tbuckley/cabot-search huggingface dataset:
A first-in-human phase I/II trial of SRA737 (a Chk1 Inhibitor) in subjects with advanced cancer.
Authors: Elizabeth Ruth Plummer, Rebecca Sophie Kristeleit, Elena Cojocaru, Noor Md Haris, Louise Carter, Robert Hugh Jones, Sarah Patricia Blagden, T.R. Jeffry Evans, Hendrik-Tobias Arkenau, Debashis Sarker, Sarah Danson, Stefan N. Symeonides, Harriet Walter, Joanita Ocen, Manreet Randhawa, Mark Marion Kowalski, Ines Verdon, Andrew Dye, Udai Banerji
Journal: Journal of Clinical Oncology (2019)

3094 Background: SRA737 is a potent, highly selective and orally-bioavailable inhibitor of checkpoint kinase 1 (Chk1). SRA737-01 was designed to investigate the safety and tolerability of continuous, daily dosing with SRA737 and to evaluate preliminary efficacy in expansion cohorts of prospectively-selected genetically-defined subjects with advanced tumors. Methods: The escalation phase employed an accelerated titration design starting at 20 mg administered orally in 28-day cycles. Incremental 100% dose escalations in single-subject cohorts were followed by a rolling-6 design once SRA737-related ≥ Grade 2 toxicity was observed during Cycle 1. The expansion phase enrolled subjects prospectively selected by next-generation sequencing with: high grade serous ovarian, colorectal, metastatic castration-resistant prostate, non-small cell lung, and head and neck cancers. Results: In escalation, 18 subjects received SRA737 in 9 dose level cohorts, from 20 to 1300 mg QD; median treatment duration 62.5 days (range 1 to 226). Of these subjects, 3 experienced dose limiting toxicity (DLT; inability to receive 75% of the planned dose); 2 at 1300 mg QD due to gastrointestinal intolerability and 1 at 500 mg BID due to thrombocytopenia. The maximum tolerated dose (MTD) was established at 1000 mg QD or 500 mg BID. The C max and AUC 0-24 at 1000 mg QD were 2391 ng/mL and 26795 ng∙h/mL respectively and the C min (411 ng/mL) exceeded that determined in preclinical models to be effective. Doses ≥ 300 mg QD also exceeded this level. Of 462 subjects prospectively screened for genetic alterations associated with Chk1 sensitivity, 93 were enrolled in expansion across all tumor types. Overall, the most commonly reported treatment-emergent adverse events were diarrhea (70%), nausea (64%), vomiting (51%), and fatigue (47%); the majority were of mild to moderate severity. Conclusions: In this first-in-human trial of SRA737 monotherapy, the MTD was 1000 mg/day and based on overall tolerability and PK, the recommended Phase 2 dose is 800 mg/day. The successful enrollment of prospectively-selected genetically-defined subjects will allow response data to be correlated with genomic profiles hypothesized to confer sensitivity to Chk1 inhibition. Clinical trial information: NCT02797964.
OP0250 INFLUENTIAL FACTORS IN PROMOTING TREAT-TO-TARGET FOR SYSTEMIC LUPUS ERYTHEMATOSUS VIA EMPOWERING PATIENTS: A COHORT STUDY FROM CHINA BY SMART SYSTEM OF DISEASE MANAGEMENT (SSDM)
Authors: Jianlin Huang, Tong Xie, Qiang Shu, Jing Yang, Yongfu Wang, Hongzhi Wang, Xiangyuan Liu, Cheng Zhao, Xinwang Duan, Guosheng Wang, Rui Wu, Yanjie Hao, Yuhua Jia, Fei Xiao, Lingyun Sun
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> T2T is routine in RA, but no comparable standard has been defined for SLE. In 2015, the definition of Lupus Low Disease Activity State (LLDAS) was generated by Asia-Pacific Lupus Collaboration, and the preliminary validation demonstrated its attainment to be associated with improved outcomes in SLE. A SLEDAI-2K score lower than 4 is the main criteria for LLDAS. SSDM is an interactive mobile disease management application, including application systems for both the doctors and patients. The patients can perform self-assessment, including SLEDAI and medical records entry through the mobile application. The data is synchronized to the SSDM of authorized rheumatologists and stored in cloud database. <h3>Objectives</h3> To evaluate the patterns of T2T and related influential factors among SLE patients after applying SSDM in real world. <h3>Methods</h3> Patients were trained to master SSDM by rheumatologists in clinics. The first assessment for SLEDAI was performed as the baseline. Patients were required to perform repeated assessments after leaving the clinics. <h3>Results</h3> From July 2015 to Jan 2019, 1,090 SLE patients from 88 hospitals were followed up for more than 12 months through SSDM, and the results were summarized in Table 1. The ratio of T2T achievers was 52.84% (576/1,090) at the baseline and improved significantly to 68.35% (745/1,090) after a 12-month follow-up, p&lt;0.01. Among T2T achievers at the baseline, 77.08% (444/576) maintained T2T, and 22.92% (132/576) relapsed. Of patients who didn' t achieve T2Tat baseline, 58.56% (301/514) of the them achieve T2T after 12-month follow-up. The impact of the times of self-assessment for SLEDAI on T2T has been analyzed. The more frequent of the self-assessments being conducted by patients, the higher improvement of T2T rate will be. We performed linear regression analysis of variables in statistics and parameter estimation by least square method. The improvement of T2T rate(y) was positively correlated with times of self-assessment for SLEDAI(x) independently. The regression equation as "y = 0.0324x + 0.0226 R2 = 0.7717", p&lt;0.01. (Figure 1) <h3>Conclusion</h3> After proactive disease management via SSDM, the rate of T2T in SLE patients increased significantly. Patients with SLEDAI-2K≤4 score at baseline had a significantly higher retention rate. The patients who performed more self-assessments through SSDM had lower probability of relapse and higher rate of T2T maintaining and achievement. SSDM is a valuable tool for long term SLE follow-up through empowering patients. <h3>Disclosure of Interests</h3> None declared
Business versus science: High drug prices conspire against oncological research.
Authors: Felipe G. Gercovich, Ernesto Gil Deza, Eduardo L. Morgenfeld, Marcelo Muino, Marvin Albert Mizrahi, Gaston Martin Reinas, Leonardo Smolje, Mariana Abal, Edgardo G. J. Rivarola
Journal: Journal of Clinical Oncology (2019)

e14580 Background: It has been consistently proven that pivotal clinical trials (PCT) for drug registration or new drug indications (NDI), sponsored by the pharmaceutical industry, obtain better results than independent confirmatory studies (ICS). The interplay between PCT and ICS has supported better treatment selections and more realistic expectations: hope vs hype.The aim of this paper is to analyze the amount of ICS publications in Oncology within the last decade. Methods: All new FDA approved oncological drugs or NDI for solid tumors between 2005 and 2017 were taken into account. The PCT that led to their approval were identified, and between October and November 2019 a thorough search for related ICS (published or ongoing) was conducted on MEDLINE, ASCO Abstracts, NEJM, Lancet Oncology, JAMA, JCO, Cancer, PLOS ONE, PLOS Medicine and www.Clinicaltrials.gov. Results: Fifty-five new drugs or NDI were analyzed (Abemaciclib, Abiraterone, Ado-trastuzumab emtasine, Afatinib, Alectinib, Atezolizumab, Avelumab, Axitinib, Bendamustine, Bevacizumab, Brigatinib, Cabozantinib, Ceritinib, Cobimetinib, Crizotinib, Dabrafenib, Degarelix, Denosumab, Durvalumab, Eribuline mesylate, Everolimus, Ipilimumab, Irinotecan liposome, Ixabepilone, Lapatinib, Lenvatinib, Necitumumab, Neratinib, Nilotinib, Niraparib, Nivolumab, Olaparib, Osimertinib, Paclitaxel protein bound, Palbociclib, Panitumumab, Pazopanib, Pembrolizumab, Pertuzumab, Ramucirumab, Regorafenib, Ribociclib, Rucaparib, SipuleucelT, Sonidegib, Sorafenib, Sunitinib, Topotecan, Trabectedin, Trametinib, Trastuzumab, Vandetanib, Vemurafenib, Vismodegib, Ziv-afilbercept). Until November 30, 2019, no published or ongoing ICS were found in any of the cases. Conclusions: a) For the past decade, none of the PCT used for approval of new oncological drugs or NDI were replicated by ICS (without pharmaceutical industry sponsorship). b) We ignore the reasons for these approval methods but they raise suspicion and unnecessary discomfort. c) From an ethical point of view, patients’ Informed Consent must specify that expected results for the new drug or NDI are based exclusively on trials sponsored by the selling pharmaceutical company, unless other ICS are published. d) The current state of affairs can only be reverted if regulatory agencies and the scientific community demand ICS as part of the drug approval process or post-marketing duties.
Combination of metformin and gefitinib as first-line therapy for nondiabetic advanced non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations: A multicenter, randomized, double-blind, placebo-controlled phase II trial.
Authors: Yong He, Li Li, Liyan Jiang, Yubo Wang, Yizhuo Zhao, Xiaoju Zhang, Guoming Wu, Xiangdong Zhou, Jianguo Sun, Jun Bai, Biyong Ren, Kun Tian, Zhi Xu, Hualiang Xiao, Qi Zhou, Rui Han, Hengyi Chen, Haidong Wang, Zhenzhou Yang, Chan Gao
Journal: Journal of Clinical Oncology (2019)

9035 Background: Several lines of evidence suggested a role for metformin in sensitizing diabetic NSCLC patients to EGFR tyrosine kinase inhibitors (TKIs). However, data regarding its effects on non-diabetic populations remained scarce. We hereby examined metformin’s first-line use alongside gefitinib in EGFR mutation positive (EGFRm) patients without diabetes. Methods: In this trial (NCT01864681), 224 non-diabetic patients with treatment naïve stage IIIB-IV EGFRm NSCLC were randomly assigned at 1:1 to receive gefitinib plus metformin or placebo. Gefitinib was administered at 250 mg once daily, while metformin/placebo was initiated at 500 mg once daily and then escalated to 1000 mg twice daily over 2 weeks. Dose reduction was permitted for metformin/placebo in case of intolerable toxicity. The primary endpoint was progression-free survival (PFS) rate at 1 year. Secondary endpoints were overall survival (OS), PFS, objective response rate (ORR), and safety. Serum levels of interleukin-6 were also subjected to exploratory analysis. Results: Baseline characteristics were well balanced between treatment groups. The median duration of follow-up was 19.15 (IQR 12.99-28.44) months. The estimated 1-year PFS rate was 41.2% (95% confidence interval [CI] 30.0-52.2) in the metformin group versus 42.9% (95% CI 32.6-52.7) in the placebo group (p = 0.6268). Metformin did not increase median PFS (10.3 months vs. 11.4 months), median OS (22.0 months vs. 27.5 months), or ORR (66.0% vs. 66.7%) over placebo. No significant treatment group differences in terms of PFS were detected across subgroups either, including those with elevated levels of interleukin-6. Metformin plus gefitinib shared a similar safety profile with the control group, except for a remarkably higher incidence of diarrhea (78.38% vs. 43.24%). Conclusions: Our study did not show enhanced gefitinib efficacy upon addition of metformin and hence does not support its concurrent use with first-line EGFR-TKI therapy in non-diabetic EGFRm NSCLC patients. Clinical trial information: NCT01864681.
Poly adenosine diphosphate ribose polymerase inhibitors maintenance in patients with ovarian cancer: A systematic review and meta-analysis of randomized controlled trials.
Authors: Thura WIN Htut, Aung Tun, Anita Sultan, Sriman Swarup, Myo Zaw, Pwint Phyu Hlaing, Yin Mon Myat, Nusrat Jahan, Francis Mogollon-Duffo, Shabnam Rehman, Fred L. Hardwicke, Kyaw Zin Thein
Journal: Journal of Clinical Oncology (2019)

e17081 Background: Ovarian cancer is the deadliest of gynecologic cancers and many recur despite achieving a clinical response to initial platinum-based chemotherapy. The use of poly adenosine diphosphate ribose polymerase (PARP) inhibitors maintenance has shown to improve survival in ovarian cancer. Methods: MEDLINE, EMBASE databases and meeting abstracts from inception through January 2019 were queried. Phase 3 randomized controlled trials (RCT) which employed PARP inhibitors maintenance in ovarian cancer were incorporated in the analysis. A generic inverse variance method was used to calculate the estimated pooled hazard ratio (HR) for progression-free survival (PFS) with 95% confidence interval (CI). Heterogeneity was assessed with Cochrane Q -statistic. Random effects were used due to some heterogeneity among studies. Results: Four phase III RCTs with a total of 1792 patients were eligible. The study arm used olaparib or niraparib or rucaparib while the control arm utilized placebo. The randomization ratio was 2:1 in all studies. Participants were sensitive to platinum-based chemotherapy, as newly diagnosed in SOLO-1 trial and had been previously on two such regimens in the other trials, with an objective response. Almost all patients in the SOLO-2 and SOLO-1 trials had a gBRCA mutation, while there were patients with and without the said mutation in the other two studies. The pooled HR for PFS was statistically significant at 0.32 (95% CI: 0.27-0.38; P &lt; 0.0001), including gBRCA cohort (HR, 0.28; 95% CI: 0.24-0.33; P &lt; 0.0001) and non-gBRCA cohort (HR, 0.39; 95% CI: 0.32-0.48; P &lt; 0.0001). Conclusions: Our meta-analysis demonstrated that the use of maintenance therapy with PARP inhibitors significantly improved PFS compared to placebo, regardless of the presence or absence of gBRCA mutation.
Weighted gene co-expression network analysis combined with machine learning to identify prognostic biomarkers for cervical squamous cell carcinoma.
Authors: Yimin Li, Mei Lan, Xinhao Peng, Zijian Zhang, Jin Yi Lang
Journal: Journal of Clinical Oncology (2019)

e17000 Background: Cervical cancer represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurate identifying high-risk patients. Here, we provide a co-expression network and machine learning-based signature to predict the survival of cervical cancer. Methods: Utilizing expression profiles of The Cancer Genome Atlas datasets, we identified differentially expressed genes (DEGs) and the most significantly module by differential expression analysis and Weighted Gene Co-expression Network Analysis, respectively. The candidate genes was obtained by combining the both results. Then the prognostic classifier was constructed by LASSO COX regression analysis and validated in testing set. Finally, survival receiver operating characteristic and Cox proportional hazards analysis was used to assess the performance of prognostic prediction. Results: We identified 190 differentially expressed genes (DEGs) between cervical squamous cell cancer(CSCC) and normal samples in purple module. Next we built a 8-mRNA-based signature, and determined a optimal cutoff value with sensitivity of 0.889 and specificity of 0.785. Patients were classified into high-risk and low-risk group with significantly different overall survival(training set: p &lt; 0.0001; testing set: p = 0.039). Furthermore, the prognostic classifier was an independent and powerful prognostic biomarker for OS (HR = 7.05, 95% CI: 2.52-19.71, p &lt; 0.001). Conclusions: The prognostic classifier is a promising predictor of CSCC patients, the novel co-expression network and machine learning-based strategy described in the study may have a broad application in precision medicine.
Therapeutic implications of a novel driver classification system for cutaneous and unknown primary melanomas.
Authors: Alexander Noor Shoushtari, Francisco Sanchez-Vega, Havish Kantheti, Margaret K. Callahan, Michael A. Postow, Christopher Andrew Barker, Walid K. Chatila, Philip Jonsson, Charlotte Eielson Ariyan, Mary Susan Brady, Daniel G. Coit, Suresh Nair, Paul B. Chapman, Klaus J. Busam, David B. Solit, Jedd D. Wolchok, Nikolaus Schultz
Journal: Journal of Clinical Oncology (2019)

9539 Background: Cutaneous and “unknown primary” melanomas frequently harbor alterations that activate the Mitogen Activated Protein Kinase (MAPK) pathway, and are often classified as BRAF, NRAS, NF1 mutant, or “triple wild type.” Multigene sequencing may identify additional oncogenic drivers, but the clinical impact of this information is unknown. Methods: Patients with BRAF inhibitor naïve melanoma underwent prospective tumor molecular profiling using a targeted capture-based assay (MSK-IMPACT), and demographic and treatment data were collected. Time to treatment failure was assessed for patients who received frontline PD-1 monotherapy or nivolumab plus ipilimumab. Results: 576 patients were successfully sequenced. 533 samples (96%) harbored a known or presumed oncogenic mutation in 1 of 28 genes in the RTK-RAS-MAPK pathway. 187 tumors (32%) had two or more drivers in this pathway, and the rates of driver co-alterations varied widely by specific driver. A hierarchical classification of 9 driver groups (BRAF V600E; V600K/R/M; BRAF non-V600; NRAS Q61; Other RAS; NF1; KIT; Other driver; Unknown driver) was significantly associated with tumor mutational burden, primary melanoma site, and patient age. Time to treatment failure varied by driver class and site of primary melanoma for PD-1 monotherapy but not nivolumab plus ipilimumab. 150 patients with BRAF V600 wild-type melanoma required systemic therapy after progression on checkpoint blockade. 21 were given genomically matched therapy, and 5/21 had clinical benefit for ≥6 months. Complete and durable responses were observed with TRK and ROS1 inhibitors in patients with NTRK1/2/3 and ROS1 fusion positive tumors. Conclusions: Oncogenic alterations in the RTK-RAS-MAPK pathway can be detected using targeted capture NGS in the vast majority of cutaneous and unknown primary melanomas. A hierarchical classification of 9 driver groups revealed clinically relevant melanoma subsets with varying clinical outcomes to PD-1 monotherapy. Select patients with oncogenic kinase fusions can achieve durable therapeutic benefit with targeted inhibitors of these rare drivers.
Case 22-1984
Authors: Richard C. Cabot, Robert E. Scully, Eugene J. Mark, Betty U. McNeely, M. B. Garnick, R. H. Young
Journal: New England Journal of Medicine (1984)

Presentation of CaseA 38-year-old man was admitted to the hospital because of an orbital mass.He was well until 22 months earlier, when he began to experience transient left upper abdominal pain. He entered another hospital, where evaluation yielded no diagnosis. The intermittent pain became more severe, and 13 months before admission he returned to the same hospital. Examination revealed a large left abdominal mass and enlargement of the left testis. X-ray films of the chest were normal. An intravenous urographic examination showed nonfunction of the left kidney. At laparotomy a large tumor was found in the left side . . .
A prospective blinded study of 1000 cases analyzing the role of artificial intelligence: Watson for oncology and change in decision making of a Multidisciplinary Tumor Board (MDT) from a tertiary care cancer center.
Authors: S.P. Somashekhar, Martín-J. Sepúlveda, Edward H Shortliffe, Rohit Kumar C, Amit Rauthan, Poonam Patil, Ramya Yethadka
Journal: Journal of Clinical Oncology (2019)

6533 Background: Artificial intelligence is being used to provide support for information-intensive decision making. In this report, we present our experience in explaining how artificial intelligence adds value to MDT’s decision making ability &amp; paves way for personalized therapy. Methods: 1000 cases involving breast, lung, and colorectal cancer were evaluated by a multidisciplinary tumor board at a major cancer center in India between 2016 and 2018. After the tumor board decision was made, MDT was presented with the Watsons recommendations contemporaneously. MDT reviewed their decision after going through Watson’s recommendations and also the evidences that it put forth supporting its decision. Cases in which decision was changed, objective assessment was done by asking MDT to quote the reasons for reviewing and changing their decision. Results: Of 1000 cases, breast, lung, colon &amp; rectal cancers were 620, 130,126 &amp; 124 respectively. There were 712 non-metastatic &amp; 288 metastatic cases. Mean age of the patients was 54.3 ± 12.2. Treatment concordance was observed in 92% for all cancers combined, 93% for rectal cancer, 92% for breast cancer, 89% for lung cancer, and 81% for colon cancer.MDT changed their decision in 136 cases (13.6%). The reasons for tumour board to change their decision was, Watson provided recent evidences for newer treatment in 55%, better personalized alternative in 30% &amp; new insights from genotypic and phenotypic data and evolving clinical experiences in 15% of time. Conclusions: The study suggest that cognitive computing decision support system holds substantial promise to reduce the cognitive burden on oncologists by providing expert, updated, recent evidence-based insights for treatment-related decision-making. The 13.6 % incremental advantage over and above in a tertiary cancer centre with functioning MDT speaks in itself the value of having a learned colleague like Watson for oncology at our disposal. It will certainly add more value in settings lacking ready access to high quality cancer expertise and information. These systems can be valuable adjuncts to strong patient-clinician relationships in the delivery of high quality cancer care.
Isolation of novel gut bifidobacteria using a combination of metagenomic and cultivation approaches
Authors: Gabriele Andrea Lugli, Christian Milani, Sabrina Duranti, Giulia Alessandri, Francesca Turroni, Leonardo Mancabelli, Danilo Tatoni, Maria Cristina Ossiprandi, Douwe van Sinderen, Marco Ventura
Journal: Genome biology (2019)

Whole metagenome shotgun (WMGS) sequencing is a method that provides insights into the genomic composition and arrangement of complex microbial consortia. Here, we report how WMGS coupled with a cultivation approach allows the isolation of novel bifidobacteria from animal fecal samples. A combination of in silico analyses based on nucleotide and protein sequences facilitate the identification of genetic material belonging to putative novel species. Consequently, the prediction of metabolic properties by in silico analyses permits the identification of specific substrates that are then employed to isolate these species through a cultivation method.