Treatment with inhibition of programmed cell death 1 (PD-1) or its ligand (PD-L1) improves survival in advanced non-small-cell lung cancer (NSCLC). Nevertheless, only a subset of patients benefit from treatment and biomarkers of response to immunotherapy are lacking. Expression of PD-L1 on tumor cells is the primary clinically-available predictive factor of response to immune checkpoint inhibitors, and its relevance in cancer immunotherapy has fostered several studies to better characterize the mechanisms that regulate PD-L1 expression. However, the factors associated with PD-L1 expression are still not well understood. Genomic alterations that activate KRAS, EGFR, and ALK, as well as the loss of PTEN, have been associated with increased PD-L1 expression. In addition, PD-L1 expression is reported to be increased by amplification of CD274, and decreased by STK11 deficiency. Furthermore, PD-L1 expression can be modulated by either tumor extrinsic or intrinsic factors. Among extrinsic factors, the most prominent one is interferon-γ release by immune cells, while there are several tumor intrinsic factors such as activation of the mechanistic target of rapamycin (mTOR), mitogen-activated protein kinase (MAPK) and Myc pathways that can increase PD-L1 expression. A deeper understanding of PD-L1 expression regulation is crucial for improving strategies that exploit inhibition of this immune checkpoint in the clinic, especially in NSCLC where it is central in the therapeutic algorithm. We reviewed current preclinical and clinical data about PD-L1 expression regulation in NSCLC.

PSMA-PET/CT is a suitable replacement for conventional imaging in the primary staging of PCa. The aim of this retrospective study was to assess the correlation between parameters discovered by PSMA PET/CT in primary staging and either prostate histopathology (pT) findings or PSMA-IHC expression in a cohort of biopsy-proven high-risk PCa candidates for surgery. Clinical information (age, iPSA-value, and grading group) and PSMA-PET/CT parameters (SUVmax, PSMA tumor volume [PSMA-TV], and total lesion [PSMA-TL]) were compared with pT (including histologic pattern, Gleason grade, and lymphovascular invasion [LVI]) and PSMA-IHC features, including visual quantification (VS) with a four-tiered score (0 = negative, 1+ = weak, 2+ = moderate, 3+ = strong), growth pattern (infiltrative vs expansive), and visual pattern (cytoplasmic vs membranous). In total, 44 patients were enrolled, with a median age of 67 (IQR 57-77); the median iPSA was 9.4 ng/dL (IQR 12.5-6.0). One patient (3%) was grading group (GG) 3, 27/44 (61%) were GG4, and 16/44 (36%) were GG5. PSMA-PET/CT detection rate for the presence of primary prostate cancer was 100%. Fused/poorly formed Gleason grade 4 features were predominant (22/44–50%); a cribriform pattern was present in 18/44 (41%) and acinar in 4/44 (9%). We found that lower PSMA-TVs were mostly related to acinar, while higher PSMA-TVs correlated with a higher probability to have a cribriform pattern (p-value 0.04). LVI was present in 21/44(48%) patients. We found that higher PSMA-TV and PSMA-TL are predictive of LVI p-value 0.002 and p-value 0.01, respectively. There was no correlation between PET-parameters and perineural invasion (PNI), probably because this was present in almost all the patients. Moreover, patients with high PSMA-TL values displayed the highest PSMA-IHC expression (VS3+) with a membranous pattern. In conclusion, PSMA-TV and PSMA-TL are predictors of a cribriform pattern and LVI. These conditions are mostly related to higher aggressiveness and worse outcomes.

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal–Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10${}^{-6}$). On the test set, the area under the curve = 0.88 (95% CI, 0.68–0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

Background: In non-small cell lung cancer (NSCLC), BRAF class 1 alterations are effectively targeted by BRAF inhibitors. Conversely, targeted therapies have very low or absent activity in patients carrying class 2 and 3 alterations. The spectrum of BRAF alterations in NSCLC patients, and their accompanying clinical features, genomic landscape and treatment outcomes have been poorly reported. Patients and methods: We identified BRAF alterations of defined functional class across different tumors through a systematic review. Then, we selected NSCLC patients carrying BRAF alterations, according to the systematic review, in the cBioPortal (cBioPortal cohort) to collect and analyze clinical, biomolecular and survival data. Finally, we identified NSCLC patients carrying BRAF non-V600 mutations enrolled in POPLAR and OAK trials (POPLAR/OAK cohort), extracting clinical and survival data for survival analyses. Results: 100 different BRAF non-V600 alterations were identified through the systematic review. In the cBioPortal cohort (n = 139), patients harboring class 2 and 3 alterations were more frequently smokers and had higher tumor mutational burden compared to those carrying class 1 alterations. The spectrum of most frequently co-altered genes was significantly different between BRAF alterations classes, including SETD2, STK11, POM121L12, MUC16, KEAP1, TERT, TP53 and other genes. In the POPLAR/OAK cohort, patients carrying non-V600 BRAF alterations were characterized by poor prognosis compared to BRAF wild-type patients. Conclusions: Different classes of BRAF alterations confer distinctive clinical features, biomolecular signature and disease behavior to NSCLC patients. Non-V600 alterations are characterized by poor prognosis, but key gene co-alterations involved in cancer cell survival and immune pathways may suggest their potential sensitivity to tailored treatments.

Background/Objectives. To evaluate T&N-staging diagnostic performance of [68Ga]Ga-FAPI-46 PET/CT (FAPI) in a suspected/confirmed lung cancer surgical cohort. Methods: Patients were enrolled in a prospective monocentric trial (EudraCT: 2021-006570-23) to perform FAPI, in addition to conventional-staging-flow-chart (including [18F]F-FDG PET/CT-FDG). For the current purpose, only surgical patients were included. PET-semiquantitative parameters were measured for T&N: SUVmax, target-to-background-ratios (using mediastinal blood pool-MBP, liver-L and pulmonary-parenchyma-P). Visual and semiquantitative T&N PET/CT performances were analysed per patient and per region for both tracers, with surgical histopathology as standard-of-truth. Results: 63 FAPI scans were performed in 64 patients enrolled (26 May 2022–30 November 2023). A total of 50/63 patients underwent surgery and were included. Agreement (%) with histopathological-T&N-StagingAJCC8thEdition was slightly in favour of FAPI (T-66% vs. 58%, N-78% vs. 70%), increasing when T&N dichotomised (T-92% vs. 80%, N-78% vs. 72%). The performance of Visual-Criteria for T-per patient (n = 50) resulted higher FAPI than FDG. For N-per patient (n = 46), sensitivity and NPV were slightly lower with FAPI. Among 59 T-regions surgically examined, malignancy was excluded in 6/59 (10%). FAPI showed (vs. FDG): sensitivity 85% (vs. 72%), specificity 67% (vs. 50%), PPV 96% (vs. 93%), NPV 33% (vs. 17%), accuracy 83% (vs. 69%). Among 217 N-stations surgically assessed (overall 746 ln removed), only 15/217 (7%) resulted malignant; FAPI showed (vs. FDG): sensitivity 53% (vs. 60%), PPV 53% (vs. 26%), NPV 97% (vs. 97%), and significantly higher specificity (97% vs. 88%, p = 0.001) and accuracy (94% vs. 86%, p = 0.018). Semiquantitative-PET parameters performed similarly, better for N (p < 0.001) than for T, slightly in favour (although not significantly) of FAPI over FDG. Conclusions: In a suspected/confirmed lung cancer surgical cohort, PET/CT performances for preoperative T&Nstaging were slightly in favour of FAPI than FDG (except for suboptimal N-sensitivity), significantly better only for N (region-based) specificity and accuracy using visual assessment. The trial’s conventional follow-up is still ongoing; future analyses are pending, including non-surgical findings and theoretical impact on patient management.