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2.
Eur J Cancer ; 157: 464-473, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34649117

RESUMO

BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). OBJECTIVES: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). METHODS: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. RESULTS: On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. CONCLUSION: Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

3.
Euro Surveill ; 26(34)2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34448448

RESUMO

BackgroundSchool attendance during the COVID-19 pandemic is intensely debated.AimIn November 2020, we assessed SARS-CoV-2 infections and seroreactivity in 24 randomly selected school classes and connected households in Berlin, Germany.MethodsWe collected oro-nasopharyngeal swabs and blood samples, examining SARS-CoV-2 infection and IgG antibodies by RT-PCR and ELISA. Household members self-swabbed. We assessed individual and institutional prevention measures. Classes with SARS-CoV-2 infection and connected households were retested after 1 week.ResultsWe examined 1,119 participants, including 177 primary and 175 secondary school students, 142 staff and 625 household members. SARS-CoV-2 infection occurred in eight classes, affecting each 1-2 individuals. Infection prevalence was 2.7% (95% confidence interval (CI): 1.2-5.0; 9/338), 1.4% (95% CI: 0.2-5.1; 2/140), and 2.3% (95% CI: 1.3-3.8; 14/611) among students, staff and household members. Six of nine infected students were asymptomatic at testing. We detected IgG antibodies in 2.0% (95%CI: 0.8-4.1; 7/347), 1.4% (95% CI: 0.2-5.0; 2/141) and 1.4% (95% CI: 0.6-2.7; 8/576). Prevalence increased with inconsistent facemask-use in school, walking to school, and case-contacts outside school. For three of nine households with infection(s), origin in school seemed possible. After 1 week, no school-related secondary infections appeared in affected classes; the attack rate in connected households was 1.1%.ConclusionSchool attendance under rigorously implemented preventive measures seems reasonable. Balancing risks and benefits of school closures need to consider possible spill-over infection into households. Deeper insight is required into the infection risks due to being a schoolchild vs attending school.


Assuntos
COVID-19 , SARS-CoV-2 , Berlim , Estudos Transversais , Alemanha/epidemiologia , Humanos , Pandemias , Instituições Acadêmicas
4.
Eur J Cancer ; 155: 191-199, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34388516

RESUMO

BACKGROUND: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. OBJECTIVE: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. METHODS: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. RESULTS: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. CONCLUSIONS: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.

5.
Eur J Cancer ; 155: 200-215, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34391053

RESUMO

BACKGROUND: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology. METHODS: Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility. RESULTS: Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation. CONCLUSIONS: Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.

6.
Lancet Respir Med ; 9(11): 1255-1265, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34391547

RESUMO

BACKGROUND: Heterologous vaccine regimens have been widely discussed as a way to mitigate intermittent supply shortages and to improve immunogenicity and safety of COVID-19 vaccines. We aimed to assess the reactogenicity and immunogenicity of heterologous immunisations with ChAdOx1 nCov-19 (AstraZeneca, Cambridge, UK) and BNT162b2 (Pfizer-BioNtech, Mainz, Germany) compared with homologous BNT162b2 and ChAdOx1 nCov-19 immunisation. METHODS: This is an interim analysis of a prospective observational cohort study enrolling health-care workers in Berlin (Germany) who received either homologous ChAdOx1 nCov-19 or heterologous ChAdOx1 nCov-19-BNT162b2 vaccination with a 10-12-week vaccine interval or homologous BNT162b2 vaccination with a 3-week vaccine interval. We assessed reactogenicity after the first and second vaccination by use of electronic questionnaires on days 1, 3, 5, and 7. Immunogenicity was measured by the presence of SARS-CoV-2-specific antibodies (full spike-IgG, S1-IgG, and RBD-IgG), by an RBD-ACE2 binding inhibition assay (surrogate SARS-CoV-2 virus neutralisation test), a pseudovirus neutralisation assay against two variants of concerns (alpha [B.1.1.7] and beta [B.1.351]), and anti-S1-IgG avidity. T-cell reactivity was measured by IFN-γ release assay. FINDINGS: Between Dec 27, 2020, and June 14, 2021, 380 participants were enrolled in the study, with 174 receiving homologous BNT162b2 vaccination, 38 receiving homologous ChAdOx1 nCov-19 vaccination, and 104 receiving ChAdOx1 nCov-19-BNT162b2 vaccination. Systemic symptoms were reported by 103 (65%, 95% CI 57·1-71·8) of 159 recipients of homologous BNT162b2, 14 (39%, 24·8-55·1) of 36 recipients of homologous ChAdOx1 nCov-19, and 51 (49%, 39·6-58·5) of 104 recipients of ChAdOx1 nCov-19-BNT162b2 after the booster immunisation. Median anti-RBD IgG levels 3 weeks after boost immunisation were 5·4 signal to cutoff ratio (S/co; IQR 4·8-5·9) in recipients of homologous BNT162b2, 4·9 S/co (4·3-5·6) in recipients of homologous ChAdOx1 nCov-19, and 5·6 S/co (5·1-6·1) in recipients of ChAdOx1 nCov-19- BNT162b2. Geometric mean of 50% inhibitory dose against alpha and beta variants were highest in recipients of ChAdOx1 nCov-19-BNT162b2 (956·6, 95% CI 835·6-1095, against alpha and 417·1, 349·3-498·2, against beta) compared with those in recipients of homologous ChAdOx1 nCov-19 (212·5, 131·2-344·4, against alpha and 48·5, 28·4-82·8, against beta; both p<0·0001) or homologous BNT162b2 (369·2, 310·7-438·6, against alpha and 72·4, 60·5-86·5, against beta; both p<0·0001). SARS-CoV-2 S1 T-cell reactivity 3 weeks after boost immunisation was highest in recipients of ChAdOx1 nCov-19-BNT162b2 (median IFN-γ concentration 4762 mIU/mL, IQR 2723-8403) compared with that in recipients of homologous ChAdOx1 nCov-19 (1061 mIU/mL, 599-2274, p<0·0001) and homologous BNT162b2 (2026 mIU/mL, 1459-4621, p=0·0008) vaccination. INTERPRETATION: The heterologous ChAdOx1 nCov-19-BNT162b2 immunisation with 10-12-week interval, recommended in Germany, is well tolerated and improves immunogenicity compared with homologous ChAdOx1 nCov-19 vaccination with 10-12-week interval and BNT162b2 vaccination with 3-week interval. Heterologous prime-boost immunisation strategies for COVID-19 might be generally applicable. FUNDING: Forschungsnetzwerk der Universitätsmedizin zu COVID-19, the German Ministry of Education and Research, Zalando SE.

7.
Eur J Cancer ; 154: 227-234, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34298373

RESUMO

AIM: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours. METHODS: A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status. RESULTS: The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% ± 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% ± 3.5%) AUROC or less. CONCLUSION: Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts.


Assuntos
Aprendizado Profundo , Melanoma/patologia , Linfonodo Sentinela/patologia , Adulto , Idoso , Humanos , Metástase Linfática , Pessoa de Meia-Idade
8.
Emerg Infect Dis ; 27(8): 2174-2178, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34102097

RESUMO

We detected delayed and reduced antibody and T-cell responses after BNT162b2 vaccination in 71 elderly persons (median age 81 years) compared with 123 healthcare workers (median age 34 years) in Germany. These data emphasize that nonpharmaceutical interventions for coronavirus disease remain crucial and that additional immunizations for the elderly might become necessary.


Assuntos
COVID-19 , Adulto , Idoso , Idoso de 80 Anos ou mais , Vacinas contra COVID-19 , Alemanha/epidemiologia , Humanos , SARS-CoV-2 , Linfócitos T , Vacinação
9.
MMW Fortschr Med ; 163(11): 45, 2021 06.
Artigo em Alemão | MEDLINE | ID: mdl-34086232
10.
Mol Ther Oncolytics ; 21: 340-355, 2021 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-34141871

RESUMO

Advanced pancreatic cancer is characterized by few treatment options and poor outcomes. Oncolytic virotherapy and chemotherapy involve complementary pharmacodynamics and could synergize to improve therapeutic efficacy. Likewise, multimodality treatment may cause additional toxicity, and new agents have to be safe. Balancing both aims, we generated an oncolytic measles virus for 5-fluorouracil-based chemovirotherapy of pancreatic cancer with enhanced tumor specificity through microRNA-regulated vector tropism. The resulting vector encodes a bacterial prodrug convertase, cytosine deaminase-uracil phosphoribosyl transferase, and carries synthetic miR-148a target sites in the viral F gene. Combination of the armed and targeted virus with 5-fluorocytosine, a prodrug of 5-fluorouracil, resulted in cytotoxicity toward both infected and bystander pancreatic cancer cells. In pancreatic cancer xenografts, a single intratumoral injection of the virus induced robust in vivo expression of prodrug convertase. Based on intratumoral transgene expression kinetics, we devised a chemovirotherapy regimen to assess treatment efficacy. Concerted multimodality treatment with intratumoral virus and systemic prodrug administration delayed tumor growth and prolonged survival of xenograft-bearing mice. Our results demonstrate that 5-fluorouracil-based chemovirotherapy with microRNA-sensitive measles virus is an effective strategy against pancreatic cancer at a favorable therapeutic index that warrants future clinical translation.

11.
Eur J Public Health ; 31(5): 1105-1107, 2021 10 26.
Artigo em Inglês | MEDLINE | ID: mdl-33956945

RESUMO

Actual surveys in kindergartens on SARS-CoV-2 infections are rare. At the beginning of the second pandemic wave, we screened 12 randomly selected kindergartens in Berlin, Germany. A total of 720 participants (pre-school children, staff and connected household members) were briefly examined and interviewed, and SARS-CoV-2 infections and anti-SARS-Cov-2 IgG antibodies were assessed. About a quarter of the participants showed common cold-resembling symptoms. However, no SARS-CoV-2 infection was detected, and only one childcare worker showed IgG seroreactivity. Against a backdrop of increased pandemic activity in the community, this cross-sectional study does not suggest that kindergartens are silent transmission reservoirs.


Assuntos
COVID-19 , SARS-CoV-2 , Anticorpos Antivirais , Berlim , Criança , Estudos Transversais , Alemanha/epidemiologia , Humanos
12.
Leukemia ; 35(10): 2948-2963, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34021250

RESUMO

Protein-coding and non-coding genes like miRNAs tightly control hematopoietic differentiation programs. Although miRNAs are frequently located within introns of protein-coding genes, the molecular interplay between intronic miRNAs and their host genes is unclear. By genomic integration site mapping of gamma-retroviral vectors in genetically corrected peripheral blood from gene therapy patients, we identified the EVL/MIR342 gene locus as a hotspot for therapeutic vector insertions indicating its accessibility and expression in human hematopoietic stem and progenitor cells. We therefore asked if and how EVL and its intronic miRNA-342 regulate hematopoiesis. Here we demonstrate that overexpression (OE) of Evl in murine primary Lin- Sca1+ cKit+ cells drives lymphopoiesis whereas miR-342 OE increases myeloid colony formation in vitro and in vivo, going along with a profound upregulation of canonical pathways essential for B-cell development or myelopoietic functions upon Evl or miR-342 OE, respectively. Strikingly, miR-342 counteracts its host gene by targeting lymphoid signaling pathways, resulting in reduced pre-B-cell output. Moreover, EVL overexpression is associated with lymphoid leukemia in patients. In summary, our data show that one common gene locus regulates distinct hematopoietic differentiation programs depending on the gene product expressed, and that the balance between both may determine hematopoietic cell fate decision.

13.
Infection ; 49(4): 703-714, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33890243

RESUMO

PURPOSE: Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course. METHODS: A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed. RESULTS: Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p < 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p < 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p < 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients. CONCLUSIONS: Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19.


Assuntos
COVID-19/epidemiologia , COVID-19/virologia , SARS-CoV-2/fisiologia , COVID-19/terapia , Estudos de Coortes , Alemanha/epidemiologia , Hospitalização , Humanos , Hipertensão/complicações , Cinética , Estudos Prospectivos , Respiração Artificial , Fatores de Risco , Centros de Atenção Terciária , Fatores de Tempo , Carga Viral , Eliminação de Partículas Virais
14.
Eur J Cancer ; 149: 94-101, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33838393

RESUMO

BACKGROUND: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. OBJECTIVES: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. METHODS: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier. RESULTS: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%. CONCLUSION: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.


Assuntos
Interpretação de Imagem Assistida por Computador , Melanoma/patologia , Microscopia , Redes Neurais de Computação , Nevo/patologia , Neoplasias Cutâneas/patologia , Adulto , Fatores Etários , Idoso , Bases de Dados Factuais , Feminino , Alemanha , Humanos , Masculino , Melanoma/classificação , Pessoa de Meia-Idade , Nevo/classificação , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Estudos Retrospectivos , Fatores Sexuais , Neoplasias Cutâneas/classificação
15.
BJU Int ; 128(3): 352-360, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-33706408

RESUMO

OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors. PATIENTS AND METHODS: Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status. RESULTS: With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM. CONCLUSION: In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

17.
J Med Internet Res ; 23(2): e23436, 2021 02 02.
Artigo em Inglês | MEDLINE | ID: mdl-33528370

RESUMO

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.


Assuntos
Inteligência Artificial/normas , Aprendizado Profundo/normas , Redes Neurais de Computação , Patologia/métodos , Humanos
18.
Nat Commun ; 12(1): 1119, 2021 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-33602930

RESUMO

Regulatory CD4+ T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4+ cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation.


Assuntos
Neoplasias da Mama/imunologia , Neoplasias da Mama/patologia , Linfócitos T Reguladores/imunologia , Antígenos de Neoplasias/metabolismo , Neoplasias da Mama/sangue , Neoplasias da Mama/genética , Linhagem Celular Tumoral , Proliferação de Células , Células Clonais , Feminino , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Ativação Linfocitária/imunologia , Estadiamento de Neoplasias , Receptores de Antígenos de Linfócitos T/imunologia , Análise de Célula Única , Células Th1/imunologia , Transcriptoma/genética
19.
Eur J Cancer ; 145: 81-91, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33423009

RESUMO

BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems. OBJECTIVE: To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)-mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing). METHODS: We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes ('brittleness') was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions. RESULTS: All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor. CONCLUSIONS: Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic.


Assuntos
Dermoscopia , Diagnóstico por Computador , Interpretação de Imagem Assistida por Computador , Melanoma/patologia , Redes Neurais de Computação , Nevo/patologia , Neoplasias Cutâneas/patologia , Diagnóstico Diferencial , Humanos , Valor Preditivo dos Testes , Reprodutibilidade dos Testes
20.
Int J Cancer ; 148(6): 1438-1451, 2021 03 15.
Artigo em Inglês | MEDLINE | ID: mdl-32949162

RESUMO

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.


Assuntos
Oncologia/métodos , Terapia de Alvo Molecular/métodos , Neoplasias , Medicina de Precisão/métodos , Proteômica/métodos , Adulto , Idoso , Biomarcadores Tumorais/análise , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Neoplasias/genética , Neoplasias/terapia , Estudo de Prova de Conceito
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