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Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation–mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

Targeted therapies have transformed treatment of driver-mutated metastatic NSCLC. We compared cardiovascular adverse events between and within targeted therapy classes.

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients’ response to treatments.

Hypertension (HTN) has been found to be the most common comorbidity in patients with cancer. In addition to increased prevalence of baseline HTN, patients with cancer may be at increased risk of HTN as a short- or long-term adverse event from cancer therapy. Different classes of cancer therapies have been implicated in the development of HTN, including inhibitors of vascular endothelial growth factor (VEGF), Bruton tyrosine kinase inhibitors, proteasome inhibitors, androgen deprivation therapy, and others. While some of these drugs may lead to increases in blood pressure through on-target effects (e.g. with VEGF inhibition), others may be associated with HTN from off-target mechanisms that are not always well-understood. The appropriate management of cancer therapy-associated HTN is also limited by a dearth of studies investigating HTN management specifically in cancer patients, leading to extrapolation of data from the general population. This manuscript aims to review the knowledge on incidence, mechanisms, and sequelae of cancer-therapy related HTN with different classes of anti-neoplastic agents. We further review management strategies as per consensus-based guidelines.

Introduction:Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used to activate the immune system against tumor cells. Despite therapeutic benefits, ICIs have the potential to cause immune related adverse events (irAE) such as myocarditis, a rare but serious side effect with up to 50% mortality in affected patients. Histologically, patients with ICI myocarditis have lymphocytic infiltrates in the heart, implicating T-cell-mediated mechanisms. However, the precise pathologic immune subsets and molecular changes in ICI myocarditis are unknown.Hypothesis:We hypothesize that ICI myocarditis is associated with significant quantitative and molecular changes in pathogenic immune cell subsets.Methods:To identify immune subset(s) associated with ICI myocarditis, we performed time-of-flight mass cytometry (CyTOF) on peripheral blood mononuclear cells from 40 patients and controls with autoimmune adverse events (irAE) on ICI including four patients with ICI myocarditis. We also profiled 15 patients/controls using single cell T-cell receptor (TCR) sequencing and RNA sequencing (scRNA-seq) with feature barcoding for surface marker expression confirmation.Results:With both methods, we found expansion of cytotoxic CD8+ T effector cells re-expressing CD45RA (Temra CD8+ cells) in ICI myocarditis patients compared to controls. Using TCR sequencing, these CD8+ Temra cells were clonally expanded in myocarditis patients. Transcriptomic analysis of these Temra CD8+ clones confirmed a highly activated and cytotoxic phenotype with expression of granzyme/perforin. Longitudinal study demonstrated progression of these Temra CD8+ cells into an exhausted phenotype two months after treatment with glucocorticoids. Differential expression of proinflammatory chemokines (CCL4/CCL4L2/CCL5) was uncovered in clonally expanded Temra CD8+ cells, and ligand-receptor analysis implicated their regulation of other inflammatory cells such as monocytes and NK cells.Conclusions:Clonal cytotoxic Temra CD8+ cells are significantly increased in patients with ICI myocarditis, and have unique transcriptional changes, including upregulation of chemokines CCL4/CCL4L2/CCL5, which may serve as attractive diagnostic/therapeutic targets.