MOTIVATION: RNA sequencing enables the characterization of a cell's transcript isoforms in healthy and disease conditions. In the context of cancer, local transcript variability may translate to splicing-derived tumor-associated peptides recognized by the immune system. A software tool that extracts such candidate peptides, is of great interest for personalized cancer therapy. RESULTS: We present the open-source software tool ImmunoPepper, which extracts a set of biologically plausible peptides from a splicing graph, derived from a set of RNA-seq datasets. This peptide set can be personalized with germline and somatic variation and takes novel RNA splice variants into account. ImmunoPepper supports several filtering options, including subtraction of normal tissue background, prediction of MHC-binding affinity, as well as MassSpec-based validation of identified peptides. We analyzed 32 ovarian cancer (TCGA-OV) and 31 breast invasive carcinoma (TCGA-BRCA) samples, with a strict cancer-specific filtering configuration, and obtained on average 834 and 569 cancer-specific predicted MHC-I binding 9-mers per sample, for each cohort, respectively. MassSpec validation with the target-decoy competition Subset-Neighbor-Search (SNS) showed an average validation rate of 4.5% per TCGA-OV sample and 5.3% per TCGA-BRCA sample. This corresponded to 25 MHC-I binders 9-mers per TCGA-OV sample, and 20 MHC-I binders 9-mers per TCGA-BRCA sample in average. Finally, we draw conclusions about the best framework for generation of splicing-derived neoepitopes and recommend to use joint data structures when processing homogeneously a cancer and a normal cohort and to focus on reproducibility of the candidates across generation pipelines. AVAILABILITY AND IMPLEMENTATION: ImmunoPepper is implemented in Python 3 and is available as open-source software at https://github.com/ratschlab/immunopepper. The online documentation can be found at https://immunopepper.readthedocs.io/en/latest/.

MOTIVATION: Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output. We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet. RESULTS: We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model's AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer. Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the findings of our manuscript can be found at: https://github.com/ratschlab/AKI-EWS.

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.