Extending expressed RNA genomics from surgical decision making for cytologically indeterminate thyroid nodules to targeting therapies for metastatic thyroid cancer

Ali, Syed Z.; Siperstein, Allan; Sadow, Peter M.; Golding, Allan C.; Kennedy, Giulia C.; Kloos, Richard T.; Ladenson, Paul W.

doi:10.1002/cncy.22132

Cited by 22 publications

(20 citation statements)

References 73 publications

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“…For patients with thyroid carcinoma in the neck or in distant sites that is refractory to radioactive iodine and may warrant systemic therapy, use of XA from FNAs of known thyroid cancer deposits may inform treatment selection, although confirmation testing by an approved companion diagnostic test may still be required for patients to access certain pharmaceuticals. Additionally, repeat testing from a site of disease progression during active treatment may provide additional genomic insights to potentially guide therapy (26).…”

Section: Discussionmentioning

confidence: 99%

“…Currently, an important tool to avoid unnecessary diagnostic surgery among cytologically indeterminate thyroid nodules is the Afirma Genomic Sequencing Classifier (GSC) (25, 26). In this context, “cytologically indeterminate” refers to the two Bethesda categories Atypia of Undetermined Significance/ Follicular Lesion of Undetermined Significance (“Bethesda III”) and Follicular Neoplasm/ Suspicious for a Follicular Neoplasm (Bethesda IV) (27) or their equivalents (28).…”

Section: Introductionmentioning

confidence: 99%

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Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples

et al. 2019

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Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation.Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results.Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86–91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%.Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples

et al. 2019

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“…Thus, assigning a risk interpretation to a panel or group of genes/variants rather than to individual specific variants may be less accurate. We also believe that it is likely that understanding tumor prediction and prognostics at the specific variant level will increase personalized prediction accuracy and treatment decisions (92).…”

Section: Importance Of Assessing Ppv By Individual Variantmentioning

confidence: 99%

Molecular Variants and Their Risks for Malignancy in Cytologically Indeterminate Thyroid Nodules

Goldner

Angell

McAdoo

et al. 2019

Thyroid

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Background: Gene panels are routinely used to assess predisposition to hereditary cancers by simultaneously testing multiple susceptibility genes and/or variants. More recently, genetic panels have been implemented as part of solid tumor malignancy testing assessing somatic alterations. One example is targeted variant panels for thyroid nodules that are not conclusively malignant or benign upon fine-needle aspiration (FNA). We systematically reviewed published studies from 2009 to 2018 that contained genetic data from preoperative FNA specimens on cytologically indeterminate thyroid nodules (ITNs) that subsequently underwent surgical resection. Pooled prevalence estimates per gene and variant, along with their respective positive predictive values (PPVs) for malignancy, were calculated. Summary: Our systematic search identified 540 studies that were supplemented by 18 studies from bibliographies or personal files. Sixty-one studies met all inclusion criteria and included >4600 ITNs. Overall, 26% of nodules contained at least 1 variant or fusion. However, half of them did not include details on the specific gene, variant, and/or complete fusion pair reported for inclusion toward PPV calculations. The PPVs of genomic alterations reported at least 10 times were limited to BRAF V600E (98%, 95% confidence interval [CI 96-99%]), PAX8/ PPARG (55% [CI 34-78%]), HRAS Q61R (45% [CI 22-72%]), BRAF K601E (42% [CI 19-68%]), and NRAS Q61R (38% [CI 23-55%]). Excluding BRAF V600E , the pooled PPV for all other specified variants and fusions was 47%. Multiple variants within the same nodule were identified in *1% of ITN and carried a cumulative PPV of 77%. Conclusions: The chance that a genomic alteration predicts malignancy depends on the individual variant or fusion detected. Only five alterations were reported at least 10 times; BRAF V600E had a PPV of 98%, while the remaining four had individual PPVs ranging from 38% to 55%. The small sample size of most variants and fusion pairs found among ITNs, however, limits confidence in their individual PPV point estimates. Better specific reporting of genomic alterations with cytological category, histological subtype, and cancer staging would facilitate better understanding of cancer prediction, and the independent contribution of the genomic profile to prognosis.

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“…Even when FNA of the thyroid is performed it does not always yield a definitive result. A final diagnosis cannot be made in one out of seven nodules with FNA (8). Molecular markers were developed to avoid surgery for benign nodules with indeterminate cytology.…”

Section: Introductionmentioning

confidence: 99%

“…Molecular markers were developed to avoid surgery for benign nodules with indeterminate cytology. The positive predictive value for these molecular tests varies between 20 to 50% (8, 9). Many times, a repeat biopsy may be required to do molecular markers.…”

Section: Introductionmentioning

confidence: 99%

AIBx, artificial intelligence model to risk stratify thyroid nodules

Thomas

Haertling

2021

Preprint

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Background Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. 1 out of 2 women over the age of 50 may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm. Methods Ultrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February of 2012 through February of 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules which underwent biopsy or surgery from March of 2017 through July of 2018. Results Negative predictive value of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value and accuracy of the model was 87.8%, 78.5%, 65.9% and 81.5% respectively. Conclusion When compared to published results of ACR TIRADS and ATA classification system, our image similarity model had comparable negative predictive value with better sensitivity specificity and positive predictive value. By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model which increases physician′s confidence in the predictions.

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Extending expressed RNA genomics from surgical decision making for cytologically indeterminate thyroid nodules to targeting therapies for metastatic thyroid cancer

Cited by 22 publications

References 73 publications

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples

Molecular Variants and Their Risks for Malignancy in Cytologically Indeterminate Thyroid Nodules

AIBx, artificial intelligence model to risk stratify thyroid nodules

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