bluuebunny/crossref_metadata_2025 huggingface dataset:
Mutation spectrum of EGFR in 21,324 patients with non-small cell lung cancer successfully tested by multiple platforms in a CAP-accredited laboratory.
Authors: Mao Linlin , Zhao Weiwei , Li Xiaoxia , Zhang Shangfei , Zhou Changhong , Zhou Danyan , Ou Xiaohua , Xu Yanyan , Tang Yuanxiao , Hu Changming , Luo Pifu , Yu Shihui
Published: May 2020

e13637

Background: Genotyping epidermal growth factor receptor ( EGFR) gene in patients with advanced non-small cell lung cancers (NSCLC) is essential to identify those patients who may benefit from targeted treatments. However, the testing methodology is still a puzzle for many clinicians. This study retrospectively reviewed the EGFR data from testing patients with NSCLC in our laboratory in 10 years. Methods: A total of 21,324 NSCLC cases successfully underwent EGFR genotyping in our laboratory for clinical therapeutic purpose, including 5,244 cases tested by Sanger sequencing, 13,329 cases tested by real-time PCR, and 2,751 tested by next generation sequencing-NGS. Results: 54.6%, 20% and 2.3% samples were recorded with tumor cell content (TCC) of < 40%, < 20% and 1% respectively. The average EGFR mutation rate was 45.1%, with 40.3% identified by Sanger sequencing, 46.5% by real-time PCR and 47.5% by NGS. Comparable EGFR mutation rates were observed in cases with 1%-20% TCC from real-time PCR and NGS platforms. In these cases with EGFR mutations, 93.3% of them carried a single EGFR mutation (92.1% with a classical mutation as 19del or L858R, and 7.9% with an uncommon mutation) and 6.7% harbored complex EGFR mutations. 65.3% of the cases carrying EGFR mutations identified by NGS also have tumor related variants in other genes. 26.5% more cases were found to have oncogenic driver mutations with therapeutic significance beyond EGFR by NGS. Conclusions: NGS may be the most optimal methodology to detect EGFR mutations in patients with NSCLC compared with Sanger sequencing and real-time PCR platforms from accuracy and comprehensiveness aspects, but some other issues, such as Turn Around Time (TAT), still should be considered in clinical practice.
Genetic predisposition to childhood cancer: A retrospective cohort analysis in a multidisciplinary genetic cancer counseling Unit.
Authors: Rodriguez Salas Nuria , Escudero Adela , Blanco Naiara , Corral Dolores , Higuera Oliver , Ruiz Gimenez Leticia , Gallego Alejandro , Sánchez Cabrero Darío , Rodriguez Laura , Perez Martinez Antonio
Published: May 2020

e13643

Background: The incidence of childhood cancer in Spain is 18, 2 cases per 100.000 inhabitants per year. The presence of predisposing genetic factors has been revealed in approximately 10% of patients, in them the detection of genetic mutations associated is essential in order to establish the correct clinical management of patients and their relatives. However, genetic counseling in this population is currently underestimated since, not all patients with pediatric malignancies have a family history of cancer or other clinical signs to suspect and inheritance syndrome. Methods: The Unit for Predisposition to Child and Adolescent Cancer of the University Hospital La Paz in Madrid, Spain, is composed by the Units of Clinical Oncology, Pediatric Hemathology-Oncology, and Genetics. Inclusion criteria are based on the characteristics of the individual's neoplasia, family characteristics and other phenotypic characteristics.The molecular study is carried out through Next Generation Sequencing, Direct Sequencing and / or MLPA. In the present study, a retrospective and descriptive analysis of the cases studied from September 2018 to December 2019 was carried out. Results: The Pediatric Cancer Predisposition Functional Unit of the La Paz University Hospital attended during the years 2018 and 2019 a 58 patients with suspected predisposition syndromes to childhood cancer belonging to 41 families. The following inheritance syndromes have been identified: hereditary retinoblastoma in 14 families with mutation in the RB1 gene, Li Fraumeni syndrome in 1 family with mutation in the TP53 gene, multiple endocrine neoplasia type 2A by RET gene mutation in 1 family, 1 hereditary melanoma syndrome with a CDKN2A mutation, and inheritance family leukemia in 1 family with a PAX5 gene mutation. Other cases studied have been negative-non informative, with the following neoplasms of the index cases: unilateral retinoblastoma, hepatoblastoma, hamartomatous colon polyposis, lymphoma, leukemia and soft tissue sarcoma. In addition to the molecular diagnosis of the index case, both cases and family members received genetic counseling and adequate clinical management, adapted to each syndrome. Conclusions: The multidisciplinary and specialized approach improves the diagnosis and monitoring of patients with suspected syndromes of predisposition to childhood cancer and allows the establishment of prevention strategies and early diagnosis individualized to each family.
An ultra-sensitive multiplexed enrichment of rare DNA variants via toehold exchange principle.
Authors: Pu Yue , Wang Qianwen , Lin Rui , Wang Tao
Published: May 2020

e13658

Background: As a major type of liquid biopsy, circulating tumor DNA (ctDNA) provides important somatic mutation information, but the feature of rare allele frequency of mutation in ctDNA hampers its wide application. Methodology with high sensitivity, cost effectiveness, and multiplexing capability is needed for analyzing ctDNA samples. Methods: We have developed a novel methodology, termed MTEA (Multiplexed Toehold Exchange Amplification), which selectively amplifies and enriches all sequence variants (including SNV and deletion) 1000-fold over the wild-type sequence within ~20-nucleotide window. The amplified products are detected by Sanger sequencing or next-generation sequencing (NGS). This novel approach takes advantage of toehold exchange principle in which PCR amplification of wild-type rather than mutants would be greatly reduced due to the formation of a duplex of wild-type sequence with a blocker targeting to the wild-type rather than the mutant alleles. Results: Using circulating cell-free DNA (cfDNA) reference standards, we demonstrated that the major driver mutations of lung adenocarcinomas, including EGFR L858R and exon 19 deletion, KRAS G12X, and BRAF V600E, were enriched and detected simultaneously with MTEA technology at abundance as low as 0.01-0.05%. The clinical implications of MTEA technology were further validated using ctDNA from liquid biopsy specimens of 29 non-small cell lung cancer patients, and results were compared with those collected from the NGS and ddPCR analysis. Consistent results were obtained from all of the three platforms, which indicated 100% accuracy. The MTEA method has advantages over ddPCR since the later lacks the multiplexing detection ability. The MTEA technology can also save cost compared with NGS method. Conclusions: MTEA technology can enrich ctDNA from cancer patients to detect ultra-low abundance of clinically relevant mutations. Upon validation using a larger cohort, the technology may have wide application in precision medicine and guide clinicians for therapeutic decisions.
Circulating exosome DANCR correlated to the recurrence of hepatitis C virus-related hepatocellular carcinoma.
Authors: Wang Shu-Chi , Huang Jee-Fu , Dai Chia-Yen , Chuang Wan-Long , Yu Ming-Lung
Published: May 2020

e13688

Background: Long non-coding RNAs (lncRNAs) have been associated with various types of neoplasms. Hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) has high risk of recurrence. Methods: Differentially-expressed candidate lncRNAs relevant to HCV-HCC were identified by RNA-seq in Screening set, validated by qPCR in Validation set, and confirmed in external Test cohort. Target lncRNAs in tissue and serum exosome were applied to predict HCC recurrence of HCV-HCC after curative surgical resection in a large Application cohort. Pathogenetic mechanisms of target lncRNA were explored by using JFH1-infectious cells. Results: Of the top ten differentially-expressed lncRNAs relevant to HCV-related HCC identified in Screening set, differentiation antagonizing non-protein coding RNA (DANCR), which was upregulated by HCV infection and identified as the most relevant lncRNA to HCV-HCC, was overexpressed in tumor than in adjacent non-tumor (ANT) tissues. Of 183 HCV-HCC patients followed for 10 years after curative HCC resection, tumor DANCR, tumor/ANT DANCR ratio and circulating exosome DANCR levels were positively associated with HCC recurrence. Circulating exosome DANCR levels by droplet digital PCR provided the best predictive power of HCC recurrence with area under the curve of 0.88, when compared to tumor DANCR levels, tumor/ANT DANCR ratio, and alfa-fetoprotein. Exosome DANCR > 50 copies/ml was the most predictive factor associated with HCC recurrence and mortality (hazard ratio/95% confidence intervals: 7.0/4.3-11.6 and 2.7/1.5-5.1 respectively, Cox regression model). Cell experiments showed that DANCR, positively associated with mesenchymal-associated β-catenin expression, promoted cell migration and invasion ability. Conclusions: lncRNA-DANCR was highly relevant to disease progression of HCV-HCC. Circulating exosome DANCR could serve as a non-invasive prognostic biomarker for HCV-HCC.
The multi-disciplinary meeting as a tool to identify patients for clinical trials and translational research.
Authors: Winter H , Rea David
Published: May 2020

e14001

Background: Cancer multi-disciplinary team meetings (MDTs) were established with one of the core aims to increase participation in clinical research. However, a recent Cancer Research UK study observing 624 patient MDT discussions revealed only 8 (1.3%) patients included a discussion on clinical trials. Current work in the UK looking at MDT effectiveness has explored the quality of discussions and team dynamics. However, recruitment to clinical trials has not been promoted as an indicator of an effective MDT. This study explored the role of MDTs in identifying patients for research across all solid tumour MDTs in a tertiary cancer centre. Methods: This study explored the outcome data from solid tumour MDTS; reports on the observation of solid tumour MDTs and completion of a trials and translational proforma about patient discussions on research; exploration of MDT members views on the role of MDTS in identifying opportunities for patients to participate in clinical trials and translational research. Results: MDT outcomes from all solid tumour MDTs were reviewed. Out of 7000 local MDT outcomes from Avon Cancer Registry April 2019- Set 2019. Only 409 discussions included an outcome registered with “Clinical Trial y/n” (5%). In the majority there was no mention of any consideration of clinical trial or an explicit trial documented. Conclusions: We report on the exploration of the MDT as integral to identifying patients for clinical trials and translational research. This requires input and buy in from MDT members and the identification of research champions attending MDTs. The integration of genomics data into MDTs will require a proactive approach to access clinical trials for patients with identified potential actionable targets.
Utilization of imaging and outcomes of care among metastatic colorectal cancer (mCRC) patients (pts) receiving bevacizumab containing regimens.
Authors: Barzi Afsaneh , Zheng Hanke , McCombs Jeffrey
Published: May 2020

e14011

Background: Chemotherapy combined with bevacizumab is the most commonly used treatment first line therapy in pts with mCRC. Decisions for continuation or change of chemotherapy are based on the findings of CT scan, the most commonly used from of imaging in this population. Modeled after clinical trials, CT scan every 2 months is adopted as a standard of care. Yet, patterns of utilization of CT scan in general population is unknown. We set to explore CT scan utilization and associated outcomes among pts with mCRC. Methods: The De-identified Clinformatics Data Mart (OptumInsight, Eden Prairie, MN) covering January 2008 to December 2016 was used for this analysis. Pts with two out-patient and/or one in-patient ICD codes for colorectal cancer were identified. Pts with at least 180 days of enrollment, no chemotherapy within 120 days prior to chemotherapy, and at least one claim for CT scan were eligible for analysis. Recipients of FOLFOX (CAPOX) or FOLFIRI (XILIRI) +bevacizumab were identified using HCPCS codes and the data of their 1sttreatment was registered as index date. The primary endpoint of the analysis was exposure to both FOLFIRI and FOLFOX, secondary endpoint was survival. SAS software was used for data processing and analysis. Results: A total of 3261 pts met the inclusion criteria 78% with oxaliplatin based regimens and 22% with irinotecan regimens. The median age of the population is 66 (19-89), and 58.3% of the identified pts were male. The median duration of first line therapy was 119 days. Median number of CT scan during first line was 2.3. The median and mean number of CT scan per 2 months were 0.82 and 0.94. There was no difference in age, gender, and comorbidities in those with less than 2 vs. 2 or more CT scans. Exposure to both regimens (measured with switching from one regimen to another) was 35% in pts with less than 2 CT scans and 44% in those with 2 or more CT scan (p-value < 0.0001). Probability of survival at 12 months was 83% for all patients regardless of the frequency of scans. Conclusions: In patients with mCRC more frequent scans is associated with higher probability of access to active agents. However, survival probability at 12 months was not different between the two groups.
Systematic analysis of chemotherapy agents for home-based administration.
Authors: Xiangyun Ye George , Binder Adam , Handley Nathan , Piddoubny Maria , Espinosa Gloria , Chung Justine , Alberto Judith
Published: May 2020

e14012

Background: Improvement in supportive care medications, feasibility of outpatient management, and regulatory changes have led to a dramatic shift in the primary location of chemotherapy delivery from the inpatient setting to outpatient infusion centers. Over the last decade, centers in Europe and Canada have taken the next logical step – home based chemotherapy infusion. To our knowledge there is no systematic large scale initiative that has transitioned chemotherapy to the home in the United States. In order to implement a home based chemotherapy infusion program, we systematically scored anti-neoplastic agents for ease of administration. Methods: We reviewed all anti-neoplastic agents administered at our infusion center. Characteristics of each medication that could be a barrier to home infusion were identified. These included route and duration of administration, vesicant status, emetogenic potential, and duration of stability at room temperature. Scoring was determined by a multi-disciplinary team of pharmacists and oncologists (see Table). Higher scores indicated greater potential for home administration. Results: We reviewed 100 medications. The highest possible score was 8; the lowest possible score was -4. Agents ranged with scores from 8 (fulvestrant) to -1 (dactinomycin), with a median score of 4. The mode score was 3 (24 medications). The largest factor lowering the ease of administration score was stability at room temperature; score of -2 and -1 in 19 and 18 medications respectively. Conclusions: It is feasible to administer the majority of our chemotherapeutic agents in the home setting. The biggest barrier to administration at home is stability of medications at room temperature. This issue can be addressed by transporting and storing the medication in a refrigerated container. Expectedly, injectable drugs and medications with short infusion times that are stable at room temperature would be the easiest to administer in the home. Further analysis in ongoing to assess the financial feasibility and establishment of our home based program. [Table: see text]
First validation of the supportive care score (SCC) scale: Alert scale for referral of solid cancer patients to supportive oncology team.
Authors: Chastenet Mathilde , Laurain Pierre-Antoine , Maupoint Typhaine , Legeay Karine , Salleron Julia , Baumard Florian , Scotte Florian
Published: May 2020

e14025

Background: Patients’ needs are still underestimated during cancer course. The development of a simple and accessible screening tool to identify supportive care needs is an innovative approach to improve the cancer care pathway. Supportive Scale sCore (SCC) is a new tool developed to trigger alert in main need of supportive care such as social, nutritional, physical, pain or psychological disorders. This study aimed to develop and validate the SCC tool for detecting supportive care needs. Methods: The SCC, the Edmonton Symptom Assessment System (ESAS), a symptom scale and the EQ-5D (for Quality Of Life) was distributed to cancer patients over a week, in an ambulatory hospital of oncology department. The acceptability was assessed by the fill rate. The validity of alerts generated by the SCC scale was assessed by their consistency with ESAS and EQ-5D scores. Results: Hundred patients were included with an average age of 67,2 years. Acceptability was good with a fill rate of over 90%. For a-priori defined risk groups by SCC with alert or not, ESAS symptom score and QOL differed significantly (p < 0,05) between groups. We observed higher ESAS symptom scores in the alert group [nutritional alert: appetite: 4 (Standard Deviation SD 2,4) vs 0 (SD 1,6), p < 0,001; physical alert: fatigue: 4 (SD 1,7) vs 2 (SD 2,2) p < 0,001; psychological alert: depressed: 3,5 (SD 2,7) vs 0 (SD 1,5), p < 0,001; anxiety: 4 (SD 2,9) vs 0 (SD 1,5), p < 0,001; unwell-being: 4,5 (SD 2,7) vs. 0 (SD 1,5), p < 0,001]. Moreover, the QOL was poorer in each domain of EQ-5D in the alert group. [Social alert, self-care: 9,3% vs 0%, p = 0,02; usual activities: 25% vs 5,4%, p = 0,005. Physical alert, usual activities: 21,3% vs 2,6%, p = 0,008; mobility 29,5% vs 2,6%, p = 0,01. Pain alert, pain: 81,8% vs 11,9%, p < 0,001. Psychological alert, psychological: 56,3% vs 11,9%, p < 0,001]. Conclusions: The SCC seems to be a reliable instrument to detect cancer patients’ supportive care needs.
Interdisciplinary risk assessment and delay of chemotherapy.
Authors: Souza Juliana , Santos Monica , Coutinho Ana Paula , Gil Natalia , Silva Alessandra , Fernandes Maria Carolina , Souza Larissa , Souza Vilceia , Neves Daniele Ferreira
Published: May 2020

e14033

Background: In 2015, in our outpatient clinic, we implemented a breast cancer interdisciplinary team with the goal of identifying risk factors for chemotherapy delay. Evaluation of individual cases and care plans were made by a medical oncologist, clinical pharmacologist, psychologist, dietician and oncology nurse. Methods: 81 patients with breast cancer in neoadjuvant/adjuvant chemotherapy were prospectively evaluated from July 2018 until July 2019 for clinical risk: age, anthracyckine use, presence of symptoms grade >3, ASGPPP questionnaire for nutritional risk, psychological risk for pre existent issues or induce by treatment and IRPO questionnaire for screening, nursing evaluation for comunication, organization and understanding issues, and pharmacologist evaluation of polypharmacy and unknown drugs or routine use. Results: Of 81 patients evaluated and had finish treatment, 55 pts (67.9%) received adjuvant chemotherapy. Median age was 57.9 years (range:29-90.6y). We defined as risk factors for delay chemotherapy treatment: anthracycline-based chemotherapy (9.34 vs 2.58 d p:0.002), psychological risk (both risks combined) (8.1 vs 4.7 p:0.047), comunication, organization and understanding issues (15.25 vs 5.83 d p: 0.004), and unknown drugs that routinely uses (11 vs 5.34d p:0.012). Patients who needed extra nutritional and psychological appointment had greater delay in chemotherapy (Dietician: 9.636 vs 4.7 d P:0.01; Psyc: 9.1 vs 4.2d p:0.008). Conclusions: The chemotherapy scheme, psychological risk, difficulty in understanding and organizing, and unaware of the drugs used routinely uses were high risk factors for delayed chemotherapy and these patients should be handled more carefully.
Optimizing the febrile neutropenia care process in cancer patients in a third level Spanish hospital.
Authors: Solis-Hernandez Maria Pilar , Mihic Góngora Luka , Contreras Toledo Debora Corina , Iglesias Gomez Clara , Fernandez Arrojo Sara , Alvarez Fernandez Carlos , Jiménez Fonseca Paula , Izquierdo Manuel Marta , Revuelta Alfonso , Gómez David , del Río Fernández Jorge , Valcárcel González Sena , Muniz Castrillo Maria , Castillo Trujillo Oscar Alfredo , Villanueva Noemí , Garcia Llano Juan Luis , Palacio Vázquez Isabel , Fernandez Perez Yolanda , Esteban Emilio ,   Hospital Universitario Central de Asturias
Published: May 2020

e14034

Background: Cancer patients under cytotoxic treatment with fever used to have an average time of antibiotics administration of 11h (median 3h43min) when admitted and identified at the Emergency Room Service in the third level Central University Hospital of Asturias. International guidelines recommend antibiotic administration in the first hour: the so called “Golden Hour”. This delay implies an increase in patient morbidity as well as the cost of managing this complication. Methods: Data was collected from our electronic registry after proper authorization and corresponding anonymization. Pareto and fishbone diagrams were built to point out the possible causes and to stratify them in related subgroups. We measured: Number of visits to ER with fever and Number of inpatients with Febrile Neutropenia diagnosis, Time from the admission to antibiotic administration, Average stay of inpatients with Febrile Neutropenia diagnosis. A protocol associated to electronic tools and validated international scores was designed for patients at Febrile Neutropenia risk and launched on March 2019. Results: 66 patients were included since January 2018. Baseline data showed that the median time to antibiotics indication was 3h50min (average 4h38min) with a median delay of 18min to the administration (average 1h06min), a median of 3h54min (average 10h09min) time to antibiotic administration. When the protocol was activated, median time to antibiotic indication was 30min (average (1h08min) with a median 41min (average 40min) delay in administration, and a median of 1h30min (average 1h42min) time to antibiotic administration. Those with activation of the protocol had average length of stay of 1d13h40min vs. 5d11h18min in those to whom this protocol was not applied. Conclusions: A continuous evaluation effort and the design of appropriate protocols and interventions will increase the efficiency of the oncology patient care process with febrile neutropenia, by reducing unnecessary variability and costs.
Lenalidomide use in multiple myeloma.
Authors: Meade Denise , Ng Marie , Alford S
Published: May 2020

e14045

Background: For patients with multiple myeloma (MM), lenalidomide’s (LEN) received its first FDA approval for use in combination with dexamethasone (DEX) for second-line treatment in 2006, and was expanded to first-line treatment in 2015 for patients not eligible for autologous stem cell transplant, and was further expanded for use as monotherapy for maintenance treatment following a transplant in 2017. We aimed to characterize the treatment patterns of LEN and analyze the impact of the expanded approval on the MM treatment. Methods: A MM disease model was built in IBM Access and Value Connect to interrogate patient demographics, payer types, clinical characteristics, clinical outcomes, utilization, costs, and treatment sequencing, informed by United States Food and Drug Administration (FDA) labels for approvals, professional society guidelines, clinical and economic literature review, and subject matter expertise. Patients were selected from IBM Marketscan Commercial and Medicare Claims database and have an approved MM treatment, at least one diagnosis claim for MM within 30 days of treatment, and a minimum of treatment period of 90 days, but do not have claims for other cancers or pregnancy during the study period. Using advanced visual analytics, we identified trends and outcomes in the treatment and performance of MM patients with LEN to evaluate the impact of LEN’s expanded approval. Results: From October 2017 to September 2018, a total of 1,422 MM patients in the database received treatment with LEN, representing 61% of all MM patients for the same period. Of the patients receiving LEN, 38% of patients were being treated with LEN on monotherapy. In comparison, for the same time period only 1% of patients were on LEN-DEX. Patients receiving LEN as a monotherapy had mean monthly all-cause costs of $15,590 vs $32,352 for the same costs for patients receiving LEN as a combination therapy. Conclusions: The expanded indication of LEN in 2017 has likely increased the population of MM patients receiving LEN as a monotherapy. Further analyses to determine whether clinical outcomes have improved for patients on LEN maintenance therapy should be completed given the increased financial costs of combination therapy.
Trends in ALK inhibitors for non-small cell lung cancer.
Authors: Meade Denise , Ng Marie , Alford S
Published: May 2020

e14046

Background: Rearrangements in the gene encoding anaplastic lymphocyte kinase (ALK) are found in 3%–5% of patients. Treatment options have significantly expanded with ALK inhibitor drug approvals including crizotinib in 2011, ceritinib in 2014, alectinib in 2015, brigatinib in 2017 and lorlatinib in 2018. We sought to better understand the real-world treatment utilization and cost of ALK inhibitors in lung cancer (LCA) over the most recent period for which adjudicated claims are available, October 2017-September 2018. Methods: A LCA disease model was built in IBM Access and Value Connect to interrogate patient demographics, payer types, clinical characteristics, clinical outcomes, utilization, costs, and treatment sequencing, informed by United States Food and Drug Administration (FDA) labels for approvals, professional society guidelines, clinical and economic literature review, and subject matter expertise. Patients were selected from IBM Marketscan Commercial and Medicare Claims database and have an approved LCA treatment, at least one diagnosis claim for LCA within 30 days of treatment, and a minimum treatment period of 90 days, but do not have claims for other cancers or pregnancy during the study period. Using advanced visual analytics, we identified trends in the cost, utilization, and clinical outcomes of LCA patients. Results: There were 2,300 LCA treated patients in our 12-month cohort from October 2017-September 2018, of which 6% were treated with an ALK inhibitor. Of those 145 patients, 59% were treated with alectinib and 33% with crizotinib. Overall all cause per-member per-month costs for alectinib and crizotinib patients were $18,541 and $19,078, respectively. While the overall costs for alectinib and crizotinib were roughly the same, ER and in-patient costs were higher for crizotinib at $1,635 and $404, respectively, compared to $503 and $57 for alectinib. Conclusions: Our analyses demonstrate alectinib is the preferred first-line ALK inhibitor in the last twelve months of available data. Furthermore, the increased ER and in-patient costs may substantiate the findings of the ALEX trial, notably higher liver toxicity and more nausea, vomiting, and diarrhea for crizotinib. There is not yet sufficient data on the newer ALK inhibitors, brigatinib and lorlatinib in the real-world.
Deep learning to develop transcriptomic model for survival prediction in TCGA patients with hepatocellular carcinoma.
Authors: Yu Hao , Dai Wei , Chiang Chi Leung , Du Shisuo , Zeng Zhao-Chong , Shi Guo-Ming , Zhang Wei , Chan Albert , Hu Chen , Kong Feng-Ming
Published: May 2020

e14057

Background: This study aimed to investigate the prognostic value of transcriptome and clinical data of Hepatocellular carcinoma (HCC) patients for overall survival (OS) by deep learning method. Methods: A total of 371 HCC patients with 20530 level three RNA-sequencing data were from The Cancer Genome Atlas (TCGA). Cox-nnet model, a deep learning model through an artificial neural network extension of the Cox regression model, was used for OS prediction. The patients were randomly split into train-set and test-set (7:3). In train-set, the significant genes associated with OS under univariate Cox regression were considered for modeling. Clinical parameters, including age, gender, pathologic stage, child pugh classification, creatinine level etc. were also considered. The Cox-nnet model was developed by cross-validation. Its discrimination was determined by the concordance index (CI) in the independent test-set and compared with multivariable Cox regression. The clustering method Uniform Manifold Approximation and Projection (UMAP) was used for revealing biological information from the hidden layer in the model. Results: In the train-set (n = 259), 1505 genes and two clinical variables (child pugh score and creatinine level) were significantly associated with OS (adjusted P-value < 0.05). To avoid overfitting, only 40 most significant genes were included in the Cox-nnet model. In the test-set (n = 112), the CI of Cox-nnet (0.76, se = 0.04) is better than the CI of multivariable Cox regression (0.71, se = 0.05). The difference between good or poor survival subgroups classified by Cox-nnet was remarkably significant ( P-value = 1e-4, median OS: 80.7 vs. 25.1 months). In the Cox-nnet model with all significant variables, the weights in the hidden layer were clustered by UMAP into 3 positive clusters and 2 negative clusters, which are enriched in GO/KEGG. The “cell cycle” and “complement and coagulation cascades” are the most important signal pathways in positive and negative clusters, respectively. Conclusions: Combining transcriptomic and clinical data, and with deep learning algorithm, we built and validated a robust model for survival prediction in HCC patients. Our study would be useful to explore the clinical implications in survival prediction and corresponding genetic mechanisms. Clinical trial information: 5U24CA143799, 5U24CA143835, 5U24CA143840, 5U24CA143843, 5U24CA143845, 5U24CA143848, 5U24CA1438.
Emerging oncology drug targets in the 21st century: An FDA analysis.
Authors: Scott Emma C. , Gong Yutao , Subedee Ashim , Kazandjian Dickran , Pazdur Richard , Rao V. Ashutosh , Beaver Julia A. , Schneider Julie A.
Published: May 2020

e14067

Background: Traditional chemotherapy agents are cytotoxic and typically act on all rapidly dividing cells. In contrast, targeted cancer therapies take advantage of unique genetic or proteomic susceptibility in cancer cells, their microenvironment, or the immune system to selectively target tumor tissue or stimulate immune activity. We catalogued oncology drug approvals in the U.S. in the 21st century to study the evolution of molecular targets over the last twenty years. Methods: We used internal FDA databases and data repositories to generate a dataset of all oncology approvals granted 1/1/2000-12/31/2019. This dataset was curated through a comprehensive survey of drug labels, drug reviews, and published literature to include target gene(s), mechanism of action, drug class, and approved indications for each drug. Approvals were grouped into five-year periods for trend analyses. Results: There has been an increase in the number of oncology approvals over the last twenty years, the vast majority of which are targeted therapies. There have been considerable recent advances in some disease sites. Three disease sites (ovary, bladder, and liver) had many approvals in the last five years after few to none 2000-2014; lung cancer and melanoma have seen substantial advances in the last ten years. Additionally, the first site-agnostic approvals occurred in the last five years. Kinase inhibitors are the most common drug class by both number of drugs and indications. Immune checkpoint inhibitors (ICI) are the second most common class by number of indications, despite having only entered the market in 2011 and being the sixth most common class by number of drugs. The target gene PD-1 has the highest number of approved indications, followed by EGFR and the BCR-ABL fusion gene. The number of novel genes targeted by drugs approved in 2010-2019 is nearly double that of 2000-2009, with the most novel gene targets in 2019 (n = 7). Conclusions: This analysis captures the landscape of targeted drug approvals in the 21st century, including the continued dominance of kinase inhibitors despite the dramatic impact of ICIs on cancer care since their introduction in 2011. The promise of targeted therapy in oncology is especially evident from the introduction of site-agnostic indications, new approvals in cancer types with limited therapy options, and ICIs with many indications due to targeting tumors indirectly via the immune system. The continual introduction of novel gene targets provides a snapshot of ongoing novel drug development, and it will be interesting to see whether this trend continues over the next twenty years.
Evaluation of an AI system for breast cancer screening in mammograms of young women.
Authors: Petrini Daniel Gustavo Pellacani , Valente Gabriel Vansuita , Shimizu Carlos , Roela Rosimeire Aparecida , de Araújo Gabriel Miranda , Tucunduva Tatiana Cardoso de Mello , Koike Folgueira Maria A. A. , Kim Hae Yong
Published: May 2020

e14068

Background: The interpretation of the mammography is challenging, especially in young women, who have dense breasts. Artificial intelligence (AI) promises to improve breast cancer detection; however these systems should be tested on different datasets. Our aim is to evaluate the performance of a publicly available deep convolutional neural network, developed by Wu et al. (IEEE Trans. Med. Imaging, 2019), applied to mammograms of young women. Methods: The test dataset consisted of mammograms obtained on a single occasion from 135 young women (up to 40 years old) on a Siemens mammography system. Each exam consisted of 4 full-field digital mammography images and had two labels (left malignant and right malignant). Mammograms were analyzed by a single mammography trained radiologist, using BI-RADS reporting tool. Among 270 labels, 170 were malignant and 100 were non-malignant. We used the program developed by Wu et al. that, according to the authors, presents AUC of 0.895 for the general population. As a preliminary test, we ran this program in a publicly available dataset named INbreast and obtained AUC of 0.8708, very close to the result reported by the authors. Results: We applied the program to our dataset of young women and obtained AUC of 0.876. We computed its standard error, obtaining 0.0290. At equal error rate point of the ROC curve, specificity and sensitivity are both 0.774. With this result we conclude that, at least for our dataset, cancer detection in young women is not substantially more difficult than in general population for an AI system. We fine-tuned the weights of the original network to the population of young women using transfer learning and obtained a slight improvement in AUC: 0.9018±0.0528, where the mean and the standard error were obtained using 5-fold cross validation. As the improvement was small and the standard errors are large, we would have to test on a larger test set to ensure that the observed improvement is real. Conclusions: We conclude, based on the experimental data, that there is no substantial degradation in accuracy when a mammogram screening program for general population is used for young women. We also conclude that it seems to be possible to obtain a slight improvement in accuracy by fine-tuning the network for the population of young women.
Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.
Authors: Akbilgic Oguz , Karabayir Ibrahim , Gunturkun Hakan , Pierre Joseph F , Rashe Ashley C , Thomas Alexandra
Published: May 2020

e14069

Background: There is growing interest in the links between cancer and the gut microbiome. However, the effect of chemotherapy upon the gut microbiome remains unknown. We studied whether machine learning can: 1) accurately classify subjects with cancer vs healthy controls and 2) whether this classification model is affected by chemotherapy exposure status. Methods: We used the American Gut Project data to build a extreme gradient boosting (XGBoost) model to distinguish between subjects with cancer vs healthy controls using data on simple demographics and published microbiome. We then further explore the selected features for cancer subjects based on chemotherapy exposure. Results: The cohort included 7,685 subjects consisting of 561 subjects with cancer, 52.5% female, 87.3% White, and average age of 44.7 (SD 17.7). The binary outcome variable represents cancer status. Among 561 subjects with cancer, 94 of them were treated with chemotherapy agents before sampling of microbiomes. As predictors, there were four demographic variables (sex, race, age, BMI) and 1,812 operational taxonomic units (OTUs) each found in at least 2 subjects via RNA sequencing. We randomly split data into 80% training and 20% hidden test. We then built an XGBoost model with 5-fold cross-validation using only training data yielding an AUC (with 95% CI) of 0.79 (0.77, 0.80) and obtained the almost the same AUC on the hidden test data. Based on feature importance analysis, we identified 12 most important features (Age, BMI and 12 OTUs; 4C0d-2, Brachyspirae, Methanosphaera, Geodermatophilaceae, Bifidobacteriaceae, Slackia, Staphylococcus, Acidaminoccus, Devosia, Proteus) and rebuilt a model using only these features and obtained AUC of 0.80 (0.77, 0.83) on the hidden test data. The average predicted probabilities for controls, cancer patients who were exposed to chemotherapy, and cancer patients who were not were 0.071 (0.070,0.073), 0.125 (0.110, 0.140), 0.156 (0.148, 0.164), respectively. There was no statistically significant difference on levels of these 12 OTUs between cancer subjects treated with and without chemotherapy. Conclusions: Machine learning achieved a moderately high accuracy identifying patients’ cancer status based on microbiome. Despite the literature on microbiome and chemotherapy interaction, the levels of 12 OTUs used in our model were not significantly different for cancer patients with or without chemotherapy exposure. Testing this model on other large population databases is needed for broader validation.
Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.
Authors: Kanan Christopher , Sue Jillian , Grady Leo , Fuchs Thomas J. , Chandarlapaty Sarat , Reis-Filho Jorge S. , Salles Paulo G O , da Silva Leonard Medeiros , Ferreira Carlos Gil , Pereira Emilio Marcelo
Published: May 2020

e14076

Background: The most common approach to diagnose prostate cancer is the “whole gland biopsy procedure,” in which numerous cores (≥12) are taken from different regions of the gland to maximize the chances of detecting small cancers; the presence of cancer in any of these cores is significant to the patient. If concerning features that are not fully diagnostic of cancer are identified, the pathologist may defer the final diagnosis until additional studies (e.g. immunohistochemistry) have been performed. We recently developed an artificial intelligence (AI)-based system for the assessment of cancer in prostate biopsies. Here, we investigated the performance of this test in an independent dataset of prostate cancers consecutively accrued. Methods: Two board-certified pathologists retrospectively reviewed 600 digitized hematoxylin-and-eosin (H&E) stained diagnostic prostate core needle biopsy slides from 100 consecutive patients, originally diagnosed at an independent hospital. Pathologists’ assessments were based on the H&E image alone; if further testing would be preferred, it was noted in the review notes. All images were assessed by Paige Prostate 1.0, an AI-based diagnostic tool; based on its outputs (either suspicious for cancer or not), the discordant images were re-reviewed by the pathologists and, in parallel, adjudicated with additional testing (e.g. ancillary immunohistochemical markers). Results: Paige Prostate's slide-level sensitivity was 98.9% and its specificity was 93.3% (100% and 78.0%, respectively, at the subject-level). The pathologists' average slide-level sensitivity and specificity without Paige Prostate was 90.9% and 98.6%, respectively. The sensitivity with their consensus read and Paige Prostate increased by 5.7% to 96.6% with only 0.8% decrease in specificity. In addition to new slide-level findings, benefits were also observed at the subject-level; with Paige, three new prostate cancer cases were discovered that were initially missed. Conclusions: The study reflects the potential benefits of the Paige Prostate system in the hands of experienced pathologists and validates the algorithm in a completely independent dataset. Paige Prostate can improve pathologists' sensitivity when reviewing digitized H&E prostate needle biopsy images with a minor impact on specificity.
The association of adaptive early phase study design and late phase study results in oncology using clinicaltrials.gov data.
Authors: Levy Donna Elise , Banerjee Srikanta , Tabung Fred K
Published: May 2020

e14078

Background: Traditional methods such as the 3+3 design developed in the 1940s continue to be used in the majority of early phase oncology studies. Researchers have found that the traditional methods identify the appropriate dose level only 30% of the time. Patients are also exposed to sub-therapeutic doses due to the conservative methods. With these limitations on traditional design options, innovative methods need to be developed, adopted and assessed. Methods: This quantitative study assessed the association of the design methods (adaptive versus traditional) used for early phase oncology studies (adaptive versus traditional) and the outcome of late stage clinical trials. Differences by cancer type and by drug classification were also assessed. A sample of studies for this analysis was extracted from the National Institute of Health Clinical registry and results database. The data used for the analysis was extracted by the Clinical Trials Transformation Initiative (CTTI) Aggregate Analysis of ClinicalTrials.gov (AACT). Results: When assessing study design and outcome, there were lower odds of a positive outcome when adaptive methods were used though this association was not statistically significant (OR [95% highest posterior density (HPD)]:0.66 [0.20, 1.21]). Among the different drug types, using adaptive compared to traditional methods was associated with significantly higher odds of a positive outcome for taxanes, OR: 2.75, 95% HPD: 1.01, 5.16) and other, OR: 3.23, 95% HPD: 1.58, 5.46) but no association among studies of monoclonal antibodies or protein kinase inhibitors. There were no significant associations between early phase study design and outcome in late phase studies by cancer type (lung, breast, other). Conclusions: While results associated with the use of adaptive methods were not significant, further research should be conducted using all completed oncology clinical trials in the database to more precisely determine the relationship between adaptive study design in early phase oncology studies and outcomes in late stage studies. In addition, improvements in traditional versus adaptive design capture in the ClinicalTrials.gov database needs to be considered.
The landscape of gastrointestinal oncologic phase III clinical trials in the last two decades.
Authors: Abi Jaoude Joseph , Kouzy Ramez , Mainwaring Walker , Lin Timothy , Miller Austin B. , Jethanandani Amit , Espinoza Andres F. , Ludmir Ethan B. , Taniguchi Cullen M.
Published: May 2020

e14087

Background: Gastrointestinal (GI) cancers are among the most common cancers in adult patients. The prognosis of GI cancers differs widely between specific subsites, with some cancers having excellent prognoses, and others (such as pancreatic and hepatobiliary cancers) having an extremely poor prognosis. We sought to characterize the landscape of GI oncologic clinical trials in the last two decades. Methods: We searched ClinicalTrials.gov for phase III RCTs between 2003 and 2018. Randomized multi-arm trials assessing a therapeutic intervention in adult cancer patients were included. GI cancer trials were identified and grouped into specific disease subsites. Differences in trial factors between GI and non-GI trials and between different GI subsites were analyzed. Results: Out of the 600 RCTs identified, 76 trials assessed gastrointestinal cancers (12.7%). By disease subsite, we identified 21 (27.6%) gastro-esophageal cancer trials, 12 (15.8%) pancreatic cancer trials, 11 (14.5%) hepatobiliary cancer trials, and 32 (42.1%) colorectal cancer trials. GI cancer trials were more likely to be industry funded when compared to non-GI cancer trials (88.2% vs 76.3% respectively, p = 0.02). Furthermore, only 18.4% of GI trials had cooperative group sponsorship (vs 33.3% for non-GI trials, p = 0.01). Most GI cancer trials assessed systemic therapy interventions (85.5%); only 6 trials (7.9%) addressed supportive care interventions (vs 20.2% for non-GI trials, p = 0.01). Around half of GI cancer trials (51.3%) met their primary endpoints, but only 8 trials (10.5%) led to subsequent FDA approval. All FDA approvals were for colorectal cancer treatments, and none for gastroesophageal, pancreatic or hepatobiliary cancers (p = 0.01). Most patients (97.1%) accrued on GI cancer trials had a good performance status (Eastern Cooperative Oncology Group of 0 to 1). Specifically, 95.9% of patients in pancreatic trials and 98.6% of patients in hepatobiliary trials had a good performance status. Conclusions: While many GI cancers have a poor prognosis, very few trials assess supportive care interventions in this populations. Moreover, most patients included in GI cancer trials have a good performance status. In that regard, future GI cancer trials addressing supportive care measures with a wider cancer population are still warranted.