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Exam Code: 156-915-80 Practice exam 2022 by Killexams.com team
156-915-80 Check Point Certified Security Expert Update - R80 (156-915.80)

Exam Title : Check Point Certified Security Expert (CCSE) R80 Update
Exam ID : 156-915.80
Exam Center Fee : $250 (USD)
Exam Duration : 90 mins
Questions in exam : 90
Passing Score : 70%
Official Training : CCSE Training
Exam Center : Pearson VUE
Real Questions : Check Point CCSE Real Questions
VCE VCE exam : Check Point 156-915.80 Certification VCE Practice Test

Upgrading
Objectives:
Perform a backup of a Security Gateway and Management Server using your Understanding of the differences between backups, snapshots, and upgrade-exports. Upgrade and troubleshoot a Management Server using a database migration. Upgrade and troubleshoot a clustered Security Gateway deployment.
Backup and Restore Security Gateways and Management Servers
- Snapshot management
- Upgrade Tools
- Backup Schedule Recommendations
- Upgrade Tools
- Performing Upgrades
- Support Contract
Upgrading Standalone Full High Availability
  Lab 1: Upgrading to Check Point
- Install Security Management Server
- Migrating Management server Data
- Importing the Check Point Database
- Launch SmartDashboard
- Upgrading the Security Gateway
Advanced Firewall
Objectives:
Using knowledge of Security Gateway infrastructure, including chain modules, packet flow and kernel tables to describe how to perform debugs on firewall processes.
Check Point Firewall Infrastructure
- GUI Clients
- Management
Security Gateway
- User and Kernel Mode Processes
- CPC Core Process
- FWM
- FWD
- CPWD
- Inbound and Outbound Packet Flow
- Inbound FW CTL Chain Modules
- Outbound Chain Modules
- Columns in a Chain
- Stateful Inspection

Kernel Tables
- Connections Table
- Connections Table Format

Check Point Firewall Key Features
- Packet Inspection Flow
- Policy Installation Flow
- Policy Installation Process
- Policy Installation Process Flow

Network Address Translation
- How NAT Works
- Hide NAT Process
- Security Servers
- How a Security Server Works
- Basic Firewall Administration
- Common Commands

FW Monitor
- What is FW Monitor
- C2S Connections and S2C Packets fw monitor

Lab 2: Core CLI Elements of Firewall Administration
- Policy Management and Status
- Verification from the CLI
- Using cpinfo
- Run cpinfo on the Security Management Server
- Analyzing cpinfo in InfoView
- Using fw ctl pstat
- Using tcpdump

Clustering and Acceleration
Objectives:
Build, test and troubleshoot a ClusterXL Load Sharing deployment on an enterprise network.
Build, test and troubleshoot a ClusterXL High Availability deployment on an enterprise network.
Build, test and troubleshoot a management HA deployment on an enterprise network.
Configure, maintain and troubleshoot SecureXL and CoreXL acceleration solutions on the corporate network traffic to ensure noted performance enhancement on the firewall.
Build, test and troubleshoot a VRRP deployment on an enterprise network.

VRRP
- VRRP vs ClusterXL
- Monitored Circuit VRRP
- Troubleshooting VRRP

Clustering and Acceleration
- Clustering Terms
- ClusterXL
- Cluster Synchronization
- Synchronized-Cluster Restrictions
- Securing the Sync Interface
- To Synchronize or Not to Synchronize

ClusterXL: Load Sharing
- Multicast Load Sharing
- Unicast Load Sharing
- How Packets Travel Through a Unicast
- LS Cluster
- Sticky Connections

Maintenance Tasks and Tools
- Perform a Manual Failover of the FW Cluster
- Advanced Cluster Configuration

Management HA
- The Management High Availability Environment
- Active vs. Standby
- What Data is Backed Up?
- Synchronization Modes
- Synchronization Status

SecureXL: Security Acceleration
- What SecureXL Does
- Packet Acceleration
- Session Rate Acceleration
- Masking the Source Port
- Application Layer Protocol - An Example with HTTP
- Factors that Preclude Acceleration
- Factors that Preclude Templating (Session Acceleration)
- Packet Flow
- VPN Capabilities

CoreXL: Multicore Acceleration
- Supported Platforms and Features
- Default Configuration
- Processing Core Allocation
- Allocating Processing Cores
- Adding Processing Cores to the Hardware
- Allocating an Additional Core to the SND
- Allocating a Core for Heavy Logging
- Packet Flows with SecureXL Enabled

Lab 3 Migrating to a Clustering Solution
- Installing and Configuring the Secondary Security Gateway
- Re-configuring the Primary Gateway
- Configuring Management Server Routing
- Configuring the Cluster Object
- Testing High Availability
- Installing the Secondary Management Server
- Configuring Management High Availability

Advanced User Management
Objectives:
Using an external user database such as LDAP, configure User Directory to incorporate user information for authentication services on the network.
Manage internal and external user access to resources for Remote Access or across a VPN.
Troubleshoot user access issues found when implementing Identity Awareness.

User Management
- Active Directory OU Structure
- Using LDAP Servers with Check Point
- LDAP User Management with User Directory
- Defining an Account Unit
- Configuring Active Directory Schemas
- Multiple User Directory (LDAP) Servers
- Authentication Process Flow
- Limitations of Authentication Flow
- User Directory (LDAP) Profiles

Troubleshooting User Authentication and User Directory (LDAP)
- Common Configuration Pitfalls
- Some LDAP Tools
- Troubleshooting User Authentication

Identity Awareness
- Enabling AD Query
- AD Query Setup
- Identifying users behind an HTTP Proxy
- Verifying there’s a logged on AD user at the source IP
- Checking the source computer OS
- Using SmartView Tracker

Lab 4: Configuring SmartDashboard to Interface with Active Directory
- Creating the Active Directory Object in SmartDashboard
- Verify SmartDashboard Communication with the AD Server

Advanced IPsec VPN and Remote Access
Objectives:
Using your knowledge of fundamental VPN tunnel concepts, troubleshoot a site-to-site or certificate-based VPN on a corporate gateway using IKEView, VPN log files and commandline debug tools.
Optimize VPN performance and availability by using Link Selection and Multiple Entry Point solutions.
Manage and test corporate VPN tunnels to allow for greater monitoring and scalability with multiple tunnels defined in a community including other VPN providers.

Advanced VPN Concepts and Practices
- IPsec
- Internet Key Exchange (IKE)
- IKE Key Exchange Process – Phase 1/ Phase 2 Stages

Remote Access VPNs
- Connection Initiation
- Link Selection

Multiple Entry Point VPNs
- How Does MEP Work
- Explicit MEP
- Implicit MEP

Tunnel Management
- Permanent Tunnels
- Tunnel Testing
- VPN Tunnel Sharing
- Tunnel-Management Configuration
- Permanent-Tunnel Configuration
- Tracking Options
- Advanced Permanent-Tunnel configuration
- VPN Tunnel Sharing Configuration

Troubleshooting
- VPN Encryption Issues

VPN Debug
- vpn debug Command
- vpn debug on | off
- vpn debug ikeon |ikeoff
- vpn Log Files
- vpn debug trunc
- VPN Environment Variables
- vpn Command
- vpn tu
- Comparing SAs

Lab 5: Configure Site-to-Site VPNs with Third Party Certificates
- Configuring Access to the Active Directory Server
- Creating the Certificate
- Importing the Certificate Chain and Generating Encryption Keys
- Installing the Certificate
- Establishing Environment Specific Configuration
- Testing the VPN Using 3rd Party Certificates

Lab 6: Remote Access with Endpoint Security VPN
- Defining LDAP Users and Groups
- Configuring LDAP User Access
- Defining Encryption Rules
- Defining Remote Access Rules
- Configuring the Client Side

Auditing and Reporting
Objectives:
Create Events or use existing event definitions to generate reports on specific network traffic using SmartReporter and SmartEvent in order to provide industry compliance information to management.
Using your knowledge of SmartEvent architecture and module communication, troubleshoot report generation given command-line tools and debug-file information.

Auditing and Reporting Process
- Auditing and Reporting Standards

SmartEvent
- SmartEvent Intro

SmartEvent Architecture
- Component Communication Process
- Event Policy User Interface

SmartReporter
- Report Types

Lab 7: SmartEvent and SmartReporter
- Configure the Network Object in SmartDashboard
- Configuring Security Gateways to work with SmartEvent
- Monitoring Events with SmartEvent
- Generate Reports Based on Activities

Check Point Certified Security Expert Update - R80 (156-915.80)
Checkpoint approach
Killexams : Checkpoint approach - BingNews http://www.bing.com:80/news/search?q=Checkpoint+approach&cc=us&format=RSS Search results Killexams : Checkpoint approach - BingNews http://www.bing.com:80/news/search?q=Checkpoint+approach&cc=us&format=RSS https://killexams.com/exam_list/Checkpoint Killexams : Whole exome sequencing predicts whether patients respond to cancer immunotherapy

Immunotherapies, such as immune checkpoint inhibitors, have transformed the treatment of advanced stage cancers. Unlike chemotherapies that kill cancer cells, these drugs help the body's immune system to find and destroy cancer cells themselves. Unfortunately, only a subset of patients responds long-term to immune checkpoint inhibitors -- and these treatments can come at a high cost and with side effects.

Researchers have developed a two-step approach using whole exome sequencing to zero in on genes and pathways that predict whether cancer patients will respond to immunotherapy. The study, published in Nature Communications and conducted by researchers at New York University, Weill Cornell Medicine, and the New York Genome Center, illustrates how the use of whole exome sequencing can better predict treatment response than current laboratory tests.

"Can we better predict who will benefit from immunotherapy? Scientists have developed various biomarkers that help anticipate immunotherapy treatment response, but there's still an unmet need for a robust, clinically practical predictive model," said Neville Sanjana, assistant professor of biology at NYU, assistant professor of neuroscience and physiology at NYU Grossman School of Medicine, a core faculty member at New York Genome Center, and the study's co-senior author.

Several biomarkers -- including age, tumor type, and the number of mutations found in cancer cells, known as tumor mutational burden -- are already known to correlate with responses to immunotherapy. Tumor mutational burden, which is calculated by analyzing a few hundred genes, is the most well-established predictor and is often used to determine a patient's eligibility for immune checkpoint inhibitors.

If scientists look at a much larger portion of our genes, could that help to better predict which patients will respond to immunotherapy? Whole exome sequencing is a method for sequencing the part of the genome that codes for proteins -- around 20,000 genes, or two percent of the genome -- to look for mutations that may be involved in disease.

While whole exome sequencing is not widely used in cancer treatment, some recent studies of immunotherapies have started to include sequencing. These studies are small, but together can help illuminate the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous immunotherapy studies of patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole exome sequencing was available for all participants, who were treated with an immune checkpoint inhibitor (either anti-PD-1 or anti-CTLA-4).

But even after combining the six studies, the number of patients -- 319 in total -- was still relatively small.

"The problem with a small study of only a few hundred people is a mismatch between the number of patients and the vast number of genes sequenced in whole exome sequencing. We'd ideally have a dataset with more patients than genes," said Zoran Gajic, a graduate student in the Sanjana Lab, and the study's first author.

To get around this problem, the researchers turned to a model called fishHook which distinguishes mutations that drive cancer from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for a range of factors that affect the rates of background mutations -- for instance, adjusting for the size of a gene, since larger genes are more likely to have mutations.

Using this model, the researchers employed a two-step approach: first, they looked at the sequencing from all patients to find any genes with a higher mutational burden than they would expect, adjusting for genomic factors like gene size or whether a particular piece of DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with suspiciously high mutational burdens.

Next, the researchers determined if any of these six genes were enriched in people who responded or did not respond to immunotherapy. Two of the genes -- KRAS, a gene often mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma -- were enriched in patients who responded to immunotherapy. In contrast, two other genes -- TP53 and BCLAF1 -- were enriched in those who did not respond to immunotherapy. BCLAF1 is not well studied, but these findings suggest that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.

Using the same two-step approach on collections of genes called pathways, the researchers determined that certain pathways (MAPK signaling, p53 associated, and immunomodulatory) also predicted immune checkpoint inhibitor response.

They then combined the four genes and three pathways with other predictive variables such as age, tumor type, and tumor mutational burden to create a tool they named the Cancer Immunotherapy Response CLassifiEr (CIRCLE). CIRCLE was able to better predict immunotherapy response by approximately 11% than tumor mutational burden alone. CIRCLE was also able to accurately predict cancer survival after immunotherapy.

"These results suggest that the use of broader diagnostics such as whole exome or even whole genome sequencing may significantly Excellerate our ability to predict who will respond to immunotherapy -- essentially, showing that more data does help to better predict treatment response," said Marcin Imieliński, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center, and the study's co-senior author.

To validate their approach, the researchers tested CIRCLE on data from 165 additional cancer patients with whole exome sequencing who underwent treatment with immunotherapy and found that CIRCLE captured predictive information beyond that obtained from tumor mutational burden alone.

Future research will involve testing CIRCLE on larger cohorts of patient data, as the researchers anticipate that the model will Excellerate with data from thousands of patients rather than hundreds. They also hope that with larger cohorts, they can begin to tease out which patients are likely to respond to different immunotherapies, given the growing number of treatments available.

"We envision that this two-step approach and use of whole exome sequencing will pave the way for better prognostic tools for cancer immunotherapy," said Sanjana.

Additional authors include Aditya Deshpande of NYGC and Weill Cornell Medicine and Mateusz Legut of NYGC and NYU. The research was funded by the National Institutes of Health (U24-CA15020, DP2HG010099, R01CA218668, and GM136573), Sidney Kimmel Foundation, Brain and Behavior Foundation, Burroughs Wellcome Fund, Doris Duke Clinical Foundation, Starr Cancer Consortium, Melanoma Research Alliance, Hope Funds for Cancer Research, and startup funds from NYU, Weill Cornell Medicine, and the New York Genome Center.

Fri, 08 Jul 2022 14:48:00 -0500 en text/html https://www.sciencedaily.com/releases/2022/07/220708123640.htm
Killexams : 2-Step Gene Sequencing Shows Who May Respond to Cancer Immunotherapy No result found, try new keyword!Scientists used a process known as whole exome sequencing to examine 20,000 genes for a clearer picture of who may benefit from targeted cancer drugs known as immune checkpoint inhibitors. Tue, 12 Jul 2022 16:00:00 -0500 en-us text/html https://www.msn.com/en-us/health/medical/2-step-gene-sequencing-shows-who-may-respond-to-cancer-immunotherapy/ar-AAZzlMQ Killexams : New TSA tech lets passengers skip boarding passes at some DFW Airport checkpoints No result found, try new keyword!Passengers can put those boarding passes away when heading through some DFW International Airport checkpoints. DFW Is one of a handful of airports where ... Tue, 05 Jul 2022 23:10:40 -0500 en-us text/html https://www.msn.com/en-us/travel/news/new-tsa-tech-lets-passengers-skip-boarding-passes-at-some-dfw-airport-checkpoints/ar-AAZg22T Killexams : Go zero trust with Check Point Infinity
Ralph Berndt, sales and marketing director at Syrex.

Ralph Berndt, sales and marketing director at Syrex.

Syrex, a provider of hyperconverged cloud technology solutions in South Africa, says the Check Point Infinity cyber security architecture has been built to prevent Gen V cyber attacks using a consolidated zero trust environment.

First coming to prominence in 2017, Gen V cyber attacks are large-scale multi-vector attacks designed to infect multiple components of ICT infrastructure spanning networks, cloud deployments, endpoints, mobile and internet of things (IOT) devices. These see cyber criminals exploiting the fact that most companies still rely on older generations of security that merely detect attacks but are insufficient when it comes to cyber defence.

Across the industry, security professionals are shifting to a zero trust security state of mind. This means that no device, user, workload or system is trusted by default, neither inside nor outside the security perimeter. However, designing or rebuilding the security infrastructure of a company around a zero trust approach using point solutions might lead to complex deployment and inherent security gaps.

“Check Point Infinity is a single, consolidated cyber security architecture that offers a practical and comprehensive approach to implement zero trust. It integrates a range of security functions and solutions that cover the gamut of what constitutes an effective zero trust security model,” says Ralph Berndt, sales and marketing director at Syrex.

Focused on threat prevention instead of detection, Check Point Infinity uses 64 different security engines to protect against known and unknown threats across all networks, endpoints, cloud, mobile and IOT. It leverages globally shared threat intelligence powered by ThreatCloud to provide threat prevention technologies with the industry’s best catch rate.

Furthermore, Check Point Infinity includes cloud security solutions that integrate with any public or private cloud infrastructure and provide full visibility and control over these ever-changing environments.

“Securing workloads, particularly those that are running in the public cloud, is essential since cloud assets such as Kubernetes containers and virtual machines are vulnerable and attractive targets to threat actors. With Check Point Infinity in place, businesses get the peace of mind that their cyber security real estate delivers the proactive defence they need for today’s modern digital landscape,” adds Berndt.

To this end, Check Point Infinity provides a zero trust security model that constantly monitors, logs, correlates and analyses all activity across the organisational network. The architecture is managed via the Check Point R80 Centralised Security Management solution, which provides security teams full visibility into their entire security posture, enabling them to quickly detect and mitigate threats in real-time.

Mon, 11 Jul 2022 18:48:00 -0500 en text/html https://www.itweb.co.za/content/KPNG878NdN2q4mwD
Killexams : Whole Exome Sequencing Helps To Predict If a Patient Will Respond to Cancer Immunotherapy

Immunotherapies, such as immune checkpoint inhibitors, have transformed the treatment of advanced stage cancers. Unlike chemotherapies that kill cancer cells, these drugs help the body’s immune system to find and destroy cancer cells themselves. Unfortunately, only a subset of patients responds long-term to immune checkpoint inhibitors—and these treatments can come at a high cost and with side effects.

Researchers have developed a two-step approach using whole exome sequencing to zero in on genes and pathways that predict whether cancer patients will respond to immunotherapy. The study, published in Nature Communications and conducted by researchers at New York University, Weill Cornell Medicine, and the New York Genome Center, illustrates how the use of whole exome sequencing can better predict treatment response than current laboratory tests.

“Can we better predict who will benefit from immunotherapy? Scientists have developed various biomarkers that help anticipate immunotherapy treatment response, but there’s still an unmet need for a robust, clinically practical predictive model,” said Neville Sanjana, assistant professor of biology at NYU, assistant professor of neuroscience and physiology at NYU Grossman School of Medicine, a core faculty member at New York Genome Center, and the study’s co-senior author.

Several biomarkers—including age, tumor type, and the number of mutations found in cancer cells, known as tumor mutational burden—are already known to correlate with responses to immunotherapy. Tumor mutational burden, which is calculated by analyzing a few hundred genes, is the most well-established predictor and is often used to determine a patient’s eligibility for immune checkpoint inhibitors.

If scientists look at a much larger portion of our genes, could that help to better predict which patients will respond to immunotherapy? Whole exome sequencing is a method for sequencing the part of the genome that codes for proteins—around 20,000 genes, or two percent of the genome—to look for mutations that may be involved in disease.

While whole exome sequencing is not widely used in cancer treatment, some recent studies of immunotherapies have started to include sequencing. These studies are small, but together can help illuminate the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous immunotherapy studies of patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole exome sequencing was available for all participants, who were treated with an immune checkpoint inhibitor (either anti-PD-1 or anti-CTLA-4).

But even after combining the six studies, the number of patients—319 in total—was still relatively small.

“The problem with a small study of only a few hundred people is a mismatch between the number of patients and the vast number of genes sequenced in whole exome sequencing. We’d ideally have a dataset with more patients than genes,” said Zoran Gajic, a graduate student in the Sanjana Lab, and the study’s first author.

To get around this problem, the researchers turned to a model called fishHook which distinguishes mutations that drive cancer from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for a range of factors that affect the rates of background mutations—for instance, adjusting for the size of a gene, since larger genes are more likely to have mutations.

Using this model, the researchers employed a two-step approach: first, they looked at the sequencing from all patients to find any genes with a higher mutational burden than they would expect, adjusting for genomic factors like gene size or whether a particular piece of DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with suspiciously high mutational burdens.

Next, the researchers determined if any of these six genes were enriched in people who responded or did not respond to immunotherapy. Two of the genes—KRAS, a gene often mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma—were enriched in patients who responded to immunotherapy. In contrast, two other genes—TP53 and BCLAF1—were enriched in those who did not respond to immunotherapy. BCLAF1 is not well studied, but these findings suggest that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.

Using the same two-step approach on collections of genes called pathways, the researchers determined that certain pathways (MAPK signaling, p53 associated, and immunomodulatory) also predicted immune checkpoint inhibitor response.

They then combined the four genes and three pathways with other predictive variables such as age, tumor type, and tumor mutational burden to create a tool they named the Cancer Immunotherapy Response CLassifiEr (CIRCLE). CIRCLE was able to better predict immunotherapy response by approximately 11% than tumor mutational burden alone. CIRCLE was also able to accurately predict cancer survival after immunotherapy.

“These results suggest that the use of broader diagnostics such as whole exome or even whole genome sequencing may significantly Excellerate our ability to predict who will respond to immunotherapy—essentially, showing that more data does help to better predict treatment response,” said Marcin Imieliński, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center, and the study’s co-senior author.

To validate their approach, the researchers tested CIRCLE on data from 165 additional cancer patients with whole exome sequencing who underwent treatment with immunotherapy and found that CIRCLE captured predictive information beyond that obtained from tumor mutational burden alone.

Future research will involve testing CIRCLE on larger cohorts of patient data, as the researchers anticipate that the model will Excellerate with data from thousands of patients rather than hundreds. They also hope that with larger cohorts, they can begin to tease out which patients are likely to respond to different immunotherapies, given the growing number of treatments available.

“We envision that this two-step approach and use of whole exome sequencing will pave the way for better prognostic tools for cancer immunotherapy,” said Sanjana.

Reference: Gajic ZZ, Deshpande A, Legut M, Imieliński M, Sanjana NE. Recurrent somatic mutations as predictors of immunotherapy response. Nat Commun. 2022;13(1):3938. doi: 10.1038/s41467-022-31055-3


This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

Sun, 10 Jul 2022 20:10:00 -0500 en text/html https://www.technologynetworks.com/tn/news/whole-exome-sequencing-helps-to-predict-if-a-patient-will-respond-to-cancer-immunotherapy-363533
Killexams : 2 men charged in separate alleged human smuggling cases

Two men have been charged in separate incidents for allegedly attempting to smuggle more than 70 migrants each through a border patrol checkpoint in Texas, weeks after the deadliest human trafficking incident in U.S. history.

The departments of Justice and Homeland Security claim that Menietto Lateet Crawford, 41, and Denny Fuentes, 41, attempted to smuggle migrants across the border on June 14 and 15, respectively. The men were also each charged with conspiracy.

The cases are nearly identical to the incident that killed more than 50 migrants near San Antonio on June 27, when dozens were found dead in a tractor-trailer.

Though the charges against Crawford and Fuentes are similar, the cases are not related.

Crawford allegedly had 80 people in a refrigerated truck, according to the Department of Justice. A K-9 found migrants inside of the vehicle when Crawford arrived at the Border Patrol checkpoint in Laredo, Texas, on June 14.

PHOTO: A migramt waits to be processed by US Border Patrol agent after illegally crossing the US-Mexico border in Yuma, Arizona, July 11, 2022.

A migramt waits to be processed by US Border Patrol agent after illegally crossing the US-Mexico border in Yuma, Arizona, July 11, 2022.

Allison Dinner/AFP via Getty Images

Fuentes arrived at the same checkpoint the day after with more than 70 migrants from Mexico, Guatemala, El Salvador and Honduras in a refrigerated truck. He claimed that he was transporting "pig meat," according to the charges.

Each man faces up to 20 years in federal prison and a possible $250,000 fine if convicted, according to Homeland Security Investigations, the department's investigative unit.

On June 27, 53 people died while trapped in a tractor-trailer in San Antonio. Temperatures reached a high of 103 degrees that day.

While the trailer was refrigerated, it did not have a working air-conditioning unit, nor were there signs of any water.

Authorities arrested and charged four men in connection with the suspected smuggling operation.

Two of the men, if convicted, face up to life in prison and possibly the death penalty. Two other men were arrested on gun charges, federal authorities said.

Texas has taken a tough approach in addressing border crossings by targeting migrants. The state's Department of Public Safety program launched the border security initiative "Operation Lone Star" in 2021 meant to restrict migrant crossings into Texas.

The DOJ is investigating the program for possible civil rights violations.

Sat, 16 Jul 2022 01:38:00 -0500 en text/html https://abcnews.go.com/US/men-charged-separate-human-smuggling-cases/story?id=86774420
Killexams : At some DFW Airport checkpoints, travelers won't be required to show a boarding pass No result found, try new keyword!DFW is one of only a few airports where the Transportation Security Administration has rolled out Credential Authentication Technology. It matches a database of daily passenger names and birth dates ... Wed, 06 Jul 2022 22:52:48 -0500 en-us text/html https://www.msn.com/en-us/travel/news/at-some-dfw-airport-checkpoints-travelers-won-t-be-required-to-show-a-boarding-pass/ar-AAZjx4v Killexams : Immune Checkpoint Blockade Response Linked to Recurrent Mutations in Genes, Pathways

NEW YORK – Mutations across many genes and pathways may provide clues to immune checkpoint immunotherapy response, new research suggests, pointing to the potential for improving treatment response prediction models with tumor exome or genome sequencing data.

"These results suggest that the use of broader diagnostics such as whole-exome or even whole-genome sequencing may significantly Excellerate our ability to predict who will respond to immunotherapy — essentially, showing that more data does help to better predict treatment response," Weill Cornell Medicine researcher Marcin Imieliński, co-senior author of a study published in Nature Communications on Friday, said in a statement.

Researchers from Weill Cornell Medicine, the New York Genome Center, and New York University started with clinical and exome sequencing data for 319 cancer patients who had received an immune checkpoint inhibitor.

The cases included patients with melanoma, non-small cell lung cancer, bladder cancer, or head and neck cancer who took part in half a dozen past studies, they explained, and had undergone exome sequencing prior to treatment and "Response Evaluation Criteria in Solid Tumor" (RECIST) classification after treatment.

Using sequence data spanning almost 19,700 protein-coding genes and a modified version of a statistical modeling analytical method called fishHook — designed for weeding out irrelevant background mutations — the team attempted to track down and expand the repertoire of potential biomarkers for predicting immune checkpoint response across the diverse set of cancer cases considered.

They noted that response to immune checkpoint blockade immunotherapy was achieved in between 6 percent and 56 percent of the patients, including more than a dozen complete responders and 80 partial responders. Another 47 patients had stable disease, and 178 patients experienced disease progression. Treatment response was particularly common in patients with NSCLC or melanoma, as well as in older patients.

From a set of more than 129,300 tumor mutations with effects that were predicted to be high or moderate, the investigators narrowed in on six recurrently mutated genes. Four of those genes — BRAF, KRAS, TP53, and BCLAF1 — showed significant ties to checkpoint immunotherapy responses, as did alterations affecting MAP kinase, immune modulating, or p53-related pathways.

In KRAS and BRAF, for example, recurrent mutations corresponded with better-than-usual immunotherapy responses. On the other hand, relatively muted checkpoint immune blockade responses were detected in the cancer patients who had recurrent TP53 or BCLAF1 mutations in their tumors.

After identifying these treatment response-related, recurrently mutated genes and pathways, the team went on to develop a so-called "Cancer Immunotherapy Response Classifier," or CIRCLE, that appeared to boost the sensitivity and specificity of immune checkpoint blockade response models that included tumor mutational burden, tumor type, patient age, and other potential response predictors.

"Scientists have developed various biomarkers that help anticipate immunotherapy treatment response, but there's still an unmet need for a robust, clinically practical predictive model," co-senior author Neville Sanjana, a researcher affiliated with NYU and the New York Genome Center, said in a statement.

"We envision that this two-step approach and use of whole-exome sequencing will pave the way for better prognostic tools for cancer immunotherapy," Sanjana added.

The predictive potential for CIRCLE classification was further validated with exome sequence data on 165 additional immunotherapy-treated cancer patients, the researchers reported, noting that they expect to continue testing and enhancing the model using data from ever larger cohorts of cancer patients with available tumor sequence and immunotherapy response data.

"We envision that CIRCLE and, more broadly, the analysis of recurrently mutated cancer genes will pave the way for better prognostic tools for cancer immunotherapy," the authors suggested.

Mon, 11 Jul 2022 04:58:00 -0500 en text/html https://www.precisiononcologynews.com/sequencing/immune-checkpoint-blockade-response-linked-recurrent-mutations-genes-pathways
Killexams : DFW offers way to skip the boarding pass

DALLAS — Passengers can put those boarding passes away when heading through some DFW International Airport checkpoints.

DFW is one of a handful of airports where the Transportation Security Administration has rolled out a new technology that matches a database of daily passenger names and birth dates with passengers booked on flights that day. That means passengers can just present a driver’s license or passport and skip the second document, a boarding pass, that has been needed at checkpoints since the agency was formed in 2001.

“Every boarding pass looks different and as a screening officer, you are dealing with hundreds, if not thousands, in an hour,” said TSA spokeswoman Lorie Danker. “This technology puts the information on a screen in the same place.”

TSA rolled out the Credential Authentication Technology in 2019, allowing TSA agents a more comprehensive look at identifying information using REAL ID data. The machines were added to identify legitimate and fraudulent driver’s licenses and passports.

The newest iteration of the machines can link that driver’s license data with information airlines provide about who is traveling through DFW over the next 24 hours.

DFW started installing the newest machines earlier this year and another few months to train screening officers before it was ready for use.

There are only about 90 of the upgraded CAT scanners operating throughout the country, including 19 at DFW.

The machines are in every terminal, but DFW has more than 50 security lanes at 15 different TSA screening checkpoints in its terminals, so many passengers won’t know if they need to show a boarding pass until they approach security agents.

Passengers used to the old procedure can still hand over boarding passes, Danker said.

Dallas Love Field should have the technology ready for use this summer.

TSA is also working on adding new baggage scanning machines that could eliminate the need to take laptops and liquids out of luggage and backpacks.

The new technology comes just weeks after TSA and American Airlines started tests on a new facial scanning technology that lets American Airlines customers enrolled in TSA PreCheck skip showing ID cards. That program allows customers to upload their driver’s licenses and scan their faces into the American Airlines app, then present a QR code in the PreCheck line.

Sat, 16 Jul 2022 23:00:00 -0500 en text/html https://www.news-journal.com/news/business/dfw-offers-way-to-skip-the-boarding-pass/article_b4780692-03c0-11ed-9e23-ff1741a2b497.html
Killexams : Whole-Exome Sequencing Predicts Response to Cancer Immunotherapy

Immunotherapies, such as immune checkpoint inhibitors, have transformed the treatment of advanced stage cancers. Unlike chemotherapies that kill cancer cells, these drugs help the body’s immune system to find and destroy cancer cells themselves. However, only a subset of patients responds long term to immune checkpoint inhibitors, and these treatments can come at a high cost and with side effects.

Researchers at New York University, Weill Cornell Medicine, and the New York Genome Center, have now developed a two-step approach, using whole-exome sequencing, to zero in on genes and pathways that predict whether cancer patients will respond to immunotherapy. The work illustrates how the use of whole-exome sequencing can better predict treatment response than current laboratory tests.

“These results suggest that the use of broader diagnostics such as whole exome or even whole genome sequencing may significantly Excellerate our ability to predict who will respond to immunotherapy—essentially, showing that more data does help to better predict treatment response,” said Marcin Imieliński, PhD, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center. Imieliński is co-senior author of the team’s published paper in Nature Communications, which is titled “Recurrent somatic mutations as predictors of immunotherapy response.”

Immune checkpoint blockade (ICB) has transformed the treatment of metastatic cancer but is hindered by variable response rates, the authors wrote. “A key unmet need is the identification of biomarkers that predict treatment response.” Several biomarkers—including age, tumor type, and the number of mutations found in cancer cells, known as tumor mutational burden—are already known to correlate with responses to immunotherapy. Tumor mutational burden (TMB), which is calculated by analyzing a few hundred genes, is the most well-established predictor and is often used to determine a patient’s eligibility for immune checkpoint inhibitors. “TMB-high tumors are thought to be more immunogenic and hence responsive to ICB due to their increased burden of neoantigens,” the authors explained.

However, as co-senior author Neville Sanjana, PhD, asked, “Can we better predict who will benefit from immunotherapy? … Scientists have developed various biomarkers that help anticipate immunotherapy treatment response, but there’s still an unmet need for a robust, clinically practical predictive model.” Sanjana is assistant professor of biology at NYU, assistant professor of neuroscience and physiology at NYU Grossman School of Medicine, a core faculty member at New York Genome Center,

If scientists look at a much larger portion of our genes, could that help to better predict which patients will respond to immunotherapy? Whole-exome sequencing is a method for sequencing the part of the genome that codes for proteins—around 20,000 genes, or 2% of the genome—to look for mutations that may be involved in disease.

Whole-exome sequencing is not widely used in cancer treatment, although some recent studies of immunotherapies have started to include sequencing. These studies are small, but together can help illuminate the relationship between genomic factors and how patients respond to immunotherapy. However, the authors noted, “Though recent whole-exome sequencing (WES) studies have attempted to go beyond TMB to link specific DNA alterations to ICB response, they have been limited by low trial sizes and underpowered (genome-wide) analytic approaches.”

For their study the researchers combined data from six previous immunotherapy studies of patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole-exome sequencing was available for all participants, who were treated using either an anti-PD-1 or an anti-CTLA-4 immune checkpoint inhibitor. But even after combining the six studies, the number of patients—319 in total—was still relatively small. “ … Although we build a larger cohort by pooling several studies, the trial size is still limiting for genome-wide significance,” the team acknowledged.

“The problem with a small study of only a few hundred people is a mismatch between the number of patients and the vast number of genes sequenced in whole-exome sequencing,” said study first author Zoran Gajic, a graduate student in the Sanjana lab. “We’d ideally have a dataset with more patients than genes.”

To get around this problem, the researchers turned to a model called fishHook, which distinguishes mutations that drive cancer from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for a range of factors that affect the rates of background mutations—for instance, adjusting for the size of a gene, since larger genes are more likely to have mutations. “To identify positively selected genes and pathways in the aggregated immunotherapy cohort, we adapted fishHook, a statistical method originally developed to study noncoding mutational recurrence in whole genome sequencing,” the team noted. “We limited the fishHook analysis to the coding regions of 19,688 genes that are consistently captured by WES.”

Using this model, the researchers employed a two-step approach: first, they looked at the sequencing from all patients to find any genes with a higher mutational burden than they would expect, adjusting for genomic factors like gene size or whether a particular piece of DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with suspiciously high mutational burdens.

Next, the researchers determined if any of these six genes were enriched in people who responded or did not respond to immunotherapy. Two of the genes—KRAS, a gene often mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma—were enriched in patients who responded to immunotherapy. In contrast, two other genes—TP53 and BCLAF1—were enriched in those who did not respond to immunotherapy. BCLAF1 is not well studied, but these findings suggest that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.” In total, we identified 4 ICB response predictive genes from our logistic regression (BCLAF1, BRAF, KRAS, and TP53),” the authors noted.

Using the same two-step approach the researchers next determined that certain pathways (MAPK signaling, p53 associated, and immunomodulatory) also predicted immune checkpoint inhibitor response.

They subsequently combined the four genes and three pathways with other predictive variables such as age, tumor type, and tumor mutational burden to create a tool they named the Cancer Immunotherapy Response CLassifiEr (CIRCLE). They found that compared to tumor mutational burden alone, CIRCLE better predicted ICB response, with a 10.5% increase in sensitivity and a 11% increase in specificity. CIRCLE was also able to accurately predict cancer survival after immunotherapy. “These results suggest that the use of broader diagnostics such as whole-exome or even whole-genome sequencing may significantly Excellerate our ability to predict who will respond to immunotherapy—essentially, showing that more data does help to better predict treatment response,” said Marcin Imieliński, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center, and the study’s co-senior author.

Figure describing steps of CIRCLE: 1. Combine immunotherapy whole-exome sequencing datasets 2. Identify mutated genes and pathways 3. Test candidate genes and pathways for response association 4. Predictive framework for therapy response (determining Responder or Non-responder) [Sanjana and Imieliński labs]
To validate their approach, the researchers tested CIRCLE on data from 165 additional cancer patients with whole exome sequencing who underwent treatment with immunotherapy and found that CIRCLE captured predictive information beyond that obtained from tumor mutational burden alone.

“Our study focuses on biomarkers derived from existing cohorts of immunotherapy patients with paired WES and response data alongside clinically relevant metadata,” the authors summarized. “It capitalizes on the advantages of both candidate gene and genome-wide approaches to achieve optimized predictive power with a modest cohort size … “We found that the CIRCLE classifier yields improved ICB response prediction when compared to TMB,” they wrote. “Taken together, these results support broader investigations into CIRCLE and more generally recurrent somatic alterations as immunotherapy biomarkers.”

Future research will involve testing CIRCLE on larger cohorts of patient data, as the researchers anticipate that the model will Excellerate with data from thousands of patients rather than hundreds. They also hope that with larger cohorts, they can begin to tease out which patients are likely to respond to different immunotherapies, given the growing number of treatments available.

“Larger immunotherapy cohorts will be needed to validate this finding, and more broadly the principle that positively selected driver alterations can help predict immunotherapy response … Due to the cancer type specificity of driver alterations, we can expect that expanding CIRCLE to broader pan-cancer cohorts will require the classifier to be revised with additional discovery analyses.”

“We envision that this two-step approach and use of whole-exome sequencing will pave the way for better prognostic tools for cancer immunotherapy,” said Sanjana. As the authors conclude, “While panel testing is already used routinely in immuno-oncology, our results suggest that the use of broader diagnostics (including WES and whole genome sequencing) may significantly Excellerate this stratification of responders and nonresponders … We envision that CIRCLE and more broadly the analysis of recurrently mutated cancer genes will pave the way for better prognostic tools for cancer immunotherapy.”

Tue, 12 Jul 2022 00:00:00 -0500 en-US text/html https://www.genengnews.com/topics/omics/sequencing/whole-exome-sequencing/whole-exome-sequencing-predicts-patient-response-to-cancer-immunotherapy/
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