Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors

Sep 21, 2026

Uploaded by Theodora

Breast cancer is a highly heterogeneous disease shaped by variable cellular, genetic, epigenetic, and molecular states that evolve within and across tumors, challenging accurate diagnosis and treatment selection. A study proposes immune cell-based metabolic profiling using an ultrafast laser-fabricated Immuno Nano Sensor. Spectral markers combining Natural Killer (NK) and T cells support machine-learning classification of cancer vs healthy samples, primary vs metastatic tumors, and single-cell ER/PR status. The approach reports 97.8% sensitivity and 92.2% specificity, with 97.5% sensitivity for ER/PR-positive vs negative discrimination, enabling minimally invasive liquid biopsy potential.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Immune cell selective pressure and tumor heterogeneity

Methods and platform concept

Immuno Nano Sensor and NK/T cell spectral markers

Machine-learning discrimination tasks

Results and performance metrics

Cancer vs healthy classification

Primary vs metastatic identification

ER/PR-positive vs negative discrimination

Significance and implications

RESEARCH ARTICLE [www.small-journal.com](www.small-journal.com) Proﬁling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors Deeptha Ishwar, Srilakshmi Premachandran, Sunit Das, Krishnan Venkatakrishnan, and Bo Tan* Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions. The detection of intratumor heterogeneity in breast cancer poses signiﬁcant challenges due to its complex multifaceted characteristics, yet its identiﬁcation is crucial for guiding eﬀective treatment decisions and understanding the disease progression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor. Combining spectral markers from both Natural Killer (NK) and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples from healthy individuals, achieving 97.8% sensitivity and 92.2% speciﬁcity, and accurately identiﬁed primary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive and negative tumors with 97.5% sensitivity. This study demonstrates the potential of immune cell-based metabolic proﬁling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management. annually.[1] Breast cancer is highly heterogeneous with various cancer cells within the same tumor existing in diverse metabolic states due to variations in cellular, genetic, epigenetic, and molecular expression. [2–4] These molecular expressions are not uniformly constant and can dynamically evolve over time as the tumor progresses to adiﬀerent location. [5,6] For example, the estrogen receptor (ER) expression is inconsistent between primary and metastatic breast cancer samples and even within the primary sample, there can be nonuniform expression. [7–9] The inter-tumor heterogeneity and intra-tumor metabolic transitions occur at the single-cell level, presenting challenges in diagnosis. Liquid biopsy methods have emerged as promising tools for detecting cancer, but they focus on speciﬁc genetic or molecular alterations associated with cancer, limiting their ability to provide a comprehensive view of tumor heterogeneity. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. Immune cells exert selective pressure on tumors controlling tumor heterogeneity. 1. Introduction Breast cancer is a signiﬁcant health concern aﬀecting women worldwide, accounting for 12.5% of all newly diagnosed cancers Both Natural Killer cells (NK) and T cells employ distinct methods of tumor recognition and eliminate speciﬁc cancer cell subpopulations. [10–12] However, few cancer cells develop immune evasion abilities and circumvent immune D. Ishwar Faculty of Dentistry Department ofStomatology University of Montreal Montreal, Quebec H3T 1J4, Canada The ORCID identiﬁcation number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/smll.202406475](https://doi.org/10.1002/smll.202406475) © 2024 The Author(s) . Small published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modiﬁcations or adaptations are made. DOI: 10.1002/s

FAQs

Why is breast tumor heterogeneity difficult to detect with existing methods?

Intratumor heterogeneity involves dynamic, multifaceted cellular, genetic, epigenetic, and molecular variations across individual cells, and current approaches cannot capture the full extent of these changes. This limits diagnostic completeness for guiding treatment decisions.

What role do NK cells and T cells play in the proposed profiling approach?

NK and T cells exert selective pressure and develop metabolic adaptations during tumor interaction. The study leverages their tumor-associated metabolic signatures as spectral markers to characterize tumor heterogeneity.

How does the Immuno Nano Sensor and machine-learning workflow support subtype identification?

An ultrafast laser-fabricated Immuno Nano Sensor collects spectral information from immune-cell signatures. A machine-learning approach then distinguishes cancer from healthy samples, identifies primary versus metastatic tumors, and determines ER/PR status at the single-cell level.

What diagnostic performance is reported for the platform?

The study reports 97.8% sensitivity and 92.2% specificity for distinguishing heterogeneous breast cancer samples from healthy individuals, and 97.5% sensitivity for discriminating ER/PR-positive versus ER/PR-negative tumors.

Sep 21, 2026

Uploaded by Theodora