Immuno-oncology gene expression profiling of formalin-fixed and paraffin-embedded clear cell renal cell carcinoma: Performance comparison of the NanoString nCounter technology with targeted RNA sequencing.

Inflammatory gene signatures are currently being explored as predictive biomarkers for immune checkpoint blockade, and particularly for the treatment of renal cell cancers. From a diagnostic point of view, the nCounter analysis platform and targeted RNA sequencing are emerging alternatives to microarrays and comprehensive transcriptome sequencing in assessing formalin-fixed and paraffin-embedded (FFPE) cancer samples. So far, no systematic study has analyzed and compared the technical performance metrics of these two approaches. Filling this gap, we performed a head-to-head comparison of two commercially available immune gene expression assays, using clear cell renal cell cancer FFPE specimens. We compared the nCounter system that utilizes a direct hybridization technology without amplification with an NGS assay that is based on targeted RNA-sequencing with preamplification. We found that both platforms displayed high technical reproducibility and accuracy (Pearson coefficient: ≥0.96, concordance correlation coefficient [CCC]: ≥0.93). A density plot for normalized expression of shared genes on both platforms showed a comparable bi-modal distribution and dynamic range. RNA-Seq demonstrated relatively larger signaling intensity whereas the nCounter system displayed higher inter-sample variability. Estimated fold changes for all shared genes showed high correlation (Spearman coefficient: 0.73). This agreement is even better when only significantly differentially expressed genes were compared. Composite gene expression profiles, such as an interferon gamma (IFNg) signature, can be reliably inferred by both assays. In summary, our study demonstrates that focused transcript read-outs can reliably be achieved by both technologies and that both approaches achieve comparable results despite their intrinsic technical differences.

RSPO2 gene rearrangement: a powerful driver of ß-catenin activation in liver tumours.

OBJECTIVE: We aimed at the identification of genetic alterations that may functionally substitute for CTNNB1 mutation in ß-catenin-activated hepatocellular adenomas (HCAs) and hepatocellular carcinoma (HCC). DESIGN: Large cohorts of HCA (n=185) and HCC (n=468) were classified using immunohistochemistry. The mutational status of the CTNNB1 gene was determined in ß-catenin-activated HCA (b-HCA) and HCC with at least moderate nuclear CTNNB1 accumulation. Ultra-deep sequencing was used to characterise CTNNB1wild-type and ß-catenin-activated HCA and HCC. Expression profiling of HCA subtypes was performed. RESULTS: A roof plate-specific spondin 2 (RSPO2) gene rearrangement resulting from a 46.4 kb microdeletion on chromosome 8q23.1 was detected as a new morphomolecular driver of ß-catenin-activated HCA. RSPO2 fusion positive HCA displayed upregulation of RSPO2 protein, nuclear accumulation of ß-catenin and transcriptional activation of ß-catenin-target genes indicating activation of Wingless-Type MMTV Integration Site Family (WNT) signalling. Architectural and cytological atypia as well as interstitial invasion indicated malignant transformation in one of the RSPO2 rearranged b-HCAs. The RSPO2 gene rearrangement was also observed in three ß-catenin-activated HCCs developing in context of chronic liver disease. Mutations of the human telomerase reverse transcriptase promoter-known to drive malignant transformation of CTNNB1-mutated HCA-seem to be dispensable for RSPO2 rearranged HCA and HCC. CONCLUSION: The RSPO2 gene rearrangement leads to oncogenic activation of the WNT signalling pathway in HCA and HCC, represents an alternative mechanism for the development of b-HCA and may drive malignant transformation without additional TERT promoter mutation.

Size matters: Dissecting key parameters for panel-based tumor mutational burden analysis.

Tumor mutational burden (TMB) represents a new determinant of clinical benefit from immune checkpoint blockade that identifies responders independent of PD-L1 expression levels and is currently being explored in clinical trials. Although TMB can be measured directly by comprehensive genomic approaches such as whole-genome and exome sequencing, broad availability, short turnaround times, costs and amenability to formalin-fixed and paraffin-embedded tissue support the use of gene panel sequencing for approximating TMB in routine diagnostics. However, data on the parameters influencing panel-based TMB estimation are limited. Here, we report an extensive in silico analysis of the TCGA data set that simulates various panel sizes and compositions. We demonstrate that panel size is a critical parameter that influences confidence intervals (CIs) and cutoff values as well as important test parameters including sensitivity, specificity, and positive predictive value. Moreover, we evaluate the Illumina TSO500 panel, which will be made available for TMB estimation, and propose dynamic, entity-specific cutoff values based on current clinical trial data. Optimizing the cost-benefit ratio, our data suggest that panels between 1.5 and 3 Mbp are ideally suited to estimate TMB with small CIs, whereas smaller panels tend to deliver imprecise TMB estimates for low to moderate TMB (0-30 muts/Mbp), connected with insufficient separation of hypermutated tumors from non-hypermutated tumors.

Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels.

Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel-based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using i) in silico analysis of TCGA (The Cancer Genome Atlas) data and ii) wet-lab sequencing of a total of 92 formalin-fixed and paraffin-embedded (FFPE) cancer samples grouped in three independent cohorts (non-small cell lung cancer, NSCLC; colorectal cancer, CRC; and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel >1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in NSCLC determined by wet-lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, our study is a valuable data source for researchers working in the field of immuno-oncology as well as for diagnostic laboratories planning TMB testing.

Combined targeted DNA and RNA sequencing of advanced NSCLC in routine molecular diagnostics: Analysis of the first 3,000 Heidelberg cases.

Tyrosine kinase inhibitors currently confer the greatest survival gain for nonsmall cell lung cancer (NSCLC) patients with actionable genetic alterations. Simultaneously, the increasing number of targets and compounds poses the challenge of reliable, broad and timely molecular assays for the identification of patients likely to benefit from novel treatments. Here, we demonstrate the feasibility and clinical utility of comprehensive, NGS-based genetic profiling for routine workup of advanced NSCLC based on the first 3,000 patients analyzed in our department. Following automated extraction of DNA and RNA from formalin-fixed, paraffin-embedded tissue samples, parallel sequencing of DNA and RNA for detection of mutations and gene fusions, respectively, was performed using PCR-based enrichment with an ion semiconductor sequencing platform. Overall, 807 patients (27%) were eligible for currently approved, EGFR-/BRAF-/ALK- and ROS1-directed therapies, while 218 additional cases (7%) with MET, ERBB2 (HER2) and RET alterations could potentially benefit from experimental targeted compounds. In addition, routine capturing of comutations, e.g. TP53 (55%), KEAP1 (11%) and STK11 (11%), as well as the precise typing of fusion partners and involved exons in case of actionable translocations including ALK and ROS1, are prognostic and predictive tools currently gaining importance for further refinement of therapeutic and surveillance strategies. The reliability, low dropout rates (<5%), minimal tissue requirements, fast turnaround times (6 days on average) and lower costs of the diagnostic approach presented here compared to sequential single-gene testing, highlight its practicability in order to support individualized decisions in routine patient care, enrollment in molecularly stratified clinical trials, as well as translational research.

Variant classification in precision oncology.

Next-generation sequencing has become a cornerstone of therapy guidance in cancer precision medicine and an indispensable research tool in translational oncology. Its rapidly increasing use during the last decade has expanded the options for targeted tumor therapies, and molecular tumor boards have grown accordingly. However, with increasing detection of genetic alterations, their interpretation has become more complex and error-prone, potentially introducing biases and reducing benefits in clinical practice. To facilitate interdisciplinary discussions of genetic alterations for treatment stratification between pathologists, oncologists, bioinformaticians, genetic counselors and medical scientists in specialized molecular tumor boards, several systems for the classification of variants detected by large-scale sequencing have been proposed. We review three recent and commonly applied classifications and discuss their individual strengths and weaknesses. Comparison of the classifications underlines the need for a clinically useful and universally applicable variant reporting system, which will be instrumental for efficient decision making based on sequencing analysis in oncology. Integrating these data, we propose a generalizable classification concept featuring a conservative and a more progressive scheme, which can be readily applied in a clinical setting.

Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity.

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

Definition of transcriptome-based indices for quantitative characterization of chemically disturbed stem cell development: introduction of the STOP-Toxukn and STOP-Toxukk tests.

Stem cell-based in vitro test systems can recapitulate specific phases of human development. In the UKK test system, human pluripotent stem cells (hPSCs) randomly differentiate into cells of the three germ layers and their derivatives. In the UKN1 test system, hPSCs differentiate into early neural precursor cells. During the normal differentiation period (14 days) of the UKK system, 570 genes [849 probe sets (PSs)] were regulated >fivefold; in the UKN1 system (6 days), 879 genes (1238 PSs) were regulated. We refer to these genes as 'developmental genes'. In the present study, we used genome-wide expression data of 12 test substances in the UKK and UKN1 test systems to understand the basic principles of how chemicals interfere with the spontaneous transcriptional development in both test systems. The set of test compounds included six histone deacetylase inhibitors (HDACis), six mercury-containing compounds ('mercurials') and thalidomide. All compounds were tested at the maximum non-cytotoxic concentration, while valproic acid and thalidomide were additionally tested over a wide range of concentrations. In total, 242 genes (252 PSs) in the UKK test system and 793 genes (1092 PSs) in the UKN1 test system were deregulated by the 12 test compounds. We identified sets of 'diagnostic genes' appropriate for the identification of the influence of HDACis or mercurials. Test compounds that interfered with the expression of developmental genes usually antagonized their spontaneous development, meaning that up-regulated developmental genes were suppressed and developmental genes whose expression normally decreases were induced. The fraction of compromised developmental genes varied widely between the test compounds, and it reached up to 60 %. To quantitatively describe disturbed development on a genome-wide basis, we recommend a concept of two indices, 'developmental potency' (D p) and 'developmental index' (D i), whereby D p is the fraction of all developmental genes that are up- or down-regulated by a test compound, and D i is the ratio of overrepresentation of developmental genes among all genes deregulated by a test compound. The use of D i makes hazard identification more sensitive because some compounds compromise the expression of only a relatively small number of genes but have a high propensity to deregulate developmental genes specifically, resulting in a low D p but a high D i. In conclusion, the concept based on the indices D p and D i offers the possibility to quantitatively express the propensity of test compounds to interfere with normal development.

Hepatotoxicity of piperazine designer drugs: up-regulation of key enzymes of cholesterol and lipid biosynthesis.

The piperazine derivatives most frequently consumed for recreational purposes are 1-benzylpiperazine, 1-(3,4-methylenedioxybenzyl) piperazine, 1-(3-trifluoromethylphenyl) piperazine and 1-(4-methoxyphenyl) piperazine. Generally, they are consumed as capsules, tablets or pills but also in powder or liquid forms. Currently, the precise mechanism by which piperazine designer drugs induce hepatotoxicity and whether they act by a common pathway is unclear. To answer this question, we performed a gene array study with rat hepatocytes incubated with the four designer drugs. Non-cytotoxic concentrations were chosen that neither induce a decrease in reduced glutathione or ATP depletion. Analysis of the gene array data showed a large overlap of gene expression alterations induced by the four drugs. This 'piperazine designer drug consensus signature' included 101 up-regulated and 309 down-regulated probe sets (p < 0.05; FDR adjusted). In the up-regulated genes, GO groups of cholesterol biosynthesis represented a dominant overrepresented motif. Key enzymes of cholesterol biosynthesis up-regulated by all four piperazine drugs include sterol C4-methyloxidase, isopentyl-diphosphate-Δ-isomerase, Cyp51A1, squalene epoxidase and farnesyl diphosphate synthase. Additionally, glycoprotein transmembrane nmb, which participates in cell adhesion processes, and fatty acid desaturase 1, an enzyme that regulates unsaturation of fatty acids, were also up-regulated by the four piperazine designer drugs. Regarding the down-regulated probe sets, only one gene was common to all four piperazine derivatives, the betaine-homocysteine-S-methyltransferase 2. Analysis of transcription factor binding sites of the 'piperazine designer drug consensus signature' identified the sterol regulatory element binding protein (SREBP-1) as strongly overrepresented in the up-regulated genes. SREBP transcription factors are known to regulate multiple genes of cholesterol metabolism. In conclusion, the present study shows that piperazine designer drugs act by up-regulating key enzymes of cholesterol biosynthesis which is likely to increase the risk of phospholipidosis and steatosis.

A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of 'mercurials' (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

Design principles of concentration-dependent transcriptome deviations in drug-exposed differentiating stem cells.

Information on design principles governing transcriptome changes upon transition from safe to hazardous drug concentrations or from tolerated to cytotoxic drug levels are important for the application of toxicogenomics data in developmental toxicology. Here, we tested the effect of eight concentrations of valproic acid (VPA; 25-1000 µM) in an assay that recapitulates the development of human embryonic stem cells to neuroectoderm. Cells were exposed to the drug during the entire differentiation process, and the number of differentially regulated genes increased continuously over the concentration range from zero to about 3000. We identified overrepresented transcription factor binding sites (TFBS) as well as superordinate cell biological processes, and we developed a gene ontology (GO) activation profiler, as well as a two-dimensional teratogenicity index. Analysis of the transcriptome data set by the above biostatistical and systems biology approaches yielded the following insights: (i) tolerated (≤25 µM), deregulated/teratogenic (150-550 µM), and cytotoxic (≥800 µM) concentrations could be differentiated. (ii) Biological signatures related to the mode of action of VPA, such as protein acetylation, developmental changes, and cell migration, emerged from the teratogenic concentrations range. (iii) Cytotoxicity was not accompanied by signatures of newly emerging canonical cell death/stress indicators, but by catabolism and decreased expression of cell cycle associated genes. (iv) Most, but not all of the GO groups and TFBS seen at the highest concentrations were already overrepresented at 350-450 µM. (v) The teratogenicity index reflected this behavior, and thus differed strongly from cytotoxicity. Our findings suggest the use of the highest noncytotoxic drug concentration for gene array toxicogenomics studies, as higher concentrations possibly yield wrong information on the mode of action, and lower drug levels result in decreased gene expression changes and thus a reduced power of the study.

From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects.

The superordinate principles governing the transcriptome response of differentiating cells exposed to drugs are still unclear. Often, it is assumed that toxicogenomics data reflect the immediate mode of action (MoA) of drugs. Alternatively, transcriptome changes could describe altered differentiation states as indirect consequence of drug exposure. We used here the developmental toxicants valproate and trichostatin A to address this question. Neurally differentiating human embryonic stem cells were treated for 6 days. Histone acetylation (primary MoA) increased quickly and returned to baseline after 48 h. Histone H3 lysine methylation at the promoter of the neurodevelopmental regulators PAX6 or OTX2 was increasingly altered over time. Methylation changes remained persistent and correlated with neurodevelopmental defects and with effects on PAX6 gene expression, also when the drug was washed out after 3-4 days. We hypothesized that drug exposures altering only acetylation would lead to reversible transcriptome changes (indicating MoA), and challenges that altered methylation would lead to irreversible developmental disturbances. Data from pulse-chase experiments corroborated this assumption. Short drug treatment triggered reversible transcriptome changes; longer exposure disrupted neurodevelopment. The disturbed differentiation was reflected by an altered transcriptome pattern, and the observed changes were similar when the drug was washed out during the last 48 h. We conclude that transcriptome data after prolonged chemical stress of differentiating cells mainly reflect the altered developmental stage of the model system and not the drug MoA. We suggest that brief exposures, followed by immediate analysis, are more suitable for information on immediate drug responses and the toxicity MoA.

Toxicogenomics directory of chemically exposed human hepatocytes.

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory ( http://wiki.toxbank.net/toxicogenomics-map/ ) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes.

Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach.

Developmental neurotoxicity (DNT) and many forms of reproductive toxicity (RT) often manifest themselves in functional deficits that are not necessarily based on cell death, but rather on minor changes relating to cell differentiation or communication. The fields of DNT/RT would greatly benefit from in vitro tests that allow the identification of toxicant-induced changes of the cellular proteostasis, or of its underlying transcriptome network. Therefore, the 'human embryonic stem cell (hESC)-derived novel alternative test systems (ESNATS)' European commission research project established RT tests based on defined differentiation protocols of hESC and their progeny. Valproic acid (VPA) and methylmercury (MeHg) were used as positive control compounds to address the following fundamental questions: (1) Does transcriptome analysis allow discrimination of the two compounds? (2) How does analysis of enriched transcription factor binding sites (TFBS) and of individual probe sets (PS) distinguish between test systems? (3) Can batch effects be controlled? (4) How many DNA microarrays are needed? (5) Is the highest non-cytotoxic concentration optimal and relevant for the study of transcriptome changes? VPA triggered vast transcriptional changes, whereas MeHg altered fewer transcripts. To attenuate batch effects, analysis has been focused on the 500 PS with highest variability. The test systems differed significantly in their responses (<20 % overlap). Moreover, within one test system, little overlap between the PS changed by the two compounds has been observed. However, using TFBS enrichment, a relatively large 'common response' to VPA and MeHg could be distinguished from 'compound-specific' responses. In conclusion, the ESNATS assay battery allows classification of human DNT/RT toxicants on the basis of their transcriptome profiles.

Targeting rare and non-canonical driver variants in NSCLC - An uncharted clinical field.

OBJECTIVES: Implementation of tyrosine kinase inhibitors (TKI) and other targeted therapies was a main advance in thoracic oncology with survival gains ranging from several months to years for non-small-cell lung cancer (NSCLC) patients. High-throughput comprehensive molecular profiling is of key importance to identify patients that can potentially benefit from these novel treatments. MATERIAL AND METHODS: Next-generation sequencing (NGS) was performed on 4500 consecutive formalin-fixed, paraffin-embedded specimens of advanced NSCLC (n = 4172 patients) after automated extraction of DNA and RNA for parallel detection of mutations and gene fusions, respectively. RESULTS AND CONCLUSION: Besides the 24.9 % (n = 1040) of cases eligible for approved targeted therapies based on the presence of canonical alterations in EGFR exons 18-21, BRAF, ROS1, ALK, NTRK, and RET, an additional n = 1260 patients (30.2 %) displayed rare or non-canonical mutations in EGFR (n = 748), BRAF (n = 135), ERBB2 (n = 30), KIT (n = 32), PIK3CA (n = 221), and CTNNB1 (n = 94), for which targeted therapies could also be potentially effective. A systematic literature search in conjunction with in silico evaluation identified n = 232 (5.5 %) patients, for which a trial of targeted treatment would be warranted according to available evidence (NCT level 1, i.e. published data showing efficacy in the same tumor entity). In conclusion, a sizeable fraction of NSCLC patients harbors rare or non-canonical alterations that may be associated with clinical benefit from currently available targeted drugs. Systematic identification and individualized management of these cases can expand applicability of precision oncology in NSCLC and extend clinical gain from established molecular targets. These results can also inform clinical trials.

Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma.

BACKGROUND: Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification. METHODS: TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath). RESULTS: Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%. CONCLUSIONS: Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.

Subtype specific expression and survival prediction of pivotal lncRNAs in muscle invasive bladder cancer.

Comprehensive transcriptome expression analyses of bladder cancer revealed distinct lncRNA clusters with differential molecular and clinical characteristics. In this study, pivotal lncRNAs were assessed for their impact on survival and their differential expression between the molecular bladder cancer subtypes. FFPE samples from chemotherapy-naïve patients with muscle invasive bladder cancer (MIBC) were analyzed on the Nanostring nCounter platform for absolute quantification. An established 36-gene panel was used for molecular subtype classification into basal, luminal and infiltrated MIBC. In a second step, 14 pivotal lncRNAs were assessed for their molecular subtype attribution, and their predictive value in disease-specific survival. In silico validation was performed on a total of 487 MIBC patients (MDA, TGCA and Chungbuk cohort). Several pivotal lncRNAs showed a distinct molecular subtype attribution: e.g. MALAT1 showed a downregulation in the basal subtype (p = 0.009), TUG1 and CBR3AS1 showed an upregulation in the luminal subtype (p ≤ 0.001). High transcript levels of SNHG16, CBR3AS1 and H19 appeared to be predictive for a shorter disease-specific survival. Patients overexpressing putative oncogenes MALAT1 and TUG1 in MIBC tissue presented prolonged survival, suggesting tumor suppressive effects of both lncRNAs. The Nanostring nCounter proved to be a valid platform for the quantification of low-abundance transcripts including lncRNAs.

Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement.

OBJECTIVES: Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. MATERIAL AND METHODS: We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. RESULTS: Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. CONCLUSION: A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.

YAP Orchestrates Heterotypic Endothelial Cell Communication via HGF/c-MET Signaling in Liver Tumorigenesis.

The oncogene yes-associated protein (YAP) controls liver tumor initiation and progression via cell extrinsic functions by creating a tumor-supporting environment in conjunction with cell autonomous mechanisms. However, how YAP controls organization of the microenvironment and in particular the vascular niche, which contributes to liver disease and hepatocarcinogenesis, is poorly understood. To investigate heterotypic cell communication, we dissected murine and human liver endothelial cell (EC) populations into liver sinusoidal endothelial cells (LSEC) and continuous endothelial cells (CEC) through histomorphological and molecular characterization. In YAPS127A-induced tumorigenesis, a gradual replacement of LSECs by CECs was associated with dynamic changes in the expression of genes involved in paracrine communication. The formation of new communication hubs connecting CECs and LSECs included the hepatocyte growth factor (Hgf)/c-Met signaling pathway. In hepatocytes and tumor cells, YAP/TEA domain transcription factor 4 (TEAD4)-dependent transcriptional induction of osteopontin (Opn) stimulated c-Met expression in EC with CEC phenotype, which sensitized these cells to the promigratory effects of LSEC-derived Hgf. In human hepatocellular carcinoma, the presence of a migration-associated tip-cell signature correlated with poor clinical outcome and the loss of LSEC marker gene expression. The occurrence of c-MET-expressing CECs in human liver cancer samples was confirmed at the single-cell level. In summary, YAP-dependent changes of the liver vascular niche comprise the formation of heterologous communication hubs in which tumor cell-derived factors modify the cross-talk between LSECs and CECs via the HGF/c-MET axis. SIGNIFICANCE: YAP-dependent changes of the liver vascular niche comprise the formation of heterologous communication hubs in which tumor cell-derived factors modify the cross-talk between EC subpopulations. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/24/5502/F1.large.jpg.