Biology subjects

Blank, C. U.

Publications and source records attributed to Blank, C. U..

3 recordsLinked to original sources

bioRxiv2025

Explainable machine learning identifies features and thresholds predictive of immunotherapy response

BackgroundImmunotherapy has improved patient survival for multiple cancer types, including melanoma. While a variety of molecular features have been linked to response to immune checkpoint inhibitors (ICI) treatment, clinically established biomarkers, such as tumour mutation burden (TMB) and PD-L1 expression, have shown limitations in accurately categorising responders versus non-responders. Due to the complex nature of ICI response, which includes cancer intrinsic and extrinsic features within the tumour microenvironment (TME), using a single biomarker to predict response is insufficient, necessitating the need to identify accurate clinical and multi-omic molecular predictors. MethodsWe integrate clinical, DNA and RNA sequencing data from four datasets, comprising 138 melanoma patients treated with ICI, to develop machine learning models for predicting ICI response. The performance of each model was evaluated using an independent dataset of patients with cutaneous melanoma (n=53). Interactions between trained models and features were rationalised using the explainability method SHAP. ResultsThe most optimal model was the multi-omic random forest model, with AUC-ROC of 0.78 when predicting response in the independent test dataset. Using SHAP, we predicted thresholds for mutational signatures, neoantigen load, immune cell-type abundance and immune receptor LAG3 expression. The relationship between these influential features and their SHAP scores revealed numerical thresholds constituting good and poor patient response. ConclusionsThis approach highlights patient response to ICI is influenced by both cancer intrinsic and extrinsic features, as well as identifies candidate biomarkers that could inform the use of ICI and potentially assist in the selection of combination therapy in melanoma treatment.

bioinformatics↗

bioRxiv2020

Nafamostat Mesylate in lipid carrier for nasal SARS-CoV2 titer reduction in a hamster model

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) has been responsible for the largest pandemic in recent decades. After seemingly being in control due to consequent lock-downs and social distancing, the majority of countries faces currently a second wave of exponentially increasing infections, hospital referrals and deaths due to SARS-CoV-2-mediated disease (COVID-19). To date, no effective vaccination has been found, and wearing masks and social distancing are the only effective approaches to reduce further spreading. However, unwillingness in the societies to distance again and consequently wear masks might be reasons for the second SARS-CoV-2 infection wave. User-friendly chemicals interfering at the host site with viral entry might be an approach to contain the pandemic. In addition, such an approach would work synergistic with vaccinations that miss new virus mutants. Nafamostat (NM) has been shown in vitro to interfere with cellular virus entry by inhibition of the host transmembrane protease serine 2 (TMPRSS2), an enzyme required for SARS-CoV-2 spike protein cleavage, a prerequisite for cell entry. We hypothesized that nasal application of NM in a liposomal layer (as additional mechanical barrier) could lower the nasal viral load and subsequently reduce the severity of COVID-19. We found, indeed, that nasal viral load one day post single NM application, was lowered in a hamster SARS-CoV-2 infection model. However, severity of subsequent local tissue destruction and weight loss due to pneumonitis was not favorably altered. In conclusion, a single NM application reduced nasal viral load, but did not favorably improve the outcome of COVID-19, likely due to the short half-time of NM. Improvement of NM stability or repetitive application (which was not permitted in this animal model according to Dutch law) might circumvent these challenges.

microbiology↗

bioRxiv2020

PREDICTIVE IMMUNE CHECKPOINT BLOCKADE CLASSIFIERS DISTINGUISHING MONO- VERSUS COMBINATION THERAPY REQUIREMENT

Although high clinical response rates are seen for immune checkpoint blockade (ICB) treatment of metastatic melanomas, both intrinsic and acquired ICB resistance remain considerable clinical challenges1. Combination ICB (anti-PD-1 + anti-CTLA-4) shows improved patient benefit2-5, but is associated with severe adverse events and exceedingly high cost. Therefore, there is a dire need to stratify individual patients for their likelihood of responding to either anti-PD-1 or anti-CTLA-4 monotherapy, or the combination. Since it is conceivable that ICB responses are influenced by both tumor cell-intrinsic and -extrinsic factors6-9, we hypothesized that a predictive genetic classifier ought to mirror both these features. In a panel of patient-derived melanoma xenografts10 (PDX), we noted that cells derived from the human tumor microenvironment (TME) that were co-grafted with the tumor cells were naturally replaced by murine cells after the first passage. Taking advantage of the XenofilteR11 algorithm we recently developed to deconvolute human from murine RNA sequence reads from PDX10, we obtained curated human melanoma tumor cell RNA reads. These expression signals were computationally subtracted from the total RNA profiles in bulk (tumor cell + TME) melanomas from patients. We thus derived one genetic signature that is purely tumor cell-intrinsic ("InTumor"), and one that comprises tumor cell-extrinsic RNA profiles ("ExTumor"). Here we report that the InTumor signature predicts patient response to anti-PD-1, but not anti-CTLA-4 treatment. This was validated in melanoma PDX and cell lines, which confirmed that InTumorLO tumors were effectively eliminated by adoptive cell transfer of T-Cell Receptor (TCR)-matched cytotoxic T cells, whereas InTumorHI melanomas were refractory and grew out as fast as tumors challenged with unmatched T cells. In contrast, the ExTumor signature predicts patient response to anti-CTLA-4 but not anti-PD-1. Most importantly, we used the InTumor and ExTumor signatures in conjunction to generate an ICB response quadrant, which predicts clinical benefit for five independent melanoma patient cohorts treated with either mono- or combination ICB. Specifically, these signatures enable identification of patients who have a much higher chance of responding to the combination treatment than to either monotherapy (p < 0.05), as well as patients who are likely to experience little benefit from receiving anti-CTLA-4 on top of anti-PD-1 (p < 0.05). These signatures may be clinically exploited to distinguish patients who need combined PD-1 + CTLA-4 blockade from those who are likely to benefit from either anti-CTLA-4 or anti-PD-1 monotherapy.

cancer biology↗