Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.Comment: Paper published in Nature Medicin

Preferential rabbit antibody responses to C-termini of NOTCH3 peptide immunogens

Abstract Antibodies raised in peptide-immunized rabbits have been used in biological research for decades. Although there has been wide implementation of this approach, specific proteins are occasionally difficult to target for multiple reasons. One consideration that was noted in mice is that humoral responses may preferentially target the carboxyl terminus of the peptide sequence which is not present in the intact protein. To shed light on the frequency of preferential rabbit antibody responses to C-termini of peptide immunogens, we present our experience with generation of rabbit antibodies to human NOTCH3. A total of 23 antibodies were raised against 10 peptide sequences of human NOTCH3. Over 70% (16 of 23) of these polyclonal antibodies were determined to be C-terminal preferring: NOTCH3 peptide-reactive antibodies largely targeted the terminating free carboxyl group of the immunizing peptide. The antibodies that preferred C-terminal epitopes reacted weakly or not at all with recombinant target sequences with extension the C-terminus that eliminated the free carboxyl group of the immunogen structure; furthermore, each of these antisera revealed no antibody reactivity to proteins truncated before the C-terminus of the immunogen. In immunocytochemical applications of these anti-peptide antibodies, we similarly found reactivity to recombinant targets that best binding to cells expressing the free C-terminus of the immunizing sequence. In aggregate, our experience demonstrates a strong propensity for rabbits to mount antibody responses to C-terminal epitopes of NOTCH3-derived peptides which is predicted to limit their use against the native protein. We discuss some potential approaches to overcome this bias that could improve the efficiency of generation of antibodies in this commonly utilized experimental paradigm

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support. Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data for end-to-end model development and validation. We provide a framework for patch-based whole slide SRH classification and inference using weak (i.e. patient-level) diagnostic labels. Finally, we benchmark two computer vision tasks: multiclass histologic brain tumor classification and patch-based contrastive representation learning. We hope OpenSRH will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support in order to improve the access, safety, and efficacy of cancer surgery in the era of precision medicine. Dataset access, code, and benchmarks are available at opensrh.mlins.org.Comment: Neural Information Processing Systems (NeurIPS) 2022 Datasets and Benchmarks Trac