Publications by authors named "Frederick Klauschen"

120 Publications

Artificial Intelligence and Pathology: from Principles to Practice and Future Applications in Histomorphology and Molecular Profiling.

Semin Cancer Biol 2021 Feb 22. Epub 2021 Feb 22.

Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pathology, Ludwig-Maximilians-Universität München, Thalkirchner Strasse, 80339 München, Germany. Electronic address:

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling and has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given its capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will support both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will likely transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.
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http://dx.doi.org/10.1016/j.semcancer.2021.02.011DOI Listing
February 2021

Immune-related gene expression predicts response to neoadjuvant chemotherapy but not additional benefit from PD-L1 inhibition in women with early triple-negative breast cancer.

Clin Cancer Res 2021 Feb 16. Epub 2021 Feb 16.

Institute of Pathology, Philipps-University Marburg and University Hospital Marburg

Purpose: We evaluated mRNA signatures to predict response to neoadjuvant PD-L1 inhibition in combination with chemotherapy in early triple-negative breast cancer.

Experimental Design: Targeted mRNA sequencing of 2559 transcripts was performed in FFPE samples from 162 patients of the GeparNuevo trial. We focused on validation of four predefined gene-signatures and differential gene expression analyses for new predictive markers.

Results: Two signatures (G6-Sig, IFN-Sig) were predictive for treatment response in a multivariate model including treatment arm (G6-Sig: OR 1.558, 95 % CI 1.130-2.182; P = 0.008, IFN-Sig: OR 1.695, 95 % CI 1.234-2.376; P = 0.002), while the CYT metric predicted pCR in the durvalumab arm, and the Prolif-Sig in the placebo arm. Expression of PD-L1 mRNA was associated with better response on both arms, indicating that increased levels of PD-L1 are a general predictor of neoadjuvant therapy response. In an exploratory analysis, we identified seven genes that were higher expressed in responders in the durvalumab arm but not the placebo arm: HLA-A, HLA-B, TAP1, GBP1, CXCL10, STAT1, CD38. These genes were associated with cellular antigen processing and presentation and interferon signaling.

Conclusions: Immune-associated signatures are associated with pCR after chemotherapy but might be of limited use for the prediction of response to additional immune-checkpoint blockade. Gene expression related to antigen presentation and interferon signaling might be interesting candidates for further evaluation.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-3113DOI Listing
February 2021

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

J Med Internet Res 2021 Feb 2;23(2):e23436. Epub 2021 Feb 2.

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Background: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems.

Objective: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects.

Methods: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%.

Results: A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin).

Conclusions: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.
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http://dx.doi.org/10.2196/23436DOI Listing
February 2021

High-protein diet more effectively reduces hepatic fat than low-protein diet despite lower autophagy and FGF21 levels.

Liver Int 2020 12 21;40(12):2982-2997. Epub 2020 Jul 21.

Department of Clinical Nutrition, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany.

Background And Aims: Non-alcoholic fatty liver disease (NAFLD) is becoming increasingly prevalent and nutrition intervention remains the most important therapeutic approach for NAFLD. Our aim was to investigate whether low- (LP) or high-protein (HP) diets are more effective in reducing liver fat and reversing NAFLD and which mechanisms are involved.

Methods: 19 participants with morbid obesity undergoing bariatric surgery were randomized into two hypocaloric (1500-1600 kcal/day) diet groups, a low protein (10E% protein) and a high protein (30E% protein), for three weeks prior to surgery. Intrahepatic lipid levels (IHL) and serum fibroblast growth factor 21 (FGF21) were measured before and after the dietary intervention. Autophagy flux, histology, mitochondrial activity and gene expression analyses were performed in liver samples collected during surgery.

Results: IHL levels decreased by 42.6% in the HP group, but were not significantly changed in the LP group despite similar weight loss. Hepatic autophagy flux and serum FGF21 increased by 66.7% and 42.2%, respectively, after 3 weeks in the LP group only. Expression levels of fat uptake and lipid biosynthesis genes were lower in the HP group compared with those in the LP group. RNA-seq analysis revealed lower activity of inflammatory pathways upon HP diet. Hepatic mitochondrial activity and expression of β-oxidation genes did not increase in the HP group.

Conclusions: HP diet more effectively reduces hepatic fat than LP diet despite of lower autophagy and FGF21. Our data suggest that liver fat reduction upon HP diets result primarily from suppression of fat uptake and lipid biosynthesis.
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http://dx.doi.org/10.1111/liv.14596DOI Listing
December 2020

Pemetrexed-Based Chemotherapy Is Inferior to Pemetrexed-Free Regimens in Thyroid Transcription Factor 1 (TTF-1)-Negative, EGFR/ALK-Negative Lung Adenocarcinoma: A Propensity Score Matched Pairs Analysis.

Clin Lung Cancer 2020 11 22;21(6):e607-e621. Epub 2020 May 22.

Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Division of Pulmonary Inflammation, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

Introduction: Thyroid transcription factor 1 (TTF-1) is a prognostic biomarker in lung adenocarcinoma; however, TTF-1-positive patients also display more favorable factors like actionable target mutations. In contrast, TTF-1-negative cancer is a poorly described entity. We performed a retrospective study to characterize a TTF-1-negative phenotype and to evaluate outcome depending on the chemotherapy regimen applied in the EGFR/ALK-negative population.

Patients And Methods: Phenotypic traits were analyzed in 741 patients with evaluable TTF-1 expression status, among them 529 patients with platinum-based first-line chemotherapy, with disease diagnosed between 2009 and 2016 at a tertiary referral university hospital. The influence of TTF-1 and several cofactors on progression-free survival and overall survival (OS) were analyzed using a 1:1 propensity score matching model, depending on the platinum doublet chemotherapy's incorporating pemetrexed or not, with subsequent Cox regression.

Results: TTF-1 negativity implied a distinct cancer phenotype with the predominance of male sex, worse Eastern Cooperative Oncology Group performance status, greater metastatic burden at primary diagnosis, and more adrenal gland metastases. These patients had improved progression-free survival (hazard ratio, 0.42; P = .001) and OS (hazard ratio, 0.40; P < .001) when gemcitabine-, taxane-, or vinorelbine-based regimens were provided instead of pemetrexed. None of the regimens was superior in TTF-1-positive patients with regard to OS. Overall, TTF-1 expression was strongly prognostic with a substantial increase in progression-free survival (hazard ratio, 0.54; P < .001) and OS (hazard ratio, 0.53; P < .001).

Conclusion: TTF-1 negativity is associated with a distinct cancer phenotype. Incorporation of this biomarker may be helpful when choosing an appropriate therapy regimen.
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http://dx.doi.org/10.1016/j.cllc.2020.05.014DOI Listing
November 2020

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis.

Lab Invest 2020 10 29;100(10):1288-1299. Epub 2020 Jun 29.

Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.
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http://dx.doi.org/10.1038/s41374-020-0455-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498367PMC
October 2020

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer.

Cancers (Basel) 2020 06 17;12(6). Epub 2020 Jun 17.

Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany.

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.
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http://dx.doi.org/10.3390/cancers12061604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352768PMC
June 2020

Distinct immune evasion in APOBEC-enriched, HPV-negative HNSCC.

Int J Cancer 2020 Oct 18;147(8):2293-2302. Epub 2020 Jun 18.

Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Hindenburgdamm 30, Berlin, 12203, Germany.

Immune checkpoint inhibition leads to response in some patients with head and neck squamous cell carcinoma (HNSCC). Robust biomarkers are lacking to date. We analyzed viral status, gene expression signatures, mutational load and mutational signatures in whole exome and RNA-sequencing data of the HNSCC TCGA dataset (n = 496) and a validation set (DKTK MASTER cohort, n = 10). Public single-cell gene expression data from 17 HPV-negative HNSCC were separately reanalyzed. APOBEC3-associated TCW motif mutations but not total single nucleotide variant burden were significantly associated with inflammation. This association was restricted to HPV-negative HNSCC samples. An APOBEC-enriched, HPV-negative subgroup was identified, that showed higher T-cell inflammation and immune checkpoint expression, as well as expression of APOBEC3 genes. Mutations in immune-evasion pathways were also enriched in these tumors. Analysis of single-cell sequencing data identified expression of APOBEC3B and 3C genes in malignant cells. We identified an APOBEC-enriched subgroup of HPV-negative HNSCC with a distinct immunogenic phenotype, potentially mediating response to immunotherapy.
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http://dx.doi.org/10.1002/ijc.33123DOI Listing
October 2020

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods.

Sci Rep 2020 04 14;10(1):6423. Epub 2020 Apr 14.

Singapore University of Technology and Design, ISTD Pillar, Singapore, 487372, Singapore.

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, many explanation methods have emerged. This work shows how heatmaps generated by these explanation methods allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. We elaborate on biases which are typically inherent in histopathological image data. In the binary classification task of tumour tissue discrimination in publicly available haematoxylin-eosin-stained images of various tumour entities, we investigate three types of biases: (1) biases which affect the entire dataset, (2) biases which are by chance correlated with class labels and (3) sampling biases. While standard analyses focus on patch-level evaluation, we advocate pixel-wise heatmaps, which offer a more precise and versatile diagnostic instrument. This insight is shown to not only be helpful to detect but also to remove the effects of common hidden biases, which improves generalisation within and across datasets. For example, we could see a trend of improved area under the receiver operating characteristic (ROC) curve by 5% when reducing a labelling bias. Explanation techniques are thus demonstrated to be a helpful and highly relevant tool for the development and the deployment phases within the life cycle of real-world applications in digital pathology.
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http://dx.doi.org/10.1038/s41598-020-62724-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156509PMC
April 2020

Mutations Predict Sensitivity to Adjuvant Gemcitabine in Patients with Pancreatic Ductal Adenocarcinoma: Next-Generation Sequencing Results from the CONKO-001 Trial.

Clin Cancer Res 2020 Jul 31;26(14):3732-3739. Epub 2020 Mar 31.

Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

Purpose: We performed next-generation sequencing (NGS) in the CONKO-001 phase III trial to identify clinically relevant prognostic and predictive mutations and conducted a functional validation in The Cancer Genome Atlas (TCGA) sequencing data.

Experimental Design: Patients of the CONKO-001 trial received curatively intended surgery for pancreatic adenocarcinoma (PDAC) followed by adjuvant chemotherapy with gemcitabine (Gem) or observation only (Obs). Tissue samples of 101 patients were evaluated by NGS of 37 genes. Cox proportional hazard models were applied for survival analysis. In addition, functional genomic analyses were performed in an NGS and RNA-sequencing dataset of 146 pancreatic tumors from TCGA.

Results: The most common mutations in the CONKO cohort were (75%), (60%), (10%), (9%), as well as SWI/SNF (12%) complex alterations. In untreated patients, mutations were a negative prognostic factor for disease-free survival (DFS; HR mut vs. WT 2.434, = 0.005). With respect to gemcitabine treatment, mutations were a positive predictive factor for gemcitabine efficacy [mut: HR for DFS Gem vs. Obs, 0.235 (0.130 - 0.423; < 0.001); wt: HR for DFS Gem vs. Obs, 0.794 (0.417 - 1.513; = 0.483)] with a significant test for interaction ( = 0.003). In the TCGA dataset, mutations were associated with shortened DFS.

Conclusions: In CONKO-001, the benefit from adjuvant gemcitabine was confined to the mut patient group. This potentially clinical relevant observation needs to be confirmed in independent prospective studies. The sensitivity of TP53mut PDAC to gemcitabine in CONKO-001 provides a lead for further mechanistic investigations.
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http://dx.doi.org/10.1158/1078-0432.CCR-19-3034DOI Listing
July 2020

Support of a molecular tumour board by an evidence-based decision management system for precision oncology.

Eur J Cancer 2020 03 23;127:41-51. Epub 2020 Jan 23.

Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany. Electronic address:

Background: Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses.

Methods: A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up.

Results: One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients.

Conclusion: The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies.
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http://dx.doi.org/10.1016/j.ejca.2019.12.017DOI Listing
March 2020

Human leucocyte antigen class I in hormone receptor-positive, HER2-negative breast cancer: association with response and survival after neoadjuvant chemotherapy.

Breast Cancer Res 2019 12 11;21(1):142. Epub 2019 Dec 11.

German Breast Group Forschungs GmbH, Neu-Isenburg, Germany.

Background: Clinical application of cancer immunotherapy requires a better understanding of tumor immunogenicity and the tumor microenvironment. HLA class I molecules present antigens to CD8 cytotoxic cells. Their loss or downregulation is frequently found in tumors resulting in reduced T cell responses and worse prognosis.

Methods: We evaluated HLA class I heavy chain expression by immunohistochemistry in 863 biopsies (GeparTrio trial). Patients received neoadjuvant chemotherapy and adjuvant endocrine treatment if tumors were hormone receptor-positive (HR+). In parallel, the expression of HLA-A was analyzed using a microarray cohort of 320 breast cancer patients from the MD Anderson Cancer Center. We evaluated its association with clinical outcome, tumor-infiltrating lymphocytes (TILs), and immune cell metagenes.

Results: In HR+/HER2- breast cancer, HLA class I heavy chain expression was associated with increased TILs and better response to chemotherapy (7% vs. 14% pCR rate, P = 0.029), but worse disease-free survival (hazard ratio (HR) 1.6 (1.1-2.4); P = 0.024). The effect was significant in a multivariate model adjusted for clinical and pathological variables (HR 1.7 (1.1-2.6); P = 0.016) and was confirmed by analysis of HLA-A in a microarray cohort. HLA-A was correlated to most immune cell metagenes. There was no association with response or survival in triple-negative or HER2+ disease.

Conclusions: The study confirms the negative prognostic role of lymphocytes in HR+ breast cancer and points at a complex interaction between chemotherapy, endocrine treatment, and tumor immunogenicity. The results point at a subtype-specific and potentially treatment-specific role of tumor-immunological processes in breast cancer with different implications in triple-negative and hormone receptor-positive disease.
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http://dx.doi.org/10.1186/s13058-019-1231-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907189PMC
December 2019

Next generation sequencing of lung adenocarcinoma subtypes with intestinal differentiation reveals distinct molecular signatures associated with histomorphology and therapeutic options.

Lung Cancer 2019 12 11;138:43-51. Epub 2019 Oct 11.

Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Institute of Pathology, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:

Objectives: We aim to provide a better understanding of the molecular landscape of primary lung adenocarcinomas with intestinal differentiation.

Material And Methods: Five invasive mucinous adenocarcinomas (IMA) and seven pulmonary enteric adenocarcinomas (PEAD) were included in this study. Furthermore, we analyzed six pulmonary colloid adenocarcinomas (CAD), including one primary tumor, one metastasis, and two sample pairs consisting of the primary colloid lung tumor and a matching metastasis and an acinar component, respectively. All samples were characterized using immunohistochemistry (TTF-1, CK7, CK20, CDX2, Ki-67, ALK and PD-L1) and a next generation sequencing panel covering 404 cancer-related genes (FoundationOne® gene panel).

Results And Conclusion: While Ki-67 expression was comparably low in IMA (range: 8-15%) and in primary CAD (range: 5-8%), we observed considerably higher proliferation rates in the non-colloid tumor compartment (16%) and metastases (72%) from CAD, as well as in the PEAD-group (36-71%). The overall tumor mutational burden was lowest in IMA (2.5 mutations per megabase), intermediate in CAD (5.8 mutations per megabase) and highest in PEAD (16.8 mutations per megabase). KRAS mutations were frequent in all three tumor subtypes, but TP53 mutations were mostly limited to PEAD. While chromosomal alterations were rare in IMA, we discovered MYC amplifications in three of four CAD. Comparing primary and metastatic CAD, we observed the acquisition of multiple mutations and chromosomal alterations. PEAD had a variety of chromosomal alterations, including two cases with RICTOR amplification. PD-L1 expression (20%, 50% and 80% of tumor cells) was limited to three PEAD samples, only. In conclusion, we provide a detailed insight into the molecular alterations across and within the different subtypes of pulmonary adenocarcinomas with intestinal differentiation. From a clinical perspective, we provide data on potential treatment strategies for patients with PEAD, including immunotherapy.
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http://dx.doi.org/10.1016/j.lungcan.2019.10.005DOI Listing
December 2019

Liquid biopsy assessment of synchronous malignancies: a case report and review of the literature.

ESMO Open 2019 16;4(4):e000528. Epub 2019 Aug 16.

Charite Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Assessment of patients with synchronous primary cancers and metastases is challenging, as it can be difficult to assign the metastases to the correct primary due to low differentiation, high similarity on histology or inaccessibility of tumour tissue. Systemic treatment for metastatic disease, however, needs to be directed at the leading histology or cover multiple tumour types with the same regimen. Considering the additional obstacles in cancer management, including tumour heterogeneity and clonal evolution, blood-based genomic profiling ('liquid biopsy') is suggested to be a useful tool to provide accessible tumour-derived biomarkers. We herein report a case of a patient with independent primary tumours of the colon and pancreas, as well as liver metastases. All lesions were resected and genotyped revealing mutations G12C and G12D in the primary tumours, respectively. The G12D mutation detected in the pancreatic tumour was retrieved in the metastasis, thus confirming the pancreatic cancer to be the origin of the liver lesions. The prevalence of the pancreatic tumour was additionally verified by the detection of the G12D variant in circulating cell-free DNA (cfDNA). This case demonstrates the utility of liquid biopsy to identify the predominant tumour burden in patients with multiple primary cancers, based on the detection of the tumour-associated gene mutation in the plasma. Serial monitoring through liquid biopsies might allow disease surveillance to guide cancer management. The review of the literature highlights the importance of liquid biopsies in personalised oncology, even though only one case report refers to the benefit of cfDNA analysis in a patient affected by synchronous primary tumours.
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http://dx.doi.org/10.1136/esmoopen-2019-000528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735668PMC
August 2019

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.

Sci Transl Med 2019 09;11(509)

Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany.

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.
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http://dx.doi.org/10.1126/scitranslmed.aaw8513DOI Listing
September 2019

Immunohistochemical analysis of Bcl-2, nuclear S100A4, MITF and Ki67 for risk stratification of early-stage melanoma - A combined IHC score for melanoma risk stratification.

J Dtsch Dermatol Ges 2019 Aug;17(8):800-808

Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Background And Objectives: Overall survival (OS) in patients with early-stage malignant melanoma differs. To date, there are no established prognostic markers. We aimed to contribute to a better understanding of potential prognostic immunohistochemical markers for risk stratification.

Patients And Methods: 161 surgically resected early-stage malignant melanomas (stage pT1 and pT2) were analyzed for expression of 20 different proteins using immunohistochemistry. The results were correlated with OS. The cohort was randomly split into a discovery and a validation cohort.

Results: High Bcl-2 expression, high nuclear S100A4 expression as well as a Ki67 proliferation index of ≥ 20 % were associated with shorter OS. Strong MITF immunoreactivity was a predictor for favorable prognosis. A combination of these four markers resulted in a multi-marker score with significant prognostic value in multivariate survival analysis (HR: 3.704; 95 % CI 1.484 to 9.246; p = 0.005). Furthermore, the score was able to differentiate a low-risk group with excellent OS rates (five-year survival rate: 100 %), an intermediate-risk group (five-year survival rate: 81.8 %) and a high-risk group (five-year survival rate: 52.6 %). The prognostic value was confirmed within the validation cohort.

Conclusions: Combined immunohistochemical analysis of Bcl-2, nuclear S100A4, Ki67 and MITF could contribute to better risk stratification of early-stage malignant melanoma patients.
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http://dx.doi.org/10.1111/ddg.13917DOI Listing
August 2019

Immunologic Profiling of Mutational and Transcriptional Subgroups in Pediatric and Adult High-Grade Gliomas.

Cancer Immunol Res 2019 Sep 2;7(9):1401-1411. Epub 2019 Jul 2.

Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Immunologic treatment strategies are under investigation for high-grade gliomas. Determining relevant immunologic pathways is required for invigorating a tumor-specific immune response. We therefore investigated the immunologic phenotypes within different subgroups of high-grade gliomas, with a focus on rare genetic subgroups of pediatric and adolescent patients to identify potentially targetable mechanisms. We gathered published gene-expression data from 1,135 high-grade glioma patients and applied a machine-learning technique to determine their transcriptional (mesenchymal, classic, neural, and proneural) and mutational [K27, G34, IDH, and wild type (WT)] subtypes. Gene signatures of infiltrating immune cells and functional immune pathways were evaluated in correlation to histologic diagnosis, age, and transcriptional and mutational subgroups. Our analysis identified four distinct microenvironmental signatures of immune cell infiltration (immune 1-4), which can be stratified into vascular, monocytic/stromal, monocytic/T-cell-, and antigen-presenting cell (APC)/natural killer (NK) cell/T-cell-dominated immune clusters. Immune cell expression profiles correlated with transcriptional and mutational subgroups but were independent of age and histologic diagnosis. By including functional pathways and correlating the expression of immunostimulatory and -inhibitory receptor-ligand interactions, we were able to define the immunologic microenvironment and identify possible immunologic subtypes associated with poor prognosis. In addition, comparison of overall survival with the immunologic landscape and with checkpoint molecules revealed correlations within the transcriptional and mutational subgroups, highlighting the potential application of PD-1/PD-L1 checkpoint inhibition in K27-mutated tumors. Our study shows that transcriptional and mutational subgroups are characterized by distinct immunologic tumor microenvironments, demonstrating the immunologic heterogeneity within high-grade gliomas and suggesting an immune-specific stratification for upcoming immunotherapy trials.
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http://dx.doi.org/10.1158/2326-6066.CIR-18-0939DOI Listing
September 2019

Clinical Impact of Rare and Compound Mutations of Epidermal Growth Factor Receptor in Patients With Non-Small-Cell Lung Cancer.

Clin Lung Cancer 2019 09 11;20(5):350-362.e4. Epub 2019 May 11.

Institute of Pathology, University Hospital Carl Gustav Carus, TU Dresden, Germany.

Background: Standard therapy of advanced non-small-cell lung cancer harboring an activating mutation in the epidermal growth factor receptor (EGFR) gene is treatment with tyrosine kinase inhibitors (TKI). However, for rare and compound mutations of the EGFR gene, the clinical evidence of TKI therapy is still unclear.

Patients And Methods: A total of 2906 lung cancer samples were analyzed for EGFR mutations during routine analysis between 2010 and 2017. The samples have been investigated by Sanger sequencing and since 2014 by next-generation sequencing.

Results: We detected EGFR mutations in 408 specimens (14%). Among these, we found 41 samples with rare and 22 with compound mutations. In these 63 samples, 56 different rare EGFR mutations occurred. Information about the clinical outcome was available for 37. Among those with rare mutations, only one patient harboring the mutation p.G874D had disease that responded to first-generation TKI therapy. In contrast, the disease of all patients with compound mutations responded to first- or second-generation TKI therapy. Furthermore, we collected data on clinical relevance regarding TKI therapy from different databases and from an additional literature search, and only found data for 36 of the 56 detected rare mutations.

Conclusion: Information about the clinical outcome of patients with rare and compound EGFR mutations remains limited. At present, second- and third-generation TKIs are available, which may represent new treatment strategies for these patients. Therefore, it is becoming increasingly important to maintain databases concerning rare EGFR mutations.
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http://dx.doi.org/10.1016/j.cllc.2019.04.012DOI Listing
September 2019

Histomorphological and molecular profiling: friends not foes! Morpho-molecular analysis reveals agreement between histological and molecular profiling.

Histopathology 2019 Nov 5;75(5):694-703. Epub 2019 Sep 5.

Institute of Pathology, Charité Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany.

Aims: Whereas current cancer diagnosis largely relies on the well-established organ and tissue typing of tumours, partially complemented by molecular properties, the comprehensive molecular profiling efforts of recent years have stimulated proposals for molecular reclassifications of tumours independently of anatomical origin. Proposals based only on mutational profiles show the least concordance with histotypes, whereas greater concordance is achieved when various genomic and proteomic data are included.

Methods And Results: The most comprehensive molecular reclassification of tumours, by Hoadley et  al (Cell, 158, 2014; 929) and Hoadley et  al (Cell, 173, 2018; 291), integrated multi-omics data, and proposes novel molecular tumour classes. To investigate the relationship between the proposed molecular classes and the original histological tumour types, we re-examined the histomorphology of molecularly reclassified cases. Our results show that the claimed molecular reclassification is associated with and explainable by specific histological subtypes in 70% of the reclassified cases.

Conclusion: Therefore, in contrast to the proclaimed reclassification and independence of molecular and histological tumour types, our analysis demonstrates that comprehensive molecular profiling, which includes gene expression and methylation as well as proteomic profiling in addition to mutational analyses, is largely consistent with histomorphological tumour properties.
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http://dx.doi.org/10.1111/his.13930DOI Listing
November 2019

Regulatory T cells differ from conventional CD4 T cells in their recirculatory behavior and lymph node transit times.

Immunol Cell Biol 2019 10 17;97(9):787-798. Epub 2019 Jun 17.

Department of Physiology, Department of Microbiology and Immunology, McGill Research Centre for Complex Traits, McGill University, Montreal, QC, Canada.

Regulatory T cells (Tregs) continuously suppress autoreactive immune responses within tissues to prevent autoimmunity, yet the recirculatory behavior of Tregs between and within tissues enabling the maintenance of peripheral tolerance remains incompletely defined. Here, we quantified homing efficiency to and the dwell time of Tregs within secondary lymphoid organs (SLOs) and used intravital two-photon microscopy to measure Treg surveillance behavior of dendritic cells. Tregs homed substantially less efficiently to SLOs compared with conventional CD4 T cells (Tconvs), despite similar expression of homing receptors. Tregs remained on average 2-3 times longer within the LN than Tconvs before exiting, and retained Tregs differed from recirculating Tregs in phenotype, motility and interaction duration with dendritic cells. Taken together, these data revealed fundamental differences in Treg versus conventional T cell in vivo recirculation and migration behaviors, identified a Treg population with prolonged LN dwell time, and provided quantitative insight into their spatiotemporal behavior within LNs.
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http://dx.doi.org/10.1111/imcb.12276DOI Listing
October 2019

Clinical and analytical validation of Ki-67 in 9069 patients from IBCSG VIII + IX, BIG1-98 and GeparTrio trial: systematic modulation of interobserver variance in a comprehensive in silico ring trial.

Breast Cancer Res Treat 2019 Aug 7;176(3):557-568. Epub 2019 May 7.

International Breast Cancer Study Group Central Pathology Office, Department of Pathology and Laboratory Medicine, IEO European Institute of Oncology IRCCS, Milan, Italy.

Purpose: Ki-67 has been clinically validated for risk assessment in breast cancer, but the analytical validation and cutpoint-definition remain a challenge. Intraclass correlation coefficients (ICCs) are a statistical parameter for Ki-67 interobserver performance. However, the maximum degree of variance among pathologists allowed for meaningful biomarker results has not been defined.

Methods: Different amounts of variance were added to central pathology Ki-67 data (n = 9069) from three cohorts (IBCSGVIII + IX, BIG1-98, GeparTrio) by simulation of 4500 evaluations for each cohort, which were grouped by ICCs, ranging from excellent (ICC = 0.9) to poor concordance (ICC = 0.1). Endpoints were disease-free survival (DFS) and pathological complete response (pCR, GeparTrio).

Results: Ki-67 was a significant continuous prognostic marker for DFS over a wide range of cutpoints between 8% and 30% in all three cohorts. In our modelling approach, Ki-67 was a stable prognostic marker despite increased interpathologist variance. Even for a poor ICC of 0.5, one or more significant Ki-67 cutoffs were detected in 86.8% (GeparTrio), 92.4% (IBCSGVIII + IX) and 100% of analyses (BIG1-98). Similarly, in GeparTrio, even with an extremely low ICC of 0.2, 99.6% of analyses were significant for pCR.

Conclusions: Our study shows that Ki-67 is a continuous marker which is extremely robust to pathologist variation. Even if only 50% of variance is attributable to true Ki-67-based proliferation (ICC = 0.5), this information is sufficient to obtain statistically significant differences in clinical cohorts. This stable performance explains the observation that many Ki-67 studies achieve significant results despite relevant interobserver variance and points to a high clinical validity of this biomarker. For clinical decisions based on analysis of individual patient data, ongoing efforts to further reduce interobserver variability, including ring trials and standardized guidelines as well as image analysis approaches, should be continued.
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http://dx.doi.org/10.1007/s10549-018-05112-9DOI Listing
August 2019

Variant classification in precision oncology.

Int J Cancer 2019 12 21;145(11):2996-3010. Epub 2019 May 21.

Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.

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.
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http://dx.doi.org/10.1002/ijc.32358DOI Listing
December 2019

Mutational Diversity and Therapy Response in Breast Cancer: A Sequencing Analysis in the Neoadjuvant GeparSepto Trial.

Clin Cancer Res 2019 07 12;25(13):3986-3995. Epub 2019 Apr 12.

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Berlin, Germany.

Purpose: Next-generation sequencing (NGS) can be used for comprehensive investigation of molecular events in breast cancer. We evaluated the relevance of genomic alterations for response to neoadjuvant chemotherapy (NACT) in the GeparSepto trial.

Experimental Design: Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational (, and ) and 8 genes for copy-number alteration analysis (, and ).

Results: The most common genomic alterations were mutations of (38.4%) and (21.5%), and 8 different amplifications ( 34.9%; 30.6%; 30.1%; 21.9%; 24.1%; 17.7%; 14.9%; 12.6%). All other alterations had a prevalence of less than 5%. The genetic heterogeneity in different breast cancer subtypes [lum/HER2neg vs. HER2pos vs. triple-negative breast cancer (TNBC)] was significantly linked to differences in NACT response. A significantly reduced pathologic complete response rate was observed in -mutated breast cancer [mut: 23.0% vs. wild-type (wt) 38.8%, < 0.0001] in particular in the HER2pos subcohort [multivariate OR = 0.43 (95% CI, 0.24-0.79), = 0.006]. An increased response to nab-paclitaxel was observed only in wt breast cancer, with univariate significance for the complete cohort ( = 0.009) and the TNBC ( = 0.013) and multivariate significance in the HER2pos subcohort (test for interaction = 0.0074).

Conclusions: High genetic heterogeneity was observed in different breast cancer subtypes. Our study shows that FFPE-based NGS can be used to identify markers of therapy resistance in clinical study cohorts. mutations could be a major mediator of therapy resistance in breast cancer.
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http://dx.doi.org/10.1158/1078-0432.CCR-18-3258DOI Listing
July 2019

Somatic genome alterations in relation to age in lung adenocarcinoma.

Int J Cancer 2019 10 28;145(8):2091-2099. Epub 2019 Mar 28.

Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Imperial College London, London, W120NN, United Kingdom.

Lung adenocarcinoma (LUAD) is the most common cause of global cancer-related mortality and the major risk factor is smoking consumption. By analyzing 486 LUAD samples from The Cancer Genome Atlas, we detected a higher mutational burden among younger patients in the global cohort as well as in the TP53-mutated subcohort. The interaction effect of patient age and TP53 mutations significantly affected the mutational rate of younger TP53-mutated patients. Furthermore, we detected a significant enrichment of the smoking-related signature SI4 (SI4) among younger TP53-mutated patients, meanwhile the age-related Signature 1 (SI1) significantly increased in proportion to patient age. Although present and past smoking is reported in the TP53 wild-type patients, we observed a lower average number of somatic mutations, with no correlation with patient age. Overall, TP53 mutations were significantly higher in younger patients and mainly characterized by SI4 and Signature 24 (SI24). Therefore, TP53 seemed to acquire a particular sensitivity to smoking related C>A mutations in younger patients. We hypothesize that TP53 mutations at a younger age might be a crucial factor enhancing the sensitivity to smoking-related mutations leading to a burst of somatic alterations. The mutational profile of cancer cell might reflect the mutational processes operative in aging in a given tissue. Therefore, TP53-mutated and TP53 wild-type patient groups might represent phenotypes which endure aging-related mutational processes with different strength. Our study provides indications of age-dependent differences in mutational backgrounds that might be relevant for cancer prevention and age-adjusted treatment approaches.
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http://dx.doi.org/10.1002/ijc.32265DOI Listing
October 2019

Tissue clonality of dendritic cell subsets and emergency DCpoiesis revealed by multicolor fate mapping of DC progenitors.

Sci Immunol 2019 03;4(33)

Immunobiology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.

Conventional dendritic cells (cDCs) are found in all tissues and play a key role in immune surveillance. They comprise two major subsets, cDC1 and cDC2, both derived from circulating precursors of cDCs (pre-cDCs), which exited the bone marrow. We show that, in the steady-state mouse, pre-cDCs entering tissues proliferate to give rise to differentiated cDCs, which themselves have residual proliferative capacity. We use multicolor fate mapping of cDC progenitors to show that this results in clones of sister cDCs, most of which comprise a single cDC1 or cDC2 subtype, suggestive of pre-cDC commitment. Upon infection, a surge in the influx of pre-cDCs into the affected tissue dilutes clones and increases cDC numbers. Our results indicate that tissue cDCs can be organized in a patchwork of closely positioned sister cells of the same subset whose coexistence is perturbed by local infection, when the bone marrow provides additional pre-cDCs to meet increased tissue demand.
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http://dx.doi.org/10.1126/sciimmunol.aaw1941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420147PMC
March 2019

Systems proteogenomics for precision oncology.

Oncotarget 2019 Jan 22;10(7):692-693. Epub 2019 Jan 22.

Frederick Klauschen: Systems Pathology Group, Institute of Pathology, Charité Universitätsmedizin Berlin, Germany; German Cancer Consortium, Berlin Partner Site and German Cancer Research Center, Heidelberg, Germany.

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http://dx.doi.org/10.18632/oncotarget.26601DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366822PMC
January 2019

DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer.

Mod Pathol 2019 06 5;32(6):855-865. Epub 2019 Feb 5.

Department of Neuropathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.

Pulmonary enteric adenocarcinoma is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome-wide methylation profiles of 600 primary pulmonary, colorectal, and upper gastrointestinal adenocarcinomas obtained from The Cancer Genome Atlas and the Gene Expression Omnibus database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal adenocarcinomas, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed methylation data of 15 pulmonary enteric adenocarcinomas as well as four pulmonary metastases and four primary colorectal adenocarcinomas with the algorithm. All 15 pulmonary enteric adenocarcinomas were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal cancer samples were identified as colorectal adenocarcinomas. In a t-distributed stochastic neighbor embedding analysis, the pulmonary enteric adenocarcinoma samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary cancers. Additional characterization of the pulmonary enteric adenocarcinoma series using fluorescence in situ hybridization, next-generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (67%), the molecular data was mostly reminiscent of standard pulmonary adenocarcinomas. In conclusion, we provide sound evidence of the pulmonary origin of pulmonary enteric adenocarcinomas and in addition provide a publicly available machine learning-based algorithm to reliably distinguish these tumors from metastatic colorectal cancer.
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http://dx.doi.org/10.1038/s41379-019-0207-yDOI Listing
June 2019

Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers.

Genome Med 2018 11 15;10(1):83. Epub 2018 Nov 15.

Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.

Background: Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers.

Methods: To determine effects of oncogenic mutations on protein profiles, we used the energy distance, which compares the Euclidean distances of protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers.

Results: We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings.

Conclusions: This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs.
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http://dx.doi.org/10.1186/s13073-018-0591-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238410PMC
November 2018