Biology subjects

Rimm, D.

Publications and source records attributed to Rimm, D..

3 recordsLinked to original sources

bioRxiv2024

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Spatial cellular profiling technologies have revolutionized our understanding of complex biological processes, from development and disease progression to immunity and aging. Despite their promise, integrating spatial information with multiplexed molecular data to accurately predict phenotypes poses significant challenges, especially in clinical settings. Here, we present SORBET, a geometric deep learning framework that directly analyzes complete spatial profiling data, eliminating the need to compress complete cell profiles into a limited set of annotations, such as cell types. SORBET models tissues as graphs of adjacent cells and applies graph convolutional networks to infer emergent phenotypes, such as responses to immunotherapy. The model leverages a novel data augmentation technique to ensure robust predictions, complemented by tailored interpretability analyses to identify the molecular and spatial patterns underlying the models phenotype inferences. We apply our method to a CosMx spatial transcriptomics dataset of pre-treatment metastatic melanoma samples annotated with response to immunotherapy; we show that spatial information significantly improves clinical endpoint, or phenotype, prediction and identifies important biological patterns. To our knowledge, SORBET is the first example of phenotype prediction on spatial transcriptomics data. We further validated our method using two spatial proteomics datasets, Imaging Mass Cytometry (IMC) and Co-detection by indexing (CODEX), obtained from Non-Small Cell Lung Cancer and Colorectal Cancer samples, respectively. SORBET demonstrates superior accuracy in phenotype prediction over leading spatial and non-spatial methods across various datasets of different observed phenotypes and technologies. SORBET sets a new benchmark for predictive analysis in spatial omics, promising to advance personalized medicine through refined patient treatment stratification, grounded in molecular and spatial tissue profiling.

bioinformatics↗

bioRxiv2021

Deep learning trained on H&E tumor ROIs predicts HER2 status and Trastuzumab treatment response in HER2+ breast cancer

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that is better than IHC and may benefit clinical evaluations.

bioinformatics↗

bioRxiv2020

CECR2 Drives Breast Cancer Metastasis by Suppressing Macrophage Inflammatory Responses

Epigenetic and transcriptional changes are critical for metastasis, the major cause of cancer-related deaths. Metastatic tumor cells escape immune surveillance more efficiently than tumor cells in the primary sites, but the mechanisms controlling their immune evasion are poorly understood. We found that distal metastases are more immune inert with increased M2 macrophages compared to their matched primary tumors. Acetyl-lysine reader CECR2 is an epigenetic regulator upregulated in metastases and positively associated with M2 macrophages. CECR2 specifically promotes breast cancer metastasis in multiple mouse models, with more profound effect in the immunocompetent setting. Mechanistically, NF-{kappa}B family member RELA recruits CECR2 to activate CSF1 and CXCL1, which are critical for macrophage-mediated immunosuppression at the metastatic sites. Furthermore, pharmacological inhibition of CECR2 bromodomain impedes NF-{kappa}B-mediated immune suppression by macrophages and inhibits breast cancer metastasis. These results reveal novel therapeutic strategies to treat metastatic breast cancer. Statement of SignificanceComparison of matched primary breast tumors and distal metastases show that metastases are more immune inert with increased tumor promoting macrophages. Depletion or pharmacological inhibition of CECR2 inhibits breast cancer metastasis by suppressing macrophage inflammatory responses, nominating CECR2 as a promising therapeutic target for cancer metastasis.

cancer biology↗