Exceptional Response to Trastuzumab Deruxtecan in a Patient With Recurrent Ovarian Clear Cell Carcinoma With Human Epidermal Growth Factor Receptor 2 Expression.

Case report of a HER2-expressed ovarian clear cell carcinoma with exceptional response to trastuzumab deruxtecan.

Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging.

Multiplex tissue imaging are a collection of increasingly popular single-cell spatial proteomics and transcriptomics assays for characterizing biological tissues both compositionally and spatially. However, several technical issues limit the utility of multiplex tissue imaging, including the limited number of RNAs and proteins that can be assayed, tissue loss, and protein probe failure. In this work, we demonstrate how machine learning methods can address these limitations by imputing protein abundance at the single-cell level using multiplex tissue imaging datasets from a breast cancer cohort. We first compared machine learning methods' strengths and weaknesses for imputing single-cell protein abundance. Machine learning methods used in this work include regularized linear regression, gradient-boosted regression trees, and deep learning autoencoders. We also incorporated cellular spatial information to improve imputation performance. Using machine learning, single-cell protein expression can be imputed with mean absolute error ranging between 0.05-0.3 on a [0,1] scale. Our results demonstrate (1) the feasibility of imputing single-cell abundance levels for many proteins using machine learning to overcome the technical constraints of multiplex tissue imaging and (2) how including cellular spatial information can substantially enhance imputation results.

Addressing persistent challenges in digital image analysis of cancerous tissues.

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Phase Ib Dose Expansion and Translational Analyses of Olaparib in Combination with Capivasertib in Recurrent Endometrial, Triple-Negative Breast, and Ovarian Cancer.

PURPOSE: On the basis of strong preclinical rationale, we sought to confirm recommended phase II dose (RP2D) for olaparib, a PARP inhibitor, combined with the AKT inhibitor capivasertib and assess molecular markers of response and resistance. PATIENTS AND METHODS: We performed a safety lead-in followed by expansion in endometrial, triple-negative breast, ovarian, fallopian tube, or peritoneal cancer. Olaparib 300 mg orally twice daily and capivasertib orally twice daily on a 4-day on 3-day off schedule was evaluated. Two dose levels (DL) of capivasertib were planned: 400 mg (DL1) and 320 mg (DL-1). Patients underwent biopsies at baseline and 28 days. RESULTS: A total of 38 patients were enrolled. Seven (18%) had germline BRCA1/2 mutations. The first 2 patients on DL1 experienced dose-limiting toxicities (DLT) of diarrhea and vomiting. No DLTs were observed on DL-1 (n = 6); therefore, DL1 was reexplored (n = 6) with no DLTs, confirming DL1 as RP2D. Most common treatment-related grade 3/4 adverse events were anemia (23.7%) and leukopenia (10.5%). Of 32 evaluable subjects, 6 (19%) had partial response (PR); PR rate was 44.4% in endometrial cancer. Seven (22%) additional patients had stable disease greater than 4 months. Tumor analysis demonstrated strong correlations between response and immune activity, cell-cycle alterations, and DNA damage response. Therapy resistance was associated with receptor tyrosine kinase and RAS-MAPK pathway activity, metabolism, and epigenetics. CONCLUSIONS: The combination of olaparib and capivasertib is associated to no serious adverse events and demonstrates durable activity in ovarian, endometrial, and breast cancers, with promising responses in endometrial cancer. Importantly, tumor samples acquired pre- and on-therapy can help predict patient benefit.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

The Pseudomonas syringae Type III Effector HopG1 Induces Actin Remodeling to Promote Symptom Development and Susceptibility during Infection.

The plant cytoskeleton underpins the function of a multitude of cellular mechanisms, including those associated with developmental- and stress-associated signaling processes. In recent years, the actin cytoskeleton has been demonstrated to play a key role in plant immune signaling, including a recent demonstration that pathogens target actin filaments to block plant defense and immunity. Herein, we quantified spatial changes in host actin filament organization after infection with Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), demonstrating that the type-III effector HopG1 is required for pathogen-induced changes to actin filament architecture and host disease symptom development during infection. Using a suite of pathogen effector deletion constructs, coupled with high-resolution microscopy, we found that deletion of hopG1 from Pst DC3000 resulted in a reduction in actin bundling and a concomitant increase in the density of filament arrays in Arabidopsis, both of which correlate with host disease symptom development. As a mechanism underpinning this activity, we further show that the HopG1 effector interacts with an Arabidopsis mitochondrial-localized kinesin motor protein. Kinesin mutant plants show reduced disease symptoms after pathogen infection, which can be complemented by actin-modifying agents. In total, our results support a model in which HopG1 induces changes in the organization of the actin cytoskeleton as part of its virulence function in promoting disease symptom development.

Analysis of genome sequences from plant pathogenic Rhodococcus reveals genetic novelties in virulence loci.

Members of Gram-positive Actinobacteria cause economically important diseases to plants. Within the Rhodococcus genus, some members can cause growth deformities and persist as pathogens on a wide range of host plants. The current model predicts that phytopathogenic isolates require a cluster of three loci present on a linear plasmid, with the fas operon central to virulence. The Fas proteins synthesize, modify, and activate a mixture of growth regulating cytokinins, which cause a hormonal imbalance in plants, resulting in abnormal growth. We sequenced and compared the genomes of 20 isolates of Rhodococcus to gain insights into the mechanisms and evolution of virulence in these bacteria. Horizontal gene transfer was identified as critical but limited in the scale of virulence evolution, as few loci are conserved and exclusive to phytopathogenic isolates. Although the fas operon is present in most phytopathogenic isolates, it is absent from phytopathogenic isolate A21d2. Instead, this isolate has a horizontally acquired gene chimera that encodes a novel fusion protein with isopentyltransferase and phosphoribohydrolase domains, predicted to be capable of catalyzing and activating cytokinins, respectively. Cytokinin profiling of the archetypal D188 isolate revealed only one activate cytokinin type that was specifically synthesized in a fas-dependent manner. These results suggest that only the isopentenyladenine cytokinin type is synthesized and necessary for Rhodococcus phytopathogenicity, which is not consistent with the extant model stating that a mixture of cytokinins is necessary for Rhodococcus to cause leafy gall symptoms. In all, data indicate that only four horizontally acquired functions are sufficient to confer the trait of phytopathogenicity to members of the genetically diverse clade of Rhodococcus.