ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer

Background: Anillin (ANLN), an actin-binding protein required for cytokinesis, has recently been presented as part of a prognostic marker panel in breast cancer. The objective of the current study was to further explore the prognostic and functional value of ANLN as a single biomarker in breast cancer. Methods: Immunohistochemical assessment of ANLN protein expression was performed in two well characterized breast cancer cohorts (n = 484) with long-term clinical follow-up data and the results were further validated at the mRNA level in a publicly available transcriptomics dataset. The functional relevance of ANLN was investigated in two breast cancer cell lines using RNA interference. Results: High nuclear fraction of ANLN in breast tumor cells was significantly associated with large tumor size, high histological grade, high proliferation rate, hormone receptor negative tumors and poor prognosis in both examined cohorts. Multivariable analysis showed that the association between ANLN and survival was significantly independent of age in cohort I and significantly independent of proliferation, as assessed by Ki-67 expression in tumor cells, age, tumor size, ER and PR status, HER2 status and nodal status in cohort II. Analysis of ANLN mRNA expression confirmed that high expression of ANLN was significantly correlated to poor overall survival in breast cancer patients. Consistent with the role of ANLN during cytokinesis, transient knock-down of ANLN protein expression in breast cancer cell lines resulted in an increase of senescent cells and an accumulation of cells in the G2/M phase of the cell cycle with altered cell morphology including large, poly-nucleated cells. Moreover, ANLN siRNA knockdown also resulted in decreased expression of cyclins D1, A2 and B1. Conclusions: ANLN expression in breast cancer cells plays an important role during cell division and a high fraction of nuclear ANLN expression in tumor cells is correlated to poor prognosis in breast cancer patients, independent of Ki-67, tumor size, hormone receptor status, HER2 status, nodal status and age

A probability-based quality measure improves the classification of unexpected sequences in in situ sequencing

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridiseringIn situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridizatio

A probability-based quality measure improves the classification of unexpected sequences in in situ sequencing

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridiseringIn situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridizatio

A probability-based quality measure improves the classification of unexpected sequences in in situ sequencing

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridiseringIn situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridizatio

Additional file 1: Figure S1. of ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer

Distribution of ANLN nuclear fraction with regard to tamoxifen treatment. Distribution of ANLN nuclear fraction was analyzed with regard to tamoxifen treatment in cohort II, a randomized prospective tamoxifen trial. The distribution of ANLN nuclear fraction was similar in the treatment and control arms with the majority of tumors expressing less than 25% of ANLN nuclear staining. (PDF 25 kb

Additional file 5: Figure S5. of ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer

Association of ANLN expression with senescence. Transient knockdown of ANLN expression induced significant levels of cellular senescence compared to controls in SKBR3 cells up to 5 days after initiation of ANLN depletion. Significant levels of cellular senescence compared to controls were noted in T47D cells 5 days after ANLN siRNA knockdown. Similar, but not significant, result was observed 3 days after knockdown. Scale bars 60 μm. (TIF 14662 kb

Multiplex plasma protein profiling identifies novel markers to discriminate patients with adenocarcinoma of the lung

Background:The overall prognosis of non-small cell lung cancer (NSCLC) is poor, and currently only patients with localized disease are potentially curable. Therefore, preferably non-invasively determined biomarkers that detect NSCLC patients at early stages of the disease are of high clinical relevance. The aim of this study was to identify and validate novel protein markers in plasma using the highly sensitive DNA-assisted multiplex proximity extension assay (PEA) to discriminate NSCLC from other lung diseases.  Methods:Plasma samples were collected from a total of 343 patients who underwent surgical resection for different lung diseases, including 144 patients with lung adenocarcinoma (LAC),68 patients with non-malignant lung disease, 83 with lung metastasis of colorectal cancers and 48 patients with typical carcinoid. One microliter of plasma was analyzed using PEA, allowing detection and quantification of 92 established cancer related proteins. The concentrations of the plasma proteins were compared between disease groups. Results:The comparison between LAC and benign samples revealed significantly different plasma levels for four proteins; CXL17, CEACAM5, VEGFR2 and ERBB3 (adjusted p-value < 0.05). A multi-parameter classifier was developed to discriminate between samples from LAC patients and from patients with non-malignant lung conditions. With a bootstrap aggregated decision tree algorithm (TreeBagger) a sensitivity of 93% and specificity of 64% was achieved to detect LAC in this risk population.  Conclusion:By applying the highly sensitive PEA, reliable protein profiles could be determined in microliter amounts of plasma. We further identified proteins that demonstrated different plasma concentration in defined disease groups and developed a signature that holds potential to be included in a screening assay for early lung cancer detection.

Infiltration of NK and plasma cells is associated with a distinct immune subset in non‐small cell lung cancer

Immune cells of the tumor microenvironment are central but erratic targets for immunotherapy. The aim of this study was to characterize novel patterns of immune cell infiltration in non-small cell lung cancer (NSCLC) in relation to its molecular and clinicopathologic characteristics. Lymphocytes (CD3+, CD4+, CD8+, CD20+, FOXP3+, CD45RO+), macrophages (CD163+), plasma cells (CD138+), NK cells (NKp46+), PD1+, and PD-L1+ were annotated on a tissue microarray including 357 NSCLC cases. Somatic mutations were analyzed by targeted sequencing for 82 genes and a tumor mutational load score was estimated. Transcriptomic immune patterns were established in 197 patients based on RNA sequencing data. The immune cell infiltration was variable and showed only poor association with specific mutations. The previously defined immune phenotypic patterns, desert, inflamed, and immune excluded, comprised 30, 13, and 57% of cases, respectively. Notably, mRNA immune activation and high estimated tumor mutational load were unique only for the inflamed pattern. However, in the unsupervised cluster analysis, including all immune cell markers, these conceptual patterns were only weakly reproduced. Instead, four immune classes were identified: (1) high immune cell infiltration, (2) high immune cell infiltration with abundance of CD20+ B cells, (3) low immune cell infiltration, and (4) a phenotype with an imprint of plasma cells and NK cells. This latter class was linked to better survival despite exhibiting low expression of immune response-related genes (e.g. CXCL9, GZMB, INFG, CTLA4). This compartment-specific immune cell analysis in the context of the molecular and clinical background of NSCLC reveals two previously unrecognized immune classes. A refined immune classification, including traits of the humoral and innate immune response, is important to define the immunogenic potency of NSCLC in the era of immunotherapy. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland

Expression of scavenger receptor MARCO defines a targetable tumor-associated macrophage subset in non-small cell lung cancer

Tumor-associated macrophages (TAMs) are attractive targets for immunotherapy. Recently, studies in animal models showed that treatment with an anti-TAM antibody directed against the scavenger receptor MARCO resulted in suppression of tumor growth and metastatic dissemination. Here we investigated the expression of MARCO in relation to other macrophage markers and immune pathways in a non-small cell lung cancer (NSCLC) cohort (n = 352). MARCO, CD68, CD163, MSR1 and programmed death ligand-1 (PD-L1) were analyzed by immunohistochemistry and immunofluorescence, and associations to other immune cells and regulatory pathways were studied in a subset of cases (n = 199) with available RNA-seq data. We observed a large variation in macrophage density between cases and a strong correlation between CD68 and CD163, suggesting that the majority of TAMs present in NSCLC exhibit a protumor phenotype. Correlation to clinical data only showed a weak trend toward worse survival for patients with high macrophage infiltration. Interestingly, MARCO was expressed on a distinct subpopulation of TAMs, which tended to aggregate in close proximity to tumor cell nests. On the transcriptomic level, we found a positive association between MARCO gene expression and general immune response pathways including strong links to immunosuppressive TAMs, T-cell infiltration and immune checkpoint molecules. Indeed, a higher macrophage infiltration was seen in tumors expressing PD-L1, and macrophages residing within tumor cell nests co-expressed MARCO and PD-L1. Thus, MARCO is a potential new immune target for anti-TAM treatment in a subset of NSCLC patients, possibly in combination with available immune checkpoint inhibitors