Inhibition of the renal apical sodium dependent bile acid transporter prevents cholemic nephropathy in mice with obstructive cholestasis.

BACKGROUND & AIMS: Cholemic nephropathy (CN) is a severe complication of cholestatic liver diseases for which there is no specific treatment. We revisited its pathophysiology with the aim of identifying novel therapeutic strategies. METHODS: Cholestasis was induced by bile duct ligation (BDL) in mice. Bile flux in kidneys and livers was visualized by intravital imaging, supported by MALDI mass spectrometry imaging and liquid chromatography-tandem mass spectrometry. The effect of AS0369, a systemically bioavailable apical sodium-dependent bile acid transporter (ASBT) inhibitor, was evaluated by intravital imaging, RNA-sequencing, histological, blood, and urine analyses. Translational relevance was assessed in kidney biopsies from patients with CN, mice with a humanized bile acid (BA) spectrum, and via analysis of serum BAs and KIM-1 (kidney injury molecule 1) in patients with liver disease and hyperbilirubinemia. RESULTS: Proximal tubular epithelial cells (TECs) reabsorbed and enriched BAs, leading to oxidative stress and death of proximal TECs, casts in distal tubules and collecting ducts, peritubular capillary leakiness, and glomerular cysts. Renal ASBT inhibition by AS0369 blocked BA uptake into TECs and prevented kidney injury up to 6 weeks after BDL. Similar results were obtained in mice with humanized BA composition. In patients with advanced liver disease, serum BAs were the main determinant of KIM-1 levels. ASBT expression in TECs was preserved in biopsies from patients with CN, further highlighting the translational potential of targeting ASBT to treat CN. CONCLUSIONS: BA enrichment in proximal TECs followed by oxidative stress and cell death is a key early event in CN. Inhibiting renal ASBT and consequently BA enrichment in TECs prevents CN and systemically decreases BA concentrations. IMPACT AND IMPLICATIONS: Cholemic nephropathy (CN) is a severe complication of cholestasis and an unmet clinical need. We demonstrate that CN is triggered by the renal accumulation of bile acids (BAs) that are considerably increased in the systemic blood. Specifically, the proximal tubular epithelial cells of the kidney take up BAs via the apical sodium-dependent bile acid transporter (ASBT). We developed a therapeutic compound that blocks ASBT in the kidneys, prevents BA overload in tubular epithelial cells, and almost completely abolished all disease hallmarks in a CN mouse model. Renal ASBT inhibition represents a potential therapeutic strategy for patients with CN.

Long-term hypercaloric diet exacerbates metabolic liver disease in PNPLA3 I148M animals.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD) is a major health burden associated with the metabolic syndrome leading to liver fibrosis, cirrhosis and ultimately liver cancer. In humans, the PNPLA3 I148M polymorphism of the phospholipase patatin-like phospholipid domain containing protein 3 (PNPLA3) has a well-documented impact on metabolic liver disease. In this study, we used a mouse model mimicking the human PNPLA3 I148M polymorphism in a long-term high fat diet (HFD) experiment to better define its role for NAFLD progression. METHODS: Male mice bearing wild-type Pnpla3 (Pnpla3WT ), or the human polymorphism PNPLA3 I148M (Pnpla3148M/M ) were subjected to HFD feeding for 24 and 52 weeks. Further analysis concerning basic phenotype, inflammation, proliferation and cell death, fibrosis and microbiota were performed in each time point. RESULTS: After 52 weeks HFD Pnpla3148M/M animals had more liver fibrosis, enhanced numbers of inflammatory cells as well as increased Kupffer cell activity. Increased hepatocyte cell turnover and ductular proliferation were evident in HFD Pnpla3148M/M livers. Microbiome diversity was decreased after HFD feeding, changes were influenced by HFD feeding (36%) and the PNPLA3 I148M genotype (12%). Pnpla3148M/M mice had more faecal bile acids. RNA-sequencing of liver tissue defined an HFD-associated signature, and a Pnpla3148M/M specific pattern, which suggests Kupffer cell and monocytes-derived macrophages as significant drivers of liver disease progression in Pnpla3148M/M animals. CONCLUSION: With long-term HFD feeding, mice with the PNPLA3 I148M genotype show exacerbated NAFLD. This finding is linked to PNPLA3 I148M-specific changes in microbiota composition and liver gene expression showing a stronger inflammatory response leading to enhanced liver fibrosis progression.

Discordance of HER2-Low between Primary Tumors and Matched Distant Metastases in Breast Cancer.

We examined differences in HER2 expression between primary tumors and distant metastases, particularly within the HER2-negative primary breast cancer cohort (HER2-low and HER2-zero). The retrospective study included 191 consecutive paired samples of primary breast cancer and distant metastases diagnosed between 1995 and 2019. HER2-negative samples were divided into HER2-zero (immunohistochemistry [IHC] score 0) and HER2-low (IHC score 1+ or 2+/in situ hybridization [ISH]-negative). The main objective was to analyze the discordance rate between matched primary and metastatic samples, focusing on the site of distant metastasis, molecular subtype, and de novo metastatic breast cancer. The relationship was determined by cross-tabulation and calculation of Cohen's Kappa coefficient. The final study cohort included 148 paired samples. The largest proportion in the HER2-negative cohort was HER2-low [primary tumor 61.4% (n = 78), metastatic samples 73.5% (n = 86)]. The discordance rate between the HER2 status of primary tumors and corresponding distant metastases was 49.6% (n = 63) (Kappa -0.003, 95%CI -0.15-0.15). Development of a HER2-low phenotype occurred most frequently (n = 52, 40.9%), mostly with a switch from HER2-zero to HER2-low (n = 34, 26.8%). Relevant HER2 discordance rates were observed between different metastatic sites and molecular subtypes. Primary metastatic breast cancer had a significantly lower HER2 discordance rate than secondary metastatic breast cancer [30.2% (Kappa 0.48, 95%CI 0.27-0.69) versus 50.5% (Kappa 0.14, 95% CI -0.03-0.32)]. This highlights the importance of evaluating potentially therapy-relevant discordance rates between a primary tumor and corresponding distant metastases.

Inhibiting the glycerophosphodiesterase EDI3 in ER-HER2+ breast cancer cells resistant to HER2-targeted therapy reduces viability and tumour growth.

BACKGROUND: Intrinsic or acquired resistance to HER2-targeted therapy is often a problem when small molecule tyrosine kinase inhibitors or antibodies are used to treat patients with HER2 positive breast cancer. Therefore, the identification of new targets and therapies for this patient group is warranted. Activated choline metabolism, characterized by elevated levels of choline-containing compounds, has been previously reported in breast cancer. The glycerophosphodiesterase EDI3 (GPCPD1), which hydrolyses glycerophosphocholine to choline and glycerol-3-phosphate, directly influences choline and phospholipid metabolism, and has been linked to cancer-relevant phenotypes in vitro. While the importance of choline metabolism has been addressed in breast cancer, the role of EDI3 in this cancer type has not been explored. METHODS: EDI3 mRNA and protein expression in human breast cancer tissue were investigated using publicly-available Affymetrix gene expression microarray datasets (n = 540) and with immunohistochemistry on a tissue microarray (n = 265), respectively. A panel of breast cancer cell lines of different molecular subtypes were used to investigate expression and activity of EDI3 in vitro. To determine whether EDI3 expression is regulated by HER2 signalling, the effect of pharmacological inhibition and siRNA silencing of HER2, as well as the influence of inhibiting key components of signalling cascades downstream of HER2 were studied. Finally, the influence of silencing and pharmacologically inhibiting EDI3 on viability was investigated in vitro and on tumour growth in vivo. RESULTS: In the present study, we show that EDI3 expression is highest in ER-HER2 + human breast tumours, and both expression and activity were also highest in ER-HER2 + breast cancer cell lines. Silencing HER2 using siRNA, as well as inhibiting HER2 signalling with lapatinib decreased EDI3 expression. Pathways downstream of PI3K/Akt/mTOR and GSK3ß, and transcription factors, including HIF1α, CREB and STAT3 were identified as relevant in regulating EDI3 expression. Silencing EDI3 preferentially decreased cell viability in the ER-HER2 + cells. Furthermore, silencing or pharmacologically inhibiting EDI3 using dipyridamole in ER-HER2 + cells resistant to HER2-targeted therapy decreased cell viability in vitro and tumour growth in vivo. CONCLUSIONS: Our results indicate that EDI3 may be a potential novel therapeutic target in patients with HER2-targeted therapy-resistant ER-HER2 + breast cancer that should be further explored.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

Long-term prognostic significance of HER2-low and HER2-zero in node-negative breast cancer.

BACKGROUND: Recently, novel antibody--drug conjugates (ADCs) showed clinical activity in a subset of advanced human epidermal growth factor receptor 2 (HER2)-negative patients. We investigated the prognostic significance of HER2-low and HER2-zero tumours. PATIENTS AND METHODS: The retrospective cohort study included 410 consecutive node-negative breast cancer patients without adjuvant systemic therapy treated between 1985 and 2000 (median follow-up: 16.73 [IQR 8.58-23.45] years). 351 (85.6%) were HER-2 negative and subdivided into HER2-zero (immunohistochemistry [IHC] score 0) and HER2-low (IHC score 1+ or 2+/in situ hybridisation [ISH]-negative). HER2 gene expression was available in 170 (48.4%) patients. Differences in HER2 status for immunohistochemistry, gene expression and clinico-pathologic parameters were assessed using Fisher's exact test, Pearson's correlation and Mann-Whitney test. Prognosis was investigated using the Kaplan-Meier method and Cox regression analyses. RESULTS: Of the 351 HER2-negative patients, 198 (56.4%) had HER2-low tumours and 153 (43.6%) were HER2-zero. Significant differences between HER2-zero and HER2-low tumours were found in histologic grading (P = 0.001), Ki-67 (P = 0.013) and HER2 gene expression (P = 0.002). HER2-low patients had significantly longer disease-free survival (DFS) (15-year rate: 67.5% [95% CI 61.0-74.7] vs. 47.3% [95% CI 39.9-56.1], P < 0.001) and overall survival (OS) (15-year rate: 75.4% [95% CI 69.4-81.9] vs. 66.8% [95% CI 59.5-74.9], P = 0.009). The OS difference was observed in hormone receptor (HR)-positive (P = 0.039) but not HR-negative (P = 0.086) tumours. The results of multivariable analyses confirmed the independent prognostic significance of HER2 status (DFS: HR, 0.546; 95% CI, 0.402-0.743; P < 0.001; OS: HR, 0.653; 95% CI, 0.458-0.932; P = 0.019). CONCLUSION: HER2-low patients had a better survival than HER2-zero patients.

Interruption of bile acid uptake by hepatocytes after acetaminophen overdose ameliorates hepatotoxicity.

BACKGROUND & AIMS: Acetaminophen (APAP) overdose remains a frequent cause of acute liver failure, which is generally accompanied by increased levels of serum bile acids (BAs). However, the pathophysiological role of BAs remains elusive. Herein, we investigated the role of BAs in APAP-induced hepatotoxicity. METHODS: We performed intravital imaging to investigate BA transport in mice, quantified endogenous BA concentrations in the serum of mice and patients with APAP overdose, analyzed liver tissue and bile by mass spectrometry and MALDI-mass spectrometry imaging, assessed the integrity of the blood-bile barrier and the role of oxidative stress by immunostaining of tight junction proteins and intravital imaging of fluorescent markers, identified the intracellular cytotoxic concentrations of BAs, and performed interventions to block BA uptake from blood into hepatocytes. RESULTS: Prior to the onset of cell death, APAP overdose causes massive oxidative stress in the pericentral lobular zone, which coincided with a breach of the blood-bile barrier. Consequently, BAs leak from the bile canaliculi into the sinusoidal blood, which is then followed by their uptake into hepatocytes via the basolateral membrane, their secretion into canaliculi and repeated cycling. This, what we termed 'futile cycling' of BAs, led to increased intracellular BA concentrations that were high enough to cause hepatocyte death. Importantly, however, the interruption of BA re-uptake by pharmacological NTCP blockage using Myrcludex B and Oatp knockout strongly reduced APAP-induced hepatotoxicity. CONCLUSIONS: APAP overdose induces a breach of the blood-bile barrier which leads to futile BA cycling that causes hepatocyte death. Prevention of BA cycling may represent a therapeutic option after APAP intoxication. LAY SUMMARY: Only one drug, N-acetylcysteine, is approved for the treatment of acetaminophen overdose and it is only effective when given within ∼8 hours after ingestion. We identified a mechanism by which acetaminophen overdose causes an increase in bile acid concentrations (to above toxic thresholds) in hepatocytes. Blocking this mechanism prevented acetaminophen-induced hepatotoxicity in mice and evidence from patients suggests that this therapy may be effective for longer periods after ingestion compared to N-acetylcysteine.

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.

BACKGROUND: Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes). In clinical practice, this is often challenging because patient cohorts are typically small and can be heterogeneous. In classical subgroup analysis, a separate prediction model is fitted using only the data of one specific cohort. However, this can lead to a loss of power when the sample size is small. Simple pooling of all cohorts, on the other hand, can lead to biased results, especially when the cohorts are heterogeneous. RESULTS: We propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the important predictors and provides a separate risk prediction model for each cohort. It allows sharing information between cohorts to increase power by assuming a graph linking predictors within and across different cohorts. The graph helps to identify pathways of functionally related genes and genes that are simultaneously prognostic in different cohorts. CONCLUSIONS: Results demonstrate that our proposed approach is superior to the standard approaches in terms of prediction performance and increased power in variable selection when the sample size is small.

Weighted Cox regression for the prediction of heterogeneous patient subgroups.

BACKGROUND: An important task in clinical medicine is the construction of risk prediction models for specific subgroups of patients based on high-dimensional molecular measurements such as gene expression data. Major objectives in modeling high-dimensional data are good prediction performance and feature selection to find a subset of predictors that are truly associated with a clinical outcome such as a time-to-event endpoint. In clinical practice, this task is challenging since patient cohorts are typically small and can be heterogeneous with regard to their relationship between predictors and outcome. When data of several subgroups of patients with the same or similar disease are available, it is tempting to combine them to increase sample size, such as in multicenter studies. However, heterogeneity between subgroups can lead to biased results and subgroup-specific effects may remain undetected. METHODS: For this situation, we propose a penalized Cox regression model with a weighted version of the Cox partial likelihood that includes patients of all subgroups but assigns them individual weights based on their subgroup affiliation. The weights are estimated from the data such that patients who are likely to belong to the subgroup of interest obtain higher weights in the subgroup-specific model. RESULTS: Our proposed approach is evaluated through simulations and application to real lung cancer cohorts, and compared to existing approaches. Simulation results demonstrate that our proposed model is superior to standard approaches in terms of prediction performance and variable selection accuracy when the sample size is small. CONCLUSIONS: The results suggest that sharing information between subgroups by incorporating appropriate weights into the likelihood can increase power to identify the prognostic covariates and improve risk prediction.

Spatio-Temporal Multiscale Analysis of Western Diet-Fed Mice Reveals a Translationally Relevant Sequence of Events during NAFLD Progression.

Mouse models of non-alcoholic fatty liver disease (NAFLD) are required to define therapeutic targets, but detailed time-resolved studies to establish a sequence of events are lacking. Here, we fed male C57Bl/6N mice a Western or standard diet over 48 weeks. Multiscale time-resolved characterization was performed using RNA-seq, histopathology, immunohistochemistry, intravital imaging, and blood chemistry; the results were compared to human disease. Acetaminophen toxicity and ammonia metabolism were additionally analyzed as functional readouts. We identified a sequence of eight key events: formation of lipid droplets; inflammatory foci; lipogranulomas; zonal reorganization; cell death and replacement proliferation; ductular reaction; fibrogenesis; and hepatocellular cancer. Functional changes included resistance to acetaminophen and altered nitrogen metabolism. The transcriptomic landscape was characterized by two large clusters of monotonously increasing or decreasing genes, and a smaller number of 'rest-and-jump genes' that initially remained unaltered but became differentially expressed only at week 12 or later. Approximately 30% of the genes altered in human NAFLD are also altered in the present mouse model and an increasing overlap with genes altered in human HCC occurred at weeks 30-48. In conclusion, the observed sequence of events recapitulates many features of human disease and offers a basis for the identification of therapeutic targets.

Prognostic Impact of Immunoglobulin Kappa C (IGKC) in Early Breast Cancer.

We studied the prognostic impact of tumor immunoglobulin kappa C (IGKC) mRNA expression as a marker of the humoral immune system in the FinHer trial patient population, where 1010 patients with early breast cancer were randomly allocated to either docetaxel-containing or vinorelbine-containing adjuvant chemotherapy. HER2-positive patients were additionally allocated to either trastuzumab or no trastuzumab. Hormone receptor-positive patients received tamoxifen. IGKC was evaluated in 909 tumors using quantitative real-time polymerase chain reaction, and the influence on distant disease-free survival (DDFS) was examined using univariable and multivariable Cox regression and Kaplan-Meier estimates. Interactions were analyzed using Cox regression. IGKC expression, included as continuous variable, was independently associated with DDFS in a multivariable analysis also including age, molecular subtype, grade, and pT and pN stage (HR 0.930, 95% CI 0.870-0.995, p = 0.034). An independent association with DDFS was also found in a subset analysis of triple-negative breast cancers (TNBC) (HR 0.843, 95% CI 0.724-0.983, p = 0.029), but not in luminal (HR 0.957, 95% CI 0.867-1.056, p = 0.383) or HER2-positive (HR 0.933, 95% CI 0.826-1.055, p = 0.271) cancers. No significant interaction between IGKC and chemotherapy or trastuzumab administration was detected (Pinteraction = 0.855 and 0.684, respectively). These results show that humoral immunity beneficially influences the DDFS of patients with early TNBC.

Gene Expression-Based Prediction of Neoadjuvant Chemotherapy Response in Early Breast Cancer: Results of the Prospective Multicenter EXPRESSION Trial.

PURPOSE: Expression-based classifiers to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) are not routinely used in the clinic. We aimed to build and validate a classifier for pCR after NACT. PATIENTS AND METHODS: We performed a prospective multicenter study (EXPRESSION) including 114 patients treated with anthracycline/taxane-based NACT. Pretreatment core needle biopsies from 91 patients were used for gene expression analysis and classifier construction, followed by validation in five external cohorts (n = 619). RESULTS: A 20-gene classifier established in the EXPRESSION cohort using a Youden index-based cut-off point predicted pCR in the validation cohorts with an accuracy, AUC, negative predictive value (NPV), positive predictive value, sensitivity, and specificity of 0.811, 0.768, 0.829, 0.587, 0.216, and 0.962, respectively. Alternatively, aiming for a high NPV by defining the cut-off point for classification based on the complete responder with the lowest predicted probability of pCR in the EXPRESSION cohort led to an NPV of 0.960 upon external validation. With this extreme-low cut-off point, a recommendation to not treat with anthracycline/taxane-based NACT would be possible for 121 of 619 unselected patients (19.5%) and 112 of 322 patients with luminal breast cancer (34.8%). The analysis of the molecular subtypes showed that the identification of patients who do not achieve a pCR by the 20-gene classifier was particularly relevant in luminal breast cancer. CONCLUSIONS: The novel 20-gene classifier reliably identifies patients who do not achieve a pCR in about one third of luminal breast cancers in both the EXPRESSION and combined validation cohorts.

Handling deviating control values in concentration-response curves.

In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC[Formula: see text] values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls: (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-effect range, low variances of the replicate measurements, and deviating controls, control values should be omitted before fitting the model. (3) When data are missing in the no-effect range, the Brain-Cousens model sometimes leads to better results than the default model.

TEAD-YAP Interaction Inhibitors and MDM2 Binders from DNA-Encoded Indole-Focused Ugi Peptidomimetics.

DNA-encoded combinatorial synthesis provides efficient and dense coverage of chemical space around privileged molecular structures. The indole side chain of tryptophan plays a prominent role in key, or "hot spot", regions of protein-protein interactions. A DNA-encoded combinatorial peptoid library was designed based on the Ugi four-component reaction by employing tryptophan-mimetic indole side chains to probe the surface of target proteins. Several peptoids were synthesized on a chemically stable hexathymidine adapter oligonucleotide "hexT", encoded by DNA sequences, and substituted by azide-alkyne cycloaddition to yield a library of 8112 molecules. Selection experiments for the tumor-relevant proteins MDM2 and TEAD4 yielded MDM2 binders and a novel class of TEAD-YAP interaction inhibitors that perturbed the expression of a gene under the control of these Hippo pathway effectors.

Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms.

BACKGROUND: With modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable. A less researched, but also relevant aspect for medical applications are costs of different biomarker candidates. These costs are often financial costs, but can also refer to other aspects, for example the decision between a painful biopsy marker and a simple urine test. In this paper, we propose extensions to two feature selection methods to control the total amount of such costs: greedy forward selection and genetic algorithms. In comprehensive simulation studies of binary classification tasks, we compare the predictive performance, the run-time and the detection rate of relevant features for the new proposed methods and five baseline alternatives to handle budget constraints. RESULTS: In simulations with a predefined budget constraint, our proposed methods outperform the baseline alternatives, with just minor differences between them. Only in the scenario without an actual budget constraint, our adapted greedy forward selection approach showed a clear drop in performance compared to the other methods. However, introducing a hyperparameter to adapt the benefit-cost trade-off in this method could overcome this weakness. CONCLUSIONS: In feature cost scenarios, where a total budget has to be met, common feature selection algorithms are often not suitable to identify well performing subsets for a modelling task. Adaptations of these algorithms such as the ones proposed in this paper can help to tackle this problem.

Model-based optimization of subgroup weights for survival analysis.

MOTIVATION: To obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building. RESULTS: We propose a subgroup-based weighted likelihood approach for survival prediction with high-dimensional genetic covariates. When predicting survival for a specific subgroup, for every other subgroup an individual weight determines the strength with which its observations enter into model building. MBO (model-based optimization) can be used to quickly find a good prediction model in the presence of a large number of hyperparameters. We use MBO to identify the best model for survival prediction of a specific subgroup by optimizing the weights for additional subgroups for a Cox model. The approach is evaluated on a set of lung cancer cohorts with gene expression measurements. The resulting models have competitive prediction quality, and they reflect the similarity of the corresponding cancer subgroups, with both weights close to 0 and close to 1 and medium weights. AVAILABILITY AND IMPLEMENTATION: mlrMBO is implemented as an R-package and is freely available at http://github.com/mlr-org/mlrMBO.

LIPG-promoted lipid storage mediates adaptation to oxidative stress in breast cancer.

Endothelial lipase (LIPG) is a cell surface associated lipase that displays phospholipase A1 activity towards phosphatidylcholine present in high-density lipoproteins (HDL). LIPG was recently reported to be expressed in breast cancer and to support proliferation, tumourigenicity and metastasis. Here we show that severe oxidative stress leading to AMPK activation triggers LIPG upregulation, resulting in intracellular lipid droplet accumulation in breast cancer cells, which supports survival. Neutralizing oxidative stress abrogated LIPG upregulation and the concomitant lipid storage. In human breast cancer, high LIPG expression was observed in a limited subset of tumours and was significantly associated with shorter metastasis-free survival in node-negative, untreated patients. Moreover, expression of PLIN2 and TXNRD1 in these tumours indicated a link to lipid storage and oxidative stress. Altogether, our findings reveal a previously unrecognized role for LIPG in enabling oxidative stress-induced lipid droplet accumulation in tumour cells that protects against oxidative stress, and thus supports tumour progression.

Prognostic Impact of Tumor Cell Programmed Death Ligand 1 Expression and Immune Cell Infiltration in NSCLC.

INTRODUCTION: Infiltration of T and B/plasma cells has been linked to NSCLC prognosis, but this has not been thoroughly investigated in relation to the expression of programmed death ligand 1 (PD-L1). Here, we determine the association of lymphocytes and PD-L1 with overall survival (OS) in two retrospective cohorts of operated NSCLC patients who were not treated with checkpoint inhibitors targeting the programmed death 1/PD-L1 axis. Moreover, we evaluate how PD-L1 positivity and clinicopathologic factors affect the prognostic association of lymphocytes. METHODS: Cluster of differentiation (CD) 3 (CD3)-, CD8-, CD4-, forkhead box P3 (FOXP3)-, CD20-, CD79A-, and immunoglobulin kappa constant (IGKC)-positive immune cells, and tumor PD-L1 positivity, were determined by immunohistochemistry on tissue microarrays (n = 705). Affymetrix data was analyzed for a patient subset, and supplemented with publicly available transcriptomics data (N = 1724). Associations with OS were assessed by Kaplan-Meier plots and uni- and multivariate Cox regression. RESULTS: Higher levels of T and B plasma cells were associated with longer OS (p = 0.004 and p < 0.001, for CD8 and IGKC, respectively). Highly proliferative tumors with few lymphocytes had the worst outcome. No association of PD-L1 positivity with OS was observed in a nonstratified patient population; however, a significant association with shorter OS was observed in never-smokers (p = 0.009 and p = 0.002, 5% and 50% cutoff). Lymphocyte infiltration was not associated with OS in PD-L1-positive tumors (50% cutoff). The prognostic association of lymphocyte infiltration also depended on the patients' smoking history and histologic subtype. CONCLUSIONS: Proliferation, PD-L1 status, smoking history, and histology should be considered if lymphocyte infiltration is to be used as a prognostic biomarker.

Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning 1 H NMR spectroscopy.

High-resolution magic angle spinning (HR MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly being used to study metabolite levels in human breast cancer tissue, assessing, for instance, correlations with prognostic factors, survival outcome or therapeutic response. However, the impact of intratumoral heterogeneity on metabolite levels in breast tumor tissue has not been studied comprehensively. More specifically, when biopsy material is analyzed, it remains questionable whether one biopsy is representative of the entire tumor. Therefore, multi-core sampling (n = 6) of tumor tissue from three patients with breast cancer, followed by lipid (0.9- and 1.3-ppm signals) and metabolite quantification using HR MAS 1 H NMR, was performed, resulting in the quantification of 32 metabolites. The mean relative standard deviation across all metabolites for the six tumor cores sampled from each of the three tumors ranged from 0.48 to 0.74. This was considerably higher when compared with a morphologically more homogeneous tissue type, here represented by murine liver (0.16-0.20). Despite the seemingly high variability observed within the tumor tissue, a random forest classifier trained on the original sample set (training set) was, with one exception, able to correctly predict the tumor identity of an independent series of cores (test set) that were additionally sampled from the same three tumors and analyzed blindly. Moreover, significant differences between the tumors were identified using one-way analysis of variance (ANOVA), indicating that the intertumoral differences for many metabolites were larger than the intratumoral differences for these three tumors. That intertumoral differences, on average, were larger than intratumoral differences was further supported by the analysis of duplicate tissue cores from 15 additional breast tumors. In summary, despite the observed intratumoral variability, the results of the present study suggest that the analysis of one, or a few, replicates per tumor may be acceptable, and supports the feasibility of performing reliable analyses of patient tissue.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.