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Sophia C Kamran

Publications and source records attributed to Sophia C Kamran.

2 recordsLinked to original sources

Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.

Tumor-only whole-exome sequencing (WES) is used when matched normal tissue is unavailable, but one sample can produce thousands of variants. Somatic likelihood tiering (SLT) is an interpretable post-calling protocol that ranks Mutect2 calls into four review-priority tiers using population-frequency, germline-quality, cancer-knowledge, PureCN posterior, and clonal-hematopoiesis evidence. Layer 2 distinguishes common, rare-callable, and unevaluable gnomAD states; missing or unmatchable gnomAD evidence is not positive rarity evidence. On the SEQC2 HCC1395 benchmark, the callability-aware SLT-A row contained 101 calls, 78 truth variants, 77.2% PPV (95% Wilson confidence interval 68.1%-84.3%), and a Number Needed to Review (NNR) of 1.29 (1.19-1.47). The conservative SLT-C catchment retained 352 of 455 truth variants (77.4%, 73.3%-81.0%) and all tiers together retained 430 of 455 truth variants. SNV performance is the primary calibration frame: SLT-C retained 341 of 439 SNV truth variants, whereas indel results were exploratory because only 16 truth indels were available. Clinical cohorts are reported as recall and concordance versus partially dependent matched-normal Mutect2 references, not independent clinical sensitivity. Patient-level bootstrap intervals were principal: HdM-BLCA-1 SLT-A recall was 18.2% (14.0%-23.5%), and LUAD-TW SLT-A recall was 49.1% (26.6%-63.3%) among 32 evaluable patients. The HdM-BLCA-1 median SLT-A queue remained 1277 variants per patient, so SLT reduces first-pass candidate counts but does not measure review time or eliminate FFPE candidate-count burden. SLT provides an auditable tumor-only WES review queue, not a substitute for matched-normal sequencing, independent orthogonal validation, or definitive somatic classification.

Humans↗

Participant Heterogeneity in the Prostate Cancer Biobank of the NRG: An Obstacle to Broadening the Reach of Precision Oncology.

PURPOSE: Precision medicine has revolutionized oncology; however, tumor biomarkers are not reflective of the heterogeneous cancer population. We evaluated NRG Oncology prostate cancer (PCa) clinical trials for demographic differences among patients with optional biospecimen collection (BC) consent and biospecimen submission (BSub). METHODS: Data from 19 NRG PCa clinical trials closed before 2015 were analyzed. Patients who consented to BC and completed BSub were evaluated by race, ethnicity, median income, area deprivation index (ADI; categorized as highest v lowest three quartiles), age at enrollment, site, and year of enrollment. T/chi-square tests were used for continuous/categorical variables, respectively, followed by logistic regression. RESULTS: Of the 15,648 randomized patients eligible for BC, 11,796 (75%) had specimens submitted. In all, 4,598 (82.2%) of 5,597 eligible patients consented for optional BC in nine clinical trials with a separate BC consent process (consent rates by race/ethnicity: 74.1% Black, 72.8% Hispanic/Latino, 83.8% White). A smaller proportion of Black and Hispanic/Latino patients consented to optional BC compared with those who did not (12.1% v 19.5% Black, P < .0001; 3.5% v 5.8% Hispanic, P = .0006). In univariable logistic regression models, high ADI (more socioeconomic disadvantage) was associated with a decreased likelihood for optional BC consent (odds ratio [OR], 0.67 [95% CI, 0.55 to 0.82]; P = .02), but not a decreased likelihood for BSub (OR, 0.74 [95% CI, 0.53 to 1.04]; P = .08). Multivariable models demonstrated that Black/Hispanic/Latino patients were less likely to consent to optional BC, and Black patients were less likely to have BSub (P < .05 for all). CONCLUSION: White/non-Hispanic patients and those with less socioeconomic disadvantage were more likely to consent to optional BC, whereas Black patients were less likely to have BSub. Targeted solutions are needed to improve biorepository representation so that precision medicine approaches better reflect the cancer population.

Aged↗