89 research outputs found
Role of combination bortezomib and pegylated liposomal doxorubicin in the management of relapsed and/or refractory multiple myeloma
The first in class proteasome inhibitor bortezomib (B) received its initial regulatory approval for therapy of patients with multiple myeloma (MM) in the relapsed/refractory setting. Modulation of proteasome function, however, is also a rational strategy for chemosensitization, and a variety of agents have shown synergistic activity with bortezomib pre-clinically, including anthracyclines. This formed the basis for evaluation of a regimen of bortezomib with pegylated liposomal doxorubicin (PLD). PLD+B, in a phase I study, induced a predictable and manageable toxicity profile, and showed encouraging anti-MM activity. In a recent international, randomized phase III trial, PLD+B demonstrated a superior overall response rate and response quality compared to bortezomib alone, as well as a longer time to progression, duration of response, progression-free survival, and overall survival. Sub-analyses revealed benefits in almost all clinically relevant subgroups, including several which would be considered to have high-risk disease. These findings have led to the establishment of the PLD+B regimen as one of the standards of care for patients with relapsed and/or refractory myeloma. Efforts are now underway to build on this combination further by adding other active anti-myeloma agents. In this review, we will discuss the role of PLD+B as an important addition to our therapeutic armamentarium for patients with MM
Blockade of interleukin-6 signalling with siltuximab enhances melphalan cytotoxicity in preclinical models of multiple myeloma
Signalling through the interleukin (IL)-6 pathway induces proliferation and drug resistance of multiple myeloma cells. We therefore sought to determine whether the IL-6-neutralizing monoclonal antibody siltuximab, formerly CNTO 328, could enhance the activity of melphalan, and to examine some of the mechanisms underlying this interaction. Siltuximab increased the cytotoxicity of melphalan in KAS-6/1, INA-6, ANBL-6, and RPMI 8226 human myeloma cell lines (HMCLs) in an additive-to-synergistic manner, and sensitized resistant RPMI 8226.LR5 cells to melphalan. These anti-proliferative effects were accompanied by enhanced activation of drug-specific apoptosis in HMCLs grown in suspension, and in HMCLs co-cultured with a human-derived stromal cell line. Siltuximab with melphalan enhanced activation of caspase-8, caspase-9, and the downstream effector caspase-3 compared with either of the single agents. This increased induction of cell death occurred in association with enhanced Bak activation. Neutralization of IL-6 also suppressed signalling through the phosphoinositide 3-kinase/Akt pathway, as evidenced by decreased phosphorylation of Akt, p70 S6 kinase and 4E-BP1. Importantly, the siltuximab/melphalan regimen demonstrated enhanced anti-proliferative effects against primary plasma cells derived from patients with myeloma, monoclonal gammopathy of undetermined significance, and amyloidosis. These studies provide a rationale for translation of siltuximab into the clinic in combination with melphalan-based therapies
A phase 2 multicentre study of siltuximab, an anti-interleukin-6 monoclonal antibody, in patients with relapsed or refractory multiple myeloma
Interleukin-6 (IL6) plays a central role in multiple myeloma pathogenesis and confers resistance to corticosteroid-induced apoptosis. We therefore evaluated the efficacy and safety of siltuximab, an anti-IL6 monoclonal antibody, alone and in combination with dexamethasone, for patients with relapsed or refractory multiple myeloma who had â„2 prior lines of therapy, one of which had to be bortezomib-based. Fourteen initial patients received siltuximab alone, 10 of whom had dexamethasone added for suboptimal response; 39 subsequent patients were treated with concurrent siltuximab and dexamethasone. Patients received a median of 4 prior lines of therapy, 83% were relapsed and refractory, and 70% refractory to their last dexamethasone-containing regimen. Suppression of serum C-reactive protein levels, a surrogate marker of IL6 inhibition, was demonstrated. There were no responses to siltuximab but combination therapy yielded a partial (17%) + minimal (6%) response rate of 23%, with responses seen in dexamethasone-refractory disease. The median time to progression, progression-free survival and overall survival for combination therapy was 4.4, 3.7 and 20.4 months, respectively. Haematological toxicity was common but manageable. Infections occurred in 57% of combination-treated patients, including â„grade 3 infections in 18%. Further study of siltuximab in modern corticosteroid-containing myeloma regimens is warranted, with special attention to infection-related toxicity
Recruitment of a SAP18-HDAC1 Complex into HIV-1 Virions and Its Requirement for Viral Replication
HIV-1 integrase (IN) is a virally encoded protein required for integration of viral cDNA into host chromosomes. INI1/hSNF5 is a component of the SWI/SNF complex that interacts with HIV-1 IN, is selectively incorporated into HIV-1 (but not other retroviral) virions, and modulates multiple steps, including particle production and infectivity. To gain further insight into the role of INI1 in HIV-1 replication, we screened for INI1-interacting proteins using the yeast two-hybrid system. We found that SAP18 (Sin3a associated protein 18 kD), a component of the Sin3a-HDAC1 complex, directly binds to INI1 in yeast, in vitro and in vivo. Interestingly, we found that IN also binds to SAP18 in vitro and in vivo. SAP18 and components of a Sin3A-HDAC1 complex were specifically incorporated into HIV-1 (but not SIV and HTLV-1) virions in an HIV-1 INâdependent manner. Using a fluorescence-based assay, we found that HIV-1 (but not SIV) virion preparations harbour significant deacetylase activity, indicating the specific recruitment of catalytically active HDAC into the virions. To determine the requirement of virion-associated HDAC1 to HIV-1 replication, an inactive, transdominant negative mutant of HDAC1 (HDAC1H141A) was utilized. Incorporation of HDAC1H141A decreased the virion-associated histone deacetylase activity. Furthermore, incorporation of HDAC1H141A decreased the infectivity of HIV-1 (but not SIV) virions. The block in infectivity due to virion-associated HDAC1H141A occurred specifically at the early reverse transcription stage, while entry of the virions was unaffected. RNA-interference mediated knock-down of HDAC1 in producer cells resulted in decreased virion-associated HDAC1 activity and a reduction in infectivity of these virions. These studies indicate that HIV-1 IN and INI1/hSNF5 bind SAP18 and selectively recruit components of Sin3a-HDAC1 complex into HIV-1 virions. Furthermore, HIV-1 virion-associated HDAC1 is required for efficient early post-entry events, indicating a novel role for HDAC1 during HIV-1 replication
Pitfalls in machine learningâbased assessment of tumorâinfiltrating lymphocytes in breast cancer: a report of the international immunoâoncology biomarker working group
The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC
The Kuramoto model in complex networks
181 pages, 48 figures. In Press, Accepted Manuscript, Physics Reports 2015 Acknowledgments We are indebted with B. Sonnenschein, E. R. dos Santos, P. Schultz, C. Grabow, M. Ha and C. Choi for insightful and helpful discussions. T.P. acknowledges FAPESP (No. 2012/22160-7 and No. 2015/02486-3) and IRTG 1740. P.J. thanks founding from the China Scholarship Council (CSC). F.A.R. acknowledges CNPq (Grant No. 305940/2010-4) and FAPESP (Grants No. 2011/50761-2 and No. 2013/26416-9) for financial support. J.K. would like to acknowledge IRTG 1740 (DFG and FAPESP).Peer reviewedPreprin
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The NeST (Neoadjuvant systemic therapy in breast cancer) study: National Practice Questionnaire of United Kingdom multi-disciplinary decision making
Abstract: Background: Neoadjuvant systemic therapy (NST) is increasingly used in the treatment of breast cancer, yet it is clear that there is significant geographical variation in its use in the UK. This study aimed to examine stated practice across UK breast units, in terms of indications for use, radiological monitoring, pathological reporting of treatment response, and post-treatment surgical management. Methods: Multidisciplinary teams (MDTs) from all UK breast units were invited to participate in the NeST study. A detailed questionnaire assessing current stated practice was distributed to all participating units in December 2017 and data collated securely usingREDCap. Descriptive statistics were calculated for each questionnaire item. Results: Thirty-nine MDTs from a diverse range of hospitals responded. All MDTs routinely offered neoadjuvant chemotherapy (NACT) to a median of 10% (range 5â60%) of patients. Neoadjuvant endocrine therapy (NET) was offered to a median of 4% (range 0â25%) of patients by 66% of MDTs. The principal indication given for use of neoadjuvant therapy was for surgical downstaging. There was no consensus on methods of radiological monitoring of response, and a wide variety of pathological reporting systems were used to assess tumour response. Twenty-five percent of centres reported resecting the original tumour footprint, irrespective of clinical/radiological response. Radiologically negative axillae at diagnosis routinely had post-NACT or post-NET sentinel lymph node biopsy (SLNB) in 73.0 and 84% of centres respectively, whereas 16% performed SLNB pre-NACT. Positive axillae at diagnosis would receive axillary node clearance at 60% of centres, regardless of response to NACT. Discussion: There is wide variation in the stated use of neoadjuvant systemic therapy across the UK, with general low usage of NET. Surgical downstaging remains the most common indication of the use of NAC, although not all centres leverage the benefits of NAC for de-escalating surgery to the breast and/or axilla. There is a need for agreed multidisciplinary guidance for optimising selection and management of patients for NST. These findings will be corroborated in phase II of the NeST study which is a national collaborative prospective audit of NST utilisation and clinical outcomes
Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer
The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer
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