Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma

Harder, Nathalie; Schönmeyer, Ralf; Nekolla, Katharina; Meier, Armin; Brieu, Nicolas; Vanegas, Carolina; Madonna, Gabriele; Capone, Mariaelena; Botti, Gerardo; Ascierto, Paolo A.; Schmidt, Günter

doi:10.1038/s41598-019-43525-8

Cited by 51 publications

(42 citation statements)

References 28 publications

(41 reference statements)

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“…Given the success of these and other computer vision models, there is growing interest in whether neural networks can be used to predict response to treatments. In 2019, Harder and colleagues proposed a workflow for predicting response to ipilimumab that relies on DCNNs to robustly segment cell nuclei and classify CD3 þ , CD8 þ , and melanin objects of interest (29). Here, we present an approach to predicting treatment response that similarly draws upon the automated assessment of digital histology images.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet

Coudray²,

Donnelly

et al. 2021

Clinical Cancer Research

115

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Purpose: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma. Experimental Design: We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan–Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400). Results: The multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400). Conclusions: Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet

Coudray²,

Donnelly

et al. 2021

Clinical Cancer Research

115

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“…2b). Notably, Harder and coworkers classified melanoma patients as responders and non-responders to ipilimumab, 51 and Madabhushi et al demonstrated a concept for the prediction of response to immunotherapy in patients with NSCLC directly from H&E-stained images. 52 However, these studies only included small patient numbers, and it can be expected that the potential of DL to predict therapy response is not yet exhausted.…”

Section: Prediction Of Genotype and Gene Expressionmentioning

confidence: 99%

Deep learning in cancer pathology: a new generation of clinical biomarkers

et al. 2020

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Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

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“…The TIDE and TIS transcriptomic signatures, as well as genes linked to T cells cytotoxicity, Th1 chemokines and antigen presentation seem useful for the identification of responders among melanoma patients ( 29 , 124 ). The mutational and neoantigen load ( 125 ) and a high ratio of CD8 + density in the intratumoral region have also been related to clinical benefit to Ipilimumab in melanoma ( 126 ), while PD-L1 staining by IHC alone does not seem to be predictive ( 127 ).…”

Section: Anti-ctla-4 Inhibitorsmentioning

confidence: 99%

Myeloid Cells as Clinical Biomarkers for Immune Checkpoint Blockade

et al. 2020

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Immune checkpoint inhibitors are becoming standard treatments in several cancer types, profoundly changing the prognosis of a fraction of patients. Currently, many efforts are being made to predict responders and to understand how to overcome resistance in non-responders. Given the crucial role of myeloid cells as modulators of T effector cell function in tumors, it is essential to understand their impact on the clinical outcome of immune checkpoint blockade and on the mechanisms of immune evasion. In this review we focus on the existing clinical evidence of the relation between the presence of myeloid cell subsets and the response to anti-PD(L)1 and anti-CTLA-4 treatment. We highlight how circulating and tumor-infiltrating myeloid populations can be used as predictive biomarkers for immune checkpoint inhibitors in different human cancers, both at baseline and on treatment. Moreover, we propose to follow the dynamics of myeloid cells during immunotherapy as pharmacodynamic biomarkers. Finally, we provide an overview of the current strategies tested in the clinic that use myeloid cell targeting together with immune checkpoint blockade with the aim of uncovering the most promising approaches for effective combinations.

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Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma

Cited by 51 publications

References 28 publications

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Deep learning in cancer pathology: a new generation of clinical biomarkers

Myeloid Cells as Clinical Biomarkers for Immune Checkpoint Blockade

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