MicroRNA Expression Characterizes Oligometastasis(es)

Cancer staging and treatment presumes a division into localized or metastatic disease. We proposed an intermediate state defined by ≤ 5 cumulative metastasis(es), termed oligometastases. In contrast to widespread polymetastases, oligometastatic patients may benefit from metastasis-directed local treatments. However, many patients who initially present with oligometastases progress to polymetastases. Predictors of progression could improve patient selection for metastasis-directed therapy.Here, we identified patterns of microRNA expression of tumor samples from oligometastatic patients treated with high-dose radiotherapy.Patients who failed to develop polymetastases are characterized by unique prioritized features of a microRNA classifier that includes the microRNA-200 family. We created an oligometastatic-polymetastatic xenograft model in which the patient-derived microRNAs discriminated between the two metastatic outcomes. MicroRNA-200c enhancement in an oligometastatic cell line resulted in polymetastatic progression.These results demonstrate a biological basis for oligometastases and a potential for using microRNA expression to identify patients most likely to remain oligometastatic after metastasis-directed treatment

role of next generation sequencing technologies in personalized medicine

Following the completion of the Human Genome Project in 2003, research in oncology has progressively focused on the sequencing of cancer genomes, with the aim of better understanding the genetic basis of oncogenesis and identifying actionable alterations. The development of next-generation-sequencing (NGS) techniques, commercially available since 2006, allowed for a cost- and time-effective sequencing of tumor DNA, leading to a "genomic era" of cancer research and treatment. NGS provided a significant step forward in Personalized Medicine (PM) by enabling the detection of somatic driver mutations, resistance mechanisms, quantification of mutational burden, germline mutations, which settled the foundation of a new approach in cancer care. In this chapter, we discuss the history, available techniques, and applications of NGS in oncology, with a particular referral to the PM approach and the emerging role of the research field of pharmacogenomics

Assessing Nitrogen-Saturation in a Seasonally Dry Chaparral Watershed: Limitations of Traditional Indicators of N-Saturation

To evaluate nitrogen (N) saturation in xeric environments, we measured hydrologic N losses, soil N pools, and microbial processes, and developed an N-budget for a chaparral catchment (Sierra Nevada, California) exposed to atmospheric N inputs of approximately 8.5 kg N ha⁻¹ y⁻¹. Dual-isotopic techniques were used to trace the sources and processes controlling nitrate (NO₃ ⁻) losses. The majority of N inputs occurred as ammonium. At the onset of the wet season (November to April), we observed elevated streamwater NO₃ ⁻ concentrations (up to 520 µmol l⁻¹), concomitant with the period of highest gaseous N-loss (up to 500 ng N m⁻² s⁻¹) and suggesting N-saturation. Stream NO₃ ⁻ δ¹⁵N and δ¹⁸O and soil N measurements indicate that nitrification controlled NO₃ ⁻ losses and that less than 1% of the loss was of atmospheric origin. During the late wet season, stream NO₃ ⁻ concentrations decreased (to <2 µmol l⁻¹) as did gaseous N emissions, together suggesting conditions no longer indicative of N-saturation. We propose that chaparral catchments are temporarily N-saturated at ≤8.5 kg N ha⁻¹ y⁻¹, but that N-saturation may be difficult to reach in ecosystems that inherently leak N, thereby confounding the application of N-saturation indicators and annual N-budgets. We propose that activation of N sinks during the typically rainy winter growing season should be incorporated into the assessment of ecosystem response to N deposition. Specifically, the N-saturation status of chaparral may be better assessed by how rapidly catchments transition from N-loss to N-retention