Resistance to immune checkpoint inhibitors in advanced lung cancer: Clinical characteristics, potential prognostic factors and next strategy

BackgroundImmune checkpoint inhibitors (ICIs) have shown unprecedented clinical benefit in cancer immunotherapy and are rapidly transforming the practice of advanced lung cancer. However, resistance routinely develops in patients treated with ICIs. We conducted this retrospective study to provide an overview on clinical characteristics of ICI resistance, optimal treatment beyond disease progression after prior exposure to immunotherapy, as well as potential prognostic factors of such resistance.Methods190 patients diagnosed with unresectable lung cancer who received at least one administration of an anti-programmed cell death 1 (PD-1)/anti-programmed cell death-ligand 1(PD-L1) at any treatment line at Zhongshan Hospital Fudan University between Sep 2017 and December 2019 were enrolled in our study. Overall survival (OS) and progression-free survival (PFS) were analyzed. Levels of plasma cytokines were evaluated for the prognostic value of ICI resistance.ResultsWe found that EGFR/ALK/ROS1 mutation and receiving ICI treatment as second-line therapy were risk factors associated with ICI resistance. Patients with bone metastasis at baseline had a significantly shorter PFS1 time when receiving initial ICI treatment. Whether or not patients with oligo-progression received local treatment seemed to have no significant effect on PFS2 time. Systemic therapies including chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. TNF, IL-6 and IL-8 were significantly elevated when ICI resistance. Lower plasma TNF level and higher plasma IL-8 level seemed to be significantly associated with ICI resistance. A nomogram was established to prognosis the clinical outcome of patients treated with ICIs.ConclusionPatients with EGFR/ALK/ROS1 mutation, or those receiving ICI treatment as second-line therapy had higher risk of ICI resistance. Patients with bone metastasis had poor prognosis during immunotherapy. For those patients with oligo-progression after ICI resistance, combination with local treatment did not lead to a significantly longer PFS2 time. Chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. Levels of plasma cytokines including TNF, IL-6 and IL-8 were associated with ICI resistance

Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features

IntroductionThe distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.MethodsBetween 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.ResultsSeven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2’s synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.ConclusionThis novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis

Expression of PD-L1 through evolution phase from pre-invasive to invasive lung adenocarcinoma

Abstract Background This study evaluated programmed cell death-ligand 1 (PD-L1) expression from pre-invasive adenocarcinoma to invasive lung adenocarcinoma, aimed to investigate the potential association of PD-L1 pathway with lung adenocarcinoma early evolution. Methods We evaluated PD-L1 expression in 1123 resected lung specimens of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) of stage IA1–IA3. PD-L1 expression was defined based on the proportion of stained tumor cells using the tumor proportion score:  < 1% (negative),  ≥ 1% (positive) and  ≥ 50% (strongly positive). Correlations between PD-L1 expression and T stage, pathological subtype, adenocarcinoma grade, spread through air space (STAS), vascular invasion, lymphatic invasion and driven genes were analyzed. Results There was almost no PD-L1 expression in AIS or MIA. However, PD-L1 expression was correlated with invasiveness of lung adenocarcinoma. The percentages of PD-L1 positive in IA1–IA3 were 7.22%, 11.29%, and 14.20%, respectively. The strongly positive rates of PD-L1 were 0.38%, 1.64%, and 3.70% in IA1–IA3, respectively. PD-L1 expression and positive rate were also associated with poor pathological subtype and poor biological behavior, such as adenocarcinoma Grade 3, micropapillary or solid dominant subtype, STAS and vascular invasion. Finally, PD-L1 positive rate seems also corrected with driven gene ALK, ROS-1 and KRAS. Conclusions PD-L1 expression was positively correlated with the emergence of invasiveness and poor pathological subtype or biological behavior of early-stage lung adenocarcinoma. PD-L1 pathway may be involved in the early evolution of lung adenocarcinoma from AIS to IAC

Image_1_Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features.tif

IntroductionThe distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.MethodsBetween 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.ResultsSeven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2’s synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.ConclusionThis novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis.</p

Image_2_Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features.tif

IntroductionThe distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.MethodsBetween 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.ResultsSeven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2’s synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.ConclusionThis novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis.</p