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  • 1
    In: Cell, Elsevier BV, Vol. 183, No. 1 ( 2020-10), p. 197-210.e32
    Type of Medium: Online Resource
    ISSN: 0092-8674
    RVK:
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2305-2305
    Abstract: Background. We hypothesized that circulating tumor DNA (ctDNA) can be used as a prognostic biomarker, improve the assessment of response and enhance detection of minimal residual disease in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy (NAT). Methods. We analyzed data from 31 LARC patients treated at Memorial Sloan Kettering as part of the Organ Preservation in Rectal Adenocarcinoma (OPRA) phase II clinical trial. Patients had stage II or III rectal adenocarcinoma and received NAT including chemoradiation and chemotherapy. Patients without a clinical complete response (cCR) underwent surgical resection, while patients with a cCR were enrolled in a watch-and-wait protocol for organ preservation. Complete response (CR) after NAT was defined as either pathological complete response or a cCR sustained for ≥2 years. Disease-free survival (DFS) was measured from the start of NAT. Median follow-up was 5.41 years [range 2.96-8.38]. ctDNA analyses were performed using the C2i Genomics platform. A patient-specific molecular profile was created by performing whole-genome sequencing (WGS) of their tumor and matched normal DNA (40x coverage). WGS (20x coverage) was performed on plasma samples collected at baseline (before NAT), interval evaluation (halfway through NAT), re-staging evaluation (8 weeks after NAT) and follow-up (3-6 months after NAT). Results. Tumor was detected in plasma samples from 24/25 patients at baseline (96% sensitivity). The tumor fraction (TF) levels detected at baseline separated responders from non-responders (median TF 6.2e-4 vs 1.4e-3; p=0.055). Tumor detection at interval was associated with a lower rate of CR (25% vs. 75%, p=0.0095) and shorter time to recurrence (58.3% vs. 94.1% 3-year DFS, p=0.02). Tumor detection at follow-up was associated with a higher rate of recurrence (p=0.037) and tumor was detected at follow-up for all 5/5 patients who developed recurrence. Overall TF dynamics showed clearance of ctDNA down to the non-detection level throughout treatment in patients with a CR, while non-responders exhibited non-decreasing and often increasing estimates of ctDNA burden. Analysis of tissue WGS data identified multiple patients with colibactin associated mutational signatures, which provides additional insights into their cancer etiology. Conclusions. The WGS-based approach for ctDNA analysis exhibited very high sensitivity for detection at baseline. TF across multiple time points separated responders from non-responders, suggesting potential value as a prognostic marker. Detection of ctDNA at follow-up for all patients who recurred is indicative of potential clinical utility for treatment de-escalation in the context of organ preservation strategies. Citation Format: Francisco Sanchez-Vega, Chin-Tung Chen, Danielle Afterman, Dana Omer, Madison Darmofal, Ino de Bruijn, Walid K. Chatila, Matthew Drescher, Grittney Tam, Tomer Lauterman, Maja Kuzman, Santiago Gonzalez, Dunja Glavas, James Samdbeck, Dillon Maloney, Jurica Levatic, Sunil Deochand, Michael Yahalom, Ryan Ptashkin, Iman Tavassoly, Zohar Donenhirsh, Eric White, Ravi Kandasamy, Ury Alon, Michael F. Berger, Brian Loomis, Paz Polak, Boris Oklander, Asaf Zviran, Julio Garcia-Aguilar. Ultra-sensitive detection of circulating tumor DNA by whole-genome sequencing of blood samples from locally advanced rectal cancer patients receiving neoadjuvant therapy and enrolled in watch-and-wait strategies for organ preservation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2305.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 5440-5440
    Abstract: Background: Knowledge of a patient’s tumor type is essential for guiding clinical treatment decisions in cancer, but histologically-based diagnosis remains challenging for a subset of cancers. Genomic alterations are highly indicative of tumor type and can be used to build classifiers that predict diagnoses, but most genomic-based classification methods use whole genome sequencing (WGS) data which is not feasible for widespread clinical implementation at present. Clinical sequencing is typically performed using cancer gene panels that target individual mutations, often drivers, but previous tumor type classifiers developed using driver-based features alone perform poorly. We hypothesize that a classifier developed using state-of-the-art deep-learning methods and a sufficiently large training cohort would be able to overcome previous accuracy limitations and support the development of a clinically-relevant tumor type prediction model. Methods: We present Deep Genome-Derived Diagnosis (GDD-ENS), an ensemble-based deep-learning tumor type classification method trained using data from cancer gene panel sequencing. We specifically use data from MSK-IMPACT, an FDA-authorized clinical sequencing assay that reports genomic alterations including mutations, indels, copy number alterations, and gene fusions across 505 cancer-associated genes. We aggregated a discovery cohort of 35,372 patients with solid tumors profiled with MSK-IMPACT across 38 common cancer types and used this set to generate 4,487 somatic mutation features for development. Results: GDD-ENS achieves 78.8% accuracy on a held out validation cohort of 6971 patients. For the 71.9% of predictions assigned a high confidence by the model, accuracy increases to 92.7%, rivaling WGS-based models. We use Shapley Values to report prediction-specific feature importance, and aggregate them across cancer types to show GDD-ENS identifies known cancer type-genomic alteration trends. GDD-ENS also, with high accuracy, identifies patients with cancer types not included in the 38 common types using metrics derived from ensemble statistics. For patients where non-genomic information could further guide predictions, we implement a customizable prediction-specific adaptive prior distribution and report improved accuracy after adjusting predictions to account for features such as metastatic sample biopsy site. Finally, we apply GDD-ENS to a set of 1,123 patients with Cancers of Unknown Primary (CUP) and return high confidence predictions for 49% of these patients, in some cases matching predictions on CUP samples with diagnoses that were later confirmed through additional sampling and disease progression. Conclusions: Integrating GDD-ENS into prospective clinical sequencing workflows will enable clinically-relevant tumor type predictions that can guide treatment decisions in real-time. Citation Format: Madison Darmofal, Shalabh Suman, Gurnit Atwal, Jie-Fu Chen, Anna Varghese, Jason C. Chang, Anoop Balakrishnan Rema, Aijazuddin Syed, Quaid Morris, Michael Berger. Deep-learning model for tumor type classification enables enhanced clinical decision support in cancer diagnosis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5440.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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