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An experiment was conducted on litchi cv. China at Horticulture garden in the Department of Horticulture (Fruit and Fruit Technology), Bihar Agricultural University, Sabour, Bhagalpur, Bihar, to show the shelf life and biochemical changes of litchi during storage. The experiment was laid out in randomized block design with five treatments and four replications. Salicylic acid was sprayed twice i.e. 15 days and 30 days before anticipated harvesting time with different concentrations of salicylic acid (T1- 50 ppm, T2-100 ppm, T3-150 ppm, T4-200 ppm and T5-control). Different concentrations have different effect on shelf life and biochemical parameters. Salicylic acid with T3-150 ppm has increased TSS (20.02 0Brix), total sugar (13.10 %), ascorbic acid (38.21 mg/100 g pulp), anthocyanin (8.06 mg/100g pulp) and shelf life (4 days) on the 6th day of storage at ambient condition.

Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.

Litchi is one of the tropical and deliciously juicy fruit. Litchi growers reported bug infestation. Many bug attacks the litchi but one of the bugs Tessaratoma javanica is the most destructive one which decreases the quantity and quality of crops. T. javanica is polyphagous in nature and has a hemimetabolous life cycle. The reason for infestation is the migration of bugs in an alternate host plant the adult and nymph mainly cause severe loss of litchi fruit about 80% loss. To protect the litchi crops use of chemical pesticides did not give positive results because of its hard hemelytra and waxy ventral surface protection from the effect of chemical pesticides. The idea of applying Anastatus japonicus (Hymenoptera: Eupelmidae) and O. phongi Trjapitzin et al. (Hymenoptera: Encyrtidae) for biological control of notorious pests, resulted in a positive effect in China and Thailand and also two natural enemies were found in the litchi crop field, viz., Anastatus bangalorensis Mani & Kurian and Ooencyrtus sps. infesting egg of litchi stink bug, if we reared in mass and released in the field, it would help integrated pest management. This outbreak is an alarming situation and needs an urgent response from the researchers of the country to develop pest management strategies to protect the litchi fruit.

Niche partitioning and resource utilization strategies play crucial roles in shaping the ecological dynamics and coexistence of species within communities. This study focuses on investigating the niche partitioning and resource utilization strategies of butterflies belonging to the order Lepidoptera, specifically the suborder Rhopalocera. Butterflies are known for their diverse ecological adaptations and behaviours, making them an ideal group for studying niche differentiation. Through an extensive review of the existing literature and field observations, we examine the various mechanisms employed by butterflies to partition resources and reduce competition within their habitats. These mechanisms include spatial, temporal, and dietary niche partitioning, as well as differences in habitat preference, larval host plant selection, and adult feeding strategies. Spatial niche partitioning is observed through variations in butterfly species' distribution patterns and preferences for specific microhabitats within their respective habitats. Some species may utilize different vertical or horizontal strata, while others display preferences for open areas or forested regions. Temporal niche partitioning is evident through variations in seasonal activity patterns, diurnal or nocturnal behavior, and specific time periods for mating or foraging. Dietary niche partitioning is a key strategy employed by butterflies, primarily through specialization on specific larval host plants. This specialization can lead to unique interactions between butterflies and plants, with some species relying on toxic or unpalatable plants for defence or mimicry. Adult butterflies also exhibit resource utilization strategies by selecting specific nectar sources based on floral morphology, availability, and optimal energy gain. Understanding the mechanisms of niche partitioning and resource utilization strategies among butterflies has important implications for conservation efforts and ecosystem management. By identifying key factors that promote coexistence and reduce competition, we can develop effective strategies to preserve butterfly populations and their associated habitats. The objectives of this study are to contribute to our knowledge of butterfly ecology and provide insights into the complex interactions between butterflies, their resources, and their environments. The findings enhance our understanding of community dynamics and the factors shaping biodiversity patterns within ecosystems, ultimately aiding in the conservation and management of butterfly populations and their habitats.

Animals have evolved different mechanisms for survival like camouflage, defence mechanisms. These characteristics behaviour of moths is essential for developing adaptation in a critical environment. Camouflage can affect behaviour, possibly influencing the selection of suitable resting backgrounds, body positions, and orientations, as well as the concealment of essential features like shadows and the ability to remain hidden while moving and altering bodies, structures, and the environment. Moths adapted themselves in many ways for their survival like camouflage, defence, foraging behaviour, and developing navigation mechanisms in search of food. Here, we explained the adaptation in moths with reference to various camouflage and defence mechanisms. Here we elaborate on the mechanism of how moths ignored and survived predators with the help of various adaptations. Survival of moths is essential because they balance the ecosystem. This study also helps in the evolution of prey defence diversity.

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally.Significance:AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data.This article is highlighted in the In This Issue feature, p. 2007

Supplementary Table from AACR Project GENIE: 100,000 Cases and Beyond

Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond

Table S4

AACR GENIE Data Guide

Figures S1-S14

Supplementary Methods

Tables S1-S12

The aim of the present investigation was to determine the potential effects of toxic weed Lantana camara on the feeding behaviour of Spodoptera litura (Fabricius) on the basis of Preference index and insecticidal activity using Residual toxicity test - Leaf residue film method (LRFM). Screening process is an important aspect in order to understand the potential activity of the crude extract against any pest, as it depends on various factors such as dose, species-specific behaviour, age of the larvae, solvent type etc. This study was carried out using methanolic extract of Lantana camara at three different concentrations (1%, 3% and 5%) against third instar larvae of the pest. Results showed significant phagostimulant feeding activity at all three concentrations with C-value (1.5, 1.34 and 1.35, respectively) over control. Insecticidal activity was found as 73.33% at 1% concentration, 60% at 3% concentration and 100% at 5% concentration. From this screening investigation, we concluded that methanolic extract Lantana camara exhibit significant phagostimulant activity at these concentrations. It could also be concluded that Lantana camara do not have antifeedant activity at all three concentrations but might have induced significant toxicity/poison after ingestion of food.

Meckel’s diverticulum is a true diverticulum of the alimentary tract occurring resulting from the persistence of remnants of the vitello-intestinal duct. They are often asymptomatic and incidentally diagnosed during surgery. Complications such as intestinal obstruction, diverticulitis, intestinal haemorrhage and perforation may occur with Meckel’s diverticulum, which renders them symptomatic. The clinical and imaging diagnosis of Meckel’s diverticulum is very challenging. As a result of the rare occurrence of complicated Meckel’s diverticulum and the difficult preoperative diagnosis, knowledge of its imaging features is limited. The presented case series describes a spectrum of complications caused by Meckel’s diverticulum and its CT imaging features. It highlights the importance of a high clinical suspicion by carefully searching for a Meckel’s diverticulum on CT in its characteristic location to avoid missing it preoperatively.

Cloud computing has brought about a transformation in the delivery model of information technology from a product to a service. It has enabled the availability of various software, platforms and infrastructural resources as scalable services on demand over the internet. However, the performance of cloud computing services is hampered due to their inherent vulnerability to failures owing to the scale at which they operate. It is possible to utilize cloud computing services to their maximum potential only if the performance related issues of reliability, availability, and throughput are handled effectively by cloud service providers. Therefore, fault tolerance becomes a critical requirement for achieving high performance in cloud computing. This paper presents a comprehensive overview of fault tolerance-related issues in cloud computing; emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods. The objective is to provide insights into the existing fault tolerance approaches as well as challenges yet required to be overcome. The survey enumerates a few promising techniques that may be used for efficient solutions and also, identifies important research directions in this area.

Tumor-infiltrating lymphocyte (TIL) density has been identified as a prognostic and predictive biomarker in select tumors treated with defined therapies. These observations suggest that TILs may be general markers of patient outcomes, but evidence in support of this hypothesis has been limited by small cohorts. We validated ImmunoPROFILE, a multiplexed immunofluorescence (MIF)-based assay coupled with machine-learning-based image analysis, to identify and quantify tumor cells (cytokeratin, PAX5, PAX8, SOX10), T cells (CD8), T-regulatory cells (FOXP3), exhausted cells (PD-1) and immunosuppressive tumor and immune cells (PD-L1). We applied the MIF panel to specimens from patients collected prospectively over three years and analyzed 2,023 cases across 27 tumor types. The association between biomarkers and overall survival (OS) was investigated using Cox models controlling for patient risk factors such as cancer type, metastatic vs. primary disease, age, and gender. Multivariable biomarker selection was based on likelihood ratios. The assay was highly robust (success rate 97%), reproducible (inter-scanning and intra-staining density controls within 1 SD, inter-staining PD-L1 scores ≤11% CV), and operator-independent (R2 >0.7 to >0.9 for each biomarker and 95% concordance in PD-L1 score-based interpretation between technicians). From whole slide images, a total of 11,932 individual regions of interest were analyzed across the cohort, resulting in >50 million spatially-resolved single cells which were summarized into cell population densities and PD-L1 scores. High densities of CD8+ (>64/mm2, p50/mm2, p30/mm2, p To our knowledge, this is the first enterprise-level immune biomarker assay using multiplexed staining, digital imaging, and machine learning to be applied in a prospective manner to clinical specimens at scale. We found that select immune cell densities are prognostic across cancer types and therapies and demonstrated that quantification of multiple cell populations yields better prognostic power than single marker analyses. Citation Format: James Lindsay, Bijaya Sharma, Kristen D. Felt, Anita Giobbie-Hurder, Ian Dryg, Jason L. Weirather, Jennifer Altreuter, Tali Mazor, Priti Kumari, Joao V. Alessi, Ajit J. Nirmal, Michael P. Manos, Ananth R. Kumar, William Lotter, Ethan Cerami, Burce E. Johnson, Neil I. Lindeman, Lynette M. Sholl, Jonathan A. Nowak, Scott J. Rodig. ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5706.

cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data sets. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. The public site (https://www.cbioportal.org) is accessed by >35,000 unique visitors each month and hosts data from >350 studies spanning individual labs and large consortia. In addition, at least 74 instances of cBioPortal are installed at academic institutions and companies worldwide. To better support all users, we unified our documentation (https://docs.cbioportal.org) and added a user guide and an ongoing series of ‘how-to’ videos to address common questions. In 2022 we added 32 studies (>38,000 samples) to the public site. In addition, we added a nonsynonymous tumor mutation burden (TMB) value for all samples and enhanced the TCGA PanCancer Atlas studies with DNA methylation and treatment data. All data is available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >165,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment history. The first BPC cohorts are now available: ~2,000 non-small cell lung cancer samples and ~1,500 colorectal cancer samples. Support for multimodal data analysis has been a major focus, including several new integrations with external tools. Single cell data is now available in the CPTAC GBM study and can be visualized throughout cBioPortal, and via integration with cellxgene. On the patient page, H&E and mIF images can be visualized via integration with Minerva, and the genomic overview now integrates IGV. We continue to enhance existing features. In the study view, users can now add charts comparing categorical vs continuous data, and the plots tab includes a heatmap option. We replaced the existing fusion data type with a generalized structural variant data type that supports detailed information including breakpoints and orientation, to enable new visualizations and analyses. Pathway level analysis has been extended with a new integration with NDEx. cBioPortal is fully open source (https://github.com/cBioPortal/). Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Ino de Bruijn, Tali Mazor, Adam Abeshouse, Diana Baiceanu, Stephanie Carrero, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Bryan Lai, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Yusuf Ziya Özgül, Oleguer Plantalech, Sander Rodenburg, Baby Anusha Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Jianjiong Gao, Nikolaus Schultz. cBioPortal for Cancer Genomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4256.

Purpose: In contrast to recurrence after initial diagnosis of stage I–III breast cancer [recurrent metastatic breast cancer (rMBC)], de novo metastatic breast cancer (dnMBC) represents a unique setting to elucidate metastatic drivers in the absence of treatment selection. We present the genomic landscape of dnMBC and association with overall survival (OS). Experimental Design: Targeted DNA sequencing (OncoPanel) was prospectively performed on either primary or metastatic tumors from 926 patients (212 dnMBC and 714 rMBC). Single-nucleotide variants, copy-number variations, and tumor mutational burden (TMB) in treatment-naïve dnMBC primary tumors were compared with primary tumors in patients who ultimately developed rMBC, and correlated with OS across all dnMBC. Results: When comparing primary tumors by subtype, MYB amplification was enriched in triple-negative dnMBC versus rMBC (21.1% vs. 0%, P = 0.0005, q = 0.111). Mutations in KMTD2, SETD2, and PIK3CA were more prevalent, and TP53 and BRCA1 less prevalent, in primary HR+/HER2− tumors of dnMBC versus rMBC, though not significant after multiple comparison adjustment. Alterations associated with shorter OS in dnMBC included TP53 (wild-type: 79.7 months; altered: 44.2 months; P = 0.008, q = 0.107), MYC (79.7 vs. 23.3 months; P = 0.0003, q = 0.011), and cell-cycle (122.7 vs. 54.9 months; P = 0.034, q = 0.245) pathway genes. High TMB correlated with better OS in triple-negative dnMBC (P = 0.041). Conclusions: Genomic differences between treatment-naïve dnMBC and primary tumors of patients who developed rMBC may provide insight into mechanisms underlying metastatic potential and differential therapeutic sensitivity in dnMBC. Alterations associated with poor OS in dnMBC highlight the need for novel approaches to overcome potential intrinsic resistance to current treatments.

Concrete is a Composite material which is composed of Cement, fine aggregate, coarse aggregate binded together with a definite proportion of water. Concrete is widely used in every single construction work around the world. Due to large scale construction activities using conventional coarse aggregate such as granite as a constructional material extreme reduction in the natural stone deposit has been encountered and is affecting the environment, hence causing ecology imbalance. In current situation of construction, price factor and the wide range of extraction and processing of materialsis matter of great concern for the people as well as environment. Therefore, introduction of alternate waste material in place of natural aggregate in concrete production not only protects environment but also make concrete a suitable, economical and environment friendly construction material. Different material like Coconut Shell and Fiber can also be used alternatively. In this project CoconutShell and fiber are used as partial replacement for coarse aggregate as well as fine aggregate, respectively. To study characteristic properties of concrete 10% and 20% for coarse aggregate and 1%, and 2% for fine aggregate are replaced by its weight with coconut shell and fiber.

PURPOSE The yield of comprehensive genomic profiling in recruiting patients to molecular-based trials designed for small subgroups has not been fully evaluated. We evaluated the likelihood of enrollment in a clinical trial that required the identification of a specific genomic change based on our institute-wide genomic tumor profiling. PATIENTS AND METHODS Using genomic profiling from archived tissue samples derived from patients with metastatic breast cancer treated between 2011 and 2017, we assessed the impact of systematic genomic characterization on enrollment in an ongoing phase II trial (ClinicalTrials.gov identifier: NCT01670877 ). Our primary aim was to describe the proportion of patients with a qualifying ERBB2 mutation identified by our institutional genomic panel (OncoMap or OncoPanel) who enrolled in the trial. Secondary objectives included median time from testing result to trial registration, description of the spectrum of ERBB2 mutations, and survival. Associations were calculated using Fisher’s exact test. RESULTS We identified a total of 1,045 patients with metastatic breast cancer without ERBB2 amplification who had available genomic testing results. Of these, 42 patients were found to have ERBB2 mutation and 19 patients (1.8%) were eligible for the trial on the basis of the presence of an activating mutation, 18 of which were identified by OncoPanel testing. Fifty-eight percent of potentially eligible patients were approached, and 33.3% of eligible patients enrolled in the trial guided exclusively by OncoPanel testing. CONCLUSION More than one half of eligible patients were approached for trial participation and, significantly, one third of those were enrolled in NCT01670877. Our data illustrate the ability to enroll patients in trials of rare subsets in routine clinical practice and highlight the need for these broadly based approaches to effectively support the success of these studies.

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally. Significance: AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data. This article is highlighted in the In This Issue feature, p. 2007

Pediatric pan-cancer genome analyses do not capture the full range of diagnoses encountered in clinical practice. To inform basket trial design and real-world precision oncology practice, we classified diagnoses and assessed the landscape of mutations, including trial-matching, in an unselected cohort of pediatric solid tumors. Since 2013 all Dana-Farber/Boston Children’s patients have been offered participation in the Profile study. Participant tumor samples were sequenced with DFCI-OncoPanel, a targeted panel test sequencing exons of up to 447 cancer genes for single nucleotide variants, insertions and deletions and copy number alterations, and introns and exons of up to 60 genes for rearrangements. Patient diagnosis was classified according to ICD-O, version 3.2. Genomic alterations were analyzed for matching to the actionable mutation lists of precision oncology basket trials (NCI-COG Pediatric MATCH, NCI-MATCH, and the ASCO TAPUR Study v.3). Data will be shared with the Childhood Cancer Data Initiative. There were 888 pediatric patients with sequencing enrolled in Profile between January 2013 and March 2019; 512 (58%) with solid tumors and 376 (42%) with CNS tumors. Fifty-five percent (491/888) of patients had one of ten common pediatric cancer diagnoses: neuroblastoma (n=80), low-grade glioma (n=72), Wilms tumor (n=57), medulloblastoma (n=55), pilocytic astrocytoma (n=47), rhabdomyosarcoma (n=44), osteosarcoma (n=42), ependymoma (n=39), Ewing sarcoma (n=28) and glioblastoma (n=27). The remaining 45% (397/888) had one of 85 distinct rare malignancies with less than 25 cases per diagnosis. Most (80/85) of these rare diagnoses are not represented in prior pediatric pan-cancer sequencing studies. Recurrent (>5%) pathogenic alterations were, in common and rare diagnoses, TP53 mutations(m) and deletions(del) and BRAFm and rearrangements(r), in common diagnoses, MYC/MYCN amplification (amp) and EWSR1r and, in rare diagnoses, CTNNB1m, CDKN2A/Bdel and NF1m/del. We found that 31% (n=271/888) of patients had at least 1 variant matching a basket trial treatment arm. Genes with matching alterations include BRAF (10%), NF1 (4%), PI3KCA (3%), NRAS (2%), BRCA2 (2%), ALK (1%), and FGFR1 (1%). Sequencing of pediatric malignancies is increasing. This study highlights opportunities to use the resulting genomic data to inform genome-selected clinical trial design and uncover drivers in pediatric cancers. The proportion of cases in this cohort with genomic alterations meeting eligibility for basket trials is equivalent to that seen in the pediatric MATCH screening study. Due to the low prevalence of the diagnoses in the long tail of cancer types in this study, defining the genomic landscape of ultra-rare cancers will require data sharing. Classifying pediatric cancer diagnoses using the ICD-O standard ontology system is feasible and will facilitate data sharing. Citation Format: Suzanne J. Forrest, Hersh Gupta, Abigail Ward, Yvonne Li, Duong Doan, Alyaa Al-Ibraheemi, Sanda Alexandrescu, Pratiti Bandopadhayay, Suzanne Shusterman, Elizabeth A. Mullen, Natalie Collins, Susan N. Chi, Karen D. Wright, Priti Kumari, Tali Mazor, Keith L. Ligon, Priyanka Shivdasani, Phani Davineni, Monica Manam, Richard L. Schilsky, Suanna S. Bruinooge, Jaime M. Guidry Auvil, Ethan Cerami, Barrett J. Rollins, Matthew L. Meyerson, Neal I. Lindeman, Laura MacConaill, Bruce E. Johnson, Andrew D. Cherniack, Alanna J. Church, Katherine A. Janeway. Sequencing of 888 pediatric solid tumors informs precision oncology trial design and data sharing initiatives in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3890.

Precision medicine (PM) drugs targeting alterations such as EGFR mutations and BCR-ABL fusions have provided great clinical benefit to patients. However, with an abundance of tumor sequencing data and trial eligibility criteria available, it can be challenging for clinicians to identify PM trial options for patients. To address this challenge at Dana-Farber Cancer Institute (DFCI), we developed MatchMiner, an open-source platform for computationally matching patients to PM trials. MatchMiner has three modes of use: (1) patient-centric, where clinicians can view all available trial matches for a patient, (2) trial-centric, where clinical trial teams identify patients for their trials based on genomic and clinical criteria, and (3) trial search, where clinicians search for available trials based on clinical and genomic eligibility. Trial matching is performed via the MatchEngine, which computes trial matches based on patient genomic and clinical data and PM trial eligibility criteria. To encode trial eligibility criteria, we developed a structured format called clinical trial markup language (CTML), which uses Boolean logic to encode inclusion and exclusion criteria. Here, we describe our implementation of MatchMiner at DFCI including strategies that were successful and MatchMiner’s impact on trial consent. Since MatchMiner first went live in March 2017, a number of strategies have helped facilitate utilization of MatchMiner. The biggest impact has come from targeted departmental collaborations (Gastrointestinal, Breast, and Center for Cancer Therapeutic Innovation or CCTI), where the MatchMiner team worked directly with key departmental stakeholders to develop customized workflows. To facilitate access to MatchMiner among individual clinicians, we integrated the patient-centric and trial search modes into the Epic electronic health record. Other implementation strategies were piloted, such as weekly emails to clinicians alerting them to potential trial matches, but were less impactful. Overall, departmental collaborations have resulted in several ongoing MatchMiner initiatives. Thus far at DFCI, we have curated 354 PM trials into MatchMiner and facilitated 220 patient consents. For PM trials, 222 genes, 7 mutational signatures and nearly all cancer types were represented, demonstrating that there is a wide range of PM trial options available to patients. We also examined the distribution of trial phases and disease centers running each trial. The majority were Phase I and Phase II trials run out of the CCTI, consistent with the frequency with which novel drugs do not progress to later phase trials. Lastly, we have identified 220 trial consents that benefitted from MatchMiner. Retrospective analysis of a subset of these trial consents (n=166) revealed a significant 22% decrease in time to consent relative to other consents to the same trials, demonstrating the clinical impact of MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, Andrea Ovalle, Pavel Trukhanov, Jason Hansel, Joyce Yu, James Lindsay, Michael Hassett, Ethan Cerami. Design and adoption of MatchMiner at Dana-Farber Cancer Institute [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4091.

cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale cancer genomics data sets. cBioPortal provides a user-friendly interface that integrates genomic and clinical data, and provides a suite of visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. cBioPortal also integrates external tools including CIViC, Cancer Digital Slide Archive, Next-Generation Clustered Heat Map, IGV and Bioconductor to facilitate interpretation. The public site (https://www.cbioportal.org) is accessed by ~35,000 unique visitors each month and hosts data from >325 studies spanning individual labs and large consortia. In addition, >67 instances of cBioPortal are installed at academic institutions and pharmaceutical/biotechnology companies worldwide. In 2021 we added data from 32 studies, totaling >24,000 samples, to the public site. All data is also available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub/. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >135,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). In addition, the GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment histories. The first BPC release contains data from >1,800 non-small cell lung cancer samples and will be released in early 2022. The growing GENIE cohort and the BPC clinical data have driven a number of recent developments, including performance improvements (the load time for the GENIE cohort was reduced from minutes to seconds). To leverage the BPC clinical data, we enabled sample selection based on treatment status, extended support for outcome analysis, and enhanced the patient timeline representation to incorporate response data. Additional development work has focused on improvements to variant interpretation, enhancements to the Mutations tab, and support for novel molecular assays via the ‘generic assay’ data type. Documentation on these new features and many others is available at https://www.cbioportal.org/news. cBioPortal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Jianjiong Gao, Tali Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoc, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Baby A. Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S. Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1155.