We present the first study of a magnetic quantum phase transition in the itinerant-electron ferromagnet Ni3Al at high pressures. Electrical resistivity measurements in a diamond anvil cell at hydrostatic pressures up to 100 kbar and temperatures as low as 50 mK indicate that the Curie temperature collapses towards absolute zero at a critical pressure pc = 82±2 kbar. Over wide ranges in pressure and temperature, both in the ferromagnetic and paramagnetic states, the temperature variation of the resistivity is found to deviate from the conventional Fermi-liquid form. We consider the extent to which this deviation can be understood in terms of a mean-field model of enhanced spin fluctuations on the border of ferromagnetism in three dimensions.

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

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Gene expression profiling (GEP) represents an important approach to inform breast cancer treatment. However, access to GEP involves challenges associated with cost, tissue transportation, and turn around time. In this work, we explore the prediction of estrogen receptor gene (ESR1) expression directly from images of hematoxylin and eosin (H&E) stained, formalin-fixed paraffin-embedded (FFPE) breast cancer tissue. Since H&E staining is a fast and inexpensive component of the standard tissue preparation in pathology, this approach is tissue preserving and requires no additional tissue processing. Our method uses a deep multiple instance learning approach to process cropped image patches from whole-slide images (WSI) into a numeric embedding vector summarizing the information in each patch. A gated attention mechanism then aggregates these embeddings into a single prediction for the WSI. We train and tune the model on a site-based split of The Cancer Genome Atlas (TCGA) BRCA dataset, and evaluate it on both a heldout split of TCGA (independent sites) and a separate dataset from a tertiary teaching hospital (TTH). All splits of TCGA have ESR1 value, immunohistochemistry (IHC) estrogen receptor (ER) status, and limited clinical outcome data. The TTH dataset has IHC-based ER status and clinical outcome, but not ESR1 expression. On the TCGA heldout test split, our model’s root mean square error (RMSE) for predicting normalized gene expression counts (TPM) was 2.90 [95% CI: 2.57, 3.23], and the Pearson correlation was 0.57 [95%CI; 0.46, 0.67]. For predicting IHC-based ER status on the same TCGA split, this weakly-supervised ESR1-predicting model had an area under the receiver-operator curve (AUROC) of 0.81 [0.74, 0.87]. This was comparable to a strongly-supervised method directly predicting ER status (AUROC: 0.85 [0.77, 0.92]). Lastly, when evaluated for association with patient outcomes (progression-free interval; PFI) using the independent TTH dataset, this ESR1-predicting model had a concordance index (c-index) of 0.59 [0.52, 0.65]. For comparison, the c-index for PFI using the IHC-based ER status for these cases was 0.61 [0.54, 0.66]. This work further demonstrates the potential to infer gene expression from H&E stained images in a manner that shows meaningful associations with clinical variables. Because obtaining H&E stained images is substantially easier and faster than genetic testing, the capability to derive molecular genetic information from these images may increase access to this type of information for patient risk stratification and provide research insights into molecular-morphological associations. Future work incorporating more comprehensive sets of genes remains a valuable next step. Citation Format: Anvita A. Srinivas, Ronnachai Jaroensri, Ellery Wulczyn, James H. Wren, Elaine E. Thompson, Niels Olson, Fabien Beckers, Melissa Miao, Yun Liu, Cameron Chen, David F. Steiner. Estrogen receptor gene expression prediction from H&E-stained whole slide images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5357.