Biology subjects

Chuang, J. H.

Publications and source records attributed to Chuang, J. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatially resolved multimodal hallmarks of response to neoadjuvant immunotherapies in the melanoma ecosystem in 2D and 3D

Neoadjuvant immunotherapy has transformed cancer treatment, yet the spatial molecular architecture governing response and resistance across distinct immune checkpoint blockade (ICB) regimens remains poorly defined. We assembled the largest neoadjuvant ICB (NICB) spatial multi-omics cohort to date, profiling over 112 million cells at single-cell resolution across three melanoma ICB regimens using MERFISH spatial transcriptomics, multiplexed immunofluorescence, and scRNA-sequencing. These analyses revealed the full multicellular spatial architecture of the NICB tumor microenvironment, including mature TLS with germinal centers, TCF7+ stem-like T cells, myeloid cells organized into spatially distinct cellular neighborhoods with unique intercellular signaling circuits, and CCL19/CCL21-expressing fibroblasts as a previously unrecognized stromal scaffold sustaining these immune hubs. We developed three purpose-built computational tools that together enabled comprehensive quantification of this microenvironment for the first time: SCIRA for whole-slide single-cell receptor-ligand quantification, GCSCAN for molecularly grounded TLS and germinal center structural delineation, and PathNet-TLS for automated TLS detection on H&E images. Applying these tools across the cohort, we defined the immune and stromal composition and cellular neighborhood organization distinguishing responders from non-responders. We also quantified cell-cell interactions and regimen-specific immune architectures, including a markedly stronger mature TLS/germinal center response with IPI-NIVO than NIVO-RELA. Importantly, GCSCAN-quantified TLS and germinal center density each stratified disease-free survival, with responders that lack germinal centers having an elevated risk of relapse. Open-top light-sheet imaging and CODA-based 3D reconstruction further uncovered interconnected germinal center-TLS tunnels invisible to standard 2D histopathology. These findings establish a discovery-to-tool paradigm linking single-cell tumor microenvironment interrogation to clinically deployable computational pathology for biomarker-driven NICB assessment across cancer types.

cancer biology↗

bioRxiv2026

Distinct senescent β-cell senotypes differentially drive islet aging and dysfunction

Biological aging greatly impacts the bodys ability to handle glucose, and represents a major risk factor the development and progression of type 2 diabetes (T2D). Nonetheless, despite advances in cellular senescence research and the development of new senolytic therapies, the heterogeneity of cellular senescence in the human endocrine pancreas, as well as its roles in normal aging, remains to be elucidated at the single-cell level. Here, we performed single-cell-resolved spatial proteomics and transcriptomics on intact pancreas from 26 donors (ages 20-80) and multiplexed single-cell RNA sequencing and functional assays on dispersed islets from 14 donors (ages 34-69). We identify two discrete SnC subpopulations distinguished by relative expression of CDKN1A and CDKN2A. CDKN1A senescent cells (SnCs) exhibit loss of {beta}-cell identity, impaired insulin secretion, and a proinflammatory SASP associated with increased islet immune infiltration. In contrast, CDKN2A SnCs retain transcriptional identity and functional competence, with lower inflammatory signaling. Together, these findings identify heterogeneous and functionally divergent senotypes in the human pancreas, distinguishing an adaptive (CDKN2A) from a maladaptive (CDKN1A) senescence program, thus providing a mechanism-guided framework for senescence-targeted therapies in T2D.

cell biology↗

bioRxiv2026

DIANNE: Segmentation-Free Localization of Histology Differential Attributes

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology annotation support and discovery approach for rapid training and inference of spatial differential attributes, enabled by train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

pathology↗

bioRxiv2025

A human and mouse subpopulation of senescent β-cells induces pathologic dysfunction through targetable paracrine signaling

Cellular senescence is a stress response mechanism marked by irreversible growth arrest, upregulation of antiapoptotic pathways, loss of cellular function, and remodelling of the cellular secretory profile. In both humans and mice, pancreatic {beta}-cells undergo senescence with age and insulin resistance. Targeted removal of senescent cells in mouse models of diabetes improves glucose homeostasis, demonstrating the role {beta}-cell senescence in diabetes progression. In contrast, {beta}-cell senescence also promotes immune surveillance, promoting {beta}-cell survival and function. Thus, a better understanding of senescent cells phenotypic and functional heterogeneity is needed to develop effective therapeutic strategies. Herein, we show that subpopulations of senescent {beta}-cells in mice and humans, which were identified through the expression of Cdkn1a (encoding p21Cip1) and Cdkn2a (encoding p16Ink4a) by single-cell RNA sequencing (scRNA-seq), flow cytometry, spatial transcriptomics, and spatial proteomics, exhibit distinct transcriptional and functional identities. The predominant senescent {beta}-cell subpopulation expressed Cdkn1a and was characterized by a lack of glucose responsiveness, high basal insulin secretion, and transcription of canonical SASP factors. The SASP of Cdkn1a-expressing {beta}-cells had non-cell autonomous effects on neighbouring cells. A subset of four SASP factors from Cdkn1a+ cells was sufficient to induce secondary senescence and {beta}-cell dysfunction in vitro. JAK inhibitors (JAK1/2 and JAK1/3) counteracted secondary senescence induction and restored {beta}-cell function in high-fat diet-fed mice and human islets from donors with or without type 2 diabetes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/648438v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9b8addorg.highwire.dtl.DTLVardef@1b9c4eborg.highwire.dtl.DTLVardef@12f2964org.highwire.dtl.DTLVardef@1468297_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

Prediction of Outcome from Spatial Protein Profiling of Triple-Negative Breast Cancers

In tumors, reciprocal spatial interactions between immune cells, their mediators, the extracellular matrix, and mutated neoplastic cells impact all aspects of treatment resistance. The operational mechanisms of these interactions are foundational for developing insights and targets for cancer therapy and prevention. Spatial quantification of the tumor microenvironment system from image data has untapped potential for patient stratification. Here, we present SparTile, a powerful computational approach for analysis of multiplex proteomics images to reveal clinically relevant structural organization in the tumor microenvironment. SparTile enables the robust unbiased identification and characterization of tumor microenvironments based on spatial relationships among protein markers without the need for cell segmentation or classification. Applied to tissues of patients with triple negative breast cancer (TNBC), an aggressive subtype of breast cancer, SparTile identified repeatable microenvironments with specific cellular relationships. These included microenvironments characterized by risk markers such as Ki67+ (p-value= 0.052) and vimentin+ (p-value<0.01) tumor cells, which correlate with poor survival. Furthermore, myeloid markers in an MX1 positive tumor environment correlate with shorter survival (p-value=0.04). Finally, we identified a relative distance between tumor and myeloid cell markers as a strong prognostic risk factor in multivariate Cox models (p-value=0.01). This distance metric was externally validated on two datasets of breast cancer multiplex images (p-values<0.01). Thus, unbiased protein-based and segmentation-free spatial analysis is effective for identifying clinically relevant biomarkers from multiplex tumor images and identifies novel predictive biology.

cancer biology↗

bioRxiv2024

Multifaceted roles of cohesin in regulating transcriptional loops

Cohesin is required for chromatin loop formation. However, its precise role in regulating gene transcription remains largely unknown. We investigated the relationship between cohesin and RNA Polymerase II (RNAPII) using single-molecule mapping and live-cell imaging methods in human cells. Cohesin-mediated transcriptional loops were highly correlated with those of RNAPII and followed the direction of gene transcription. Depleting RAD21, a subunit of cohesin, resulted in the loss of long-range (>100 kb) loops between distal (super-)enhancers and promoters of cell-type-specific genes. By contrast, the short-range (<50 kb) loops were insensitive to RAD21 depletion and connected genes that are mostly housekeeping. This result explains why only a small fraction of genes are affected by the loss of long-range chromatin interactions due to cohesin depletion. Remarkably, RAD21 depletion appeared to up-regulate genes located in early initiation zones (EIZ) of DNA replication, and the EIZ signals were amplified drastically without RAD21. Our results revealed new mechanistic insights of cohesins multifaceted roles in establishing transcriptional loops, preserving long-range chromatin interactions for cell-specific genes, and maintaining timely order of DNA replication.

genomics↗

bioRxiv2024

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of SignificanceTumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

cancer biology↗

bioRxiv2023

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, {gamma}{delta}) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

genomics↗

bioRxiv2023

SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images

Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken into smaller images called tiles, and a neural network backbone encodes each tile in a feature space. Many recent works have applied attention based deep learning models to aggregate tile-level features into a slide-level representation, which is then used for slide-level prediction tasks. However, training attention models is computationally intensive, necessitating hyperparameter optimization and specialized training procedures. Here, we propose SAMPLER, a fully statistical approach to generate efficient and informative WSI representations by encoding the empirical cumulative distribution functions (CDFs) of multiscale tile features. We demonstrate that SAMPLER-based classifiers are as accurate or better than state-of-the-art fully deep learning attention models for classification tasks including distinction of: subtypes of breast carcinoma (BRCA: AUC=0.911 {+/-} 0.029); subtypes of non-small cell lung carcinoma (NSCLC: AUC=0.940{+/-}0.018); and subtypes of renal cell carcinoma (RCC: AUC=0.987{+/-}0.006). A major advantage of the SAMPLER representation is that predictive models are >100X faster compared to attention models. Histopathological review confirms that SAMPLER-identified high attention tiles contain tumor morphological features specific to the tumor type, while low attention tiles contain fibrous stroma, blood, or tissue folding artifacts. We further apply SAMPLER concepts to improve the design of attention-based neural networks, yielding a context aware multi-head attention model with increased accuracy for subtype classification within BRCA and RCC (BRCA: AUC=0.921{+/-}0.027, and RCC: AUC=0.988{+/-}0.010). Finally, we provide theoretical results identifying sufficient conditions for which SAMPLER is optimal. SAMPLER is a fast and effective approach for analyzing WSIs, with greatly improved scalability over attention methods to benefit digital pathology analysis.

bioinformatics↗

bioRxiv2023

Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples

HighlightsWe have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. SummaryWe designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.

bioinformatics↗

bioRxiv2023

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 m z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with <3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

bioinformatics↗

bioRxiv2022

Development and application of genetic ancestry reconstruction methods to study diversity of patient-derived models in the NCI PDXNet Consortium

Personalized medicine holds great promise for improving cancer outcomes, yet there is a large inequity in the demographics of patients from whom genomic data and models, including patient derived xenografts (PDX), are developed and for whom treatments are optimized. In this study we develop a genetic ancestry pipeline for the Cancer Genomics Cloud, which we use to assess the diversity of models currently available in the National Cancer Institute (NCI) supported PDX Development and Trial Centers Research Network (PDXNet). We show that there is an over-representation of models derived from patients of European ancestry, which is consistent with other cancer model resources. We discuss these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US. For example, for the top cancer health disparities affecting African Americans and Latinos, there is a significant lack of ethnic/race appropriate models needed to advance pre-clinical research and personalized clinical treatment. For stomach and liver tumors, which represent disparities in these two minority populations, there are only three available models derived from patients from such backgrounds. Fortunately, ongoing NCI-funded efforts in minority focused PDXNet centers are actively addressing these gaps. We further discuss these results in the context of power analyses to highlight the immediate need for the development of models from minority populations to address cancer health equity in personalized medicine.

cancer biology↗

bioRxiv2022

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality

Colorectal cancers are the fourth most commonly diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. We first observe that deep learning models based only on whole slide images (WSIs) from The Cancer Genome Atlas accurately separate high risk (OS<3years, N=38) from low risk (OS>5years, N=25) patients (AUC=0.81{+/-}0.08, 5year survival p-value=2.13e-25, 5year relative risk=5.09{+/-}0.05) though such models are less effective at predicting OS for moderate risk (3years<OS<5years, N=45) patients (5year survival p-value=0.5, 5year relative risk=1.32{+/-}0.09). However, we find that novel integrative models combining whole slide images, clinical variables, and mutation signatures can improve patient stratification for moderate risk patients (5year survival p-value=6.69e-30, 5year relative risk=5.32{+/-}0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset generated by our team (3year survival p-value=1.14e-09, 5year survival p-value=2.15e-05, 3year relative risk=3.25{+/-}0.06, 5year relative-risk=3.07{+/-}0.08). The integrative model substantially outperforms models using only images or only clinical variables, indicating beneficial cross-talk between the data types. Pathologist review of image-based heatmaps suggests that nuclear shape, nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high risk, while low risk regions tend to have more regular and small cells. The improved stratification of colorectal cancer patients from our computational methods can be beneficial for preemptive development of management and treatment plans for individual patients, as well as for informed enrollment of patients in clinical trials.

pathology↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2021

Deep Learning Features Encode Interpretable Morphologies within Histological Images

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture), which we show can be construed as abstract morphological genes ("mones") with strong independent associations to biological phenotypes. We observe that many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC=97.1% {+/-} 2.8% for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC=99.2% {+/-} 0.12%). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values.

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↗

bioRxiv2021

A breast cancer patient-derived xenograft and organoid platform for drug discovery and precision oncology

Model systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowing in vivo validation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/433268v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@134d5cdorg.highwire.dtl.DTLVardef@1c0a419org.highwire.dtl.DTLVardef@556a4forg.highwire.dtl.DTLVardef@1b68c9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Defects in translation-dependent quality control pathways lead to convergent molecular and neurodevelopmental pathology

Translation-dependent quality control pathways such as no-go decay (NGD), non-stop decay (NSD) and nonsense-mediated decay (NMD) govern protein synthesis and proteostasis by resolving non-translating ribosomes and preventing the production of potentially toxic peptides derived from faulty and aberrant mRNAs. However, how translation is altered and the in vivo defects that arise in the absence of these pathways are poorly understood. Here, we show that the NGD/NSD factors Pelo and Hbs1l are critical for cerebellar neurogenesis but expendable for survival of these neurons after development. Analysis of mutant embryonic fibroblasts revealed translational pauses, alteration of signaling pathways, and translational reprogramming. Similar effects on signaling pathways, the translatome and cerebellar development were observed upon deletion of the NMD factor Upf2. These data reveal that these quality control pathways that function to mitigate errors at distinct steps in translation can evoke similar cellular responses.

cell biology↗