scholarly journals Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Konrad H Stopsack ◽  
Svitlana Tyekucheva ◽  
Molin Wang ◽  
Travis A Gerke ◽  
J Bailey Vaselkiv ◽  
...  
Keyword(s):  
Measurement Error ◽  
Tumor Tissue ◽  
Tissue Microarrays ◽  
R Package ◽  
Tumor Biomarker ◽  
Batch Effects ◽  
Protein Biomarkers ◽  
Individual Study ◽  
Inherent Feature ◽  
Prostate Cancers

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

scholarly journals Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

2021 ◽  
Author(s):  
Konrad H. Stopsack ◽  
Svitlana Tyekucheva ◽  
Molin Wang ◽  
Travis A. Gerke ◽  
J. Bailey Vaselkiv ◽  
...  
Keyword(s):  
Measurement Error ◽  
Tumor Tissue ◽  
Tissue Microarrays ◽  
R Package ◽  
Tumor Biomarker ◽  
Batch Effects ◽  
Protein Biomarkers ◽  
Individual Study ◽  
Inherent Feature ◽  
Prostate Cancers

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

Abstract 893: Batch effects in tumor biomarker studies using tissue microarrays: Extent, impact, and remediation

2021 ◽  
Author(s):  
Konrad H. Stopsack ◽  
Molin Wang ◽  
Svitlana Tyekucheva ◽  
Travis A. Gerke ◽  
J. Bailey Vaselkiv ◽  
...  
Keyword(s):  
Tissue Microarrays ◽  
Tumor Biomarker ◽  
Batch Effects

scholarly journals High Prevalence of 5T4/Trophoblast Glycoprotein in Soft Tissue Sarcomas

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4841
Author(s):  
Patrick Groothuis ◽  
Nicola Penel ◽  
Antoine Italiano ◽  
Nuria Kotecki ◽  
Fred Dijcks ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Tumor Tissue ◽  
Staining Method ◽  
Soft Tissue Sarcomas ◽  
Tissue Microarrays ◽  
Histological Subtypes ◽  
Scoring Method ◽  
Drug Conjugates ◽  
High Prevalence ◽  
Quantitative Scoring

The expression of 5T4/trophoblast glycoprotein was evaluated in several histological subtypes of soft tissue sarcoma (STS) to determine whether the prevalence and level of expression of this membrane-associated glycoprotein is sufficient for use in targeted therapies. Tumor tissue microarrays containing cores from different histological subtypes of STS were stained using a standardized immunohistochemical staining method to detect 5T4; the level of staining was assessed using a semi-quantitative scoring method. No 5T4 staining was seen in the angiosarcomas and liposarcomas investigated in this study. 5T4 staining in the other STS subtypes was seen in more than 50% of cases, warranting further investigation into whether this antigen could evoke an anti-tumor immune response or can be used as target for the delivery of more potent toxins through antibody drug conjugates.

scholarly journals STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

2020 ◽  
Author(s):  
Massimo Andreatta ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Distance Measure ◽  
Source Code ◽  
Cell Types ◽  
R Package ◽  
Computational Method ◽  
Biological Variability ◽  
Rna Seq ◽  
Batch Effects ◽  
Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

Multiplexed tissue protein assay as a predictor of response to targeted therapy in advanced renal cell carcinoma.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4577-4577
Author(s):  
Ulka N. Vaishampayan ◽  
Alexandrine Derrien-Colemyn ◽  
Ye Hong ◽  
Seema Sethi ◽  
Wei Chen ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Mtor Inhibitors ◽  
Predictive Score ◽  
Protein Biomarkers ◽  
Scoring Method ◽  
Tissue Samples ◽  
Protein Assay ◽  
Advanced Renal Cancer ◽  
Formalin Fixed Paraffin Embedded ◽  
Multiple Biomarkers

4577 Background: A number of prognostic biomarkers have been explored in advanced renal cancer, but to date none have been useful in therapeutic outcome prediction. Methods: Following regulatory approval, formalin-fixed paraffin embedded (FFPE) pre-therapy (sunitinib and/or mTOR inhibitors) tissue samples and clinical data on kidney cancer patients (pts) were obtained. The FFPE tissue was analyzed using layered immunohistochemistry which allows analysis of multiple biomarkers using a single tissue section. Multiplexed panels of protein biomarkers were used to probe tissue sections for proteins along the PI3K/AKT/mTOR and/or the VEGFR/PDGFR signaling pathways. Expression of biomarkers in tumor tissue was scored and predictive scores which correlated with the pt’s clinical outcome status generated. Results: Tissue samples of 51 pts treated with sunitinib (S)were analyzed. A predictive score was generated by combining the scores assigned to VEGFR1 and VEGFR2 and multiplying the sum with the score of VEGFA. A predictive score equal to or above 24, was associated with response or stable disease (SD) at 12 weeks. Patients with a score <24 were predicted to have progression (PD) on S. Using this scoring method, 27 of the 33 responders tested and 15 of the 18 non responders were accurately identified. The accuracy of the test was noted to be 82.6% and sensitivity and specificity were 81.8% and 83.3% respectively. Tissue samples of 33 pts treated with mTOR inhibitor were analyzed using the same technique. Three biomarkers in the mTOR pathway (pmTOR (Ser 2448), p4EBP1 (Ser 65), p4EBP1 (Thr 37-46)) were used to create a predictive score. Eight of 12 OR/SD pts (sensitivity 67%) and 17 of 21 PD pts (specificity 76%) were accurately predicted using a score cut-off of 6 for an accuracy of 71.5%. Statistical modeling, results of ongoing validation testing, and score correlation with time to progression will be presented. Conclusions: These results indicate that an assay based on multiplexed protein analysis of tumor tissue is capable of providing clinically applicable information to help guide therapy. Funding source: Supported in part by NCI BRIDGE Grant 5R44CA123994-06 and by NCI SBIR Contract No. HHSN261201000135C.

scholarly journals TAF1 Differentially Enhances Androgen Receptor Transcriptional Activity via Its N-Terminal Kinase and Ubiquitin-Activating and -Conjugating Domains

2010 ◽  
Vol 24 (4) ◽  
pp. 696-708 ◽  
Author(s):  
Peyman Tavassoli ◽  
Latif A. Wafa ◽  
Helen Cheng ◽  
Amina Zoubeidi ◽  
Ladan Fazli ◽  
...  
Keyword(s):  
Androgen Receptor ◽  
Cancer Cells ◽  
Transcriptional Activity ◽  
Specific Antigen ◽  
Tissue Microarrays ◽  
Compensatory Mechanism ◽  
Castration Resistance ◽  
Aberrant Expression ◽  
Androgen Withdrawal ◽  
Prostate Cancers

Abstract Aberrant expression of androgen receptor (AR) coregulators has been linked to progression of prostate cancers to castration resistance. Using the repressed transactivator yeast two-hybrid system, we found that TATA binding protein-associated factor 1 (TAF1) interacted with the AR. In tissue microarrays, TAF1 was shown to steadily increase with duration of neoadjuvant androgen withdrawal and with progression to castration resistance. Glutathione S-transferase pulldown assays established that TAF1 bound through its acetylation and ubiquitin-activating/conjugating domains (E1/E2) directly to the AR N terminus. Coimmunoprecipitation and ChIP assays revealed colocalization of TAF1 and AR on the prostate-specific antigen promoter/enhancer in prostate cancer cells. With respect to modulation of AR activity, overexpression of TAF1 enhanced AR activity severalfold, whereas small interfering RNA knockdown of TAF1 significantly decreased AR transactivation. Although full-length TAF1 showed enhancement of both AR and some generic gene transcriptional activity, selective AR coactivator activity by TAF1 was demonstrated in transactivation experiments using cloned N-terminal kinase and E1/E2 functional domains. In keeping with AR coactivation by the ubiquitin-activating and -conjugating domain, TAF1 was found to greatly increase the cellular amount of polyubiquitinated AR. In conclusion, our results indicate that increased TAF1 expression is associated with progression of human prostate cancers to the lethal castration-resistant state. Because TAF1 is a coactivator of AR that binds and enhances AR transcriptional activity, its overexpression could be part of a compensatory mechanism adapted by cancer cells to overcome reduced levels of circulating androgens.

scholarly journals Monitoring of Technical Variation in Quantitative High-Throughput Datasets

2013 ◽  
Vol 12 ◽  
pp. CIN.S12862 ◽  
Author(s):  
Martin Lauss ◽  
Ilhami Visne ◽  
Albert Kriegner ◽  
Markus Ringnér ◽  
Göran Jönsson ◽  
...  
Keyword(s):  
High Throughput ◽  
Principal Components ◽  
General Procedure ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Batch Effects ◽  
Cancer Genome Atlas ◽  
Technical Bias ◽  
Microarray Datasets ◽  
High Dimensional Datasets

High-dimensional datasets can be confounded by variation from technical sources, such as batches. Undetected batch effects can have severe consequences for the validity of a study's conclusion(s). We evaluate high-throughput RNAseq and miRNAseq as well as DNA methylation and gene expression microarray datasets, mainly from the Cancer Genome Atlas (TCGA) project, in respect to technical and biological annotations. We observe technical bias in these datasets and discuss corrective interventions. We then suggest a general procedure to control study design, detect technical bias using linear regression of principal components, correct for batch effects, and re-evaluate principal components. This procedure is implemented in the R package swamp, and as graphical user interface software. In conclusion, high-throughput platforms that generate continuous measurements are sensitive to various forms of technical bias. For such data, monitoring of technical variation is an important analysis step.

scholarly journals Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy

2021 ◽  
Author(s):  
Kamdin Mirsanaye ◽  
Leonardo Uribe Castaño ◽  
Yasmeen Kamaliddin ◽  
Ahmad Golaraei ◽  
Renaldas Augulis ◽  
...  
Keyword(s):  
Second Harmonic Generation ◽  
Harmonic Generation ◽  
Tumor Tissue ◽  
Second Harmonic ◽  
Tissue Microarrays ◽  
Cancer Diagnostics ◽  
Label Free ◽  
Second Harmonic Generation Microscopy ◽  
Trained Classifier ◽  
Omission Rate

The extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor tissue from normal with 94.2% accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.

scholarly journals Performance of Different Diagnostic PD-L1 Clones in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Julika Ribbat-Idel ◽  
Franz F. Dressler ◽  
Rosemarie Krupar ◽  
Christian Watermann ◽  
Finn-Ole Paulsen ◽  
...  
Keyword(s):  
Head And Neck ◽  
Tumor Cells ◽  
Squamous Cell ◽  
Tumor Tissue ◽  
Checkpoint Inhibitors ◽  
Principal Component ◽  
Tissue Microarrays ◽  
Daily Routine ◽  
Therapeutic Decision Making ◽  
Reported Data

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 testing in Germany different clones are used in daily routine. While the correlation of staining results has been tested in various entities, there is no data for head and neck squamous cell carcinomas (HNSCC) so far.Methods: We tested five different PD-L1 clones (SP263, SP142, E1L3N, 22-8, 22C3) on primary HNSCC tumor tissue of 75 patients in the form of tissue microarrays. Stainings of both immune and tumor cells were then assessed and quantified by pathologists to simulate real-world routine diagnostics. The results were analyzed descriptively and the resulting staining pattern across patients was further investigated by principal component analysis and non-negative matrix factorization clustering.Results: Percentages of positive immune and tumor cells varied greatly. Both the resulting combined positive score as well as the eligibility for certain checkpoint inhibitor regimens was therefore strongly dependent on the choice of the antibody. No relevant co-clustering and low similarity of relative staining patterns across patients was found for the different antibodies.Conclusions: Performance of different diagnostic anti PD-L1 antibody clones in HNSCC is less robust and interchangeable compared to reported data from other tumor entities. Determination of PD-L1 expression is critical for therapeutic decision making and may be aided by back-to-back testing of different PD-L1 clones.

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