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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. A better understanding of the temporal dynamics and specific pathways leading into and out of the persister state is needed to identify strategies to prevent treatment failure. Using spatial transcriptomics in patient-derived xenograft models, we captured clonal lineage evolution during treatment. The persister state showed increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identified intrinsic and acquired resistance mechanisms (e.g., dual specific phosphatases, reticulon-4, and CDK2) and suggested specific temporal windows of potential therapeutic susceptibility. Deep learning-enabled analysis of histopathological slides revealed morphological features correlating with specific cell states, demonstrating that juxtaposition of transcriptomics and histological data enabled identification of phenotypically distinct populations from using imaging data alone. In summary, this study defined 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.

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states., (© 2024. The Author(s).)

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories., (©2024 The Authors; Published by the American Association for Cancer Research.)

We 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 genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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 Significance: Tumor 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.

Highlights: We 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., Summary: We 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.

Motivation: Drug synergy prediction is approached with machine learning techniques using molecular and pharmacological data. The published Cancer Drug Atlas (CDA) predicts a synergy outcome in cell-line models from drug target information, gene mutations and the models' monotherapy drug sensitivity. We observed low performance of the CDA, 0.339, measured by Pearson correlation of predicted versus measured sensitivity on DrugComb datasets., Results: We augmented the approach CDA by applying a random forest regression and optimization via cross-validation hyper-parameter tuning and named it Augmented CDA (ACDA). We benchmarked the ACDA's performance, which is 68% higher than that of the CDA when trained and validated on the same dataset spanning 10 tissues. We compared the performance of ACDA to one of the winning methods of the DREAM Drug Combination Prediction Challenge, the performance of which was lower than ACDA in 16 out of 19 cases. We further trained the ACDA on Novartis Institutes for BioMedical Research PDX encyclopedia data and generated sensitivity predictions for PDX models. Finally, we developed a novel approach to visualize synergy-prediction data., Availability and Implementation: The source code is available at https://github.com/TheJacksonLaboratory/drug-synergy and the software package at PyPI., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (© The Author(s) 2023. Published by Oxford University Press.)

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research., Significance: Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations., (©2022 The Authors; Published by the American Association for Cancer Research.)

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals., (© 2022. The Author(s).)

In the course of our studies aiming to discover vascular bed-specific endothelial cell (EC) mitogens, we identified leukemia inhibitory factor (LIF) as a mitogen for bovine choroidal EC (BCE), although LIF has been mainly characterized as an EC growth inhibitor and an anti-angiogenic molecule. LIF stimulated growth of BCE while it inhibited, as previously reported, bovine aortic EC (BAE) growth. The JAK-STAT3 pathway mediated LIF actions in both BCE and BAE cells, but a caspase-independent proapoptotic signal mediated by cathepsins was triggered in BAE but not in BCE. LIF administration directly promoted activation of STAT3 and increased blood vessel density in mouse eyes. LIF also had protective effects on the choriocapillaris in a model of oxidative retinal injury. Analysis of available single-cell transcriptomic datasets shows strong expression of the specific LIF receptor in mouse and human choroidal EC. Our data suggest that LIF administration may be an innovative approach to prevent atrophy associated with AMD, through protection of the choriocapillaris., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)