Why Biomarker-Driven Treatment Fails: The Hidden Role of Histology

Why Biomarker-Driven Treatment Fails The Hidden Role of Histology

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Introduction

Biomarker-driven treatment has fundamentally transformed oncology, ushering in an era of precision medicine in which therapeutic decisions are increasingly guided by the molecular characteristics of tumors rather than their anatomical origin alone. A pivotal moment occurred in 2017 when the United States Food and Drug Administration granted its first tissue-agnostic drug approval, signaling a paradigm shift in cancer treatment. This regulatory milestone established that therapies could be approved based on the presence of specific genomic alterations regardless of tumor site. As of 2025, ten tissue-agnostic therapies have received FDA approval, with pembrolizumab becoming the first to achieve accelerated approval for tumors characterized by microsatellite instability high or mismatch repair deficiency. Despite these advances, real-world clinical outcomes have often proven more variable than initially anticipated, particularly when tumor histology is not fully considered in treatment planning.

The scientific rationale for tumor-agnostic therapies arose from the recognition that certain oncogenic drivers function across multiple malignancies. When a mutation plays a central role in tumor biology, inhibiting that pathway may theoretically produce therapeutic benefit independent of tissue origin. This concept has expanded treatment opportunities for patients with rare cancers and has helped streamline drug development by allowing the inclusion of multiple tumor types within a single clinical framework. However, identifying a clinically meaningful biomarker requires more than detecting its presence. An effective biomarker must demonstrate consistent predictive validity, meaning it reliably correlates with treatment response across diverse biological contexts. In practice, the predictive performance of many biomarkers is influenced by factors such as tumor lineage, co-occurring mutations, epigenetic regulation, and the surrounding tumor microenvironment.

Diagnostic biomarkers therefore serve as essential tools for treatment selection, but their interpretive value becomes more complex when histological features are incorporated. Evidence from a large real-world analysis of nearly 300,000 molecularly profiled tumors revealed that approximately 21.5 percent harbored at least one alteration associated with a tissue-agnostic indication. While this finding highlights the broad applicability of molecular testing, it also underscores a key clinical challenge: responses to targeted therapies can differ significantly across histological subtypes, even when the same genomic alteration is present. Such variability complicates clinical decision-making and introduces uncertainty into treatment expectations.

These challenges extend beyond the clinical setting to regulatory agencies and health technology assessment bodies. Tissue-agnostic therapies often rely on evidence derived from basket trials or single-arm studies, as conducting large randomized controlled trials may be impractical for rare molecular subsets. Although these designs accelerate access to promising therapies, they frequently limit the availability of direct comparative efficacy data. As a result, regulators must balance the urgency of unmet clinical need against the uncertainty surrounding long-term benefit, durability of response, and cost effectiveness.

Serum-based biomarkers have further improved the accessibility of molecular testing by enabling less invasive diagnostic pathways. However, blood-based assays cannot fully capture the intricate biological interactions that occur within tissue-specific microenvironments. Tumor behavior is shaped not only by genomic alterations but also by stromal composition, immune infiltration, angiogenesis, and metabolic factors that vary widely between organ systems. These contextual influences can modify drug sensitivity and resistance patterns, reinforcing the continued relevance of histopathological evaluation.

An integrated approach that combines molecular profiling with detailed histological assessment is therefore essential for optimizing precision oncology outcomes. Rather than viewing tissue-agnostic strategies as a replacement for traditional pathology, emerging evidence supports a complementary framework in which genomic data enhances, rather than overrides, histological insight. Multidisciplinary interpretation involving oncologists, pathologists, and molecular diagnosticians is increasingly necessary to ensure accurate patient selection and appropriate therapeutic sequencing.

This article explores the enduring importance of histology in the interpretation of biomarker data and examines how harmonizing molecular and tissue-based perspectives can strengthen clinical decision-making. By evaluating current evidence, regulatory considerations, and real-world treatment patterns, the discussion aims to clarify how precision medicine can evolve toward a more nuanced model that recognizes both the power of genomic discovery and the biological specificity of tumor origin.

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Why Biomarker-Driven Treatment Looks Promising on Paper

Personalized medicine represents a fundamental shift in cancer management, moving away from traditional approaches toward molecularly guided treatments. The integration of biomarkers into oncology has yielded remarkable advances in cancer therapeutics and patient prognosis [1]. This paradigm allows oncologists to customize treatment regimens based on each tumor’s unique molecular profile rather than relying solely on anatomical classification.

Biomarker-targeted therapies and the rise of precision oncology

Biomarker-targeted therapies form the cornerstone of precision oncology, enabling tailored interventions aligned with specific molecular alterations within tumors [1]. These treatments generally fall into two categories: small molecule drugs that penetrate cells to alter signaling pathways, and monoclonal antibodies that interact with extracellular targets [2]. In contrast to conventional chemotherapy, targeted therapies specifically engage molecular structures within cells, often resulting in fewer side effects [2].

Recent FDA approval trends reveal a striking pattern—between 2000 and 2022, 573 oncology therapeutics received approval, with targeted drugs and biologics comprising 91% of these approvals [2]. Patients receiving treatment through biomarker-guided approaches consistently demonstrate improved outcomes compared to those treated with traditional methods [2].

What is a diagnostic biomarker and how is it used?

A diagnostic biomarker is a biological molecule found in blood, other body fluids, or tissues that indicates disease presence or condition [1]. These molecular indicators can be DNA, RNA, protein, or metabolomic profiles specific to the tumor [3]. Diagnostic biomarkers serve multiple critical functions:

• Assessing an individual’s risk of developing cancer
• Determining cancer recurrence risk
• Predicting likelihood of response to specific therapies
• Monitoring disease progression to evaluate treatment efficacy [3]

Once biomarkers are identified, clinicians determine whether any alterations are “actionable”—meaning they represent genetic changes driving tumor growth that can be targeted with available medications [3]. Notably, cancer biomarkers are categorized as either prognostic (providing insights into disease outcomes) or predictive (useful for evaluating drug responses) [2].

The promise of tissue-agnostic drug approvals

Tissue-agnostic oncology drugs target specific molecular alterations across multiple cancer types regardless of organ or tissue origin [4]. This innovative approach enables treatment based on biomarker expression rather than anatomical location [1]. The FDA defines tissue-agnostic drugs as those targeting molecular alterations across multiple cancers—for instance, the same targeted alteration affecting a single pathway in various tumor types such as colorectal, thyroid, and breast cancers [4].

Since 2017, six drugs have received FDA approval for seven tissue-agnostic indications [5]. These include:

• Larotrectinib (Vitrakvi), entrectinib (Rozlytrek), and repotrectinib (Augtyro) for NTRK gene alterations
• Pembrolizumab (Keytruda) and dostarlimab (Jemperli) as immune checkpoint inhibitors
• Dabrafenib (Tafinlar) with trametinib (Mekinist) for certain BRAF gene changes
• Selpercatinib (Retevmo) targeting abnormal RET protein [4]

Essentially, this approach expands treatment options for patients with rare or difficult-to-treat cancers by focusing on underlying tumor biology rather than anatomical site [1]. Nevertheless, the development of tissue-agnostic oncology drugs raises unique challenges not encountered in traditional approaches [4]. Most drug approvals lacked concurrent approval of a diagnostic test, with post-marketing studies playing a vital role in confirming clinical benefit and ensuring companion diagnostic performance across diverse tumor types [5].

For many cancer types, biomarker testing has become routine in guiding treatment decisions [6]. Yet, research continues across multiple fronts, including identifying biomarkers that predict immunotherapy response, developing liquid biopsies, detecting minimal residual disease, and discovering pharmacodynamic markers [3]. As precision medicine evolves, more in-depth tumor characterization will likely advance personalized cancer medicine, optimize treatment selection, and improve patient outcomes [3].

 

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The Overlooked Influence of Tumor Histology

Tumor histology—the microscopic examination of tissue structure—often plays a decisive yet underappreciated role in treatment outcomes, even in the era of molecular medicine. Clinical data increasingly shows that tissue context fundamentally alters how biomarkers function as predictive tools, creating scenarios where identical molecular alterations yield dramatically different treatment responses.

Histology-specific drug response patterns

Histological subtypes exhibit distinct treatment sensitivities that cannot be explained by molecular profiling alone. In non-small cell lung cancer (NSCLC), squamous cell carcinoma (SCC) demonstrates substantially better outcomes with platinum-based neoadjuvant chemotherapy compared to non-SCC subtypes. Patients with SCC achieved higher major pathologic response rates (p=0.021) and lower recurrence rates (p=0.009) [7]. Moreover, SCC patients showed superior five-year progression-free survival (56.9±5.9% vs 34.1±5.2%, p=0.0072) and overall survival rates (68.2±5.6% vs 52.2±5.6%, p=0.046) [7].

This pattern extends beyond NSCLC. For breast cancer, histology influences predicted sensitivity to both chemotherapy and hormonal therapy. Paclitaxel sensitivity positively correlates with tumor aggression markers, whereas tamoxifen effectiveness shows an opposite association, demonstrating increased efficacy in low-grade estrogen receptor-positive tumors [2]. Additionally, among NSCLC patients with tumors ≥4cm treated with stereotactic body radiation therapy, adjuvant chemotherapy improved overall survival in SCC and large cell histologies but showed no benefit for adenocarcinoma (p=.262) [8].

Why histology still matters despite biomarker presence

Histology provides crucial contextual information that molecular biomarkers alone cannot capture. Tumor microenvironment—including stromal characteristics, immune cell infiltration, and tissue architecture—fundamentally alters drug delivery, metabolism, and efficacy. The presence of tumor-infiltrating lymphocytes (TILs), for instance, independently predicts chemotherapy response [2].

Examination of histological features also reveals treatment-relevant information at the spatial level. Advanced imaging analysis can identify regions within tumors demonstrating high or low drug sensitivity based on morphological patterns [2]. For example:

• High paclitaxel sensitivity areas: Pleomorphic tumor cells with lymphocytic infiltration
• Low paclitaxel sensitivity areas: Dense sclerotic stroma with myxoid changes
• High tamoxifen sensitivity areas: Tumor cells with low nuclear pleomorphism
• Low tamoxifen sensitivity areas: Regions showing necrosis and increased mitotic activity [2]

Furthermore, poorly differentiated clusters (PDCs) and desmoplastic reaction (DR) patterns provide critical prognostic insights that molecular profiling misses, particularly in colorectal cancer metastasis [9].

Mismatch between biomarker expression and drug efficacy

The correlation between biomarker presence and therapeutic response varies considerably across histological contexts. PD-L1 expression exemplifies this limitation—while approved as a companion diagnostic for multiple cancer types, its predictive value fluctuates substantially between tumor types [10]. As diagnostic tools, PD-L1 immunohistochemistry assays face several challenges:

1. Multiple different assays exist with varied cutoff points
2. Predictive significance differs between tumor vs immune cell expression
3. Poor interobserver reproducibility in scoring immune cell PD-L1 expression
4. Inability to assess PD-L1 within the broader tumor microenvironment context [10]

Tissue-agnostic approvals for NTRK fusion-targeting drugs illustrate this disconnect. Though these fusions appear in multiple cancer types, their prevalence ranges dramatically—from 90-100% in mammary analog secretory carcinoma to less than 1% in common cancers like colorectal (0.7-1.5%) and lung (0.2-0.3%) [2]. More importantly, response rates vary by tissue origin, with some histologies showing primary resistance despite biomarker presence.

The basket trial approach, which groups patients based on molecular alterations rather than tumor type, often masks these histology-dependent variations. For instance, in a pembrolizumab trial, cohorts defined by tumor mutation load showed dramatically different outcomes based on tumor type rather than mutation burden alone [11].

 

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When Biomarkers Mislead: Clinical Scenarios

Clinical experiences reveal that identical biomarkers can yield markedly different treatment outcomes based on tumor origin. These discrepancies undermine the tissue-agnostic approach to cancer treatment, creating critical challenges for practitioners interpreting molecular data.

Case: NTRK fusion in pancreatic vs. thyroid cancer

NTRK gene fusions illustrate how histology profoundly influences biomarker utility. In pancreatic adenocarcinoma, NTRK fusions appear at exceedingly low frequencies (0.3-0.8%) [6], making them rare molecular events. Conversely, thyroid cancers harbor these same alterations at substantially higher rates—10-20% in adults and up to 60% in pediatric populations under 10 years [3].

Beyond prevalence differences, treatment efficacy varies remarkably between these histologies despite targeting identical molecular alterations. NTRK inhibitors demonstrate higher efficacy in thyroid cancer, with larotrectinib achieving approximately 86% response rates versus 66-69% with entrectinib [3]. In pancreatic cancer, efficacy data remains limited due to the scarcity of NTRK-positive cases [12]. This striking disparity exists even as both inhibitors target the same oncogenic driver.

Tumor microenvironment and drug resistance

The tumor microenvironment (TME) fundamentally alters drug efficacy independently of biomarker status. TME comprises cellular components (cancer cells, immune cells, fibroblasts) and non-cellular elements that collectively influence treatment outcomes [13]. Extracellular matrix serves as a physical barrier, dissolving drugs or delaying delivery to tumor cells [14].

Immunosuppressive cytokines within the TME, chiefly TGF-β and IL-10, impede antitumor immune responses through multiple mechanisms [14]. TGF-β hinders immune effector cell proliferation, suppresses dendritic cell maturation, and reduces cytotoxic T-lymphocyte activation. Meanwhile, IL-10 decreases costimulatory molecule expression on dendritic cells and blocks T-cell attacks against tumor cells [14].

Histology-driven immune response variability

Inflammatory responses differ substantially across histological subtypes, explaining inconsistent outcomes with biomarker-matched treatments. In lung cancer, CBC-derived inflammatory markers progressively increase with tumor stage in both NSCLC and SCLC, yet NSCLC-NOS demonstrates particularly elevated values [13]. Furthermore, adenocarcinoma patients show statistically significant increases in MLR (p=0.02) and NLR (p=0.01) with advancing tumor stage [13].

Sarcoma subtypes exemplify this histology-driven response variability. Among patients receiving immune checkpoint inhibitors, objective response rates ranged from 66.7% in Kaposi sarcoma to 0% in osteosarcoma and synovial sarcoma [4]. Likewise, median progression-free survival varied dramatically—from “not reached” in Kaposi sarcoma to extremely short durations in liposarcoma—despite identical biomarker-driven treatment approaches [4].

This disparity explains why many initially promising biomarkers never reach clinical implementation. Of the numerous biomarkers discovered between 1965-1980, remarkably few have entered routine practice [5]. Beyond technical limitations, many fail because they provide statistically significant yet clinically insufficient information [5]. Consequently, practitioners often prefer treatment approaches based primarily on histopathology rather than relying exclusively on biomarker data with imperfect predictive value.

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Limitations of Basket Trials in Real-World Settings

Basket trials represent an evolving approach in biomarker-driven treatment evaluation, yet several methodological limitations hinder their translation to clinical practice. These trials, which group patients based on biomarkers rather than tumor types, face substantial challenges in real-world implementation.

Lack of histology stratification in trial design

Basket trials typically organize patients with different histologies under unified treatment protocols, often without accounting for tissue-specific variations [15]. This approach, while efficient for protocol startup and regulatory procedures, requires coordination between different specialist teams at research institutions—a substantial logistical challenge [15]. Importantly, among 36 analyzed studies, 86% used non-randomized and open-label designs, raising concerns about bias and external validity [16]. The absence of stratification by histology means treatment effects may be obscured by biological variations between tumor types [1]. Consequently, if a therapy works exceptionally well in one tumor type but poorly in others, the overall efficacy assessment becomes misleading [17].

Small sample sizes and statistical power issues

The average enrollment per basket trial stands at approximately 203 patients, ranging from 20 to 825 participants, with a median of 154 [18]. These relatively small cohorts become problematic when divided into histology-specific subgroups. Consider the pembrolizumab study—while it included 90 colorectal cancer patients, other tumor types were represented by merely 1-5 patients each [19]. Understandably, such minimal representation precludes meaningful statistical analysis within specific cancer subtypes. Furthermore, trials across multiple centers (average 56 centers per trial) face additional coordination challenges [18]. Given that biomarker prevalence often falls below 10% of tumors, identifying suitable candidates becomes extraordinarily difficult, expensive, and time-consuming [15].

Why basket trials may overestimate generalizability

Without rigorous standardized testing approaches, patients may be incorrectly classified as having specific genetic alterations and treated accordingly—leading to suboptimal outcomes [19]. Heterogeneity in disease progression and treatment response across subgroups further complicates interim analyzes [16]. In the real world, molecular match rates for targeted therapy trials have been reported as low as 4%, with single-institution studies showing only 13% of profiled patients successfully enrolling in matched studies [1]. The predominance of non-randomized designs (86%) raises additional concerns about reproducibility [16]. Finally, uncertainty regarding response duration across tumor types makes cost-effectiveness estimations exceptionally difficult [19].

These limitations underscore why basket trial results often fail to translate into consistent clinical benefits across diverse histological contexts.

 

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Improving Biomarker-Driven Approaches with Histology Context

Advancing beyond biomarker presence alone, effective clinical practice now calls for integrating tissue context into molecular profiling. Histopathology provides crucial biological information that molecular tests cannot capture independently.

Integrating histology into biomarker interpretation

Hematoxylin and eosin (H&E) staining, standard in clinical pathology, offers valuable prognostic and predictive information that enhances treatment decisions [20]. Recent artificial intelligence applications have revolutionized biomarker discovery through histopathology analysis. Single-cell mapping of tumors enables identification of four key cell types—tumor cells, lymphocytes, neutrophils, and macrophages—throughout tissue samples [20]. Indeed, several spatial features have shown significant associations with prognosis across multiple cancer types [20]. This approach achieved substantially higher correlation to gene expression-based cell estimates versus manual annotation (mean Pearson correlation: 0.61 vs 0.11) [20].

What is a good biomarker? Criteria beyond mutation presence

Effective biomarkers extend past mutation presence to include:

• Clinical applicability – Testing must align with clinical workflows, as excessive biomarkers increase cost and turnaround time in daily practice [2]
• Reproducibility across testing methods – Results should remain consistent regardless of testing approach [21]
• Histology-specific validation – Performance must be verified within specific tissue contexts [22]
• Predictive value – The biomarker should reliably predict treatment response [23]

Deep learning methods now extract previously hidden molecular information directly from routine histology images, creating novel opportunities even when tissue quantities limit additional molecular testing [2].

Role of multi-omic profiling in refining treatment decisions

Multi-omic approaches integrate genomics, transcriptomics, proteomics, and metabolomics data to provide comprehensive tumor characterization [7]. Following standard diagnostic workup, extended targeted NGS provides modest impact to molecular tumor boards, whereas functional and multi-omic data deliver substantial added value to clinical decision-making [22]. Importantly, the TuPro framework established a robust multi-omic analysis pipeline that yields individual tumor profiles with timely insights for treatment decisions [22]. Among treatments recommended through this approach, 87% were subsequently administered to patients [22]. Tumor mutational burden, T cell infiltration, HLA-ABC, and PD-L1 emerged as the markers most frequently informing recommendations [22].

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Conclusion   

The promise of biomarker-driven cancer therapies has certainly transformed oncology practice. Nevertheless, real-world evidence demonstrates that molecular alterations alone cannot predict treatment response with consistent reliability across different tissue contexts. Tumor histology fundamentally shapes how biomarkers function, often determining whether targeted therapies succeed or fail despite seemingly favorable molecular profiles.

Clinical data repeatedly shows identical genetic alterations yielding drastically different outcomes based on tissue origin. NTRK fusions, for instance, respond differently to targeted inhibitors in thyroid cancer compared to pancreatic adenocarcinoma. Similarly, PD-L1 expression carries variable predictive weight across cancer types, underscoring how tissue context modifies biomarker utility.

Basket trials, though innovative, face substantial challenges when translating results to routine clinical practice. Small sample sizes within histological subgroups, lack of proper stratification, and predominantly non-randomized designs limit their generalizability. Additionally, the tumor microenvironment—with its unique cellular composition, extracellular matrix, and cytokine profiles—creates histology-specific barriers to drug delivery and efficacy regardless of biomarker status.

Therefore, oncologists must approach biomarker data through a histological lens. Routine H&E staining provides valuable prognostic and predictive information that complements molecular testing. Artificial intelligence applications have likewise enhanced this integration, extracting molecular signatures directly from histopathology images and identifying spatial relationships between tumor and immune cells that predict treatment response.

Multi-omic profiling represents the next evolution in precision oncology, offering comprehensive characterization beyond single biomarkers. This approach combines genomics, transcriptomics, proteomics, and metabolomics to generate detailed tumor profiles that guide treatment decisions with greater accuracy than isolated molecular markers.

The future of precision medicine thus depends not on choosing between biomarkers or histology but rather on their thoughtful integration. Tissue context will undoubtedly remain crucial for interpreting molecular data, especially as treatment options expand. Ultimately, patients receive optimal care when clinicians recognize both the power and limitations of biomarker-driven approaches, always considering the biological nuances of tumor histology before making treatment decisions.

Key Takeaways

While biomarker-driven cancer treatment has revolutionized oncology, clinical success requires understanding how tumor histology fundamentally shapes treatment outcomes beyond molecular profiles alone.

• Histology determines biomarker effectiveness: Identical genetic alterations like NTRK fusions show dramatically different response rates across tissue types, with thyroid cancers achieving 86% response versus limited efficacy in pancreatic cancer.
• Tumor microenvironment creates tissue-specific barriers: Physical barriers, immunosuppressive cytokines, and cellular composition vary by histology, affecting drug delivery and resistance patterns regardless of biomarker presence.
• Basket trials have real-world limitations: Small sample sizes per histology subgroup, lack of stratification, and predominantly non-randomized designs limit generalizability of tissue-agnostic treatment approaches.
• Integration improves precision medicine: Combining routine histopathology with molecular profiling through AI-enhanced analysis and multi-omic approaches provides more accurate treatment predictions than biomarkers alone.
• Clinical practice requires dual perspective: Effective oncologists interpret biomarker data through histological context, recognizing that tissue origin fundamentally influences whether targeted therapies will succeed or fail.

The future of precision oncology lies not in choosing between molecular and morphological approaches, but in their thoughtful integration to optimize patient outcomes across diverse cancer contexts.

Frequently Asked Questions:   

FAQs

Q1. Why is tumor histology important in biomarker-driven cancer treatment? Tumor histology is crucial because it fundamentally shapes how biomarkers function and influences treatment outcomes. Identical genetic alterations can yield drastically different responses based on the tissue of origin, affecting drug efficacy and patient prognosis.

Q2. What are the limitations of basket trials in cancer research? Basket trials face challenges such as small sample sizes within histological subgroups, lack of proper stratification, and predominantly non-randomized designs. These limitations can affect the generalizability of results to routine clinical practice.

Q3. How does the tumor microenvironment impact treatment effectiveness? The tumor microenvironment, which varies by histology, creates tissue-specific barriers to drug delivery and efficacy. It includes physical barriers, immunosuppressive cytokines, and unique cellular compositions that can affect treatment outcomes regardless of biomarker status.

Q4. What is multi-omic profiling and how does it improve cancer treatment decisions? Multi-omic profiling integrates genomics, transcriptomics, proteomics, and metabolomics data to provide comprehensive tumor characterization. This approach offers more detailed tumor profiles, guiding treatment decisions with greater accuracy than isolated molecular markers.

Q5. How can artificial intelligence enhance biomarker interpretation in cancer diagnosis? Artificial intelligence applications can extract molecular signatures directly from histopathology images and identify spatial relationships between tumor and immune cells. This enhances biomarker discovery and provides valuable prognostic and predictive information that complements molecular testing.

 

 

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