Ketosynthase (KS) domains of assembly line polyketide synthases (PKSs) catalyze intermodular translocation of the growing polyketide chain as well as chain elongation via decarboxylative Claisen condensation. The mechanistic roles of ten conserved residues in the KS domain of Module 1 of the 6-deoxyerythronolide B synthase were interrogated via site-directed mutagenesis and extensive biochemical analysis. Although the C211A mutant at the KS active site exhibited no turnover activity, it was still a competent methylmalonyl-ACP decarboxylase. The H346A mutant exhibited reduced rates of both chain translocation and chain elongation, with a greater effect on the latter half-reaction. H384 contributed to methylmalonyl-ACP decarboxylation, whereas K379 promoted C–C bond formation. S315 played a role in coupling decarboxylation to C–C bond formation. These findings support a mechanism for the translocation and elongation half-reactions that provides a well-defined starting point for further analysis of the key chain-building domain in assembly line PKSs.

Background With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types. Methods Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression. Results In a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively. Conclusions We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

Introduction Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8–11 months. Methods Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype. Results We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Discussion Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-023-00650-5.

Cancers assume a variety of distinct histologies and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision making based on consensus guidelines such as NCCN is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for CUP patients, with a median survival of 8-11 months. Here we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-seq-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. We show that the Tempus TO model is 91% accurate when assessed on retrospectively and prospectively held out cohorts of containing 9,210 samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

Background: Tumors of unknown origin account for up to 5% of newly diagnosed cancers and the average survival time is 9 to 12 months from diagnosis. Establishing tumor type and subtype guides standard of care treatment for several NCCN targeted therapy guidelines. Methods: Targeted DNA sequencing for more than 500 cancer-associated genes and exome-capture RNA sequencing was carried out in more than 25,000 fresh frozen or paraffin embedded tumor samples, including both primary and metastatic tumors. Mutations, copy number variants, and viral sequences were detected from DNA sequencing while gene expression and fusion events were determined from RNA sequencing. We aimed to predict cancer type by utilizing multiple machine learning models trained on individual data types and harmonize predictions across multiple data types. Results: The transcriptome model predicts more than 60 unique diagnoses covering both solid and hematological cancers with >90% overall accuracy on a held-out test set. Of note, the model can accurately predict 10 subtypes of sarcoma and 6 subtypes of neuroendocrine tumors. Gene expression and splicing were the most informative data types, but a performant DNA-only model was also evaluated for application when only DNA data is available. Finally, we evaluated the model on an unlabeled cohort of poorly differentiated samples with inconclusive diagnosis. Conclusions: The incorporation of multiple modes of omics data can improve the interpretability and robustness of machine learning models to predict cancer diagnosis. Citation Format: Jackson Michuda, Benjamin Leibowitz, Shlomit Amar-Farkash, Crystal Bevis, Alessandra Breschi, Joshuah Kapilivsky, Catherine Igartua, Joshua S. Bell, Kyle A. Beauchamp, Kevin White, Martin Stumpe, Nike Beaubier, Timothy Taxter. Multimodal prediction of diagnosis for cancers of unknown primary [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5423.