Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology

Shachi Mittal, Kevin Yeh, L. Suzanne Leslie, Seth Kenkel, Andre Kajdacsy-Balla, Rohit Bhargava

Research output: Contribution to journalArticle

Abstract

Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients' caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

Original languageEnglish (US)
Pages (from-to)E5651-E5660
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number25
DOIs
StatePublished - Jun 19 2018

Fingerprint

Tumor Microenvironment
Confocal Microscopy
Breast
Epithelial Cells
Research
Semiconductor Lasers
Signal-To-Noise Ratio
Fourier Analysis
Pathology
Breast Neoplasms
Technology
Carcinoma
Biopsy

Keywords

  • Breast
  • Cancer
  • Imaging
  • Pathology
  • Spectroscopy

ASJC Scopus subject areas

  • General

Cite this

Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology. / Mittal, Shachi; Yeh, Kevin; Suzanne Leslie, L.; Kenkel, Seth; Kajdacsy-Balla, Andre; Bhargava, Rohit.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 115, No. 25, 19.06.2018, p. E5651-E5660.

Research output: Contribution to journalArticle

@article{4de8a6368f4f48cf9099bf24ade4ac16,
title = "Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology",
abstract = "Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients' caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.",
keywords = "Breast, Cancer, Imaging, Pathology, Spectroscopy",
author = "Shachi Mittal and Kevin Yeh and {Suzanne Leslie}, L. and Seth Kenkel and Andre Kajdacsy-Balla and Rohit Bhargava",
year = "2018",
month = "6",
day = "19",
doi = "10.1073/pnas.1719551115",
language = "English (US)",
volume = "115",
pages = "E5651--E5660",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
number = "25",

}

TY - JOUR

T1 - Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology

AU - Mittal, Shachi

AU - Yeh, Kevin

AU - Suzanne Leslie, L.

AU - Kenkel, Seth

AU - Kajdacsy-Balla, Andre

AU - Bhargava, Rohit

PY - 2018/6/19

Y1 - 2018/6/19

N2 - Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients' caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

AB - Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients' caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

KW - Breast

KW - Cancer

KW - Imaging

KW - Pathology

KW - Spectroscopy

UR - http://www.scopus.com/inward/record.url?scp=85048738854&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048738854&partnerID=8YFLogxK

U2 - 10.1073/pnas.1719551115

DO - 10.1073/pnas.1719551115

M3 - Article

C2 - 29866827

AN - SCOPUS:85048738854

VL - 115

SP - E5651-E5660

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 25

ER -