Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data

1. A method of creating a system for predicting cancer recurrence risk, comprising:

receiving spatial multi-parameter cellular and sub-cellular imaging data labelled with a plurality of different biomarkers for a plurality of cancer patients;

performing a spatial dissection of the spatial multi-parameter cellular and sub-cellular imaging data including cell segmentation to divide the multiplexed immunofluorescence biomarker imaging data into a plurality of intra-tumor spatial domains;

generating a base feature set for each of the intra-tumor spatial domains, wherein for each intra-tumor spatial domain the base feature set includes: (i) a computed intensity expression value for each of the plurality of different biomarkers, wherein the computed intensity expression value for each biomarker is a mean intensity value for the biomarker averaged across all cells within the intra-tumor spatial domain expressing the biomarker, and (ii) a plurality of Kendall rank correlation values, wherein each Kendall rank correlation value is between a respective pair of biomarkers of the plurality of different biomarkers for all cells within the intra-tumor spatial domain expressing the respective pair of biomarkers;

for each of the intra-tumor spatial domains, determining an optimal subset of features from the base feature set for the intra-tumor spatial domain by testing each feature of the base feature set of the intra-tumor spatial domain using a regression method and determining those specific features from the base feature set that constitute the optimal subset;

for each of the intra-tumor spatial domains, developing and training a spatial domain specific multivariate prognostic model for predicting cancer recurrence risk using the optimal subset of features of the intra-tumor spatial domain;

combining the spatial domain specific multivariate prognostic model of each of the intra-tumor spatial domains into a joint prognostic model for predicting cancer recurrence risk.

2. The method according to claim 1 , wherein the spatial multi-parameter cellular and sub-cellular imaging data is hyperplexed immunofluorescence (HxIF) multiplexed immunofluorescence biomarker data.

3. The method according to claim 1 , wherein the plurality of intra-tumor spatial domains comprise an epithelial spatial domain, a stromal spatial domain, and an epithelial-stromal domain.

4. The method according to claim 3 , wherein the performing the dissection comprises segmenting the spatial multi-parameter cellular and sub-cellular imaging data into epithelial and stromal regions differentiated by epithelial E-cadherin staining.

5. The method according to claim 4 , wherein cells in the epithelial region are identified using E-cadherin cell-cell adhesion labeling and pan-cytokeratin, with individual epithelial cells being segmented using a Na+K+ATPase cell membrane marker, a ribosomal protein S 6 cytoplasmic marker, and DAPI-based nuclear staining.

6. The method according to claim 3 , wherein the epithelial-stromal domain captures a boundary wherein stroma and malignant epithelial cells interact in close proximity.

7. The method according to claim 6 , wherein the boundary is a 100 gm boundary.

8. The method according to claim 1 , wherein, for each of the intra-tumor spatial domains, the selecting the subset of features from the base feature set for the intra-tumor spatial domain employs model selection based on an L1-penalized Cox proportional hazard regression method.

9. The method according to claim 8 , wherein, for each of the intra-tumor spatial domains, the method includes learning coefficients for each feature in the subset of features using L2 penalty in a penalized Cox regression model with only the subset of features be used as inputs.

10. The method according to claim 9 , wherein, for each of the intra-tumor spatial domains, the selecting the subset of features from the base feature set for the intra-tumor spatial domain further comprises testing for stability of contribution to recurrence prognosis through testing stability of the sign of each of the coefficients at a predetermined threshold.

11. The method according to claim 10 , wherein the predetermined threshold is 90%.

12. The method according to claim 1 , wherein the spatial multi-parameter cellular and sub-cellular imaging data is multiplexed immunofluorescence biomarker data, wherein, for each of the intra-tumor spatial domains, the selecting the subset of features from the base feature set for the intra-tumor spatial domain comprises testing the biomarkers used to generate the multiplexed immunofluorescence biomarker data together for prognostic power.

13. The method according to claim 1 , wherein the spatial multi-parameter cellular and sub-cellular imaging data is multiplexed immunofluorescence biomarker data, wherein, for each of the intra-tumor spatial domains, the selecting the subset of features from the base feature set for the intra-tumor spatial domain comprises testing the biomarkers used to generate the multiplexed immunofluorescence biomarker data individually for prognostic power.