been on the genetic alterations in individual cancers, including genome rearrangements, gene amplifications, and specific cancer-driving mutations. Using systems-level approaches, it is now also possible to define downstream effects of individual genetic alterations in a genome-wide manner. RATIONALE In our study, we used a systems-level approach to analyze the transcriptome of 17 major cancer types with respect to clinical outcome, based on a genome-wide transcriptomics analysis of ~8000 individual patients with clinical metadata. The study was made possible through the availability of large open-access knowledge-based efforts such as the Cancer Genome Atlas and the Human Protein Atlas. Here, we used the data to perform a systems-level analysis of 17 major human cancer types, describing both interindividual and intertumor variation patterns. RESULTS The analysis identified candidate prognostic genes associated with clinical outcome for each tumor type; the results show that a large fraction of cancer protein-coding genes are differentially expressed and, in many cases, have an impact on overall patient survival. Systems biology analyses revealed that gene expression of individual tumors within a particular cancer varied considerably and could exceed the variation observed between distinct cancer types. No general prognostic gene necessary for clinical outcome was applicable to all cancers. Shorter patient survival was generally associated with up-regulation of genes involved in mitosis and cell growth and down-regulation of genes involved in cellular differentiation. The data allowed us to generate personalized genome-scale metabolic models for cancer patients to identify key genes involved in tumor growth. In addition, we explored tissue-specific genes associated with the dedifferentiation of tumor cells and the role of specific cancer testis antigens on a genome-wide scale. For lung and colorectal cancer, a selection of prognostic genes identified by the systems biology effort were analyzed in independent, prospective cancer cohorts using immunohistochemistry to validate the gene expression patterns at the protein level. CONCLUSION A Human Pathology Atlas has been created as part of the Human Protein Atlas program to explore the prognostic role of each protein-coding gene in 17 different cancers. Our atlas uses transcriptomics and antibody-based profiling to provide a standalone resource for cancer precision medicine. The results demonstrate the power of large systems biology efforts that make use of publicly available resources. Using genome-scale metabolic models, cancer patients are shown to have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. With more than 900,000 Kaplan-Meier plots, this resource allows exploration of the specific genes influencing clinical outcome for major cancers, paving the way for further in-depth studies incorporating systems-level analyses of cancer. All data presented are available in an interactive open-access database (www.proteinatlas.org/pathology) to allow for genome-wide exploration of the impact of individual proteins on clinical outcome in major human cancers. Schematic overview of the Human Pathology Atlas. A systems-level approach enables analysis of the protein-coding genes of 17 different cancer types from ~8000 patients. Results are available in an interactive open-access database. Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Cause of death in patients with dementia disorders.

Background:  Investigations on cause of death may provide valuable information about life expectancy and on conditions of terminal dementia care, which perhaps can be ameliorated.

Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study.

Background Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. Methods and findings We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01–1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15–2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79–1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84–0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25–2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. Conclusions Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.

Prevalence of dementia subtypes: A 30-year retrospective survey of neuropathological reports

We investigated the distribution of neuropathologically defined dementia subtypes among individuals with dementia disorder. The neuropathological reports were studied on all patients (n=524; 55.3% females; median age 80, range 39-102 years) with clinically diagnosed dementia disorder who underwent complete autopsy including neuropathological examination within the Department of Pathology at the University Hospital in Lund, Sweden, during the years 1974-2004. The neuropathological diagnosis was Alzheimers disease (AD) in 42.0% of the cases, vascular dementia (VaD) in 23.7%, dementia of combined Alzheimer and vascular pathology in 21.6%, and frontotemporal dementia in 4.0% of the patients. The remaining 8.8% of the patients had other dementia disorders, including combinations other than combined Alzheimer and vascular pathology. The registered prevalence of dementia subtypes depends on many variables, including referral habits, clinical and neuropathological judgments and diagnostic traditions, all of these variables potentially changing over time. This, however, does not seem to obscure the delineation of the major dementia subgroups. In this material of 30 years from Lund in the south of Sweden, AD by far dominated among dementia subtypes, while cerebrovascular pathology corresponded with the dementia disorder, either entirely or partly, in almost half of the demented patients.

Staf50 is a novel p53 target gene conferring reduced clonogenic growth of leukemic U-937 cells

The tumor suppressor gene p53 is a transcription factor that mediates both cell cycle arrest and apoptosis. Interestingly, p53 also induces differentiation of a number of tissues, including leukemic cells. However, although p53-mediated differentiation of leukemic U-937 cells depends on the transcriptional activity of p53, a p53 target gene mediating differentiation has hitherto not been identified. To screen for novel p53 target genes in leukemic cells, a cDNA microarray analysis was performed with U-937-4/ptsp53 cells, expressing a temperature-sensitive p53 mutant. We report that transcription of the Staf50 (stimulated transacting factor of 50 kDa) gene is upregulated in response to wild-type p53 in U-937-4, K562 and MCF-7 cells. Staf50 was directly activated by p53, as determined by the independence of de novo protein synthesis. Moreover, while the proximal promoter of Staf50 was found not to be p53 responsive, a functional enhancer-like p53-response element in intron 1 of the Staf50 gene was identified that was also transactivated by the p53-family member p73. Direct binding of p53 to the response element was shown by electrophoretic mobility shift analysis. Ectopic expression of Staf50 in U-937 cells resulted in reduced clonogenic growth. Moreover, levels of endogenous Staf50 mRNA correlated to all-trans retinoic acid-induced differentiation of promyelocytic NB-4 and HL60 cells, suggesting that Staf50 could be involved in proliferation and/or differentiation of leukemic cells.

Immunohistochemistry in the differential diagnostics of primary lung cancer: an investigation within the southern Swedish lung cancer study.

OBJECTIVES To assess immunohistochemical (IHC) stains differentially expressed between different types of lung cancer. METHODS We evaluated 16 different IHC stains in 209 prospectively included, surgically treated primary lung cancers, including 121 adenocarcinomas, 65 squamous cell carcinomas, 15 large-cell carcinomas, 5 adenosquamous carcinomas, 2 sarcomatoid carcinomas, and 1 small-cell carcinoma, using the tissue microarray technique. RESULTS Cytokeratin 5 (CK5) and P63 were both positive in 10% or more of the cells in 97% of the squamous cell carcinomas, with the former being positive (<10% of the cells) in only 2 non-squamous cell carcinomas. Thyroid transcription factor 1 (TTF1) and napsin A were positive in 10% or more of the cells in 88% and 87% of the adenocarcinomas, respectively, with 94% of the adenocarcinomas being positive in at least 1 marker. Fifteen percent of the adenocarcinomas were positive for estrogen receptor. CONCLUSIONS CK5, TTF1, and napsin A are sensitive markers for squamous cell carcinoma and adenocarcinoma of the lung.

Cerebrospinal fluid biomarker results in relation to neuropathological dementia diagnoses

Clinical dementia diagnoses are not always consistent with neuropathological findings. As correct diagnosis is important for treatment and care, new diagnostic possibilities for dementia are in demand. Cerebrospinal fluid biomarkers should ideally be able to identify ongoing processes in the brain, but need to be further compared with neuropathological findings for evaluation of their diagnostic validity.

Genome-wide DNA Methylation Analysis of Lung Carcinoma Reveals One Neuroendocrine and Four Adenocarcinoma Epitypes Associated with Patient Outcome

Purpose: Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer remains unclear. Experimental Design: Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors, including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), 1 adenosquamous cancer, 5 large cell carcinomas, 9 large cell neuroendocrine carcinomas (LCNEC), and 3 small-cell carcinomas (SCLC). Unsupervised bootstrap clustering was performed to identify DNA methylation subgroups, which were validated in 695 adenocarcinomas and 122 SqCCs. Subgroups were characterized by clinicopathologic factors, whole-exome sequencing data, and gene expression profiles. Results: Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, remaining four epitypes were associated with unsupervised and supervised gene expression phenotypes, and differences in molecular features, including global hypomethylation, promoter hypermethylation, genomic instability, expression of proliferation-associated genes, and mutations in KRAS, TP53, KEAP1, SMARCA4, and STK11. Furthermore, these epitypes were associated with clinicopathologic features such as smoking history and patient outcome. Conclusions: Our findings highlight one neuroendocrine and four adenocarcinoma epitypes associated with molecular and clinicopathologic characteristics, including patient outcome. This study demonstrates the possibility to further subgroup lung cancer, and more specifically adenocarcinomas, based on epigenetic/molecular classification that could lead to more accurate tumor classification, prognostication, and tailored patient therapy. Clin Cancer Res; 20(23); 6127–40. ©2014 AACR.

Profiling cancer testis antigens in non–small-cell lung cancer

Cancer testis antigens (CTAs) are of clinical interest as biomarkers and present valuable targets for immunotherapy. To comprehensively characterize the CTA landscape of non-small-cell lung cancer (NSCLC), we compared RNAseq data from 199 NSCLC tissues to the normal transcriptome of 142 samples from 32 different normal organs. Of 232 CTAs currently annotated in the Caner Testis Database (CTdatabase), 96 were confirmed in NSCLC. To obtain an unbiased CTA profile of NSCLC, we applied stringent criteria on our RNAseq data set and defined 90 genes as CTAs, of which 55 genes were not annotated in the CTdatabase, thus representing potential new CTAs. Cluster analysis revealed that CTA expression is histology dependent and concurrent expression is common. IHC confirmed tissue-specific protein expression of selected new CTAs (TKTL1, TGIF2LX, VCX, and CXORF67). Furthermore, methylation was identified as a regulatory mechanism of CTA expression based on independent data from The Cancer Genome Atlas. The proposed prognostic impact of CTAs in lung cancer was not confirmed, neither in our RNAseq cohort nor in an independent meta-analysis of 1,117 NSCLC cases. In summary, we defined a set of 90 reliable CTAs, including information on protein expression, methylation, and survival association. The detailed RNAseq catalog can guide biomarker studies and efforts to identify targets for immunotherapeutic strategies.

PD-L1 immunohistochemistry in clinical diagnostics of lung cancer: inter-pathologist variability is higher than assay variability

Assessment of programmed cell death ligand 1 (PD-L1) immunohistochemical staining is used for decision on treatment with programmed cell death 1 and PD-L1 checkpoint inhibitors in lung adenocarcinomas and squamous cell carcinomas. This study aimed to compare the staining properties of tumor cells between the antibody clones 28-8, 22C3, SP142, and SP263 and investigate interrater variation between pathologists to see if these stainings can be safely evaluated in the clinical setting. Using consecutive sections from a tissue microarray with tumor tissue from 55 resected lung cancer cases, staining with five PD-L1 assays (28-8 from two different vendors, 22C3, SP142, and SP263) was performed. Seven pathologists individually evaluated the percentage of positive tumor cells, scoring each sample applying cutoff levels used in clinical studies: <1% positive tumor cells (score 0), 1–4% (score 1), 5–9% (score 2), 10–24% (score 3), 25–49% (score 4), and >50% positive tumor cells (score 5). Pairwise analysis of antibody clones showed weighted kappa values in the range of 0.45–0.91 with the highest values for comparisons with 22C3 and 28-8 and the lowest involving SP142. Excluding SP142 resulted in kappa 0.75–0.91. Weighted kappa for interobserver variation between pathologists was 0.71–0.96. Up to 20% of the cases were differently classified as positive or negative by any pathologist compared with consensus score using ≥1% positive tumor cells as cutoff. A significantly better agreement between pathologists was seen using ≥50% as cutoff (0–5% of cases). In conclusion, the concordance between the PD-L1 antibodies 22C3, 28-8 and SP263 is relatively good when evaluating lung cancers and suggests that any one of these assays may be sufficient as basis for decision on treatment with nivolumab, pembrolizumab, and durvalumab. The scoring of the pathologist presents an intrinsic source of error that should be considered especially at low PD-L1 scores.