Mapping of truncated O-glycans in cancers of epithelial and non-epithelial origin.

BACKGROUND: Novel immunotherapies targeting cancer-associated truncated O-glycans Tn (GalNAcα-Ser/Thr) and STn (Neu5Acα2-6GalNacα-Ser/Thr) are promising strategies for cancer treatment. However, no comprehensive, antibody-based mapping of truncated O-glycans in tumours exist to guide drug development. METHODS: We used monoclonal antibodies to map the expression of truncated O-glycans in >700 tissue cores representing healthy and tumour tissues originating from breast, colon, lung, pancreas, skin, CNS and mesenchymal tissue. Patient-derived xenografts were used to evaluate Tn expression upon tumour engraftment. RESULTS: The Tn-antigen was highly expressed in breast (57%, n = 64), colorectal (51%, n = 140) and pancreatic (53%, n = 108) tumours, while STn was mainly observed in colorectal (80%, n = 140) and pancreatic (56%, n = 108) tumours. We observed no truncated O-glycans in mesenchymal tumours (n = 32) and low expression of Tn (5%, n = 87) and STn (1%, n = 75) in CNS tumours. No Tn-antigen was found in normal tissue (n = 124) while STn was occasionally observed in healthy gastrointestinal tissue. Surface expression of Tn-antigen was identified across several cancers. Tn and STn expression decreased with tumour grade, but not with cancer stage. Numerous xenografts maintained Tn expression. CONCLUSIONS: Surface expression of truncated O-glycans is limited to cancers of epithelial origin, making Tn and STn attractive immunological targets in the treatment of human carcinomas.

De novo expression of human polypeptide N-acetylgalactosaminyltransferase 6 (GalNAc-T6) in colon adenocarcinoma inhibits the differentiation of colonic epithelium.

Aberrant expression of O-glycans is a hallmark of epithelial cancers. Mucin-type O-glycosylation is initiated by a large family of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferases (GalNAc-Ts) that target different proteins and are differentially expressed in cells and organs. Here, we investigated the expression patterns of all of the GalNAc-Ts in colon cancer by analyzing transcriptomic data. We found that GalNAc-T6 was highly up-regulated in colon adenocarcinomas but absent in normal-appearing adjacent colon tissue. These results were verified by immunohistochemistry, suggesting that GalNAc-T6 plays a role in colon carcinogenesis. To investigate the function of GalNAc-T6 in colon cancer, we used precise gene targeting to produce isogenic colon cancer cell lines with a knockout/rescue system for GALNT6 GalNAc-T6 expression was associated with a cancer-like, dysplastic growth pattern, whereas GALNT6 knockout cells showed a more normal differentiation pattern, reduced proliferation, normalized cell-cell adhesion, and formation of crypts in tissue cultures. O-Glycoproteomic analysis of the engineered cell lines identified a small set of GalNAc-T6-specific targets, suggesting that this isoform has unique cellular functions. In support of this notion, the genetically and functionally closely related GalNAc-T3 homolog did not show compensatory functionality for effects observed for GalNAc-T6. Taken together, these data strongly suggest that aberrant GalNAc-T6 expression and site-specific glycosylation is involved in oncogenic transformation.

Drug dosage modifications in 24 million in-patient prescriptions covering eight years: A Danish population-wide study of polypharmacy.

Polypharmacy has generally been assessed by raw counts of different drugs administered concomitantly to the same patients; not with respect to the likelihood of dosage-adjustments. To address this aspect of polypharmacy, the objective of the present study was to identify co-medications associated with more frequent dosage adjustments. The data foundation was electronic health records from 3.2 million inpatient admissions at Danish hospitals (2008-2016). The likelihood of dosage-adjustments when two drugs were administered concomitantly were computed using Bayesian logistic regressions. We identified 3,993 co-medication pairs that associate significantly with dosage changes when administered together. Of these pairs, 2,412 (60%) did associate with readmission, mortality or longer stays, while 308 (8%) associated with reduced kidney function. In comparison to co-medications pairs that were previously classified as drug-drug interactions, pairs not classified as drug-drug interactions had higher odds ratios of dosage modifications than drug pairs with an established interaction. Drug pairs not corresponding to known drug-drug interactions while still being associated significantly with dosage changes were prescribed to fewer patients and mentioned more rarely together in the literature. We hypothesize that some of these pairs could be associated with yet to be discovered interactions as they may be harder to identify in smaller-scale studies.

Optimizing drug selection from a prescription trajectory of one patient.

It is unknown how sequential drug patterns convey information on a patient's health status and treatment guidelines rarely account for this. Drug-agnostic longitudinal analyses of prescription trajectories in a population-wide setting are needed. In this cohort study, we used 24 years of data (1.1 billion prescriptions) from the Danish prescription registry to model the risk of sequentially redeeming a drug after another. Drug pairs were used to build multistep longitudinal prescription trajectories. These were subsequently used to stratify patients and calculate survival hazard ratios between the stratified groups. The similarity between prescription histories was used to determine individuals' best treatment option. Over the course of 122 million person-years of observation, we identified 9 million common prescription trajectories and demonstrated their predictive power using hypertension as a case. Among patients treated with agents acting on the renin-angiotensin system we identified four groups: patients prescribed angiotensin converting enzyme (ACE) inhibitor without change, angiotensin receptor blockers (ARBs) without change, ACE with posterior change to ARB, and ARB posteriorly changed to ACE. In an adjusted time-to-event analysis, individuals treated with ACE compared to those treated with ARB had lower survival probability (hazard ratio, 0.73 [95% CI, 0.64-0.82]; P < 1 × 10-16). Replication in UK Biobank data showed the same trends. Prescription trajectories can provide novel insights into how individuals' drug use change over time, identify suboptimal or futile prescriptions and suggest initial treatments different from first line therapies. Observations of this kind may also be important when updating treatment guidelines.

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining.

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.

[Precision cardiology].

Cardiology relies on a huge amount of data derived from clinical observations, case-specific considerations and randomised controlled trials. At best, medical intervention relieves symptoms and reduces disease consequences or complications. Rarely, it redirects the cause. However, an accelerated array of opportunities within a digital and molecular discourse may mark a medical era which acknowledges individual variation rather than accepts a pragmatic pooling of similarities. This review aims at providing a brief overview of academic achievements, clinical considerations and translational perspectives related to precision cardiology.