SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Although programmed death-ligand 1-programmed death 1 (PD-L1-PD-1) inhibitors are broadly efficacious, improved outcomes have been observed in patients with high PD-L1 expression or high tumor mutational burden (TMB). PD-L1 testing is required for checkpoint inhibitor monotherapy in front-line non-small-cell lung cancer (NSCLC). However, obtaining adequate tumor tissue for molecular testing in patients with advanced disease can be challenging. Thus, an unmet medical need exists for diagnostic approaches that do not require tissue to identify patients who may benefit from immunotherapy. Here, we describe a novel, technically robust, blood-based assay to measure TMB in plasma (bTMB) that is distinct from tissue-based approaches. Using a retrospective analysis of two large randomized trials as test and validation studies, we show that bTMB reproducibly identifies patients who derive clinically significant improvements in progression-free survival from atezolizumab (an anti-PD-L1) in second-line and higher NSCLC. Collectively, our data show that high bTMB is a clinically actionable biomarker for atezolizumab in NSCLC.

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.

Software code and documentation freely available here: http://miso.readthedocs.org/en/fastmiso/sashimi.html

Rationale: Plasma-detectable biomarkers that rapidly and accurately diagnose bacterial infections in children with suspected pneumonia could reduce the morbidity of respiratory disease and decrease the unnecessary use of antibiotic therapy.Objectives: Using 56 markers measured in a multiplexed immunoassay, we sought to identify proteins and protein combinations that could discriminate bacterial from viral or malarial diagnoses. Methods:We selected 80 patients with clinically diagnosed pneumonia (as defined by the World Health Organization) who also met criteria for bacterial, viral, or malarial infection based on clinical, radiographic, and laboratory results. Ten healthy community control subjects were enrolled to assess marker reliability. Patients were subdivided into two sets: one for identifying potential markers and another for validating them.Measurements and Main Results: Three proteins (haptoglobin, tumor necrosis factor receptor 2 or IL-10, and tissue inhibitor of metalloproteinases 1) were identified that, when combined through a classification tree signature, accurately classified patients into bacterial, malarial, and viral etiologies and misclassified only one patient with bacterial pneumonia from the validation set. The overall sensitivity and specificity of this signature for the bacterial diagnosis were 96 and 86%, respectively. Alternative combinations of markers with comparable accuracy were selected by support vector machine and regression models and included haptoglobin, IL-10, and creatine kinase-MB.Conclusions: Combinations of plasma proteins accurately identified children with a respiratory syndrome who were likely to have bacterial infections and who would benefit from antibiotic therapy. When used in conjunction with malaria diagnostic tests, they may improve diagnostic specificity and simplify treatment decisions for clinicians.