Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model.

BACKGROUND: Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute. OBJECTIVE: We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size. METHODS: We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records. RESULTS: The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist. CONCLUSIONS: Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.

Mapping the Intratumoral Heterogeneity in Glioblastomas with Hyperspectral Stimulated Raman Scattering Microscopy.

Recent genomic studies on the glioblastoma (GBM) subtypes (e.g., mesenchymal, proneural, and classical) pave a way for effective clinical treatments of the recurrent brain tumor. However, identification of the GBM subtype is complicated by the intratumoral heterogeneity that results in coexistence of multiple subtypes within the tissue specimen. Here, we present the use of hyperspectral stimulated Raman scattering (SRS) microscopy for rapid, label-free molecular assessment of GBM intratumoral heterogeneity with submicron resolution. We develop a unique label-free Raman imaging diagnostic platform consisting of the spectral focusing hyperspectral SRS imaging of the large-area GBM tissue specimens, SRS images, and spectrum retrieval using the multivariate curve resolution algorithm and subtype classification based on the quadratic support vector machine model for rapid molecular subtyping of GBMs. Both the stain-free SRS histological images and 2D subtype maps can be obtained within 20-30 min which is superior to the days of the conventional single-cell RNA sequencing. While the SRS histology assesses the demyelination status as a new diagnostic feature, the SRS mapping provides a new insight into intratumoral heterogeneity across GBM tissue specimens. We find that the major proportions of the GBM tissues agree with the diagnostic results of the genomic analysis, but nontrivial portions of the remaining SRS image tiles in the specimens are found to belong to other molecular subtypes, implying the substantial degree of GBM heterogeneity. The rapid SRS imaging diagnostic platform developed has shown the ability of unveiling tumor heterogeneity in GBM tissues accurately, which would promote the improvement of the GBM-targeted therapy in near future.

Spatial light-modulated stimulated Raman scattering (SLM-SRS) microscopy for rapid multiplexed vibrational imaging.

High speed imaging is pre-requisite for monitoring of dynamic processes in biological events. Here we report the development of a unique spatial light-modulated stimulated Raman scattering (SLM-SRS) microscopy that tailors the broadband excitation beam with sparse-sampling masks designed for rapid multiplexed vibrational imaging to monitor real-time cancer treatment effects and in vivo transport of drug solvent. Methods: We design an optimal mask pattern that enables selection of predominant windows in SRS spectrum for collective excitation at the highest possible peak power, thus providing an improved signal-to-noise ratio (SNR) without compromise of chemical specificity. The mask pattern generated is applied to the broad excitation beam using a flexible spatial light modulator. The SLM module further offers complementary function whereby rapid scanning of SRS spectrum can be facilitated prior to the mask generation, thereby making the SLM-SRS system a stand-alone imaging platform. Results: We demonstrate that SLM-SRS microscopy permits rapid multiplexed SRS imaging of polystyrene and polymethyl methacrylate beads in Brownian motion in dimethyl sulfoxide (DMSO) at 70 ms intervals without motion artiacts. We further apply SLM-SRS to monitor the therapeautic effect of mild alkaline solution on cancer cells, which shows immediate apoptotic response. Finally, we visualize in vivo penetration of DMSO into the plant tissue and evaluate acute toxicity of DMSO on cellulose and proteins within the tissue. Conclusion: We develop novel SLM-SRS microscopy and affirm its broad applicability for rapid monitoring of dynamic biological processes at the subcellular and molecular level.

Real-Time Monitoring of Pharmacokinetics of Mitochondria-Targeting Molecules in Live Cells with Bioorthogonal Hyperspectral Stimulated Raman Scattering Microscopy.

The dynamics of mitochondria in live cells play a pivotal role in biological events such as cell metabolism, early stage apoptosis, and cell differentiation. Triphenylphosphonium (TPP) is a commonly used mitochondria-targeting agent for mitochondrial studies. However, there has been a lack of understanding in intracellular behaviors of TPP in the course of targeting mitochondria due to the difficulty in tracking and quantifying small molecules in a biological environment. Here, we report the utility of hyperspectral stimulated Raman scattering (SRS) microscopy associated with a Raman tag synthesized for real-time visualization and quantitation of TPP dynamics within live cells at the subcellular level. With the myriad of merits offered by a synthesized aryl-diyne-based Raman tag such as excellent photostability, negligible background interferences, and a linear dependence of the SRS signal on the TPP concentration, we successfully establish a quantitative model to associate the mitochondrial membrane potential with the key pharmacokinetic parameters of TPP inside the live cells. The model reveals that reduction in the mitochondrial membrane potential leads to significant decreases in both the uptake rate and intracellular concentrations of TPP. Further, on the basis of the multiplexed SRS images concurrently highlighting the cellular proteins and lipids without further labeling, we find that the TPP uptake causes little cytotoxicity to the host cells. The bioorthogonal hyperspectral SRS microscopy imaging reveals that TPP can maintain stable affinity to mitochondria during the restructuring of mitochondrial networking, demonstrating its great potential for real-time monitoring of pharmacokinetics of small molecules associated with live biological hosts, thereby promoting the development of mitochondria-targeting imaging probes and therapies in the near future.

Real-Time Monitoring of Pharmacokinetics of Antibiotics in Biofilms with Raman-Tagged Hyperspectral Stimulated Raman Scattering Microscopy.

Antibiotics resistance developed by biofilms has posed a clinical challenge in the effective treatment of bacterial infections. However, the resistance mechanisms have not been well understood due to a lack of suitable tools for dynamic observation of the interplay between antibiotics and biofilm. In this work, with the use of rapid hyperspectral stimulated Raman scattering microscopy associated with an aryl-alkyne-based Raman tag synthesized, we investigate dynamic interactions between vancomycin and Staphylococcus aureus (S. aureus) biofilm to gain new insights into the resistance mechanisms of the biofilm. Methods: We utilize spectral focusing hyperspectral stimulated Raman scattering microscopy ensued with multivariate curve resolution analysis to spectrally decompose S. aureus biofilm into its major components (i.e., bacteria and extracellular polymeric substances). Concurrently, vancomycin is conjugated with aryl-alkyne Raman tag (Raman peak at 2218 cm-1) for in vivo tracking of its uptake into biofilm without tissue interference. Results: We find that vancomycin penetration is a non-uniform diffusion process with penetration depths limited by the preferential affinity to the cell clusters. Semi-quantitative analysis shows that the majority of vancomycin binds to the bacteria, achieving intracellular concentrations of up to 4- to 10- fold higher than the administered dosage. The diffusion constant of ~3.16 µm2/min based on the diffusion and antibiotic binding equations is obtained that well accounts for the antibiotic penetration into the biofilm. SRS longitudinal monitoring of antibiotic effect on the growth of biofilms shows that the antibiotics can eradicate the upper layer of the biofilm exposed to sufficient dosages, while the lower layer of the biofilm at a sub-inhibitory dose remains viable, eventually re-growing to significant bio-volume. Conclusion: The Raman-tagged hyperspectral SRS microscopy developed is a powerful imaging tool for dynamic monitoring of inhibitory effects of antibiotics on the growing biofilm in vivo, which would facilitate the formulation of new antibiotics for more effective treatments of bacterial infections in near future.

Epi-Detected Hyperspectral Stimulated Raman Scattering Microscopy for Label-Free Molecular Subtyping of Glioblastomas.

We report the development and implementation of an epi-detected spectral-focusing hyperspectral stimulated Raman scattering (SRS) imaging technique for label-free biomolecular subtyping of glioblastomas (GBMs). The hyperspectral SRS imaging technique developed generates SRS image stacks (from 2800 to 3020 cm-1 at 7 cm-1 intervals) within 30 s through controlling the time delay between the chirped pump and Stokes beams. SRS images at representative Raman shifts (e.g., 2845, 2885, and 2935 cm-1) delineate the biochemical variations and morphological differences between proneural and mesenchymal subtypes of GBMs. Multivariate curve resolution (MCR) analysis on hyperspectral SRS images enables the quantification of major biomolecule distributions in mesenchymal and proneural GBMs. Further principal component analysis (PCA) and linear discriminant analysis (LDA) together with leave-one SRS spectrum-out, cross-validation (LOOCV) yields a diagnostic sensitivity of 96.7% (29/30) and specificity of 88.9% (28/36) for differentiation between mesenchymal and proneural subtypes of GBMs. This study shows great potential of applying hyperspectral SRS imaging technique developed for rapid, label-free molecular subtyping of GBMs in neurosurgery.