Diversity in cancer invasion phenotypes indicates specific stroma regulated programs

Abstract

Tumor dissemination into the surrounding stroma is the initial step in a metastatic cascade. Invasion into stroma is a non-autonomous process for cancer, and its progression depends upon the stage of cancer, as well as the cells residing in the stroma. However, a systems framework to understand how stromal fibroblasts resist, collude, or aid cancer invasion has been lacking, limiting our understanding of the role of stromal biology in cancer metastasis. We and others have shown that gene perturbation in stromal fibroblasts can modulate cancer invasion into the stroma, highlighting the active role stroma plays in regulating its own invasion. However, cancer invasion into stroma is a complex higher-order process and consists of various sub-phenotypes that together can result in an invasion. Stromal invasion exhibits a diversity of modalities in vivo. It is not well understood if these diverse modalities are correlated, or they emanate from distinct mechanisms and if stromal biology could regulate these characteristics. These characteristics include the extent of invasion, formation, and persistence of invasive forks by cancer as opposed to a collective frontal invasion, the persistence of invading velocity by leader cells at the tip of invasive forks, etc. We posit that quantifying distinct aspects of collective invasion can provide useful suggestions about the plausible mechanisms regulating these processes, including whether the process is regulated by mechanics or by intercellular communication between stromal cells and cancer. Here, we have identified the sub-characteristics of invasion, which might be indicative of broader mechanisms regulating these processes, developed methods to quantify these metrics, and demonstrated that perturbation of stromal genes can modulate distinct aspects of collective invasion. Our results highlight that the genetic state of stromal fibroblasts can regulate complex phenomena involved in cancer dissemination and suggest that collective cancer invasion into stroma is an outcome of the complex interplay between cancer and stromal fibroblasts.

Keywords: Cancer-stroma interaction; Collective invasion; Stromal invasion.

Fig. 1

Multiplexed stromal gene knockdown to test melanoma invasion into skin stromal fibroblasts. a Schematic showing the fabrication workflow of the collective stromal invasion device substrate; PUA polyurethane; b Schematic showing assay setup and experimental workflow consisting of patterned seeding of labeled invasive A375 cells using stencils, and subsequent seeding of stromal BJ fibroblasts, and observing formation and penetration of invasive fronts; c Heatmap showing relative expression of selected genes with high expression in human endometrial stromal fibroblasts (hESFs) compared to bovine endometrial stromal fibroblasts (bESFs) with or without co-culture of the species-specific trophoblasts (HTR8, and F3, respectively); Heatmap shows the Z-score; d Representative image showing an invasive A375 front into the BJ stromal fibroblast monolayer at time 0 h and 24 h, scale bar = 200 μm. Inset shows a magnified view of the juxtaposed A375 (red) and BJ monolayer, scale bar = 100 μm; e Time-lapse images showing initial (yellow line, 0 h) and final (red line, 24 h) position of the invasive A375 fronts into the stromal BJ monolayer, wherein BJ cells are transduced with siRNA listed above the panels; f Quantified extent of A375 frontal invasion across different stromal BJ conditions listed in e. The end of boxes refer to as upper and lower quartile, the horizontal bar refers to the mean, and the end of lines refer to the lowest or highest data (*p value < 0.05, **p value < 0.005, ***p value < 0.0005)

Fig. 2

Phenotypic characterization of the invasive fronts. a Schematic showing formation of invasive fronts and identification of the leader cells in the invasive fronts; and images of invading A375 cells showing a representative example of the interface at time 0 (yellow line), and after 24 h (red line); Also shown below are examples of invasive forks penetrating BJ cells transduced with siRNA targeting SFRP1, or control; gray dots depict the tip of the invasive fronts; Scale bar = 200 μm; b Number of invasive fronts penetrating into BJ cells with different siRNA knockdowns. c Rate of penetration of invasive forks in all conditions listed in b. d Schematic showing new area occupied by stromal cells after 24 h (indicated by arrows). e The normalized extent of BJ cells’ invasion of the cancer cells. The end of boxes refer to as upper and lower quartile, the horizontal bar refers to the mean, and the end of lines refer to the lowest or highest data. Statistical significance was calculated for each siRNA as an unpaired t-test against the control Scr. (*p value < 0.05, **p value < 0.005, ***p value < 0.0005, ****p value < 0.0001). In each of the above figures, gene name refers to the siRNA knockdown of the specific gene

Fig. 3

Phenotypic characterization of the speed and the path of the leader cells. a Schematic showing calculation of instantaneous velocity and migration rate of the tip of invasive forks calculated from acquired time-lapse images; t_i: time-stamp, V_i: velocity, L_i: length traversed by the leader cell in the i-th interval; b average trajectory of a representative leader cell for each condition; instantaneous velocity is color coded; c Violin plot showing persistence of movement of the leader cells; each dot refers to an instance of an invasive fork; d Difference in the maximal, and minimal velocity of penetrating A375 leader cells in the different stromal background; e Representative instantaneous acceleration of leader cells plotted over time. f The difference in the maximal and the minimal acceleration of penetrating A375 leader cells in the different stromal background. Statistical significance was calculated for each siRNA as an unpaired t test against the control Scr. (*p value < 0.05, **p value < 0.005, ***p value < 0.0005)