Predicting Immune Checkpoint Inhibitor Response via RNA-Seq CellCarta

June 6, 2023

Various biomarkers, such as PD-L1 expression, tumor mutation burden (TMB), cytotoxic T-cell infiltration, Microsatellite Instability (MSI), and immune gene signatures, have been proposed to predict immune checkpoint inhibitor response.

However, individual biomarkers have limited accuracy. To overcome this, a computational pipeline has been developed to quantify biomarkers including expressed mutation burden (eTMB), MSI status, infiltrating immune cells, and immune gene expression signatures from tumor RNA-sequencing data.

Machine learning and computational deconvolution algorithms enhance performance, validated on large cohorts.

Integration of these biomarkers improves the prediction of checkpoint inhibition therapy response.

Viewers will gain insights into:

  • A unique computational pipeline that defines several characteristics directly from the RNA-sequencing profile of a tumor such as:
    • Expressed mutation burden (eTMB)
    • Fraction of infiltrating immune cells
    • Various immune gene expression signatures
  • How this analysis is compatible with formalin-fixed paraffin-embedded (FFPE) tumor samples and does not require a matched germline DNA sample

Pieter Mestdagh, Computational Biology Manager, CellCarta

Pieter Mestdagh is a Computational Biology Manager at CellCarta, and Professor at Ghent University, Belgium. He holds master’s degrees in industrial engineering (2004) and in bioscience engineering (2006) and obtained a PhD in biomedical sciences (2011).

He is the author of more than 100 scientific articles in international journals and co-inventor on several European patents.

Predicting Immune Checkpoint Inhibitor Response via RNA-Seq CellCarta

June 6, 2023

Various biomarkers, such as PD-L1 expression, tumor mutation burden (TMB), cytotoxic T-cell infiltration, Microsatellite Instability (MSI), and immune gene signatures, have been proposed to predict immune checkpoint inhibitor response.

However, individual biomarkers have limited accuracy. To overcome this, a computational pipeline has been developed to quantify biomarkers including expressed mutation burden (eTMB), MSI status, infiltrating immune cells, and immune gene expression signatures from tumor RNA-sequencing data.

Machine learning and computational deconvolution algorithms enhance performance, validated on large cohorts.

Integration of these biomarkers improves the prediction of checkpoint inhibition therapy response.

Viewers will gain insights into:

  • A unique computational pipeline that defines several characteristics directly from the RNA-sequencing profile of a tumor such as:
    • Expressed mutation burden (eTMB)
    • Fraction of infiltrating immune cells
    • Various immune gene expression signatures
  • How this analysis is compatible with formalin-fixed paraffin-embedded (FFPE) tumor samples and does not require a matched germline DNA sample

Pieter Mestdagh, Computational Biology Manager, CellCarta

Pieter Mestdagh is a Computational Biology Manager at CellCarta, and Professor at Ghent University, Belgium. He holds master’s degrees in industrial engineering (2004) and in bioscience engineering (2006) and obtained a PhD in biomedical sciences (2011).

He is the author of more than 100 scientific articles in international journals and co-inventor on several European patents.

NextGen Oncology: Precision Proteomics in Diagnostic Solutions

May 2, 2023

Personalized oncology aims to match each patient to a specific therapy based on the molecular characteristics of their tumor.

Currently, tumor DNA is sequenced, and genomics reports are given to physicians to help select targeted therapies for patients.

While this genome-centric approach to precision oncology has extended the lives of subsets of patients, many patients do not respond to the selected therapy and many whose tumors initially respond have a high chance of recurrence with resistant disease.

New approaches are required to capture complex clinical phenotypes and better match patients to efficacious therapies.

Viewers will gain insights into:

  • An understanding of state-of-the-art proteomic analyses
  • Application of proteogenomic approaches to identifying novel biomarkers
  • How to develop multiplex, targeted proteomic assays to support preclinical and clinical studies

Dr. Amanda Paulovich, MD, PhD, Professor and Aven Foundation Endowed Chair, Director, CLIA Targeted Proteomic Laboratory, Clinical Research Division, Fred Hutchinson Cancer Center

As an oncologist, Dr. Amanda Paulovich was struck by the paucity of quantitative assays for measuring clinically relevant phenotypes in her patients, and the limitations that this put on her ability to practice “personalized medicine.” Through these experiences, she became passionate about developing technologies and strategies for the translation of NextGen diagnostics and therapeutics to enable precision medicine.

Over the past 19 years, Dr. Paulovich’s interdisciplinary laboratory has focused on proteogenomic approaches to understanding cancer biology and laying the groundwork for the clinical translation of NextGen diagnostics incorporating targeted, multiple reaction monitoring (MRM) mass spectrometry.

Dr. Paulovich completed a residency in internal medicine at Massachusetts General Hospital and a fellowship in oncology at Dana-Farber Cancer Institute. She completed her PhD training in genetics with Dr. Lee Hartwell at the University of Washington and postdoctoral training in genomics at the Massachusetts Institute of Technology with Dr. Eric Lander.

NextGen Oncology: Precision Proteomics in Diagnostic Solutions

May 2, 2023

Personalized oncology aims to match each patient to a specific therapy based on the molecular characteristics of their tumor.

Currently, tumor DNA is sequenced, and genomics reports are given to physicians to help select targeted therapies for patients.

While this genome-centric approach to precision oncology has extended the lives of subsets of patients, many patients do not respond to the selected therapy and many whose tumors initially respond have a high chance of recurrence with resistant disease.

New approaches are required to capture complex clinical phenotypes and better match patients to efficacious therapies.

Viewers will gain insights into:

  • An understanding of state-of-the-art proteomic analyses
  • Application of proteogenomic approaches to identifying novel biomarkers
  • How to develop multiplex, targeted proteomic assays to support preclinical and clinical studies

Dr. Amanda Paulovich, MD, PhD, Professor and Aven Foundation Endowed Chair, Director, CLIA Targeted Proteomic Laboratory, Clinical Research Division, Fred Hutchinson Cancer Center

As an oncologist, Dr. Amanda Paulovich was struck by the paucity of quantitative assays for measuring clinically relevant phenotypes in her patients, and the limitations that this put on her ability to practice “personalized medicine.” Through these experiences, she became passionate about developing technologies and strategies for the translation of NextGen diagnostics and therapeutics to enable precision medicine.

Over the past 19 years, Dr. Paulovich’s interdisciplinary laboratory has focused on proteogenomic approaches to understanding cancer biology and laying the groundwork for the clinical translation of NextGen diagnostics incorporating targeted, multiple reaction monitoring (MRM) mass spectrometry.

Dr. Paulovich completed a residency in internal medicine at Massachusetts General Hospital and a fellowship in oncology at Dana-Farber Cancer Institute. She completed her PhD training in genetics with Dr. Lee Hartwell at the University of Washington and postdoctoral training in genomics at the Massachusetts Institute of Technology with Dr. Eric Lander.

Digital PCR Applications: Advancing Clinical Research

June 1, 2022

In this webinar, Dr. Jan Hellemans describes the intrinsic potential of droplet digital PCR (ddPCR) in clinical research for three different applications:

  • rare variant analysis in plasma samples,
  • small changes in alternative splicing, and
  • CAR-T studies.

Since becoming commercially available in 2011, digital PCR (dPCR) has been gaining in popularity as it enables nucleic acid quantification with superior accuracy and precision compared to quantitative PCR (qPCR). In brief, the technology consists of the following steps:

  1. The PCR reaction mixture is partitioned into thousands of water-in-oil droplets with target and background DNA randomly distributed among the reactions.
  2. The target DNA is amplified by PCR using standard thermal cycling with fluorescent dyes or probes.
  3. Each reaction provides a fluorescent positive or negative signal indicating that target DNA was present or absent in partitioning.
  4. The fraction of positive droplets is used to calculate the target DNA concentration using Poisson correction.

With data acquisition taking place at the end of the reaction, and since absolute concentrations can be determined, data can be easily interpreted and compared across different samples and experiments, especially in a clinical setting.

Additionally, dPCR doesn’t suffer from the inherent complications of qPCR that result from the requirement of a standard curve and excels in the quantification of low abundant genes or small differences.

Learn more about digital PCR.

Digital PCR Applications: Advancing Clinical Research

June 1, 2022

In this webinar, Dr. Jan Hellemans describes the intrinsic potential of droplet digital PCR (ddPCR) in clinical research for three different applications:

  • rare variant analysis in plasma samples,
  • small changes in alternative splicing, and
  • CAR-T studies.

Since becoming commercially available in 2011, digital PCR (dPCR) has been gaining in popularity as it enables nucleic acid quantification with superior accuracy and precision compared to quantitative PCR (qPCR). In brief, the technology consists of the following steps:

  1. The PCR reaction mixture is partitioned into thousands of water-in-oil droplets with target and background DNA randomly distributed among the reactions.
  2. The target DNA is amplified by PCR using standard thermal cycling with fluorescent dyes or probes.
  3. Each reaction provides a fluorescent positive or negative signal indicating that target DNA was present or absent in partitioning.
  4. The fraction of positive droplets is used to calculate the target DNA concentration using Poisson correction.

With data acquisition taking place at the end of the reaction, and since absolute concentrations can be determined, data can be easily interpreted and compared across different samples and experiments, especially in a clinical setting.

Additionally, dPCR doesn’t suffer from the inherent complications of qPCR that result from the requirement of a standard curve and excels in the quantification of low abundant genes or small differences.

Learn more about digital PCR.

Flow Cytometry Data Analysis: Fast & Visual with CellEngine

February 4, 2022

Common challenges faced with existing flow cytometry software include speed, ability to create complex visualizations of large studies and meeting regulatory compliance.

CellEngine was built from the ground up by CellCarta to solve these challenges.

Topics in this presentation include:

  • Extensive end-to-end analysis features and unmatched performance of the cloud-based cytometry analysis software
  • Rigorous validation and comprehensive 21 CFR part 11-compliance features which are an excellent fit for regulated environments
  • Analyzing longitudinal clinical studies with metadata-driven visualizations
  • The ease of reproducibly analyzing datasets containing thousands of samples with speed and rich API

This webinar was hosted with XTalks and featured the following speakers:

Zachary Bjornson-Hooper, PhD, Sr. Director, Informatics, CellCarta

Dr. Bjornson-Hooper completed his undergraduate studies in biology at the Massachusetts Institute of Technology and earned his PhD in microbiology and immunology at Stanford University. He has extensive wet lab experience in comparative immunology, flow & mass cytometry and viral genomics, including leading the development of an atlas of immune signaling responses in various animal models relevant to infectious disease research.

At CellCarta, he oversees the development of the CellEngine single-cell analysis platform.

Dave McIlwain, PhD, Senior Research Scientist, Stanford University School of Medicine, Nolan Lab

Dr. McIlwain studies host response to infections using high-dimensional single-cell and spatial proteomics tools. He trained for his PhD at the University of Toronto, yielding insights into alternative mRNA splicing and iRhom2 as a new factor controlling the production of inflammatory mediator TNF.

As a post-doctoral fellow, Dr. McIlwain investigated host response to viral infection in animal models at the University of Dusseldorf in Germany before moving to Stanford University.

Pier Federico Gherardini, PhD, Computational Biology Consultant, Pragmatica.Bio

Dr. Gherardini has 14 years of experience in computational biology and bioinformatics and obtained his PhD from the University of Rome Tor Vergata before moving to Stanford for postdoctoral training.

Dr. Gherardini has developed computational tools for the analysis of cytometry data and a technology to measure gene expression in single cells using mass cytometry.

Flow Cytometry Data Analysis: Fast & Visual with CellEngine

February 4, 2022

Common challenges faced with existing flow cytometry software include speed, ability to create complex visualizations of large studies and meeting regulatory compliance.

CellEngine was built from the ground up by CellCarta to solve these challenges.

Topics in this presentation include:

  • Extensive end-to-end analysis features and unmatched performance of the cloud-based cytometry analysis software
  • Rigorous validation and comprehensive 21 CFR part 11-compliance features which are an excellent fit for regulated environments
  • Analyzing longitudinal clinical studies with metadata-driven visualizations
  • The ease of reproducibly analyzing datasets containing thousands of samples with speed and rich API

This webinar was hosted with XTalks and featured the following speakers:

Zachary Bjornson-Hooper, PhD, Sr. Director, Informatics, CellCarta

Dr. Bjornson-Hooper completed his undergraduate studies in biology at the Massachusetts Institute of Technology and earned his PhD in microbiology and immunology at Stanford University. He has extensive wet lab experience in comparative immunology, flow & mass cytometry and viral genomics, including leading the development of an atlas of immune signaling responses in various animal models relevant to infectious disease research.

At CellCarta, he oversees the development of the CellEngine single-cell analysis platform.

Dave McIlwain, PhD, Senior Research Scientist, Stanford University School of Medicine, Nolan Lab

Dr. McIlwain studies host response to infections using high-dimensional single-cell and spatial proteomics tools. He trained for his PhD at the University of Toronto, yielding insights into alternative mRNA splicing and iRhom2 as a new factor controlling the production of inflammatory mediator TNF.

As a post-doctoral fellow, Dr. McIlwain investigated host response to viral infection in animal models at the University of Dusseldorf in Germany before moving to Stanford University.

Pier Federico Gherardini, PhD, Computational Biology Consultant, Pragmatica.Bio

Dr. Gherardini has 14 years of experience in computational biology and bioinformatics and obtained his PhD from the University of Rome Tor Vergata before moving to Stanford for postdoctoral training.

Dr. Gherardini has developed computational tools for the analysis of cytometry data and a technology to measure gene expression in single cells using mass cytometry.

Biomarkers and Correlates in Infection and Immunity

June 26, 2020

Fast-tracking the development of treatments and ensuring early selection of a promising one depends on your ability to identify significant correlates during both pre-clinical and clinical phases.

In this webinar, we use case studies to show the importance of a multi-disciplinary systems approach in the identification of these correlates.

Understanding both pathogen and host, and their interactions, is essential to fully assess the effect of infectious diseases. The gold standard for diagnosis of an infection has been to demonstrate the presence of pathogen.

However, disease severity, progression, as well as response to treatment are often more dependent on the status of the host. Therefore, an appropriately detailed assessment of the host response will substantially inform the evaluation of antimicrobial prophylactics and therapeutics.

The immune response is complex and collaborative. A robust immune response requires multiple cell lineages and soluble factors to work in concert.

The immune response, however, can be influenced by multiple host factors including co-morbidities, levels of immune competence, type of immune response and age of the host. These factors are often meaningfully represented in specific at-risk populations and therefore need to be accounted for in development programs.

All of these factors are prevalent in the high-risk groups susceptible to the COVID19 pandemic, and age and immune function have been shown to be factors affecting vaccination generally. Furthermore, the immune correlates of protection necessary for successful vaccine development may not be the same as those required for successful therapy development.

A multi-disciplinary systems approach for the measurement of the host response is thus required to successfully address all these variables.

This presentation will support this argument by drawing examples from our extensive previous experience identifying infectious disease biomarkers in pre-clinical and clinical trial settings for diagnostic, disease progression, or treatment response applications using a variety of platforms.

The case studies will survey the identification of predictive biomarkers of disease progression and response to vaccination, as well as cellular and soluble protein correlates of vaccine response.

Speaker: Eustache Paramithiotis PhD, Vice-President, Research & Development, Caprion Biosciences Inc.

Learn more about CellCarta.

Biomarkers and Correlates in Infection and Immunity

June 26, 2020

Fast-tracking the development of treatments and ensuring early selection of a promising one depends on your ability to identify significant correlates during both pre-clinical and clinical phases.

In this webinar, we use case studies to show the importance of a multi-disciplinary systems approach in the identification of these correlates.

Understanding both pathogen and host, and their interactions, is essential to fully assess the effect of infectious diseases. The gold standard for diagnosis of an infection has been to demonstrate the presence of pathogen.

However, disease severity, progression, as well as response to treatment are often more dependent on the status of the host. Therefore, an appropriately detailed assessment of the host response will substantially inform the evaluation of antimicrobial prophylactics and therapeutics.

The immune response is complex and collaborative. A robust immune response requires multiple cell lineages and soluble factors to work in concert.

The immune response, however, can be influenced by multiple host factors including co-morbidities, levels of immune competence, type of immune response and age of the host. These factors are often meaningfully represented in specific at-risk populations and therefore need to be accounted for in development programs.

All of these factors are prevalent in the high-risk groups susceptible to the COVID19 pandemic, and age and immune function have been shown to be factors affecting vaccination generally. Furthermore, the immune correlates of protection necessary for successful vaccine development may not be the same as those required for successful therapy development.

A multi-disciplinary systems approach for the measurement of the host response is thus required to successfully address all these variables.

This presentation will support this argument by drawing examples from our extensive previous experience identifying infectious disease biomarkers in pre-clinical and clinical trial settings for diagnostic, disease progression, or treatment response applications using a variety of platforms.

The case studies will survey the identification of predictive biomarkers of disease progression and response to vaccination, as well as cellular and soluble protein correlates of vaccine response.

Speaker: Eustache Paramithiotis PhD, Vice-President, Research & Development, Caprion Biosciences Inc.

Learn more about CellCarta.