DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Quantifcation of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specifcities. Bioinformatic tools to assess the diferent cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data

Adrenal gland as a sanctuary site for immunotherapy in patients with microsatellite instability-high metastatic colorectal cancer

Metastatic colorectal cancers (mCRC) harboring microsatellite instability (MSI) are sensitive to immune checkpoint inhibitors (ICIs), but the mechanisms of resistance to ICIs remain unclear. Dissociated responses in patients with ICI-treated cancer suggest that certain organs may serve as sanctuary sites due to the tumor microenvironment. This case series describes five patients with ICI-treated MSI mCRC with disease progression limited to the adrenal glands. At ICI initiation, three patients were free of metastasis in the adrenal glands. Four patients experienced objective response per RECIST (Response Evaluation Criteria in Solid Tumors) while treated with ICI. ICI treatment was discontinued due to progressive disease limited to the adrenal glands (n=3) or toxicity (n=2). The time between ICI initiation and progression in the adrenal glands ranged from 11 to 39 months. Adrenalectomy (n=3) and stereotactic body radiation therapy (n=2) were performed. At the last follow-up, all patients were alive and progression free. Molecular analyses were performed in one patient. A significant impairment of the antigen presentation pathway was observed in the ICI-resistant lesion of the adrenal gland, which could be explained by the presence of glucocorticoids in the adrenal gland microenvironment. We also detected an overexpression of TSC22D3, a glucocorticoid-target gene that functions as a mediator of anti-inflammation and immunosuppression. This case series suggests that the adrenal glands may be the sanctuary sites for ICI-treated MSI mCRC through the glucocorticoid-induced impairment of the antigen presentation machinery

Investigation of radiosensitivity gene signatures in cancer cell lines

Intrinsic radiosensitivity is an important factor underlying radiotherapy response, but there is no method for its routine assessment in human tumours. Gene signatures are currently being derived and some were previously generated by expression profiling the NCI-60 cell line panel. It was hypothesised that focusing on more homogeneous tumour types would be a better approach. Two cell line cohorts were used derived from cervix [n = 16] and head and neck [n = 11] cancers. Radiosensitivity was measured as surviving fraction following irradiation with 2 Gy (SF2) by clonogenic assay. Differential gene expression between radiosensitive and radioresistant cell lines (SF2</> median) was investigated using Affymetrix GeneChip Exon 1.0ST (cervix) or U133A Plus2 (head and neck) arrays. There were differences within cell line cohorts relating to tissue of origin reflected by expression of the stratified epithelial marker p63. Of 138 genes identified as being associated with SF2, only 2 (1.4%) were congruent between the cervix and head and neck carcinoma cell lines (MGST1 and TFPI), and these did not partition the published NCI-60 cell lines based on SF2. There was variable success in applying three published radiosensitivity signatures to our cohorts. One gene signature, originally trained on the NCI-60 cell lines, did partially separate sensitive and resistant cell lines in all three cell line datasets. The findings do not confirm our hypothesis but suggest that a common transcriptional signature can reflect the radiosensitivity of tumours of heterogeneous origins

Adrenal gland as a sanctuary site for immunotherapy in patients with microsatellite instability-high metastatic colorectal cancer

International audienceMetastatic colorectal cancers (mCRC) harboring microsatellite instability (MSI) are sensitive to immune checkpoint inhibitors (ICIs), but the mechanisms of resistance to ICIs remain unclear. Dissociated responses in patients with ICI-treated cancer suggest that certain organs may serve as sanctuary sites due to the tumor microenvironment. This case series describes five patients with ICI-treated MSI mCRC with disease progression limited to the adrenal glands. At ICI initiation, three patients were free of metastasis in the adrenal glands. Four patients experienced objective response per RECIST (Response Evaluation Criteria in Solid Tumors) while treated with ICI. ICI treatment was discontinued due to progressive disease limited to the adrenal glands (n=3) or toxicity (n=2). The time between ICI initiation and progression in the adrenal glands ranged from 11 to 39 months. Adrenalectomy (n=3) and stereotactic body radiation therapy (n=2) were performed. At the last follow-up, all patients were alive and progression free. Molecular analyses were performed in one patient. A significant impairment of the antigen presentation pathway was observed in the ICI-resistant lesion of the adrenal gland, which could be explained by the presence of glucocorticoids in the adrenal gland microenvironment. We also detected an overexpression of TSC22D3, a glucocorticoid-target gene that functions as a mediator of anti-inflammation and immunosuppression. This case series suggests that the adrenal glands may be the sanctuary sites for ICI-treated MSI mCRC through the glucocorticoid-induced impairment of the antigen presentation machinery

Characterisation of a head and neck squamous cell carcinoma (HNSCC) cell line cohort.

<p><b>A)</b> Graph showing the mean SF2 (log10) (y-axis) for each of the 11 cervix cancer cell lines (x-axis). Error bars show the standard error of mean of 2–3 independent experiments. <b>B)</b> Graph showing that there is no difference in TP63 expression between the SF2 high and low groups. Bar shows the median expression. <b>C)</b> Unsupervised hierarchical clustering of the top 1000 genes ranked by coefficient of variation (from U133 array data). Heatmap colouring is by log₂ expression value. Rows represent genes and columns are cell lines. x-axis dendrogram (clusters) indicates the similarity of the cell lines and y-axis dendrogram the similarity of genes. Cluster 1 represents two samples with the lowest TP63 values (p63 negative). Cluster 2 shows the grouping of the other p63− cell line, along with low TP63 expressing lines. Cluster 3 groups together all HNSCC lines with >6.0 (log2 expression) TP63 expression. <b>D)</b> Diagram to represent the integrated SF2 analysis of the cervix and HNSCC cell lines. Rank product analysis (FDR <0.05) identified 96 genes in the cervix cohort differentially expressed between SF2 low and high cell lines. An identical analysis in the HNSCC cell lines identifies 97 probesets (42 genes) differentially expressed between SF2 low and high cell lines. PCA of the cervix genes shows that they are capable of separating the cell lines by SF2. PCA of the HNSCC genes is equally capable of separating the samples based on SF2. The Venn diagram shows that only 4/138 genes are common between the two cohorts and of these only 2/138 are “congruent” and associated with the same directionality (high SF2/low SF2 in both HNSCC and cervix). PCA shows probeset expression of these two “common” and “congruent” genes (MGST1 and TFPI) in the NCI-60 dataset. The NCI-60 upper PCA shows data-points coloured for median SF2 and lower PCA coloured for 0.2, used previously to partition radiosensitive and radioresistant cell lines in this cohort.</p

Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications

Malignant pleural mesothelioma (MPM) is a rare and aggressive form of cancer. Here, the authors show MPM is heterogeneously composed of epithelioid and sarcomatoid components and their proportions associate with prognosis and could inform therapeutic strategies

Assessment of established radiosensitivity gene signatures.

<p><b>A)</b> PCA of the Tewari radiosensitivity gene signature. The original signature consists of 49 genes, with mapping to the NCI-60 (60 Plus2 probesets) HNSCC (60 Plus2 probesets) and cervix cell line (48/49 genes) datasets. The x-axis shows PC1, accounting for the largest amount of variation in the experiment and the y-axis shows the second principal component (PC2). Colouring based on median SF2, blue data-points are radiosensitive cell lines (below the median SF2) with red data-points being the radioresistant lines (above the median SF2). <b>B)</b> Implementation of the Eschrich radiosensitivity model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086329#pone.0086329-Eschrich2" target="_blank">[12]</a>. Applied to a training set of 16 samples from the NCI-60 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086329#pone.0086329-Eschrich3" target="_blank">[13]</a>. xy-scatterplot with the x-axis showing reported SF2 values, generated with these cell lines on a earlier array type (U95) against values generated by implementing the model in the current U133 plus 2.0 dataset (y-axis). Line indicates perfect correlation. <b>C)</b> Applied to the HNSCC and cervix cancer cell line cohorts. The y-axis indicates the predicted SF2 determined from the radiosensitivity model. The x-axis shows the empirically derived SF2 values. <b>D)</b> Principal component analysis of the Amundson radiosensitivity gene signature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086329#pone.0086329-Amundson1" target="_blank">[10]</a>. The original signature consists of 22 genes (33 Plus2 probesets), with mapping to the NCI-60 (33 Plus2 probesets), HNSCC (33 Plus2 probesets) and cervix cell line (21/22 genes) datasets. The x-axis shows PC1, accounting for the largest amount of variation in the experiment and the y-axis shows the second principal component (PC2). In the NCI-60 data colouring is based a threshold of 0.2 (previously defined <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086329#pone.0086329-Hall1" target="_blank">[21]</a> where the HNSCC and cervix cell line datasets are coloured by median SF2. In all cases blue data-points are radiosensitive cell lines (below the median SF2) with red data-points being the radioresistant lines (above the median SF2).</p

ZeptoMARK protein profiling of the cervix cancer cell lines.

<p><b>A)</b> Histogram displaying the ZeptoMARK protein-array derived abundance for the 16 cervix cancer cell lines. The y-axis displays E-cadherin protein level (relative fluorescent intensity (RFI) for each of the cell lines (x-axis). Cell lines are ranked based on TP63 expression. Grouping into p63 negative and p63 positive cell lines confirms the association of E-cadherin with p63. The p value is T-test derived comparing the difference in E-cadherin expression between the p63 positive and negative groups, error bars display standard deviation of two biological replicates. <b>B)</b> x–y scatterplot showing E-cadherin gene expression (Exon array) on the y-axis against E-cadherin protein expression on the x-axis. Dashed line represents perfect correlation. Exon array data-points represent the average of multiple exonic probesets (n = 19) from a single Exon expression array, where protein data are the mean of two biological replicates. <b>C)</b> Heatmap showing clustering of proteins with similar expression (y-axis) in the ZeptoMARK protein profiling data. Cell lines ranked by SF2. Heatmap colouring is based on row Z-score. <b>D)</b> xy-scatter plot showing the expression (y-axis) of the top 5 proteins from LIMMA against SF2 (x-axis). Table summarises the results of Limma differential protein expression analysis between high and low SF2 groups and Pearson correlation of protein expression (RFI) against SF2. p values denote those proteins with differential expression (* p<0.05 or ** p<0.01) between SF2 low and high groups according to LIMMA analysis. However these fail to pass false discovery rate correction.</p