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Simple Summary: Due to chemotherapy-related toxicity, metastatic colorectal cancer is often treated initially by induction therapy followed by maintenance therapy with less cytotoxic molecules or a chemotherapy-free interval. The maintenance therapy period is a time with theoretically fewer cancer-related symptoms and less treatment-related toxicity. For patients with unresectable metastatic colorectal cancer, one of the aims of palliative treatment is to maximise the duration of maintenance therapy. In this study, we aimed to determine the factors that influence the duration of maintenance therapy. In our cohort of patients, patient and colorectal cancer characteristics but also primary tumour resection and local treatment of liver and lung metastases determined the duration of maintenance therapy. The induction chemotherapy regimen influenced the access to local treatment of metastases, whereas the maintenance chemotherapy regimen did not modify survival. Background/Objectives: Metastatic colorectal cancer (mCRC) is mainly treated with 5-Fluoro-Uracil (5-FU), Oxaliplatin and Irinotecan chemotherapies and anti-Epidermal Growth Factor Receptor (EGFR) or anti-Vascular Endothelial Growth Factor (VEGF) targeted therapies. Due to chemotherapy-related toxicity, patients receive induction treatment to achieve tumour response followed by maintenance therapy with less cytotoxic molecules or a chemotherapy-free interval to reduce chemotherapy-related toxicity. In this study, the aim was to determine the patient, cancer and treatment factors that influence the duration of maintenance therapy (DMT). Methods: We collected retrospective data on a cohort of 133 patients treated at the Centre Georges François Leclerc (CGFL) cancer centre in Dijon between March 2014 and June 2022. Patients had unresectable or potentially resectable diseases. They received first-line induction treatment with chemotherapy and/or targeted therapy and maintenance treatment, defined as the interruption of at least one chemotherapy agent. Results: In the multivariate analysis, age (HR: 1.02, 95% CI 1.00–1.04, p = 0.031), N2 nodal status (HR: 1.78, 95% CI 1.09–2.89, p = 0.021) and the presence of peritoneal metastases (HR: 2.05, 95% CI 1.25–3.36, p = 0.004), as well as baseline carcino-embryonic antigen (CEA) level (HR: 1.10, 95% CI 1.00–1.20, p = 0.052), were significantly associated to poor DMT. Local treatment of liver metastases also significantly reduced the DMT (HR: 0.49, 95% CI 0.28–0.86, p = 0.013). In our cohort, induction triplet chemotherapy significantly increased the CEA delta (70% vs. 44%, p = 0.047) compared to doublet chemotherapy and led to a higher rate of liver surgery (40% vs. 21%, p = 0.014) and a trend for a higher rate of local treatment of metastases (62% vs. 45%, p = 0.059). Conclusions: Duration of maintenance therapy is determined by the initial patient and colorectal cancer characteristics. However, it is significantly increased by local treatment of liver metastases. By reducing the tumour burden, a triplet induction chemotherapy regimen increases the rate of liver metastase resection. [ABSTRACT FROM AUTHOR]

Stratification of the prognosis of pancreatic cancer (PDAC) patients treated by surgery is based solely on clinical variables, such as tumor stage and node status. The development of biomarkers of relapse is needed, especially to drive administration of adjuvant therapy in this at-risk population. Our study evaluates the prognostic performance of a CD3- and CD8-based immune score. CD3, CD8 and Foxp3 expression were evaluated on whole slides in two retrospective PDAC cohorts totaling 334 patients. For this study, we developed an immune score to estimate CD3 and CD8 infiltration in both tumor core and invasive margin using computer-guided analysis with QuPath software. Variables were combined in a dichotomous immune score. The association between immune and clinical scores, and both PFS and OS was investigated. We observed that a dichotomous immune score predicts both PFS and OS of localized PDAC. By univariate and multivariate analysis, immune score, tumor grade, adjuvant therapy, lymph node status, and adjuvant chemotherapy administration were associated with PFS and OS. We subsequently associated the PDAC immune score and clinical variables in a combined score. This combined score predicted patient outcomes independently of adjuvant or neoadjuvant treatment, and improved patient prognostic prediction compared to clinical variables or immune score alone. [ABSTRACT FROM AUTHOR]

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a cancer with very poor prognosis despite early surgical management. To date, only clinical variables are used to predict outcome for decision-making about adjuvant therapy. We sought to generate a deep learning approach based on hematoxylin and eosin (H&E) or hematoxylin, eosin and saffron (HES) whole slides to predict patients' outcome, compare these new entities with known molecular subtypes and question their biological significance; Methods: We used as a training set a retrospective private cohort of 206 patients treated by surgery for PDAC cancer and a validation cohort of 166 non-metastatic patients from The Cancer Genome Atlas (TCGA) PDAC project. We estimated a multi-instance learning survival model to predict relapse in the training set and evaluated its performance in the validation set. RNAseq and exome data from the TCGA PDAC database were used to describe the transcriptomic and genomic features associated with deep learning classification; Results: Based on the estimation of an attention-based multi-instance learning survival model, we identified two groups of patients with a distinct prognosis. There was a significant difference in progression-free survival (PFS) between these two groups in the training set (hazard ratio HR = 0.72 [0.54;0.96]; p = 0.03) and in the validation set (HR = 0.63 [0.42;0.94]; p = 0.01). Transcriptomic and genomic features revealed that the poor prognosis group was associated with a squamous phenotype. Conclusions: Our study demonstrates that deep learning could be used to predict PDAC prognosis and offer assistance in better choosing adjuvant treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality. This study investigates the clinical interest of whole exome sequencing for analyzing somatic mutational signatures in patients with advanced or metastatic NSCLC treated with the current standard of care. Investigating somatic mutational signatures as well as structural variations, we evaluated the association between genomic features and patient outcomes in a cohort of 132 patients. This study identified specific signatures associated with poor response to immune checkpoint inhibitor (ICI) therapy and chemotherapy, potentially aiding treatment selection and identifying patients unlikely to benefit from these approaches. Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality. This study investigates the clinical interest of whole exome sequencing (WES) for analyzing somatic mutational signatures in patients with advanced or metastatic NSCLC treated with the current standard of care. Methods: Exome sequencing data and clinical characteristics from 132 patients with advanced or metastatic NSCLC were analyzed. Somatic mutational signatures including single base substitutions (SBSs), double base substitutions (DBSs), and copy number signatures were evaluated. Structural variations including tumor mutational burden (TMB), the number of neoantigens, TCR clonality, homologous recombination deficiency (HRD), copy number alterations (CNAs), and microsatellite instability (MSI) score were determined. The association between these genomic features, NSCLC subtypes, and patient outcomes (progression-free and overall survival) was evaluated. Conclusions: Exome sequencing offers valuable insights into somatic mutational signatures in NSCLC. This study identified specific signatures associated with a poor response to immune checkpoint inhibitor (ICI) therapy and chemotherapy, potentially aiding treatment selection and identifying patients unlikely to benefit from these approaches. [ABSTRACT FROM AUTHOR]