ABSTRACT
Colorectal carcinoma (CRC) is a heterogeneous group of tumors with varying therapeutic response and prognosis, and evidence suggests the tumor immune microenvironment (TIME) plays a pivotal role. Using advanced molecular and spatial biology technologies, we aimed to evaluate the TIME in patients with CRC to determine whether specific alterations in the immune composition correlated with prognosis. We identified primary and metastatic tumor samples from 31 consented patients, which were profiled with whole-exome sequencing and bulk RNA-seq. Immune cell deconvolution followed by gene set enrichment analysis and unsupervised clustering was performed. A subset of tumors underwent in situ analysis of the TIME spatial composition at single-cell resolution through Imaging Mass Mass Cytometry. Gene set enrichment analysis revealed two distinct groups of advanced CRC, one with an immune activated phenotype and the other with a suppressed immune microenvironment. The activated TIME phenotype contained increased Th1 cells, activated dendritic cells, tertiary lymphoid structures, and higher counts of CD8+ T cells whereas the inactive or suppressed TIME contained increased macrophages and a higher M2/M1 ratio. Our findings were further supported by RNA-seq data analysis from the TCGA CRC database, in which unsupervised clustering also identified two separate groups. The immunosuppressed CRC TIME had a lower overall survival probability (HR 1.66, p=0.007). This study supports the pertinent role of the CRC immune microenvironment in tumor progression and patient prognosis. We characterized the immune cell composition to better understand the complexity and vital role that immune activity states of the TIME play in determining patient outcome.
ABSTRACT
Circulating tumor cells (CTCs) captured from the bloodstream of patients with solid tumors have the potential to accelerate precision oncology by providing insight into tumor biology, disease progression and response to treatment. However, their potential is hampered by the lack of standardized CTC enrichment platforms across tumor types. EpCAM-based CTC enrichment, the most commonly used platform, is limited by EpCAM downregulation during metastasis and the low EpCAM expression in certain tumor types, including the highly prevalent and lethal NSCLC. In this study we demonstrate that Transferrin Receptor (TfR) is a selective, efficient biomarker for CTC identification and capture in patients with prostate, pancreatic and NSCLC. TfR identifies significantly higher CTC counts than EpCAM, and TfR + -CTC enumeration correlates with disease progression in metastatic prostate and pancreatic cancers, and overall survival and osimetrinib-resistance in non-small cell lung cancer (NSCLC). Profiling of TfR + -CTCs provides a snapshot of the molecular landscape of each respective tumor type and identifies potential mechanisms underlying treatment response to EGFR TKi and immune checkpoint inhibitors in NSCLC. One sentence summary: Transferrin Receptor identifies circulating tumor cells in solid tumors.
ABSTRACT
OBJECTIVE: Approximately half of ischemic strokes (IS) in cancer patients are cryptogenic, with many presumed cardioembolic. We evaluated whether there were specific miRNA and mRNA transcriptome architectures in peripheral blood of IS patients with and without comorbid cancer, and between cardioembolic versus noncardioembolic IS etiologies in comorbid cancer. METHODS: We studied patients with cancer and IS (CS; n = 42), stroke only (SO; n = 41), and cancer only (n = 28), and vascular risk factor-matched controls (n = 30). mRNA-Seq and miRNA-Seq data, analyzed with linear regression models, identified differentially expressed genes in CS versus SO and in cardioembolic versus noncardioembolic CS, and miRNA-mRNA regulatory pairs. Network-level analyses identified stroke etiology-specific responses in CS. RESULTS: A total of 2,085 mRNAs and 31 miRNAs were differentially expressed between CS and SO. In CS, 122 and 35 miRNA-mRNA regulatory pairs, and 5 and 3 coexpressed gene modules, were associated with cardioembolic and noncardioembolic CS, respectively. Complement, growth factor, and immune/inflammatory pathways showed differences between IS etiologies in CS. A 15-gene biomarker panel assembled from a derivation cohort (n = 50) correctly classified 81% of CS and 71% of SO participants in a validation cohort (n = 33). Another 15-gene panel correctly identified etiologies for 13 of 13 CS-cardioembolic and 11 of 11 CS-noncardioembolic participants upon cross-validation; 11 of 16 CS-cryptogenic participants were predicted cardioembolic. INTERPRETATION: We discovered unique mRNA and miRNA transcriptome architecture in CS and SO, and in CS with different IS etiologies. Cardioembolic and noncardioembolic etiologies in CS showed unique coexpression networks and potential master regulators. These may help distinguish CS from SO and identify IS etiology in cryptogenic CS patients. ANN NEUROL 2024;96:565-581.
Subject(s)
Gene Regulatory Networks , Ischemic Stroke , MicroRNAs , Neoplasms , RNA, Messenger , Humans , Male , Female , RNA, Messenger/blood , MicroRNAs/blood , MicroRNAs/genetics , Middle Aged , Ischemic Stroke/genetics , Ischemic Stroke/blood , Ischemic Stroke/epidemiology , Aged , Neoplasms/genetics , Neoplasms/blood , Neoplasms/complications , Comorbidity , TranscriptomeABSTRACT
BACKGROUND & AIMS: Genetic testing uptake for cancer susceptibility in family members of patients with cancer is suboptimal. Among relatives of patients with pancreatic ductal adenocarcinoma (PDAC), The GENetic Education, Risk Assessment, and TEsting (GENERATE) study evaluated 2 online genetic education/testing delivery models and their impact on patient-reported psychological outcomes. METHODS: Eligible participants had ≥1 first-degree relative with PDAC, or ≥1 first-/second-degree relative with PDAC with a known pathogenic germline variant in 1 of 13 PDAC predisposition genes. Participants were randomized by family, between May 8, 2019, and June 1, 2021. Arm 1 participants underwent a remote interactive telemedicine session and online genetic education. Arm 2 participants were offered online genetic education only. All participants were offered germline testing. The primary outcome was genetic testing uptake, compared by permutation tests and mixed-effects logistic regression models. We hypothesized that Arm 1 participants would have a higher genetic testing uptake than Arm 2. Validated surveys were administered to assess patient-reported anxiety, depression, and cancer worry at baseline and 3 months postintervention. RESULTS: A total of 424 families were randomized, including 601 participants (n = 296 Arm 1; n = 305 Arm 2), 90% of whom completed genetic testing (Arm 1 [87%]; Arm 2 [93%], P = .014). Arm 1 participants were significantly less likely to complete genetic testing compared with Arm 2 participants (adjusted ratio [Arm1/Arm2] 0.90, 95% confidence interval 0.78-0.98). Among participants who completed patient-reported psychological outcomes questionnaires (Arm 1 [n = 194]; Arm 2 [n = 206]), the intervention did not affect mean anxiety, depression, or cancer worry scores. CONCLUSIONS: Remote genetic education and testing can be a successful and complementary option for delivering genetics care. (Clinicaltrials.gov, number NCT03762590).