Indiana University-Purdue University Indianapolis (IUPUI) / Triple-negative breast cancers (TNBCs) are pathologically defined by the absence of estrogen, progesterone, and HER2 receptors. Compared to other breast cancers, TNBC has a relatively high mortality. In addition, TNBC patients are more likely to relapse in the first few years after treatment, and experiencing a shorter median time from recurrence to death. Detecting the presence of tumor in patients who are technically “disease-free” after neoadjuvant chemotherapy and surgery as early as possible might be able to predict recurrence of patients, and then provide timely intervention for additional therapy. To this end, I applied the analysis of “liquid biopsies” for early detection of minimal residual disease (MRD) on early-stage TNBC patients using next-generation sequencing. For the first part of this study, I focused on detecting circulating tumor DNA (ctDNA) from TNBC patients after neoadjuvant chemotherapy and surgery. First, patient-specific somatic mutations were identified by sequencing primary tumors. From these data, 82% of the patients had at least one TP53 mutation, followed by 16% of the patients having at least one PIK3CA mutation. Next, I sequenced matched plasma samples collected after surgery to identify ctDNA with the same mutations. I observed that by detecting corresponding ctDNA I was able to predict rapid recurrence, but not distant recurrence. To increase the sensitivity of MRD detection, in the second part I developed a strategy to co-detect ctDNA along with circulating tumor RNA (ctRNA). An advantage of ctRNA is its active release into the circulation from living cancer cells. Preliminary data showed that more mutant molecules were identified after incorporating ctRNA with ctDNA detection in a metastatic breast cancer setting. A validation study in early-stage TNBC is in progress. In summary, my study suggests that co-detection of ctDNA and ctRNA could be a potential solution for the early detection of disease recurrence. / 2021-08-05

Glioblastomas are the most common malignant brain tumors in adults. Despite resection of the tumor, combined radio- and chemotherapy and new-targeted therapy approaches, the average life expectancy is only 15 months with a 3-year survival rate of less than 5%. Invasive growth of tumor cells into the surrounding brain tissue complicates treatment and causes recurrence. Imaging techniques such as magnetic resonance imaging (MRI) are conventionally used for diagnosis and for monitoring after surgery in order to detect tumor lesions. However, next to tumor progression, contrast enhancement could also result from pseudoprogression or radiation necrosis. Similarly, next to therapy response, a reduction of contrast enhancement could also mimic a success in therapy (pseudoresponse). Furthermore, the use of targeted therapies requires the molecular detection of biomarkers, which is often performed on tissue biopsies. During therapy, resistance mechanisms to therapy may develop, which have a significant impact on the success of the therapy. Repeated biopsies are needed to distinguish tumor progress from therapy-associated tissue changes and simultaneously detect therapy resistance. Often they are not feasible due to the high risk for the patient. Currently, there are methods to improve monitoring in glioblastoma patients. Besides improved imaging techniques, liquid biopsies are a promising method to detect tumor progression. At the same time, liquid biopsies also allow molecular characterization of the lesions. A key advantage over tissue biopsies is that they are minimally invasive, making them well suited for longitudinal tumor monitoring. For clinical application of liquid biopsies in the form of blood and CSF, components such as circulating tumor cells, circulating tumor DNA, extracellular vesicles, or tumor-educated platelets are used, with the former two probably being the most common tumor markers at present. In this study, a highly sensitive next generation sequencing-based method was established to detect tumor mutations in plasma and CSF of glioblastoma patients. To this end, crucial sample preparation steps were initially optimized. Therefore, four isolation kits extracting cell-free DNA from plasma were compared and the best-performing kit was chosen for the analysis of patient material. Furthermore, a multi-gene next generation sequencing panel (cfDNA-GBM panel) was designed specifically for use in liquid biopsies from glioblastoma patients and tested on a reference standard for cell-free DNA with mutations of different allele frequencies. The newly designed cfDNA-GBM panel, which uses hybrid-capture based enrichment, was compared to a primer-extension based panel. Due to the more consistent coverage of target regions, superior sensitivity, and better clinical applicability due to its smaller size, the cfDNA-GBM panel was used for subsequent clinical investigations. In both enrichments, unique molecular barcodes were used to label all original fragments and thus identify and eliminate errors in subsequent data analysis that occur during amplification in library preparation This increases the sensitivity of the method. During the establishment of the methodology, the use of molecular barcodes clarified the limitation of sequence information due to the small input amount of cell-free DNA. For tumor tissue analysis, an already established larger panel was adapted and extended based on the results of a proof of concept study. After the establishment of the sensitive method, clinical samples of glioblastoma patients were analyzed. Isolation and analysis of cell-free DNA from plasma and CSF resulted in highly variable amounts of cell-free DNA with characteristic oligonucleosomal fragment lengths. Furthermore, no difference in the amount or fragmentation of cell-free DNA from pre- or post-surgery plasma was determined. To increase the amount of cell-free DNA for sequencing, the amount of blood samples was increased from 10 ml (5 patients) to 50 ml (9 patients). Detection of circulating tumor DNA in plasma and CSF from glioblastoma patients was performed using two strategies. In a first approach, somatic tumor mutations were detected in genomic DNA from tumor tissue, which were searched for in the sequence data of liquid biopsies in a second step. In this approach, an increased detection rate (in 56% of patients compared to 25% in 10 ml blood samples) of circulating tumor DNA was detected in blood samples with a volume of 50 ml. Almost all tumor mutations lying in the target region of the smaller cfDNA-GBM panel could be detected in CSF (92%) with similar allele frequencies to those detected in the tumor. In a second strategy, somatic variants were detected in plasma and CSF without prior knowledge of variants in tumor tissue. Interestingly, three variants were detected in the CSF of one patient, which occurred with an allele frequency over 5% in the CSF, while they were not detectable in the analyzed tumor tissue. These variants may be indicators for tumor heterogeneity that was not accessible by analyzing sections of tumor tissue. Finally, mutation-specific probes were designed for three tumor-somatic variants detected in the patients' plasma to validate them by digital PCR. A pair of probes for a mutation in PTEN was successfully established to robustly detect minimal allele frequencies down to 0.1% but could not validate the mutation in cell-free DNA from plasma of the respective patient. Overall, it was shown that analysis of tumor somatic mutations using the cfDNA-GBM sequencing panel designed and established in this study is limited in the analysis of cell-free DNA from plasma of patients with glioblastomas, whereas the detection of circulating tumor DNA from CSF is promising.

MicroRNAs are a class of small non-coding RNAs involved in post-transcriptional regulation. Their role in almost all processes of the cell, make microRNAs ubiquitary players of cell development, growth, differentiation, cell to cell communication and cell death. Thus, cells’ physiological or pathological conditions are reflected by variations in the levels of expression of microRNAs, enabling them to be used as biomarkers of such states. In the past decade, there has been an exponential increase of studies using microRNAs as potential biomarkers for cancer, neurodegenerative diseases, inflammation and cardiac diseases, from tissues and liquid biopsies. However, none of them has reached the clinics yet, due to inconsistency of results through the literature and lack of assay standardization and reproducibility. Technological limitations of microRNAs detection have been, to date, the biggest challenge for using these molecules in clinical settings. In fact, although microarrays, RT-qPCR and RNA-seq are well-established technologies, they all require complex procedures and trained personnel, for performing RNA extraction, labelling of the target and PCR amplification. All these steps introduce variability and, in addition, since no universally standardized protocol – from sample extraction to analyte detection - has been produced yet, methodological procedures are difficult to reproduce. For this reason, we developed a new platform for the rapid detection of microRNAs in biofluids composed of an innovative silicon-photomultiplier (SiPM) based detector and a new chemistry for nucleic acid testing (Chem-NAT). Chem-NAT exploits a dynamic labelling chemistry which allows the sensitive detection of nucleic acids till single base level. On the other hand, SiPM-based device, compared to normal vacuum photomultipliers, grants miniaturization and higher capacity of fitting in a bench-top solution for clinical settings, among other advantages. The new platform – ODG – has been validated for the direct detection – neither RNA extraction nor PCR amplification needed - of microRNA-21 in plasma of lung cancer patients. In this work, we also explored the use of microRNAs as biomarkers in metastatic castration resistant prostate cancer (mCRPC). We collected plasma samples from mCRPC patients before and after abiraterone acetate treatment – androgen deprivation type of drug – and performed a miRnome analysis for discovering microRNAs predicting the efficacy of the drug. We chose miR-103a-3p and miR-378a-5p and we validated them via TaqMan RT-qPCR. We discovered that the ratio between the two microRNAs is able to predict the efficacy of abiraterone acetate and follow the responsiveness in time. In liquid biopsies, extracellular vesicles are getting increasing importance for diagnostic and prognostic purposes. Therefore, in this work we also explored the expression of some microRNAs in extracellular vesicles from plasma, isolated via nickel-based method. We discovered that microRNA-21 and microRNA-223 are not enriched in vesicles from healthy individuals.