Tissue Microarray Analysis Reveals Heterogeneous Expression of Talin-1 and Lactate Dehydrogenase A in Non-Small Cell Lung Cancer: Implications for Biomarker Reliability

Tissue Microarray Analysis Reveals Heterogeneous Expression of Talin-1 and Lactate Dehydrogenase A in Non-Small Cell Lung Cancer: Implications for Biomarker Reliability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tissue Microarray Analysis Reveals Heterogeneous Expression of Talin-1 and Lactate Dehydrogenase A in Non-Small Cell Lung Cancer: Implications for Biomarker Reliability Abduladim Hmmier, Paul Dowling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7004726/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tumour heterogeneity significantly impacts biomarker reliability in non-small cell lung cancer (NSCLC), complicating the validation of diagnostic proteins such as lactate dehydrogenase A (LDHA) and Talin-1. This study investigated their expression heterogeneity in tissue microarrays (TMAs) from 40 non-metastatic NSCLC cases (24 squamous cell carcinomas, 16 adenocarcinomas) and 10 normal controls, using standardised immunohistochemistry (IHC). Methods Formalin-fixed, paraffin-embedded (FFPE) TMAs were stained with anti-LDHA and anti-Talin-1 antibodies. Expression was scored (0–3) for intensity and analysed against tumour grade/stage. Results Normal tissues showed minimal reactivity (scores 0–1), while tumours exhibited marked heterogeneity. In adenocarcinomas, 20/32 cores (62.5%) showed moderate LDHA expression (score 2–3), predominantly in stage IIB/IIIA tumours. Talin-1 expression varied widely, with 14/32 (43.6%) adenocarcinoma cores scoring 2–3, with almost same IIIA/IIB stage ratio. Squamous cell carcinomas displayed greater inconsistency, with LDHA scores 2–3 in 34/48 cores (70.8%), and Talin-1 scored 2–3 in 12/48 (25%), the majority were in stage IIB/IIIA but no grade/stage correlation. Such findings demonstrate substantial intra- and inter-tumour heterogeneity for both biomarkers, independent of conventional clinicopathological parameters. This variability explains their inconsistent performance in prior studies and underscores the need for multiplexed biomarker panels to overcome heterogeneity-driven limitations. Conclusion Our findings reveal significant heterogeneity in LDHA and Talin-1 expression across NSCLC subtypes, independent of tumor grade/stage. This underscores the need for standardized IHC protocols and spatial profiling in biomarker development. The variability observed supports using multiplexed panels rather than single-marker approaches for reliable clinical applications. Laboratory Diagnostics Cancer Biology Immunohistochemistry (IHC) Tissue microarrays (TMAs) formaldehyde-fixed paraffin-embedded (FFPE) tissues Lactate dehydrogenase A (LDHA) Talin-1 inconsistent tissue expression biomarkers reliability Non-small cell lung cancer (NSCLC) Figures Figure 1 Introduction Tumour heterogeneity and treatment outcomes Tumour heterogeneity manifests at multiple levels, ranging from within individual tumours to between different cancer subtypes and tissues of origin ( 1 ). This variability significantly influences immune-tumour interactions, underscoring the need to understand its role in shaping these critical biological relationships ( 2 ). Highly heterogeneous tumours often demonstrate resistance to targeted therapies designed against specific oncogenic drivers, limiting treatment efficacy ( 3 ). Paradoxically, molecularly targeted agents that induce partial tumour cell death may inadvertently increase heterogeneity and promote more aggressive phenotypes, further complicating therapeutic management ( 3 ). Mathematical modelling suggests that optimized chemotherapy regimens, particularly those employing lower dose rates, may prove more effective against tumours containing resistant subpopulations ( 4 ). The contribution of intra-tumour heterogeneity to drug resistance highlights the necessity of incorporating evolutionary principles into clinical trial design and therapeutic development ( 5 , 6 ). Advances in tumour sampling and analysis Obtaining representative tumour samples remains crucial for treatment planning. Current biopsy techniques including; core needle, laparoscopic/thoracoscopic, and open excisional/incisional approaches, are selected based on clinical context, tumour location, and patient status ( 7 ). Image-guided methods utilizing ultrasound, CT, MRI, or fusion imaging have enhanced diagnostic accuracy through precise tumour targeting ( 8 ). Recent innovations now enable concurrent metabolomic and histologic evaluation of single biopsy specimens, maximizing the information yield from limited tissue samples ( 9 ). However, immunohistochemical (IHC) analysis continues to face challenges due to pre-analytical variability in sample handling and processing ( 10 ). Despite being the gold standard for malignancy confirmation, IHC suffers from reproducibility issues stemming from inconsistent staining protocols and lack of standardization in fixation and antigen retrieval methods ( 11 ). Variability in both pre-analytical procedures and post-analytical interpretation, particularly in threshold determination, contributes significantly to these inconsistencies ( 12 ). Addressing these limitations through standardized protocols is essential for improving diagnostic accuracy ( 13 ). Challenges in serological biomarker development The development of non-invasive biomarkers for treatment monitoring and relapse detection faces substantial obstacles due to tumour heterogeneity ( 14 ). Even histologically similar tumours from the same organ often exhibit divergent molecular profiles and therapeutic responses ( 15 ). This genetic variability complicates the identification of biomarkers with sufficient sensitivity and specificity for clinical use ( 16 ). While current biomarkers may show limited overall sensitivity, certain markers could have utility in specific patient subsets ( 17 ). The observed variability in biomarker performance across study populations primarily reflects inter-tumour heterogeneity ( 18 ), which can produce inconsistent expression patterns and weak biomarker outcome correlations ( 19 ). Overcoming these challenges requires careful pilot studies to characterize heterogeneity patterns and rigorous biomarker reporting standards to ensure study transparency and reproducibility. Study objectives This investigation examines heterogeneity patterns in tissue microarrays (TMAs) derived from non-metastatic NSCLC specimens with documented grade and TNM staging. Specifically, we evaluate the inconsistent expression profiles of LDHA and Talin-1 both within and between tumour nodules, while assessing the potential influence of tumour grade and stage on these variability patterns. Experimental Design This study investigated the inconsistent protein expression patterns of Talin-1 and lactate dehydrogenase A (LDHA) in non-metastatic non-small cell lung cancer (NSCLC), focusing on both intra-tumour and inter-tumour heterogeneity. The work aimed to understand how these spatial variations impact the clinical utility of Talin-1 and LDHA as potential serological biomarkers for NSCLC diagnosis and monitoring. Talin-1 and LDHA were chosen for detailed examination based on their well-documented involvement in tumour progression. Talin-1 plays crucial roles in cell adhesion and migration, while LDHA is a key enzyme in tumour metabolism through aerobic glycolysis. Both proteins showed elevated expression in various cancers, including NSCLC. Our selection was further supported by preliminary findings from our bronchoalveolar lavage fluid (BALF) analysis published earlier in 2017, which suggested their potential diagnostic value in NSCLC ( 20 ). Using tissue microarrays containing non-metastatic NSCLC samples, we conducted systematic analysis of protein expression patterns across different tumour regions. This approach allowed us to assess the degree of heterogeneity in Talin-1 and LDHA expression and investigate its relationship with clinical parameters such as tumour grade and stage. The study design specifically addresses how tumour heterogeneity contributes to the observed inconsistency of these otherwise promising biomarkers in clinical applications. Materials and methods Tissue microarray processing and immunohistochemistry The study utilized two commercially available NSCLC tissue microarrays (LC10011b series from TissueArrays.com LLC) containing duplicate cores from 40 non-metastatic NSCLC cases (24 squamous cell carcinoma and 16 adenocarcinoma) along with 10 normal lung tissue controls. All immunohistochemical staining was performed at the National Institute of Cellular Biotechnology at Dublin City University (NICB-DCU) using a Dako automated stainer. Primary antibodies against Talin-1 (C45F1 rabbit mAb #4021) and lactate dehydrogenase-A (C4B5 rabbit mAb #3582) were purchased from Cell Signalling Technology and used at manufacturer recommended dilutions of 1:50 and 1:400 respectively. Tissue sections underwent antigen retrieval in citrate buffer at pH 6 for 20 minutes followed by 30-minute primary antibody incubation. Counterstaining was performed with haematoxylin to visualize nuclei, with subsequent dehydration through an ethanol series and xylene before applying the cover slips ( 21 ). Stained sections were evaluated under 20× magnification with staining intensity scored using a four-category system: negative (0), weak ( 1 ), moderate ( 2 ), or strong ( 3 ). The complete scoring results by tissue type and antibody are summarised in Table 1 and endorsed in the supplementary data, with representative examples shown in Fig. 1 . Duplicate cores from each case were analysed independently to assess staining consistency across tissue samples ( 21 ). Results LDHA expression patterns across NSCLC subtypes We looked at the overall LDHA expression patterns in relation to clinical staging across different NSCLC subtypes. In normal lung tissues (N), including two cases with pulmonary oedema (PO), all twenty cores showed limited LDHA expression, with eight cores demonstrating very weak staining (zero score) and twelve showing weak staining including the PO tissue samples. No normal tissue cores exhibited moderate or strong (scores 2–3) LDHA expression. Among the thirty-two adenocarcinoma (AD) cores analysed, staining intensity varied across tumour stages. Three cores showed negative staining, originating from stage IIIA and IIB tumours. Nine cores displayed weak staining (score 1), comprising three stage IIB tumours, two stage IIA and four stage IIIA cases. Moderate staining (score 2) was observed in sixteen cores, including five stage IIIA and eleven stage IIB tumours. The four strongly staining cores were all came from stage IIIA tumours. The forty-eight squamous cell carcinoma (Sqcc) cores revealed a broader expression pattern. Four cores were completely negative, including one stage IA and three stage IIIA tumours (two of which (Sparse). Weak staining (score 1) appeared in ten cores representing stages IA, IIB, and IIIB. Twenty cores exhibited moderate staining (score 2) across stages IIIA, IIB, and IIIB, while fourteen cores showed strong staining (score 3), predominantly in stage IIIA tumours with some stage IIB cases (Table 1 ). Two additional large cell lung cancer cores, not included in our results, demonstrated weak staining and were both stage IIA. Notably, the analysis found no clear correlation between tumour grade or stage and LDHA expression intensity. The staining patterns observed in both adenocarcinoma and squamous cell carcinoma samples showed considerable variability within each stage category, suggesting that LDHA expression levels in NSCLC may be influenced by factors beyond conventional staging parameters. Complete scoring details for all specimens are available in the supplementary materials and a representative core per score shown in Fig. 1 . Talin-1 expression patterns in NSCLC subtypes Figure 1 (right panel) demonstrates the immunohistochemical reactivity of Talin-1 across TMAs cores representing normal lung tissue (N), lung adenocarcinoma, and squamous cell carcinoma. Tissue scoring was: In normal lung tissues, comprising twenty cores including two with pulmonary oedema, Talin-1 expression showed limited intensity. Seven cores, including pulmonary oedema cores, exhibited no detectable staining (score-0). Nine cores displayed weak positivity (score-1), while only two cores each demonstrated moderate or strong reactivity (score-2&3). The adenocarcinoma (AD) cores revealed variable Talin-1 expression patterns across the thirty-two evaluated cores. A single core from a stage IIIA tumour showed complete absence of staining (score-0). Fifteen cores exhibited weak staining (score-1), distributed between stage IIB and IIIA tumours. Moderate staining (score-2) appeared in twelve cores, equally divided between stage IIB and IIIA cases. Only one core, from a stage IIIA tumour, showed strong (score-3), Talin-1 immunoreactivity. Three cores could not be evaluated due to technical issues. Squamous cell carcinoma specimens (Sqcc) displayed more heterogeneous Talin-1 expression. Among forty-eight cores, eight showed no reactivity (score-0), representing tumours across stages IA to IIIB. Twenty-seven cores demonstrated weak staining (score-1) across all stages, while eleven showed moderate reactivity (score-2) predominantly in stage IIIA and IIB tumours. A single stage IIB tumour core exhibited strong staining (score-3). One additional core failed technical evaluation. This comprehensive analysis revealed no consistent correlation between Talin-1 expression intensity and tumour stage or grade. The complete dataset, including detailed scoring of all individual cores, is available in the supplementary materials for reference. Table 1 Tissue microarrays (TMA) scoring Score Biomarker Tissue type 0 1 2 3 Normal LT 8/20(40%) 12/20(60%) 0/20(0%) 0/20(0%) LDHA AD 3/32(9.4%) 9/32(28.1%) 16/32(50%) 4/32(12.5%) Sqcc 4/48(8.3%) 10/48(20.8%) 20/48(39.5%) 14/48(31.25%) Normal LT 7/20(35%) 9/20(45%) 2/20(10%) 2/20(10%) Talin-1 AD* 1/32(3.1%) 15/32(48.9%) 12/32(37.5%) 1/32(6.3%) Sqcc* 8/48(16.7%) 27/48(56.3%) 11/48(23%) 1/48(2%) L/T: lung tissue, AD: adenocarcinoma, Sqcc: squamous cell carcinoma, LDHA: Lactate dehydrogenase A.* staining failure in Talin-1 (three AD and one Sqcc cores). Discussion Challenges in lung cancer biomarker reliability The reliability of lung cancer biomarkers continues to face significant challenges, primarily due to three key factors: variability in study methodologies, heterogeneity in patient populations, and inconsistencies in results interpretation ( 22 , 23 ). This problem proves particularly complex in adenocarcinomas, where many diagnostic biomarkers simultaneously serve prognostic functions, creating potential confounding effects in clinical interpretation ( 22 , 24 ). The dual diagnostic-prognostic nature of these markers necessitates careful validation across diverse clinical settings to establish their true utility ( 25 ). Emerging approaches in biomarker discovery Emerging approaches in biomarker discovery show promise in overcoming these limitations. Metabolomic profiling has emerged as a particularly valuable tool for early detection, with advanced analytical methods like the Shapiro-Wilk Test and Recursive Feature Elimination with Random Forest improving the identification of clinically relevant metabolic signatures ( 26 – 28 ). Simultaneously, research into urine and blood-derived biomarkers has identified promising candidates among bronchial and vascular proteins, especially those displaying tumour-specific glycosylation patterns that may serve as molecular fingerprints of malignancies ( 29 ). The field has concurrently made significant progress in characterizing circulating biomarkers, including tumour-associated antigens, autoantibody profiles, and exosomal protein cargoes, which collectively offer non-invasive options for early detection, prognosis evaluation, and therapeutic monitoring ( 30 – 34 ). The role of FFPE tissues in biomarker research The critical role of formaldehyde-fixed, paraffin-embedded (FFPE) tissues in biomarker research cannot be overstated. As the cornerstone of histopathological diagnosis, these specimens provide both architectural context and molecular information that fresh-frozen samples often lack ( 35 , 36 ). The global availability of FFPE archives, containing hundreds of thousands of well-characterized cases, presents an unmatched resource for studying lung cancer biomarkers across all histological subtypes and clinical stages ( 37 ). This vast repository enables research at scales impossible to achieve with prospectively collected frozen specimens. Technological advancements in FFPE-based biomarker studies Technological advancements have significantly enhanced the research utility of FFPE materials. The development of tissue microarrays (TMAs) technology has revolutionized high-throughput biomarker validation, allowing simultaneous analysis of hundreds of specimens while conserving precious tissue resources ( 38 ). Although formaldehyde fixation historically posed challenges for proteomic analysis through protein cross-linking, modern extraction and digestion protocols have largely overcome these limitations ( 21 , 39 ). Current mass spectrometry techniques can now reliably identify and quantify proteins from FFPE tissues with accuracy approaching that achieved with fresh samples ( 21 , 40 ). Future directions in biomarker research Looking forward, the integration of targeted proteomics with comprehensive FFPE tissue banks opens new avenues for biomarker discovery. This approach enables systematic validation of candidate markers across all phases of translational research, from initial discovery to clinical implementation ( 41 , 42 ). The coupling of these molecular data with detailed clinical annotations creates powerful opportunities for developing personalized diagnostic and prognostic tools. Heterogeneity of LDHA expression across NSCLC stages and grades The study revealed significant heterogeneity in LDHA expression across different NSCLC stages and histological subtypes, with no clear correlation to tumour grade or stage. In adenocarcinomas, moderate to strong LDHA expression (scores 2–3) was observed in over 60% of cores, predominantly in stage IIB/IIIA tumours, yet weak or negative staining was also present in the same stages. Similarly, squamous cell carcinomas exhibited even greater variability, with 70% of cores showing moderate to strong (score 2–3) LDHA reactivity, but without a consistent association with the tumour stage or grade. This lack of a definitive expression pattern suggests that LDHA upregulation may be influenced by factors beyond conventional staging, such as metabolic adaptations or microenvironmental pressures ( 43 ), complicating its reliability as a standalone early detection biomarker in NSCLC ( 44 , 45 ). Talin-1 expression variability independent of tumour progression Talin-1 expression displayed marked inter- and intra-tumour heterogeneity, with no significant correlation to tumour stage or grade. While adenocarcinomas showed moderate Talin-1 expression (score 2) in 37.5% of cores, these were evenly distributed between stage IIB and IIIA cases, and only one cores exhibited strong staining. Squamous cell carcinomas demonstrated even greater inconsistency, with 56% of cores showing weak staining (score 1) across all stages, 23% of cores with moderate staining intensity, and only a single core displaying higher reactivity. The absence of a stage-dependent expression trend implies that Talin-1’s role in cell adhesion and migration may be context-dependent, potentially regulated by post-translational modifications or stromal interactions rather than tumour progression alone ( 46 , 47 ). In summary , the reliability of lung cancer biomarkers continues to face significant challenges, primarily due to three key factors: variability in study methodologies, heterogeneity in patient populations, and inconsistencies in results interpretation ( 22 ). This problem proves particularly complex in adenocarcinomas, where many diagnostic biomarkers simultaneously serve prognostic functions, creating potential confounding effects in clinical interpretation. The dual diagnostic-prognostic nature of these markers necessitates careful validation across diverse clinical settings to establish their true utility. Emerging approaches in biomarker discovery including; metabolomic profiling, liquid biopsy technologies, and advanced proteomic analysis of FFPE tissues show promise in overcoming these limitations. However, the persistent heterogeneity observed in markers like LDHA and Talin-1 underscores the need for standardized, multiplexed detection strategies that account for spatial and temporal variations in tumour biology. 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PubMed PMID: 38332915; PubMed Central PMCID: PMC10850336 Additional Declarations The authors declare no competing interests. Supplementary Files LC10011bspecssupplimentarydataforFJPC.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7004726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478055111,"identity":"76447186-be74-4ba9-bee5-a915e8191ce9","order_by":0,"name":"Abduladim Hmmier","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACAyA+wMBgw8PADmTxkKAljYeBmRQtQHCYgXgt5mKnEw983HNehr+Z+ZjEGwY7Od0GAlosZ+duODjj2W0eicNsaZJzGJKNzQ4Qctjt3A2HeQ7c5jFg5jGT5mE4kLiNKC1/DpwjVQvDgQMkajnYcyAZ5JdkyzkGxPll84cfB+zs+dubD954U2EnR1ALugmkKR8Fo2AUjIJRgAMAAOWEQCeCPuj9AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6553-9096","institution":"Libyan Centre for biotechnology research","correspondingAuthor":true,"prefix":"","firstName":"Abduladim","middleName":"","lastName":"Hmmier","suffix":""},{"id":478055112,"identity":"4d527d3a-0179-481d-a33c-a9d9bc2d2823","order_by":1,"name":"Paul Dowling","email":"","orcid":"","institution":"Maynooth University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Dowling","suffix":""}],"badges":[],"createdAt":"2025-06-29 20:32:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7004726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7004726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85926522,"identity":"23b5cbaa-7dcf-4c95-9b2e-0b9fdbc6c5b7","added_by":"auto","created_at":"2025-07-03 08:40:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":971284,"visible":true,"origin":"","legend":"\u003cp\u003eTMAs to detect the impact of tumour stage/grade on LDHA and talin-1 level to visualize the inter/intra -tumour heterogeneity and to know at what stage of non-metastatic cancer lactate dehydrogenase- A (LDHA) and Talin-1 are hugely produced , further work was conducted on LDHA and Talin-1 in NSCLC tissue microarrays (TMAs) for the sake of better consideration when evaluating their clinical utility. Scoring system used is per tissue type [Normal n=20 cores from duplicates of 10 non-cancer individuals (two of which with pulmonary oedema), Adenocarcinoma n=32 cores from duplicates of 16 AD patients (one of which with necrosis), squamous cell carcinoma n=48 cores from duplicates of 24 patients with Squamous cell carcinomas, Sqcc (one of which with sparse)]. Scoring system was [0: no/very week staining, 1: weakly stained, 2: moderate staining, 3: strong staining].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7004726/v1/7dca869eddbdab926386e940.png"},{"id":85928170,"identity":"1b023796-7590-4239-9a0a-1d2aab5d4e5e","added_by":"auto","created_at":"2025-07-03 08:56:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2069755,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7004726/v1/c189920b-aaae-4b66-bf14-d1ac2433925f.pdf"},{"id":85926517,"identity":"0c8cfaba-0322-4efa-bdda-097152095acb","added_by":"auto","created_at":"2025-07-03 08:40:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17928,"visible":true,"origin":"","legend":"","description":"","filename":"LC10011bspecssupplimentarydataforFJPC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7004726/v1/b40df0e2c3994d6781d83b77.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTissue Microarray Analysis Reveals Heterogeneous Expression of Talin-1 and Lactate Dehydrogenase A in Non-Small Cell Lung Cancer: Implications for Biomarker Reliability\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eTumour heterogeneity and treatment outcomes\u003c/h2\u003e \u003cp\u003eTumour heterogeneity manifests at multiple levels, ranging from within individual tumours to between different cancer subtypes and tissues of origin (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This variability significantly influences immune-tumour interactions, underscoring the need to understand its role in shaping these critical biological relationships (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Highly heterogeneous tumours often demonstrate resistance to targeted therapies designed against specific oncogenic drivers, limiting treatment efficacy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Paradoxically, molecularly targeted agents that induce partial tumour cell death may inadvertently increase heterogeneity and promote more aggressive phenotypes, further complicating therapeutic management (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Mathematical modelling suggests that optimized chemotherapy regimens, particularly those employing lower dose rates, may prove more effective against tumours containing resistant subpopulations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The contribution of intra-tumour heterogeneity to drug resistance highlights the necessity of incorporating evolutionary principles into clinical trial design and therapeutic development (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAdvances in tumour sampling and analysis\u003c/h2\u003e \u003cp\u003eObtaining representative tumour samples remains crucial for treatment planning. Current biopsy techniques including; core needle, laparoscopic/thoracoscopic, and open excisional/incisional approaches, are selected based on clinical context, tumour location, and patient status (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Image-guided methods utilizing ultrasound, CT, MRI, or fusion imaging have enhanced diagnostic accuracy through precise tumour targeting (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Recent innovations now enable concurrent metabolomic and histologic evaluation of single biopsy specimens, maximizing the information yield from limited tissue samples (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, immunohistochemical (IHC) analysis continues to face challenges due to pre-analytical variability in sample handling and processing (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Despite being the gold standard for malignancy confirmation, IHC suffers from reproducibility issues stemming from inconsistent staining protocols and lack of standardization in fixation and antigen retrieval methods (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Variability in both pre-analytical procedures and post-analytical interpretation, particularly in threshold determination, contributes significantly to these inconsistencies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Addressing these limitations through standardized protocols is essential for improving diagnostic accuracy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChallenges in serological biomarker development\u003c/h3\u003e\n\u003cp\u003eThe development of non-invasive biomarkers for treatment monitoring and relapse detection faces substantial obstacles due to tumour heterogeneity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Even histologically similar tumours from the same organ often exhibit divergent molecular profiles and therapeutic responses (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This genetic variability complicates the identification of biomarkers with sufficient sensitivity and specificity for clinical use (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While current biomarkers may show limited overall sensitivity, certain markers could have utility in specific patient subsets (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The observed variability in biomarker performance across study populations primarily reflects inter-tumour heterogeneity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which can produce inconsistent expression patterns and weak biomarker outcome correlations (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Overcoming these challenges requires careful pilot studies to characterize heterogeneity patterns and rigorous biomarker reporting standards to ensure study transparency and reproducibility.\u003c/p\u003e\n\u003ch3\u003eStudy objectives\u003c/h3\u003e\n\u003cp\u003eThis investigation examines heterogeneity patterns in tissue microarrays (TMAs) derived from non-metastatic NSCLC specimens with documented grade and TNM staging. Specifically, we evaluate the inconsistent expression profiles of LDHA and Talin-1 both within and between tumour nodules, while assessing the potential influence of tumour grade and stage on these variability patterns.\u003c/p\u003e\n\u003ch3\u003eExperimental Design\u003c/h3\u003e\n\u003cp\u003eThis study investigated the inconsistent protein expression patterns of Talin-1 and lactate dehydrogenase A (LDHA) in non-metastatic non-small cell lung cancer (NSCLC), focusing on both intra-tumour and inter-tumour heterogeneity. The work aimed to understand how these spatial variations impact the clinical utility of Talin-1 and LDHA as potential serological biomarkers for NSCLC diagnosis and monitoring.\u003c/p\u003e \u003cp\u003eTalin-1 and LDHA were chosen for detailed examination based on their well-documented involvement in tumour progression. Talin-1 plays crucial roles in cell adhesion and migration, while LDHA is a key enzyme in tumour metabolism through aerobic glycolysis. Both proteins showed elevated expression in various cancers, including NSCLC. Our selection was further supported by preliminary findings from our bronchoalveolar lavage fluid (BALF) analysis published earlier in 2017, which suggested their potential diagnostic value in NSCLC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing tissue microarrays containing non-metastatic NSCLC samples, we conducted systematic analysis of protein expression patterns across different tumour regions. This approach allowed us to assess the degree of heterogeneity in Talin-1 and LDHA expression and investigate its relationship with clinical parameters such as tumour grade and stage. The study design specifically addresses how tumour heterogeneity contributes to the observed inconsistency of these otherwise promising biomarkers in clinical applications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTissue microarray processing and immunohistochemistry\u003c/h2\u003e \u003cp\u003eThe study utilized two commercially available NSCLC tissue microarrays (LC10011b series from TissueArrays.com LLC) containing duplicate cores from 40 non-metastatic NSCLC cases (24 squamous cell carcinoma and 16 adenocarcinoma) along with 10 normal lung tissue controls. All immunohistochemical staining was performed at the National Institute of Cellular Biotechnology at Dublin City University (NICB-DCU) using a Dako automated stainer.\u003c/p\u003e \u003cp\u003ePrimary antibodies against Talin-1 (C45F1 rabbit mAb #4021) and lactate dehydrogenase-A (C4B5 rabbit mAb #3582) were purchased from Cell Signalling Technology and used at manufacturer recommended dilutions of 1:50 and 1:400 respectively. Tissue sections underwent antigen retrieval in citrate buffer at pH 6 for 20 minutes followed by 30-minute primary antibody incubation. Counterstaining was performed with haematoxylin to visualize nuclei, with subsequent dehydration through an ethanol series and xylene before applying the cover slips (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStained sections were evaluated under 20\u0026times; magnification with staining intensity scored using a four-category system: negative (0), weak (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), moderate (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), or strong (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The complete scoring results by tissue type and antibody are summarised in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and endorsed in the supplementary data, with representative examples shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Duplicate cores from each case were analysed independently to assess staining consistency across tissue samples (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLDHA expression patterns across NSCLC subtypes\u003c/h2\u003e \u003cp\u003eWe looked at the overall LDHA expression patterns in relation to clinical staging across different NSCLC subtypes. In normal lung tissues (N), including two cases with pulmonary oedema (PO), all twenty cores showed limited LDHA expression, with eight cores demonstrating very weak staining (zero score) and twelve showing weak staining including the PO tissue samples. No normal tissue cores exhibited moderate or strong (scores 2\u0026ndash;3) LDHA expression.\u003c/p\u003e \u003cp\u003eAmong the thirty-two adenocarcinoma (AD) cores analysed, staining intensity varied across tumour stages. Three cores showed negative staining, originating from stage IIIA and IIB tumours. Nine cores displayed weak staining (score 1), comprising three stage IIB tumours, two stage IIA and four stage IIIA cases. Moderate staining (score 2) was observed in sixteen cores, including five stage IIIA and eleven stage IIB tumours. The four strongly staining cores were all came from stage IIIA tumours.\u003c/p\u003e \u003cp\u003eThe forty-eight squamous cell carcinoma (Sqcc) cores revealed a broader expression pattern. Four cores were completely negative, including one stage IA and three stage IIIA tumours (two of which (Sparse). Weak staining (score 1) appeared in ten cores representing stages IA, IIB, and IIIB. Twenty cores exhibited moderate staining (score 2) across stages IIIA, IIB, and IIIB, while fourteen cores showed strong staining (score 3), predominantly in stage IIIA tumours with some stage IIB cases (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two additional large cell lung cancer cores, not included in our results, demonstrated weak staining and were both stage IIA.\u003c/p\u003e \u003cp\u003eNotably, the analysis found no clear correlation between tumour grade or stage and LDHA expression intensity. The staining patterns observed in both adenocarcinoma and squamous cell carcinoma samples showed considerable variability within each stage category, suggesting that LDHA expression levels in NSCLC may be influenced by factors beyond conventional staging parameters. Complete scoring details for all specimens are available in the supplementary materials and a representative core per score shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTalin-1 expression patterns in NSCLC subtypes\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (right panel) demonstrates the immunohistochemical reactivity of Talin-1 across TMAs cores representing normal lung tissue (N), lung adenocarcinoma, and squamous cell carcinoma. Tissue scoring was: In normal lung tissues, comprising twenty cores including two with pulmonary oedema, Talin-1 expression showed limited intensity. Seven cores, including pulmonary oedema cores, exhibited no detectable staining (score-0). Nine cores displayed weak positivity (score-1), while only two cores each demonstrated moderate or strong reactivity (score-2\u0026amp;3).\u003c/p\u003e \u003cp\u003eThe adenocarcinoma (AD) cores revealed variable Talin-1 expression patterns across the thirty-two evaluated cores. A single core from a stage IIIA tumour showed complete absence of staining (score-0). Fifteen cores exhibited weak staining (score-1), distributed between stage IIB and IIIA tumours. Moderate staining (score-2) appeared in twelve cores, equally divided between stage IIB and IIIA cases. Only one core, from a stage IIIA tumour, showed strong (score-3), Talin-1 immunoreactivity. Three cores could not be evaluated due to technical issues.\u003c/p\u003e \u003cp\u003eSquamous cell carcinoma specimens (Sqcc) displayed more heterogeneous Talin-1 expression. Among forty-eight cores, eight showed no reactivity (score-0), representing tumours across stages IA to IIIB. Twenty-seven cores demonstrated weak staining (score-1) across all stages, while eleven showed moderate reactivity (score-2) predominantly in stage IIIA and IIB tumours. A single stage IIB tumour core exhibited strong staining (score-3). One additional core failed technical evaluation.\u003c/p\u003e \u003cp\u003eThis comprehensive analysis revealed no consistent correlation between Talin-1 expression intensity and tumour stage or grade. The complete dataset, including detailed scoring of all individual cores, is available in the supplementary materials for reference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTissue microarrays (TMA) scoring\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eScore\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTissue type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal LT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/20(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/20(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/20(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0/20(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLDHA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/32(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/32(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16/32(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4/32(12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSqcc\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/48(8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/48(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20/48(39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14/48(31.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal LT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/20(35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/20(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/20(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2/20(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTalin-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/32(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/32(48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12/32(37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/32(6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSqcc*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/48(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27/48(56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/48(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1/48(2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eL/T: lung tissue, AD: adenocarcinoma, Sqcc: squamous cell carcinoma, LDHA: Lactate dehydrogenase A.* staining failure in Talin-1 (three AD and one Sqcc cores).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eChallenges in lung cancer biomarker reliability\u003c/h2\u003e \u003cp\u003eThe reliability of lung cancer biomarkers continues to face significant challenges, primarily due to three key factors: variability in study methodologies, heterogeneity in patient populations, and inconsistencies in results interpretation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This problem proves particularly complex in adenocarcinomas, where many diagnostic biomarkers simultaneously serve prognostic functions, creating potential confounding effects in clinical interpretation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The dual diagnostic-prognostic nature of these markers necessitates careful validation across diverse clinical settings to establish their true utility (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEmerging approaches in biomarker discovery\u003c/h2\u003e \u003cp\u003eEmerging approaches in biomarker discovery show promise in overcoming these limitations. Metabolomic profiling has emerged as a particularly valuable tool for early detection, with advanced analytical methods like the Shapiro-Wilk Test and Recursive Feature Elimination with Random Forest improving the identification of clinically relevant metabolic signatures (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Simultaneously, research into urine and blood-derived biomarkers has identified promising candidates among bronchial and vascular proteins, especially those displaying tumour-specific glycosylation patterns that may serve as molecular fingerprints of malignancies (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The field has concurrently made significant progress in characterizing circulating biomarkers, including tumour-associated antigens, autoantibody profiles, and exosomal protein cargoes, which collectively offer non-invasive options for early detection, prognosis evaluation, and therapeutic monitoring (\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe role of FFPE tissues in biomarker research\u003c/h2\u003e \u003cp\u003eThe critical role of formaldehyde-fixed, paraffin-embedded (FFPE) tissues in biomarker research cannot be overstated. As the cornerstone of histopathological diagnosis, these specimens provide both architectural context and molecular information that fresh-frozen samples often lack (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The global availability of FFPE archives, containing hundreds of thousands of well-characterized cases, presents an unmatched resource for studying lung cancer biomarkers across all histological subtypes and clinical stages (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This vast repository enables research at scales impossible to achieve with prospectively collected frozen specimens.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTechnological advancements in FFPE-based biomarker studies\u003c/h2\u003e \u003cp\u003eTechnological advancements have significantly enhanced the research utility of FFPE materials. The development of tissue microarrays (TMAs) technology has revolutionized high-throughput biomarker validation, allowing simultaneous analysis of hundreds of specimens while conserving precious tissue resources (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Although formaldehyde fixation historically posed challenges for proteomic analysis through protein cross-linking, modern extraction and digestion protocols have largely overcome these limitations (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Current mass spectrometry techniques can now reliably identify and quantify proteins from FFPE tissues with accuracy approaching that achieved with fresh samples (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions in biomarker research\u003c/h2\u003e \u003cp\u003eLooking forward, the integration of targeted proteomics with comprehensive FFPE tissue banks opens new avenues for biomarker discovery. This approach enables systematic validation of candidate markers across all phases of translational research, from initial discovery to clinical implementation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The coupling of these molecular data with detailed clinical annotations creates powerful opportunities for developing personalized diagnostic and prognostic tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity of LDHA expression across NSCLC stages and grades\u003c/h2\u003e \u003cp\u003eThe study revealed significant heterogeneity in LDHA expression across different NSCLC stages and histological subtypes, with no clear correlation to tumour grade or stage. In adenocarcinomas, moderate to strong LDHA expression (scores 2\u0026ndash;3) was observed in over 60% of cores, predominantly in stage IIB/IIIA tumours, yet weak or negative staining was also present in the same stages. Similarly, squamous cell carcinomas exhibited even greater variability, with 70% of cores showing moderate to strong (score 2\u0026ndash;3) LDHA reactivity, but without a consistent association with the tumour stage or grade. This lack of a definitive expression pattern suggests that LDHA upregulation may be influenced by factors beyond conventional staging, such as metabolic adaptations or microenvironmental pressures (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), complicating its reliability as a standalone early detection biomarker in NSCLC (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTalin-1 expression variability independent of tumour progression\u003c/h2\u003e \u003cp\u003eTalin-1 expression displayed marked inter- and intra-tumour heterogeneity, with no significant correlation to tumour stage or grade. While adenocarcinomas showed moderate Talin-1 expression (score 2) in 37.5% of cores, these were evenly distributed between stage IIB and IIIA cases, and only one cores exhibited strong staining. Squamous cell carcinomas demonstrated even greater inconsistency, with 56% of cores showing weak staining (score 1) across all stages, 23% of cores with moderate staining intensity, and only a single core displaying higher reactivity. The absence of a stage-dependent expression trend implies that Talin-1\u0026rsquo;s role in cell adhesion and migration may be context-dependent, potentially regulated by post-translational modifications or stromal interactions rather than tumour progression alone (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn summary\u003c/b\u003e, the reliability of lung cancer biomarkers continues to face significant challenges, primarily due to three key factors: variability in study methodologies, heterogeneity in patient populations, and inconsistencies in results interpretation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This problem proves particularly complex in adenocarcinomas, where many diagnostic biomarkers simultaneously serve prognostic functions, creating potential confounding effects in clinical interpretation. The dual diagnostic-prognostic nature of these markers necessitates careful validation across diverse clinical settings to establish their true utility. Emerging approaches in biomarker discovery including; metabolomic profiling, liquid biopsy technologies, and advanced proteomic analysis of FFPE tissues show promise in overcoming these limitations. However, the persistent heterogeneity observed in markers like LDHA and Talin-1 underscores the need for standardized, multiplexed detection strategies that account for spatial and temporal variations in tumour biology. Moving forward, integrating multi-omics data with clinically annotated FFPE repositories will be critical for developing robust, clinically actionable biomarkers that improve NSCLC diagnosis, prognosis, and personalized therapeutic strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhatib S, Pomyen Y, Dang H, Wang XW (2020) Understanding the Cause and Consequence of Tumor Heterogeneity. Trends Cancer 6(4):267\u0026ndash;271 Epub 20200213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trecan.2020.01\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2020.01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnoche SM, Larson AC, Sliker BH, Poelaert BJ, Solheim JC (2021) The role of tumor heterogeneity in immune-tumor interactions. 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PubMed PMID: 38332915; PubMed Central PMCID: PMC10850336\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"2806851f-e6b5-4ccc-87a3-d870c0d07430","identifier":"10.13039/100017057","name":"Ministry of Higher Education and Scientific Research","awardNumber":"Scholarship","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Maynooth University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immunohistochemistry (IHC), Tissue microarrays (TMAs), formaldehyde-fixed paraffin-embedded (FFPE) tissues, Lactate dehydrogenase A (LDHA), Talin-1, inconsistent tissue expression, biomarkers reliability, Non-small cell lung cancer (NSCLC)","lastPublishedDoi":"10.21203/rs.3.rs-7004726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7004726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTumour heterogeneity significantly impacts biomarker reliability in non-small cell lung cancer (NSCLC), complicating the validation of diagnostic proteins such as lactate dehydrogenase A (LDHA) and Talin-1. This study investigated their expression heterogeneity in tissue microarrays (TMAs) from 40 non-metastatic NSCLC cases (24 squamous cell carcinomas, 16 adenocarcinomas) and 10 normal controls, using standardised immunohistochemistry (IHC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFormalin-fixed, paraffin-embedded (FFPE) TMAs were stained with anti-LDHA and anti-Talin-1 antibodies. Expression was scored (0\u0026ndash;3) for intensity and analysed against tumour grade/stage.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNormal tissues showed minimal reactivity (scores 0\u0026ndash;1), while tumours exhibited marked heterogeneity. In adenocarcinomas, 20/32 cores (62.5%) showed moderate LDHA expression (score 2\u0026ndash;3), predominantly in stage IIB/IIIA tumours. Talin-1 expression varied widely, with 14/32 (43.6%) adenocarcinoma cores scoring 2\u0026ndash;3, with almost same IIIA/IIB stage ratio. Squamous cell carcinomas displayed greater inconsistency, with LDHA scores 2\u0026ndash;3 in 34/48 cores (70.8%), and Talin-1 scored 2\u0026ndash;3 in 12/48 (25%), the majority were in stage IIB/IIIA but no grade/stage correlation. Such findings demonstrate substantial intra- and inter-tumour heterogeneity for both biomarkers, independent of conventional clinicopathological parameters. This variability explains their inconsistent performance in prior studies and underscores the need for multiplexed biomarker panels to overcome heterogeneity-driven limitations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings reveal significant heterogeneity in LDHA and Talin-1 expression across NSCLC subtypes, independent of tumor grade/stage. This underscores the need for standardized IHC protocols and spatial profiling in biomarker development. The variability observed supports using multiplexed panels rather than single-marker approaches for reliable clinical applications.\u003c/p\u003e","manuscriptTitle":"Tissue Microarray Analysis Reveals Heterogeneous Expression of Talin-1 and Lactate Dehydrogenase A in Non-Small Cell Lung Cancer: Implications for Biomarker Reliability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 08:40:50","doi":"10.21203/rs.3.rs-7004726/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a54f5df7-01fc-4049-964f-44db0cc6b62e","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50754145,"name":"Laboratory Diagnostics"},{"id":50754146,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2025-07-03T08:40:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 08:40:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7004726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7004726","identity":"rs-7004726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}