Clustering result of the NSCLC dataset.

<p>Clustering result of the NSCLC dataset.</p

Clustering result of the ccRCC dataset.

<p>Clustering result of the ccRCC dataset.</p

Clustering result of the ccRCC and NSCLC datasets.

<p>Clustering result of the ccRCC and NSCLC datasets.</p

Classes of cancer evolutionary trees in the simulations.

<p><b>Simulation I</b>: Five classes of tree topologies were considered: monoclonal (MC), polyclonal-low (PL), polyclonal-middle (PM), polyclonal-high (PH), and mutator-phenotype (MT). <b>Simulation II</b>: Three classes of edge lengths of the tree are considered: trunk accumulation (TR), branched accumulation (BR), and balanced accumulation (BL). <b>Simulation III</b>: Nine classes of trees are considered: polyclonal-low trunk accumulation (PL-TR), polyclonal-low balanced accumulation (PL-BL), polyclonal-low branch accumulation (PL-BR), polyclonal-middle trunk-accumulation (PM-TR), polyclonal-middle balanced-accumulation (PM-BL), polyclonal-middle branch-accumulation (PM-BR), polyclonal-high trunk accumulation (PH-TR), polyclonal-high balanced accumulation (PH-BL), and polyclonal-high branch accumulation (PH-BR)</p

phyC: Clustering cancer evolutionary trees

<div><p>Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from <a href="https://github.com/ymatts/phyC" target="_blank">https://github.com/ymatts/phyC</a>.</p></div

Results of the simulations.

<p>Each row of panels represents the simulation type (simulation I, II, and III), and each column represents the external clustering validation indices: purity (PR), normalized mutual information (NMI), and Rand index (RI). The horizontal axis of each graph is the variance parameter defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005509#pcbi.1005509.s001" target="_blank">S1 Text</a>, and the vertical axis is the external validation index. The bold lines and the bands indicate the mean and 95% confidence interval of the index for 100 replicates of each dataset.</p

Overview of the proposed method.

<p>(A) Example of cancer evolution. A founder cell is established after a normal cell acquires several passenger mutations and driver mutations (founder SSNVs), and sub-clones evolve by acquiring progressor SSNVs. Each color (purple, orange, dark blue, light blue, and green) of circles represents different sub-clones. (B) Example of a cancer evolutionary tree in the case of (A). A root and its immediate node represent the normal cell and founder cell, respectively. Subsequent nodes indicate sub-clones and edge lengths indicate the number of SSNVs acquired in the sub-clones. (C) Example of the registration of a tree. To resolve (p1)–(p4) for comparison of the evolutionary trees, a sufficiently large bifurcated tree is constructed, which is the reference tree (note that we have omitted bifurcation from the root for clearer visualization). The tree topologies and attributes are mapped to the reference tree beginning with those with the largest depths to those with the smallest depths. In the case of a tie, the sub-trees are mapped from those with the largest edge lengths. Zero-length edges are regarded as degenerated edges (dashed lines). Edge lengths are normalized by the sum of all edge lengths within tumors. The resulting trees can be represented as edge length vectors <b>z</b>_<i>i</i>. (D) Clustering cancer evolutionary trees to summarize the evolutionary history of cancer for each patient. The trees are reconstructed based on the VAFs and then <i>n</i> cancer sub-clonal evolutionary trees are divided into <i>K</i> subgroups based on tree topologies and edge attributes. Through the registration, <i>n</i> evolutionary trees can be represented as <i>m</i>-dimensional <i>n</i> vectors in Euclidean space, and a standard clustering algorithm can be applied.</p

<i>C</i>. <i>elegans</i> can respond to cancer cell culture medium and cancer tissue, and detect cancer smells in human urine.

<p>(A) Chemotaxis of wild-type <i>C</i>. <i>elegans</i> to 10^-6 and 10^-7 dilutions of MEM, EMEM or RPMI medium only, or culture medium from fibroblast (KMST-6 and CCD-112CoN), colorectal cancer (SW480, COLO201 and COLO205), breast cancer (MCF7) or gastric cancer (NUGC4, MKN1 and MKN7) cells (n ≥ 5 assays). (B) Chemotaxis of wild type and <i>odr-3</i> mutants (n ≥ 5 assays) in response to a 10^-6 dilution of conditioned culture medium from colorectal, breast or gastric cancer cells. (C) Chemotaxis of wild type to 10^-2, 10^-3 and 10^-4 dilutions of saline with normal and cancer tissue (n ≥ 5 assays). (D) Chemotaxis to normal and cancer tissue by wild-type and <i>odr-3</i> mutants (n ≥ 5 assays). (E) Chemotaxis of wild type to human urine samples from control subjects (blue bars; c1–c10) or cancer patients (orange bars; p1–p20) at 10^-1 dilution (n = 5 assays). (F) Chemotaxis to urine from cancer patients by wild-type and <i>odr-3</i> mutants at 10^-1 dilution (n ≥ 6 assays). Error bars represent SEM. Significant differences from control samples are indicated by * (<i>P</i> < 0.05); ** (<i>P</i> < 0.01); *** (<i>P</i> < 0.001) by Dunnett’s tests (A) or Student’s <i>t</i>-tests (B, C, D, F). † indicates a significant difference (<i>P</i> < 0.05) by Student’s <i>t</i>-tests (A).</p

NSDT of 242 urine samples.

<p>Box plots (A) and dot plots (B) of chemotactic responses of wild-type <i>C</i>. <i>elegans</i> to urine samples from control subjects (n = 218) or cancer patients (n = 24). Whiskers indicate 10th and 90th percentiles.</p

A Highly Accurate Inclusive Cancer Screening Test Using <i>Caenorhabditis elegans</i> Scent Detection

<div><p>Early detection and treatment are of vital importance to the successful eradication of various cancers, and development of economical and non-invasive novel cancer screening systems is critical. Previous reports using canine scent detection demonstrated the existence of cancer-specific odours. However, it is difficult to introduce canine scent recognition into clinical practice because of the need to maintain accuracy. In this study, we developed a Nematode Scent Detection Test (NSDT) using <i>Caenorhabditis elegans</i> to provide a novel highly accurate cancer detection system that is economical, painless, rapid and convenient. We demonstrated wild-type <i>C</i>. <i>elegans</i> displayed attractive chemotaxis towards human cancer cell secretions, cancer tissues and urine from cancer patients but avoided control urine; in parallel, the response of the olfactory neurons of <i>C</i>. <i>elegans</i> to the urine from cancer patients was significantly stronger than to control urine. In contrast, G protein α mutants and olfactory neurons-ablated animals were not attracted to cancer patient urine, suggesting that <i>C</i>. <i>elegans</i> senses odours in urine. We tested 242 samples to measure the performance of the NSDT, and found the sensitivity was 95.8%; this is markedly higher than that of other existing tumour markers. Furthermore, the specificity was 95.0%. Importantly, this test was able to diagnose various cancer types tested at the early stage (stage 0 or 1). To conclude, <i>C</i>. <i>elegans</i> scent-based analyses might provide a new strategy to detect and study disease-associated scents.</p></div