Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology

Abstract

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.

Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas

“…These 5 ARGs are probably mechanistically involved in the onset and development of APA, then they can also be potential diagnostic tARGsets for APA. Of course, the diagnostic performance of these 5 ARGs for APA still needs to be verified by artificial neural network modeling ( MANDAIR et al, 2023 ). At present, the clinical approach for diagnosing APA still has some drawbacks.…”

Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects

“…The assessment of premalignant lesions and potential precursor lesions that may or may not become invasive in a patient's lifetime suffers from poor inter-observer reproducibility [50][51][52][53][54]. Grading of oral dysplasia [52,53] and ductal carcinoma in situ (DCIS) [38,51], a non-obligate precursor and risk factor of invasive breast cancer, are prime candidates for AI-based approaches. Given our limited knowledge of precancerous tissue characteristics, AI-assisted computational pathology pipelines may reveal new features in seemingly normal tissue and, as such, offer new tools for early cancer detection approaches.…”

“…Indeed, CNN models applied to routine H&Estained tumour sections have been described for the prediction of biomarkers such as mutations in KRAS [23,34], BRAF [33,34], TP53 [23,34,36,37], microsatellite instability [34], and tumour mutational burden (TMB) [35]. Moreover, weakly supervised CNN model-based frameworks utilise information from histopathology and other clinical reports as the ground truth label for an entire WSI in a classification or segmentation task [38]. Without a priori manual annotations, these networks learn to localise specific regions associated with a particular clinical, pathological, or genomic label [33,34,39,40].…”

“…Spatial analysis powered by AI has helped to decipher the TME and revealed pathways contributing to both immune escape and the lack of immune cell ingress [41]. Computational pathology methodologies capturing tumour-infiltrating lymphocytes (TILs) on H&E-stained breast and other cancers according to guidelines defined by the International Immuno-Oncology Biomarker Working Group, also called the TILs-WG [42], have shown potential in predicting clinical outcome [38,[43][44][45]. The TILs-WG has organised a public grand challenge for computational assessment of TIL-counts alone and integrated into nomograms with established prognostic variables.…”

“…A looming question remains as to whether clinicalgrade computational pathology tools need to be fully interpretable for medical diagnostics. AI-based models are commonly described as unaccountable 'black-boxes', due to the opaque nature of their decision process [1,8,38,66]. Explainable models require methods that abstract the exact underlying rules that form a neural network's decision.…”