JONT Journal

Discussion

The results of this real-world study underscore the potential clinical impact of comprehensive tumor profiling in metastatic cancer patients, particularly those who have exhausted standardof-care options. The observed improvement in progression-free survival (PFS2) following precision oncology-guided therapy, as compared to PFS1 under conventional treatment, highlights the feasibility and translational relevance of integrating extensive molecular analyses into therapeutic decision-making. Notably, the differential outcomes associated with biological targeted therapy, combination regimens, and gene expression-driven recommendations suggest that molecular profiling alone may not be a definitive predictor of therapeutic efficacy. These findings necessitate a broader discussion on the interplay between genomic alterations, transcriptional activity, the tumor microenvironment as well as other individualized therapeutic response shaping factors. Furthermore, the significant variation in PFS2/PFS1 ratios across treatment modalities underscores the importance of optimizing patient stratification criteria and refining bioinformatics-driven ranking systems for therapeutic prioritization. In light of these insights, the following discussion contextualizes these findings within the broader landscape of precision oncology, addressing key challenges, potential limitations, and future directions for optimizing patient-centered therapeutic strategies.

Extensive tumor profiling inherently detects more potential targets, including previously uncharacterized variants for which therapies are available. For instance, a young patient with a therapy-resistant breast carcinoma, finally endocrine-sensitive according to hormone receptor status, HER2-negative, underwent extensive tumor profiling after all available therapy modalities failed to show benefit. This profiling revealed an uncharacterized ERBB2pV697L mutation, retrospectively identified as a gain-offunction mutation with high clinical impact.

A fundamental challenge is the translatability of targeted therapy information across different tumor entities. This issue is influenced by several etiological components, such as the functional heterogeneity of alterations with varying sensitivities to targeted therapies, entity-specific transcription and gene expression profiles with diverse efficacies, and epigenetic factors, among others [6-8].

This context of functional heterogeneity of oncogenic alterations across tumor entities is exemplified by the divergent therapeutic response to BRAFpV600E mutations in different tumor types. The efficacy of vemurafenib as a monotherapy in patients with BRAFpV600E-mutated malignant melanoma was substantiated by the phase III BRIM-3 trial [57]. In contrast, colorectal carcinoma with the same mutation shows intrinsic resistance to BRAF inhibition due to feedback activation of EGFR signaling.

Effective treatment therefore requires combination regimens targeting multiple nodes of the MAPK pathway, as demonstrated in the BEACON CRC trial.

Similarly, the SUMMIT Basket Trial [12, 13] showed that neratinib elicited responses in HER2-mutated solid tumors with the pS310x alterations in various solid tumors (breast, colon, cervix, cholangiocarcinoma, etc.), but not in bladder carcinoma, despite the presence of the same mutation, indicating significantly varying response rates.

To explain the value of comprehensive tumor testing and the associated individual challenges, while simultaneously demonstrating the insights derived from our approach, we have curated a selection of illustrative case examples.

In cases of solid tumors where a targetable genetic alteration predominates with respect to tumor-biological behaviour and considering additional functionally critical alterations, a purely molecularly guided therapy can achieve high efficacy as exemplified by the following case of a young patient with therapy resistant Her 2 negative breast cancer. The initial tumor profiling, conducted in the 38th month of therapy, revealed the HER2 p.V697L mutation. This mutation was subsequently targeted multiple times, considering various potential resistance factors. For example, the presence of an NF2 mutation can lead to upregulation of EGFR and the mTOR pathway, suggesting a potential benefit from the additional administration of lapatinib and everolimus, sometimes associated with long-lasting remissions [37, 41]. Upon the disappearance of this clinically significant mutation, mutations related to homologous recombination deficiency were targeted, such as a germline BRCA1 and a somatic BRCA1 mutation, alongside a synchronous ARID1A mutation. The therapeutic consequence of this included the use of olaparib and carboplatin to induce a prompt therapeutic response in the setting of extensive tumor progression. Throughout the treatment course, various subclones were detected, with peritoneal and hepatic recurrences influencing therapy choices based on remission driven selective pressure.

This example demonstrates that NGS can reflect clinically significant aspects of the tumor phenotype, effectively guiding treatment over six years with varying targets.

In contrast to the aforementioned example, the complexity of tumor progression presents a distinct challenge in the following context: genome, transcriptome, proteome, signal transduction pathways, interconnected pathways, microenvironment, immune system dysfunction, and cell-cell contacts. All these factors contribute to intra- and intertumoral heterogeneity [7,14-16].

Our data also demonstrate that gene alterations do not necessarily lead to the expected gene expression [17, 18, 20]. For example, a patient with neuroendocrine breast carcinoma developed multiple therapy-resistant recurrences after adjuvant chemotherapy. In the absence of guideline recommendations and the possibility of participating in a clinical trial, extensive tumor profiling was conducted. This profiling detected a PIK3CApE545K mutation, which is predictive of the potential efficacy of mTOR or PIK3CA inhibitors. However, mTOR inhibition showed limited therapeutic response. Retrospective analysis revealed that mRNA profiles of the mTOR pathway exhibited a downregulation of peripheral effector mechanisms such as S6 Kinase 1 and 2 and eIF4B. Thus, the therapeutic blockade of an already downregulated pathway could explain the treatment failure [21-24].

Additionally, other signal transduction pathways within the PIK3CA pathway, such as cell cycle progression (e.g., MYC, CDK20) and cell survival pathways (e.g., MCL1, BIM, BCL2), were also downregulated. Therefore, PIK3CA inhibition alone appeared insufficient for a therapeutic response.

The combination of alpelisib (in 2020 available through compassionate use) and carboplatin demonstrated, as per the NCT 05472220 study, a therapeutic response for the first time, with improvement in quality of life despite prior extensive tumor progression.

Another example illustrating the indication for extensive tumor profiling shows that co-alterations as potential emerging markers can influence signal transduction pathways and therapy efficacy. In another patient with neuroendocrine breast carcinoma, hepatic metastases were detected immediately after completing adjuvant therapy. Extensive tumor profiling revealed a loss-of-function mutation in STK11 (STK11pY131*) and a TP53pR290fs alteration, which could lead to upregulation of the PIK3CA pathway even in the absence of an activating PIK3CA mutation [19, 38].

The potential discrepancy between molecular genetics and gene expression [17, 18 ,20, 25] necessitated evaluating this pathway at the mRNA level. This analysis revealed an upregulation of S6 kinase 1 and 2, indicating a potential benefit from mTOR pathway inhibitors. Additionally, the synchronous presence of FGFR1/2 amplification, which can be associated with PIK3CA-mTOR pathway stimulation and potential resistance to endocrine therapy, CDK4/6 inhibitors, and PIK3CA inhibitors, while maintaining sensitivity to mTOR inhibitors [26, 27], suggests that mTOR inhibition could be promising in this individual case.

However, there are counterarguments against monotherapy. Everolimus, as an allosteric inhibitor of mTORC1, has a more significant effect on S6 Kinase 1and 2 and less on 4E-BP1/ eIF4E axis, which strongly inhibits tumor protein synthesis [2224]. Thus, complete inhibition of mTOR cannot be expected. Additionally, arguments against Everolimus monotherapy include the potential suppression of negative feedback loops, such as IRS1, leading to subsequent PIK3CA-mTOR activation [28], and the downregulation of S6 Kinase 1 and 2by Everolimus may induce inverse AKT and mTOR activation [29]. Moreover, mTOR inhibition is known to trigger interconnected pathways, such as the MAPK pathway [30].

Based on the literature, a combination of mTOR inhibition and anti-angiogenesis appears reasonable, particularly in the context of upregulated VEGF-A and HIF-alpha at the mRNA level in this patient [31-35]. Given data on the effective combination of an mTOR inhibitor with chemotherapy [29], the administration of Everolimus, Bevacizumab, and 5-FU resulted in a therapeutic response for the first time over four months.

These cases illustrate that the efficacy of targeted therapy does not necessarily correlate with the number of genes or expression parameters addressed. Instead, it highlights the relevance of targeting key oncogenic drivers rather than maximizing the breadth of intervention. Specifically, statistical analysis does not indicate a meaningful association between the extent of targeted NGS or gene expression profiling and improved progression-free survival (PFS), nor with the PFS2/PFS1 ratio.

The integration of artificial intelligence and machine learning in drug ranking could enhance the precision of treatment recommendations by systematically analyzing vast datasets of genomic, transcriptomic, proteomic, and clinical data. This approach can identify complex patterns and interactions that may not be immediately apparent to human experts. Furthermore, AIdriven drug ranking can adapt to new data and evolving knowledge, continuously refining therapeutic strategies.

In conclusion, while interdisciplinary expertise remains crucial, incorporating AI-based methodologies could significantly improve the accuracy and efficacy of personalized cancer therapy. Future research should focus on developing and validating AI models that can integrate diverse biological data to provide robust and individualized treatment recommendations.

These examples raise the question of utilizing a drug ranking system via digital learning processes to better predict individual therapeutic efficacy [42]. It is evident that therapy recommendations based solely on interdisciplinary human expertise or even with AIassisted prioritization, sometimes necessitate non-evaluated and non-established therapy combinations for clinical benefit. This involves considerations of independent drug actions, additivity, synergism, and other related aspects [43].

In cases of extensive metastasis, complementing cytostatics may be required, potentially following sensitivity testing and considering pharmacogenetic aspects. This dual approach aims to achieve a rapid antiproliferative effect while maintaining a broad therapeutic range, mindful of overlaps in substance classes and targets [1, 44].

A significant challenge lies in estimating the therapeutic index. Often, there is no overlapping toxicity between cytostatics and biological targeted therapies, especially with the application of two antibodies [44]. Key aspects to consider include patient age, organ function, comorbidities, and concomitant medications, as outlined by Borad et al., where a median of eight medications per patient are reported for Phase 2 studies [45].

Pharmacogenetics also plays a crucial role, with extensive tumor profiling providing additional insights into individual toxicity profiles. This comprehensive approach can inform personalized treatment plans, enhancing both efficacy and safety.

In general, combination therapies require dose reduction based on data from Phase 1 studies, where applicable. This dose optimization principle applies to the same substance class, such as small molecules, but does not apply to combinations of antibodies with each other. For drug combinations targeting the same pathway, such as anti-angiogenesis agents dose adjustment is necessary. The combination of mTOR inhibitors affecting DNA repair mechanisms by influencing CDK 1 and cytotoxic agents along with PARP- and HDAC inhibitors, also modulating DNA expression and repair necessitates dose optimizations [46].

The lowest safe additive starting dosage is considered to be 60% of each drug when there is no overlap and no mTOR inhibitor is applied. For combinations with overlap of class and/or target or application of mTOR inhibition, the initial dosage can be reduced to 30% of each drug. The literature review on therapeutic efficacy in the era of biological targeted therapies indicates that patients receiving lower doses in Phase 1 trials do not ultimately fare worse than those treated with the maximum tolerated dose. Efficacy parameters, including response rate, progression-free survival, and overall survival, remain comparable between these dosing cohorts [46].

In the absence of contemporaneous data at the time of patient recruitment and treatment, our therapeutic decision was initially guided by Liu´s recommendations [1].

This approach ensures the preservation of the therapeutic index while mitigating the risk of overlapping toxicity, thereby enhancing the safety and effectiveness of combination therapies in clinical practice.

These encouraging results from our trial, based on a small study population under real-world conditions, indicate at least directional trends in precision oncology. Significant studies such as the WINTHER trial and the I-PREDICT study confirm the clinical benefit of broad-based diagnostics with partially entity-agnostic therapy. Molecular differences in subclones can account for variations in tumor response, necessitating more targeted therapies for resistant subclones. This etiological aspect may explain the significant positive correlation between higher matching scores and improved overall response, progression-free survival, and overall survival, as already noted in the aforementioned studies.

The I-PREDICT study highlights the challenge of using multiple drugs simultaneously, raising the question of whether “more is better.” The study demonstrated improved outcomes in a subset of patients with a high matching score.

Due to ethical and pharmacoeconomic considerations, our trial did not use a matching score. Instead, we focused on extracting clinically relevant information from extensive tumor profiling to identify a few highly effective therapies tailored to the tumor’s biological context where possible information on tumor characteristics was obtained and analysed at prognostically and clinically relevant primary tumor and metastasis sites. Our evaluation suggests that this approach can extend patient survival while maintaining functional status, alongside with reduced side effects.

The integration of extensive tumor profiling, including gene expression analysis, has the potential to enhance clinical outcomes. While recommendations based solely on nextgeneration sequencing (NGS) exhibit a notable improvement in progression-free survival compared to the absence of precision oncology guidance, the incorporation of gene expression profiling suggests a further, albeit statistically non-significant, increase in therapeutic benefit. These findings underscore the potential value of comprehensive tumor characterization in optimizing treatment strategies, although the extent of its clinical impact requires further validation.

Summary and Outlook

In summary, expanded tumor profiling, when combined with adequate bioinformatic analysis and resulting therapy recommendations, can lead to improved therapeutic outcomes. A problematic aspect remains the complexity due to the enormous amounts of data being generated. This necessitates the selection of relevant information for oncological real-world application using machine learning algorithms [47, 48]. Moreover, practitioners must effectively implement this information in clinical practice.

The appropriate development of modern techniques based on machine learning for diagnostics, bioinformatics, prognostics, and prediction, coupled with closely coordinated interdisciplinary therapy recommendations, offers the potential for a paradigm shift from reactive to proactive oncology.

Abbreviations: ctDNA: circulating tumor DNA

CTX: Chemotherapy

ECOG: Eastern Cooperative Oncology Group (performance status)

EXACTA: trade name for advanced molecular tumor profiling system

IQR: inter quartile range (statistics)

MDN: median

NGS: Next Generation Sequencing

p: propability of observing a result at least as extreme under the null hypothesis (statistics) pat.: patients

PFS1: progression-free survival in first therapy line based on standard of care therapy

PFS2: progression-free survival based on NGS navigated therapy

Q1: First quartile (statistics) Q3: Third quartile (statistics) r: Pearson correlation coefficient (statistics)

W: sum of ranks of positive or negative differences in Wilcoxon signed rank test

Ethical Considerations: Under the reference number2023-086f-S, the Ethics Committee of westfalen-Lippe in Münster issued a positive ethics vote on March 14, 2023.

Conflict of Interest: There is no conflict of interest for any of the authors.

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