tbuckley/cabot-search huggingface dataset:
The European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) in daily practice: A real-life feasibility analysis at the Medical University Vienna.
Authors: Barbara Kiesewetter, Markus Raderer, Guenther G. Steger, Rupert Bartsch, Robert Pirker, Sabine Zochbauer-Muller, Gerald W. Prager, Michael Krainer, Matthias Preusser, Manuela Schmidinger, Thorsten Fuereder, Christine Marosi, Gottfried J. Locker, Thomas Brodowicz, Christoph Zielinski
Journal: Journal of Clinical Oncology (2017)

e18151 Background: The ESMO-MCBS constitutes a new tool to quantify the clinical benefit that may be anticipated from a novel anticancer treatment based on original data from randomized or controlled trials. In contrast to comparable concepts, this score appears somehow unique as it concentrates on the clinical benefit for the individual patient irrespective of socioeconomic factors. Being introduced 2015; the MCBS has excited great public interest in Europe in the last year. We present here the first feasibility analysis of the MCBS under real-life conditions at the Medical University Vienna (MUV), one of Europe’s largest centers. Methods: This study evaluated systematically the practicability of the MCBS for advanced/metastatic malignant diseases treated at the MUV ( &gt; 15 tumor entities). A 3-step approach including data analysis of regimens in regular use, assessment of scores (1 = not recommended to 5 = highly recommended) and evaluation of results with a focus on clinical feasibility was applied. Results: In the majority of tumor entities and clinical settings the assessed MCBS scores were consistent with our daily practice and particularly with our 1 st line standards for common tumors like metastatic breast-, colorectal- or lung cancer. However, there were certain limitations including orphan diseases with a lack of randomized data (e.g. rare sarcoma subtypes) or tumor entities with cascade like treatment settings (e.g. colorectal cancer). Interestingly, new immunomodulatory approaches and checkpoint inhibitors in particular, scored strikingly high in comparison to conventional chemo due to a favorable toxicity profile, positive influence on quality of life and strong efficacy data in many cases. A broad selection of data will be presented at the meeting. Conclusions: Our analysis encompasses a wide range of tumor entities and clinical settings and reflects the daily experience of a university clinic. Mostly, the MCBS scores supported treatment decisions based on the clinical benefit to be expected from a novel therapy and might also support socioeconomic questions exemplified by high scoring of immunomodulatory approaches.
Oncologic outcomes of advanced gynecologic malignancies managed with the aid of comprehensive genomic profiling (CGP) in a rural community setting.
Authors: Luis Alexander Rojas-Espaillat, Kirstin Anne Williams, David Starks, Casey B. Williams, Bing Xu, Nandini Dey, Pradip Dey, Brian Leyland-Jones
Journal: Journal of Clinical Oncology (2016)

e17077 Background: Managing advanced gynecologic malignancies represents a challenge. Chemotherapies for recurrent disease have limited efficacy, as they are selectied ignoring the role that tumor heterogeneity plays in achieving a lasting response. We utilized multiplatform comprehensive genomic profiling (CGP) to identify relevant genomic alterations (CRGA) to be targeted in combination with standard chemotherapies. Methods: CGP were performed from DNA extracted from formalin-fixed paraffin-embedded tissue of advanced gynecologic cancers. All classes of GA were evaluated, and clinically relevant genomic alterations (CRGA) were defined as GA linked to drugs on the market or under evaluation in mechanism driven clinical trials. Therapy recommendations combining standard of care single agent chemotherapies and agents targeting CRGA were made. Results: A total of 66 patients with advanced, recurrent or progressive gynecologic malignancies have been enrolled in the study. Twenty-five subjects (38%) with a median age of 62 (32 – 88) are evaluable at this time. There were 17 (68%) ovarian carcinomas, 4 (16%) endometrial carcinomas, 2 (8%) uterine sarcomas, and 1 (4%) case each of vaginal melanoma and a tumor of unknown gynecologic primary. Sixteen percent were stage I (16%), 1 (4%) stage II, 17 (68%) stage III and 3 (12%) stage IV. CGP revealed that 80% of cases exhibit at least 1 CRGA, for an average of 1.83 CRGA per case. All subjects tumors demonstrated at least one GA, for a total of 107 GA and an average of 4.56 GA/case. The most frequently detected GA was in TP53 (37%), and the most common CRGA were PTEN, MYC, PIK3CA, PIK3R1, NF1, KRAS, BRCA2 and BRAF. Overall response rate (ORR) was 68%, for an overall clinical benefit of 76% (CR 32% + PR 36% + 8% SD). Progressive disease was seen in 6/25 (24%) subjects. Conclusions: Management of advanced gynecologic malignancies targeting CRGA in combination with standard chemotherapy is feasible and results in superior therapeutic outcomes compared to historical controls. Further applications of CGP for precision oncology in gynecologic malignancies appear to be warranted and deserve further evaluation.
Results of the first thousand patients from a multidisciplinary prostate cancer clinic.
Authors: Pandora Rudd, John Hines, Paul Cathcart, Karen Wilkinson, Greg Shaw, Peter Wilson, Jonathan Shamash, Thomas Murray Richards, Paula Wells, John D Kelly, Pui Ying Chan, James Green, Thomas Powles, Karen Tipples
Journal: Journal of Clinical Oncology (2016)

e16602 Background: Patients with early prostate cancer have a spectrum of treatment options, delivered by different healthcare professionals. To address this a multidisciplinary prostate cancer clinic (MDC) was started at Barts Cancer Centre in 2011, covering all treatment options in one appointment. Methods: Between 2011-15 all patients with early prostate cancer and fit enough for radical therapy were referred to the MDC from a catchment area of 1.2 million patients. Clinical data and treatment choice on the first 1000 patients was collected within this audit. Patient demographics and tumour characteristics were recorded. Only patients with confirmed prostate cancer were included in the analysis. This was either by histology from a biopsy specimen, or by the demonstration of metastatic disease on imaging and a PSA test > 100. Patients were separated into risk groups using the D’Amico classification. Results: 1069 male patients attended the clinic. 1000 patients had confirmed prostate cancer. Median age was 66 years (IQR = 14). Median presenting PSA was 9 (IQR = 8). 87% of patients had over 12 core biopsies samples. 75% saw more than 1 clinician at the first appointment. At presentation; 341 (34.1%) had high risk disease, 442 (44.2%) intermediate risk, 167 (16.7%) low risk, 18 (1.8%) had nodal disease N1, and 32 (3.2%) had metastatic disease M1. Treatments included androgen deprivation therapy with radiotherapy (RT) in 360 (36%), followed by Active Surveillance (S); 281 (28.1%), Radical Prostatectomy (RP) 259 (25.9%), HIFU 30 (3%), ADT alone 29 (2.9%), and Brachytherapy 25 (2.5%). S, RP and RT were the commonest treatments in low, intermediate and high risk disease. Patients above the median age were more likely to have S and RT (p < 0.05). Treatment decisions changed over time. The most recent cohort of patients (2015) were more likely to have S and less likely to have RT (p < 0.05). A patient satisfaction survey showed that 90% of patients were pleased or very pleased with their management. Conclusions: MDCs enable informed decision making opportunities for patients, and the ability to consider multiple treatment options at one time. Patients report high levels of satisfaction with this model of care.
The Path to the First FDA-Approved Cannabis-Derived Treatment and What Comes Next
Authors: Rita Rubin
Journal: JAMA (2018)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA HomeNew OnlineCurrent IssueFor Authors Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) Podcasts Clinical Reviews Editors' Summary Medical News Author Interviews More JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA journal
Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data
Authors: Nrupen A. Bhavsar, Aijing Gao, Matthew Phelan, Neha J. Pagidipati, Benjamin A. Goldstein
Journal: JAMA Network Open (2018)

<h3>Importance</h3> Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies. <h3>Objective</h3> To quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models. <h3>Design, Setting, and Participants</h3> Cohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation. <h3>Main Outcomes and Measures</h3> Outcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. <h3>Results</h3> Among the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome. <h3>Conclusions and Relevance</h3> Social determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.
P01.098 Have we been still defeated with treatment of GBM IDH wild-type recurrency
Authors: O Kalita, M Vaverka, L Hrabalek, D Vrana, R Trojanec, Z Sporikova, M Hajduch, J Drabek, P Hok, P Hlustik, L Tuckova, J Vrbkova
Journal: Neuro-Oncology (2018)

Regardless of GBM patient prognosis remains dire, there is a patient group, in which profit from current diagnostic methods and treatment strategies progress has yielded extension of both OS and PFS, that again recall question of recurrent GBM solution.Our work is aimed at result evaluation of our treatment strategy for the most malignant subtype of GBM - IDH wild type. 116 consecutive patients with GBM IDH wild-type were enrolled. Recurrent GBM was defined as increasing more than 20–30% of previous, residual tumour volume, or tumour regrowing after radiologically radical surgery. We consider as a best solution repeated surgery following oncotherapy. In case unsuitable for redo-surgery, we accepted oncotherapy rechallenge. Time of recurrency evolving ≥ 6 months after surgery, Clinical condition - Karnofsky score (KS) ≥ 70% and Performance status (PS) WHO ≤ gr. 2, Local recurrency, without multilocality, Option for considerable tumor volume reduction, minimally ≤ 70%; tumour location; Time of recurrency evolving ≥ 6 motnts after prime surgery, Clinical condition - Karnofsky score (KS) ≥ 70% and Performance status (PS) WHO ≤ gr. 2, Local recurrency, without multilocality, No option for considerable tumour volume reduction, minimally ≤ 70%; tumour location. 23 patients met these criteria. Such treatment selection drew improvement of both PFS (10,5 months; 8.94,17.91)) and OS (19,2 months; (15.28,30.82)). A good clinical condition, longer time to the tumour recurrency commitment, primary radical surgery, following Stupp protocol a radical redo-surgery, following chemotherapy were positive clinical prognostic factors. Solely rechallenge oncotherapy (reradiation, chemotherapy/temozolomid) had lesser positive impact. Regarding cytogenetic tumour profile, based on FISH, increasing copy number P53 and CCND1 were approved as a positive prognostic factor. GBMs IDH wild-type are the most malignant subset, but these patients have relatively heterogenous life expectancy. Thera are missed relevant prognostic factors and our work attempted to change it. As emerged from this analysis, OS and PFS predominantly rely on genetic features (P53, CCND1) of certain tumour. The most important issue for successful recurrent GBM treatment was set up appropriate indication criteria. When we evaluated our patient group, the patients, who un-fulfilled criteria, underwent redo-surgery, predominantly for the patient′s and family urges.
Comprehensive genomic profiling of sarcomas in Chinese population.
Authors: Jie Lin, Qing Bi, Hong-mei Shen, Ye Jiang, Yang Liu, Xing-kui Mo, Yong-hong Lei, Yan-bin Xiao, Su-wei Dong, Yi-hao Yang, Tao Yuan, Fang Yang, Miao Han, Hua Dong, Jing Wang, Fu-Gen Li, Yi-qin Ai
Journal: Journal of Clinical Oncology (2018)

11526 Background: Sarcomas consist of a broad family of mesenchymal malignancies with remarkable diversity. Currently, there are no targeted drugs approved for sarcomas except for pazopanib and imatinib. In this study, comprehensive genomic profiling of 221 sarcomas by targeted next generation sequencing (NGS) was performed, the clinical relevant genomic alterations (CRGAs) and potential therapeutic targets were explored. Methods: 211 sarcomas, including 30 leiomyosarcoma (LMS), 13 osteosarcoma (OS), 13 rhabdomyosarcoma (RMS), 9 synovial sarcoma (SS), 22 liposarcoma (LPS), and 124 other subtypes were sequenced by cancer gene panel (CGP) with all coding exons and splicing sites of 365 cancer-related genes plus selected introns from 25 frequently rearranged genes. Genomic alterations (GAs) including point mutations, short insertions and deletions, copy number variations, and rearrangements were called, and further confirmed manually using IGV. These CRGAs were annotated based on literature review and some of them are actionable. Results: There are 1215 GAs for all 211 sarcomas and average 5.5 mutations per sample. Among these subtypes, LPS has the highest number of mutations per sample (average 7.5), SS has the lowest number of mutations per sample (average 2.7). Interestingly, 49% GAs are structure variations (SVs). In addition, the top ranked altered genes were TP53 (36%), RB1 (13%), MDM2 (12%) and CDK4 (11%) in all sarcomas. The genomic alterations across all subtypes of sarcomas are enriched in cell cycle pathway. Furthermore, 88% of patients have at least one CRGA, 10 patients with CRGAs in FGFR1/2/3, VEGFA, KIT, FLT4 or KDR could benefit from pazopanib. 57 patients have at least one actionable mutation. Among them, 24 patients with CRGAs in CDK4, CDKN2A/B could benefit from palbociclib, 22 patients with CRGAs in MTOR, PIK3CA, NF1/2, PTEN, TSC1 or STK11 could benefit from everolimus, and 6 patients with CRGAs in BRCA2, ARD1A or ATM could benefit from olaparib. Conclusions: This study demonstrated that sarcomas are characterized predominantly by structure variations, most of sarcomas have at least one actionable mutation. In conclusion, comprehensive genomic profiling in a large cohort highlights the promise of targeted therapies.
Prediction of cancer patient responses to different treatments using personalized tumor models or models with matching genomic profiles.
Authors: X. Katherine Ye, Jingjing Jiang, Zhongguang Luo, Jia Wei, Jia He, Guanglei Zhuang, Ying Yan, Tengfei Yu, Wei Du, Jiali Gu, Jie Liu, Zhenyu Gu
Journal: Journal of Clinical Oncology (2016)

11595 Background: The challenge in cancer treatment management is to find the right drug for a patient at the right time. Progress in cancer target identification and new target drug development has presented many opportunities and successful examples. However, for the majority of patients, finding an effective treatment based on the unique mutations in their tumor cells is still hard. New approaches for precision medicine are needed and thus being investigated. Methods: A mouse “Avatar” is a personalized cancer xenograft model derived from a patient’s tumor sample (PDX model) and can be used to test different drugs for the patient. “GIFTS” (Genomic Information Fitting based Therapeutics Selection) is a method of comparing a patient’s cancer genomic information with the archived PDX model genomic profiles in GenenDesign Genomic Information Database to find the best genomic fit and related therapeutic options in GenenDesign Drug Response Database. Results: We have successfully derived over 1000 PDX models from patient tumor tissues of various cancer types including lung, gastric, liver, esophageal, colorectal and pancreatic cancers. In some cases, both patients and their mouse “Avatars” were treated with the same cancer drugs including target drugs and chemotherapies. A comparison of drug responses in mouse “Avatars” with patients' clinical results reveals high correlations in both sensitivity and unresponsiveness. We also developed a bioinformatics algorithm to analyze PDX model genomic profiles and drug response information. Both drug sensitivity and resistance biomarkers were used to match cancer patient genomic profiles to those of the PDX models in GenenDesign databases. The preliminary result shows that there is a high degree of similarities in drug response profiles between the patients and PDX models matched through the GIFTS method. Conclusions: Mouse “Avatar” drug testing and the genomic matching GIFTS method can provide personalized experimental data for predicting therapeutic effectiveness of cancer drugs. Both methods are especially useful for patients with late stage cancers that can’t be readily treated with known target cancer drugs.
Perceptions on coordination of care in prostate cancer patients treated with radiotherapy (RT) and androgen deprivation therapy (ADT): Implications for survivorship care planning.
Authors: Jordan A. Holmes, Roger F. Anderson, Leroy G. Hoffman, Timothy Norman Showalter, Mohit S. Kasibhatla, Sean P. Collins, Michael A. Papagikos, Brittney Diane Barbosa, Lori Stravers, Zahra Mahbooba, Andrew Wang, Ronald C. Chen
Journal: Journal of Clinical Oncology (2016)

10102 Background: Survivorship care planning upon treatment completion is especially important for prostate cancer patients treated with ADT. These patients need coordinated oncologic and primary care, the latter monitoring cardiovascular disease risk factors. In a multicenter prospective cohort, we assessed coordination of care (COC) and satisfaction with care (SC) using validated patient-reported outcome instruments. Methods: 100 men with non-metastatic prostate cancer to be treated with RT and ADT at 8 institutions were enrolled before treatment and followed prospectively for 1 year. Patients completed validated surveys on COC (specifically between radiation oncologist and primary care; 4 questions each scored on 5 point Likert scale) and SC (9 questions each scored on a 6 point Likert scale). Overall score for each instrument was calculated as the mean of all questions. Results: Median age was 67 and 89% had ACE-27 score >0 indicating baseline comorbid illness. At baseline (pre-treatment) 66-70% of patients indicated that their cancer provider communicates with other providers and follows up on problems, but only 17% indicated that their cancer provider was aware of other medical care (Table). At one year COC did not change dramatically. No significant difference in COC was seen among patients with ACE-27 score 0-1 vs. >1. Change in overall COC score from baseline to 1 year was significantly associated with change in patient-reported SC score (p=.03). Conclusions: Patients reported that their cancer specialist was not aware of health care received elsewhere. This is one of the first studies to show that improved coordination is associated with increased patient satisfaction. For prostate cancer survivors receiving ADT, this is especially relevant given the need for coordinated oncologic and primary care. Clinical trial information: NCT02003417.% of patients reporting agree/strongly agree on Coordination of Care questions. My cancer provider Baseline (%) 1 Year (%) - always knows about the care I receive other places 17 24 - communicates with the other providers I see 71 70 - knows the results of my visits with other doctors 66 69 - always follow up on a problem I’ve had 71 83