OpenCell: Endogenous tagging for the cartography of human cellular organization.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

Patient-Reported Symptom Severity Among 22,650 Cancer Outpatients in the Last Six Months of Life.

CONTEXT: Understanding the magnitude and risk factors for symptom burden of patients with cancer at the end of life is critical to guiding effective patient- and system-level interventions. OBJECTIVES: We aimed to estimate the prevalence of severe patient-reported symptoms among cancer outpatients during the six months before death and to identify patient groups at a higher risk for reporting severe symptoms. METHODS: This was a retrospective cohort study of cancer decedents at regional cancer centers from 2010 to 2016. Patient-reported Edmonton Symptom Assessment System (ESAS) scores from the last six months of life were linked to administrative databases. The proportion of patients reporting severe symptom scores (≥7) for anxiety, depression, drowsiness, lack of appetite, nausea, pain, shortness of breath, tiredness, and overall well-being during the six months before death was described. Multivariable modified Poisson regression analyses were used to identify risk factors for reporting severe symptom scores. RESULTS: Of 39,084 cancer decedents, 22,650 had one or more symptom assessments recorded in the last six months of life, resulting in 92,757 ESAS assessments. Severe scores were highest for tiredness (56%), lack of appetite (46%), and impaired well-being (45%). The proportion of patients reporting severe symptom scores was stable before progressively increasing at three months before death. Elderly, women, patients with high comorbidity, immigrants, and living in urban areas or with high material deprivation were at increased risk of reporting severe scores. CONCLUSIONS: Despite an integrated symptom screening program, rates of severe patient-reported symptom scores before death were high for outpatients with cancer. Patient subgroups at increased risk of severe symptom burden may benefit from targeted interventions. Ongoing review of routinely collected symptom data may be used to assess the supportive care needs and guide targeted interventions at the health-system level.

Large dataset enables prediction of repair after CRISPR-Cas9 editing in primary T cells.

Understanding of repair outcomes after Cas9-induced DNA cleavage is still limited, especially in primary human cells. We sequence repair outcomes at 1,656 on-target genomic sites in primary human T cells and use these data to train a machine learning model, which we have called CRISPR Repair Outcome (SPROUT). SPROUT accurately predicts the length, probability and sequence of nucleotide insertions and deletions, and will facilitate design of SpCas9 guide RNAs in therapeutically important primary human cells.

Pediatric leukemia: Moving toward more accurate models.

Leukemia is a complex genetic disease caused by errors in differentiation, growth, and apoptosis of hematopoietic cells in either lymphoid or myeloid lineages. Large-scale genomic characterization of thousands of leukemia patients has produced a tremendous amount of data that have enabled a better understanding of the differences between adult and pediatric patients. For instance, although phenotypically similar, pediatric and adult myeloid leukemia patients differ in their mutational profiles, typically involving either chromosomal translocations or recurrent single-base-pair mutations, respectively. To elucidate the molecular mechanisms underlying the biology of this cancer, continual efforts have been made to develop more contextually and biologically relevant experimental models. Leukemic cell lines, for example, provide an inexpensive and tractable model but often fail to recapitulate critical aspects of tumor biology. Likewise, murine leukemia models of leukemia have been highly informative but also do not entirely reproduce the human disease. More recent advances in the development of patient-derived xenografts (PDXs) or human models of leukemias are poised to provide a more comprehensive, and biologically relevant, approach to directly assess the impact of the in vivo environment on human samples. In this review, the advantages and limitations of the various current models used to functionally define the genetic requirements of leukemogenesis are discussed.