Modern pathology is being transformed by the innovative technology of digital pathology. Pathologists have historically examined tissue samples (typically mounted on glass slides) as part of their practice. Even though this workflow has long been the gold standard for the diagnosis of many diseases, including cancer, it has a built-in degree of variability because of subjectivity and human error.
What is Digital Pathology?
Many of the difficulties posed by traditional glass slide workflows have been solved by advancements in digital pathology technologies. In general, the term "digital pathology" refers to the collection, analysis, and dissemination of digitized pathology specimens.
The development of potent whole slide imaging (WSI) technologies has made it possible to digitally scan an entire specimen at diagnostic quality. Occasionally, digitized slides may offer a better resolution of features that help with diagnosis. When combined with potent AI algorithms built on machine learning and/or deep learning, the technology's full potential is realized.
Digital Pathology - Based Biomarker Analysis in Practice
It is no secret that biomarkers are becoming more crucial to the development of all types of cancer medications. This is since many new oncology medications are being created as precision medicines, in which a patient's biomarker profile determines which medication is best for them.
Thus, to increase the likelihood of discovering biomarkers that successfully stratify patients based on who is most likely to respond or develop resistance, the pharmaceutical industry is integrating biomarker strategies earlier in their R&D programmers.
It is possible to identify biomarkers that support efficacy, safety, pharmacodynamics, and/or mechanisms of action (MoA), all of which can be difficult to do but are incredibly instructive.
Digital Pathology - Based Biomarker Analysis Technologies
Various techniques are currently employed to find tissue biomarkers. These include fundamental stains that distinguish between various cellular compartments (such as H&E) or molecular assays for protein biomarkers like immunohistochemistry (IHC) or immunofluorescence (IF). In situ hybridization (ISH) with chromogenic (CSH) or fluorescent (FISH) options is still a common technique for detecting RNA/DNA biomarkers, despite advances in sequencing technology.
For several significant cancer targets, robust IHC and IF protocols have been developed because many tissue biomarkers are protein-based. These consist of:
TAAs (Tumor Associated Antigens), such as growth factor receptors, are antigens associated with tumours (e.g., EGFR, HER2)
Angiogenesis, tumour growth, apoptosis (Ki67, Cleaved Caspase-3), and other markers of the cancer mechanism (CD31, VEGF)
Immune checkpoint inhibitors (ICIs) PD-1 and PD-L1 that can predict a cancer type's response to immunotherapy
Biomarkers in Decisions...
HER2:
HER2-positive Human epidermal growth factor receptor 2 -positive breast cancer is one type of breast cancer. The growth of cancer cells is encouraged by this protein.
The gene that produces the HER2 protein is duplicated in the cancer cells of about 1 out of every 5 breast cancers. Breast cancers that are HER2-positive are typically more aggressive than other subtypes.
While HER2 promotes the division and growth of cancer cells. A monoclonal antibody is a class of targeted cancer medication that includes tratuzumab. In order to prevent the cancer cells from proliferating and dividing, it binds to HER2.
ER & PR:
It is widespread practice for the diagnosis of breast cancer to examine the tumor for both estrogen and progesterone receptors. The accuracy of the results is crucial because they are used to direct treatment. Immunohistochemistry, or IHC, is currently the most popular technique for examining a tumor for the presence of estrogen and progesterone receptors.
From a tissue sample, IHC testing can identify oestrogen and progesterone receptors in cancer cells. This tissue may come from a biopsy, which is the removal of a small sample of tissue for microscopic examination, or from a surgical procedure in which the entire tumor and all or a portion of the breast are removed.
PD - L1
A protein called Programmed Death-Ligand 1 (PD-L1) is present on the surface of numerous cells all over the body. Large amounts of PD-L1, present in some tumor cells but not all, aid tumor cells in evading the immune system, the body's natural defense mechanism. A laboratory test determines the quantity of PD-L1 on tumor cells, which may help determine how certain cancers should be treated.
Our immune system uses T cells (also known as T lymphocytes), a category of white blood cells, as one weapon against diseases like cancer. Certain T-cell subtypes can identify and attacking cancer cells as well as other abnormal or infected cells directly
IHC - A Diagnostic Companion:
In breast pathology, IHC is used as a companion diagnostic for a particular therapy as well as for diagnostic purposes and prognostic stratification of malignant lesions. In IHC, antibodies are used to recognize the epitope(s) of a particular target, also known as a "marker," which is typically a protein but can also be a lipid or a carbohydrate.
IHC, which is used with cytological preparations, fresh frozen samples, and formalin-fixed paraffin-embedded tissue samples, detects the presence of a marker(s) and pattern of expression in situ using antigen-antibody reactions. There are pitfalls and staining quality issues associated with different markers, despite the importance of quantifying IHC staining patterns of diagnostic, prognostic, and predictive markers.
Pitfalls of the Manual Pathology Workflows
The issue is that each of these IHC processes can be altered, and there is no "universal" IHC protocol that can be applied to all IHC markers. There are differences and inconsistent data regarding antibodies, IHC staining protocol, interpretation, and scoring.
Each IHC marker's specificity, sensitivity, staining patterns, validation procedure, antibody clones, detection kits, cross-reactivity, controls, and staining pitfalls can vary and depend on several variables. Different tissue types, fixation characteristics, the IHC staining kit and reagent used, the staining condition, and between different observers who may use different thresholds and subjective interpretation can all cause significant variations in IHC performance.
AI Assisted Diagnosis for the Future
Digital pathology-based biomarker analysis offers enormous potential for selecting cancer drug candidates with the highest likelihood of being successful in clinical trials, including immuno-oncology agents.
The transition to a digital and automated framework lessens the effects of subjectivity and human error while encouraging greater research collaboration for faster and accurate diagnosis.
References:
1. Topalian SL, Hodi FS, Brahmer JR, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366(26):2443-2454. PMC free article PubMed CrossRef Google Scholar
2. Frati A, Chereau E, Coutant C, et al. Comparison of two nomograms to predict pathologic complete responses to neoadjuvant chemotherapy for breast cancer: evidence that HER2-positive tumours need specific predictors. Breast Cancer Res Treat. 2012;132(2):601-607.
3. Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, et al. Artificial intelligence & tissue biomarkers: advantages, risks, and perspectives for pathology. Cells. 2021; 10:787.
4. Li AC, Zhao J, Zhao C, Ma Z, Hartage R, Zhang Y, et al. Quantitative digital imaging analysis of HER2 immunohistochemistry predicts the response to anti-HER2 neoadjuvant chemotherapy in HER2-positive breast carcinoma. Breast Cancer Res Treat. 2020; 180:321–9.
5. Khameneh FD, Razavi S, Kamasak M. Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Comput Biol Med. 2019; 110:164–74.
6. Skaland I, Ovestad I, Janssen EA, Klos J, Kjellevold KH, Baak JP. Comparing subjective and digital image analysis HER2/neu expression scores with conventional and modified FISH scores in breast cancer. J Clin Pathol. 2008; 61:68–71.
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