Efficacy end points were not significantly different between experimental and reference arms, although toxicities showed differences. These findings suggest that chemotherapy in NSCLC has reached a therapeutic plateau.

Biological changes that occur during metastatic progression of breast cancer are still incompletely characterized. In this study, we compared intrinsic molecular subtypes and gene expression in 123 paired primary and metastatic tissues from breast cancer patients. Intrinsic subtype was identified using a PAM50 classifier and χ2 tests determined the differences in variable distribution. The rate of subtype conversion was 0% in basal-like tumors, 23.1% in HER2-enriched (HER2-E) tumors, 30.0% in luminal B tumors, and 55.3% in luminal A tumors. In 40.2% of cases, luminal A tumors converted to luminal B tumors, whereas in 14.3% of cases luminal A and B tumors converted to HER2-E tumors. We identified 47 genes that were expressed differentially in metastatic versus primary disease. Metastatic tumors were enriched for proliferation-related and migration-related genes and diminished for luminal-related genes. Expression of proliferation-related genes were better at predicting overall survival in metastatic disease (OSmet) when analyzed in metastatic tissue rather than primary tissue. In contrast, a basal-like gene expression signature was better at predicting OSmet in primary disease compared with metastatic tissue. We observed correlations between time to tumor relapse and the magnitude of changes of proliferation, luminal B, or HER2-E signatures in metastatic versus primary disease. Although the intrinsic subtype was largely maintained during metastatic progression, luminal/HER2-negative tumors acquired a luminal B or HER2-E profile during metastatic progression, likely reflecting tumor evolution or acquisition of estrogen independence. Overall, our analysis revealed the value of stratifying gene expression by both cancer subtype and tissue type, providing clinicians more refined tools to evaluate prognosis and treatment.

BackgroundConcomitant medications, such as steroids, proton pump inhibitors (PPI) and antibiotics, might affect clinical outcomes with immune checkpoint inhibitors.MethodsWe conducted a multicenter observational retrospective study aimed at evaluating the impact of concomitant medications on clinical outcomes, by weighing their associations with baseline clinical characteristics (including performance status, burden of disease and body mass index) and the underlying causes for their prescription. This analysis included consecutive stage IV patients with cancer, who underwent treatment with single agent antiprogrammed death-1/programmed death ligand-1 (PD-1/PD-L1) with standard doses and schedules at the medical oncology departments of 20 Italian institutions. Each medication taken at the immunotherapy initiation was screened and collected into key categories as follows: corticosteroids, antibiotics, gastric acid suppressants (including proton pump inhibitors - PPIs), statins and other lipid-lowering agents, aspirin, anticoagulants, non-steroidal anti-inflammatory drugs (NSAIDs), ACE inhibitors/Angiotensin II receptor blockers, calcium antagonists, β-blockers, metformin and other oral antidiabetics, opioids.ResultsFrom June 2014 to March 2020, 1012 patients were included in the analysis. Primary tumors were: non-small cell lung cancer (52.2%), melanoma (26%), renal cell carcinoma (18.3%) and others (3.6%). Baseline statins (HR 1.60 (95% CI 1.14 to 2.25), p=0.0064), aspirin (HR 1.47 (95% CI 1.04 to 2.08, p=0.0267) and β-blockers (HR 1.76 (95% CI 1.16 to 2.69), p=0.0080) were confirmed to be independently related to an increased objective response rate. Patients receiving cancer-related steroids (HR 1.72 (95% CI 1.43 to 2.07), p<0.0001), prophylactic systemic antibiotics (HR 1.85 (95% CI 1.23 to 2.78), p=0.0030), prophylactic gastric acid suppressants (HR 1.29 (95% CI 1.09 to 1.53), p=0.0021), PPIs (HR 1.26 (95% CI 1.07 to 1.48), p=0.0050), anticoagulants (HR 1.43 (95% CI: 1.16 to 1.77), p=0.0007) and opioids (HR 1.71 (95% CI 1.28 to 2.28), p=0.0002) were confirmed to have a significantly higher risk of disease progression. Patients receiving cancer-related steroids (HR 2.16 (95% CI 1.76 to 2.65), p<0.0001), prophylactic systemic antibiotics (HR 1.93 (95% CI 1.25 to 2.98), p=0.0030), prophylactic gastric acid suppressants (HR 1.29 (95% CI 1.06 to 1.57), p=0.0091), PPI (HR 1.26 (95% CI 1.04 to 1.52), p=0.0172), anticoagulants (HR 1.45 (95% CI 1.14 to 1.84), p=0.0024) and opioids (HR 1.53 (95% CI 1.11 to 2.11), p=0.0098) were confirmed to have a significantly higher risk of death.ConclusionWe confirmed the association between baseline steroids administered for cancer-related indication, systemic antibiotics, PPIs and worse clinical outcomes with PD-1/PD-L1 checkpoint inhibitors, which can be assumed to have immune-modulating detrimental effects.

Background Immune-inflammatory biomarkers (IIBs) showed a prognostic relevance in patients with metastatic CRC (mCRC). We aimed at evaluating the prognostic power of a new comprehensive biomarker, the Pan-Immune-Inflammation Value (PIV), in patients with mCRC receiving first-line therapy. Methods In the present pooled-analysis, we included patients enrolled in the Valentino and TRIBE trials. PIV was calculated as: (neutrophil count × platelet count × monocyte count)/lymphocyte count. A cut-off was determined using the maximally selected rank statistics method. Generalised boosted regression (GBR), the Kaplan–Meier method and Cox hazards regression models were used for survival analyses. Results A total of 438 patients were included. Overall, 208 patients (47%) had a low-baseline PIV and 230 (53%) had a high-baseline PIV. Patients with high PIV experienced a worse PFS (HR, 1.66; 95% CI, 1.36–2.03, P < 0.001) and worse OS (HR, 2.01; 95% CI, 1.57–2.57; P < 0.001) compared to patients with low PIV. PIV outperformed the other IIBs in the GBR model and in the multivariable models. Conclusion PIV is a strong predictor of survival outcomes with better performance than other well-known IIBs in patients with mCRC treated with first-line therapy. PIV should be prospectively validated to better stratify mCRC patients undergoing first-line therapy.