Utilization of anonymization techniques to create an external control arm for clinical trial data.

BACKGROUND: Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment. In the Nordic countries, legal requirements may require that the original subject-level data be anonymized, i.e., modified so that the risk to identify any individual is minimal. The aim of this study was to conduct initial exploration on how well pseudonymized and anonymized RWD perform in the creation of an ECA for an RCT. METHODS: This was a hybrid observational cohort study using clinical data from the control arm of the completed randomized phase II clinical trial (PACIFIC-AF) and RWD cohort from Finnish healthcare data sources. The initial pseudonymized RWD were anonymized within the (k, ε)-anonymity framework (a model for protecting individuals against identification). Propensity score matching and weighting methods were applied to the anonymized and pseudonymized RWD, to balance potential confounders against the RCT data. Descriptive statistics for the potential confounders and overall survival analyses were conducted prior to and after matching and weighting, using both the pseudonymized and anonymized RWD sets. RESULTS: Anonymization affected the baseline characteristics of potential confounders only marginally. The greatest difference was in the prevalence of chronic obstructive pulmonary disease (4.6% vs. 5.4% in the pseudonymized compared to the anonymized data, respectively). Moreover, the overall survival changed in anonymization by only 8% (95% CI 4-22%). Both the pseudonymized and anonymized RWD were able to produce matched ECAs for the RCT data. Anonymization after matching impacted overall survival analysis by 22% (95% CI -21-87%). CONCLUSIONS: Anonymization may be a viable technique for cases where flexible data transfer and sharing are required. As anonymization necessarily affects some aspects of the original data, further research and careful consideration of anonymization strategies are needed.

Classification and Locoregional Treatment of Rectal Neuroendocrine Tumors.

A 43-year-old male presented to his primary care physician's office with a complaint of painless rectal bleeding with a concomitant weight loss of 10-15 pounds and intermittent abdominal pain. Endoscopic evaluation was remarkable for a 5 mm rectal polyp roughly 10 cm from the anal verge. Resection was performed and the pathology was consistent with a low-grade neuroendocrine/carcinoid tumor. Immunostaining for synaptophysin, chromogranin, CD56, and CAM5.2 were positive while staining for CK20 was negative. Given the absence of metastasis on radiographic and endoscopic evaluation, the patient was managed conservatively thereafter with observation. Despite having an indolent clinical course, resection is recommended for all rectal neuroendocrine tumors. Locoregional endoscopic resection versus radical resection can be used for adequate tissue removal depending on the characteristics of the tumor and the degree of invasion.

Treatment Outcomes Of Patients With Newly Diagnosed Acute Promyelocytic Leukemia; Experience From A Developing Country.

BACKGROUND: Acute promyelocytic leukaemia (APL) characterized by t (15;17) leading to formation of fusion protein PML-RARA is an acute leukaemia with highest mortality. A remarkable improvement in the outcomes has been witnessed due to evolution of highly effective targeted therapies replacing the traditional chemotherapy is most patients. However limited data is available regarding treatment outcomes of APL using various novel regimens from developing countries like Pakistan. METHODS: This was a retrospective descriptive study which included APL patients treated at AFBMTC Rawalpindi from 2005 to 2020. It included a total of 51 eligible patients with a diagnosis of de novo APL confirmed by the presence of PML-RARA transcript or presence of t (15;17) by cytogenetics or FISH analysis. The protocols used for treatment included the UKAML MRC 12, the LPA-99/LPA-2005 PETHEMA, the APML4 and non-chemotherapy based ATO-ATRA protocol. RESULTS: The study included 51 patients in which 31 (60.78%) were male and 20 (39.2%) were female. The median age at diagnosis was 30 years (range 5-70). The commonest symptom was fever seen in 43 (84.3%) patients and bruising was the commonest physical finding present in 44 (86.3%) patients. High-risk patients were 23 (46.1%), 18 (35.3%) were intermediate risk and 10 (19.6%) were low risk. The LPA99/LPA2005 was most frequently employed protocol being used in 36 (72%) patients. There were 2 deaths during induction and 44 (86.3%) achieved CR post induction. The median follow up time was 32 months (range 1 to 190 months) with an overall survival (OS) of 76.5% and a relapse free survival (RFS) of 66.7. CONCLUSIONS: Our study shows APL is a highly curable malignancy and outcomes have improved with newer non chemotherapy based therapies. It can also be concluded that outcomes of APL gradually improved over the past 2 decades due to improvement in supportive care, provision of blood products and use of newer protocols. The prognosis remains less favourable in high risk patients.

Machine learning and feature selection for drug response prediction in precision oncology applications.

In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input "big data" require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

Motivation: Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results: Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Availability and implementation: Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. Contact: mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary information: Supplementary data are available at Bioinformatics online.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.