A novel in-house deep sequencing method for non-invasive disease monitoring in multiple myeloma patients

Background: Novel and more effective treatment strategies have sig- nificantly prolonged multiple myeloma (MM) survival and raised inter- est in the depth of response. This implies the need of highly sensitive assays such as the determination of minimal residual disease (MRD) by multiparametric flow cytometry (MFC) and next generation sequencing (NGS) of immunoglobulin (IGH) gene rearrangements. Ongoing studies are examining circulating cell-free tumor DNA (cfDNA) as a sensitive measure of small amounts of residual cells. In the present study, we de- scribe and analytically validate a simplified in-house deep-sequencing method to identify and quantify residual tumor burden in MM patients from plasma samples. Methods: We retrospectively analyzed 25 MM paired tumor (n=25) and plasma samples (n=48) obtained at diagnosis and at specified time points during treatment. Genomic DNA (gDNA) and cfDNA were extracted from selected CD138+ plasma cells (PC) and from plasma (Qiagen). IGH gene rearrangements were amplified, qual- ity assessed (Agilent hsDNA kit) and sequenced on Ion Torrent PGM. Raw reads were filtered and aligned using IMGT germline database andaggregated into clonotypes. Post-processing analyses were performed using VDJtools and customized R scripts. Results: Our sequencing method successfully identified a IGH MM clonotype in 88% of tumor samples (22/25), subsequently detected in plasma of all 22 cases (me- dian 4.7% of total filtered reads). Levels of the IGH clonotype in cfDNA distinguished between groups of patients with different prognosis: pa- tients with levels >4.7% prior to therapy, had significantly shorter PFS than patients with levels10-5 vs 15\ub15 months for frequencies=10-5 vs 37\ub14 months for frequencies<10- 5). Those patients are in CR and characterized by PC frequencies <10- 5 by MFC, and are therefore defined as MRD-negative. Conclusions: Results of this study support the clinical applicability of quantifying tumor levels by our in-house deep-sequencing of IGH gene rearrange- ments in plasma of MM patients

Monitoring the Fate of Orally Administered PLGA Nanoformulation for Local Delivery of Therapeutic Drugs

One of the goals of the pharmaceutical sciences is the amelioration of targeted drug delivery. In this context, nanocarrier-dependent transportation represents an ideal method for confronting a broad range of human disorders. In this study, we investigated the possibility of improving the selective release of the anti-cancer drug paclitaxel (PTX) in the gastro-intestinal tract by encapsulating it into the biodegradable nanoparticles made by FDA-approved poly(lactic-co-glycolic acid) (PLGA) and coated with polyethylene glycol to improve their stability (PLGA-PEG-NPs). Our study was performed by combining the synthesis and characterization of the nanodrug with in vivo studies of pharmacokinetics after oral administration in mice. Moreover, fluorescent PLGA-nanoparticles (NPs), were tested both in vitro and in vivo to observe their fate and biodistribution. Our study demonstrated that PLGA-NPs: (1) are stable in the gastric tract; (2) can easily penetrate inside carcinoma colon 2 (CaCo2) cells; (3) reduce the PTX absorption from the gastrointestinal tract, further limiting systemic exposure; (4) enable PTX local targeting. At present, the oral administration of biodegradable nanocarriers is limited because of stomach degradation and the sink effect played by the duodenum. Our findings, however, exhibit promising evidence towards our overcoming these limitations for a more specific and safer strategy against gastrointestinal disorders

A targeted sequencing approach in multiple myeloma reveals a complex landscape of genomic lesions that has implications for prognosis

Background: Next-generation sequencing (NGS) studies have shown that mul- tiple myeloma is a heterogeneous disease with a complex subclonal architecture and few recurrently mutated genes. The analysis of smaller regions of interest in the genome (\u201ctargeted studies\u201d) allows interrogation of recurrent genomic events with reduces complexity of downstream analysis at a lower price. Aims: Here, we performed the largest targeted study to date in multiple myelo- ma to analyze gene mutations, deletions and amplifications, chromosomal copy number changes and immunoglobulin heavy chain locus (IGH) translo- cations and correlate results with biological and clinical features. Methods: We used Agilent SureSelect cRNA pull down baits to target: 246 genes implicated in myeloma or cancer in general in a mixed gene discovery/confirmation effort; 2538 single nucleotide polymorphisms to detect amplifications and deletions at the single-gene and chromosome level; the IGH locus to detect translocations. We sequenced unmatched DNA from CD138- purified plasma cells from 418 patients with multiple myeloma at diagnosis, with a median follow-up of 5.3 years. We sequenced at an average depth of 337x using Hiseq2000 machines (Illumina Inc.). We applied algorithms developed in- house to call genomic events, filtering out potential artifacts and germline vari- ants. We then ranker each event on its likelihood of being \u201concogenic\u201d based on clustering, recurrence and cross-reference with the COSMIC database. Results: We identified 2270 gene mutations in 412/418 patients, and of those 688 were oncogenic. 342 patients harbored at least one oncogenic mutation. 215/246 genes showed at lease one likely somatic mutation, but only 106 showed at least one oncogenic mutation. 63% of oncogenic mutations were accounted for by the top 9 driver genes previously identified (KRAS, NRAS, TP53, FAM46C, BRAF, DIS3, TRAF3, SP140, IRF4), implying our gene discov- ery effort did not identify novel mutated genes. We included deletion of tumor suppressors, amplification of oncogenes, chromosomal copy number changes and IGH translocations for a total of 76 variables, so that 413/418 patients showed at least one informative driver genomic event, (median 4/patient). We investigated pairwise associations between events and found significant corre- lations, such as TP53 mutations and del(17p), CYLD mutations and del(16), FAM46C mutations and del(1p), SF3B1 mutations and t(11;14). Hotspots muta- tions of IRF4 lysine p.123 showed an inverse correlation with a hyperdiploid karyotype and del(16) as opposed to other missense mutations scattered along the gene, which has pathogenic implications. Survival was negatively affected by the cumulative burden of lesions in an almost linear fashion, with median survival of 10.97 and 4.07 years in patients with =7 lesions respectively, and this was independent of the nature of the genomic events. Given the het- erogeneity and complex interplay of the variables we fitted a cox-proportional hazard model to predict survival. We found that mutations in TP53, amplifications of MYC, deletions of CYLD, amp(1q), del12p13.31 and del17p13 where the only significant events, all promoting shorter survival. In particular, TP53 muta- tions and deletions, often co-occurring, had an additive effect so that carriers of both showed a dismal survival of 17 months (Figure 1).Summary/Conclusions: Due to the complex genomic landscape in MM, a discovery effort still requires large studies to derive significant associations. We conclude that a targeted sequencing approach may provide prognostic models and give insights into myeloma biology

Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

In multiple myeloma, next-generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number abnormalities (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and immunomodulatory drug-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication

A mixed sideslip yaw rate stability controller for over-actuated vehicles

Electronic stability control (ESC) has become a fundamental safety feature for passenger cars. Commonly employed ESCs are based on differential braking. Nevertheless, electric vehicles' growth, particularly those featuring an over-actuated configuration with individual wheel motors, allows for maintaining driveability without slowing down the vehicle. Standard control strategies are based on yaw rate tracking. The reference signal is model-based and needs precise knowledge of the friction coefficient. To increase the system robustness, more sophisticated approaches that include vehicle sideslip are introduced. Still, it is unclear how the two signals have to be weighted, and rarely proposed controllers have been experimentally validated. In this paper, we present a mixed sideslip and yaw rate stability controller. The mixed approach allows to address the control design as a single-input single-output problem simplifying the tuning process. Furthermore, we explain the rationale behind the choice of the weighting parameter. Eventually, the proposed ESC is validated following EU regulation in simulation and with an experimental vehicle on dry asphalt and snow. The results obtained in all the performed tests demonstrate that the proposed control strategy is robust and effective. The mixed approach is able to halve the sideslip in critical conditions with respect to a pure yaw rate approach

A Yaw Rate Based Stability Control for Under-Actuated Vehicles

Full electric vehicles (FEVs) are becoming more and more common. For these vehicles, a widely adopted driveline configuration consists in all-wheel drive (AWD) technology realized by independently drive front and rear axles. In this paper, we investigate the possibility of implementing an electronic stability control (ESC) exploiting only front and rear motor torques. The proposed control is robust with respect to friction variation and easily tunable thanks to a control allocation that permits single-input single-output formulation. The allocator is based on a multi-dimensional linearized tire model that accounts for longitudinal and lateral slip. Despite being an under-actuated configuration, incapable of generating arbitrary yaw moments, experimental tests show that this configuration still passes the EU homologation tests for ESC

Experimental Validation of a Nonlinear Slip Control for 4-Wheel Drive Full Electric Vehicles

Electric vehicles are becoming widely adopted; besides environmental advantages, electric power trains present peculiar characteristics that pave the way for reconsidering classical vehicle dynamics control (e.g. ABS, TC) and improving their performance. In this paper, we propose an ABS/TC system for full electric vehicles with 4-wheel drive. The regulator is derived from a nonlinear brake-based slip control. We modelled the wheel dynamics with an augmented single-corner model that includes the transmission, a crucial element. The controller has been tuned in simulation on the identified grey-box model and validated with an instrumented vehicle on ice and snow. It shows good performance relieving the driver of limiting the slips. The wheels are kept controlled and the magnitude of the average acceleration is increased with respect to professional driver performance

Non-invasive molecular monitoring in multiple myeloma patients using cell-free tumor DNA: a pilot study

Novel treatments for multiple myeloma (MM) have increased rates of complete response raising interest in more accurate methods to evaluate residual disease. Cell-free tumor DNA (cfDNA) analysis may represent a minimally invasive approach complementary to multiparameter flow cytometry (MFC) and molecular methods on bone marrow aspirates. A sequencing approach using the Ion Torrent Personal Genome Machine was applied to identify clonal immunoglobulin heavy chain (IGH) gene rearrangements in tumor plasma cells (PCs) and in serial plasma samples of 25 MM patients receiving second-line therapy. The same clonal IGH rearrangement identified in tumor PCs was detected in paired plasma samples and levels of IGH cfDNA correlated with outcome and mirrored tumor dynamics evaluated using conventional laboratory parameters. In addition IGH cfDNA levels reflected the number of PCs enumerated by MFC immunophenotyping even in the complete response context. Minimal residual disease negative patients by MFC were characterized by low frequencies of tumor clonotypes in cfDNA and longer survival. This pilot study supports the clinical applicability of the non-invasive monitoring of tumor levels in plasma samples of MM patients by IGH sequencing

An Efficient Eco-Planner for Autonomous Vehicles With Focus on Passengers Comfort

Speed planning is one of the tasks that a self-driving vehicle carries out. A complete planner should consider and balance passengers comfort, trip time and energy consumption. This paper proposes a computationally efficient global speed planner for autonomous vehicles that explicitly includes comfort as one of the main objectives. In particular, our approach considers the trip time as a user-specified constraint and optimizes a cost function that accounts for both energy consumption and comfort. Since passenger comfort plays a critical role for self driving vehicle, we propose a comfort model that captures different aspects: planar and vertical accelerations and the contribution of different frequency components. We test the algorithm on a realistic case study and we quantify the trade-off between energy consumption and comfort