Exploring the biology of ctDNA release in colorectal cancer.

BACKGROUND: Circulating tumor DNA (ctDNA) has emerged as a promising tool for early cancer detection and minimal residual disease monitoring. However, the biology underlying ctDNA release and its variation across cancer types and histologies remains poorly understood. This study investigated the biology behind ctDNA shedding in colorectal cancer. METHODS: The study included a local cohort of 747 stage I-III colorectal cancer patients. All patients had ctDNA measurement prior to treatment and extensive clinical data. Primary tumor RNA sequencing and whole exome sequencing was performed in 95 and 652 patients respectively. Additionally, the study evaluated 89 non-small cell lung cancer patients from the TRACERx cohort, comprising primary tumor RNA sequencing and ctDNA measurement. RESULTS: We found tumor size and proliferative capacity to be key factors associated with ctDNA shedding in colorectal cancer. Furthermore, we found that the secretory and CMS3 colorectal cancer subtypes exhibited lower ctDNA shedding, while microsatellite instability (MSI) tumors had higher levels of ctDNA. Mutational analysis did not reveal any genes or pathways associated with ctDNA shedding in colorectal cancer. A comparison of transcriptomic profiles across multiple cancer types demonstrated that colorectal cancer and lung squamous cell carcinoma tumors shared a high-proliferative ctDNA shedding phenotype, while lung adenocarcinoma tumors displayed a distinct low-proliferative subgroup. Additionally, proliferation levels correlated with ctDNA detection sensitivity across multiple cancer types. CONCLUSION: These findings suggest that tumor size and proliferative capacity are drivers of ctDNA release in colorectal cancer and provide insights into the biology of ctDNA shedding on a pan-cancer level.

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating T cells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Catatonia in Patients with Autism Spectrum Disorder.

Catatonia was first described by Karl Ludwig Kahlbaum in 1874, occurring in association with other psychiatric and medical disorders. However, in the nineteenth century the disorder was incorrectly classified as a subtype of schizophrenia. This misclassification persisted until the publication of DSM-5 in 2013 when important changes were incorporated. Although the etiology is unknown, disrupted gamma-aminobutyric acid has been proposed as the underlying pathophysiological mechanism. Key symptoms can be identified under 3 clinical domains: motor, speech, and behavioral. Benzodiazepines and electroconvulsive therapy are the only known effective treatments. Timely recognition and treatment have important outcome, and sometimes lifesaving, implications.

Catatonia in Patients with Autism Spectrum Disorder.

Catatonia was first described by Karl Ludwig Kahlbaum in 1874, occurring in association with other psychiatric and medical disorders. However, in the nineteenth century the disorder was incorrectly classified as a subtype of schizophrenia. This misclassification persisted until the publication of DSM-5 in 2013 when important changes were incorporated. Although the etiology is unknown, disrupted gamma-aminobutyric acid has been proposed as the underlying pathophysiological mechanism. Key symptoms can be identified under 3 clinical domains: motor, speech, and behavioral. Benzodiazepines and electroconvulsive therapy are the only known effective treatments. Timely recognition and treatment have important outcome, and sometimes lifesaving, implications.