The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review

Lorkowski, Shuhui Wang; Dermawan, Josephine K.; Rubin, Brian P.

doi:10.1007/s00428-023-03708-1

Cited by 2 publications

(3 citation statements)

References 52 publications

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“…Considering the ambivalent data from rather small study cohorts supporting CUP being a unique cancer type [ 12 ], it is most commonly assumed to be a metastatic disease stemming from a broad variety of different cancer entities [ 10 , 13 ]. This hypothesis is further supported by successful approaches to demarcate the primary tumor sites based on DNA methylation analysis of CUP, as will be discussed below [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 94%

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles

Stark,

Kasajima,

Stögbauer

et al. 2024

Clin Epigenet

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Background The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. Results The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. Conclusions On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.

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Section: Introductionmentioning

confidence: 94%

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles

Stark,

Kasajima,

Stögbauer

et al. 2024

Clin Epigenet

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“…Biotheranostics Laboratory in the United States utilizes real-time reverse-transcription polymerase chain reaction (RT-PCR) to detect the expression of 92 genes in patient tumors, classifying them by matching gene expression patterns with a database of over 2000 known tumor types and subtypes, differentiating 50 different tumor types and subtypes, covering over 95% of solid tumors, with an accuracy rate of 85% [32]. Currently, artificial intelligence (AI) and bioinformatics-based multi-omics analysis technologies are also increasingly used in identifying the origin of CUP, emerging as high-performance, low-cost methods for cancer tracing, and they are expected to further improve the accuracy of identification and the efficacy of guided therapy [33].…”

Section: Figure 3 Gene Expression Profiling (Gep) Tissue-of-origin Cl...mentioning

confidence: 99%

“…Artificial intelligence (AI), as an emerging disruptive technology, is gradually unleashing the immense energy of the technological revolution and industrial transformation, continually expanding its application domains [33]. Recently, a team from Harvard Medical School, led by Faisal Mahmood, published a research paper in Nature, titled "AI-based pathology predicts origins for cancers of unknown primary" [49] (Figure 5).…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies

Liu,

Jiang,

Wang

2024

Biosensors

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Cancers of unknown primary (CUP) exhibit significant cellular heterogeneity and malignancy, which poses significant challenges for diagnosis and treatment. Recent years have seen deeper insights into the imaging, pathology, and genetic characteristics of CUP, driven by interdisciplinary collaboration and the evolution of diagnostic and therapeutic strategies. However, due to their insidious onset, lack of evidence-based medicine, and limited clinical understanding, diagnosing and treating CUP remain a significant challenge. To inspire more creative and fantastic research, herein, we report and highlight recent advances in the diagnosis and therapeutic strategies of CUP. Specifically, we discuss advanced diagnostic technologies, including 12-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET/CT) or 68Ga-FAPI (fibroblast activation protein inhibitor) PET/CT, liquid biopsy, molecular diagnostics, self-assembling nanotechnology, and artificial intelligence (AI). In particular, the discussion will extend to the effective treatment techniques currently available, such as targeted therapies, immunotherapies, and bio-nanotechnology-based therapeutics. Finally, a novel perspective on the challenges and directions for future CUP diagnostic and therapeutic strategies is discussed.

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The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review

Cited by 2 publications

References 52 publications

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles

Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies

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