SBIR-STTR Award

Multi-Omic Single-Cell System for Improved Combination Cancer Immunotherapy Monitoring and Implementation
Award last edited on: 10/8/2021

Sponsored Program
SBIR
Awarding Agency
NIH : NCI
Total Award Amount
$2,149,181
Award Phase
2
Solicitation Topic Code
NCI
Principal Investigator
Timothy S McConnell

Company Information

Isoplexis Inc (AKA: Isoplexis Corporation)

35 Ne Industrial Road
Branford, CT 06405
   (203) 208-4111
   info@isoplexis.com
   www.isoplexis.com
Location: Single
Congr. District: 03
County: New Haven

Phase I

Contract Number: 1R43HG010025-01
Start Date: 4/16/2018    Completed: 9/30/2018
Phase I year
2018
Phase I Amount
$163,731
Although solid tumors oftentimes arise from the uninhibited growth of a single aberrant cell, the population of cells within a tumor are genetically and molecularly heterogenous. This makes it very difficult to evaluate a therapy’s efficacy when using bulk population measurements. Furthermore, recent research has implicated microRNAs (miRNAs) as drivers of progression and malignancy in many tumor types. MiRNAs, small non- coding RNAs that are typically about 20 nucleotides long, can modulate target gene expression by binding to mRNA, signaling for its degradation or inactivation. The relationship between miRNA and target genes becomes even more complex since miRNAs can target multiple mRNAs, and most of what is known about miRNA targets is theoretical and based on sequence-based predication algorithms. It is the correlation between miRNAs and target protein that therapy developers need to be able to monitor to tell if their therapy is going to be effective for a certain tumor type. The only way to quickly discover accurate correlations between RNA and proteins, is to monitor the transcriptome and proteome on a single cell level. This will tell drug developers, exactly what the relationship is between a specific miRNA and protein of intertest, and in what cell types these correlations are most prevalent. The IsoPlexis Single-Cell Barcode (SCBC) is the only technology that currently exists that can obtain this correlation data from single cells, measuring up to 42 secreted or intracellular proteins along with up to 4 phenotypic surface markers from a single cell making our technology able to measure 10-fold more proteins per cell than our competitors and requires only a few thousand cells to provide significant, and importantly quantitative, results. Our platform has recently been further developed to lyse cells on-chip in a cell specific manner, allowing for the capture of circulating RNA species, including miRNA and mRNA. Herein, we propose to use this phase I grant to develop a new tool, using the IsoPlexis SCBC platform, for multi-omic analysis that can (i) make highly multiplexed measurements from a single cell (ii) while being able to gather both proteomic and transcriptomic data from the same cell (iii) in a manner that is high throughout and capable of being fully automated. We propose the following specific aims: Aim 1. Develop and optimize SCBC flow cell for dual capture of protein and RNA on-chip. Aim 2. Deliver validation for ability to monitor multi-omic GBM biomarkers from a single-cell using 10 patient samples in collaboration with UCLA and Caltech.

Public Health Relevance Statement:
NARRATIVE MicroRNAs are small non-coding RNAs that bind target transcripts, marking them either for inactivation or degradation. The relationship between microRNAs and their targets is a complex one, where a single microRNA may have tens, or even hundreds, of potential protein targets. Being able to elucidate the correlations between miRNAs and protein at a single cell level, may allow us to uncover network interactions that contribute to therapeutic resistance and that are not predicted by analyzing pathways in isolation. Further development of the IsoPlexis platform would allow us to (i) make highly multiplexed measurements from a single cell (ii) while being able to gather both proteomic and transcriptomic data from the same cell (iii) in a manner that is high throughout and capable of being fully automated.

NIH Spending Category:
Biotechnology; Brain Cancer; Brain Disorders; Cancer; Cancer Genomics; Genetics; Human Genome; Rare Diseases

Project Terms:
Algorithms; base; Base Sequence; Benchmarking; Binding; Biological Assay; Biological Markers; biomarker panel; cancer therapy; cancer type; cell type; Cells; Cellular Assay; Cellular biology; Charge; Clinical; clinically relevant; Collaborations; Complex; cross reactivity; Cytolysis; Data; Data Correlations; Detection; Development; Environment; Evaluation; Flow Cytometry; Gene Expression; Gene Targeting; Genes; Genomics; Glioblastoma; Grant; Growth; Heterogeneity; Human; Industry Standard; Institutes; instrument; interest; Malignant Neoplasms; Masks; Measurement; Measures; Messenger RNA; Methods; MicroRNAs; Modeling; Molecular; Monitor; multiple omics; novel; Nucleotides; outcome forecast; Pathway Analysis; Patients; Pharmaceutical Preparations; Phase; Phenotype; Population; predictive marker; Process; Protein Analysis; Proteins; Proteome; Proteomics; Protocols documentation; Research; Research Personnel; RNA; RNA Binding; Sampling; Signal Transduction; single molecule; Solid Neoplasm; Subgroup; success; Surface; System; Technology; Testing; therapy resistant; Time; tool; Transcript; transcriptome; transcriptome sequencing; transcriptomics; Translations; Treatment Efficacy; tumor; Tumor Markers; Untranslated RNA; Validation

Phase II

Contract Number: 9R44CA243949-02
Start Date: 8/1/2019    Completed: 7/31/2021
Phase II year
2019
(last award dollars: 2020)
Phase II Amount
$1,985,450

IsoPlexis proposes to deliver a novel multi-omic method for targeted profiling of both the TCR sequence and proteome from an array of 1000+ single cells. Specifically, we will deliver a single-cell, TCR sequencing and protein capture assay for identifying responsive antigen specific TCRs, and concurrently evaluate these T-cells for functional response to that antigen. The challenge remains to link the activation of quiescent T-cell embedded in tumors by combination immunotherapies to patient outcome. Determining the combination of therapies to which each individual patient best responds indicates the best course of treatment. The quality of single-cell polyfunctional response of these immune cells correlates to positive outcomes far better than traditional bulk analysis. For example, PD-1 is upregulated upon T-cell activation while PD-L1 is expressed by a range of cell types. Since PD-1/PD-L1 interactions negatively regulate T cell immune function, PD-1/PD-L1 blockade can rescue effector T cell function. Critical to analyzing TILs is to assess (1) these T-cells’ function in the tumor environment in order to enable trial leaders to predict responders vs non-responders, a critical problem in immuno-oncology, and (2) to understand the TCR Sequence of the highest functioning cells. IsoPlexis single- cell secretion analysis exceeds its competition in the generation and quantitation of highly-multiplexed, single- cell data. Additional single-cell data from the TCR sequence would help to link antigen specificity to polyfunctional T cells involved in patient response, for improved biomarkers and targeted T-cell therapy development. We propose the following specific aims: (1) develop SCBC flow cell for the dual capture of multiplexed proteins and transcriptome on-device. (2a) produce a miniaturized and benchtop automated instrument of the existing instrument for multi-omic applications. 2b) develop a software suite for automated data processing and intuitive integrated informatics of polyfunctional and transcriptome data. 3) Establish patient learning of phenotype & genotype information in multiple trials, applied with machine learning of large patient genotype/phenotype data. At the end of our Phase II grant, we will demonstrate a dual TCR/proteomic assay on a fully-automated miniaturized SCBC instrument, the IsoMini, and software suite that will be successfully used across three combination therapy trials at Yale, Stanford and Fred Hutch.

Public Health Relevance Statement:
As successes in personalized medicine have continued to move the field forward, there is a growing need to predict responder differences in combination cancer therapies. The challenge remains to link the activation of quiescent T-cell embedded in tumors (TILs) by combination immunotherapies to patient outcome. IsoPlexis automated high-throughput platform using highly-multiplexed, single-cell measurements allows the simultaneous identification of both antigen specificity by TCR sequencing and functional effects by polyfunctional cytokine secretion. This would allow clinicians to finally have a tool to identify the specific TCR of the highest responding TILs necessary for the effective development of combination therapies.

Project Terms:
Algorithms; American Society of Clinical Oncology; Antigens; Automatic Data Processing; base; Biological Assay; Biological Markers; cancer immunotherapy; CD8-Positive T-Lymphocytes; Cell Count; Cell physiology; Cell secretion; cell type; Cells; combination cancer therapy; Combination immunotherapy; Combined Modality Therapy; comparative; Computer software; computerized data processing; cost; cytokine; Data; Development; Devices; effective therapy; effector T cell; Environment; Evaluation; Generations; Genes; Genomics; Genotype; genotyped patients; Grant; Hour; Imagery; immune function; Immune response; Immunooncology; improved; individual patient; Industry; Informatics; instrument; Intuition; Lead; Learning; Life; Link; Machine Learning; Measurement; Messenger RNA; Methods; miniaturize; Monitor; multiple omics; novel; Outcome; Output; patient response; Patient-Focused Outcomes; Patients; PD-1/PD-L1; PDCD1LG1 gene; personalized medicine; Phase; Phenotype; phenotypic data; Proteins; Proteome; Proteomics; Publishing; responders and non-responders; response; RNA; Running; Sampling; SLEB2 gene; software development; Specificity; standard care; success; System; T cell response; T cell therapy; T-Cell Activation; T-Lymphocyte; targeted treatment; Technology; Testing; therapy development; Therapy trial; Time; tool; Toxic effect; transcriptome; transcriptome sequencing; transcriptomics; tumor; Tumor Antigens