Clinical Nomogram Using Novel Computed Tomography-Based Radiomics Predicts Survival in Patients With Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.

PURPOSE: Improved survival prediction and risk stratification in non-small-cell lung cancer (NSCLC) would lead to better prognosis counseling, adjuvant therapy selection, and clinical trial design. We propose the persistent homology (PHOM) score, the radiomic quantification of solid tumor topology, as a solution. MATERIALS AND METHODS: Patients diagnosed with stage I or II NSCLC primarily treated with stereotactic body radiation therapy (SBRT) were selected (N = 554). The PHOM score was calculated for each patient's pretreatment computed tomography scan (October 2008-November 2019). PHOM score, age, sex, stage, Karnofsky Performance Status, Charlson Comorbidity Index, and post-SBRT chemotherapy were predictors in the Cox proportional hazards models for OS and cancer-specific survival. Patients were split into high- and low-PHOM score groups and compared using Kaplan-Meier curves for overall survival (OS) and cumulative incidence curves for cause-specific death. Finally, we generated a validated nomogram to predict OS, which is publicly available at Eashwarsoma.Shinyapps. RESULTS: PHOM score was a significant predictor for OS (hazard ratio [HR], 1.17; 95% CI, 1.07 to 1.28) and was the only significant predictor for cancer-specific survival (1.31; 95% CI, 1.11 to 1.56) in the multivariable Cox model. The median survival for the high-PHOM group was 29.2 months (95% CI, 23.6 to 34.3), which was significantly worse compared with the low-PHOM group (45.4 months; 95% CI, 40.1 to 51.8; P < .001). The high-PHOM group had a significantly greater chance of cancer-specific death at post-treatment month 65 (0.244; 95% CI, 0.192 to 0.296) compared with the low-PHOM group (0.171; 95% CI, 0.123 to 0.218; P = .029). CONCLUSION: The PHOM score is associated with cancer-specific survival and predictive of OS. Our developed nomogram can be used to inform clinical prognosis and assist in making post-SBRT treatment considerations.

Maintaining, masking, and mimicking selection: the interplay of cell-intrinsic and cell-extrinsic effects upon eco-evolutionary dynamics.

Evolution is a stochastic yet inevitable process that lies at the heart of biology yet in the multi-cellular environments within patients, ecological complexities arise via heterogeneity and microenvironments. The interplay of ecology and mutation is thus fundamental to predicting the evolution of complex diseases and engineering optimal treatment solutions. As experimental evidence of ecological interactions between disease agents continues to grow, so does the need for evolutionary theory and modeling that incorporates these interaction effects. Inspired by experimental cell biology, we transform the variables in the interaction payoff matrix to encode cell-cell interactions in our mathematical approach as growth-rate modifying, frequency-dependent interactions. In this way, we can show the extent to which the presence of these cell-extrinsic ecological interactions can modify the evolutionary trajectories that would be predicted from cell-intrinsic properties alone. To do this we form a Fokker-Planck equation for a genetic population undergoing diffusion, drift, and interactions and generate a novel, analytic solution for the stationary distribution. We use this solution to determine when these interactions can modify evolution in such ways as to maintain, mask, or mimic mono-culture fitness differences. This work has implications for the interpretation and understanding of experimental and patient evolution and is a result that may help to explain the abundance of apparently neutral evolution in cancer systems and heterogeneous populations in general. In addition, the derivation of an analytical result for stochastic, ecologically dependent evolution paves the way for treatment approaches requiring knowledge of a stationary solution for the development of control protocols.

Combining measures of immune infiltration shows additive effect on survival prediction in high-grade serous ovarian carcinoma.

BACKGROUND: In colorectal and breast cancer, the density and localisation of immune infiltrates provides strong prognostic information. We asked whether similar automated quantitation and combined analysis of immune infiltrates could refine prognostic information in high-grade serous ovarian carcinoma (HGSOC) and tested associations between patterns of immune response and genomic driver alterations. METHODS: Epithelium and stroma were semi-automatically segmented and the infiltration of CD45RO+, CD8+ and CD68+ cells was automatically quantified from images of 332 HGSOC patient tissue microarray cores. RESULTS: Epithelial CD8 [p = 0.027, hazard ratio (HR) = 0.83], stromal CD68 (p = 3 × 10-4, HR = 0.44) and stromal CD45RO (p = 7 × 10-4, HR = 0.76) were positively associated with survival and remained so when averaged across the tumour and stromal compartments. Using principal component analysis, we identified optimised multiparameter survival models combining information from all immune markers (p = 0.016, HR = 0.88). There was no significant association between PTEN expression, type of TP53 mutation or presence of BRCA1/BRCA2 mutations and immune infiltrate densities or principal components. CONCLUSIONS: Combining measures of immune infiltration provided improved survival modelling and evidence for the multiple effects of different immune factors on survival. The presence of stromal CD68+ and CD45RO+ populations was associated with survival, underscoring the benefits evaluating stromal immune populations may bring for prognostic immunoscores in HGSOC.