Correction:How the COVID-19 pandemic highlights the necessity of animal research (vol 30, pg R1014, 2020)

(Current Biology 30, R1014–R1018; September 21, 2020) As a result of an author oversight in the originally published version of this article, a number of errors were introduced in the author list and affiliations. First, the middle initials were omitted from the names of several authors. Second, the surname of Dr. van Dam was mistakenly written as “Dam.” Third, the first name of author Bernhard Englitz was misspelled as “Bernard” and the surname of author B.J.A. Pollux was misspelled as “Pullox.” Finally, Dr. Keijer's first name was abbreviated rather than written in full. These errors, as well as various errors in the author affiliations, have now been corrected online

Imaging windows for long-term intravital imaging: General overview and technical insights

Intravital microscopy is increasingly used to visualize and quantitate dynamic biological processes at the (sub)cellular level in live animals. By visualizing tissues through imaging windows, individual cells (e.g., cancer, host, or stem cells) can be tracked and studied over a time-span of days to months. Several imaging windows have been developed to access tissues including the brain, superficial fascia, mammary glands, liver, kidney, pancreas, and small intestine among others. Here, we review the development of imaging windows and compare the most commonly used long-term imaging windows for cancer biology: the cranial imaging window, the dorsal skin fold chamber, the mammary imaging window, and the abdominal imaging window. Moreover, we provide technical details, considerations, and trouble-shooting tips on the surgical procedures and microscopy setups for each imaging window and explain different strategies to assure imaging of the same area over multiple imaging sessions. This review aims to be a useful resource for establishing the long-term intravital imaging procedure

Breast cancer dormancy is associated with a 4NG1 state and not senescence

Reactivation of dormant cancer cells can lead to cancer relapse, metastasis, and patient death. Dormancy is a nonproliferative state and is linked to late relapse and death. No targeted therapy is currently available to eliminate dormant cells, highlighting the need for a deeper understanding and reliable models. Here, we thoroughly characterize the dormant D2.OR and ZR-75-1, and proliferative D2A1 breast cancer cell line models in vivo and/or in vitro, and assess if there is overlap between a dormant and a senescent phenotype. We show that D2.OR but not D2A1 cells become dormant in the liver of an immunocompetent model. In vitro, we show that D2.OR and ZR-75-1 cells in response to a 3D environment or serum-free conditions are growth-arrested in G1, of which a subpopulation resides in a 4NG1 state. The dormancy state is reversible and not associated with a senescence phenotype. This will aid future research on breast cancer dormancy

Low Transforming Growth Factor-β Pathway Activity in Cervical Adenocarcinomas

Cervical cancer is the fourth most common cancer in women worldwide. Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are the most common histological types, with AC patients having worse prognosis. Over the last two decades, incidence rates of AC have increased, highlighting the importance of further understanding AC tumorigenesis, and the need to investigate new treatment options. The cytokine TGF-β functions as a tumour suppressor in healthy tissue. However, in tumour cells this suppressive function can be overcome. Therefore there is an increasing interest in using TGF-β inhibitors in the treatment of cancer. Here, we hypothesize that TGF-β plays a different role in SCC and AC. Analysis of RNA-seq data from the TCGA, using a TGF-β response signature, resulted in separate clustering of the two subtypes. We further investigated the expression of TGF-β-signalling related proteins (TβR1/2, SMAD4, pSMAD2, PAI-1, αvβ6 and MMP2/9) in a cohort of 62 AC patients. Low TβR2 and SMAD4 expression was associated with worse survival in AC patients and interestingly, high PAI-1 and αvβ6 expression was also correlated with worse survival. Similar correlations of TβR2, PAI-1 and αvβ6 with clinical parameters were found in previously reported SCC analyses. However, when comparing expression levels between SCC and AC patient samples, pSMAD2, SMAD4, PAI-1 and αvβ6 showed lower expression in AC compared to SCC. Because of the low expression of core TβR1/2, (p-)SMAD2 and SMAD4 proteins and the correlation with worse prognosis, TGF-β pathway most likely leads to tumour inhibitory effects in AC and therefore the use of TGF-β inhibitors would not be recommended. However, given the correlation of PAI-1 and αvβ6 with poor prognosis, the use of TGF- β inhibitors might be of interest in SCC and in the subsets of AC patients with high expression of these TGF-β associated proteins

Neutralization of TGF-β reduces tumor infiltrating lymphocytes and A375 tumor growth.

<p><i>(</i>A) TGF-β signaling in the TME. IHC quantification on A375 NLS-mCerulean tumors treated with IgG control (white bars) or 1D11 (green bars) antibody for 3 weeks. Per field of view, cells negative for mCerulean (TME cells) but with nuclear pSMAD2/3 were counted and plotted as a fraction of total mCerulean^negative (TME) cells. N ≥ 4 animals per condition. (B-C) IHC quantification on A375 NLS-mCerulean tumors treated with IgG control (white bars) or 1D11 (green bars) antibody. The number of (B) CD3⁺CD4⁺, (C) CD3⁺CD8⁺ T cells was counted per field of view (FOV), and the average of ≥4 FOV in ≥4 tumors per condition was plotted in the bar graphs. (D) Mean tumor volume of A375 NLS-mCerulean tumors in <i>nu/nu</i> mice treated with IgG control antibody (dashed black line) or treated with 1D11 (solid green line). Treatments are indicated with an arrow. N ≥ 6 mice per group. (E) <i>In vitro</i> proliferation assay. A375 cells were treated with PBS or rTGF-β1 for 2 or 7 days. Proliferation was measured by calculating green fluorescent (living) cells and comparing it to a live control. N = 3 experiments performed in triplicate. (F) <i>In vitro</i> cell viability assay. A375 cells were treated with PBS or rTGF-β1 for 2 or 7 days. Viability was measured by calculating the red fluorescent (dead) cells and comparing it to a dead control. N = 3 experiments performed in triplicate. G) <i>In vitro</i> proliferation assay. A375 cells were treated with IgG control antibody or 1D11 antibody for 0, 1 or 2 days. After addition of cell titel 96 aquaous solution proliferation was assessed by measuring optical density at 490 nm and normalizing it to day 0. N = 3 experiments performed in triplicate. Error bars, SEM; Double-sided unpaired T-Test (A to C) or repeated measures two-way ANOVA (D-G): * P-value ≤ 0.05, n.s. P-value > 0.05.</p

TGFB1 correlations for various tumor types.

<p>Heatmap displaying correlations between TGFB1 expression and active ITGB1 (ITGB1 metagene) and TGF-β activity (SERPINE1/PAI1) for various tumor types (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175300#pone.0175300.s010" target="_blank">S4 Table</a>). The heatmap also includes rows with mean mutation rates (mean mut. rate), TGFB1 Kaplan-Meier treatment outcome log-rank p-values (Treatment outcome), and GSEA Normalized Enrichment Score (NES) for T-cell (T cell genes) and stromal (Stromal genes) gene sets for each of the tumor types.</p

TS2/16 attenuates tumor growth by indirectly increasing apoptosis.

<p>(A-B) IHC quantification on A375 NLS-mCerulean tumors treated with IgG control (white bars) or TS2/16 (blue bars) antibody. The number of (A) CD3⁺CD4⁺, (B) CD3⁺CD8⁺ T cells was counted per field of view (FOV), and the average of ≥5 FOV in ≥3 tumors per condition was plotted in bar graphs. D10: tumors were treated once and were harvested 10 days after injection (2 days after treatment). D28: Tumors were treated 3x and were harvested 28 days after injection (5 days after last treatment). (C) Mean tumor volume of A375 tumors in <i>nu/nu</i> mice left untreated (dashed line), treated with TS2/16 (blue line) or Paclitaxel (grey line). Treatments are indicated with an arrow. N ≥ 5 mice per group. (D) A375 tumor growth measurements in <i>nu/nu</i> mice. Each curve represents the growth of a single tumor. Treatments are indicated with an arrow: TS2/16 or IgG (blue), paclitaxel (grey). Mice were sacrificed when tumors reached > 130 mm³, cured mice are indicated by the number on the right. N ≥ 8 tumors per group. (E) Mean tumor volume of A375 tumors shown in D. Mice were left untreated (dashed line), treated with paclitaxel/IgG/Paclitaxel (grey line) or treated with paclitaxel/TS2/16/paclitaxel (blue line). Treatments are indicated with an arrow: TS2/16 or IgG (blue), paclitaxel (grey). Mice were sacrificed when tumors reached > 130 mm³. N ≥ 8 mice per group. (F-G) IHC quantification on A375 NLS-mCerulean tumors treated with IgG control (white bars) or TS2/16 (blue bars) antibody for 5 weeks. The number of KI67⁺ cells (proliferation, F) or Cl. Casp3⁺ cells (apoptosis,G) per FOV was measured. N ≥ 10 FOV per condition. (H) Kaplan Meier survival curves of <i>nu/nu</i> and NSG mice respectively injected with A375 tumor cells and treated with indicated treatment regimen: IgG (IgG control antibody), P (paclitaxel), TS (TS2/16). Mice were sacrificed when tumors reached >130 mm³. N ≥ 10 mice per group. Survival analyses with Bonferroni post-hoc: in both <i>nu/nu</i> and NSG mice IgG/P vs TS/P and P/IgG/P vs P/TS/P was significant (P-value≤ 0.05). Error bars, SEM; Two-sided unpaired T-tests (A—B) or 1-way ANOVA with Bonferroni post-hoc test comparing treatments to control (F—G): * P-value ≤ 0.05, n.s. P-value > 0.05.</p

TGFB1 is positively correlated with overall survival and the TS2/16 gene signature in melanoma.

<p>(A) Heatmap showing TCGA human skin cutaneous melanoma (SKCM) patient sample Pearson correlations for TGFB1 RNA-seq expression levels and tumor microenvironmental genes. Active TGF-β signaling (SERPINE1), a metagene for integrin β1 activity (ITGB1 metagene), the immune score (imm score) and Non-Synonymous Mutation rate (non-syn Mut) are included as well. (B) Gene set enrichment analysis (GSEA) for the stromal gene signature on TCGA SKCM patient samples with genes ranked based on Pearson correlation with TGFB1 expression level. (C) Kaplan-Meier curve displaying overall survival (OS) for TCGA SKCM skin/distant melanoma patients who were subdivided into a TGFB1^high and a TGFB1^low group. TGFB1^high > median (N = 63), TGFB1^low < median (N = 70). (D) GSEA analyses for T cell gene signature on TCGA SKCM patient samples with genes ranked based on Pearson correlation with TGFB1 expression level.</p