RESUMO
Case report of a HER2-expressed ovarian clear cell carcinoma with exceptional response to trastuzumab deruxtecan.
Assuntos
Recidiva Local de Neoplasia , Neoplasias Ovarianas , Receptor ErbB-2 , Trastuzumab , Humanos , Feminino , Trastuzumab/uso terapêutico , Neoplasias Ovarianas/tratamento farmacológico , Neoplasias Ovarianas/genética , Receptor ErbB-2/genética , Recidiva Local de Neoplasia/tratamento farmacológico , Adenocarcinoma de Células Claras/tratamento farmacológico , Adenocarcinoma de Células Claras/patologia , Adenocarcinoma de Células Claras/genética , Antineoplásicos Imunológicos/uso terapêutico , Camptotecina/análogos & derivados , Camptotecina/uso terapêutico , Pessoa de Meia-Idade , Imunoconjugados/uso terapêuticoRESUMO
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.
RESUMO
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.
Assuntos
Neoplasias da Mama , Biópsia , Neoplasias da Mama/genética , Feminino , Humanos , Microambiente Tumoral/genéticaRESUMO
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.
Assuntos
Processamento de Imagem Assistida por Computador , Neoplasias , Diagnóstico por Imagem , Humanos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias/diagnóstico por imagem , Neoplasias/patologia , SoftwareRESUMO
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.
RESUMO
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.