Frequent post-operative monitoring of colorectal cancer using individualised ctDNA validated by multiregional molecular profiling

The Clinical Validity of Urinary Pellet DNA Monitoring for the Diagnosis of Recurrent Bladder Cancer

The aim of this study was to evaluate the clinical validity of monitoring urine pellet DNA (upDNA) of bladder cancer (BC) by digital PCR (dPCR) as a biomarker for early recurrence prediction, treatment efficacy evaluation, and no-recurrence corroboration. Tumor panel sequencing was first performed to select patient-unique somatic mutations to monitor both upDNA and circulating tumor DNA (ctDNA) by dPCR. For longitudinal monitoring using upDNA as well as plasma ctDNA, an average of 7.2 (range, 2 to 12) time points per case were performed with the dPCR assay for 32 previously treated and untreated patients with BC. Clinical recurrence based on imaging and urine cytology was compared using upDNA variant allele frequency (VAF) dynamics. A continuous increasing trend of upDNA VAF ≥1% was considered to indicate molecular recurrence. Most (30/32; 93.8%) cases showed at least one traceable somatic mutation. In 5 of 7 cases (71.4%) with clinical recurrence, upDNA VAF >1% was detected 7 to 15 months earlier than the imaging diagnosis. The upDNA VAF remained high after initial treatment for locally recurrent cases. The clinical validity of upDNA monitoring was confirmed with the observation that 26 of 30 cases (86.7%) were traceable. Local recurrences were not indicated by ctDNA alone. The results support the clinical validity of upDNA monitoring in the management of recurrent BC.

Postoperative recurrence detection using individualized circulating tumor DNA in upper tract urothelial carcinoma

Biomarkers that could detect the postoperative recurrence of upper tract urothelial carcinoma (UTUC) have not been established. In this prospective study, we aim to evaluate the utility of individualized circulating tumor DNA (ctDNA) monitoring using digital PCR (dPCR) as a tumor recurrence biomarker for UTUC in the perioperative period. Twenty-three patients who underwent radical nephroureterectomy (RNU) were included. In each patient, whole exome sequencing by next-generation sequencing and TERT promoter sequencing of tumor DNA were carried out. Case-specific gene mutations were selected from sequencing analysis to examine ctDNA by dPCR analysis. We also prospectively collected plasma and urine ctDNA from each patient. The longitudinal variant allele frequencies of ctDNA during the perioperative period were plotted. Case-specific gene mutations were detected in 22 cases (96%) from ctDNA in the preoperative samples. Frequently detected genes were TERT (39%), FGFR3 (26%), TP53 (22%), and HRAS (13%). In all cases, we obtained plasma and urine samples for 241 time points and undertook individualized ctDNA monitoring for 2 years after RNU. Ten patients with intravesical recurrence had case-specific ctDNA detected in urine at the time of recurrence. The mean lead time of urinary ctDNA in intravesical recurrence was 60 days (range, 0-202 days). Two patients with distal metastasis had case-specific ctDNA in plasma at the time of metastasis. In UTUC, tumor-specific gene mutations can be monitored postoperatively as ctDNA in plasma and urine. Individualized ctDNA might be a minimally invasive biomarker for the early detection of postoperative recurrence.

Optimizing the number of variants tracked to follow disease burden with circulating tumor DNA assays in metastatic colorectal cancer

Background

The number of somatic mutations detectable in circulating tumor DNA (ctDNA) is highly heterogeneous in metastatic colorectal cancer (mCRC). The optimal number of mutations required to assess disease kinetics is relevant and remains poorly understood.

Objectives

To determine whether increasing panel breadth (the number of tracked variants in a ctDNA assay) would alter the sensitivity in detecting ctDNA in patients with mCRC.

Design

We used archival tissue sequencing to perform an in silico assessment of the optimal number of tracked mutations to detect and monitor disease kinetics in mCRC using sequencing data from the Canadian Cancer Trials Group CO.26 trial.

Methods

For each patient, 1, 2, 4, 8, 12, or 16 of the most clonal (highest variant allele frequency) somatic variants were selected from archival tissue-based whole-exome sequencing and assessed for the proportion of variants detected in matched ctDNA at baseline, week 8, and progression timepoints.

Results

Data from 110 patients were analyzed. Genes most frequently encountered among the top four highest VAF variants in archival tissue were TP53 (51.9% of patients), APC (43.3%), KRAS (42.3%), and SMAD4 (9.6%). While the frequency of detecting at least one tracked variant increased when expanding beyond variant pool sizes of 1 and 2 in baseline (p = 0.0030) and progression (p = 0.0030) ctDNA samples, we observed no significant benefit to increases in variant pool size past four variants in any of the ctDNA timepoints (p < 0.05).

Conclusion

While increasing panel breadth beyond two tracked variants improved variant re-detection in ctDNA samples from patients with treatment refractory mCRC, increases beyond four tracked variants yielded no significant improvement in variant re-detection.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Diagnostic and prognostic biomarkers in colorectal cancer and the potential role of exosomes in drug delivery

Colorectal cancer (CRC) is third most common cancer with second most common cause of death worldwide. One fourth to one fifth of the CRC cases are detected at advance stage. Early detection of colorectal cancer might help in decreasing mortality and morbidity worldwide. CRC being a heterogeneous disease, new non-invasive approaches are needed to complement and improve the screening and management of CRC. Reliable and early detectable biomarkers would improve diagnosis, prognosis, therapeutic responses, and will enable the prediction of drug response and recurrence risk. Over the past decades molecular research has demonstrated the potentials of CTCs, ctDNAs, circulating mRNA, ncRNAs, and exosomes as tumor biomarkers. Non-invasive screening approaches using fecal samples for identification of altered gut microbes in CRC is also gaining attention. Exosomes can be potential candidates that can be employed in the drug delivery system. Further, the integration of in vitro, in vivo and in silico models that involve CRC biomarkers will help to understand the interactions occurring at the cellular level. This review summarizes recent update on CRC biomarkers and their application along with the nanoparticles followed by the application of organoid culture in CRC.