Monoclonal antibodies targeting PD1/PD-L1 are game changers in advanced non-small cell lung cancer (NSCLC), but biomarkers are lacking. We previously reported the prognostic role of splenic volume in digestive cancer and its correlation with the presence of immunosuppressive cells. The aim of this study was to evaluate the prognostic role of splenic volume in NSCLC patients treated with immune checkpoint inhibitors (ICIs). We conducted a retrospective study of 276 patients receiving ICIs for advanced NSCLC in the Georges François Leclerc Cancer Center. The association between splenic volume at baseline and at two months of therapy and progression-free survival (PFS) during ICI treatment or overall survival (OS) from ICI initiation was evaluated using univariate and multivariable Cox analyses. Splenic volume during treatment and the change in splenic volume were associated with poor PFS (respectively p = 0.02 and p = 0.001) and with OS (respectively p < 1.10⁻³ and p < 1.10⁻³). Baseline splenic volume at the first evaluation was also associated with poor OS (p = 0.001). LDH rate and dNLR were positively correlated with splenic volume, as well as with its evolution. After the adjustment of clinical variables, splenic volumes remained a predictive marker of immunotherapy efficacy. Splenic volume is a prognostic biomarker in patients with advanced NSCLC treated with ICIs.

Purpose: Immune infiltration is a prognostic factor in high-grade serous ovarian carcinoma (HGSC) but immunotherapy efficacy is disappointing. Genomic instability is now used to guide the therapeutic value of PARP inhibitors. We aimed to investigate exome-derived parameters to assess the tumor microenvironment according to genomic instability profile. Methods: We used the HGSC TCGA (the cancer genome atlas) dataset with genomic characteristics, including homologous recombination deficiency (HRD), copy number variant (CNV) signatures, TCR (T cell receptor) clonality and abundance of tissue-infiltrating immune and stromal cell populations. We then investigated the relationship with survival data. Results: In 578 HGSC patients, HRD status, CNV signature 7 and TCR clonality were associated with longer survival. The combination of high CNV signature 7 expression and HRD status or high CNV signature 3 expression and high TCR clonality was associated with a trend towards longer survival compared to each variable alone. Combining T cell infiltrate and TCR clonality improved the prognostic value compared to T cells infiltration alone. Prognostic value of TCR clonality was confirmed in an independent cohort. Conclusions: TCR clonality is an emerging prognostic biomarker that improves T cell infiltrate information. Analysis of TCR clonality combined with genomic instability could be an interesting prognostic biomarker.

Immune checkpoint inhibitors (ICIs) have improved the care of patients in multiple cancer types. However, PD-L1 status, high Tumor Mutational Burden (TMB), and mismatch repair deficiency are the only validated biomarkers of efficacy for ICIs. These markers remain imperfect, and new predictive markers represent an unmet medical need. Whole-exome sequencing was carried out on 154 metastatic or locally advanced cancers from different tumor types treated by immunotherapy. Clinical and genomic features were investigated using Cox regression models to explore their capacity to predict progression-free survival (PFS). The cohort was split into training and validation sets to assess validity of observations. Two predictive models were estimated using clinical and exome-derived variables, respectively. Stage at diagnosis, surgery before immunotherapy, number of lines before immunotherapy, pleuroperitoneal, bone or lung metastasis, and immune-related toxicity were selected to generate a clinical score. KRAS mutations, TMB, TCR clonality, and Shannon entropy were retained to generate an exome-derived score. The addition of the exome-derived score improved the prediction of prognosis compared with the clinical score alone. Exome-derived variables could be used to predict responses to ICI independently of tumor type and might be of value in improving patient selection for ICI therapy.

Purpose: The first line treatment of advanced pancreatic ductal adenocarcinoma cancer (PDAC) comprises a FOLFIRINOX regimen for most patients with good performance status. However, no biomarker to predict efficacy is currently available. We investigated whether exome sequencing could be used to predict progression-free and overall survival in patients undergoing FOLFIRINOX for PDAC. Methods: In this single-center observational study, we included 78 patients with advanced PDAC who underwent somatic and germline exome analyses during first line therapy with FOLFIRINOX or gemcitabine. Exome-derived variables associated with outcome were then used in Cox regression models to generate a composite biomarker. Results: Performance status, tumor stage, liver metastasis, and lung metastasis were retained to generate a prognostic clinical score associated with overall and progression-free survival. Clonality, ploidy, and copy number variant (CNV) signatures 1 and 5, as well as gene variants in the calcium, non-homologous end-joining (NHEJ), and spliceosome pathways, were retained to generate a genomic prognostic score. The addition of genomic score improved the prediction of prognosis compared to the clinical score alone. Conclusions: This study underlines that structural and pathway genomic features could be used to predict FOLFIRINOX survival in patients with advanced PDAC.