Pathogenic variants in<i>PIK3CA</i>are associated with clinical phenotypes of kaposiform lymphangiomatosis, generalized lymphatic anomaly, and central conducting lymphatic anomaly

Pediatric Blood & Cancer · 2023 · 18 citations

• Medicine
• Pathology
• Radiology

Complex lymphatic anomalies are debilitating conditions characterized by aberrant development of the lymphatic vasculature (lymphangiogenesis). Diagnosis is typically made by history, examination, radiology, and histologic findings. However, there is significant overlap between conditions, making accurate diagnosis difficult. Recently, genetic analysis has been offered as an additional diagnostic modality. Here, we describe four cases of complex lymphatic anomalies, all with PIK3CA variants but with varying clinical phenotypes. Identification of PIK3CA resulted in transition to a targeted inhibitor, alpelisib. These cases highlight the genetic overlap between phenotypically diverse lymphatic anomalies.

Genomic profiling informs diagnoses and treatment in vascular anomalies

Nature Medicine · 2023 · 60 citations

• Medicine
• Pathology
• Biology

MITI minimum information guidelines for highly multiplexed tissue images

Nature Methods · 2022 · 85 citations

• Computer Science
• Computer Science
• Computational biology

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Cell · 2020 · 581 citations

• Biology
• Computational biology
• Bioinformatics

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.