Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data

Abstract

The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.

Keywords: biomarker; cancer; intratumor heterogeneity; mass spectrometry imaging; t-SNE.

Fig. 1.

Nonlinear clustering of tumor cell-specific MSI data from 63 patients with gastric cancer. (A) The t-SNE scatterplot reveals clear structural separations based on molecular heterogeneity. (B) In the t-SNE image, each pixel is colored according to its location in the 3D t-SNE space using L*a*b* color coordinates, revealing a patchwork of subpopulations throughout the tumors. (C) Illustration of the discretization process of the spatially mapped t-SNE. (Upper) The molecularly distinct regions found by t-SNE are separated in the t-SNE space, yielding transitional boundaries in the image that can be highlighted using the Canny edge detector. (Lower) The same image after discretization (clustering) and Canny edge detection-based demarcation of cluster boundaries. (D) Pearson correlation metric of the edge images of the t-SNE and k-clustered images is then used to determine the discrete representation with the greatest correlation, here k = 3. (E) The k = 3 discrete approximation of the 63-tumor sample t-SNE image.

Fig. 2.

(A) Clustering the t-SNE scatterplot using the highest-ranked value of k for the gastric cancer MSI data; k = 3. (B) Kaplan–Meier survival analysis shows the survival distribution for each of the clusters (subpopulations). (C) There are significant differences in survival between clusters 1 and 2; highlighting these clusters in the t-SNE scatterplot in A shows that they are from distinct regions of the t-SNE space. (D) The number of patients contributing to each of the clusters is shown as a bar plot in which the bar is colored according to the Cox hazard ratio.

Fig. 3.

(A) Nonlinear visualization of tumor cell-specific MSI data from 32 patients with breast cancer using t-SNE. (B) An edge-based image correlation is then used to determine the discrete representation with the highest correlation, here k = 8. (C) Visualization of the metastasis-associated subpopulations in the breast cancer MSI data as revealed by t-SNE shows the contributions of metastatic (black) and nonmetastatic patients (gray) to the eight clusters in a grouped histogram. A statistical analysis found cluster 7 to be exclusively associated with a metastatic phenotype. (D) This subpopulation, highlighted in red in the t-SNE scatterplot, occupies a distinct region of the t-SNE space.

Fig. 4.

(A) A 3D t-SNE map of the gastric cancer MSI dataset (Fig. 1A) color-coded with the intensities of m/z 3,374 and 3,445, protein ions detected in all LOPO runs, with localized overexpression in the yellow, poor-survival subpopulation (Fig. 2A). (B) A 3D t-SNE map of the breast cancer MSI dataset (SI Appendix, Fig. S5), color-coded with the intensities of m/z 4,965 and 4,999, protein ions detected in all LOPO runs, and with localized underexpression in the exclusively metastatic subpopulation (Fig. 3D).

Fig. 5.

Comparison of tissue histology and MSI of α-defensin protein ion detected at m/z 3,374. (A) Histological image of a tissue section from a patient with gastric cancer. (B) Higher-magnification image of a selected region in A showing uniform histology. (C) Close-up of a tissue section in the k = 3 discrete approximation of the 63-tumor sample t-SNE image, showing the presence of the poor survival subpopulation (cluster number 2, yellow). (D) MSI of the α-defensin protein ion detected at m/z = 3,374 showing heterogeneity within the histologically uniform tissue, in which it is highly expressed in the poor-survival subpopulation.