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.

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer.

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo.

Balamuthia mandrillaris infection in China: a retrospective report of 28 cases.

Balamuthia mandrillaris infection is a rare and fatal disease. We have recorded 28 cases of Balamuthia mandrillaris infection during the past 20 years. Eighteen patients (64%) were male and 10 (36%) were female. Patient age ranged from 3 to 74 (mean, 27) years. Patient locations were distributed among 12 Provinces in China. Twenty-seven (96%) patients lived in rural areas, and 17 (61%) patients reported a history of trauma before the appearance of skin lesions. All cases presented with skin lesions as the primary symptom, and 16 (57%) cases developed encephalitis. Histopathology of skin lesions revealed granulomatous changes with histiocytes, lymphocytes, and plasma cells infiltration. Amebas were identified in all cases with immunohistochemical staining. Follow-up information was available in 27 (96%) cases. Fifteen (56%) patients died due to encephalitis and 12 (44%) were free of disease after treatment. Our results show that the clinical characteristics of Balamuthia mandrillaris infection in China are very different from those in the US. Infection of traumatized skin may play an important role in the pathogenesis of the disease in China. Encephalitis usually develops 3-4 years after skin lesions in Chinese cases. Patients with only skin lesions have a higher cure rate than patients with encephalitis.

Transcellular traversal of the blood-brain barrier by the pathogenic Propionibacterium acnes.

BACKGROUND: Propionibacterium acnes (P. acnes) is an anaerobe commonly stay in the body as part of the commensal microbiota, and a dominant bacterium of the human skin and hair follicles. It has been found that this bacterium could participate in brain inflammation that causes Alzheimer's disease (AD) and Parkinson's disease (PD). But how P. acnes invade the brain remains elusive. METHODS: We established the in vitro blood-brain barrier (BBB) model by culturing the HBMEC/D3 cell line on collagen-coated PFTE membrane. The BBB model was verified by the transepithelial electrical resistance (TEER) and horseradish peroxidase (HRP) permeability rate, and observed by the scanning electron microscope (SEM), transmission electron microscope (TEM), as well as confocal microscope. The P. acnes was then cocultured with the in vitro BBB model and the permeability of P. acnes was measured by counting the bacteria clones collected from the lower chamber of the model. RESULTS: High local concentration of P. acnes invaded the in vitro BBB model through the transcellular traversal pathway. The permeability for P. acnes was increased by the treatment of lipopolysaccharide (LPS), but not mannitol. P. acnes invasion elevated the expression of cell adhesion molecules E-selectin, ICAM-1, and VCAM-1 in HBMEC cells. CONCLUSION: P. acnes has the ability to penetrate the brain though transcellular invasion of the blood-brain barrier.