A platform-independent AI tumor lineage and site (ATLAS) classifier.

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

Machine Learning & Molecular Radiation Tumor Biomarkers.

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.

A new GTSeq resource to facilitate multijurisdictional research and management of walleye Sander vitreus.

Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research, which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping-in-thousands (GTSeq) is one approach used to create standardized marker panels for nonmodel organisms. Here, we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (Sander vitreus) from the Great Lakes region of North America. High genome-coverage sequencing conducted using RAD capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen, which were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave-one-out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false-positive rates of parent-offspring and full-sibling kinship identification were found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region.

Intrinsic Molecular Subtypes of Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Although numerous biology-driven subtypes have been described previously in metastatic castration-resistant prostate cancer (mCRPC), unsupervised molecular subtyping based on gene expression has been less studied, especially using large cohorts. Thus, we sought to identify the intrinsic molecular subtypes of mCRPC and assess molecular and clinical correlates in the largest combined cohort of mCRPC samples with gene expression data available to date. EXPERIMENTAL DESIGN: We combined and batch-effect corrected gene expression data from four mCRPC cohorts from the Fred Hutchinson Cancer Research Center (N = 157), a small-cell neuroendocrine (NE) prostate cancer (SCNC)-enriched cohort from Weill Cornell Medicine (N = 49), and cohorts from the Stand Up 2 Cancer/Prostate Cancer Foundation East Coast Dream Team (N = 266) and the West Coast Dream Team (N = 162). RESULTS: Hierarchical clustering of RNA-sequencing data from these 634 mCRPC samples identified two distinct adenocarcinoma subtypes, one of which (adeno-immune) was characterized by higher gene expression of immune pathways, higher CIBERSORTx immune scores, diminished ASI benefit, and non-lymph node metastasis tropism compared with an adeno-classic subtype. We also identified two distinct subtypes with enrichment for an NE phenotype, including an NE-liver subgroup characterized by liver metastasis tropism, PTEN loss, and APC and SPOP mutations compared with an NE-classic subgroup. CONCLUSIONS: Our results emphasize the heterogeneity of mCRPC beyond currently accepted molecular phenotypes, and suggest that future studies should consider incorporating transcriptome-wide profiling to better understand how these differences impact treatment responses and outcomes.

Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone.

DNA mutations in specific genes can confer preferential benefit from drugs targeting those genes. However, other molecular perturbations can "phenocopy" pathogenic mutations, but would not be identified using standard clinical sequencing, leading to missed opportunities for other patients to benefit from targeted treatments. We hypothesized that RNA phenocopy signatures of key cancer driver gene mutations could improve our ability to predict response to targeted therapies, despite not being directly trained on drug response. To test this, we built gene expression signatures in tissue samples for specific mutations and found that phenocopy signatures broadly increased accuracy of drug response predictions in-vitro compared to DNA mutation alone, and identified additional cancer cell lines that respond well with a positive/negative predictive value on par or better than DNA mutations. We further validated our results across four clinical cohorts. Our results suggest that routine RNA sequencing of tumors to identify phenocopies in addition to standard targeted DNA sequencing would improve our ability to accurately select patients for targeted therapies in the clinic.

Longitudinal Molecular Profiling of Circulating Tumor Cells in Metastatic Renal Cell Carcinoma.

PURPOSE: Liquid biopsies in metastatic renal cell carcinoma (mRCC) provide a unique approach to understand the molecular basis of treatment response and resistance. This is particularly important in the context of immunotherapies, which target key immune-tumor interactions. Unlike metastatic tissue biopsies, serial real-time profiling of mRCC is feasible with our noninvasive circulating tumor cell (CTC) approach. METHODS: We collected 457 longitudinal liquid biopsies from 104 patients with mRCC enrolled in one of two studies, either a prospective cohort or a phase II multicenter adaptive immunotherapy trial. Using a novel CTC capture and automated microscopy platform, we profiled CTC enumeration and expression of HLA I and programmed cell death-ligand 1 (PD-L1). Given their diametric immunological roles, we focused on the HLA I to PD-L1 ratio (HP ratio). RESULTS: Patients with radiographic responses showed significantly lower CTC abundances throughout treatment. Furthermore, patients whose CTC enumeration trajectory was in the highest quartile (> 0.12 CTCs/mL annually) had shorter overall survival (median 17.0 months v 21.1 months, P < .001). Throughout treatment, the HP ratio decreased in patients receiving immunotherapy but not in patients receiving tyrosine kinase inhibitors. Patients with an HP ratio trajectory in the highest quartile (≥ 0.0012 annually) displayed significantly shorter overall survival (median 18.4 months v 21.2 months, P = .003). CONCLUSION: In the first large longitudinal CTC study in mRCC to date to our knowledge, we identified the prognostic importance of CTC enumeration and the change over time of both CTC enumeration and the HP ratio. These insights into changes in both tumor burden and the molecular profile of tumor cells in response to different treatments provide potential biomarkers to predict and monitor response to immunotherapy in mRCC.

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

The ghosts of propagation past: haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus).

Stocking of fish is an important tool for maintaining fisheries but can also significantly alter population genetic structure and erode the portfolio of within-species diversity that is important for promoting resilience and adaptability. Walleye (Sander vitreus) are a highly valued sportfish in the midwestern United States, a region characterized by postglacial recolonization from multiple lineages and an extensive history of stocking. We leveraged genomic data and recently developed analytical approaches to explore the population structure of walleye from two midwestern states, Minnesota and Wisconsin. We genotyped 954 walleye from 23 populations at ~20,000 loci using genotyping by sequencing and tested for patterns of population structure with single-SNP and microhaplotype data. Populations from Minnesota and Wisconsin were highly differentiated from each other, with additional substructure found in each state. Population structure did not consistently adhere to drainage boundaries, as cases of high intra-drainage and low inter-drainage differentiation were observed. Low genetic structure was observed between populations from the upper Wisconsin and upper Chippewa river watersheds, which are found as few as 50 km apart and were likely homogenized through historical stocking. Nevertheless, we were able to differentiate these populations using microhaplotype-based co-ancestry analysis, providing increased resolution over previous microsatellite studies and our other single SNP-based analyses. Although our results illustrate that walleye population structure has been influenced by past stocking practices, native ancestry still exists in most populations and walleye populations may be able to purge non-native alleles and haplotypes in the absence of stocking. Our study is one of the first to use genomic tools to investigate the influence of stocking on population structure in a nonsalmonid fish and outlines a workflow leveraging recently developed analytical methods to improve resolution of complex population structure that will be highly applicable in many species and systems.

A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms.

Targeted amplicon sequencing methods, such as genotyping-in-thousands by sequencing (GT-seq), facilitate rapid, accurate, and cost-effective analysis of hundreds of genetic loci in thousands of individuals. Development of GT-seq panels is nontrivial, but studies describing trade-offs associated with different steps of GT-seq panel development are rare. Here, we construct a dual-purpose GT-seq panel for walleye (Sander vitreus), discuss trade-offs associated with different development and genotyping approaches, and provide suggestions for researchers constructing their own GT-seq panels. Our GT-seq panel was developed using an ascertainment set consisting of restriction site-associated DNA data from 954 individuals sampled from 23 populations in Minnesota and Wisconsin, USA. We conducted simulations to test the utility of all loci for parentage analysis and genetic stock identification and designed 600 primer pairs to maximize joint accuracy for these analyses. We then performed three rounds of primer optimization to remove loci that overamplified and our final panel consisted of 436 loci. We also explored different approaches for DNA extraction, multiplexed polymerase chain reaction (PCR) amplification, and cleanup steps during the GT-seq process and discovered the following: (i) inexpensive Chelex extractions performed well for genotyping; (ii) the exonuclease I and shrimp alkaline phosphatase (ExoSAP) procedure included in some current protocols did not improve results substantially and was probably unnecessary; and (iii) it was possible to PCR amplify panels separately and combine them prior to adapter ligation. Well-optimized GT-seq panels are valuable resources for conservation genetics and our findings and suggestions should aid in their construction in myriad taxa.

Attack of the PCR clones: Rates of clonality have little effect on RAD-seq genotype calls.

Interpretation of high-throughput sequence data requires an understanding of how decisions made during bioinformatic data processing can influence results. One source of bias that is often cited is PCR clones (or PCR duplicates). PCR clones are common in restriction site-associated sequencing (RAD-seq) data sets, which are increasingly being used for molecular ecology. To determine the influence PCR clones and the bioinformatic handling of clones have on genotyping, we evaluate four RAD-seq data sets. Data sets were compared before and after clones were removed to estimate the number of clones present in RAD-seq data, quantify how often the presence of clones in a data set causes genotype calls to change compared to when clones were removed, investigate the mechanisms that lead to genotype call changes and test whether clones bias heterozygosity estimates. Our RAD-seq data sets contained 30%-60% PCR clones, but 95% of RAD-tags had five or fewer clones. Relatively few genotypes changed once clones were removed (5%-10%), and the vast majority of these changes (98%) were associated with genotypes switching from a called to no-call state or vice versa. PCR clones had a larger influence on genotype calls in individuals with low read depth but appeared to influence genotype calls at all loci similarly. Removal of PCR clones reduced the number of called genotypes by 2% but had almost no influence on estimates of heterozygosity. As such, while steps should be taken to limit PCR clones during library preparation, PCR clones are likely not a substantial source of bias for most RAD-seq studies.