jhavlek

2020 AACR: Improved Tumor-Only Somatic Variant Calling Using a Gradient Boosted Machine Learning Algorithm Accurate identification of somatic variants in a tumor sample is often accomplished by utilizing a paired normal tissue sample from the same patient to enable the separation of private germline mutations from somatic variants. However, a paired normal sample is [...]

2020 AACR: A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome While the success of immune checkpoint blockade (ICB) has been hugely promising, it’s increasingly apparent that predicting response to immunotherapies requires a more comprehensive approach to tumor immunogenomic profiling. There [...]

2020 AACR: A Pan-Cancer Characterization of Both the Tumor and Micro Environment Highlights the Importance of an Integrated Approach for Immuno-Oncology A better understanding of the characteristics of cancer across different indications is required to drive the development of personalized treatments, inform therapy decisions, and improve outcomes. Integrating data from the tumor and the [...]

2020 AACR: Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome Comprehensive characterization of the tumor and tumor microenvironment (TME) can improve our understanding of tumor progression and treatment outcomes. In particular, quantification of the immune infiltrate has the potential to inform mechanisms of immune escape and predict response to therapy. Standard experimental [...]