Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study.

BACKGROUND: Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS: This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS: Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION: The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers. Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance. Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively. Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

The prognostic impact of KRAS, TP53, STK11 and KEAP1 mutations and their influence on the NLR in NSCLC patients treated with immunotherapy.

BACKGROUND: PD-L1 expression is used to predict NSCLC response to ICIs, but its performance is suboptimal. The impact of KRAS mutations in these patients is unclear. Studies evaluating co-mutations in TP53, STK11 and KEAP1 as well as the NLR showed that they may predict the benefit of ICIs. PATIENTS & METHODS: This is a retrospective study of patients with NSCLC treated with ICIs at the CHUM between July 2015 and June 2020. OS and PFS were compared using Kaplan-Meier and logrank methods. Co-mutations in TP53, STK11 and KEAP1 as well as the NLR were accounted for. ORR and safety were compared using Wald method. RESULTS: From 100 patients with known KRAS status, 50 were mutated (KRASMut). Mutation in TP53, STK11 and KEAP1 were present, and their status known in, respectively, 19/40 (47.5 %), 8/39 (20.5 %) and 4/38 (10.5 %) patients. STK11Mut and KEAP1Mut were associated with shorter overall survival when compared with wild type tumors (respectively median OS of 3.3 vs 20.4, p = 0.0001 and 10.1 vs 17.7, p = 0.24). When KRAS status was compounded with STK11/KEAP1, KRASMut trended to a better prognosis in STK11+KEAP1WT tumors (median OS 21.1 vs 15.8 for KRASWT, p = 0.15), but not for STK11+/-KEAP1Mut tumors. The NLR was strongly impacted by STK11 (6.0Mutvs 3.6WT, p = 0.014) and TP53 (3.2Mutvs 4.8WT, p = 0.048), but not by KEAP1 or KRAS mutations. CONCLUSION: STK11Mut and KEAP1Mut are adverse predictors of ICI therapy benefit. The NLR is strongly impacted by STK11Mut but not by KEAP1Mut, suggesting differences in their resistance mechanism. In STK11-KEAP1WT tumors, KRASMut seem associated with improved survival in NSCLC patients treated with ICIs. MICROABSTRACT: Response of NSCLC to immunotherapy is not easily predictable. We conducted a retrospective study in 100 patients with NSCLC and a known KRAS status. By accounting for different co-mutations, KRAS mutation was found to be associated with a better median overall survival in STK11 and KEAP1 wild-type tumors (21.1 vs 15.8, p = 0.15). NLR was impacted by STK11, but not KEAP1 mutation, suggesting a difference in their resistance mechanism.

Characterization and description of Gabonibacter chumensis sp. nov., isolated from feces of a patient with non-small cell lung cancer treated with immunotherapy.

A polyphasic taxonomic approach, incorporating analysis of phenotypic features, cellular fatty acid profiles, 16S rRNA gene sequences, and determination of average nucleotide identity (ANI) plus digital DNA-DNA hybridization (dDDH), was applied to characterize an anaerobic bacterial strain designated KD22T isolated from human feces. 16S rRNA gene-based phylogenetic analysis showed that strain KD22T was found to be most closely related to species of the genus Gabonibacter. At the 16S rRNA gene level, the closest species from the strain KD22T corresponded with Gabonibacter massiliensis GM7T, with a similarity of 97.58%. Cells of strain KD22T were Gram-negative coccobacillus, positive for indole and negative for catalase, nitrate reduction, oxidase, and urease activities. The fatty acid analysis demonstrated the presence of a high concentration of iso-C15: 0 (51.65%). Next, the complete whole-genome sequence of strain KD22T was 3,368,578 bp long with 42 mol% of DNA G + C contents. The DDH and ANI values between KD22T and type strains of phylogenetically related species were 67.40% and 95.43%, respectively. These phylogenetic, phenotypic, and genomic results supported the affiliation of strain KD22T as a novel bacterial species within the genus Gabonibacter. The proposed name is Gabonibacter chumensis and the type strain is KD22T (= CSUR Q8104T = DSM 115208 T).

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. METHODS: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. RESULTS: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. CONCLUSIONS: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods: Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results: The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion: We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study.

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.

Tractidigestivibacter montrealensis sp. nov., a new member of human gut microbiota isolated from a healthy volunteer.

Strain KD21T, isolated from the fecal sample of a healthy female volunteer, is a strictly anaerobic, non-motile, Gram-staining-positive, saccharolytic small rod that does not produce spores. Strain KD21T was able to grow in the range of temperature 28°C-37°C (optimum, 37 °C), pH 6.0-8.0 (optimum, pH 7.0), and with 0-5.0 g/l NaCl (optimum, 0 g/l NaCl). Bacteria cells reduced nitrates to nitrites. Its major fatty acids were C18:1ω9c, C16:0, C18:0, and summed in feature 8 (C18:1ω7c and/or C18:1ω6c). 16S rRNA gene phylogenetic analysis revealed that KD21T is a member of the genus Tractidigestivibacter and is distinct from any species with validly published names. The sequence showed 98.48% similarity with T. scatoligenes SK9K4T. The DNA G + C content of strain KD21T was 62.6 mol%. The DNA-DNA hybridization and OrthoANI values between strain KD21T and T. scatoligenes SK9K4T were 40.2% and 90.2%, respectively. Differences in phenotypic, phylogenetic, chemotaxonomic, and genomic characteristics indicated that strain KD21T represents a novel species within the genus Tractidigestivibacter. The name T. montrealensis sp. nov. is proposed and the type strain is KD21T (= CSUR Q8103T =  DSM 115111T).

Helicobacter pylori serology is associated with worse overall survival in patients with melanoma treated with immune checkpoint inhibitors.

The microbiome is now regarded as one of the hallmarks of cancer and several strategies to modify the gut microbiota to improve immune checkpoint inhibitor (ICI) activity are being evaluated in clinical trials. Preliminary data regarding the upper gastro-intestinal microbiota indicated that Helicobacter pylori seropositivity was associated with a negative prognosis in patients amenable to ICI. In 97 patients with advanced melanoma treated with ICI, we assessed the impact of H. pylori on outcomes and microbiome composition. We performed H. pylori serology and profiled the fecal microbiome with metagenomics sequencing. Among the 97 patients, 22% were H. pylori positive (Pos). H. pylori Pos patients had a significantly shorter overall survival (p = .02) compared to H. pylori negative (Neg) patients. In addition, objective response rate and progression-free survival were decreased in H. pylori Pos patients. Metagenomics sequencing did not reveal any difference in diversity indexes between the H. pylori groups. At the taxa level, Eubacterium ventriosum, Mediterraneibacter (Ruminococcus) torques, and Dorea formicigenerans were increased in the H. pylori Pos group, while Alistipes finegoldii, Hungatella hathewayi and Blautia producta were over-represented in the H. pylori Neg group. In a second independent cohort of patients with NSCLC, diversity indexes were similar in both groups and Bacteroides xylanisolvens was increased in H. pylori Neg patients. Our results demonstrated that the negative impact of H. pylori on outcomes seem to be independent from the fecal microbiome composition. These findings warrant further validation and development of therapeutic strategies to eradicate H. pylori in immuno-oncology arena.

A Natural Polyphenol Exerts Antitumor Activity and Circumvents Anti-PD-1 Resistance through Effects on the Gut Microbiota.

Several approaches to manipulate the gut microbiome for improving the activity of cancer immune-checkpoint inhibitors (ICI) are currently under evaluation. Here, we show that oral supplementation with the polyphenol-rich berry camu-camu (CC; Myrciaria dubia) in mice shifted gut microbial composition, which translated into antitumor activity and a stronger anti-PD-1 response. We identified castalagin, an ellagitannin, as the active compound in CC. Oral administration of castalagin enriched for bacteria associated with efficient immunotherapeutic responses (Ruminococcaceae and Alistipes) and improved the CD8+/FOXP3+CD4+ ratio within the tumor microenvironment. Moreover, castalagin induced metabolic changes, resulting in an increase in taurine-conjugated bile acids. Oral supplementation of castalagin following fecal microbiota transplantation from ICI-refractory patients into mice supported anti-PD-1 activity. Finally, we found that castalagin binds to Ruminococcus bromii and promoted an anticancer response. Altogether, our results identify castalagin as a polyphenol that acts as a prebiotic to circumvent anti-PD-1 resistance. SIGNIFICANCE: The polyphenol castalagin isolated from a berry has an antitumor effect through direct interactions with commensal bacteria, thus reprogramming the tumor microenvironment. In addition, in preclinical ICI-resistant models, castalagin reestablishes the efficacy of anti-PD-1. Together, these results provide a strong biological rationale to test castalagin as part of a clinical trial. This article is highlighted in the In This Issue feature, p. 873.

Venous thrombotic events in patients treated with immune checkpoint inhibitors for non-small cell lung cancer: A retrospective multicentric cohort study.

OBJECTIVES: Venous thrombotic events (VTEs) are a frequent complication of non-small cell lung cancer (NSCLC) and are associated with increased morbidity. Immune checkpoint inhibitors (ICIs) are revolutionizing the management of NSCLC, but little is known about their impact on thrombosis. This study aims to define the incidence and clinical relevance of VTEs in NSCLC patients receiving these treatments. METHODS: A retrospective multicentric cohort study including 593 patients from three centers in Canada and France was performed. The cumulative incidence of VTEs after ICIs was estimated using competing risk analysis, and the association of these events with survival and response to treatment was determined. Finally, univariate and multivariate tests were performed to identify VTE risk factors. RESULTS: The cumulative incidence of VTEs in the cohort was 14.8% (95% CI = 7.4-22.2%) for an incidence rate of 76.5 (95% CI = 59.9-97.8) thrombosis per 1000 person-years, with most thromboses occurring rapidly after treatment initiation. VTEs were not correlated with overall survival, progression-free survival, or objective response to ICIs. Age ˂ 65 years old (HR = 2.00; 95% CI = 1.11-3.59) and tumors with PD-L1 1-49% (HR = 3.36; 95% CI = 1.19-9.50) or PD-L1 ≥ 50% (HR = 3.22; 95% CI = 1.21-8.57) were associated with more VTEs after 12 months of ICI initiation. Also, a delay of less than 12 months from diagnosis to the first ICI treatment (HR = 2.06; 95% CI = 1.09-3.89) and active smoking (HR = 2.00; 95% CI = 1.12-3.58) are probable risk factors of VTEs. CONCLUSION: This study suggests that the incidence of VTEs in NSCLC patients treated with ICIs is comparable to what is reported in other cohorts of patients treated with chemotherapy. In our cohort, VTEs were not associated with a decreased survival or response to therapy. Patient age < 65 and tumors with PD-L1 ≥ 1% were associated with a higher risk of VTEs under ICIs.

Physiologic colonic uptake of 18F-FDG on PET/CT is associated with clinical response and gut microbiome composition in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICI) represent the backbone treatment for advanced non-small cell lung cancer (NSCLC). Emerging data suggest that increased gut microbiome diversity is associated with favorable response to ICI and that antibiotic-induced dysbiosis is associated with deleterious outcomes. 18F-FDG physiologic colonic uptake on PET/CT increases following treatment with antibiotics (ATB) and could act as a surrogate marker for microbiome composition and predict prognosis. The aim of this study was to determine if 18F-FDG physiologic colonic uptake prior to ICI initiation correlates with gut microbiome profiling and clinical outcomes in patients with advanced NSCLC. METHODS: Seventy-one patients with advanced NSCLC who underwent a PET/CT prior to ICI were identified. Blinded colonic contouring was performed for each colon segment and patients were stratified according to the median of the average colon SUVmax as well as for each segment in low vs. high SUVmax groups. Response rate, progression-free survival (PFS), and overall survival (OS) were compared in the low vs. high SUVmax groups. Gut microbiome composition was analyzed for 23 patients using metagenomics sequencing. RESULTS: The high colon SUVmax group had a higher proportion of non-responders (p = 0.033) and significantly shorter PFS (4.1 vs. 11.3 months, HR 1.94, 95% CI 1.11-3.41, p = 0.005). High caecum SUVmax correlated with numerically shorter OS (10.8 vs. 27.6 months, HR 1.85, 95% CI 0.97-3.53, p = 0.058). Metagenomics sequencing revealed distinctive microbiome populations in each group. Patients with low caecum SUVmax had higher microbiome diversity (p = 0.046) and were enriched with Bifidobacteriaceae, Lachnospiraceae, and Bacteroidaceae. CONCLUSIONS: Lower colon physiologic 18F-FDG uptake on PET/CT prior to ICI initiation was associated with better clinical outcomes and higher gut microbiome diversity in patients with advanced NSCLC. Here, we propose that 18F-FDG physiologic colonic uptake on PET/CT could serve as a potential novel marker of gut microbiome composition and may predict clinical outcomes in this population.

Durvalumab therapy following chemoradiation compared with a historical cohort treated with chemoradiation alone in patients with stage III non-small cell lung cancer: A real-world multicentre study.

BACKGROUND: The PACIFIC trial demonstrated that durvalumab therapy following chemoradiation (CRT) was associated with improved overall survival (OS) in patients with stage III non-small cell lung cancer (NSCLC). It is unclear whether the results obtained as part of randomised controlled trials are a reflection of real-world (RW) data. Several questions remain unanswered with regard to RW durvalumab use, such as optimal time to durvalumab initiation, incidence of pneumonitis and response in PD-L1 subgroups. METHODS: In this multicentre retrospective analysis, 147 patients with stage III NSCLC treated with CRT followed by durvalumab were compared with a historical cohort of 121 patients treated with CRT alone. Survival curves were estimated using the Kaplan-Meier method and compared with the log-rank test in univariate analysis. Multivariate analysis was performed to evaluate the effect of standard prognostic factors for durvalumab use. RESULTS: Median OS was not reached in the durvalumab group, compared with 26.9 months in the historical group (hazard ratio [HR]: 0.56, 95% confidence interval [CI]: 0.37-0.85, p = 0.001). In the durvalumab group, our data suggest improved 12-month OS in patients with PD-L1 expression ≥50% (100% vs 86%, HR: 0.25, 95% CI: 0.11-0.58, p = 0.007). There was no difference in OS between patients with a PD-L1 expression of 1-49% and patients with PD-L1 expression <1%. Delay in durvalumab initiation beyond 42 days did not impact OS. Incidence of pneumonitis was similar in the durvalumab and historical groups. In the durvalumab group, patients who experienced any-grade pneumonitis had a lower 12-month OS than patients without pneumonitis (85% vs 95%, respectively; HR: 3.3, 95% CI: 1.2-9.0, p = 0.006). CONCLUSIONS: This multicentre analysis suggests that PD-L1 expression ≥50% was associated with favourable OS in patients with stage III NSCLC treated with durvalumab after CRT, whereas the presence of pneumonitis represented a negative prognostic factor.

High mortality among hospital-acquired COVID-19 infection in patients with cancer: A multicentre observational cohort study.

INTRODUCTION: Studies suggest that patients with cancer are more likely to experience severe outcomes from COVID-19. Therefore, cancer centres have undertaken efforts to care for patients with cancer in COVID-free units. Nevertheless, the frequency and relevance of nosocomial transmission of COVID-19 in patients with cancer remain unknown. The goal of this study was to determine the incidence and impact of hospital-acquired COVID-19 in this population and identify predictive factors for COVID-19 severity in patients with cancer. METHODS: Patients with cancer and a laboratory-confirmed diagnosis of COVID-19 were prospectively identified using provincial registries and hospital databases between March 3rd and May 23rd, 2020 in the provinces of Quebec and British Columbia in Canada. Patient's baseline characteristics including age, sex, comorbidities, cancer type and type of anticancer treatment were collected. The exposure of interest was incidence of hospital-acquired infection defined by diagnosis of SARS-CoV-2 ≥ 5 days after hospital admission for COVID-unrelated cause. Co-primary outcomes were death or composite outcomes of severe illness from COVID-19 such as hospitalisation, supplemental oxygen, intensive-care unit (ICU) admission and/or mechanical ventilation. RESULTS: A total of 252 patients (N = 249 adult and N = 3 paediatric) with COVID-19 and cancer were identified, and the majority were residents of Quebec (N = 233). One hundred and six patients (42.1%) received active anticancer treatment in the last 3 months before COVID-19 diagnosis. During a median follow-up of 25 days, 33 (13.1%) required admission to the ICU, and 71 (28.2%) died. Forty-seven (19.1%) had a diagnosis of hospital-acquired COVID-19. Median overall survival was shorter in those with hospital-acquired infection than that in a contemporary community-acquired population (27 days versus unreached, hazard ratio (HR) = 2.3, 95% CI: 1.2-4.4, p = 0.0006. Multivariate analysis demonstrated that hospital-acquired COVID-19, age, Eastern Cooperative Oncology Group status and advanced stage of cancer were independently associated with death. INTERPRETATION: Our study demonstrates a high rate of nosocomial transmission of COVID-19, associated with increased mortality in both univariate and multivariate analysis in the cancer population, reinforcing the importance of treating patients with cancer in COVID-free units. We also validated that age and advanced cancer were negative predictive factors for COVID-19 severity in patients with cancer.

Antibiotics are associated with decreased progression-free survival of advanced melanoma patients treated with immune checkpoint inhibitors.

Background: The gut microbiota has been shown to be an important determinant of the efficacy of immune checkpoint inhibitions (ICI) in cancer. Several lines of evidence suggest that antibiotic (ATB) usage prior to or within the first month of ICI initiation negatively impacts clinical outcomes. Methods: We examined patients with advanced melanoma treated with an anti-PD-1 monoclonal antibody (mAb) or an anti-CTLA-4 mAb alone or in combination with chemotherapy. Those receiving ATB within 30 days of beginning ICI were compared with those who did not receive ATB. Response rates as determined by RECIST 1.1, progression-free survival (PFS), overall survival (OS) and immune-related toxicities were assessed. Results: Of these 74 patients analyzed, a total of 10 patients received ATB (13.5%) within 30 days of initiation of ICI. Patients who received ATB 30 days prior to the administration of ICI experienced more primary resistance (progressive disease) (0% of the objective response rate compared to 34%), and progression-free survival (PFS) was significantly shorter (2.4 vs 7.3 months, HR 0.28, 95% CI (0.10-0.76) p = 0.01). Overall survival (OS) was also shorter; however, this was not statistically significant (10.7 vs 18.3 months, HR:0.52, 95% CI (0.21-1.32) p = 0.17). The multivariate analysis further supported that ATB administration was associated with worse PFS (HR 0.32 (0.13-0.83) 95% CI, p = 0.02). Conclusion: These findings suggest that ATB use within 30 days prior to ICI initiation in patients with advanced melanoma may adversely affect patient outcomes.

Cellular response to micropatterned growth promoting and inhibitory substrates.

BACKGROUND: Normal development and the response to injury both require cell growth, migration and morphological remodeling, guided by a complex local landscape of permissive and inhibitory cues. A standard approach for studying by such cues is to culture cells on uniform substrates containing known concentrations of these molecules, however this method fails to represent the molecular complexity of the natural growth environment. RESULTS: To mimic the local complexity of environmental conditions in vitro, we used a contact micropatterning technique to examine cell growth and differentiation on patterned substrates printed with the commonly studied growth permissive and inhibitory substrates, poly-L-lysine (PLL) and myelin, respectively. We show that micropatterning of PLL can be used to direct adherence and axonal outgrowth of hippocampal and cortical neurons as well as other cells with diverse morphologies like Oli-neu oligodendrocyte progenitor cell lines and fibroblast-like COS7 cells in culture. Surprisingly, COS7 cells exhibited a preference for low concentration (1 pg/mL) PLL zones over adjacent zones printed with high concentrations (1 mg/mL). We demonstrate that micropatterning is also useful for studying factors that inhibit growth as it can direct cells to grow along straight lines that are easy to quantify. Furthermore, we provide the first demonstration of microcontact printing of myelin-associated proteins and show that they impair process outgrowth from Oli-neu oligodendrocyte precursor cells. CONCLUSION: We conclude that microcontact printing is an efficient and reproducible method for patterning proteins and brain-derived myelin on glass surfaces in order to study the effects of the microenvironment on cell growth and morphogenesis.