Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Esophageal adenocarcinoma with any component of signet ring cells portends poor prognosis and response to neoadjuvant therapy.

BACKGROUND: Multiple investigations have shown inferior outcomes for esophageal cancer patients with signet ring cell (SRC) histology. Traditionally, SRC adenocarcinoma has been defined by ≥50% of the tumor composed of SRC. We hypothesized that patients with SRC even <50% would show resistance to standard multimodality therapy with poorer long-term outcomes. METHODS: Patients treated with trimodality therapy for adenocarcinoma from 2006 to 2018 were evaluated for SRC on pretreatment biopsy specimens. Available hematoxylin and eosin slides containing SRC tumors were re-reviewed by an esophageal pathologist to quantify the percent composition of SRC. RESULTS: SRC histology was identified on at least 1 pathologic specimen in 106 of 819 (13%) patients. Rates of pathologic complete response (pCR) among usual-type and SRC tumors were 25% (177/713) and 10% (11/106), respectively (P = .006). The pretreatment SRC components did not independently affect the rate of pCR (1%-10% SRC: 4% [2/46] pCR; 11%-49% SRC: 25% [7/28] pCR; 50%-100% SRC: 7% [2/30] pCR). Kaplan-Meier analysis demonstrated worse survival among patients with any degree of SRC present on pretreatment biopsy, as compared with usual-type esophageal adenocarcinoma (P < .0001). Cox multivariable analysis failed to identify a relationship between increasing SRC component and poorer survival. CONCLUSIONS: We present the only known evaluation of the percentage of SRC component in esophageal carcinoma. Our data support the hypothesis that esophageal adenocarcinoma with any component of SRC are more resistant to chemoradiation with poorer survival. Pathologic reporting of esophageal adenocarcinoma should include any component of SRC. Alternative therapies in patients with any SRC component may be indicated.

Diets enriched in foods with high antioxidant activity reverse age-induced decreases in cerebellar beta-adrenergic function and increases in proinflammatory cytokines.

Antioxidants and diets supplemented with foods high in oxygen radical absorbance capacity (ORAC) reverse age-related decreases in cerebellar beta-adrenergic receptor function. We examined whether this effect was related to the antioxidant capacity of the food supplement and whether an antioxidant-rich diet reduced the levels of proinflammatory cytokines in the cerebellum. Aged male Fischer 344 rats were given apple (5 mg dry weight), spirulina (5 mg), or cucumber (5 mg) either in 0.5 ml water by oral gavage or supplied in the rat chow daily for 14 d. Electrophysiologic techniques revealed a significant decrease in beta-adrenergic receptor function in aged control rats. Spirulina reversed this effect. Apple (a food with intermediate ORAC) had an intermediate effect on cerebellar beta-adrenergic receptor physiology, and cucumber (low ORAC) had no effect, indicating that the reversal of beta-adrenergic receptor function decreases might be related to the ORAC dose. The mRNA of the proinflammatory cytokines tumor necrosis factor-alpha (TNFalpha) and TNFbeta was also examined. RNase protection assays revealed increased levels of these cytokines in the aged cerebellum. Spirulina and apple significantly downregulated this age-related increase in proinflammatory cytokines, whereas cucumber had no effect, suggesting that one mechanism by which these diets work is by modulation of an age-related increase in inflammatory responses. Malondialdehyde (MDA) was measured as a marker of oxidative damage. Apple and spirulina but not cucumber decreased MDA levels in the aged rats. In summary, the improved beta-adrenergic receptor function in aged rats induced by diets rich in antioxidants is related to the ORAC dose, and these diets reduce proinflammatory cytokine levels.