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.

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.

Isolation of Viable Epithelial and Mesenchymal Circulating Tumor Cells from Breast Cancer Patients.

Circulating tumor cells (CTCs) undergoing epithelial-mesenchymal transition (EMT) may exhibit more aggressive features than epithelial CTCs and are more frequently observed during disease progression. Therefore, detection and characterization of both epithelial and mesenchymal CTCs in cancer patients are urgently needed to allow for a better understanding of the metastatic process and more effective treatment. Here we describe a method for detection and isolation of viable epithelial and mesenchymal CTCs from peripheral blood of breast cancer patients. The method is based on density gradient centrifugation, multiplex immunofluorescent staining, and negative anti-CD45 selection. Cells obtained after the procedure are suitable for genomic or transcriptomic profiling, and they can also be isolated by micromanipulation for single-cell analysis.

Improved Characterization of Circulating Tumor Cells and Cancer-Associated Fibroblasts in One-Tube Assay in Breast Cancer Patients Using Imaging Flow Cytometry.

Circulating tumor cells (CTCs) and circulating cancer-associated fibroblasts (cCAFs) have been individually considered strong indicators of cancer progression. However, technical limitations have prevented their simultaneous analysis in the context of CTC phenotypes different from epithelial. This study aimed to analyze CTCs and cCAFs simultaneously in the peripheral blood of 210 breast cancer patients using DAPI/pan-keratin (K)/vimentin (V)/alpha-SMA/CD29/CD45/CD31 immunofluorescent staining and novel technology-imaging flow cytometry (imFC). Single and clustered CTCs of different sizes and phenotypes (i.e., epithelial phenotype K+/V- and epithelial-mesenchymal transition (EMT)-related CTCs, such as K+/V+, K-/V+, and K-/V-) were detected in 27.6% of the samples and correlated with metastases. EMT-related CTCs interacted more frequently with normal cells and tended to occur in patients with tumors progressing during therapy, while cCAFs coincided with CTCs (mainly K+/V- and K-/V-) in seven (3.3%) patients and seemed to correlate with the presence of metastases, particularly visceral ones. This study emphasizes the advantages of imFC in the field of liquid biopsy and highlights the importance of multimarker-based analysis of different subpopulations and phenotypes of cancer progression-related cells, i.e., CTCs and cCAFs. The co-detection of CTCs and cCAFs might improve the identification of patients at higher risk of progression and their monitoring during 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.

Skin Microbiome in Prurigo Nodularis.

Prurigo nodularis (PN) is a chronic condition characterized by the presence of nodular lesions accompanied by intense pruritus. The disease has been linked to several infectious factors, but data on the direct presence of microorganisms in the lesions of PN are scarce. The aim of this study was to evaluate the diversity and composition of the bacterial microbiome in PN lesions by targeting the region V3-V4 of 16S rRNA. Skin swabs were obtained from active nodules in 24 patients with PN, inflammatory patches of 14 patients with atopic dermatitis (AD) and corresponding skin areas of 9 healthy volunteers (HV). After DNA extraction, the V3-V4 region of the bacterial 16S rRNA gene was amplified. Sequencing was performed using the Illumina platform on the MiSeq instrument. Operational taxonomic units (OTU) were identified. The identification of taxa was carried out using the Silva v.138 database. There was no statistically significant difference in the alpha-diversity (intra-sample diversity) between the PN, AD and HV groups. The beta-diversity (inter-sample diversity) showed statistically significant differences between the three groups on a global level and in paired analyses. Staphylococcus was significantly more abundant in samples from PN and AD patients than in controls. The difference was maintained across all taxonomic levels. The PN microbiome is highly similar to that of AD. It remains unclear whether the disturbed composition of the microbiome and the domination of Staphylococcus in PN lesions may be the trigger factor of pruritus and lead to the development of cutaneous changes or is a secondary phenomenon. Our preliminary results support the theory that the composition of the skin microbiome in PN is altered and justify further research on the role of the microbiome in this debilitating condition.

Single-Cell Enzymatic Screening for Epithelial Mesenchymal Transition with an Ultrasensitive Superwetting Droplet-Array Microchip.

The phenotypic changes of circulating tumor cells (CTCs) during the epithelial-mesenchymal transition (EMT) have been a hot topic in tumor biology and cancer therapeutic development. Here, an integrated platform of single-cell fluorescent enzymatic assays with superwetting droplet-array microchips (SDAM) for ultrasensitive functional screening of epithelial-mesenchymal sub-phenotypes of CTCs is reported. The SDAM can generate high-density, volume well-defined droplet (0.66 nL per droplet) arrays isolating single tumor cells via a discontinuous dewetting effect. It enables sensitive detection of MMP9 enzyme activities secreted by single tumor cells, correlating to their epithelial-mesenchymal sub-phenotypes. In the pilot clinical double-blind tests, the authors have demonstrated that SDAM assays allow for rapid identification and functional screening of CTCs with different epithelial-mesenchymal properties. The consistency with the clinical outcomes validates the usefulness of single-cell secreted MMP9 as a biomarker for selective CTC screening and tumor metastasis monitoring. Convenient addressing and recovery of individual CTCs from SDAM have been demonstrated for gene mutation sequencing, immunostaining, and transcriptome analysis, revealing new understandings of the signaling pathways between MMP9 secretion and the EMT regulation of CTCs. The SDAM approach combined with sequencing technologies promises to explore the dynamic EMT plasticity of tumors at the single-cell level.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry-A Perspective Study.

Tumor dissemination is one of the most-investigated steps of tumor progression, which in recent decades led to the rapid development of liquid biopsy aiming to analyze circulating tumor cells (CTCs), extracellular vesicles (EVs), and circulating nucleic acids in order to precisely diagnose and monitor cancer patients. Flow cytometry was considered as a method to detect CTCs; however, due to the lack of verification of the investigated cells' identity, this method failed to reach clinical utility. Meanwhile, imaging flow cytometry combining the sensitivity and high throughput of flow cytometry and image-based detailed analysis through a high-resolution microscope might open a new avenue in CTC technologies and provide an open-platform system alternative to CellSearch®, which is still the only gold standard in this field. Hereby, we shortly review the studies on the usage of flow cytometry in CTC identification and present our own representative images of CTCs envisioned by imaging flow cytometry providing rationale that this novel technology might be a good tool for studying tumor dissemination, and, if combined with a high CTC yield enrichment method, could upgrade CTC-based diagnostics.

Alpha-smooth muscle actin-positive cancer-associated fibroblasts secreting osteopontin promote growth of luminal breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) have been shown to support tumor development in a variety of cancers. Different markers were applied to classify CAFs in order to elucidate their impact on tumor progression. However, the exact mechanism by which CAFs enhance cancer development and metastasis is yet unknown. METHODS: Alpha-smooth muscle actin (α-SMA) was examined immunohistochemically in intratumoral CAFs of nonmetastatic breast cancers and correlated with clinicopathological data. Four CAF cell lines were isolated from patients with luminal breast cancer (lumBC) and classified according to the presence of α-SMA protein. Conditioned medium (CM) from CAF cultures was used to assess the influence of CAFs on lumBC cell lines: MCF7 and T47D cells using Matrigel 3D culture assay. To identify potential factors accounting for promotion of tumor growth by α-SMAhigh CAFs, nCounter PanCancer Immune Profiling Panel (NanoString) was used. RESULTS: In luminal breast cancer, presence of intratumoral CAFs expressing high level of α-SMA (13% of lumBC group) correlated with poor prognosis (p = 0.019). In in vitro conditions, conditioned medium obtained from primary cultures of α-SMA-positive CAFs isolated from luminal tumors was observed to enhance growth of lumBC cell line colonies in 3D Matrigel, in contrast to CM derived from α-SMA-negative CAFs. Multigene expression analysis indicated that osteopontin (OPN) was overexpressed in α-SMA-positive CAFs in both clinical samples and in vitro models. OPN expression was associated with higher percentage of Ki67-positive cells in clinical material (p = 0.012), while OPN blocking in α-SMA-positive CAF-derived CM attenuated growth of lumBC cell line colonies in 3D Matrigel. CONCLUSIONS: Our findings demonstrate that α-SMA-positive CAFs might enhance tumor growth via secretion of OPN.

FGF7/FGFR2-JunB signalling counteracts the effect of progesterone in luminal breast cancer.

We have recently demonstrated that fibroblast growth factor receptor 2 (FGFR2)-mediated signalling alters progesterone receptor (PR) activity and response of oestrogen receptor α (ER)-positive (ER+) breast cancer (BCa) cell lines to anti-ER agents. Little is known about whether the crosstalk between ER and PR, shown to be modulated by the hormonal background, might also be affected by FGFR2. Here, PR-dependent behaviour of ER+ BCa cells was studied in the presence of oestrogen (E2) and progesterone (P4) and/or FGF7. In vitro analyses showed that FGF7/FGFR2 signalling: (a) abolished the effect of P4 on E2-promoted 3D cell growth and response to tamoxifen; (b) regulated ER and PR expression and activity; (c) increased formation of ER-PR complexes; and (d) reversed P4-triggered deregulation of ER-dependent genes. Analysis of clinical data demonstrated that the prognostic value of FGFR2 varied between patients with different menopausal status; that is, high expression of FGFR2 was significantly associated with longer progression-free survival (PFS) in postmenopausal patients, whereas there was no significant association in premenopausal patients. FGFR2 was found to positively correlate with the expression of JunB proto-oncogene, AP-1 transcription factor subunit (JUNB), an ER-dependent gene, only in premenopausal patients. Molecular analyses revealed that the presence of JunB was a prerequisite for FGFR2-mediated abrogation of P4-induced inhibition of cell growth. Our results demonstrate for the first time that the FGF7/FGFR2-JunB axis abolishes the modulatory effects of PR on ER-associated biological functions in premenopausal ER+ BCa. This may provide foundations for a better selection of patients for FGFR-targeting therapeutic strategies.

ERα36-High Cancer-Associated Fibroblasts as an Unfavorable Factor in Triple-Negative Breast Cancer.

Background: Cancer-associated fibroblasts (CAFs) are the most abundant cell type in the tumor microenvironment (TME). Estrogen receptor alpha 36 (ERα36), the alternatively spliced variant of ERα, is described as an unfavorable factor when expressed in cancer cells. ERα can be expressed also in CAFs; however, the role of ERα36 in CAFs is unknown. Methods: Four CAF cultures were isolated from chemotherapy-naïve BC patients and characterized for ERα36 expression and the NanoString gene expression panel using isolated RNA. Conditioned media from CAF cultures were used to assess the influence of CAFs on triple-negative breast cancer (TNBC) cells using a matrigel 3D culture assay. Results: We found that ERα36high CAFs significantly induced the branching of TNBC cells in vitro (p < 0.001). They also produced a set of pro-tumorigenic cytokines compared to ERα36low CAFs, among which hepatocyte growth factor (HGF) was the main inducer of TNBC cell invasive phenotype in vitro (p < 0.001). Tumor stroma rich in ERα36high CAFs was correlated with high Ki67 expression (p = 0.041) and tumor-associated macrophages markers (CD68 and CD163, p = 0.041 for both). HGF was found to be an unfavorable prognostic factor in TCGA database analysis (p = 0.03 for DFS and p = 0.04 for OS). Conclusions: Breast cancer-associated fibroblasts represent distinct subtypes based on ERα36 expression. We propose that ERα36high CAFs could account for an unfavorable prognosis for TNBC patients.

Chimeric virus-like particles presenting tumour-associated MUC1 epitope result in high titers of specific IgG antibodies in the presence of squalene oil-in-water adjuvant: towards safe cancer immunotherapy.

BACKGROUND: Immunotherapy is emerging as a powerful treatment approach for several types of cancers. Modulating the immune system to specifically target cancer cells while sparing healthy cells, is a very promising approach for safer therapies and increased survival of cancer patients. Tumour-associated antigens are favorable targets for cancer immunotherapy, as they are exclusively expressed by the cancer cells, minimizing the risk of an autoimmune reaction. The ability to initiate the activation of the immune system can be achieved by virus-like particles (VLPs) which are safe and potent delivery tools. VLP-based vaccines have evolved dramatically over the last few decades and showed great potential in preventing infectious diseases. Immunogenic potency of engineered VLPs as a platform for the development of effective therapeutic cancer vaccines has been studied extensively. This study involves recombinant VLPs presenting multiple copies of tumour-specific mucin 1 (MUC1) epitope as a potentially powerful tool for future immunotherapy. RESULTS: In this report VLPs based on the structural protein of Norovirus (NoV VP1) were genetically modified to present multiple copies of tumour-specific MUC1 epitope on their surface. Chimeric MUC1 particles were produced in the eukaryotic Leishmania tarentolae expression system and used in combination with squalene oil-in-water emulsion MF59 adjuvant to immunize BALB/c mice. Sera from vaccinated mice demonstrated high titers of IgG and IgM antibodies which were specifically recognizing MUC1 antigen. CONCLUSIONS: The obtained results show that immunization with recombinant chimeric NoV VP1- MUC1 VLPs result in high titers of MUC1 specific IgG antibodies and show great therapeutic potential as a platform to present tumour-associated antigens.

Breast cancer circulating tumor cells with mesenchymal features-an unreachable target?

Circulating tumor cells (CTCs) mediate dissemination of solid tumors and can be an early sign of disease progression. Moreover, they show a great potential in terms of non-invasive, longitudinal monitoring of cancer patients. CTCs have been extensively studied in breast cancer (BC) and were shown to present a significant phenotypic plasticity connected with initiation of epithelial-mesenchymal transition (EMT). Apart from conferring malignant properties, EMT affects CTCs recovery rate, making a significant portion of CTCs from patients' samples undetected. Wider application of methods and markers designed to isolate and identify mesenchymal CTCs is required to expand our knowledge about the clinical impact of mesenchymal CTCs. Therefore, here we provide a comprehensive review of clinical significance of mesenchymal CTCs in BC together with statistical analysis of previously published data, in which we assessed the suitability of a number of methods/markers used for isolation of CTCs with different EMT phenotypes, both in in vitro spike-in tests with BC cell lines, as well as clinical samples. Results of spiked-in cell lines indicate that, in general, methods not based on epithelial enrichment only, capture mesenchymal CTCs much more efficiently that CellSearch® (golden standard in CTCs detection), but at the same time are not much inferior to Cell Search®, though large variation in recovery rates of added cells among the methods is observed. In clinical samples, where additional CTCs detection markers are needed, positive epithelial-based CTCs enrichment was the most efficient in isolating CTCs with mesenchymal features from non-metastatic BC patients. From the marker side, PI3K and VIM were contributing the most to detection of CTCs with mesenchymal features (in comparison to SNAIL) in non-metastatic and metastatic BC patients, respectively. However, additional data are needed for more robust identification of markers for efficient detection of CTCs with mesenchymal features.

Higher platelet counts correlate to tumour progression and can be induced by intratumoural stroma in non-metastatic breast carcinomas.

BACKGROUND: Platelets support tumour progression. However, their prognostic significance and relation to circulating tumour cells (CTCs) in operable breast cancer (BrCa) are still scarcely known and, thus, merit further investigation. METHODS: Preoperative platelet counts (PCs) were compared with clinical data, CTCs, 65 serum cytokines and 770 immune-related transcripts obtained using the NanoString technology. RESULTS: High normal PC (hPC; defined by the 75th centile cut-off) correlated with an increased number of lymph node metastases and mesenchymal CTCs in the 70 operable BrCa patients. Patients with hPC and CTC presence revealed the shortest overall survival compared to those with no CTC/any PC or even CTC/normal PC. Adverse prognostic impact of hPC was observed only in the luminal subtype, when 247 BrCa patients were analysed. hPC correlated with high content of intratumoural stroma, specifically its phenotype related to CD8+ T and resting mast cells, and an increased concentration of cytokines related to platelet activation or even production in bone marrow (i.e. APRIL, ENA78/CXCL5, HGF, IL16, IL17a, MDC/CCL22, MCP3, MMP1 and SCF). CONCLUSIONS: Preoperative platelets evaluated alone and in combination with CTCs have prognostic potential in non-metastatic BrCa and define patients at the highest risk of disease progression, putatively benefiting from anti-platelet therapy.

Low Tumor-to-Stroma Ratio Reflects Protective Role of Stroma against Prostate Cancer Progression.

Tumor-to-stroma ratio (TSR) is a prognostic factor that expresses the relative amounts of tumor and intratumoral stroma. In this study, its clinical and molecular relevance was evaluated in prostate cancer (PCa). The feasibility of automated quantification was tested in digital scans of tissue microarrays containing 128 primary tumors from 72 PCa patients stained immunohistochemically for epithelial cell adhesion molecule (EpCAM), followed by validation in a cohort of 310 primary tumors from 209 PCa patients. In order to investigate the gene expression differences between tumors with low and high TSR, we applied multigene expression analysis (nCounter® PanCancer Progression Panel, NanoString) of 42 tissue samples. TSR scores were categorized into low (<1 TSR) and high (≥1 TSR). In the pilot cohort, 31 patients (43.1%) were categorized as low and 41 (56.9%) as high TSR score, whereas 48 (23.0%) patients from the validation cohort were classified as low TSR and 161 (77.0%) as high. In both cohorts, high TSR appeared to indicate the shorter time to biochemical recurrence in PCa patients (Log-rank test, p = 0.04 and p = 0.01 for the pilot and validation cohort, respectively). Additionally, in the multivariate analysis of the validation cohort, TSR predicted BR independent of other factors, i.e., pT, pN, and age (p = 0.04, HR 2.75, 95%CI 1.07-7.03). Our data revealed that tumors categorized into low and high TSR score show differential expression of various genes; the genes upregulated in tumors with low TSR score were mostly associated with extracellular matrix and cell adhesion regulation. Taken together, this study shows that high stroma content can play a protective role in PCa. Automatic EpCAM-based quantification of TSR might improve prognostication in personalized medicine for PCa.

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.

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.

Activation of epithelial-mesenchymal transition process during breast cancer progression - the impact of molecular subtype and stromal composition.

Breast cancer (BC) is a heterogeneous disease with different molecular subtypes, which can be defined by oestrogen (ER), progesterone (PR) and human epidermal growth factor (HER2) receptors' status as luminal, HER2+ and triple negative (TNBC). Molecular subtypes also differ in their epithelial-mesenchymal phenotype, which might be related to their aggressiveness, as activation of the epithelial-mesenchymal transition (EMT) is linked with increased ability of cancer cells to survive and metastasize. Nevertheless, the reverse process of mesenchymal-epithelial transition was shown to be required to sustain metastatic colonization. In this study we aimed to analyse activation of the EMT process in primary tumours (PT), which have (N+) or have not (N-) colonized the lymph nodes, as well as the lymph nodes metastases (LNM) themselves in 88 BC patients. We showed that luminal N- PT have the lowest activation of the EMT process (27%), in comparison to N+ PT (48%, p=0.06). On the other hand, TNBC do not show statistically significant EMT activation at the stage before lymph colonization (N-, 83%) and after colonization of the lymph nodes (N+, 63%, p=0.58). TNBC are also the least plastic (unable to change the EMT phenotype) in terms of turning EMT on or off between matched PT and LNM (0% EMT plasticity in TNBC vs 36% plasticity in luminal tumours). Moreover, in TNBC activation of EMT was correlated with increased cell division rate of the PT- in mesenchymal TNBC PT median Ki-67 was 45% in comparison to 10% in epithelial TNBC PT (p=0.002), whereas in PT of luminal subtypes Ki-67 did not differ between epithelial and mesenchymal phenotypes. Profiling of immunotranscriptome of epithelial and mesenchymal luminal BC with Nanostring technology revealed that N- PT with epithelial phenotype were enriched in inflammatory response signatures, whereas N+ mesenchymal cancers showed elevated MHC class II antigen presentation. Overall, activation of EMT changes during cancer progression and metastatic colonization of the lymph nodes depending on the PT molecular subtype and is related to differences in stromal signatures. Activation of EMT is associated with colonizing phenotype in luminal PT and proliferative phenotype of TNBC.

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.