Knowledge bases and software support for variant interpretation in precision oncology.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.

A Concept for Mining Transitive Sequential Patterns from Pancreatic Cancer Patient Journeys.

Pancreatic cancer, renowned for its aggressive nature and poor prognosis, necessitates the optimization of treatment strategies. The sequence of procedures in clinical trials is critical, such as evaluating the potential benefits of preoperative chemo-radio-therapy for pancreatic cancer. Nevertheless, we might not be aware of other temporal sequences which have an effect on therapy response or the general outcome. Extracting transitive sequential patterns from patients' medical trajectories allows researchers to identify temporal characteristics for complex diseases. We illustrate how such sequential patterns can be discovered and might be utilized in pancreatic cancer research as well as patient care.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

KRASG 12C-inhibitor-based combination therapies for pancreatic cancer: insights from drug screening.

Pancreatic ductal adenocarcinoma (PDAC) has limited treatment options, emphasizing the urgent need for effective therapies. The predominant driver in PDAC is mutated KRAS proto-oncogene, KRA, present in 90% of patients. The emergence of direct KRAS inhibitors presents a promising avenue for treatment, particularly those targeting the KRASG12C mutated allele, which show encouraging results in clinical trials. However, the development of resistance necessitates exploring potent combination therapies. Our objective was to identify effective KRASG12C-inhibitor combination therapies through unbiased drug screening. Results revealed synergistic effects with son of sevenless homolog 1 (SOS1) inhibitors, tyrosine-protein phosphatase non-receptor type 11 (PTPN11)/Src homology region 2 domain-containing phosphatase-2 (SHP2) inhibitors, and broad-spectrum multi-kinase inhibitors. Validation in a novel and unique KRASG12C-mutated patient-derived organoid model confirmed the described hits from the screening experiment. Our findings propose strategies to enhance KRASG12C-inhibitor efficacy, guiding clinical trial design and molecular tumor boards.

Predicting the Effect of Variants of Unknown Significance in Molecular Tumor Boards with the VUS-Predict Pipeline.

Precision oncology utilizing molecular biomarkers for targeted therapies is one of the hopes to treat cancer. The availability of patient specific molecular profiling through next-generation sequencing, though, increases the amount of available data per patient to an extent that computational support is required to identify potential driver alterations for targeted therapies and rational decision-making in molecular tumor boards (MTBs). For some genetic variants evidence-based drug recommendations are available in public databases, but for the majority, the variants of unknown significance (VUS), this clinical information is missing. Additionally, for most of these variants no information about the functional impact on the protein is accessible. To acquire maximal functional evidence for VUS, the VUS-Predict pipeline collects estimations about the effect of a VUS by integrating multiple pre-existing tools. Pre-existing tools implement different approaches for their predictions, which are summarized by our newly developed tool with a common score and classification in neutral or deleterious variants. The primary tools are chosen based on their sensitivity and specificity on well-known variants of the transcription factor TP53. Resulting negative and positive predictive values are used to calibrate the VUS-Predict pipeline. Further, the pipeline is evaluated using data from public cancer databases and cases of the MTB in Göttingen, both also in comparison with the ensemble method REVEL. The results show that VUS-Predict has clear advantages in a clinical setting due to clear and traceable predictions. In particular, VUS outperforms REVEL in the real-life setting of a MTB. Likewise, an evaluation on variants of public cancer databases confirms the good results of VUS-Predict and shows the need for a reliable gold standard and unambiguous results of the tools under test.