ALK-rearranged renal cell carcinoma with TPM3::ALK gene fusion and review of the literature

Galea, Laurence A.; Hildebrand, Michael S.; Witkowski, Tom; Joy, Christopher; McEvoy, Christopher R. E.; Hanegbi, Uri; Aga, Ahmad

doi:10.1007/s00428-022-03451-z

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Mutations in PTPRC, also known as CD45, have been associated with RA patient response to anti-TNF therapy [22,23]. TPM3 has been reported as an autoantigen in in RA, and also to involved in myopathy and renal cancer [24,25,26]. ARHGDIB expression has been reported to be upregulated in RA, whereas SIAH1 is downregulated [28,29].…”

Section: Discussionmentioning

confidence: 99%

AI based on evolutionary computation yields algorithmic biomarker summary of a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient outcomes, enabling precision medicine

Horgan,

McDermott,

Harrington

et al. 2024

Preprint

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Background Producing transparent interpretable algorithms summarizing clinical trial outcomes to accurately predict individual patient’s responses would be a significant advance. We hypothesized that software designed to analyze biomedical data, based on evolutionary computation (EC), could produce summary algorithmic biomarkers from a clinical trial, predictive of individual responses to therapy. Methods and Findings A previously published randomized double-blind placebo controlled clinical trial was analyzed. Patients with active rheumatoid arthritis on a stable dose of methotrexate and naive to anti-tumor necrosis factor biologic therapy, were randomized to receive infliximab or placebo. The primary endpoint was synovial disease activity assessed by magnetic resonance imaging. Secondary endpoints included the Disease Activity Score 28 (DAS28). Baseline peripheral blood gene expression variable data were available for 59 patients, plus the treatment variable, infliximab or placebo, yielding a total of 52,379 baseline variables. The binary dependent variable for analysis was DAS28 response, defined by a decrease in DAS28 score of ≥1.2, at 14 weeks. At 14 weeks, 20 of the 30 patients receiving infliximab had responded, and ten of the 29 patients receiving placebo had responded. The software derived an algorithm, with 4 gene expression variables plus treatment assignment and 12 mathematical operations, that correctly predicted responders versus non-responders for all 59 patients with available gene expression data, giving 100% accuracy, 100% sensitivity and 100% specificity. We present the algorithm to provide transparency and to enable verification. Excluding the 4 gene expression variables, we then derived similarly predictive algorithms with 4 other gene expression variables. We hypothesized that the software could derive algorithms as predictors of treatment response to anti-tumor necrosis factor biologic therapy using just these 8 gene expression variables using previously published independent datasets from 6 rheumatoid arthritis studies. In each validation analysis the accuracy of the predictors we derived surpassed those previously reported by the original study authors. Conclusions and Relevance Software based on EC summarized the outcome of a clinical trial, with transparent biomarker algorithms correctly predicted the clinical outcome for all 59 RA patients. The biomarker variables were validated in 6 independent RA cohorts. This approach simplifies and expedites the development of algorithmic biomarkers accurately predicting individual treatment response, thereby enabling the deployment of precision medicine, and, in the future, providing a basis for dynamic labeling of prescription drugs. Original Trial Registration used for analysis: ClinicalTrials.gov registration:NCT01313520

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Section: Discussionmentioning

confidence: 99%

AI based on evolutionary computation yields algorithmic biomarker summary of a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient outcomes, enabling precision medicine

Horgan,

McDermott,

Harrington

et al. 2024

Preprint

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“…These drugs specifically target the abnormal ALK fusion proteins, disrupting their signaling pathways and inhibiting tumor growth. Hence, precise diagnosis of ALK-rearranged RCC are crucial in order to determine suitable treatment options and optimize patient outcomes [10][11][12][13].…”

Section: Alk-rearranged Rccmentioning

confidence: 99%

Molecularly Defined Renal Carcinomas

Amann-Arévalo,

Ballestín Martínez,

Vidal Cassinello

et al. 2024

KCA

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RCC has witnessed a significant increase in its incidence over the last five decades, ranking as the ninth most common cancer globally. Although survival rates have improved substantially, RCC remains one of the deadliest urological cancers. Traditionally, RCC subtypes were classified based on histopathological features. However, in recent years, there has been a paradigm shift towards molecular and genomic characterization of RCC, leading to the recognition of distinct molecular subtypes. The 2022 World Health Organization (WHO) classification introduced a new category called “molecularly defined renal carcinomas,” encompassing various subtypes, including SMARCB1-deficient medullary carcinoma, ALK-rearranged RCC, FH-deficient RCC, SDH-deficient RCC, ELOC-mutated RCC, TFEB-altered RCC, and TFE3-rearranged RCC. These molecular subgroups have significant consequences for diagnosis, prognosis, and treatment. Molecularly defined RCCs are frequently underrepresented in clinical trials, encouraging additional research to identify beneficial therapeutics. Immune checkpoint inhibitors and tyrosine- kinase inhibitors have shown promising results in some subtypes, while others may benefit from specific inhibitors targeting their molecular drivers. Additionally, these classifications have important prognostic implications, guiding treatment decisions and genetic counseling.

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ALK-Rearranged Epithelioid Mesenchymal Neoplasm: Expanding the Spectrum of Tyrosine Kinase–Altered Mesenchymal Tumors

Gestrich,

Davis,

Biederman

et al. 2023

Modern Pathology

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ALK-rearranged renal cell carcinoma with TPM3::ALK gene fusion and review of the literature

Cited by 6 publications

References 27 publications

AI based on evolutionary computation yields algorithmic biomarker summary of a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient outcomes, enabling precision medicine

AI based on evolutionary computation yields algorithmic biomarker summary of a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient outcomes, enabling precision medicine

Molecularly Defined Renal Carcinomas

ALK-Rearranged Epithelioid Mesenchymal Neoplasm: Expanding the Spectrum of Tyrosine Kinase–Altered Mesenchymal Tumors

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