Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Immune escape of B-cell lymphoblastic leukemic cells through a lineage switch to acute myeloid leukemia.

Acute leukemia (AL) with a lineage switch (LS) is associated with poor prognosis. The predisposing factors of LS are unknown, apart from KMT2A rearrangements that have been reported to be associated with LS. Herein, we present two cases and review all 104 published cases to identify risk factors for LS. Most of the patients (75.5%) experienced a switch from the lymphoid phenotype to the myeloid phenotype. Eighteen patients (17.0%) experienced a transformation from acute myelogenous leukemia (AML) to acute lymphoblastic leukemia (ALL). Forty-nine (46.2%) patients carried a KMT2A rearrangement. Most of the cases involved LS from B-cell ALL (B-ALL) to AML (59.4%), and 49 patients (46.2%) carried KMT2A-rearrangements. Forty patients (37.7%) received lineage-specific immunotherapy. Our findings suggest that the prevalence of KMT2A rearrangements together with the lineage-specific immunotherapy may trigger LS, which supports the thesis of the existence of leukemia stem cells that are capable of lymphoid or myeloid differentiation.

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. Methods: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Results: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Conclusions: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies.

Background: IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients. Methods: We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics. Results: We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting). Conclusions: Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning.

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

Long-term mortality after transcatheter aortic valve implantation for aortic stenosis in immunosuppression-treated patients: a propensity-matched multicentre retrospective registry-based analysis.

Introduction: Data regarding patients with a previous medical record of immunosuppression treatment who have undergone transcatheter aortic valve implantation (TAVI) are limited and extremely inconclusive. Available studies are mostly short term observations; thus there is a lack of evidence on efficacy and safety of TAVI in this specific group of patients. Aim: To compare the in-hospital and long-term outcomes between patients with or without a medical history of immunosuppressive treatment undergoing TAVI for aortic valve stenosis (AS). Material and methods: We conducted a retrospective registry-based analysis including patients undergoing TAVI for AS at 5 centres between January 2009 and August 2017. The primary endpoint was long-term all-cause mortality. Secondary endpoints comprised major vascular complications, life-threatening or disabling bleeding, stroke and new pacemaker implantation. Results: Of 1451 consecutive patients who underwent TAVI, two propensity-matched groups including 25 patients with a history of immunosuppression and 75 patients without it were analysed. No differences between groups in all-cause mortality were found in a median follow-up time of 2.7 years following TAVI (p = 0.465; HR = 0.73; 95% CI: 0.30-1.77). The rate of major vascular complications (4.0% vs. 5.3%) was similar in the two groups (p = 1.000). There were no statistically significant differences in the composite endpoint combining life-threatening or disabling bleeding, major vascular complications, stroke and new pacemaker implantation (40.0% vs. 20.0%, p = 0.218). Conclusions: Patients who had undergone TAVI for AS had similar long-term mortality regardless of whether they had a previous medical record of immunosuppression. Procedural complication rates were comparable between the groups.

Significance of Dermoscopy in Association with Clinical Features in Differentiation of Basal Cell Carcinoma and Benign Trichoblastic Tumours.

Background: Although basal cell carcinoma (BCC) can, in the majority of cases, be diagnosed based on clinical and dermoscopic assessment, a potential overlap with benign adnexal skin tumours seems to exist, including trichoblastic tumours (TT). Methods: Retrospective analysis of clinical and dermoscopic features of benign TT and BCC cases was performed to develop a diagnostic algorithm with a potential utility in clinical practice. Results: In the study, 502 histopathologically confirmed BCC cases were compared with 61 TT (including 44 TB (72.13%), 10 TE (16.39%) and 7 DTE (11.48%]). Patients in the BCC group were statistically older (mean age was 71.4 vs. 64.4 years, respectively; p = 0.009). BCC presented generally as larger tumours (mean tumour size 11.0 vs. 8.2 mm for the TT group; p = 0.001) and was more frequently associated with clinically visible ulceration (59.4% vs. 19.7%, respectively; p < 0.001). Comparison of lesion morphology, clinically visible pigmentation, and anatomical location did not show significant differences between the analysed groups. Dermoscopically visible ulceration was significantly more common in the BCC group compared to the TT group (52.2% vs. 14.8%; p < 0.0001). Pigmented structures, specifically brown dots and brown globules, were significantly more prevalent in the TT group (32.8% vs. 11.4%; p = 0.0001 and 29.5% vs. 8.2%; p <0.0001). Similarly, TT more commonly than BCC showed the presence of cloudy/starry milia-like cysts (26.2% vs. 11.6%; p = 0.0031) and yellow globules (16.4% vs. 7.2%; p = 0.033). Conclusions: Despite differences in frequency of clinical and dermoscopic features between BCC and TT in the studied group, differential diagnosis based on these variables is not reliable. Histopathological examination remains a diagnostic gold standard in differentiation of BCC and TT.

Modelling of dark fermentation of glucose and sour cabbage.

In the article, modified Anaerobic Digestion Models 1 (ADM-1) was tested for modelling dark fermentation for hydrogen production. The model refitting was done with the Euler method. The new model was based on sets of differential equations. The model was checked for hydrogen production from sour cabbage in batch and semi-batch in 5 g VSS (volatile solid suspension)/L and at the semi-batch process from glucose at 5 and 10 g VSS/L. Added parameters determined the conversion of a substrate, hydrogen production, and stress parameters. In the case of a semi-batch process, for one month, cumulative hydrogen production from sour cabbage of 5 g VSS/L was 0.9 L of cumulative hydrogen volume and from glucose 5 g VSS/L (in case of feeding 2 g VSS/L every two days) 2.5 L of cumulative hydrogen volume. At the bacterial population level, hydrogen production was a continuous process at an adequate range of population size and environmental parameters.

Transcriptomic landscape of blood platelets in healthy donors.

Blood platelet RNA-sequencing is increasingly used among the scientific community. Aberrant platelet transcriptome is common in cancer or cardiovascular disease, but reference data on platelet RNA content in healthy individuals are scarce and merit complex investigation. We sought to explore the dynamics of platelet transcriptome. Datasets from 204 healthy donors were used for the analysis of splice variants, particularly with regard to age, sex, blood storage time, unit of collection or library size. Genes B2M, PPBP, TMSB4X, ACTB, FTL, CLU, PF4, F13A1, GNAS, SPARC, PTMA, TAGLN2, OAZ1 and OST4 demonstrated the highest expression in the analysed cohort, remaining substantial transcription consistency. CSF3R gene was found upregulated in males (fold change 2.10, FDR q < 0.05). Cohort dichotomisation according to the median age, showed upregulated KSR1 in the older donors (fold change 2.11, FDR q < 0.05). Unsupervised hierarchical clustering revealed two clusters which were irrespective of age, sex, storage time, collecting unit or library size. However, when donors are analysed globally (as vectors), sex, storage time, library size, the unit of blood collection as well as age impose a certain degree of between- and/or within-group variability. Healthy donor platelet transcriptome retains general consistency, with very few splice variants deviating from the landscape. Although multidimensional analysis reveals statistically significant variability between and within the analysed groups, biologically, these changes are minor and irrelevant while considering disease classification. Our work provides a reference for studies working both on healthy platelets and pathological conditions affecting platelet transcriptome.

Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer.

BACKGROUND: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. METHODS: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. CONCLUSIONS: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.

Bortezomib induces methylation changes in neuroblastoma cells that appear to play a significant role in resistance development to this compound.

The anticancer activity of bortezomib (BTZ) has been increasingly studied in a number of indications and promising results for the use of this treatment have been shown in neuroblastoma. As BTZ treatment is usually administered in cycles, the development of resistance and side effects in patients undergoing therapy with BTZ remains a major challenge for the clinical usage of this compound. Common resistance development also means that certain cells are able to survive BTZ treatment and bypass molecular mechanisms that render BTZ anticancer activity. We studied the methylome of neuroblastoma cells that survived BTZ treatment. Our results indicate that BTZ induces pronounced genome wide methylation changes in cells which recovered from the treatment. Functional analyses of identified methylation changes demonstrated they were involved in key cancer pathology pathways. These changes may allow the cells to bypass the primary anticancer activity of BTZ and develop a treatment resistant and proliferative phenotype. To study whether cells surviving BTZ treatment acquire a proliferative phenotype, we repeatedly treated cells which recovered from the first round of BTZ treatment. The repetitive treatment led to induction of the extraordinary proliferative potential of the cells, that increased with subsequent treatments. As we did not observe similar effects in cells that survived treatment with lenalidomide, and non-treated cells cultured under the same experimental conditions, this phenomenon seems to be BTZ specific. Overall, our results indicate that methylation changes may play major role in the development of BTZ resistance.

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics.

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.