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.

Management of head and neck carcinomas with synchronous or metachronous oligometastatic disease: Role of locoregional radiotherapy and metastasis-directed radiotherapy

Head and neck carcinomas are initially metastatic in about 15% of cases. Radiotherapy is a cornerstone in the multimodal strategy at the locoregional phase. In patients with head and neck cancer, often heavily pretreated and with comorbidities, who relapse locoregionally or at distant sites, radiotherapy has also become increasingly important at the metastatic phase. Data on the optimal sequence of systemic treatments and metastasis-directed treatments including stereotactic irradiation are still lacking. Several randomized head and neck trials have been initiated that should provide important answers, including one recent GORTEC trial.

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.

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.

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.

Agreement among Radiation Oncologists and Radiologist in Contouring GTV on Planning and Mid-Treatment MRI for Locally Advanced Oropharynx Cancer

PURPOSE/OBJECTIVE(S)

Over the 7-week radiotherapy course, patients with head and neck cancers undergo significant anatomical changes, including weight loss and tumor shrinkage (with complete response at mid-treatment in as high as 50% of patients). The current standard of care is to maintain the same plan for the entire treatment duration, unless major dosimetric deviations are suspected. The use of MRI for treatment adaptation has the advantage of increased soft tissue contrast and is being integrated into several clinical practices with the recent implementation of the MR-Linac technology. In this interim secondary analysis of a phase 2, randomized trial, we assessed inter-observer variability (IOV) in the delineation of GTV at planning and mid-treatment in locally advanced oropharynx cancer (OPC).

MATERIALS/METHODS

Three expert radiation oncologists (RO) and 1 expert radiologist, with a minimum of 5 years' experience in MRI contouring, blinded to each other's volumes, were provided with planning and mid-treatment MRI (T1 gadolinium, T2-weighted and DWI) of 9 patients with locally advanced OPC. Primary gross tumor volume (GTV) was independently contoured by all 3 ROs and the radiologist. Contouring agreement was analyzed using the dice similarity coefficient (DSC) index and the average Hausdorff distance.

RESULTS

Nine cases of locally advanced OPC were included. Fifty percent of the patients were stage 3 (AJCC 8th edition) and all were p16+ OPC. There was moderate IOV in the delineation of GTV on planning MRI as evidenced by mean DSC index and Hausdorff distance of 0.75 (range 0.53 - 0.87) and 0.30 cm (max 2.14 cm). At mid-treatment, the GTV IOV had a mean DSC index and Hausdorff distance of 0.30 (range 0.19 - 0.76) and 0.55 cm (max 2.83), respectively. The contours of the radiologist compared to RO shown a mean DSC index and Hausdorff distance on planning MRI 0.63 (range 0.53 - 0.69) and 0.41 cm (max 2.14 cm), and on mid-treatment MRI of 0.36 (range 0.19 - 0.59) and 0.68 cm (max 1.08 cm).

CONCLUSION

Although this study demonstrates the presence of moderate IOV between 3 experienced head and neck ROs and one radiologist in an academic institution for the delineation of GTV on planning MRI, we observed a higher IOV between the 4 experts on mid-treatment MRI. We also experienced a higher IOV between the radiologist and RO on planning and mid-treatment images. These results show high degree of GTV volume variability on mid-treatment MRI. Therefore, peer review of contours is important and guidelines for MR adaptive tumor delineation are needed.

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.

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.

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.

Abstract 3524: Role of the gut microbiota in the development of immune-mediated diarrhea and colitis induced by immune checkpoint inhibitors

The accelerating use of combined immune checkpoint inhibitors (ICI; anti-PD-1 plus anti-CTLA-4 antibodies) enhanced therapeutic responses in various cancers while significantly increasing immune-related adverse events (irAE). The mechanism involved in the occurrence of immune-mediated diarrhea and colitis (IMDC) and its potential implication on ICI efficacy remain unclear. The gut microbiome is progressively recognized as a key biomarker of ICI efficacy and preliminary results suggest its potential association with irAE. However, the link between microbiota and IMDC still needs to be elucidated. In this study, we investigated IMDC-associated intestinal bacteria.We performed metagenomics sequencing of baseline pre-ICI fecal samples from 26 advanced melanoma patients and recorded IMDC. Culturomics using MALDI-TOF was used to isolate bacteria from 19 patient samples: 11 IMDC-free (fIMDC), 5 baseline pre-IMDC (pIMDC) and 8 during IMDC (dIMDC). Serum multiplex cytokine panel was measured for 36 patients. Then, we induced colitis with Dextran Sulfate Sodium (DSS) in MCA-205 murine tumor model treated with oral supplementation of human bacteria and ICI.Metagenomics profiling demonstrated lower α-diversity at baseline in patients that developed irAE. Moreover, pIMDC samples present a lower Simpson α-diversity than fIMDC samples (p=0.08). ß-diversity was also different between both groups (p=0.10) suggesting a different baseline microbiome composition. Lefse analysis revealed an overrepresentation of Enterococcus faecium and Bilophila wadsworthia in pIMDC samples compared to fIMDC samples. Culturomics showed that dIMDC samples exhibited a shift in gut microbiota with a significant decrease in bacterial diversity mostly anaerobic species and an enrichment of Clostridium paraputricum, Clostridium perfringens, Paraclostridium bifermentans and Paeniclostridium sordelii compared to fIMDC and pIMDC samples. Immunological analysis of serum revealed that IMDC patients had a higher concentration of pro-inflammatory cytokines including GMCSF (p=0.04), IL12-23p40 (p=0.04) and IL-16 (p=0.03) compared to fIMDC patients. In our DSS-induced colitis mouse model, we showed that P. bifermentans oral supplementation related with diarrhea and a shorter colon length, indicating more severe colitis. Moreover, we showed that P. bifermentans increased anti-CLTA-4 and anti-PD-1 anti-tumor activity.Results showed that gut microbiota composition of dIMDC patients differed from that of pIMDC and fIMDC patients with a decrease of diversity and colonization of P.bifermentans and P.sordelii. In IMDC patients, we isolated bacteria that aggravated colitis in murine models and are known to be associated with inflammatory bowel disease. These results provide more insight on potential microbiome-modifying strategies to damper IMDC and increase anti-cancer efficacy.

Citation Format: Khoudia Diop, Benlaïfaoui Myriam, Wiam Belkaïd, Alexis Nolin-Lapalme, Julie Malo, Marion Tonneau, Ponce Mayra, Afnan Al-Saleh, Catalin Mihalcioiu, Ian Watson, Arielle Elkrief, Karla Lee, Meriem Messaoudene, Bertrand Routy, Corentin Richard. Role of the gut microbiota in the development of immune-mediated diarrhea and colitis induced by immune checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3524.

Association between lung immune prognostic index, microbiome, and immunotherapy outcomes in non–small cell lung cancer

9050

Background: Host-related inflammatory biomarkers and gut microbiome are two major prognostic factors for non-small cell lung cancer (NSCLC) patients (pts) treated with immune checkpoint inhibitors (ICI). In this study, we aimed to assess an association between the Lung Immune Prognostic Index (LIPI) and the microbiome composition on ICI outcomes in two independent cohorts of NSCLC pts. Methods: We included 205 patients with advanced NSCLC treated with ICI (monotherapy or in combination with chemotherapy) from two independent cohorts. Metagenomics microbiome profiling was performed on the Canadian discovery cohort of 72 pts while 16S rRNA microbiome sequencing was used in the Japanese validation cohort of 133 pts. The LIPI score was calculated using the dNLR (neutrophils/[leucocytes-neutrophils]) and lactate deshydrogenase (LDH). Pts were classified as Good (G: 0 high factor), Intermediate (I: 1 high factor) and Poor (P: 2 high factors). Median overall survival (OS) was estimated using the Kaplan-Meier method. Microbiome diversity indexes and bacterial relative abundances were compared according to LIPI groups. Results: Among the 72 pts included in the discovery cohort, the median follow-up of 20.6 months (mos). The LIPI was distributed as follows: G (n = 31, 43.1%), I (31, 43.1%), P (10, 13.8%) and baseline characteristics were well balanced between the 3 groups. When segregating pts according to LIPI, the OS was 25.6 mo, 19.8 mo, and 5.7 mo in the G, I (HR: 1.71, 95%CI: 0.80-3.65) and P (HR: 3.97, 95%CI: 1.60-9.82) groups, respectively (p = 0.003). The microbiome alpha diversity was lower in the P group compared with the G group (p = 0.03), and there was a trend towards different microbiome composition in beta diversity (p = 0.055) between both groups. Pts in the G group had a favorable microbiome (enriched in Ruminococcus and Anaerostipes), while pts from the P group had an unfavorable microbiome (enriched in Enterobacteriaceae and Clostridium symbiosum and lavalense). Next, in the validation cohort of 133 pts, LIPI was distributed as follows: G (n = 62, 46.6%), I (51, 38.3%), P (20, 15%). Pts with G LIPI had not reached their median OS compared to pts in the I [15.7 mo HR: 1.60 (0.88-2.94)] and P [8.8 mo groups, HR: 2.02 (0.98-4.19)], p = 0.03. Similar to the discovery cohort, at the genus level, G LIPI group had enrichment of Ruminococcus as well as Anaerostipes compared with pts in the P and I groups with an overrepresentation of Hungatella. Conclusions: Host-related inflammatory biomarkers, represented by the LIPI, seemed to be associated with microbiome and ICI outcomes in pts treated with NSCLC. This observation was validated in an external validation cohort. This link could be in relation to the presence of proinflammatory bacteria in pts with poor LIPI.

Association between immune-related adverse events and microbiome composition in patients with advanced non–small cell lung cancer treated with immunotherapy

9036

Background: Despite immune-checkpoint inhibitors (ICI) achieving high response rates in patients (pts) with advanced non-small cell lung cancer (NSCLC), immune-related adverse events (irAE) remain an important therapeutic hurdle. The gut microbiome represents a novel prognostic marker of ICI response and early clinical evidence suggests that the microbiome may also regulate the development of irAE. We aimed to assess the association between irAE and the gut microbiome composition in advanced NSCLC pts amenable to ICI. Methods: In this study, we enrolled 464 pts from two independent cohorts (Montreal and Tokyo) with advanced NSCLC treated with ICI monotherapy or combination with chemotherapy. Fecal samples were collected prior to ICI initiation. Metagenomics microbiome profiling and 16S rRNA microbiome sequencing were performed for 81 pts and 133 pts respectively. The irAE were classified as CTCEA grade 0-1 (G0-1 irAE) and grade > 2 (G2-5 irAE). Microbiome composition of both groups was represented using alpha diversity, beta diversity indexes and LEfSe relative abundances. Results: Median follow-up was 28.3 months (mos) and the incidence of G2-5 irAE was 25.1% in the Montreal cohort. For the Tokyo cohort, the median follow-up was 19.6 mos and 30.8% pts developed G2-5 irAE. First, in both cohorts, overall survival (OS) and progression-free survival (PFS) were significantly improved for pts with G2-5 irAE compared to G0-1 irAE (OS: HR: 1.71, p = 0.008 and PFS: HR: 1.65, p < 0.001) and (OS: HR: 2.34, p = 0.001, and PFS: HR = 2.34, p = 0.006) respectively. Second, metagenomics analysis of 81 pts demonstrated a numerical decrease of the alpha diversity in terms of observed species in the G2-5 irAE. Dorea sp CAG 31, Anaerosporobacter mobilis, Butyricicococcus, and Enterococcus faecium were overrepresented in pts with G2-5 irAE. Conversely, Gordonibacter was increased in G0-1 irAE. Next, 16S rRNA profiling from the Tokyo cohort revealed an enrichment of Dorea, Butyricicococcus, and Eubacterium ventriosum in G2-5 irAE . Finally, we observed an enrichment in Dorea and Butyricicococcus in the pts with PFS > 6 months, as observed in the G2-5 irAE group. Conclusions: IrAE correlated with clinical outcome and microbiome composition in two independent cohorts of pts with advanced NSCLC. These results prompt further research on the potential mechanism underling the role of the microbiome in the development of irAE.

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.

[Radiotherapy for patients with early-stage glottic squamous cell carcinoma of the larynx: Interest of hypofractionation?]

Hypofractionated radiotherapy of early-stage squamous cell carcinoma of the glottic larynx is a promising treatment option. This can be divided into radiotherapy with moderate hypofractionation (up to 2.5Gy per fraction), more intense hypofractionation (between 2.5 and 4.5Gy per fraction) and stereotactic radiotherapy (above 4.5Gy per fraction). Most studies evaluating moderate hypofractionation show a local control rate between 85 and 95%. Acute laryngeal toxicity is superior to conventional treatment, but only for grades 1 and 2, with no significant difference reported for severe toxicity. Stereotactic radiotherapy in this pathology is also an emerging entity, but some authors have reported significant toxicity. There are currently no standardized guidelines for treatment and management regimen. We conducted a systemic review of published prospective and retrospective trials to evaluate efficacy, toxicity, and discuss future directions.

[Stereotactic body radiotherapy for locally advanced pancreatic cancer: A systemic review]

Stereotactic body radiation therapy (SBRT) for locally advanced pancreatic cancer (LAPC) is an emerging treatment option. Most studies showed local control of approximately 75% with no evidence of improved overall survival. Gastrointestinal toxicities could be significant, ranging up to 22% for acute toxicities≥grade 3+ and 44% for late toxicities≥grade 3+. Currently, no standardized guidelines for treatment and management scheme. We conducted a systemic review of published prospective and retrospective trials to evaluate the efficacy, safety, technical data, and discuss future directions.

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.