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Colorectal cancer (CRC) is the second most metastatic disease with the majority of cases detected in Western countries. Metastases are formed by circulating altered phenotype tumor cells causing 20% of CRC related deaths. Metastatic cells may show higher expression of surface molecules such as CD44, and changes in morphological properties are associated with increased invasiveness and poor prognosis. In this study, we intended to mimic the environment for metastasizing cells. Here, we used digital holographic cytometry (DHC) analysis to determine cellular morphological properties of three metastatic and two non-metastatic colorectal cancer cell lines to show differences in morphology between the CRC cells and peripheral blood mononuclear cells (PBMCs). By establishing differences in cell area, cell thickness, cell volume, and cell irregularity even when the CRC cells were in minority (5% out of PBMCs), DHC does discriminate between CRC cells and the PBMCs in vitro. We also analyzed the epithelial marker EpCAM and migration marker CD44 using flow cytometry and demonstrate that the CRC cell lines and PBMC cells differ in EpCAM and CD44 expression. Here, we present DHC as a new powerful tool in discriminating cells of different sizes in suspension together with a combination of biomarkers. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite advances in treatment of subtypes of breast cancer, there still lacks reliable biomarkers with precision to predict treatment response at diagnosis. We used machine-learning tools and developed and validated a novel 12-Gene Algorithm as a biomarker for prediction of treatment response for breast cancer patients, especially those suffering triple-negative cancer. The 12-Gene Algorithm based on KRAS-associated gene-mutation profiles showed high accuracy at predicting the response of breast cancer patients including triple-negative subtype to first-line chemotherapy treatment in two independent patient cohorts. Our study suggests that the 12-Gene Algorithm has a potential to be used in clinical practice to improve breast cancer treatment decision-making, especially for triple-negative breast cancer patients. Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan–Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94–0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3–41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7–101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96–0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4–79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality. [ABSTRACT FROM AUTHOR]

Molecularly imprinted polymers (MIPs) against sialic acid (SA) have been developed as a detection tool to target cancer cells. Before proceeding to in vivo studies, a better knowledge of the overall effects of MIPs on the innate immune system is needed. The aim of this study thus was to exemplarily assess whether SA-MIPs lead to inflammatory and/or cytotoxic responses when administered to phagocytosing cells in the innate immune system. The response of monocytic/macrophage cell lines to two different reference particles, Alhydrogel and PLGA, was compared to their response to SA-MIPs. In vitro culture showed a cellular association of SA-MIPs and Alhydrogel, as analyzed by flow cytometry. The reference particle Alhydrogel induced secretion of IL-1β from the monocytic cell line THP-1, whereas almost no secretion was provoked for SA-MIPs. A reduced number of both THP-1 and RAW 264.7 cells were observed after incubation with SA-MIPs and this was not caused by cytotoxicity. Digital holographic cytometry showed that SA-MIP treatment affected cell division, with much fewer cells dividing. Thus, the reduced number of cells after SA-MIP treatment was not linked to SA-MIPs cytotoxicity. In conclusion, SA-MIPs have a low degree of inflammatory properties, are not cytotoxic, and can be applicable for future in vivo studies. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the high incidence and mortality of metastatic colorectal cancer (mCRC), there are no new biomarker tools available for predicting treatment response at diagnosis. We used machine learning using gene mutations from primary tumors of patients and developed a new biomarker model termed a 7-Gene Algorithm. We showed that this algorithm can be used as a biomarker classifier to predict treatment response with better precision than the current predictive factors. The 7-Gene Algorithm showed high accuracy to predict treatment response for patients suffering mCRC. The novel 7-Gene Algorithm can be further developed as a biomarker model for improvement of personalized therapies. Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer cells often have aberrant sialic acid expression. We used molecularly imprinted polymers in this study as novel tools for analyzing sialic acid expression as a biomarker on cancer cells. The sialic acid imprinted polymer shell was synthesized on a polystyrene core, providing low-density support for improving the suspension stability and scattering properties of the molecularly imprinted particles compared to previous core-shell formats. Our results show that these particles have an increased ability to bind to cancer cells. The binding of these particles may be inhibited by two different pentavalent sialic acid conjugates, pointing to the specificity of the sialic acid imprinted particles. Sialic acid (SA) is a monosaccharide usually linked to the terminus of glycan chains on the cell surface. It plays a crucial role in many biological processes, and hypersialylation is a common feature in cancer. Lectins are widely used to analyze the cell surface expression of SA. However, these protein molecules are usually expensive and easily denatured, which calls for the development of alternative glycan-specific receptors and cell imaging technologies. In this study, SA-imprinted fluorescent core-shell molecularly imprinted polymer particles (SA-MIPs) were employed to recognize SA on the cell surface of cancer cell lines. The SA-MIPs improved suspensibility and scattering properties compared with previously used core-shell SA-MIPs. Although SA-imprinting was performed using SA without preference for the α2,3- and α2,6-SA forms, we screened the cancer cell lines analyzed using the lectins Maackia Amurensis Lectin I (MAL I, α2,3-SA) and Sambucus Nigra Lectin (SNA, α2,6-SA). Our results show that the selected cancer cell lines in this study presented a varied binding behavior with the SA-MIPs. The binding pattern of the lectins was also demonstrated. Moreover, two different pentavalent SA conjugates were used to inhibit the binding of the SA-MIPs to breast, skin, and lung cancer cell lines, demonstrating the specificity of the SA-MIPs in both flow cytometry and confocal fluorescence microscopy. We concluded that the synthesized SA-MIPs might be a powerful future tool in the diagnostic analysis of various cancer cells. [ABSTRACT FROM AUTHOR]

Sialylations are post-translational modifications of proteins and lipids that play important roles in many cellular events, including cell-cell interactions, proliferation, and migration. Tumor cells express high levels of sialic acid (SA), which are often associated with the increased invasive potential in clinical tumors, correlating with poor prognosis. To overcome the lack of natural SA-receptors, such as antibodies and lectins with high enough specificity and sensitivity, we have used molecularly imprinted polymers (MIPs), or "plastic antibodies", as nanoprobes. Because high expression of epithelial cell adhesion molecule (EpCAM) in primary tumors is often associated with proliferation and a more aggressive phenotype, the expression of EpCAM and CD44 was initially analyzed. The SA-MIPs were used for the detection of SA on the cell surface of breast cancer cells. Lectins that specifically bind to the a-2,3 SA and a-2,6 SA variants were used for analysis of SA expression, with both flow cytometry and confocal microscopy. Here we show a correlation of EpCAM and SA expression when using the SA-MIPs for detection of SA. We also demonstrate the binding pattern of the SA-MIPs on the breast cancer cell lines using confocal microscopy. Pre-incubation of the SA-MIPs with SA-derivatives as inhibitors could reduce the binding of the SA-MIPs to the tumor cells, indicating the specificity of the SA-MIPs. In conclusion, the SA-MIPs may be a new powerful tool in the diagnostic analysis of breast cancer cells. [ABSTRACT FROM AUTHOR]

In this study, an epitope-imprinting strategy was employed for the dynamic display of bioactive ligands on a material interface. An imprinted surface was initially designed to exhibit specific affinity towards a short peptide (i.e., the epitope). This surface was subsequently used to anchor an epitope-tagged cell-adhesive peptide ligand (RGD: Arg-Gly-Asp). Owing to reversible epitope-binding affinity, ligand presentation and thereby cell adhesion could be controlled. As compared to current strategies for the fabrication of dynamic biointerfaces, for example, through reversible covalent or host-guest interactions, such a molecularly tunable dynamic system based on a surface-imprinting process may unlock new applications in in situ cell biology, diagnostics, and regenerative medicine. [ABSTRACT FROM AUTHOR]

In this study, an epitope-imprinting strategy was employed for the dynamic display of bioactive ligands on a material interface. An imprinted surface was initially designed to exhibit specific affinity towards a short peptide (i.e., the epitope). This surface was subsequently used to anchor an epitope-tagged cell-adhesive peptide ligand (RGD: Arg-Gly-Asp). Owing to reversible epitope-binding affinity, ligand presentation and thereby cell adhesion could be controlled. As compared to current strategies for the fabrication of dynamic biointerfaces, for example, through reversible covalent or host-guest interactions, such a molecularly tunable dynamic system based on a surface-imprinting process may unlock new applications in in situ cell biology, diagnostics, and regenerative medicine. [ABSTRACT FROM AUTHOR]

PTEN and SHP1 are tumor suppressor genes involved in the regulation of cell cycle control and apoptosis. The authors investigated the protein expression of PTEN and SHP1, by immunohistochemistry in tissue microarrays from bone marrow samples in children, diagnosed with acute lymphoblastic leukaemia and nonmalignant controls. PTEN was overexpressed in diagnostic ALL samples, while SHP1 showed a low expression. Both proteins showed a significant difference in expression compared to nonmalignant controls. The roles of PTEN and SHP1 are not well investigated in pediatric leukemia and could in the future play a role as prognostic factors. [ABSTRACT FROM AUTHOR]

Although many pediatric B-cell lymphoma patients are being cured today, much is still unknown about the pathogenesis of this disease. Protein tyrosine phosphatases are involved in the control of survival, growth, and differentiation of cells. The authors have analyzed 26 pediatric B-cell lymphoma cases for the expression of a panel of phosphatases and report a statistically significant lower expression intensity of PTEN and HePTP and higher nuclear SHP2 expression in B-cell lymphoma cases compared to lymphoid tissue. Knowledge about the expression of key regulatory proteins in pediatric B-cell lymphomas is necessary for revealing the complex molecular background of this disease. [ABSTRACT FROM AUTHOR]

Patients diagnosed with diffuse large B-cell lymphoma (DLBCL) show varying responses to conventional therapy, and this might be contributed to the differentiation stage of the tumor B-cells. The aim of the current study was to evaluate a panel of kinases (ZAP70, PKC-β I and II and phosphorylated PKB/Akt) and phosphatases (PTEN, SHP1 and SHP2) known to be frequently deregulated in lymphoid malignancies. De novo DLBCL cases were divided into two subgroups, the germinal center (GC) group (14/28) and the non-germinal center (non-GC) or activated B-cell (ABC) group (14/28). ZAP70 and PKC-β II were expressed in a significantly higher percentage of tumor cells in the clinically more aggressive non-GC group compared with the prognostically favourable GC group. Also, the subcellular localization of PKC-β I and II differed in DLBCL cells, with the PKC-β I isoform being expressed in both the cytoplasm and nucleus, while PKC-β II was found exclusively in the cytoplasm. Loss of nuclear PTEN correlated with poor survival in cases from both subgroups. In addition, five cell lines of DLBCL origin were analyzed for protein expression and for mRNA levels of PTEN and SHP1. For the first time, we show that ZAP70 is expressed in a higher percentage of tumor cells in the aggressive non-GC subgroup of DLBCL and that PKC-β I and II are differently distributed in the two prognostic subgroups of de novo DLBCL. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented that discusses a study which establishes and characterizes a cell line from a patient with diffuse large B-cell lymphomas (DLBCL) that showed multi-drug resistance.

Interleukin(IL)-1 differs from most other cytokines in its lack of a signal sequence. This results in intracellular retention of the immature proform. The release of IL-1 has been shown to be restricted predominantly to activated monocytes and macrophages and to be associated with apoptosis of the producer cell. These features have limited the investigation of IL-1 in early immune responses. In order to study the biological effects of local IL-1β release during an antitumour immune response, we used B16 mouse melanoma cells transduced with mature human IL-1β cDNA constructs. To obtain a released form of human IL-1β (ssIL-1β), the signal sequence from the related IL-1 receptor antagonist was ligated to the cDNA that encoded the mature form of IL-1β. When cells of the poorly immunogenic B16 melanoma cell line were transduced with IL-1β by retroviral infection, high levels of the protein were detected intracellularly, whereas cells transduced with IL-1β containing the signal sequence secreted most of their protein. The in vitro growth of the melanoma cells was unaffected by the IL-1β or ssIL-1β gene transfer. In contrast, the in vivo subcutaneous tumour growth of the ssIL-1β-transduced B16 cells in syngeneic C57BL/6 mice was significantly reduced compared with the IL-1β- and the mock-transduced controls. Immunohistochemical analysis revealed the infiltration of macrophages to be strong in B16/ssIL-1β, moderate in B16/IL-1β and minimal in control tumours. Furthermore, a moderate infiltration of CD4+ cells and of scattered dendritic cells was detected in B16/ssIL-1β tumours whereas very few or no CD4+ cells and dendritic cells were seen in the B16/IL-1β or control tumours. Following in vivo growth, all the tumours up-regulated ICAM-1 on their cell surfaces. However, the percentage of ICAM-1-expressing cells was two- to fourfold higher in B16/ssIL-1β tumours compared to the control. The data suggest that IL-1β acts in vivo, either directly or indirectly, as a chemotactic factor for monocytes, T helper cells and dendritic cells. This supports IL-1β having a regulatory effect on tumour growth when locally released in the tumour area. [ABSTRACT FROM AUTHOR]

Molecularly imprinted synthetic receptors are approaching the perfection of natural receptors! In their Communication on page 15959, G. Pan, B. Sellergren, and co‐workers report an epitope imprinting strategy for reversible anchoring of the cell‐adhesive peptide RGD to give controllable cell‐adhesion behavior. [ABSTRACT FROM AUTHOR]

Molekular geprägte synthetische Rezeptoren erreichen die Perfektion natürlicher Rezeptoren! In ihrer Zuschrift auf S. 16175 berichten G. Pan, B. Sellergren und Mitarbeiter über eine Methode der Epitop‐Prägung zur reversiblen Verankerung des Zelladhäsionspeptids RGD und demonstrieren damit ein steuerbares Zelladhäsionsverhalten. [ABSTRACT FROM AUTHOR]