Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body

Abstract

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Keywords: antibody; cancer; deep learning; drug targeting; imaging; light-sheet; metastasis; microscopy; tissue clearing; vDISCO.

Figure 1. Experimental design and schematic of the DeepMACT pipeline for analysis of cancer metastases and antibody drug targeting

(A) Illustration of the experimental workflow for tumor transplantation and antibody application. (B) Steps of the DeepMACT pipeline on full-body mouse scans. First, the mice are fixed and processed with the vDISCO protocol to enhance the fluorescent signal of cancer cells. Transparent mice are subsequently imaged from head to toe using light-sheet microscopy, revealing all metastases. Light-sheet images are assembled into a complete 3D image of the mouse. Next, convolutional neural networks are trained to identify and segment all micrometastases in the fluorescence signal. The trained algorithms are then applied to 3D images to detect cancer metastases and an antibody-based drug targeting in full-body mouse scans.

Figure 2. DeepMACT step 1: vDISCO visualization of metastases in a full-body scan of a mouse

(A) Bioluminescence image of a NSG female mouse before vDISCO which was taken 2 months after MDA-MB-231 cancer cell implantation into the mammary fat pad. (B-G) Epifluorescence images of the same mouse after vDISCO show metastases (magenta) in greater detail compared to bioluminescence, including small micrometastases that can be readily detected in the lungs (E, red arrowhead) and in the leg (G), in addition to the primary tumor (F) and major metastases (C and D) that are also visible in bioluminescence as bulk signal (A). (H) 3D visualization of the transparent mouse body imaged by light-sheet microscopy. (I) Lateral views of the 3D segmentation obtained from the light-sheet imaging data corresponding to the magenta-boxed region indicated in (A, B, and H). For simplicity, only a few organs are segmented: the heart (cyan) and the lungs (yellow); the mouse body is shown in transparent gray and the metastases are in magenta. (J-L) Original light-sheet microscopy data (500 μm projections) showing metastases from the sagittal planes indicated in (J). (M-N) Single cell metastases identified in the brain and in the lungs by full-body light-sheet microscopy scans using a 1.1x objective with 6 μm lateral resolution (tumor cells in magenta and nucleus labeled with propidium iodide, PI, in cyan) (red arrowheads in M). The same metastases were further imaged by light-sheet microscopy with a 12x objective. Single plane images showed the colocalization of each micrometastasis with a single nucleus (yellow arrowheads in N). See Figure S1–S3 and Videos S1, S2.

Figure 3.. DeepMACT step 2: Schematic and performance of the deep learning algorithm

(A) Representation of the deep learning inference workflow to efficiently derive 3D detection and segmentation exploiting three 2D computational operations. (B) Visualization of the computational stages; the green arrow shows successful detection of a metastasis, the red arrow shows elimination of a false positive detection in the 3D reconstruction stage. (C) High-level representation of the network architecture with an encoding and a decoding path. (D-E) Comparison of our deep learning pipeline, DeepMACT, to alternative automated methods and manual segmentation by a human expert in terms of detection performance (D) and processing time (E).

Figure 4.. Deep learning-based detection and segmentation enables quantitative analysis at the level of individual metastases

(A,B) 3D rendering of a mouse transplanted with MDA-MB-231 cells in the mammary fat pad after light-sheet microscopy imaging in lateral and ventral views, respectively. Metastases in the mouse body are shown in magenta. The white arrow indicates the primary tumor and the yellow arrow indicates metastases in the axillary lymph node (A.L.N.). (C,D) Deep learning reconstructions of all detected metastases (A.L.N. and primary tumor indicated with dashed circles) color-coded by organ (C) and depth along the z-axis (D), cropped to the white box in (B) to show higher level of detail. (E,F) Detailed view of metastases in the lung region (corresponding to the black box in C) in a maximum intensity projection of a 3D light-sheet scan (E) and projection of 3D deep learning-based detection, with metastases registered to individual lung lobes (shown in different colors) (F). (G-L) Deep learning-based distributions; blue bars show individual metastases, the black line shows the Gaussian kernel density estimation. (G) 3D distance to nearest neighboring metastasis. (H) Estimates of cell counts per metastasis. (I) Metastasis diameter averaged in 3D space. (J-L) Quantitative comparison between metastases in the lungs and the rest of the torso; bars indicate 95%-confidence intervals. (J) Tumor density as share of metastatic tissue of the entire volume is two orders of magnitude higher in lungs versus rest of torso. (K) Metastasis diameter (averaged in 3D space) is significantly higher in lungs (p<0.001; two-sided t-test). (L) Cell count estimate per metastasis is significantly higher in lungs (p<0.001; two-sided t-test).

Figure 5.. DeepMACT reliably detects metastases in all organs for a variety of tumor models

Metastasis detections in full-body 3D light-sheet microscopy scans; each dot represents a metastasis, color-coded by organ; black metastasis within organ outlines are not inside that organ but rather above or below it. (A) A control mouse was perfused immediately after implant of a solid tumor (MDA-MB-231; dashed circle), leaving no time for metastases to form. (B) MCF-7 breast cancer cells were intracardially injected in a nude mouse. (C) Pancreatic cancer cells (R254) were transplanted into the pancreas (dashed circle) of a C57BL/6 mouse. (D) H2030-BrM3 lung cancer cells were intracardially injected in a nude mouse. (E-G) Three NSG mice were intracardially injected of MDA-MB-231 breast cancer cells and sacrificed after 2 days (E), 6 days (F), and 14 days (G). (H) DeepMACT analysis shows increase in tumor burden over time. Yellow arrows indicate metastases in bones; the magenta arrow indicates a metastasis in the peritoneum.

Figure 6.. The DeepMACT pipeline enables quantitative analysis of drug delivery efficacy at the level of single metastases

A mouse transplanted in the mammary fat pad with MDA-MB-231 cells was intravenously injected with 6A10 anti-CA12 antibody 9 weeks later. (A) Epifluorescence images of a processed mouse show details (B-E) of both tumor metastases (boosted with Alexa647N nanobody, shown in magenta) and 6A10 antibody (conjugated with Alexa568, shown in cyan) distributions and their overlay. While most of the micrometastases are targeted by the antibody (C, white arrow), there are some that are not (D, yellow arrow). (F) Full-body 30 light-sheet scan, cropped to the chest region, shows the distributions of metastases (magenta) and antibody (cyan). (G) Detailed view of the boxed region in (F) showing very small micrometastases targeted by the therapeutic antibody (white arrows). (H) 30 rendering of a mouse body light-sheet scan showing the tumor signal in magenta and the 6A10 antibody signal in cyan (co-localization of the signals is shown in white). The cyan inset shows an example of off-target accumulation of the 6A10 antibody. (I) Deep learning-based reconstruction of the animal in (H) showing targeted metastases in green and untargeted metastases in red; the dashed circles represent the primary tumor A.L.N metastases. (J) A significantly higher share of metastases are targeted in the lungs versus the rest of torso (p<0.001, two-sided t-test). (K) Comparison of the distributions of 6A10 antibody signal ratio (signal strength in metastasis versus local surrounding; see the methods for further details) per metastasis in the lungs versus the rest of torso. The dashed line indicates a ratio of 1 (equal signal strengths). (L) Share of targeted metastases as a function of their size (split into quartiles of average metastasis diameter) (p<0.001, two-sided t-test).

Figure 7.. Potential mechanisms of metastasis targeting by therapeutic antibody

(A) Confocal images of a large and a small metastasis (less than 5 cancer cells) in lungs of a mouse transplanted with MDA-MB-231 cells and intravenously injected with 6A10 anti-CA12 antibody, labeled with lectin (green) and Hoechst (blue). (B) Distribution of metastasis size and distance to the nearest vessel, showing that most of the metastases are close to vessels (distance less than 6 μm) (n=50). (C) Deep learning-based reconstruction of lung metastases with and without 6A10 antibody targeting. (D) Deep learning-based quantification of distance between metastases and their nearest neighbor. The average distance from an untargeted to the nearest targeted metastasis is significantly (p<0.001; two-sided t-test) larger than from a targeted one; this shows local clustering of targeted and untargeted metastases (see the methods for further details).