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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]

Simple Summary: Prostate cancer is driven by androgen receptor-regulated transcription and is a leading cause of cancer deaths. For this reason, androgen deprivation therapies are commonly used to treat advanced prostate cancer. These treatments are often effective for short durations before the emergence of treatment resistance and disease progression to castrate resistant prostate cancer or neuroendocrine-like disease. The aim of this study was to address whether new therapies targeting the epitranscriptome may suppress androgen signalling and thus represent a novel approach to prostate cancer treatment. Prostate cancer (PCa) is a leading cause of cancer-related deaths and is driven by aberrant androgen receptor (AR) signalling. For this reason, androgen deprivation therapies (ADTs) that suppress androgen-induced PCa progression either by preventing androgen biosynthesis or via AR signalling inhibition (ARSi) are common treatments. The N6-methyladenosine (m6A) RNA modification is involved in regulating mRNA expression, translation, and alternative splicing, and through these mechanisms has been implicated in cancer development and progression. RNA-m6A is dynamically regulated by the METTL3 RNA methyltransferase complex and the FTO and ALKBH5 demethylases. While there is evidence supporting a role for aberrant METTL3 in many cancer types, including localised PCa, the wider contribution of METTL3, and by inference m6A, in androgen signalling in PCa remains poorly understood. Therefore, the aim of this study was to investigate the expression of METTL3 in PCa patients and study the clinical and functional relevance of METTL3 in PCa. It was found that METTL3 is aberrantly expressed in PCa patient samples and that siRNA-mediated METTL3 knockdown or METTL3-pharmacological inhibition significantly alters the basal and androgen-regulated transcriptome in PCa, which supports targeting m6A as a novel approach to modulate androgen signalling in PCa. [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]

Simple Summary: JAK3 plays an important role in the pathogenesis of cutaneous T cell lymphoma. JAK3 belongs to the Janus kinase family of receptor-associated tyrosine kinases located in cytoplasm adjacent to the plasma membrane. In this study, we show that JAK3 can also be ectopically expressed in the nucleus in CTCL cell lines and primary cells from CTCL patients. Importantly, JAK3 interacts with the nuclear protein RNA polymerase II and phosphorylates Histone H3. Thus, our data provide first evidence for nuclear expression of JAK3 and interactions with key nuclear proteins in malignant T cells suggesting a novel non-canonical role in CTCL. Perturbation in JAK-STAT signaling has been reported in the pathogenesis of cutaneous T cell lymphoma (CTCL). JAK3 is predominantly associated with the intra-cytoplasmic part of IL-2Rγc located in the plasma membrane of hematopoietic cells. Here we demonstrate that JAK3 is also ectopically expressed in the nucleus of malignant T cells. We detected nuclear JAK3 in various CTCL cell lines and primary malignant T cells from patients with Sézary syndrome, a leukemic variant of CTCL. Nuclear localization of JAK3 was independent of its kinase activity whereas STAT3 had a modest effect on nuclear JAK3 expression. Moreover, JAK3 nuclear localization was only weakly affected by blockage of nuclear export. An inhibitor of the nuclear export protein CRM1, Leptomycin B, induced an increased expression of SOCS3 in the nucleus, but only a weak increase in nuclear JAK3. Importantly, immunoprecipitation experiments indicated that JAK3 interacts with the nuclear protein POLR2A, the catalytic subunit of RNA Polymerase II. Kinase assays showed tyrosine phosphorylation of recombinant human Histone H3 by JAK3 in vitro—an effect which was blocked by the JAK inhibitor (Tofacitinib citrate). In conclusion, we provide the first evidence of nuclear localization of JAK3 in malignant T cells. Our findings suggest that JAK3 may have a cytokine-receptor independent function in the nucleus of malignant T cells, and thus a novel non-canonical role in CTCL. [ABSTRACT FROM AUTHOR]

Simple Summary: Metastatic castration-resistant PCa (mCRPC) is a clinically highly lethal disease; the mechanisms underlying the lethal disease remain poorly understood. Furthermore, no effective treatment for cancer metastasis exists. In this study, we have demonstrated that prostate cancer cells required bone marrow-derived cells for their growth, survival and metastasis to the host bone marrow. Our findings have provided new evidence suggesting that cancer cell-specific signals may mediate interactions between prostate cancer cells and bone marrow cells during progression of mCRPC. Therapeutic interventions using a selective inhibitor of lipid kinase PIP5K1α may not only inhibit the growth of primary tumors but may also target the lethal mCRPC within tumor-microenvironment. Cancer cells facilitate growth and metastasis by using multiple signals from the cancer-associated microenvironment. However, it remains poorly understood whether prostate cancer (PCa) cells may recruit and utilize bone marrow cells for their growth and survival. Furthermore, the regulatory mechanisms underlying interactions between PCa cells and bone marrow cells are obscure. In this study, we isolated bone marrow cells that mainly constituted populations that were positive for CD11b and Gr1 antigens from xenograft PC-3 tumor tissues from athymic nu/nu mice. We found that the tumor-infiltrated cells alone were unable to form tumor spheroids, even with increased amounts and time. By contrast, the tumor-infiltrated cells together with PCa cells formed large numbers of tumor spheroids compared with PCa cells alone. We further utilized xenograft athymic nu/nu mice bearing bone metastatic lesions. We demonstrated that PCa cells were unable to survive and give rise to colony-forming units (CFUs) in media that were used for hematopoietic cell colony-formation unit (CFU) assays. By contrast, PC-3M cells survived when bone marrow cells were present and gave rise to CFUs. Our results showed that PCa cells required bone marrow cells to support their growth and survival and establish bone metastasis in the host environment. We showed that PCa cells that were treated with either siRNA for PIP5K1α or its specific inhibitor, ISA-2011B, were unable to survive and produce tumor spheroids, together with bone marrow cells. Given that the elevated expression of PIP5K1α was specific for PCa cells and was associated with the induced expression of VEGF receptor 2 in PCa cells, our findings suggest that cancer cells may utilize PIP5K1α-mediated receptor signaling to recruit growth factors and ligands from the bone marrow-derived cells. Taken together, our study suggests a new mechanism that enables PCa cells to gain proliferative and invasive advantages within their associated host microenvironment. Therapeutic interventions using PIP5K1α inhibitors may not only inhibit tumor invasion and metastasis but also enhance the host immune system. [ABSTRACT FROM AUTHOR]

Cutaneous T-cell lymphoma (CTCL) represents a heterogeneous group of potentially devastating primary skin malignancies. Despite decades of intense research efforts, the pathogenesis is still not fully understood. In the early stages, both clinical and histopathological diagnosis is often difficult due to the ability of CTCL to masquerade as benign skin inflammatory dermatoses. Due to a lack of reliable biomarkers, it is also difficult to predict which patients will respond to therapy or progress towards severe recalcitrant disease. In this review, we discuss recent discoveries concerning dysregulated microRNA (miR) expression and putative pathological roles of oncogenic and tumor suppressive miRs in CTCL. We also focus on the interplay between miRs, histone deacetylase inhibitors, and oncogenic signaling pathways in malignant T cells as well as the impact of miRs in shaping the inflammatory tumor microenvironment. We highlight the potential use of miRs as diagnostic and prognostic markers, as well as their potential as therapeutic targets. Finally, we propose that the combined use of miR-modulating compounds with epigenetic drugs may provide a novel avenue for boosting the clinical efficacy of existing anti-cancer therapies in CTCL. [ABSTRACT FROM AUTHOR]

Glutamate-ammonia ligase (GLUL) is important for acid-base homeostasis, ammonia detoxification, cell signaling, and proliferation. Here, we reported that GLUL ablation conferred resistance to several anticancer drugs in specific cancer cell lines while leaving other cell lines non-resistant to the same drugs. To understand the biochemical mechanics supporting this drug resistance, we compared drug-resistant GLUL knockout (KO) A549 non-small-cell lung carcinoma (NSCLC) cells with non-resistant GLUL KO H1299 NSCLC cells and found that the resistant A549 cells, to a larger extent, depended on exogenous glucose for proliferation. As GLUL activity is linked to the tricarboxylic acid (TCA) cycle via reversed glutaminolysis, we probed carbon flux through both glycolysis and TCA pathways by means of 13 C5 glutamine, 13 C5 glutamate, and 13 C6 glucose tracing. We observed increased labeling of malate and aspartate in A549 GLUL KO cells, whereas the non-resistant GLUL KO H1299 cells displayed decreased 13 C-labeling. The malate and aspartate shuttle supported cellular NADH production and was associated with cellular metabolic fitness. Inhibition of the malate-aspartate shuttle with aminooxyacetic acid significantly impacted upon cell viability with an IC 50 of 11.5 μM in resistant GLUL KO A549 cells compared to 28 μM in control A549 cells, linking resistance to the malate-aspartate shuttle. Additionally, rescuing GLUL expression in A549 KO cells increased drug sensitivity. We proposed a novel metabolic mechanism in cancer drug resistance where the increased capacity of the malate-aspartate shuttle increased metabolic fitness, thereby facilitating cancer cells to escape drug pressure.