Assessing the Tumor Microenvironment by Recovery of Immune Receptor - - PowerPoint PPT Presentation

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Assessing the Tumor Microenvironment by Recovery of Immune Receptor - - PowerPoint PPT Presentation

Assessing the Tumor Microenvironment by Recovery of Immune Receptor V(D)J Recombination Reads from Tumor Specimen Exome Files George Blanck, Ph.D. Professor, Molecular Medicine Morsani College of Medicine, USF Immunology Program, Moffitt


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Assessing the Tumor Microenvironment by Recovery of Immune Receptor V(D)J Recombination Reads from Tumor Specimen Exome Files

George Blanck, Ph.D. Professor, Molecular Medicine Morsani College of Medicine, USF Immunology Program, Moffitt Cancer Center (not for publication or public web page)

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Learning objectives:

  • 1. To appreciate the availability of T-cell receptor

recombination information from tumor specimen DNA samples.

  • 2. To understand the correlation between T-cell receptor

recombinations, in tumor specimen DNA, and other, clinically relevant information for bladder cancer and kidney renal cell carcinoma.

  • 3. To understand the importance of HLA alleles in

assessing the impact of T-cell receptor recombinations from tumor specimen DNA.

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  • Fig. 1.

Immune receptor genes recombine during development to generate many different receptor molecules among the B-cells and T- cells, throughout the body, through life, to bind many different antigens,

including tumor antigens.

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Seven immune receptor genes total:

  • Three related to antibodies, not

further discussed.

  • Two related to gamma-delta T-cells,

not further discussed

  • Two required for alpha-beta T-cells,

the subject of this presentation.

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Alpha-beta T-cells:

  • Most numerous.
  • Best understood, medically speaking.
  • Generally target peptide antigen, in the

case of tumors, a mutant peptide.

  • The TRB part of the TRA/TRB receptor is

considered most important in antigen binding.

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The tumor specimen and the exome

  • Surgically remove tumor, obtain DNA

sequence (all exons = exome), for tumor mutations, which can guide therapy.

  • Other cells in the specimen, particularly

T-cells.

  • The DNA representing the T-cell receptor

can be identified above the tumor DNA “background”.

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SLIDE 7
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  • Fig. 1 again.
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Tabulate, millions and millions, along with clinical information:

TCGA BLCA Barcode Reads

Number

  • f reads

detected

Clinical status V usage D usage J usage

TCGA-FD-A5BS

TCCCAGACATCTGTGTAC TTCTGTGCCAGCAGTGCC CGGACAGGGAAGTATGG CTACACCTTCGGTTCGGG GACCA

7

No subsequent tumor/positive

  • utcome

TRBV10-3 TRBD1 TRBJ1-2

TCGA-FD-A5C1

AGCCCCAACCAGACCTCT CTGTACTTCTGTGCCAGC AGTTTATCGGGCGGGGG AGTATACGAGCAGTACTT CGGGC

20

No subsequent tumor/positive

  • utcome

TRBV27- 01 TRBD2 TRBJ2-7

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Results

  • 1. For bladder cancer, the more TRB recombination

reads, the better the outcome.

  • 2. For melanoma and renal cell carcinoma, the more

TRA and TRB reads, the higher the levels of immune checkpoint protein expression.

  • 3. For certain cancers, the combination of usage of a

particular V or J segment, by T-cells in the tumor microenvironment, and a particular HLA type, corresponds to a dramatic survival distinction.

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SLIDE 11

Data collection redacted in anticipation of separate publication.

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Fig 3.

The T-cell receptor binds to antigen by simultaneously binding the HLA molecules on other cells.

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References for preceding slides, results

  • Anne Mai et al submitted
  • Kendall Clark et al in prep
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The future:

  • Exome detectable TcR recombinations, that correlate

with survival or immune checkpoint protein expression are likely to be of use in settings where cost-efficiency is paramount, or as a confirmation.

  • Linking TcR gene segment usage to HLA alleles for

prognosis may be doable, routinely, with exome based recovery of TcR recombinations, particularly in settings where cost-efficiency is paramount; or, TcR segment usage may be more reliably assessed with other methods, for matching to HLA alleles.

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Question: What is the surrogate for detection of tumor microenvironment T-cells, when relying on an exome file?

  • A. HLA allele sequence
  • B. Oncogene mutations
  • C. PD1
  • D. V-J recombination read
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USF research group Wei Lue Tong Yaping Tu Blake Callahan Anne Mai Jacob Kinskey Dhiraj Sikaria Seyed Ahmad Falasiri Diana Fisler John Yavorski Tasnif Rahman Kendall Clark Tommy Gill Jay Patel USF research computing

  • Dr. Timothy Fawcett

Moffitt Collaborators Wade J. Sexton Domenico Coppola Minjung Kim