CXCL13 Positive Cells Localization Predict Response to Anti-PD-1/PD-L1 in Pulmonary Non-Small Cell Carcinoma.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancers (NSCLCs) treatment, but only 20-30% of patients benefit from these treatments. Currently, PD-L1 expression in tumor cells is the only clinically approved predictor of ICI response in lung cancer, but concerns arise due to its low negative and positive predictive value. Recent studies suggest that CXCL13+ T cells in the tumor microenvironment (TME) may be a good predictor of response. We aimed to assess if CXCL13+ cell localization within the TME can predict ICI response in advanced NSCLC patients. Methods: This retrospective study included 65 advanced NSCLC patients treated with Nivolumab/Pembrolizumab at IUCPQ or CHUM and for whom a pretreatment surgical specimen was available. Good responders were defined as having a complete radiologic response at 1 year, and bad responders were defined as showing cancer progression at 1 year. IHC staining for CXCL13 was carried out on a representative slide from a resection specimen, and CXCL13+ cell density was evaluated in tumor (T), invasive margin (IM), non-tumor (NT), and tertiary lymphoid structure (TLS) compartments. Cox models were used to analyze progression-free survival (PFS) and overall survival (OS) probability, while the Mann-Whitney test was used to compare CXCL13+ cell density between responders and non-responders. Results: We showed that CXCL13+ cell density localization within the TME is associated with ICI efficacy. An increased density of CXCL13+ cells across all compartments was associated with a poorer prognostic (OS; HR = 1.22; 95%CI = 1.04-1.42; p = 0.01, PFS; HR = 1.16; p = 0.02), or a better prognostic when colocalized within TLSs (PFS; HR = 0.84, p = 0.03). Conclusion: Our results support the role of CXCL13+ cells in advanced NSCLC patients, with favorable prognosis when localized within TLSs and unfavorable prognosis when present elsewhere. The concomitant proximity of CXCL13+ and CD20+ cells within TLSs may favor antigen presentation to T cells, thus enhancing the effect of PD-1/PD-L1 axis inhibition. Further validation is warranted to confirm the potential relevance of this biomarker in a clinical setting.

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.

Gut microbiome predicts gastrointestinal toxicity outcomes from chemoradiation therapy in patients with head and neck squamous cell carcinoma.

OBJECTIVES: Chemoradiation (CRT) in patients with locally advanced head and neck squamous cell cancer (HNSCC) is associated with significant toxicities, including mucositis. The gut microbiome represents an emerging hallmark of cancer and a potentially important biomarker for CRT-related adverse events. This prospective study investigated the association between the gut microbiome composition and CRT-related toxicities in patients with HNSCC, including mucositis. MATERIALS AND METHODS: Stool samples from patients diagnosed with locally advanced HNSCC were prospectively collected prior to CRT initiation and analyzed using shotgun metagenomic sequencing to evaluate gut microbiome composition at baseline. Concurrently, clinicopathologic data, survival outcomes and the incidence and grading of CRT-emergent adverse events were documented in all patients. RESULTS: A total of 52 patients were included, of whom 47 had baseline stool samples available for metagenomic analysis. Median age was 62, 83 % patients were men and 54 % had stage III-IV disease. All patients developed CRT-induced mucositis, including 42 % with severe events (i.e. CTCAE v5.0 grade ≥ 3) and 25 % who required enteral feeding. With a median follow-up of 26.5 months, patients with severe mucositis had shorter overall survival (HR = 3.3, 95 %CI 1.0-10.6; p = 0.02) and numerically shorter progression-free survival (HR = 2.8, 95 %CI, 0.8-9.6; p = 0.09). The gut microbiome beta-diversity of patients with severe mucositis differed from patients with grades 1-2 mucositis (p = 0.04), with enrichment in Mediterraneibacter (Ruminococcus gnavus) and Clostridiaceae family members, including Hungatella hathewayi. Grade 1-2 mucositis was associated with enrichment in Eubacterium rectale, Alistipes putredinis and Ruminococcaceae family members. Similar bacterial profiles were observed in patients who required enteral feeding. CONCLUSION: Patients who developed severe mucositis had decreased survival and enrichment in specific bacteria associated with mucosal inflammation. Interestingly, these same bacteria have been linked to immune checkpoint inhibitor resistance.

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.

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.

A competing risk analysis of the patterns and risk factors of recurrence in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.

INTRODUCTION: To assess patterns of recurrence after stereotactic ablative radiotherapy (SABR) in patient ineligible to surgery with early-stage non-small cell lung cancer (ES-NSCLC), report survival and treatment after first recurrence. METHODS: We performed a retrospective analysis on 1068 patients with ES-NSCLC and 1143 lesions. Between group differences were estimated using competing risk analysis and cause-specific hazard ratios were calculated. Overall survival (OS) after first recurrence was calculated. RESULTS: Median follow-up was 37.6 months. Univariate analysis demonstrated that ultra-central location was associated with higher risk of regional recurrence (RR) and distant metastasis (DM) (p = 0.004 and 0.01). Central lesions were associated with higher risk of local recurrence (LR) and RR (p < 0.001). Ultra-central lesions were associated with shorter OS (p = 0.002) compared to peripheral lesions. In multivariate analysis, central location was the only factor associated with increased LR and RR risks (p = 0.016 and 0.005). Median OS after first recurrence was 14.8 months. There was no difference in OS after first recurrence between ultra-central, central, and peripheral lesions (p = 0.83). Patients who received a second SABR course had an OS of 51.3 months, compared to 19.5 months with systemic therapy and 8.1 months with supportive care (p < 0.0001). DISCUSSION: The main prognostic factor for LR and RR risks was central location. Ultra-central and central tumors might benefit from treatment intensification strategies such as dose escalation and/or addition of systemic therapy to improve radiotherapy outcomes. After a first recurrence post SABR, patients with contralateral lung recurrences and those who were eligible to receive a second course of SABR had improved OS.

Short- and Long-term Repercussions of Vancomycin on Immune Surveillance and the Efficacy of Antitumor Treatments.

ABSTRACT: Although antibiotic is a major contributor to shifts in the intestinal flora that may persist for up to several months after cessation, it is now increasingly recognized that its prescription may differentially influence clinical outcome of different anticancer treatments. Intense clinical and basic research efforts aim then at gaining sufficient insights about how the cooperative action between the intestinal ecosystem and immune surveillance modulates the efficacy of anticancer treatments. In this review, we summarize multiple levels of knowledge between vancomycin exposure, the gut microbiota, and a meaningful therapeutic response. Furthermore, we discuss the mode of action of antibiotic therapy that is prescribed for prophylaxis of bacteremia and neutropenia and outline the opportunity for judiciously improving the efficacy of anticancer drugs.

Second-line treatment outcomes after progression from first-line chemotherapy plus immunotherapy in patients with advanced non-small cell lung cancer.

INTRODUCTION: Chemotherapy plus immunotherapy is the standard of care for patients with metastatic NSCLC. No study has evaluated the outcomes of second-line chemotherapy treatments after progression following first-line chemo-immunotherapy. METHOD: This multicenter retrospective study evaluated the efficacy of second line (2L) chemotherapies after progression under first-line (1L) chemo-immunotherapy, measured by overall survival (2L-OS) and progression free survival (2L-PFS). RESULTS: A total of 124 patients were included. The mean age was 63.1 years, 30.6 % of the patients were female, 72.6 % had an adenocarcinoma and 43.5 % had a poor ECOG-performance status prior to 2L initiation. Sixty-four (52.0 %) patients were considered resistant to first line chemo-immunotherapy. (1L-PFS < 6 months). In 2L treatments, 57 (46.0 %) patients received taxane monotherapy, 25 (20.1 %) taxane plus anti-angiogenic, 12 (9.7 %) platinum-based chemotherapy and 30 (24.2 %) other chemotherapy. At a median follow-up of 8.3 months (95 %CI: 7.2-10.2), post initiation of 2L treatment, the median 2L-OS was 8.1 months (95 % CI: 6.4-12.7) and the median 2L-PFS was 2.9 months (95 %CI: 2.4-3.3). Overall, the 2L-objective response and 2L-disease control rates were 16.0 %, and 42.5 %, respectively. Taxane plus anti-angiogenic and platinum rechallenge achieved longest median 2L-OS: not reached (95 %CI: 5.8-NR) and 17.6 months (95 %CI 11.6-NR), respectively (p = 0.05). Patients resistant to the 1L treatment had inferior outcomes (2L-OS 5.1 months, 2L-PFS 2.3 months) compared with 1L responders (2L-OS 12.7 months, 2L-PFS 3.2 months). CONCLUSION: In this real-life cohort, 2L chemotherapy achieved modest activity following progression under chemo-immunotherapy. 1L-resistant patients remained a refractory population, highlighting a need for new 2L strategies.

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.

Current Therapy for Metastatic Head and Neck Cancer: Evidence, Opportunities, and Challenges.

Management of metastatic head and neck squamous cell carcinoma is evolving as new systemic therapies have led to improvements in survival, and as advances in locoregional therapy and the increased numbers of patients with HPV-associated cancers who develop oligometastases raise the possibility of ablation of limited numbers of metastases. We review the data regarding first-line immunotherapy in PD-L1-expressing metastatic head and neck squamous cell carcinoma, the experience with aggressive local management of oligometastases, and promising novel immunotherapies, targeted therapies, and HPV-specific treatments. For patients with metastatic head and neck squamous cell carcinoma that is PD-L1 expressing, first-line systemic therapy is pembrolizumab or pembrolizumab with chemotherapy. Inclusion of chemotherapy is associated with higher objective response proportion in all biomarker subgroups and may have a greater impact on survival in HPV-associated cancers. For patients with oligometastatic disease, particularly when metastases are metachronous, current evidence supporting the role of local ablation is limited to a small number of retrospective studies. Based on retrospective data, patients with a smaller number of metastases, lung metastases, and/or virally associated head and neck squamous cell carcinoma are most likely to benefit from an aggressive ablative approach. Additionally, we review emerging evidence for targeted therapy in metastatic head and neck squamous cell carcinoma, including with agents that inhibit mutant HRAS or NOTCH1, or overexpressed EGFR. Studies of antiangiogenic agents in combination with immune checkpoint blockade, and combination immunotherapy, are also under study.

Stereotactic Ablative Radiotherapy for oligo-progressive disease refractory to systemic therapy in Non-Small Cell Lung Cancer: A registry-based phase II randomized trial (SUPPRESS-NSCLC).

BACKGROUND: Management of Non-Small Cell Lung Cancer (NSCLC) patients with oligoprogression remains controversial. There is limited data to support the strategy of Stereotactic Ablative Radiotherapy (SABR) targeting the oligoprogressive disease in combination with ongoing systemic treatment. We aim to assess the benefit of this approach compared to standard of care in the treatment of oligoprogressive NSCLC. METHODS: This phase II study will enroll 68 patients with oligoprogressive NSCLC, defined as 1-5 progressive extracranial lesions ≤5 cm involving ≤3 organs. Patients on active systemic therapy (chemotherapy, immunotherapy, targeted therapy or a combination) will be randomized 1:1 to either continue their current systemic therapy in combination with SABR to all lesions or the standard of care (switch to the next line of treatment, continue same treatment or observation). The co-primary endpoints are progression-free survival (PFS) and overall survival (OS). Secondary endpoints include time to next systemic treatment, patient-reported quality of life, cost effectiveness as well as translational analysis to characterize both adaptive immunity and immunogenic cell death markers in the peripheral blood. DISCUSSION: There is an unmet need to carefully examine the efficacy, safety and quality of life impact of SABR in the context of oligoprogressive disease. The present study will provide higher level randomized evidence on the role of SABR in oligoprogressive NSCLC.

Stereotactic body radiotherapy for intramedullary metastases: a retrospective series at the Oscar Lambret center and a systematic review.

BACKGROUND: Intramedullary metastasis (IMM) is a rare disease with poor prognosis. The incidence of IMMs has increased, which has been linked to improved systemic treatment in many cancers. Surgery and/or radiotherapy are the most commonly used treatments; only small-sample retrospective studies and case reports on stereotactic body radiotherapy (SBRT) have reported acceptable results in terms of local control and clinical improvement, with no reported toxicity. Thus, we performed this monocentric retrospective study on five cases treated with SBRT for IMMs, which we supplemented with a systematic review of the literature. METHODS: We included all patients treated for IMM with SBRT. The target tumor volume, progression-free survival, prescription patterns in SBRT, survival without neurological deficit, neurological functional improvement after treatment, and overall survival were determined. RESULTS: Five patients treated with a median dose of 30 Gy in a median number of fractions of 5 (prescribed at a median isodose of 86%) included. The median follow-up duration was 23 months. Two patients showed clinical improvement. Three patients remained stable. Radiologically, 25% of patients had complete response and 50% had stable disease. No significant treatment-related toxicity was observed. CONCLUSION: SBRT appears to be a safe, effective, and rapid treatment option for palliative patients.

The role of the gut microbiome on radiation therapy efficacy and gastrointestinal complications: A systematic review.

Radiation therapy (RT) is an essential component of therapy either curative or palliative armamentarium in oncology, but its efficacy varies considerably among patients through many extrinsic and intrinsic mechanisms of the tumour, which are beginning to be better understood. Recent studies have shown that the gut microbiome represents a key factor in the modulation of the systemic immune response and consequently on patients' outcome. Moreover, the emergence of biomarkers that are derived from the gut microbiota has fuelled the development of adjuvant strategies for patients treated with immunotherapy in combination or not with RT. Despite progress in development of more precise radiotherapy techniques, almost all patients undergoing RT to the abdomen, pelvis, or rectum develop acute adverse events as a consequence of several dose-limiting parameters such as the location of irradiation that may subsequently damage normal tissue including the intestinal epithelium. Several lines of evidence in preclinical models identified that vancomycin improves RT-induced gastrointestinal toxicities such as diarrhea and oral mucositis. In order to gain further insight into this rapidly evolving field, we have systematically reviewed the studies that have described how the gut microbiome may directly or indirectly modulate RT efficacy and its gastro-intestinal toxicities. Lastly, we outline current knowledge gaps and discuss potentially more satisfactory therapeutic options to restore the functionality of the gut microbiome of patients treated with RT.