Additional file 6: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Table S5. Analysis of the Discovery and Validation datasets was performed using Weka 3. The first number in each column represented the number of patient treatment responses correctly classified by the model. The second number represented the number of incorrectly classified patient treatment responses. The GOAL row at the bottom of each column described the number of correctly and incorrectly classified patients in the simulation models. The Test Set columns described the output from applying the model trained on the Discovery set to the Validation set. The “Test and Train” columns described test set accuracy (test set column) plus the training error (results obtained by applying the model to the training set, i.e. training error). (DOCX 19 kb

Additional file 2: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Table S2. Individual mutational profiles of patient drug responders (n = 11) and nonresponders (n = 18). (DOCX 19 kb

Additional file 4: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Supplementary Materials and Methods (DOCX 109 kb

Additional file 8: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Table S6. Comparisons of clinical and predicated responses and match scores. We used a cross-validation approach to assess the match scores in Table 1 of the PD-1 predicted responses against the PD-1 clinical responses in the Rizvi et al. 2015 Discovery dataset vs. the Validation dataset. We then pooled and re-partitioned the dataset into two new Training and Test datasets. We then used a similar cross-validation approach to assess the match scores of the PD-1 predicted responses vs. the PD-1 clinical responses. (DOCX 17 kb

Additional file 1: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Table S1. Molecules with immunosuppressive functions used in simulation models to predict PD-1 drug responder status. (DOCX 55 kb

Additional file 5: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Table S4. Creation of the dendritic cell infiltration index for the patient SA97V5-specific simulation model. Chemokines CCL11, CCL20, CCL2, CCL3, CCL4, CCL5, CCL7, CX3CL1, and CXCL14, capable of trafficking of dendritic cells into the tumor microenvironment, were used to create the index. Individual chemokine percent expression (with respect to non-tumorigenic baseline controls) was predicted and given weightage so as to normalize the total to 1. The index was then calculated to be the sum of each prediction % change * weightage. (DOCX 16 kb

Additional file 7: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Figure S2. An example of the relationship between PD-L1 expression and predicted TGFB1 expression using Weka 3 algorithms for all patients in the dataset. Similar trends were seen when comparing the PD-L1 expression level to the other 13 predicted molecules. For this, the number of gene mutations identified for each patient ranged from 2 to 36 with a total of 264 unique genes between all patients. This categorical data was preprocessed and expanded into a gene vector of length 264 to represent each of the unique genes. For each gene in the vector, the data was represented in binary; a 1 was assigned if the patient had a mutation in this gene, a 0 otherwise. Two datasets, one including gene mutations (Molecules and Gene Mutations) and one without (Molecules), were both used to learn prediction models. The Discovery and Validation datasets were determined based on the split provided to allow for comparable results. The performance of a subset of these models on the testing and training sets for both Molecules and Molecules and Gene Mutations datasets are shown. The SMO support vector machine with a normalized polynomial kernel had the best performance when applied to the molecule dataset. This model correctly identified 24 out of 29 patients whereas the simulation models correctly identified 25 of 29. This was only a difference of one match between the two prediction methods. Still, several other methods, while not performing as well overall, were able to identify 9 patients in the test dataset accurately. This was near the computational simulation model prediction capability in which 10 patients were successfully identified in the test dataset. In general, adding the gene mutation data to the molecule data either maintained or decreased the performance of a model. (DOCX 4114 kb

Melanoma cells undergo aggressive coalescence in a 3D Matrigel model that is repressed by anti-CD44

<div><p>Using unique computer-assisted 3D reconstruction software, it was previously demonstrated that tumorigenic cell lines derived from breast tumors, when seeded in a 3D Matrigel model, grew as clonal aggregates which, after approximately 100 hours, underwent coalescence mediated by specialized cells, eventually forming a highly structured large spheroid. Non-tumorigenic cells did not undergo coalescence. Because histological sections of melanomas forming in patients suggest that melanoma cells migrate and coalesce to form tumors, we tested whether they also underwent coalescence in a 3D Matrigel model. Melanoma cells exiting fragments of three independent melanomas or from secondary cultures derived from them, and cells from the melanoma line HTB-66, all underwent coalescence mediated by specialized cells in the 3D model. Normal melanocytes did not. However, coalescence of melanoma cells differed from that of breast-derived tumorigenic cell lines in that they 1) coalesced immediately, 2) underwent coalescence as individual cells as well as aggregates, 3) underwent coalescence far faster and 4) ultimately formed long, flat, fenestrated aggregates that were extremely dynamic. A screen of 51 purified monoclonal antibodies (mAbs) targeting cell surface-associated molecules revealed that two mAbs, anti-beta 1 integrin/(CD29) and anti-CD44, blocked melanoma cell coalescence. They also blocked coalescence of tumorigenic cells derived from a breast tumor. These results add weight to the commonality of coalescence as a characteristic of tumorigenic cells, as well as the usefulness of the 3D Matrigel model and software for both investigating the mechanisms regulating tumorigenesis and screening for potential anti-tumorigenesis mAbs.</p></div

Differential interference (DIC) microscopy images over time of representative bridges formed by facilitator cells to move small aggregates into large aggregates in the process of coalescence.

<p>The time series of optical sections in each of the three examples was taken at one depth over time for comparison. A. In each example, a single or multiple faciltator (f) cells connect a small (sa) to a large aggregate (la), and through contraction of multicellular cable moves the small into the large aggregate, mediating coalescence. B. A single facilitator (f) cell connects a small (sa) and large (la) aggregate. Additional cells extend out of the small aggregate, join the facilitator cell, forming a cable that contracts, moving the small to large aggregate. C. Multiple facilitator cells form and cable end to end. Additional cells join the cable, then contract, bringing the small into the large aggregate. The large aggregates are not in view in panels B and C.</p

Coalescence of cells of the established melanoma cell line HTB-66 begins immediately after Matrigel coalescence and is rapid.

<p>Only the cells that coalesced into large aggregates in the 43 hour period of reconstruction are numbered.</p