Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Kumar, Abhishek; Rao, Arvind; Bhavani, Santosh; Newberg, Justin Y.; Murphy, Robert F.

doi:10.1073/pnas.1415120112

Cited by 39 publications

(27 citation statements)

References 25 publications

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“…In addition, linked multiplatform genomic annotation (gene expression, copy number, targeted sequencing) will be made available for a subset of cases, following primary reporting of the data. While invaluable resources such as the Human Protein Atlas already provide access to an enormous array of tissue images [ 33 ], some already utilized in translational studies [ 34 , 35 ], widely available digital images of tumour tissue from large high-quality clinical studies with molecular annotation such as ours are currently exceedingly rare. However, general access to such resources will be necessary to fulfil the potential of computational pathology as a novel modality that spans the research and clinical arenas.…”

Section: Discussionmentioning

confidence: 99%

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

et al. 2016

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BackgroundThere is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.MethodsWe digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.ResultsMedian lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).ConclusionsA data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.Trial registrationClinicalTrials.gov NCT00070278; 03/10/2003Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0682-8) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

et al. 2016

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“…However, technical problems diminishing the accuracy of quantifying the expression of drug targets by IHC hinder this biomarker development. The recent emergence of whole slide imaging and digital image analysis generates quantitative numerical data associated with protein expression in FFPE tissues . In theory, this approach could provide new opportunities for measuring the expression of drug targets in cancer tissues obtained from patients during routine medical care.…”

Section: Introductionmentioning

confidence: 99%

Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET

Huang

Pollan

et al. 2016

The Journal of Pathology CR

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The limited clinical success of anti‐HGF/MET drugs can be attributed to the lack of predictive biomarkers that adequately select patients for treatment. We demonstrate here that quantitative digital imaging of formalin fixed paraffin embedded tissues stained by immunohistochemistry can be used to measure signals from weakly staining antibodies and provides new opportunities to develop assays for detection of MET receptor activity. To establish a biomarker panel of MET activation, we employed seven antibodies measuring protein expression in the HGF/MET pathway in 20 cases and up to 80 cores from 18 human cancer types. The antibodies bind to epitopes in the extra (EC)‐ and intracellular (IC) domains of MET (MET4EC, SP44_METIC, D1C2_METIC), to MET‐pY1234/pY1235, a marker of MET kinase activation, as well as to HGF, pSFK or pMAPK. Expression of HGF was determined in tumour cells (T_HGF) as well as in stroma surrounding cancer (St_HGF). Remarkably, MET4EC correlated more strongly with pMET (r = 0.47) than SP44_METIC (r = 0.21) or D1C2_METIC (r = 0.08) across 18 cancer types. In addition, correlation coefficients of pMET and T_HGF (r = 0.38) and pMET and pSFK (r = 0.56) were high. Prediction models of MET activation reveal cancer‐type specific differences in performance of MET4EC, SP44_METIC and anti‐HGF antibodies. Thus, we conclude that assays to predict the response to HGF/MET inhibitors require a cancer‐type specific antibody selection and should be developed in those cancer types in which they are employed clinically.

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“…Previous multi-level classi cation studies, including a recent Kaggle challenge 26 have used immuno uorescence (IF) images of human cell lines, where antibody staining determined different subcellular localizations of the protein, related to the Subcellular Atlas of the HPA 5,27 . There have however been fewer automated classi cation studies using human tissue images stained with IHC 11,[28][29][30] .…”

Section: Discussionmentioning

confidence: 99%

Semi-automated annotation of cell type-specific protein expression patterns in human testis based on immunohistochemistry

Ghoshal¹,

Tucker²,

Fh³

et al. 2020

Preprint

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Immunohistochemistry (IHC) provides the basis for cell type-specific localization of protein expression patterns in human tissues. Manual annotation of complex IHC images is however expensive and may lead to errors or inter-observer variability. Artificial Intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. To present a reliable model for annotation of IHC images, we developed a semi-automated framework for multi-label classification of 7,848 human testis samples stained with IHC, and manually annotated in situ protein expression in eight different cell types. The dataset was used as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, the average diagnostic performance improved from 86.9% to 96.3%. The streamlined workflow has important implications for accurate large-scale efforts mapping the human cell type-specific proteome in health and disease.

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Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Cited by 39 publications

References 25 publications

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET

Semi-automated annotation of cell type-specific protein expression patterns in human testis based on immunohistochemistry

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