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IMPORTANCERCC encompasses a set of histologically distinct cancers with a high estimated genetic heritability, of which only a portion is currently explained. Previous rare germline variant studies in RCC have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification that may significantly impact the interpretation and discovery of certain candidate risk genes.OBJECTIVETo evaluate the enrichment of germline PVs in established cancer-predisposing genes (CPGs) in clear cell and non-clear cell RCC patients compared to cancer-free controls using approaches that account for population stratification and to identify unconventional types of germline RCC risk variants that confer an increased risk of developing RCC.DESIGN, SETTING, AND PARTICIPANTSIn 1,436 unselected RCC patients with sufficient data quality, we systematically identified rare germline PVs, cryptic splice variants, and copy number variants (CNVs). From this unselected cohort, 1,356 patients were ancestry-matched with 16,512 cancer-free controls, and gene-level enrichment of rare germline PVs were assessed in 143 CPGs, followed by an investigation of somatic events in matching tumor samples.MAIN OUTCOMES AND MEASURESGene-level burden of rare germline PVs, identification of secondary somatic events accompanying the germline PVs, and characterization of less-explored types of rare germline PVs in RCC patients.RESULTSIn clear cell RCC (n = 976 patients), patients exhibited significantly higher prevalence of PVs inVHLcompared to controls (OR: 39.1, 95% CI: 7.01-218.07, p-value:4.95e-05, q-value:0.00584). In non-clear cell RCC (n = 380 patients), patients carried enriched burden of PVs inFH(OR: 77.9, 95% CI: 18.68-324.97, p-value:1.55e-08, q-value: 1.83e-06) andMET(OR: 1.98e11, 95% CI: 0-inf, p-value: 2.07e-05, q-value: 3.50e-07). In aCHEK2-focused analysis with European cases and controls, clear cell RCC patients (n=906 European patients) harbored nominal enrichment of the previously reported low-penetranceCHEK2variants, p.Ile157Thr (OR:1.84, 95% CI: 1.00-3.36, p-value:0.049) and p.Ser428Phe (OR:5.20, 95% CI: 1.00-26.40, p-value:0.045) while non-clear cell RCC patients (n=295 European patients) exhibited nominal enrichment ofCHEK2LOF germline PVs (OR: 3.51, 95% CI: 1.10-11.10, p-value: 0.033). RCC patients with germline PVs inFH, MET, and VHLexhibited significantly earlier age of cancer onset compared to patients without any germline PVs in CPGs (Mean: 46.0 vs 60.2 years old, Tukey adjusted p-value < 0.0001), and more than half had secondary somatic events affecting the same gene (n=10/15, 66.7%, 95% CI: 38.7-87.0%). Conversely, patients with rare germline PVs inCHEK2exhibited a similar age of disease onset to patients without any identified germline PVs in CPGs (Mean: 60.1 vs 60.2 years old, Tukey adjusted p-value: 0.99), and only 30.4% of the patients carried secondary somatic events inCHEK2(n=7/23, 95% CI: 14.1-53.0%). Finally, rare pathogenic germline cryptic splice variants underexplored in RCC were identified inSDHAandTSC1, and rare pathogenic germline CNVs were found in 18 patients, including CNVs inFH, SDHA, andVHL.CONCLUSIONS AND RELEVANCEThis systematic analysis supports the existing link between several RCC risk genes and elevated RCC risk manifesting in earlier age of RCC onset. Our analysis calls for caution when assessing the role of germline PVs inCHEK2due to the burden of founder variants with varying population frequency in different ancestry groups. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants, such as cryptic splice variants and CNVs.KEY PointsQuestionCan we improve the assessment of germline genetic risk determinants for clear cell and non-clear cell renal cell carcinoma (RCC) with approaches that are aware to population-stratification and RCC histological subtypes?FindingsIn this systematic case-control study of 1,356 RCC patients and 16,512 ancestry-matched cancer-free controls strictly controlling for population stratification, clear-cell RCC patients exhibited a significantly higher prevalence of rare germline pathogenic variants (PVs) inVHL, and non-clear cell RCC patients carried significantly more rare germline PVs inFHandMET. European clear-cell RCC patients harbored a nominally significant enrichment of two low-penetranceCHEK2variants (p.Ser428Phe and p.Ile157Thr) while European non-clear RCC patients carried a nominally significant enrichment of rare germline loss-of-function (LOF) variants inCHEK2. Subsequent somatic analyses identified secondary somatic events in genes significantly enriched for germline PVs (VHL, FH, MET), and these variant carriers presented with earlier age of disease onset, butCHEK2germline variant carriers harbored relatively fewer somatic events inCHEK2and did not present with earlier age of onset. Finally, we identified 6 RCC patients with rare germline cryptic splice and copy number variants that impacted known kidney cancer risk genes, increasing the diagnostic yield of pathogenic variants in RCC risk genes from 2.1% to 2.5%.MeaningClear and non-clear RCCs have distinct germline pathogenic variant enrichment patterns and somatic variants. Accurate risk assessment ofCHEK2in RCC requires careful adjustment for population stratification. In addition, previously underappreciated forms of germline variants may explain a portion of the missing heritability in RCC.

Tumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles. Van Allen and colleagues develop a data-integration framework with an underlying knowledge base supporting clinical decision making and also serving as a hypothesis-generating platform, which the authors benchmark and validate across several retrospective cohorts and a prospective precision oncology trial.

Although papillary thyroid carcinoma (PTC) is the most frequent endocrine tumor with a generally excellent prognosis, a patient developed a clinically aggressive PTC eleven years after receiving platinum chemotherapy for ovarian endometrioid adenocarcinoma. Germline and somatic analyses of multi-temporal and multi-regional molecular profiles indicated that ovarian and thyroid tumors did not share common genetic alterations. PTC tumors had driver events associated with aggressive PTC behavior, an RBPMS-NTRK3 fusion and a TERT promoter mutation. Spatial and temporal genomic heterogeneity analysis indicated a close link between anatomical locations and molecular patterns of PTC. Mutational signature analyses demonstrated a molecular footprint of platinum exposure, and that aggressive molecular drivers of PTC were linked to prior platinum-associated mutagenesis. This case provides a direct association between platinum chemotherapy exposure and secondary solid tumor evolution, in specific aggressive thyroid carcinoma, and suggests that uniform clinical assessments for secondary PTC after platinum chemotherapy may warrant further evaluation.

RNA-binding proteins (RBPs) are critical regulators of gene expression and RNA processing that are required for gene function. Yet the dynamics of RBP regulation in single cells is unknown. To address this gap in understanding, we developed STAMP (Surveying Targets by APOBEC-Mediated Profiling), which efficiently detects RBP–RNA interactions. STAMP does not rely on ultraviolet cross-linking or immunoprecipitation and, when coupled with single-cell capture, can identify RBP-specific and cell-type–specific RNA–protein interactions for multiple RBPs and cell types in single, pooled experiments. Pairing STAMP with long-read sequencing yields RBP target sites in an isoform-specific manner. Finally, Ribo-STAMP leverages small ribosomal subunits to measure transcriptome-wide ribosome association in single cells. STAMP enables the study of RBP–RNA interactomes and translational landscapes with unprecedented cellular resolution. Surveying targets by APOBEC-mediated profiling identifies binding sites of RBPs by C-to-U RNA editing. STAMP is isoform specific, can be multiplexed and enables detection of ribosome association in single cells.

The COVID-19 pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The betacoronvirus has a positive sense RNA genome which encodes for several RNA binding proteins. Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs in authentic virus-infected cells. SARS-CoV-2 proteins, NSP8, NSP12, and nucleocapsid display distinct preferences to specific regions in the RNA viral genome, providing evidence for their shared and separate roles in replication, transcription, and viral packaging. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4773 unique host coding RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 and ORF9c, whose RNA substrates are associated with pathways in protein N-linked glycosylation ER processing and mitochondrial processes. Furthermore, siRNA knockdown of host genes targeted by viral proteins in human lung organoid cells identify potential antiviral host targets across different SARS-CoV-2 variants. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens cytokine productions, including interleukin-1α/β. Our viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics.

The development and functional potential of metazoan cells is dependent on combinatorial roles of transcriptional enhancers and promoters. Macrophages provide exceptionally powerful model systems for investigation of mechanisms underlying the activation of cell-specific enhancers that drive transitions in cell fate and cell state. Here, we review recent advances that have expanded appreciation of the diversity of macrophage phenotypes in health and disease, emphasizing studies of liver, adipose tissue, and brain macrophages as paradigms for other tissue macrophages and cell types. Studies of normal tissue-resident macrophages and macrophages associated with cirrhosis, obese adipose tissue, and neurodegenerative disease illustrate the major roles of tissue environment in remodeling enhancer landscapes to specify the development and functions of distinct macrophage phenotypes. We discuss the utility of quantitative analysis of environment-dependent changes in enhancer activity states as an approach to discovery of regulatory transcription factors and upstream signaling pathways.

The COVID-19 pandemic was caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs. SARS-CoV-2 proteins, NSP8 and NSP12, bind to specific regions in the RNA viral genome, providing evidence for their central and potential roles in replication, transcription and genome recombination. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4,821 unique host RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 which upregulates mitochondrial and N-linked glycosylation proteins. Furthermore, siRNA knockdown of host genes targeted by the viral proteins in human lung organoid cells demonstrates substantial antiviral effects. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens inflammatory responses such as interleukin-1α/β production. Our extensive viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics. Funding Information: The work has been supported by Emergency COVID-19 Research Seed Funding (#R00RG2636) from the University of California Office of the President. This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (#S10 OD026929). J.S.X as a visiting fellow is partially supported by Agency for Science, Technology and Research (A*STAR) and Industrial Alignment Fund Pre-Positioning (IAF-PP) grant H17/01/a0/012. ELVN is supported by the NHGRI (R00HG009530). Declaration of Interests: J.S.X, F.E.T, J.C.S and G.W.Y declare a pending patent application. ELVN is co-founder, member of the Board of Directors, on the SAB, equity holder, and paid consultant for Eclipse BioInnovations. ELVN’s interests have been reviewed and approved by the Baylor College of Medicine in accordance with its conflict of interest policies. The authors declare no other competing interests Ethics Approval Statement: This study protocol was approved by the Institutional Review Board of UCSD's Human Research Protections Program (181180).

Individual tumor molecular profiling is routinely used to detect single gene-variant (“first-order”) genomic alterations that may inform therapeutic actions -- for instance, a tumor with aBRAFp.V600E variant might be considered for RAF/MEK inhibitor therapy. Interactions between such first-order events (e.g., somatic-germline) and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these “second order” alterations are not yet generally accounted for in clinical interpretation algorithms and knowledge bases. Here, we introduce the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. We compared MOAlmanac to first-order interpretation methodology in multiple retrospective patient cohorts and observed that the inclusion of preclinical and inferential evidence as well as second-order molecular features increased the number of nominated clinical hypotheses. MOAlmanac also performed matchmaking between patient molecular profiles and cancer cell lines to further expand individualized clinical actionability. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles. Overall, we present a novel computational method to perform integrative clinical interpretation of individualized molecular profiles. MOAlmanc increases clinical actionability over conventional approaches by considering second-order molecular features and additional evidence sources, and is available as an open-source framework.

Importance Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection. Objective To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer. Design, setting, and participants A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017. Exposures Germline variant detection using standard or deep learning methods. Main outcomes and measures The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach. Results The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, -1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, -2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]). Conclusions and relevance Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.

1518 Background: Germline genetic analysis is an essential tool for implementing precision cancer prevention and treatment. However, only a small fraction of cancer patients, even those with features suggestive of a cancer-predisposition syndrome, have detectable pathogenic germline events, which may in part reflect incomplete pathogenic variant detection by current gold-standard methods. Here, we leveraged deep learning approaches to expand the diagnostic utility of genetic analysis in cancer patients. Methods: Systematic analysis of the detection rate of pathogenic cancer-predisposition variants using the standard clinical variant detection method and a deep learning approach in germline whole-exome sequencing data of 2367 cancer patients (n = 1072 prostate cancer, 1295 melanoma). Results: Of 1072 prostate cancer patients, deep learning variant detection identified 16 additional prostate cancer patients with clinically actionable pathogenic cancer-predisposition variants that went undetected by the gold-standard method (198 vs. 182), yielding higher sensitivity (94.7% vs. 87.1%), specificity (64.0% vs. 36.0%), positive predictive value (95.7% vs. 91.9%), and negative predictive value (59.3% vs. 25.0%). Similarly, germline genetic analysis of 1295 melanoma patients showed that, compared with the standard method, deep learning detected 19 additional patients with validated pathogenic variants (93 vs. 74) with fewer false-positive calls (78 vs. 135) leading to a higher diagnostic yield. Collectively, deep learning identified one additional patient with a pathogenic cancer-risk variant, that went undetected by the standard method, for every 52 to 67 cancer patients undergoing germline analysis. Superior performance of deep learning, for detecting putative loss-of-function variants, was also seen across 5197 clinically relevant Mendelian genes in these cohorts. Conclusions: The gold-standard germline variant detection method, universally used in clinical and research settings, has significant limitations for identifying clinically relevant pathogenic disease-causing variants. We determined that deep learning approaches have a clinically significant increase in the diagnostic yield across commonly examined Mendelian gene sets.

Immune checkpoint blockade (ICB) therapies, which potentiate the body’s natural immune response against tumor cells, have shown immense promise in the treatment of various cancers. Currently, tumor mutational burden (TMB) and programmed death ligand 1 (PD-L1) expression are the primary biomarkers evaluated for clinical management of cancer patients across histologies. However, the wide range of responses has demonstrated that the specific molecular and genetic characteristics of each patient’s tumor and immune system must be considered to maximize treatment efficacy. Here, we review the various biological pathways and emerging biomarkers implicated in response to PD-(L)1 and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) therapies, including oncogenic signaling pathways, human leukocyte antigen (HLA) variability, mutation and neoantigen burden, microbiome composition, endogenous retroviruses (ERV), and deficiencies in chromatin remodeling and DNA damage repair (DDR) machinery. We also discuss several mechanisms that have been observed to confer resistance to ICB, such as loss of phosphatase and tensin homolog (PTEN), loss of major histocompatibility complex (MHC) I/II expression, and activation of the indoleamine 2,3-dioxygenase 1 (IDO1) and transforming growth factor beta (TGFβ) pathways. Clinical trials testing the combination of PD-(L)1 or CTLA-4 blockade with molecular mediators of these pathways are becoming more common and may hold promise for improving treatment efficacy and response. Ultimately, some of the genes and molecular mechanisms highlighted in this review may serve as novel biological targets or therapeutic vulnerabilities to improve clinical outcomes in patients.

Secondary oral squamous cell carcinoma (OSCC) is a late complication in allogeneic hematopoietic stem cell transplantation (HSCT) patients, but little is known about long-term outcomes and prognostication. Additionally, molecular alterations and immunologic insights unique to this disease remain largely unexplored.We present a cohort of 31 patients with post-HSCT OSCC and reported on clinicopathologic predictors of survival. Whole-exome sequencing was performed on 6 (19%) matched pairs of peripheral blood (post-conditioning, pre-HSCT) and tumor samples. The entire cohort had archival tumor available for immunoprofiling with PD-1/L1 immunohistochemistry.Five-year overall survival (OS) was 57% (95% CI: 46.1-69.8) with a median disease-free survival (DFS) of 13.3 months. Advanced initial staging, a buccal or oral tongue subsite, chronic oral graft-versus-host disease (GVHD) and smoking all negatively impacted survival. High tumor mutational burden (TMB) (median 11.3 vs. 5.0) and unique mutational signatures were noted between unrelated and related donor groups - with a strong correlation between infiltrating PD-1+ lymphocytes and TMB (R = 0.98, p 0.01). Some differences were observed when comparing commonly mutated genes among our cohort and TCGA, with a predominance of TP53 events.Survival outcomes appear similar in HSCT survivors with OSCC compared with non-HSCT OSCC populations. We identified somatic alterations in genes with therapeutic potential unique to this subpopulation of oral cancers.

Background: Tumor molecular profiling is increasingly used to detect first-order genomic alterations associated with therapeutic actions (e.g. BRAF V600E & RAF/MEK inhibition). Simultaneously, more complex molecular features are being discovered and applied to clinical scenarios (e.g. mutational signatures, somatic-germline interactions). As patients receive expanded profiling, such as clinical whole-exome and RNA sequencing, novel algorithms are needed to integrate interpretation of multiple data modalities. Furthermore, the clinical-preclinical gap continues to widen as data from high-throughput screens of cancer cell lines are generated without accessibility at the point of care. Here, we introduce a paired interpretation algorithm and knowledge system for cancer genomic data, the Molecular Oncology Almanac, to inform treatment decisions through rapid assessment of tumor actionability. Methods: We implemented a cloud-based interpretation algorithm that annotates and evaluates variants from WES and RNA-seq (SNVs from WES and RNA-seq, InDels, CNAs, and fusions) and infers additional features such as mutational burden, mutational signatures, MSI, somatic-germline interactions, and aneuploidy. Predictive implication levels were assigned to reflect confidence in the database's catalogued relationships to therapeutic response and prognosis for each molecular feature. We also developed a patient-preclinical matchmaker function to expand the theoretical therapeutic modalities for any given patient. Towards timeliness of updates and knowledge system accessibility, we developed API endpoints, a browser extension for suggesting citations, and workflows in the FireCloud framework. Results: A total of 260 patients with metastatic castration-resistant prostate cancer (n=150) and metastatic melanoma (n=110) were evaluated with 569 alteration-action relationships catalogued in the Molecular Oncology Almanac. Overall 80% of patients had at least one alteration suggesting therapeutic sensitivity based on FDA approval, clinical trials, or studies in humans; which increased to 95.8% by also considering preclinical and inferential associations. Per patient, the matchmaker function on average highlighted 1.56 additional therapies that would not have otherwise been nominated. At least one feature associated with resistance or prognosis was observed in 85% and 90% of patients, respectively. Conclusion: Clinical actionability of sequence data was increased by including integrative molecular profiling of DNA and RNA, global molecular features, and preclinical alteration-action relationships. Increased accessibility of clinical interpretation through our cloud-based web portals and API endpoints may aid in sample contextualization. Source code and a web portal for this project are available at moalmanac.org. Citation Format: Brendan Reardon, Nicholas Moore, Nathanael Moore, Eric Kofman, Eliezer Van Allen. A molecular oncology almanac for integrative clinical interpretation of molecular profiles to guide precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2470.

Background: Clinical interpretation algorithms and knowledge bases (e.g. PHIAL, OncoKB) are being used for clinical decision making. These approaches are typically based generally limited to “first order” genomic relationships (e.g. BRAFV600E & RAF/MEK inhibition). The increasing complexity of molecular data generated at the point of care, including whole exome and transcriptome results, along with the expanded therapeutic landscape in cancer, necessitate novel algorithms to enable robust and modern clinical interpretation of a cancer patient's molecular data to accelerate precision cancer medicine. We introduce a paired feature-based clinical interpretation algorithm and knowledge system for cancer genomic data to inform treatment decisions at the point of care and provide researchers with rapid assessment of tumor actionability. Methods: We expand upon PHIAL to predict actionability based on first-order genomics using SNVs (from both whole-exome sequencing and bulk RNA-seq), InDels, SCNAs, and fusions to further to infer global features of an individual tumor such as mutational burden, mutational signature profile, MSI-status, somatic-germline interaction, and connections between events. Predictive implication values were assigned to reflect the validities of the database's drug sensitivity, resistance, and prognostic claims. Results: We benchmarked the feature-based approach against PHIAL & TARGET across two cohorts that include both whole exome and transcriptomic data - 150 castrate resistant prostate cancers and 110 metastatic melanomas. PHIAL identified 1281 putatively actionable or biologically relevant alterations, with a median of 3 events per patient and 94% of patients having at least 1 event. The feature-based approach identified 1767 putatively actionable or biologically relevant variants or features, with a median of 5 events per patient and 97% of patients having at least 1 event. Of the these patients, 27% had at least 1 variant associated with an FDA-approved therapy and 18% had events associated with a clinical trial. It also identified 29% of samples as having a putatively actionable global feature. Conclusion: A DNA and RNA based interpretation method was able to identify and rank more putatively actionable first-order genomic alterations than PHIAL & TARGET, while also providing insight to global features of individual tumors. Increased accessibility of clinical interpretation through our cloud-based web portals and genomic reports may aid in sample contextualization, especially at the point of care. Source code and an interactive web portal for this project are available at chips.broadinstitute.org. Citation Format: Brendan Reardon, Nathanael Moore, Eric Kofman, Eliezer Mendell Van Allen. Feature-based clinical interpretation of whole exome and transcriptome data for precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2286.

Interventions for behavior change in domestic energy consumption rely critically on energy usage data. To obtain this data, collection systems must be established. Pervasive sensing systems enable such monitoring, but populating homes with sensors is challenging. We offer an alternative to feedback approaches that depend on the assumption that users are motivated by energy data in its raw state. Physical Experiential Technology Systems (PETS) is a behavior-and sensor-based platform supporting rich experiences and the diffusion of sensors in homes. In this paper, we present our novel approach to building sensor feedback systems and our initial product concepts.