Arjun Khunger

Minocycline for acute stroke treatment: a systematic review and meta-analysis of randomized clinical trials

BackgroundVarious randomized-controlled clinical trials (RCTs) have investigated the neuroprotective role of minocycline in acute ischemic stroke (AIS) or acute intracerebral hemorrhage (ICH) patients. We sought to consolidate and investigate the efficacy and safety of minocycline in patients withxa0acute stroke.MethodsLiterature search spanned through November 30, 2017 across major databases to identify all RCTs that reported following efficacy outcomes among acute stroke patients treated with minocycline vs. placebo: National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin Scale (mRS) scores. Additional safety, neuroimaging and biochemical endpoints were extracted. We pooled mean differences (MD) and risk ratios (RR) from RCTs using random-effects models.ResultsWe identified 7 RCTs comprising a total of 426 patients. Of these, additional unpublished data was obtained on contacting corresponding authors of 5 RCTs. In pooled analysis, minocycline demonstrated a favorable trend towards 3-month functional independence (mRS-scores of 0–2) (RRu2009=u20091.31; 95% CI 0.98–1.74, pu2009=u20090.06) and 3-month BI (MDu2009=u20096.92; 95% CI −u20090.92, 14.75; pu2009=u20090.08). In AIS subgroup, minocycline was associated with higher rates of 3-month mRS-scores of 0–2 (RRu2009=u20091.59; 95% CI 1.19–2.12, pu2009=u20090.002; I2u2009=u200958%) and 3-month BI (MDu2009=u200912.37; 95% CI 5.60, 19.14, pu2009=u20090.0003; I2u2009=u200947%), whereas reduced the 3-month NIHSS (MD −u20092.84; 95% CI −u20095.55, −u20090.13; pu2009=u20090.04; I2u2009=u200986%). Minocycline administration was not associated with an increased risk of mortality, recurrent stroke, myocardial infarction and hemorrhagic conversion.ConclusionsAlthough data is limited, minocycline demonstrated efficacy and seems a promising neuroprotective agent in acute stroke patients, especiallyxa0in AIS subgroup. Further RCTs are needed to evaluate the efficacy and safety of minocycline among ICH patients.

Dabrafenib in combination with trametinib in the treatment of patients with BRAF V600-positive advanced or metastatic non-small cell lung cancer: clinical evidence and experience:

Mutations in the BRAF oncogene are found in 2–4% of all non-small cell lung cancer (NSCLC) patients. The most common activating mutation present within the BRAF oncogene is associated with valine substitution for glutamate at position 600 (V600E) within the BRAF kinase. BRAF-targeted therapies are effective in patients with melanoma and NSCLC harboring BRAF V600E mutation. In both melanoma and NSCLC, dual inhibition of both BRAF and the downstream mitogen-activated protein kinase (MEK) improves response rates compared with BRAF inhibition alone. BRAF-MEK combination therapy (dabrafenib plus trametinib) demonstrated tolerability and efficacy in a recent phase II clinical trial and was approved by the European Medicines Agency and United States Food and Drug Administration for patients with stage IV NSCLC harboring BRAF V600E mutation. Here, in this review, we outline the preclinical and clinical data for BRAF and MEK inhibitor combination treatment for NSCLC patients with BRAF V600E mutation.

Post-treatment changes in hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer patients

Background The absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC) and neutrophil to lymphocyte ratio (NLR) are known markers of inflammation. We evaluated whether ANC, ALC, AMC and NLR, both before and after treatment with nivolumab, are indicative markers of overall survival (OS) and evaluated change in NLR as a predictive marker of response in non -small cell lung cancer (NSCLC) patients treated with nivolumab. Methods A total of 109 patients with advanced NSCLC treated with nivolumab were included. ANC, ALC, AMC and NLR were examined at initiation of nivolumab therapy and after two cycles. The prognostic role of ANC, ALC, AMC and NLR with OS and changes in NLR ratio were examined with Kaplan-Meier curves and proportional hazard model. Result Post-treatment NLR ≥5 after two cycles of nivolumab was associated with poor OS (median OS in NLR = <5 vs NLR = ≥5 was 29.1 (16.2–40.9) vs 24.2(16.1–36.2) months respectively, p<0.001). In addition NLR increased in non-responders after two cycles of nivolumab by 6.6±21.8 as compared to responders (p = 0.027). Conclusions Post-treatment ANC, ALC and NLR are independent prognostic factors in NSCLC patients treated with nivolumab. Changes in NLR can be an early biomarker for response in NSCLC patients treated with nivolumab.

RaPtomics: Integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. The treatment of choice for early stage NSCLC is surgical resection followed by adjuvant chemotherapy for high risk patients. Currently, the decision to offer chemotherapy is primarily dependent on several clinical and visual radiographic factors as there is a lack of a biomarker which can accurately stratify and predict disease risk in these patients. Computer extracted image features from CT scans (radiomic) and (pathomic) from H&E tissue slides have already shown promising results in predicting recurrence free survival (RFS) in lung cancer patients. This paper presents new radiology-pathology fusion approach (RaPtomics) to combine radiomic and pathomic features for predicting recurrence in early stage NSCLC. Radiomic textural features (Gabor, Haralick, Law, Laplace and CoLlAGe) from within and outside lung nodules on CT scans and intranuclear pathology features (Shape, Cell Cluster Graph and Global Graph Features) were extracted from digitized whole slide H&E tissue images on an initial discovery set of 50 patients. The top most predictive radiomic and pathomic features were then combined and in conjunction with machine learning algorithms were used to predict classifier. The performance of the RaPtomic classifier was evaluated on a training set from the Cleveland Clinic (n=50) and independently validated on images from the publicly available cancer genome atlas (TCGA) dataset (n=43). The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) (AUC 0.78; p<0.005) as compared to radiomic (AUC 0.74; p<0.01) and pathomic (AUC 0.67; p<0.05) features alone.