Analysis of the whole transcriptome from gingivo-buccal squamous cell carcinoma reveals deregulated immune landscape and suggests targets for immunotherapy

<div><p>Background</p><p>Gingivo-buccal squamous cell carcinoma (GBSCC) is one of the most common oral cavity cancers in India with less than 50% patients surviving past 5 years. Here, we report a whole transcriptome profile on a batch of GBSCC tumours with diverse tobacco usage habits. The study provides an entire landscape of altered expression with an emphasis on searching for targets with therapeutic potential.</p><p>Methods</p><p>Whole transcriptomes of 12 GBSCC tumours and adjacent normal tissues were sequenced and analysed to explore differential expression of genes. Expression changes were further compared with those in TCGA head and neck cohort (n = 263) data base and validated in an independent set of 10GBSCC samples.</p><p>Results</p><p>Differentially expressed genes (n = 2176) were used to cluster the patients based on their tobacco habits, resulting in 3 subgroups. Immune response was observed to be significantly aberrant, along with cell adhesion and lipid metabolism processes. Different modes of immune evasion were seen across 12 tumours with up-regulation or consistent expression of <i>CD47</i>, unlike other immune evasion genes such as <i>PDL1</i>, <i>FUT4</i>, <i>CTLA4</i> and <i>BTLA</i> which were downregulated in a few samples. Variation in infiltrating immune cell signatures across tumours also indicates heterogeneity in immune evasion strategies. A few actionable genes such as <i>ITGA4</i>, <i>TGFB1</i> and <i>PTGS1/COX1</i> were over expressed in most samples.</p><p>Conclusion</p><p>This study found expression deregulation of key immune evasion genes, such as <i>CD47</i> and <i>PDL1</i>, and reasserts their potential as effective immunotherapeutic targets for GBSCC, which requires further clinical studies. Present findings reiterate the idea of using transcriptome profiling to guide precision therapeutic strategies.</p></div

A Quest for miRNA Bio-Marker: A Track Back Approach from Gingivo Buccal Cancer to Two Different Types of Precancers

<div><p>Deregulation of miRNA expression may contribute to tumorigenesis and other patho-physiology associated with cancer. Using TLDA, expression of 762 miRNAs was checked in 18 pairs of gingivo buccal cancer-adjacent control tissues. Expression of significantly deregulated miRNAs was further validated in cancer and examined in two types of precancer (leukoplakia and lichen planus) tissues by primer-specific TaqMan assays. Biological implications of these miRNAs were assessed bioinformatically. Expression of <i>hsa-miR-1293, hsa-miR-31, hsa-miR-31*</i> and <i>hsa-miR-7</i> were significantly up-regulated and those of <i>hsa-miR-206, hsa-miR-204</i> and <i>hsa-miR-133a</i> were significantly down-regulated in all cancer samples. Expression of only <i>hsa-miR</i>-31 was significantly up-regulated in leukoplakia but none in lichen planus samples. Analysis of expression heterogeneity divided 18 cancer samples into clusters of 13 and 5 samples and revealed that expression of 30 miRNAs (including the above-mentioned 7 miRNAs), was significantly deregulated in the cluster of 13 samples. From database mining and pathway analysis it was observed that these miRNAs can significantly target many of the genes present in different cancer related pathways such as “proteoglycans in cancer”, <i>PI3K-AKT</i> etc. which play important roles in expression of different molecular features of cancer. Expression of <i>hsa-miR-31</i> was significantly up-regulated in both cancer and leukoplakia tissues and, thus, may be one of the molecular markers of leukoplakia which may progress to gingivo-buccal cancer.</p></div

Reported targets associated with 7 miRNAs significantly deregulated in 18 cancer samples.

@<p>: Up-regulation of expression of miRNAs.</p>$<p>: Down regulation of expression of miRNAs.</p>∧<p>Benjamini-Hochberg corrected p-value cut off at 5% level: 6.5E⁻⁰⁴.</p><p><b>ΔΔCt</b> =  ΔCt _{of a gene in cancer tissue} - ΔCt _{of that gene in control tissue}.</p>#<p>; Expression of <i>has-miR-1</i> is not significantly deregulated and shown for comparison only.</p><p>MN- Mouth Neoplasm, HN- Head and Neck Cancer, SCC- Squamous Cell Carcinoma, LN- Laryngeal Neoplasm, EN- Esophageal Neoplasm, OLP-Oral Leukoplakia, <i>GCN1L1</i>- general control of amino-acid synthesis 1-like 1.</p

Demography of 18 leukoplakia patients.

<p><b>*</b>All except two (LK5 & LK9) patients had tobacco habits.</p

A Quest for miRNA Bio-Marker: A Track Back Approach from Gingivo Buccal Cancer to Two Different Types of Precancers - Figure 3

<p><b>A: Manhattan plot of p-values for 520 miRNAs from the cluster of 13 samples.</b> The plot of relative location of 520 miRNAs (along the horizontal axis) across the human chromosome and their corresponding –log₁₀ transformed p-value (along the vertical axis). Benjamini-Hochberg corrected P-value cut off was 0.00298 (Horizontal line in the middle of figure). <b>B: Heat map diagram of ΔΔCt values of 30 miRNAs</b>. Expression of these miRNAs was significantly deregulated in the cluster of 13 samples. Each row represents a miRNA and each column represents a sample. Sky-blue colored cells stand for failed assay (i.e.no data in those cells). Red and green colors signify up- and down-regulation of expression, respectively. Heat map was constructed using Heatmap 2 of R's “gplot” package. <b>C: Highly correlated expression of miR-411* and miR-411</b>.</p

Single Nucleotide Polymorphism Network: A Combinatorial Paradigm for Risk Prediction

<div><p>Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes <i>P2RY1</i> and <i>P2RY12</i>. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</p> </div

Demography of 18 cancer patients and their tumor differentiation status.

<p><b>*</b>All patients had tobacco habits;</p>@<p>Mx: metastasis status not known.</p

Demography of 12 lichen planus patients.

<p>*2 out of 12 (Patient LP1 and LP6) patients had tobacco habits.</p

Network representation of segregation of combined genotypic data of three population (oral cancer, leukoplakia and control).

<div><p>Five SNPs (studied with leukoplakia, cancer and control samples) of each individual are combined to form super-set genotypes. One hundred fourty three unique genotype-sets are observed of which 18 are specific to each control, leukoplakia and cancer individuals, 12 genotype-sets are present in both control and leukoplakia individuals, 18 genotype-sets are present in both control and cancer individuals, only 6 genotype-sets are common between leukoplakia and cancer individuals and as many as 53 genotype-sets are common to case, control and leukoplakia. The number of occurrences of each particular genotype-set is illustrated through its corresponding edge-width.</p> <p>The circles in i) red ii) violet iii) yellow iv) prussian blue v) grey vi) orange and vii) blue respectively represents the following groups i) cancer only ii) cancer and control iii) leukoplakia only iv) control only v) control and leukoplakia vi) cancer and leukoplakia and vii) cancer, leukoplakia and control.</p></div

Hierarchical clustering of GBSCC tumours by 2176 deregulated gene expressions.

<p>Hierarchical clusters were constructed based on log2 transformed expression values of 1002 upregulated (represented by colours of negative values in heatmap) and 1174 downregulated genes (represented by colours of positive values in heatmap). Across all 12 tumours there is a gross similarity in deregulation pattern, with some exceptions. As a result, 3 distinct sample clusters were noticed. The coloured panel below, represent subject's smoking (orange) and/or chewing tobacco (red) and/or alcohol abuse (green)status.</p