Giacomo Massa

Background: Early integration of specialist palliative care (PC) improves outcomes for patients with advanced cancer, yet implementation of needs-based referral remains inconsistent in routine oncology practice. The Palliative Care Referral System (PCRS) is a digital clinical decision-support tool designed to support referral using patient-reported symptoms, prognostic indicators, and clinical complexity. We evaluated the impact of PCRS implementation on referral patterns, patient-centered outcomes, healthcare utilization, and end-of-life (EoL) care. Patients and Methods: In this single-center prospective quasi-experimental pre–post implementation study, 380 patients with advanced solid tumors were enrolled before (pre-PCRS, n = 190) and after (post-PCRS, n = 190) PCRS implementation. During the post-PCRS phase, oncologists completed the PCRS at each outpatient visit. Patient and caregiver satisfaction, quality of life, healthcare utilization, and EoL indicators were assessed monthly for 6 months. Time to first PC referral was analyzed using competing-risk methods. Results: Overall 24-week referral rates were similar in the post-PCRS and pre-PCRS cohorts (21% vs 20%). However, referral timing and patterns changed across tumor types, with earlier referrals in breast and prostate cancer after PCRS implementation. The proportion of patients with at least 1 hospitalization was numerically lower after implementation (16% vs 27%; odds ratio, 0.53; 95% CI, 0.32-0.88; P = .017), but the difference was not significant after correction for multiple comparisons (P = .085). No significant differences were observed in patient or caregiver satisfaction, quality of life, emergency department visits, oncology visits, or PC visits. Indicators of aggressive EoL care were not significantly different between cohorts. Conclusions: PCRS implementation did not increase the overall referral rate but was associated with changes in referral timing and patterns across tumor types. Whether these changes reflect more appropriate, needs-based care and translate into lower healthcare utilization requires confirmation in larger studies.

Background Relapsed/metastatic anaplastic thyroid carcinoma (R/M ATC) is associated with poor prognosis. Approximately 40% of ATCs harbor the BRAFV600E mutation, for which targeted therapy is available. However, the clinical characteristics of BRAFV600E-mutant versus wild-type ATC and the role of liquid biopsy (LB) for molecular assessment and monitoring remain incompletely defined. Methods Consecutive patients with R/M ATCs treated at a tertiary referral center were stratified as BRAF wild-type (Cohort A) or BRAFV600E-mutant (Cohort B). Patients received chemotherapy, dabrafenib and trametinib (BRAF/MEKi), immune checkpoint inhibitors (ICI) or best supportive care. Tumor tissue (TB) was analyzed by RT-PCR and/or next-generation sequencing (NGS). Circulating cell-free DNA was assessed by droplet digital PCR (ddPCR) and/or a 52-gene NGS panel on LB at baseline in both cohorts and longitudinally in Cohort B. Clinical characteristics, treatment outcomes, and concordance between TB and LB were evaluated. Results Between 2018 and 2021, 24 patients were included (13 Cohort A, 11 Cohort B). BRAFV600E tumors more frequently presented with metastatic disease at diagnosis (81.8% vs. 15.4%, p = 0.003). Overall, 79% received first-line therapy and 41.6% second-line treatment. All patients in Cohort B received BRAF/MEK inhibitors, achieving a median progression-free survival of 3.2 months (95% CI 1.2–17.5). Concordance for BRAFV600E status between TB and LB assessed by ddPCR was 93.7% (specificity 100%, sensitivity 85.7%). Among 12 paired TB/LB NGS analyses, overall mutation concordance was 30%. In longitudinal assessment, LB anticipated disease progression in 3 of 6 cases and, in one case, identified emerging secondary alterations during BRAF/MEK inhibitor therapy. Conclusions BRAFV600E-mutant ATC displays distinct clinical features and improved survival when treated with targeted therapy; ddPCR-based liquid biopsy provides a rapid and sensitive method for BRAFV600E detection and may support timely therapeutic decision-making. Serial LB analysis may contribute to disease monitoring and detection of resistance mechanisms in selected patients.

Background uterine tumor resembling sex-cord tumors of the ovary (UTROSCT) is a rare form of mesenchymal uterine tumor of uncertain malignancy. Sporadic cases of advanced disease are reported, but little is known about its management in the metastatic setting. Pazopanib is a multi-tyrosine kinase inhibitor (TKI), approved for the treatment of advanced renal cell carcinoma and advanced soft tissue sarcomas, which affects several targets, including VEGFR1-2-3, PDGFRα-β and FGFR1-3. Case Presentation here we report a case of metastatic UTROSCT with strong and prolonged clinical response to pazopanib. Conclusions these findings suggest that this anti-angiogenic approach should be considered in planning future therapeutic strategies for this rare disease. Furthermore, we noted the importance of including patients with rare diseases in clinical trials and investigating molecular pathogenesis.

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

First-line immune-checkpoint inhibitor (ICI)-based therapy has deeply changed the treatment landscape and prognosis in advanced non-small cell lung cancer (aNSCLC) patients with no targetable alterations. Nonetheless, a percentage of patients progressed on ICI as monotherapy or combinations. Open questions remain on patients’ selection, the identification of biomarkers of primary resistance to immunotherapy and the treatment strategies to overcome secondary resistance to first-line immunotherapy. Local ablative approaches are the main therapeutic strategies in oligoprogressive disease, and their role is emerging in patients treated with immunotherapy. Many therapeutic strategies can be adapted in aNSCLC patients with systemic progression to personalize the treatment approach according to re-characterization of the tumors, previous ICI response, and type of progression. This review’s aim is to highlight and discuss the current and potential therapeutic approaches beyond first-line ICI-based therapy in aNSCLC patients based on the pattern of disease progression (oligoprogression versus systemic progression).