Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types. Plasma ctDNA is a promising method to determine patient outcome in multiple cancer types. Here, the authors use shallow WGS to create machine learning signatures to identify tumor phenotypes and predict therapy response in patients with metastatic breast cancer. Detection of somatic gene mutations, amplifications, and gene fusions allows the delivery of targeted therapies in multiple cancer-types, such as lung cancer, colorectal, melanoma and breast cancer1. In addition, detection of a high number of somatic mutations , or a microsatellite instability-high phenotype, can help identify candidates for anti-PD1/PDL1 immune checkpoint inhibitors3. Importantly, sequencing of tumor DNA in blood samples  allows an easy access to some tumor-based genetic information at any given timepoint and can replace a tumor tissue biopsy in some cases, thus avoiding delays and complications of a solid tumor invasive biopsy procedure, which can be quite challenging sometimes in the metastatic setting.Sequencing of tumor DNA has brought many new biomarkers and possibilities to precision oncology1. However, cancer is highly complex and additional biological information is likely needed to refine the prediction of patients´ prognosis and/or treatment benefit4. Breast cancer is the perfect example since RNA-based profiling tests provide clinical and biological useful information beyond individual somatic gene mutations or amplifications such as PIK3CA or ERBB2 8. In early disease, multi-gene RNA-based prognostic assays  are available and recommended by clinical guidelines. In advanced disease, RNA-based profiling is becoming a promising prognostic and predictive tool9. Unfortunately, tissue samples in patients with advanced disease are not readily available and, even so, the type of metastatic organ or site can compromise the expression patterns obtained from bulk RNA and might not reflect the intra-patient tumor heterogeneity.Identification of single tumor DNA alterations can be clinically useful10. Specifically, we found 150 multi-feature DNA-based signatures tracking a variety of breast cancer biological processes, including proliferation status, clinical ER tumor status, tumor histology, and estrogen-signaling pathway activity, with a high predictive performance 10. This finding opened the possibility to use DNA sequencing of ctDNA in blood to capture clinically relevant information beyond single genetic alterations and tumor fraction, the latter being associated with patient’s prognosis11. Our approach could be highly relevant in the metastatic setting, where ctDNA might be the only readily available genetic material from tumors.To help overcome these challenges, we previously reported a supervised learning integrative computational approach to predict RNA-based tumor expression signatures values using data from DNA copy-number alterationsHere, we hypothesized that (1) DNA-based signatures tracking breast cancer biological processes can be detected in ctDNA and provide clinically useful information and (2) DNA-signatures detected in plasma and tissue can help identify tumor subtypes within hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancer. Our work demonstrates that complex tumor phenotypic traits can be identified in ctDNA and might be clinically relevant in advanced breast cancer. Our ctDNA-based multi-gene signature approach opens potential avenues for discovering and implementing biomarkers in oncology, especially in the advanced disease setting where liquid biopsies are becoming commonplace.10 on 0.5X shallow whole genome sequencing (shWGS) data from a first dataset, hereafter Plasma-1 cohort  were from 174 patients with advanced HR+/HER2- breast cancer. Samples from the same patient were obtained at different timepoints. Additional samples from the other clinical subtypes were also assayed including 19 plasma samples from 16 patients with advanced HER2-positive (HER2+) disease, 17 plasma samples from 16 patients with advanced triple-negative breast cancer (TNBC) and 1 plasma sample from 1 patient with advanced disease but unknown HR and HER2 status.To demonstrate that ctDNA can capture complex tumor phenotypes, 150 previously defined multi-feature DNA-based signatures  had a Tumor cell Fraction (TF) of ≥3% , indicating presence of tumor, according to the ichorCNA tools10 Fig. . Of note12. As expected, this normalization step decreased the strength of association between TF and the number of altered copy-number segments, and between TF and each ctDNA-based signature score  Fig. . In addin = 27) or more than 8 weeks (n = 27). Genome-wide copy number data was obtained from formalin-fixed paraffin-embedded (FFPE) tumors using a capture-based panel, and from ctDNA using shWGS (see methods). Across all 150 signatures, the average correlation coefficient between tumor and plasma was 0.40 (range 0.02 to 0.66) and 40 signatures (26.7%) had a correlation coefficient ≥0.50. When the correlations were evaluated in the 27 cases where plasma and tumor were obtained within a timeframe of 8.0 weeks, the number of signatures with a correlation coefficient ≥0.50 was 63 , including 36 of the 40 (90%) signatures with a correlation coefficient ≥0.50 in all patients  versus tumor tissue DNA across 54 patients with available paired sample-types obtained within a timeframe of 8-weeks -based data from plasma allows the identification of similar biological states as in tissue, we calculated the intra-patient correlation of the CNA-based signals of 514 DNA segments across 54 paired samples (tumor tissue versus plasma). Overall, 57% of patients had a correlation coefficient between plasma and tissue >0.50, which increased to 83% when we evaluated 29 patients with plasma TF >10%. In these 29 patients with a plasma TF >10%, 59 and 24% had a correlation coefficient of >0.70 and >0.80, respectively Fig. .15 was available for 108 cases with a TF ≥3% in plasma. Tissue samples were obtained at various timepoints. To further explore the association between ctDNA enrichments and RNA expression data, we evaluated the correlation of 6 PAM50 RNA-based tissue signatures with each of the 150 ctDNA-based signatures (adjusted by TF)  was positively correlated with luminal ctDNA-based signatures, while proliferation and cell cycle-related genes by mRNA  were positively correlated with proliferation-related ctDNA-based signatures  or HER2 status  across 177 of 178 samples with a TF ≥3% with known ER and HER2 IHC status from tumor tissue. As expected, ctDNA-based signatures tracking luminal biological processes  were found enriched in ER-positive (ER+) disease  compared to ER-negative disease. Similarly, ctDNA-based signatures tracking HER2 expression or amplification  were found significantly enriched  in HER2+ disease compared to HER2- disease , and HER2 overexpression by IHC and/or amplification by in-situ hybridization, are key biological features of breast cancerase Fig. . OverallTo evaluate the association of each ctDNA-based signatures with patient’s outcome, we analyzed baseline pre-treatment plasma samples from 124 patients with advanced HR+/HER2- breast cancer treated with endocrine therapy and a CDK4/6 inhibitor . Eighty-seven plasma samples had a TF ≥3% Fig. . The medc-MYC amplification)20. Conversely, ctDNA-signatures associated with better outcome were tracking luminal A-related biological processes. Similar results were obtained when the analysis was performed in the subset of 52 patients treated with endocrine therapy and a CDK4/6 inhibitor in the first-line setting (Supplementary Data From the 150 ctDNA-based signatures (adjusted by TF), 36 (24%) and 37 (25%) were found significantly associated with PFS and OS, respectively , 3q , 8q , 20q  and 21q , and deletion of 2q , 4q, 5q, 12q, 13q , 15q and 17p. Interestingly, multivariable analysis showed that the association of the ctDNA RB-LOH signature with PFS and OS was independent of TF (as a continuous variable), type of CDK4/6 inhibitor, line of treatment (first-line versus second-line versus later lines), presence of visceral disease and number of metastatic sites  was found one of the top biomarkers associated with poor outcome and treatment response Fig. . Since ltes Fig. . FinallyWe analyzed tissue samples of 63 patients with advanced HR+/HER2- breast cancer treated with endocrine therapy and a CDK4/6 inhibitor. RB-LOH signature determined in tumor tissue DNA was significantly associated with PFS and OS Fig. . Twenty-RB1 locus (13q14.2) among 224 other features10  but was significantly associated with better OS (p = 0.020), while the RB-LOH signature was strongly associated with PFS and OS in patients with advanced HR+/HER2-negative breast cancer treated with CDK4/6 inhibitors. In a bivariate cox model with both variables, only the ctDNA RB-LOH signature was prognostic. Overall, the RB-LOH ctDNA-based signature better captured the clinical behavior than an individual DNA region looking only at RB1, thus highlighting the power of a multi-feature algorithm for sensing pathway activity , and using the previously defined cutoffs  derived from the CDK-Validation-1 cohort  , suggesting that our approach can work in patients with low TF (3–10%).To further validate the prognostic value of the RB-LOH signature, we evaluated pre-treatment baseline plasma samples from a second independent cohort of 357 plasma samples, hereafter Plasma-2 cohort, which included 121 patients with advanced HR+/HER2- breast cancer treated with endocrine therapy and a CDK4/6 inhibitor at Hospital Clínic of Barcelona and the Vall d’Hebron Institute of Oncology (VHIO) and assayed using our ctDNA WGS-assay. Baseline characteristics of the 121 patients of CDK-Validation-2 cohort are reported in Table 26. RB-LOH signature scores were obtained from next-generation-sequencing data of tumor tissue DNA profiled under the MSK-IMPACT platform, which includes copy-number data from 468 cancer-associated genes27. With a median follow-up of 71.0 months, the RB-LOH signature was significantly associated with PFS   Scores from the 150 ctDNA-based signatures (adjusted by TF) were evaluated in paired plasma samples  in 7 patients with advanced HR+/HER2- breast cancer treated with endocrine therapy and a CDK4/6 inhibitor Fig. . Among t7. To evaluate if the biology displayed by the 150 ctDNA-based signatures (adjusted by TF) can identify subtypes with clinical relevance, we performed an unsupervised hierarchical cluster analysis of all 150 signatures across 178 plasma samples with a TF ≥3%  is prognostic and might predict treatment benefit≥3% Fig. . Four ma≥3% Fig.  and thenp < 0.001). Cluster 2 showed high enrichment of differentiation and luminal B-related signatures, and low enrichment of basal-like related biology. Visually, cluster 2 could be further subdivided (minimum 20 samples and a correlation coefficient >0.75) into Cluster2A and Cluster2B, both of which showed differences in the enrichment of ctDNA-based proliferation features, and luminal A-related signatures. Consistent with the luminal A-related biology identified in Custer 2 A, this group was characterized by 16p amplification and 16q deletion , p < 0.001; Fig. Clusters 3 and 4 showed high scores of ctDNA-based proliferation-related signatures and low scores of differentiation status and of luminal A-related signatures. Compared to Cluster 4, Cluster 3 showed high expression of basal-like-related gene expression  compared to the other clusters  .RNA-based expression data from FFPE tissue using a research-based PAM50 intrinsic subtype assayp < 0.001). Regarding the relationship between cluster 1–4 and prior endocrine sensitivity, this was observed in 91.7%, 80.0%, 57.1%, and 55.6% in Cluster 1, Cluster 2, Cluster 3 and Cluster 4, respectively (p = 0.004)  of 52.7%, 34.0%, 7.1% and 16.7% in Cluster 1, Cluster 2, Cluster 3 and Cluster 4, respectively (04) Fig. .n = 152). Compared to Cluster 1 (median PFS = 18.3 months), Clusters 2, 3 and 4 were found significantly associated with worse PFS . Median OS was not reached for Cluster 1 and 2, while median OS were 16.8 and 17.8 months for Clusters 3 and 4, respectively. Cluster 3, which has the highest ctDNA-based RB-LOH signature score   Fig. .29, and 381 patients with advanced HR+/HER2- breast cancer from the MSK-IMPACT dataset    Fig. . In addi01) Fig. . Finally01) Fig. , and PFS01) Fig. . Similar01) Fig. .Fig. 5DNp = 0.010). This enrichment for a more aggressive tumor biology in the metastatic setting is consistent with previous studies32.Finally, to understand whether the identification of the 4 clusters/subtypes is independent of the tissue profiled , we analyzed the 150 DNA-based signatures across 158 tumor tissues (either primary tumors or metastatic tumors). The proportion of primary tumors falling in Clusters 1, 2, 3 and 4 was 47.6%, 27.0%, 11.1% and 14.3%, respectively. The proportion of metastatic tumors falling in Clusters 1, 2, 3 and 4 was 27.0%, 39.3%, 19.1% and 14.6%, respectively . In this report, we predict complex tumor phenotypes like tumor cell proliferation rates using blood samples from patients with metastatic cancer. Our discovery should lead to opportunities for biomarker discovery, validation, and implementation in oncology.Through in-silico supervised learning analysis of The Cancer Genome Atlas (TCGA) early-stage breast cancer dataset we previously identified 150 DNA copy-number-based multi-feature predictors able to track a variety of biological processes at the RNA and protein level7. Specifically, both non-luminal subtypes within HR+/HER2- disease have been associated with a poor outcome in the context of endocrine-based treatment. In addition, the Basal-like subtype within HR+ disease is associated with a lack of benefit from endocrine therapy and CDK4/6 inhibition5, a finding highly concordant with ours. Another example within metastatic HER2+ disease is the result of a phase II PATRICIA trial (NCT02448420), where the benefit of endocrine therapy and palbociclib seemed restricted to ∼50% of patients with a Luminal A or B subtype33. Finally, in advanced TNBC, the identification of the Basal-like versus non-Basal-like subtypes revealed significant differences in response rates to carboplatin versus docetaxel in non-Basal-like disease in the TNT phase III trial9. Based on these findings, several prospective clinical trials are currently selecting patients based on their tumor’s RNA-based phenotype , PATRICIA II (NCT02448420), NEREA (NCT04460430) and HARMONIA (NCT05207709)).In metastatic breast cancer, phenotype-based classification using tumor tissue RNA expression profiling  is prognostic and might predict treatment benefit16 was found as one of the top signatures associated with survival outcomes in patients with advanced HR+/HER2- breast cancer treated with endocrine therapy in combination with a CDK4/6 inhibitor. CDK4/6 kinases associate with cyclin D proteins during transition from G1 to S phase of the cell cycle21. The cyclin D/CDK4/6 complex phosphorylates RB, dissociating it from the E2F transcription factors, which are ultimately responsible for cell cycle progression34. In this scenario, loss of RB is an evident cause of resistance to CDK4/6 inhibitors21, and various preclinical studies support this hypothesis23. In addition, mutations in RB, which are rare, are responsible for resistance to CDK4/6 inhibition in a few patients35.Among the different phenotypic features identified in ctDNA, the RB-LOH signature16. Expression of 452 genes varied with RB-LOH status and 423 were highly expressed in tumors with RB-LOH. These genes were associated with cell cycle-related biological processes, including cell division, DNA metabolism, spindle organization and biogenesis and response to DNA damage, and many were known E2F-regualted genes16. If one closely examines the selected features of the DNA-copy number based RB-LOH signature, it includes 10s of features that encompass many chromosomal regions known to harbor unambiguous acknowledged players of the cell cycle including RB1, E2F1,3, CCND1,2,3, CDK6 and MYC; thus an important quality of these multi-feature Elastic Net predictors is that they have been trained to ‘sense’ many parts of a given pathway and to combine these into one objective machine learning trained predictor, which links these together into a single quantitative score. As expected, the frequency of the RNA-based RB-LOH signature varied by intrinsic subtype with the lowest and highest LOH frequencies observed in the Luminal A (15%) and Basal-like (72%) subtypes, respectively, concordant with the known lack of RB function in Basal-like tumors and thus a lack of benefit of CDK4/6 inhibitors biology5.The RB-LOH mRNA-signature was originally developed by comparing gene expression between DNA assessed RB-LOH-positive breast tumors versus LOH-normal tumors36. On the other hand, tumor biology evolves over time and the biology captured in a tissue sample might not fully recapitulate the biology present at the latter times decisions are often made in the metastatic setting. For example, ∼1/3 of primary breast tumors switch molecular subtype when relapse occurs37, a proportion that increases to ∼50% if the primary tumor was Luminal A. In addition, large relative changes in tissue gene expression have been found between primary and metastatic breast cancer. For example, 47 of 105 breast cancer-related genes (45%) were found differentially expressed across 123 paired primary-metastatic tumors31. Interestingly, expression of proliferation-related genes was better at predicting OS in metastatic disease when analyzed in metastatic tissue rather than primary tissue31. This is concordant with our findings that the RB-LOH ctDNA-signature is better associated with prognosis when measured in ctDNA prior to initiating therapy compared to the same signature evaluated in archival tissue samples coming from the primary tumor, which typically predate the metastatic specimen by many years.The ability to detect and monitor tumor phenotypes in blood samples opens opportunities for precision oncology. On one hand, tumor tissue biopsies of metastatic disease are difficult to perform, pose risks to the patient and do not fully capture the intra-patient tumor heterogeneityOur findings should facilitate the development of prospective trials in specific clinical scenarios selecting patients with a more homogenous tumor biology. For example, patients with HR+/HER2- advanced breast cancer and a high score in the ctDNA-based RB-LOH signature could be randomized to a chemotherapy-based treatment strategy versus the standard-of-care of endocrine therapy and CDK4/6 inhibition. In addition, tracking RB-LOH and other ctDNA-signatures might better help to understand why tumors progress and make treatment decisions based on that information. For example, a trial could evaluate endocrine therapy alone in patients with HR+/HER2- advanced breast cancer with a high score of the ctDNA-based Luminal A signature, and then add CDK4/6 inhibition when the ctDNA-based Luminal B signature increases during therapy versus initiating endocrine therapy and CDK4/6 inhibition upfront. Another clinical situation is when one is forced to change treatment after a single metastatic lesion progresses while the other metastatic lesions are radiologically under control and the patient is doing clinically well. In this context, detection of a low proliferation status in ctDNA might open opportunities such as considering local therapy approaches on the progressing lesion and continue with the same systemic therapy.38. Second, this was an exploratory study, and we did not use a sample size calculation but we used all the samples available for the correlative analysis. The lack of formal design through pre-planned analysis prohibits inference of negative results. Third, we did not address if any of the 150 signatures predict clinical outcome in advanced HER2+ or TNBC or benefit from other targeted therapies. Fourth, we decided to focus on signatures previously identified and validated. Fifth, although both independent datasets collected plasma samples prospectively from patients treated with endocrine therapy and a CDK4/6 inhibitor, the cohorts are not from clinical trials and are prone to potential biases such as patient selection, inconsistent evaluation of the disease during therapy and subjectivity in determining drug response and progressive events. Sixth, we did not compare the prognostic value of the ctDNA-based signatures measured at baseline with the prognostic value of early ctDNA dynamics after 2–4 weeks of initiating therapy39. Seventh, ctDNA-signatures might not capture well tumor immune-related biology as tumor tissue DNA-signatures.Our study has limitations. First, ∼39% of patients had a TF < 3%, and ctDNA-signatures could not be evaluated. In addition, ∼30% of patients had a TF of 3–10% and this might limit the detection of the ctDNA-based signatures. Further studies evaluating deeper ctDNA sequencing strategies and signature detection in patients with very low TF, including those with early-stage disease, is warranted. For example, expressed genes might be inferred by evaluating nucleosome footprints from whole-genome sequencing of plasma DNATo date, DNA sequencing has identified few FDA-approved actionable genetic alterations in cancer, especially breast cancers. Phenotypic characterization using multi-gene RNA-based expression might add biological and clinically relevant information. However, implementation of tumor-based RNA-based gene expression profiling in the metastatic setting is a major challenge since tumor tissue is often not readily available. Here, we demonstrate that complex tumor phenotypic traits can be identified in ctDNA and may provide clinical value. Our ctDNA-based multi-gene signature approach opens avenues for discovering and implementing biomarkers in oncology, especially in the advanced disease setting where liquid biopsies are becoming commonplace.The Hospital Clínic Barcelona institutional ethics committee approved the study in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki, and other applicable local regulations (HCB/2019/0666). Written informed consent was obtained from all patients before enrolment. The medical records were retrospectively reviewed to obtain the necessary clinical data.n = 190), Hospital 12 de Octubre in Madrid (n = 15) and Institut Català d’Oncologia (n = 2) at different stages of their metastatic disease: 209 plasmas from 179 patients with advanced HR+/HER2- breast cancer, 19 plasmas from 16 patients with HER2+ advanced breast cancer, 17 plasmas from 16 patients with advanced TNBC and 1 plasma from 1 patient with unknown ER and HER2 status  treated at Hospital Clinic of Barcelona  and Vall d’Hebrón Institute of Oncology (n = 71)   Fig. , includi-2) Fig. . Only baIn addition, 185 FFPE tumor tissues from patients treated at Hospital Clinic of Barcelona were collected, including samples from 110 patients with available plasma samples (in Plasma-1 cohort), and samples from 17 patients with HR+/HER2- advanced breast cancer who did not have plasma samples but were treated with endocrine therapy and a CDK4/6 inhibitor.Publicly available DNA, RNA and clinical data from METABRIC and the MSKCC datasets were obtained from cBioportal.https://github.com/shahcompbio/hmmcopy_utils) and ichorCNA v0.2.0 (https://github.com/broadinstitute/ichorCNA), with a bin size of 500 kb and default parameters12. ichorCNA is a previously reported tool by Adalsteinsson et al.12 for estimating the fraction of tumor in cfDNA from ultra-low-pass WGS. ichorCNA uses a probabilistic model, implemented as a hidden Markov model, to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the TF of an ultra-low-pass whole genome sequencing samples. ichorCNA is optimized for low coverage (~0.1X) sequencing of samples and has been benchmarked using patient and healthy donor cfDNA samples. Adalsteinsson et al. reported that, using a TF cutoff of 3%, ichorCNA achieves a sensitivity of 0.95 for detecting presence of tumor and a specificity of 0.91 for correctly classifying a healthy donor12.Approximately 30 mL of peripheral blood was collected into K2-EDTA Vacutainer tubes (Becton Dickinson) and plasma isolation was performed within 2 h of blood collection through two centrifugation steps. Centrifugation at 1600 x g for 10 min at 4 °C separated plasma from peripheral-blood cells. Approximately 12 mL of plasma were obtained per patient, which were subsequently centrifugated at 16,000 x g for 10 min at 4 °C to remove the residual supernatant and any remaining contaminants including cells. Plasma samples were then aliquoted in 1.5 mL tubes and immediately stored at −80 °C. cfDNA was obtained from 3 mL of plasma using the QIAamp Circulating Nucleic Acid Kit (QIAGEN Inc.) according to the manufacturer’s instructions and quantified with a Qubit dsDNA high-sensitivity assay kit and the Qubit 4.0 fluorometer . cfDNA was concentrated using SpeedVac to fulfil the requirements for library preparation. Library preparation was performed by ligating unique dual indexes (UDI) custom adapters to a minimum of 10 ng of the isolated cfDNA (10–50 ng dsDNA). More specifically, the fragment ends of cfDNA were blunted and 5′ phosphorylated and, after that, 3′ ends were A-tailed to favour adapter ligation. Adapters were 10 bp – UDI as recommended to mitigate errors introduced by index-hopping or switching in Illumina instruments with patterned flow cells, such as the NovaSeq 6000. Indexed libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche Sequencing Solutions), pooled, and sequenced in a NovaSeq 6000 Illumina at 0.5X mean coverage with read length of 2 ×150 bp. ShWGS was analyzed with hmmcopy_utils , we performed an in-silico downsampling of sample coverage for 12 samples in order to assess performance at different coverage. Different TFs were represented within these 12 samples. For each sample, a subset of aligned reads was selected to account for: 2X , 1X , 0.5X  and 0.1X  coverages. Reported TFs were consistent across coverages  performs similarly to 1X   and evaluated its association with PFS. We repeated this analysis 500 times and estimated the average hazard ratio and Finally, shWGS was performed in 14 healthy individuals, and the log2 values by ichorCNA estimates were determined to be on average 0  required a minimum of 7 reads supporting the variant allele to call a mutation. The sensitivity of the technique is 5% Minor allele frequency (MAF) for Single nucleotide variant (SNVs) and 10% MAF for INDELs. Frequent single nucleotide polymorphisms (SNPs) in the population were removed based on the gnomAD database . CNA were calculated from an in-built genome-wide SNP backbone targeting 20000 SNPs using CNVkit (v0.9.6.dev). Data was manually curated, and classification of identified variants was performed using publicly available databases .DNA obtained from FFPE-derived tissues was purified with the QIAamp DNA FFPE Tissue kit (QIAGEN Inc.) for all samples available, following manufacturer’s instructions. Quantification was performed with a Qubit dsDNA broad-range assay kit and the Qubit 4.0 fluorometer . A minimum of 100 ng of extracted DNA was processed for library preparation using a custom hybridization-based capture panel targeting 435 genes with reported somatic mutations in different tumor types (VHIO-300 v4 panel) performed with Agilent SureSelectXT Low Input Target Enrichment System . Indexed libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche Sequencing Solutions), pooled, and sequenced in a HiSeq 2500 Illumina (2 x 100 bp) at an average coverage of 500X. Reads were aligned to the hg19 reference genome with BWA10. Briefly, each segment score was calculated as the mean copy number score across genes within the segment. The coefficients of DNA segments for predicting gene signatures were obtained from Xia et al. DNA-based signature scores were calculated as the weighted average of DNA segment values for each sample.For both tumor DNA sequencing and plasma cell-free ctDNA sequencing, segmentation files from CNVkit output (for tumor DNA) and ichorCNA output (ctDNA) were first mapped to gene-level feature. Values from 514 DNA segments were then determined as described in Xia et al.10. Segments with values above the gain threshold or below the loss threshold were called altered.For ctDNA, TF and tumor ploidy were estimated by ichorCNA. For ctDNA samples with TF > 0, TF and tumor ploidy adjusted signature scores were calculated by first adjusting copy number values in ichorCNA segmentation file: adjusted_copy_number_ratio = log2(logR_copy_number/tumor_ploidy). Then DNA-based signature scores were derived the same as described for tumor tissue. For calculating the number of altered segments, we used arbitrary gain/loss threshold of +/− 0.07 for unadjusted segment values and 0.32/−0.42 for adjusted segment values42  using the 770-gene Breast Cancer 360TM Gene Panel, which includes the 50 PAM50 genes. Gene expression for each sample was independently normalized to the geometric mean of 5 housekeeping , and research-based PAM50 subtyping was determined15.RNA was extracted using the High Pure FFPET RNA isolation kit  following manufacturer’s protocol. One to five 10-μm FFPE slides depending on tumor cellularity were used for each tumor sample, and macrodissection was performed, when needed, to avoid normal tissue contamination. A minimum of ∼100 ng of total RNA was analyzed on the nCounter platform43. Processed DNA segment values were downloaded, and DNA-based signature scores were calculated as the weighted average of DNA segment values for each sample. Treatment and clinical outcome information from the MSK dataset was obtained from Table 26.Clinical-pathological data was obtained from cbioportal44 with <5% FDR. Then we used the selected gene list and calculated 4 centroids from the training data. For every new sample in METABRIC29, we calculated the Euclidean distances to the 4 centroids and assigned a cluster class to each sample based on the nearest centroid.To identify the 4 subtype clusters using DNA-based data, we selected signatures that were significantly differentially expressed across the 4 clusters identified in ctDNA using a multi-class significance analysis of microarrays (SAM)χ2 test or Fisher’s exact test. Differentially expressed signatures between two groups were identified using a two-class unpaired SAM with an FDR < 5%. Differentially expressed signatures between two timepoints  were identified using a two-class paired SAM with an FDR < 5%. Estimates of survival were from the Kaplan–Meier curves and tests of differences by the log-rank test. Univariate and multivariable Cox models for PFS and OS were used to test the prognostic significance of each variable. The Bonferroni correction method was used to control the family-wise error rate in case of multiple comparisons. PFS was defined as the period from initiation of endocrine therapy and a CDK4/6 inhibitor until disease progression or date of last follow-up. OS was defined as the period from initiation of endocrine therapy and a CDK4/6 inhibitor until death or date of last follow-up. All cluster analyses were displayed using Java Treeview version 1.1.3. Average linkage hierarchical clustering was performed using Cluster v3.045. Two-sided p < 0.05 were considered statistically significant. Statistical computations were carried out in R 4.0.3 (http://cran.r-project.org).Categorical variables were expressed as number (%) and compared by Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Reporting Summary


Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data. Predicting drug responses in cancer patients requires robust computational frameworks. Here, the authors develop an integral genomic signature —iGenSig— approach to predict drug responses using multi-omics data from tumour samples, and validate this approach using genomic datasets from multiple clinical studies. The current standard practices for precision oncology include detecting actionable mutations via genetic testing , or detecting small-sized predictive or prognostic gene signatures via targeted expression assays . Such assays, however, require at least one assay per decision, which limit their cost-effectiveness. On the other hand, the past ten years have observed a stunning reduction of sequencing costs for a human genome from $300,000 to $1000, with $100 whole-genome sequencing expected soon2. With this rate, it is expected that transcriptome and genome sequencing will become the clinical routine for patients. With the advent of low-cost genome sequencing, precision oncology is at the cusp of a deep transformation via leveraging the big data to provide a wide array of clinical decision supports which is deemed to be cost-effective. The computational approaches that can leverage these big data to facilitate clinical decisions and provide tailored health care are in high demand. For example, in metastatic lung cancer, the target therapies prescribed based on the current modeling of genomic sequencing data produced only minimal gain of quality-adjusted life year3. Innovative and robust clinical big data-based decision support models for precision oncology will be of vital importance.Precision oncology, defined as molecular profiling of tumors to achieve customized patient care, has entered the mainstream of cancer patient care4. The main challenge is that multi-OMIC big data typically contain daunting amounts of high-dimensional features but a limited number of subjects which poses great challenges to the computational power and training process of artificial intelligence (AI) -based methods. In addition, AI approaches are “black box” tools, so the algorithmic and biological mechanisms underlying the models are largely unknown. The modeling process is controlled by AI which makes it difficult to interpret complex model predictions and is often plagued with the problems of overfitting and overweighing. In addition, there is a lack of big data-based methods specifically addressing the insufficient performance of the prediction models for crossing dataset modeling resulting from the common biases in detected genomic features across different datasets arising from sequencing errors, different library preparation methods and platforms, discordant sequencing depth and read-length, heterogeneous sample qualities, and experimental variations, etc. This calls for robust, transparent, and explainable methods that can predict clinical treatment outcomes from multi-OMIC data with substantially improved tolerance of sequencing biases.In recent years, there has been great enthusiasm about the potential of artificial intelligence-based clinical decision support systems for big data-based precision medicine, however, to date only few examples exist that impact clinical practice5  analysis, which is designed to provide more robust clinical decision support with higher transparency and cross-dataset applicability Fig. . Due to 6  format for compiling binary multi-OMIC features, similar to that used for compiling gene concepts9. Using this format, we analyzed the expression profiling data and exome sequencing data from GDSC and compiled an integrated dataset combining the genomic features including upregulated genes, downregulated genes, mutated genes, and mutation hotspots. To increase the cross-dataset applicability of the iGenSig models, we intentionally introduced de novo feature redundancy by generating twelve overlapping levels of differentially expressed gene lists  for each drug. As a result, 196 drugs showed an AUROC > 0.75 on the testing sets (54%), and 20 drugs showed a ROCAUC >0.85 set Fig. . This maTo test if the iGenSig predictions rely on the genomic features of the primary drug targets, we removed the drug target genes for Erlotinib, Lapatinib, or Nilotinib from GDSC and CCLE genomic feature sets. We then built the iGenSig models for these drugs based on the genomic features devoid of drug targets and assessed their performance on GDSC internal test set or the CCLE validation set. Our result showed that the performances of these iGenSig models are not affected by deleting the genomic features for known drug targets  analysis suggested that our GDSC iGenSig model for Erlotinib significantly predicted the favorable response of these patients in the Erlotinib arm, with a hazard ratio of 0.2  profiled non-small cell lung cancer (NSCLC) tumors from 131 patients by gene expression array, among which 28 patients are treated with Erlotinib monotherapy, 47 patients are treated with Sorafenib monotherapy, and 20 patients are treated with vandetanib. Overall, the patient responses to Erlotinib in this trial are limited, and all patients treated with Erlotinib progressed within six months. This may be due to the selection of pretreated chemorefractory NSCLC patients as enrollment criteria16. Recent evidence suggested that the addition of Bevacizumab to Erlotinib exhibits increased therapeutic efficacy. As bevacizumab alone is known to lack efficacy in lung cancer, this effect is thought to be the result of enhanced erlotinib activity16. The SAKK 19/05 trial is a multi-center single-arm trial in previously untreated patients. The endpoint provided by this study is objective response at 12 weeks after Erlotinib and bevacizumab treatment, and no survival data are available. Our result showed that the GDSC iGenSig model for Erlotinib showed a predictive AUROC of 0.795 795 Fig. , left, atus Fig. , right. 18, we wonder if the EMT signature contributes to the iGenSig predictions. We thus examined the pathways characteristic of the integral genomic signature for Erlotinib sensitivity in our iGenSig model. This can be achieved by extracting the genes contributing to the genomic features predicting sensitive responses in our GDSC iGenSig model. The resulting gene list can be then used to explore the enriched pathways based on the concept signature enrichment analysis (CSEA) developed in our previous study, which is designed for deep functional assessment of the pathways enriched in an experimental gene list9. Our result showed that the most significantly downregulated pathways characteristic of Erlotinib sensitive responses include MYC and E2F target gene signatures  has been previously reported to mediate EGFR resistance in the BATTLE trial studyres Fig. . Consist20. Correlating these EMT signatures with the Erlotinib iGenSig scores revealed that the downregulated and upregulated EMT signatures are indeed enriched in the subjects with high or low iGenSig scores respectively in the BATTLE trial dataset , with a hazard ratio of 0.27  program that tested 5- Fluorouracil (5-FU) monotherapy on postsurgical colon cancer patient.27 Fig. . Whereas.27 Fig. . To exam.27 Fig. . This suEGFR mutated tumors in lung cancer23, whereas EGFR inhibition has been found to sensitize 5-Fu-resistant colon cancer cells24. Moreover, the interferon γ signature is associated with an inflammatory response triggered by the double-strand breaks resulting from the DNA damaging effect of 5-FU. The upregulation of Interferon γ regulated genes in cancer cells may confer better therapeutic effects through the interferon γ induced growth arrest and apoptosis in cancer cells26, and this signature appears to be captured from leukemia and lymphoma cell lines in the GDSC panel  is known to confer resistance to HER2-targeted therapy through driving HER2-independent signaling28, thus may confound the predictive effect of our models, we stratified the patients based on their ER positivity. Following stratification, the lapatinib model showed significant predictive value in ER-negative patients , the lapatinib model ranks the highest in explaining the variation in patient response, followed by PR status, and the composite model based on both lapatinib and paclitaxel models produced a higher predictive value than the individual models , with a hazard ratio of 0.65 , followed by the Epirubicin model (p = 0.069) and tumor grade (p = 0.093)  Fig. . When st93) Fig. .30. This study included both ER-positive and negative patients, and only pCR is provided as a clinical endpoint. Consistently, the paclitaxel model showed significant predictive value on pCR in the stage I-II breast cancer patients only (p = 0.0004). Within the Stage I-II patient group, the iGenSig models for paclitaxel, 5-fluorouracil, and epirubicin showed an AUROC of 0.74, 0.71, and 0.67 respectively, and the composite predictive models based on all three models achieved an AUROC of 0.81. Taken together, these data suggest that in combination therapy, the iGenSig models derived from single-drug treatment data can be confounded by the therapeutic benefits derived from other drugs in the combination, as well as confounding drug resistance factors, such as ER overexpression and late tumor stage. Stratifying the patients based on known confounding factors and combining the models for different drugs included in the regimen will help better observe the predictive effect of the iGenSig models on patient response to combination drug therapies.To verify this result, we examined another neoadjuvant clinical study carried out in a Japanese breast cancer patient cohort at Osaka University Hospital (OUH) testing neoadjuvant paclitaxel followed by 5-fluorouracil, epirubicin, and cyclophosphamide (P-FEC)32. The resulting synthetic features were then fed to the machine learning methods for supervised learning on drug responses, such as elastic net, support vector machine (SVM), or Random Forest (RF) , which are the limited available machine learning algorithms capable of carrying out predictive modeling using ultra-high-dimensional features. In addition, we also performed ridge regression and Support Vector Machine (SVM) modeling based on binomial drug sensitivity labels. For AI-based methods, we computed the unsupervised representation of the genomic features for dimensionality reduction based on the autoencoder deep learning method, as previously reported33. These include nine GDSC profiled drugs, seven of which have more than one PDX responder in this dataset. Our results show that the GDSC models for these seven drugs showed diverse predictive values on these PDX models so all modeling methods showed large deviations in predictive results for different drugs  are known to induce VEGF overexpression in endothelial cells and promote angiogenesis38. Correlating these biomarkers with the iGenSig scores revealed that in the Sorafenib treated arm of the BATTLE trial, the sensitive tumors with low iGenSig scores appear to overexpress VEGFA and CXCL8, as well as the AMP-activated protein kinases (PRKAA1/2) important for stimulating VEGF expression and angiogenesis39  or chest wall or skin invasion. Such regional metastasis may create a tumor microenvironment leading to drug resistance, which cannot be recreated when the cell lines are generated from primary tumors.The remaining issue to be addressed for iGenSig modeling is how to eliminate the effect of confounding genomic features resulting from the imbalanced distribution of confounding factors such as gender or prognostic factors that can impact patient outcomes such as metastasis, etc. While this issue may be less impactful in modeling cell line responses when a large number of cell line subjects are included, it could become more consequential when a smaller number of subjects are tested in the clinical trial. In this case, the genomic features associated with the confounding factor may be identified and excluded from the iGenSig model through multivariate statistics. In addition, the confounding clinical variables that affect prognosis such as local or distant metastasis should be accounted for via multivariate statistics or stratification methods when assessing the performance of iGenSig models in predicting patient survival outcomes. This could be particularly helpful for modeling the clinical trials testing combination drug therapies where the predictive powers of the iGenSig models derived from single-drug treatment are relatively weak due to the therapeutic benefits from combinatory drugs. Future studies will be required to further optimize the iGenSig methods for modeling clinical trial datasets and taking into consideration of these biological variables and confounding factors.40. Drug sensitivity data, mutation data, and cell line annotations were downloaded from the GDSC (http://www.cancerrxgene.org/downloads) or CCLE (http://www.broadinstitute.org/ccle) websites. The newly released batch 2 drug sensitivity dataset are downloaded from the GDSC website as of May 2021. The TCGA Pan-cancer gene expression and mutation datasets were retrieved from the UCSC Xena browser (https://xenabrowser.net/datapages/). The gene expression and mutation data for the PDX tumors were retrieved from the supplementary dataset of the original publication33. The clinical trial datasets are retrieved from Gene Expression Omnibus  or the database of Genotypes and Phenotypes , and the detailed access information are provided in the Data Availability section. All the genomic datasets included in this study are summarized in Supplementary Table The drug response data, gene expression data, and mutation data are from the Genomics of Drug Sensitivity in Cancer Project (GDSC), and the Cancer Cell Line Encyclopedia (CCLE) as of September 2018. The GDSC and CCLE gene expression data were retrieved from ArrayExpress (E-MTAB-783) and NCBI GEO (GSE36133), respectively and normalized using Robust Multi-Array Averaging (RMA)Based on gene expression and somatic point mutation datasets, we extracted genome-wide differential gene expression (DGE) and mutation features and generated an integrated genomic feature file. For gene expression datasets, quantile normalizations were performed and genes with standard deviations of less than 20% percentile are filtered. We then calculated log2 transformed fold changes of the expression values compared to the trimmed mean of expression values . To eliminate zero values during log2 transformation, we added 1 to the expression value across all cell lines or tumors. Based on the mean and standard deviation (SD) of fold changes, we assigned the cell lines or tumors into the following overlapping groups: “Up_Level1” group with the fold change above Mean + 1 × SD for a given gene; “Up_Level2” group with the fold change above Mean + 2 × SD; …, and “Up_Level6” group with the fold change above Mean + 6 × SD Likewise, “Down_Level1”, “Down_Level2”, … and “Down_Level6” grouped cell lines based on Mean − 1 × SD, Mean − 2 × SD, and Mean − 6 × SD. The 12 “Levels” were labeled as genotypic features for each given gene and the binary genomic features are compiled as a genomic matrix transposed (GMT) file format. Similarly, we extracted binary genomic features to represent point mutations. The mutation hotspots and nonsynonymous somatic mutations such as missense, nonsense, and frameshift are assigned as mutation features. Each recurrent mutation hotspot and each recurrently mutated gene were assigned as separate features.41. We first tested the skewness of the AUC measurements for each drug in the GDSC dataset. A negative skewness distribution indicates that the drug has high AUC measurements (lack of responses) in most of the cell lines, but low AUC measurements (sensitive responses) in a small subset of the cell lines, and a lower level of skewness indicates a higher level of outstanding responses. To ensure the drugs have sufficient outstanding responders for training and testing the algorithm, the GDSC drugs with negative skewness and more than 20 sensitive cell line subjects are included in our iGenSig cancer cell line modeling. We then defined sensitive drug responses of cell lines based on Act Areas using the standard waterfall method described in the CCLE study (implemented in the “define.response.AUC” and “define.response.ActArea” function of the iGenSig R package)11. The Act Area measurements for CCLE or GDSC cell lines for a given compound are sorted in ascending order to generate a waterfall distribution. The cut-off for defining sensitive subjects was determined as the maximal distance to a line drawn between the start and endpoints of the distribution. The cut-off for non-responders was determined as “median of Act Area - median absolute deviation (MAD).” The cell lines with Act Area above the sensitivity cut-off were labeled as drug-sensitive and below the resistance cut-off were labeled as drug-resistant. The cell lines with Act Areas between the cut-offs for drug sensitivity and resistance were labeled as intermediate.Drug responses of cancer cell lines are represented by the area under the dose-response curve (AUC) in GDSC or the area over the dose-response curve (Act Area) in CCLE10 to test the enrichment of the feature-positive cell line in the cell line panel sorted in descending order based on Act Area  for each genomic feature is calculated using the same algorithm as that implemented in Gene set enrichment analysis (GSEA)10. To prevent bias, we excluded the genomic features defining fewer than 5 cell lines during the calculation of GenSig scores. Likewise, we calculated the weights for each genomic feature in predicting resistant drug responses based on the cell line panel sorted by AUC in descending order. To prevent overfitting, for a given cell line x in the training set, cell line x is excluded from calculating the ES scores for the genomic features associated with cell line x. We assessed the significance of the observed ES by comparing that to the random ES scores calculated by random features with the same numbers of positive cell lines. Repeat this step until 2000 random enrichment scores were calculated, then the normalized enrichment score (NES) was calculated by:p values were determined based on the chance of random ES scores to be above the observed ES score for feature i, and the false discovery rate (FDR) were calculated using the R package “qvalue” for multiple comparisons. In this study, we used a universal FDR q value cutoff of 0.1 to select significant genomic features for calculating iGenSig scores. This parameter can be tuned for different drugs to further refine the model as the signal-to-noise levels of these predictive genomic features could be different for different drugs. Furthermore, we observed that some of the genes have both upregulation and downregulation features ranked as significant for predicting the sensitive drug response. We thus filtered the genes that have both upregulated features with FDR <0.1, and downregulated features with FDR <0.3, and the genes that have both downregulated features with FDR <0.1, and upregulated features with FDR <0.3. On the other hand, some of the genes have only level-1 DGE features selected as significant based on FDR <0.1, but none of their corresponding high levels of DGE features have an FDR more than 0.3 even if they define more than ten cell lines. These genomic features represent noises and are thus filtered as well.To define the weight (mentclass2pt{minimij). We then performed clustering of the cosine similarity matrix based on Ward’s method (D2) using the R module “hClust”. The correlated feature groups are then determined based on an adaptive dynamic cluster detection method42, using the parameters: dynamic.method = “hybrid”, cutTree.depth = 2, and minClusterSize = 40. We then introduced a penalization factor (ε) which is calculated for each genomic feature i based on the similarity indices of the colinear genomic features associated with a given cell line x and of the same cluster as a feature i:ij is the Otsuka–Ochiai coefficient between feature i and a given genomic feature j from the same cluster group as a feature i associated with cell line x. To eliminate the cumulative effect of small overlaps between genomic features, the Otsuka–Ochiai coefficients were adjusted to 0 if Kij < 0.1. Here εi is an estimator of redundancy among the genomic features of the same cluster group associated with cell line x. The penalization factor ranges from 1  to n . We then penalized the weight ωi using εi, resulting in effective weight (EW):εi (trim = 0.3) was then used to calculate the effective feature number (EFN):x is computed as:To prevent the inflation of iGenSig scores from feature redundancy, we leveraged the TCGA Pan-Cancer RNAseq and exome datasets to assess the co-occurrence between genomic features associated with each cell line and generated the cosine similarity matrix of genomic features based on Otsuka–Ochiai coefficient between these features (Kq < 0.1) calculated based on GDSC datasets will be used for calculating the iGenSig scores based on the presence of these features in a patient or cell line subject in the validation dataset. The weights of these features associated with that subject were then penalized based on the feature redundancy levels assessed using the TCGA Pan-cancer dataset (for CCLE dataset) or cancer type-specific TCGA dataset , and the iGenSig scores were calculated using the same algorithm. Thus, here only the weights from the GDSC dataset are used for modeling the validation datasets.When applying the iGenSig models to the validation datasets, the weights of the significant genomic features  of the iGenSig scores was calculated based on the binary response of the cell lines determined based on the sensitive cutoff discussed above, and the optimal cut-points of iGenSig scores are determined using the R module “coords” of the “pROC” package. The cell line subjects were divided into sensitive cell lines and other cell lines that include both intermediate and resistant cell lines, and the sensitive iGenSig scores are used when assessing the predictive values of the iGenSig models.To test if the iGenSig predictions rely on the genomic features of the primary drug targets, we removed the drug target gene features for Erlotinib, Lapatinib, or Nilotinib from GDSC and CCLE genomic feature sets. We then built the iGenSig models based on the genomic features devoid of drug targets and assessed their performance on the internal test set (20% of GDSC cell lines) or external validation set (100% of CCLE cell lines). To examine if excluding the hematologic cancer cell lines from the GDSC training dataset can improve the prediction performances of iGenSig models on the drug sensitivity of CCLE solid cancer cell lines, we removed leukemia, lymphoma, and myeloma cell lines from the GDSC dataset when performing the modeling for the shared 14 drugs, and then applied the models to CCLE solid cancer cell lines . For the CALGB40601 trial, pCR in the breast and axilla is used in our analysis, as no tumor residuals in both breast and lymph nodes can best discriminate patient outcomes44. Other endpoints will be used in the analysis if OS and pCR are not available.To examine the predictive values of the iGenSig models on patient subjects, we compiled six clinical trial or prospective clinical study datasets. The detailed information of the clinical datasets used in this study including enrollment details, treatment arms, available endpoints, available genomic datasets, and source publications are summarized in Supplementary Table 45 to perform unsupervised representation learning for dimensionality reduction and machine learning prediction algorithms for supervised learning of therapeutic responses using the low dimensional features generated by autoencoder, as previously reported32. The models are developed based on 80% of GDSC datasets (five permutated training sets). The autoencoder model was built with three hidden layers with the unit sizes in each layer designed in accordance with a previous report31. We then applied the unsupervised representation of the genomic correlates to supervised learning methods including elastic net, artificial neural network, Random Forest (RF), and support vector machine (SVM) for prediction modeling. Elastic net is a regression method that combines lasso and ridge regularization with the two hyperparameters, alpha and lambda. Alpha is a mixing parameter to define the relative weight of the lasso and ridge penalization terms and lambda determines the amount of shrinkage46. We identified alpha with the best tuning and optimized for predictive performance over a range of lambdas. Regression was performed using the glmnet R package (ver. 4.0.2). We implemented an RF regression model using randomForest R package (ver.4.6.14). We specified 1,000 trees to grow and ensure every object gets predicted multiple times. We used SVM with the linear kernel method, “svmLinear2”, provided by the caret R package (ver. 6.0.86). We specified tuneLength = 10 in the tuning parameter grid and accuracy metric. For all modeling methods, the models were developed using the same genome-wide gene expression and mutation features we compiled, and we used the same training and external validation sets of cell lines and patient subjects as in iGenSig modeling. One model is developed for each drug based on each permutation set, which are then applied to the CCLE and clinical trial datasets. To match the genomic features, the genomic features are set to zero if they do not present in the validation sets as in the iGenSig modeling.All machine learning and deep learning methods used in this study are implemented in R. For ridge regression and support vector regression, we performed the modeling directly based on the original high-dimensional binary genomic features. For ridge regression, we implemented using the R package “glmnet”. The Gaussian family is used for drug sensitivity measurements and the Binary family is used for binary sensitivity classifications. For tuning the ridge regression model, the best “lambda.1se” was obtained from tenfold cross-validation. Support vector regression is implemented using the “svm” function of the “e1071” package using default parameters based on either drug sensitivity measurements or binomial sensitivity labels. For AI-based methods, we applied the deep learning method autoencoder9 using the Hallmark pathway gene sets from MSigdb (http://www.gsea-msigdb.org). The resulting top pathways are disambiguated via correcting the crosstalk effects between pathways, to reveal independent pathway modules47. A p value <0.01 is used as a cutoff for disambiguation. The functional associations between the significant pathways are then assessed using our CSEA method that computes the enrichment of each pathway x on the human gene list sorted descendingly based on the universal concept signature scores of each pathway y9. The pathway network was visualized using the “igraph” R package (ver. 1.2.4.2).To identify the pathways characteristic of the integral genomic signature for Erlotinib resistance modeled from the GDSC dataset, we first extracted the genes involved in the iGenSig signature and then classified these genes into positive contributing genes and negative contributing genes. The positive contributing genes are defined as upregulated genes or genes with hotspot mutations. The negative contributing genes are defined as downregulated genes or mutated genes without mutation hotspots. The pathways enriched in the positive or negative contributing genes for predicting Erlotinib or 5-FU sensitive responses are analyzed by the Concept Signature Enrichment Analysis (CSEA) developed in our previous studyP values are calculated based on log-rank tests and a data-driven cut point of high iGenSig scores was determined using the R-package “maxstat”48. The concordance is used as a measure of goodness-of-fit for the predictive models in Coxph survival regression, which defines the probability that the predictive scores goes in the same direction as the survival data. To estimate the percent of outcome variations that can be explained by different factors, we fitted a logistic regression model and calculated the Pseudo-R2 for different predictive factors and clinical variables such as iGenSig scores, clinical stage, tumor grade, etc. For interaction analysis, multiple logistic regression models are generated for pair-wise interactions of the iGenSig model with each of the confounding factors, and the multiple models are compared with the simple logistic regression model via the Chi-Square test implemented in the “anova” function. P values of the box or violin plots were analyzed by paired or unpaired Student’s t-tests. Two-sided tests are used when both directions are to be tested, such as when comparing the different modeling methods. One-sided tests are used to determine if there is a difference between groups in a specific direction such as that defined by the iGenSig scores.The correlation between the predictive powers of the iGenSig models (AUROCs) with the total number of significant predictive genomic features (square root transformed) for each drug was determined using Spearman’s correlation coefficient. For survival analysis, the Further information on research design is available in the Supplementary MaterialSupplementary data 1Reporting Summary


Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural networks based on gene expression have become a hot research direction in lung cancer diagnosis in recent years, which provide an effective way of early diagnosis for lung cancer. Thus, building a deep neural network model is of great significance for the early diagnosis of lung cancer. However, the main challenges in mining gene expression datasets are the curse of dimensionality and imbalanced data. The existing methods proposed by some researchers can’t address the problems of high-dimensionality and imbalanced data, because of the overwhelming number of variables measured (genes) versus the small number of samples, which result in poor performance in early diagnosis for lung cancer.Given the disadvantages of gene expression data sets with small datasets, high-dimensionality and imbalanced data, this paper proposes a gene selection method based on KL divergence, which selects some genes with higher KL divergence as model features. Then build a deep neural network model using Focal Loss as loss function, at the same time, we use k-fold cross validation method to verify and select the best model, we set the value of k is five in this paper.The deep learning model method based on KL divergence gene selection proposed in this paper has an AUC of 0.99 on the validation set. The generalization performance of model is high.The deep neural network model based on KL divergence gene selection proposed in this paper is proved to be an accurate and effective method for lung cancer prediction. Lung cancer is the malignant tumor with the highest incidence and mortality . GloballMost lung cancers are diagnosed at an advanced stage, when the prognosis remains poor. Although LDCT screening has led to progress in early lung cancer detection, improvement in patient outcomes has been incremental and the accuracy is not satisfactory. With the rapid development of High-throughput sequencing, some researchers are beginning to explore the genomic evolution of premalignancy throughout the course of tumorigenesis . These sWith the rapid development of computer technology, machine learning methods are playing an increasingly important role in the diagnosis of lung cancer. Researchers continue to explore various lung cancer prediction algorithms, including support vector machines (SVM) , K-neareAt the same time, gene expression dataset has problems such as small dataset, high-dimensionality, unbalanced data, etc. There are tens of thousands of genes in a data set, but the number of samples is very small. Most genes have nothing to do with lung cancer, so we need exclude the irrelevant genes. If the existing machine learning methods are directly applied, the generalization performance of the model is very poor and it is difficult to converge, therefore, gene selection is needed. In the previous papers, difference analysis is usually used to select related genes. However, difference analysis requires data sets to conform to specific distributions and requires large data sets. At the same time, its calculation is more complicated, which leads to a small amount of data that cannot measure the importance of genes. There are also many researchers who try to use machine learning methods to select features, but gene expression data sets are unbalanced small data sets with high dimensionality, which leads to insufficient learning of machine learning methods and a underfitting model. The selected genes are often not the most relevant genes.In view of the shortcomings of traditional machine learning methods and the problems of gene expression level data sets, we hope to get a new simple and convenient method for gene selection, and on this basis, we can find a new machine learning method for lung cancer prediction. In recent years some deep learning researches in the biomedical field has been successful , which pIn order to solve the problem of high latitude of gene expression level data and few samples, this paper tries to use KL divergence for gene selection to build a deep neural network model. In order to solve the problem of imbalanced data, focal loss  is used The process of the deep neural network lung cancer prediction model based on KL divergence gene selection proposed in this paper is shown in Fig. https://dcc.icgc.org/). The TCGA dataset and ICGC dataset used in this paper is the RNA-seq gene expression data of lung adenocarcinoma (LUAD) samples from the TCGA dataset. The TCGA dataset contains 533 lung cancer samples and 59 normal samples. The ICGC dataset contains 488 lung cancer samples and 55 normal samples.The data we used were extracted from TCGA portal (https://tcga‐data.nci.nih.gov/tcga/) and ICGC portal . In practice, P represents the true distribution of the data, and Q represents the theoretical distribution of the data or the approximate distribution of P.KL divergence  Formula) is an aThe KL divergence is always greater than or equal to zero. When the two data distributions are the same, the value of the KL divergence is 0. The greater the difference between the two distributions, the greater the value of the KL divergence.For gene expression data, we can easily get the data distribution of each gene in the disease group and the control group. We can easily get the data distribution using a small sample data set, and then use KL divergence to measure the difference between the two distributions. If the two distributions are consistent, it means that the gene has nothing to do with the disease. If the two distributions are quite different, it means that the gene is related to the disease.KL divergence has the advantage of simple calculation. We can easily calculate the difference between the two distributions using small data set, which is suitable for small dataset such as gene expression dataset.This paper uses a deep neural network model to predict lung cancer. Deep neural network is inspired by the working principle of the brain and has been widely used in many fields. A deep neural network generates output based on input variables. Given a set of features and a target, it can learn to generate nonlinear function approximations value. Between the input and output, there are one or more nonlinear layers, called hidden layers. The deep neural network has multiple nonlinear hidden layers, which enable the deep neural network to learn complex nonlinear function relationships from high-dimensional raw data without the guidance of artificial rules .Figure input is the input information. W1 is the parameter of the first layer, relu(x) is a non-linear function, which is defined as Formula (sigmod function as the activation function[sigmod function is defined as shown in Formula (Formula  and Form Formula . Formula Formula  is the p function. The sig Formula , and itsThe binary classification model generally uses cross-entropy as the loss function, as shown in Formula , where y Formula . The croIn order to solve the problem of imbalanced data, we use focal loss function. Its formula is as shown in Formula . Focal lWe use the gradient descent method to adjust W1 and W2. Taking into account the shortcomings of existing methods and the characteristics of gene expression data sets, this paper uses TensorFlow to establish a three layers deep neural network and uses Adam as the gradient descent optimizer . In ordeWe evaluated the proposed method using k-fold cross validation method on the LUAD RNA-seq dataset, which comes from the TCGA dataset and ICGC dataset. These data sets include all stages of lung cancer, collected from patients of different clinical conditions and different ages and genders. The specific information of the data set is shown in Table This article analyzes the gene expression distribution of each gene in normal group and lung cancer group and calculates the KL divergence of two distributions. We believe that genes with larger KL divergence are genes that are related to lung cancer.After analysis and comparison, setting the KL divergence threshold to 3 can select lung cancer-related genes, the number of selected genes is small. In this paper, a total of 194 genes were selected.We use Accuracy, Recall, Precision to evaluate the performance of gene selection models and non-gene selection models. These indicators all rely on the confusion matrix  for calcThe accuracy rate is defined as the proportion of the predicted correct samples in the total samples, and the calculation formula is shown in Formula .9\documeThe recall rate is defined as the proportion of all samples whose true values are positive that are predicted to be correct. The calculation formula is shown in Formula .10\documThe precision is defined as the proportion of samples whose true values are positive among all samples whose predicted values are positive. The calculation formula is shown in Formula .11\documWe use the deep neural network model proposed in this article to train and evaluate all gene data and selected gene data respectively. The results are shown in Table We built a deep neural network model with two hidden layers, using focal loss as the loss function, taking the genes selected in the previous section as the model input, and then using k-fold cross validation method to evaluate the performance of the model, we set the value of k is five. After feature selection, the dimensionality of the data set is reduced, much lower than the sample size, which makes the application of deep neural network models possible.Compared with the SVM, LR, KNNThe AUC of the SVM, LR, KNN, RF model and the model proposed by this paper on TCGA and ICGC dataset are shown in Table In this paper, we found that the deep neural network model based on feature selection proposed in this paper has achieved better results than normal classification models in lung cancer prediction. Given the high incidence and mortality of lung cancer, early and accurate detection is very important. Therefore, computer artificial intelligence technology is of great help to improve the accuracy of lung cancer prediction.In this paper, we compare the deep learning model proposed in this paper with the SVM model. The SVM model has been widely used in disease prediction. The SVM can achieve better results on a balanced big data set, but on the gene expression data set, the generalization performance of SVM is poor. This is probably because the feature dimension is too high and the SVM cannot be fully learned, resulting in low prediction accuracy. Therefore, feature selection for high-dimensional data can reduce the cost of model learning, which makes it easier for the model to converge, so we can obtain better prediction accuracy.In addition, we observed that our deep learning-based model achieved higher accuracy and AUC scores than SVM. The result may be attributed to the inability of SVM to perform automatic feature crossover. The deep learning method we proposed can automatically learn the direct interaction and non-linear relationship of features and perform the best fit. Therefore, the accuracy of lung cancer prediction is higher. Our research results also confirm that deep learning has the ability to fit complex relationships, especially non-linear relationships, and does not require manual intervention. We believe that deep learning will become more and more important in the field of disease diagnosis and has a broad space for development.Finally, we need to point out that deep learning-based models require high computational costs. In order to overcome this problem, we use feature selection techniques in the data preprocessing stage, which greatly reduces model training and running time and makes the model easier to converge. With the rapid increase in the amount of gene expression data and the diversity of features, feature selection is a very important and necessary means. In general, in the discovery and research of important genes, feature selection is more and more worthy of attention.Aiming at the shortcomings of existing lung cancer prediction methods, this paper proposes a lung cancer prediction model based on KL divergence gene selection using deep neural network, which can solve the problem of high dimensionality, few samples, and model unfitting on gene expression data. Compared with traditional algorithms, it has the advantages of fast training and high accuracy, and it performs better on the verification data set.


The difference in cancer morbidity and mortality between individuals of different racial groups is complex. Health disparities provide a framework to explore potential connections between poor outcomes and individuals of different racial backgrounds. This study identifies genomic changes in African American patients with gynecologic malignancies, a population with well-established disparities in outcomes. Our data explore whether social health disparities might mediate interactions between the environment and tumor epigenomes and genomes that can be identified. Using The Cancer Genetic Ancestry Atlas, which encodes data from The Cancer Genome Atlas by ancestry and allows for systematic analyses of sequencing data by racial group, we performed large-scale, comparative analyses to identify novel targets with alterations in methylation, transcript, and miRNA expression between tumors from women of European American or African American racial groups across all gynecologic malignancies. We identify novel discrete genomic changes in these complex malignancies and suggest a framework for identifying novel therapeutic targets for future investigation.Common genetic changes in breast, ovarian, cervix, and uterine tumors can be identified in African American patients. Understanding why these changes occur may help improve outcomes for all patients with cancer. Variations in morbidity, mortality, and treatment responses exist between individuals of different races and across multiple cancer types . In addiAfrican American (AA) patients with gynecologic cancers are consistently found to have higher disease-associated mortality . Many stHigh-throughput sequencing analyses of tumor samples are an important tool for identifying the molecular mechanisms of tumor initiation, progression, and metastasis . MalignaHistorically, oncologic databases and clinical trials have not reflected the diversity of the United States population, underscoring the need for research centered on minorities . Recent This study used The Cancer Genome Atlas (TCGA) database, which contains genomic sequencing data for many different tumor types, in tandem with The Cancer Genome Ancestry Atlas (TCGAA), which encodes TCGA data by ethnic ancestry and allows for systematic race-associated analyses of sequencing data previously not racially coded. Using these two databases, we performed large-scale, comparative, pan-cancer analyses of gynecologic cancers focusing on alterations that differed in tumors from individuals of AA and EA racial groups. First, we performed sequential examinations of mRNA, miRNA, and methylation changes in breast, uterine, cervical, and ovarian cancers to determine whether any race-associated changes existed. Next, we characterized the identified race-associated alterations and identified molecular connections among them. We identify coordinate and epigenetically driven genomic changes specific to gynecologic cancers which may be suggestive of environmental impacts on individuals of different racial groups. Our analysis supports a model whereby epigenetic and genetic changes may be contextualized within social determinants of health to understand disparities in gynecologic malignancies.Racial tumor sample assignments previously identified by the TCGAA project were mapped to each tumor, and samples were allocated to AA or EA groups according to these TGCAA designations . Each tuPublicly available, processed, aggregated data generated via Illumina HiSeq gene expression profiling of ovarian, breast, cervical, uterine corpus endometrial cancer, and uterine carcinosarcoma tumors from TCGA were gathered using the FireHose data repository in accordance with the TCGA's data usage policy (RRID:SCR_003193). TCGA contains molecular, histopathologic, and limited clinical data for more than 11,000 patients with approximately 30 tumor types. RSEM normalized counts generated by the TCGA consortium analyses were used for downstream transcriptional analysis  and multiple hypothesis testing–adjusted P values (FDR) less than 0.05. Given the lower expression levels of miRNAs, significantly altered miRNAs were defined more permissively as those with 50% increased or decreased expression and multiple hypothesis testing–adjusted P values less than 0.05. Differentially expressed transcripts or miRNAs were identified for all gynecologic malignancies combined via a comparison of AA and EA ancestry samples.Significantly altered mRNAs were considered transcripts with 2-fold changes  correction for multiple hypothesis testing was used to assess FDR. For probes with differential methylation of FDR < 0.05, the ∆ (AA – EA) of the beta values was calculated. The ∆ values for each probe were used to rank the probes for downstream gene-set enrichment analyses and are represented scaled and normalized in P value less than 0.05.The normalized and batch-effect–corrected beta values of 5 TCGA tumor types were generated using a probe-by-probe proportional rescaling method to yield a common set of probes with comparative methylation levels. This included methylation levels of 22601 CpG positions from 2,279 samples (367 AAs and 1912 EAs) of 981 breast invasive carcinoma (BRCA), 225 cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), 534 ovarian serous cystadenocarcinoma (OV), 486 uterine corpus endometrial carcinoma (UCEC) and 53 uterine carcinosarcoma (UCS). Illumina Infinium DNA methylation bead arrays, including both HumanMethylation27 (HM27) and Human Methylation450 (HM450) were incorporated. Briefly, difference between HM27 and HM450 by two distinct technical replicates was measured and a proportional rescaling method applied to remove platform effects. Please see original publication for full description . Individq value of less than 0.05 and a normalized enrichment score or primary metric for scaling degree of enrichment of 2 or −2. Briefly, GSEA identifies biologically relevant groups of genes based on published or computationally predicted relationships. For example, the MIR330 gene set includes genes with predicted binding sites for the miRNA MIR330. See publication for full description of GSEA methodology . All pathways displayed in the paper were significantly enriched (q < 0.05). We also used Ingenuity Pathway Analysis software  to identify upstream regulation pathways with multiple hypothesis testing corrected (FDR) P values less than 0.05. The R environment was used to create all graphics and perform all associated statistical analyses. The t test was used to test against the null hypothesis of no difference between groups with the exception of P values displayed. In χ2 test comparing all probes to significantly altered probes was used for Functional pathway analysis was performed using Gene Set Enrichment Analysis  with the rank-based method for molecular profiling data and the Molecular Signatures Database v7.4. Significantly enriched pathways were those with a FDR (corrected for multiple hypothesis testing) Publications with social determinants of health-related epigenetic analysis were mined . All stuThe data generated in this study are available within the article and its Supplementary Data files.n = 1,424) and 18.2% AA (n = 317)] identified around 70 significantly altered transcripts when all tumors from AA and EA patients were combined and compared  as well as in other pathways of interest, such as the Wnt signaling pathway, and the pathway associated with the histone-modifying polycomb molecules We defined multiple, interconnected genomic landscape, and individual locus changes correlated with racial groups in breast and gynecologic malignancies. Our study and other large-scale genomic analyses based on race support the hypothesis that epigenetic alterations may be important mediators of genomic differences. Given these and other data identifying epigenetic differences between racial groups, elucidating a link between social determinants of health, environmental factors, and downstream genome alterations is an important mission for future study . EpigeneTM4SF1 and SYN2 were identified as potential candidate environment–genetic mediators and significantly altered in the “Pan-Gyn” cohort (To explore this possibility more closely, we evaluated our identified significant methylation probes for known associations with social determinants of health factors. Both probes identified from tumor-specific methylation analysis and “Pan-Gyn” methylation analysis were evaluated. Published studies identifying methylations probes associated with smoking, maternal toxin exposure, and dietary alterations were mined and a library of probes was created . The 11 ” cohort . Yet onlOur study is notably restricted by limited numbers of samples from AA patients and limited clinical information about all patients. Factors such as smoking status, BMI, comorbid conditions, or place of residence cannot be assessed in our data set. Without this information, causal links between clinical outcomes and social determinants of health cannot be specifically identified. In addition, as with the majority of pooled sequencing studies, the skewing contributions of different types of cells within the tumor or tumor heterogeneity cannot be parsed.While this particular study cannot directly evaluate interactions between individual outcomes, race, and the environment, considering potential links between social determinants of health and tumor biology represents a new framework for therapeutic focused translational research.Supplemental Table 1Differentially expressed transcripts in Pan-Gyn cohort EA vs AAClick here for additional data file.Supplemental Table 2Differentially expressed transcripts in individual tumor typesClick here for additional data file.Supplemental Table 3Gene Set Enrichment Analysis of transcriptional changes in AA samplesClick here for additional data file.Supplemental Table 4Differentially expressed microRNAs in Pan-Gyn cohort EA vs AAClick here for additional data file.Supplemental Table 5Differentially methylated probes in Pan-Gyn cohortClick here for additional data file.Supplemental Table 6Differentially methylated probes analyzed by tumor siteClick here for additional data file.Supplemental Table 7Differentially methylated probes identified independently in epigenetic studies of environmentClick here for additional data file.Supplemental Figure S1Detailed patient racial assignments by tumor type and additional pathway analysisClick here for additional data file.Supplemental Figure S2Analysis of mRNA expression by tumor typeClick here for additional data file.Supplemental Figure S3Expression levels of RNA-processing pathway membersClick here for additional data file.Supplemental Figure S4Analysis of methylation changes by tumor typeClick here for additional data file.Supplemental Figure S5Analysis of differentially methylated probe locationsClick here for additional data file.


Genome-scale data have revealed daily rhythms in various species and tissues. However, current methods to assess rhythmicity largely restrict their focus to quantifying statistical significance, which may not reflect biological relevance. To address this limitation, we developed a method called LimoRhyde2 (the successor to our method LimoRhyde), which focuses instead on rhythm-related effect sizes and their uncertainty. For each genomic feature, LimoRhyde2 fits a curve using a series of linear models based on periodic splines, moderates the fits using an Empirical Bayes approach called multivariate adaptive shrinkage (Mash), then uses the moderated fits to calculate rhythm statistics such as peak-to-trough amplitude. The periodic splines capture non-sinusoidal rhythmicity, while Mash uses patterns in the data to account for different fits having different levels of noise. To demonstrate LimoRhyde2’s utility, we applied it to multiple circadian transcriptome datasets. Overall, LimoRhyde2 prioritized genes having high-amplitude rhythms in expression, whereas a prior method (BooteJTK) prioritized “statistically significant” genes whose amplitudes could be relatively small. Thus, quantifying effect sizes using approaches such as LimoRhyde2 has the potential to transform interpretation of genomic data related to biological rhythms. Much of life on Earth shows rhythms that follow the ~24-hour cycle of day and night. To produce these daily rhythms, each organism has a system of cell-autonomous oscillators, or circadian clocks, that senses environmental cues and drives cellular, physiological, and behavioral outputs . In mammNonetheless, current methods for analyzing rhythms in genome-scale data have multiple limitations. Some methods require that timepoints be equally spaced, or assume that rhythms are sinusoidal. Most importantly, almost all current methods—including our method LimoRhyde —focus onAn alternative to calculating p-values is to estimate effect sizes such as log fold-change or rhythm amplitude directly. The broader field of genomics has developed multiple methods for estimating effects –14, whicTherefore, we developed LimoRhyde2, a new approach to quantify rhythmicity in genomic data. LimoRhyde2 builds on previous work by our group and others to rigorously analyze data from genomic experiments ,16,17, chttps://limorhyde2.hugheylab.org. Code, data, and results for this study are available on Figshare (https://doi.org/10.6084/m9.figshare.22001519).The LimoRhyde2 R package is available at Similarly to LimoRhyde, LimoRhyde2 starts by fitting a linear model to the measurements of each genomic feature in the dataset . By default in LimoRhyde2, the model terms for time  are based on a periodic cubic spline with three internal knots. Alternatively, the terms for time can be based on sine and cosine components . The model can include additional terms for covariates, e.g., to account for batch effects, but assumes all samples come from the same condition. Thus, the model could bemma-voom ,21, or Dmma-voom , all staTo reduce the dependence of the fitted curves on the locations of the spline knots if the linear model is based on periodic splines, LimoRhyde2 repeats the above procedure multiple times, fitting a series of models for each feature such that the locations of the knots in each model are shifted by a different amount. For The overall raw fit LimoRhyde2 then moderates the model coefficients to obtain posterior fits using multivariate adaptive shrinkage (Mash) , which uLimoRhyde2 then uses the moderated coefficients  to calculate the following rhythm statistics, i.e., properties of the fitted curve with respect to time between 0 and τ of each feature:mesor peak or maximum valuepeak phase trough or minimum valuetrough phase peak-to-trough amplitude root mean square (RMS) amplitude, calculated as To quantify uncertainty in the fits, LimoRhyde2 can draw samples from the posterior distributions computed by Mash. For each posterior sample, which corresponds to a set of possible model coefficients for each feature, LimoRhyde2 can calculate the expected measurement of each feature as a function of time (and possibly any covariates), as well as the corresponding rhythm statistics. LimoRhyde2 then uses the resulting posterior distributions to calculate quantities such as the 90%  credible interval, the Bayesian analogue of a confidence interval.Because amplitude estimates are non-negative, a credible interval based only on the posterior samples’ amplitudes would nearly always be strictly positive (implying that that feature is rhythmic), even if the rhythm were quite weak and the posterior samples’ phases were highly variable. To account for this uncertainty, LimoRhyde2 constructs credible intervals for peak-to-trough amplitude and RMS amplitude by first changing the sign (from positive to negative) of amplitudes for posterior samples whose peak phase is greater than 2-transformed expression values. For RNA-seq data from mouse liver (GSE67305) and suprachiasmatic nucleus (SCN) (GSE72095), we used seeker to download the sample metadata and to download and process the raw reads. We processed the data using Trim Galore for adapter and quality trimming [For microarray data from mouse lung (GSE59396), we used the seeker R package  to downltrimming , FastQC trimming  and Multtrimming  for qualtrimming  and tximtrimming  for quantrimming . Based oWe ran LimoRhyde2 using three knots (or using a cosinor model where noted), a period of 24 h, and either limma-trend (for GSE59396) or limma-voom (for GSE67305 and GSE72095). Where applicable, we calculated 90% equal-tailed credible intervals based on 200 posterior samples.https://github.com/alanlhutchison/BooteJTK). BooteJTK performs hypothesis testing for rank-order correlation between a feature’s time series and a set of reference waveforms, using parametric bootstrapping to account for measurement uncertainty. We ran BooteJTK using the default settings. In particular, we generated 25 bootstrap resamples of each gene’s time series, used a period of 24 h, treated samples collected 24 h apart as replicates, and searched for phases and asymmetries at 2-h intervals from 0 to 22 h. For each dataset, we passed BooteJTK the log-transformed microarray expression values or the log2(cpm + 1) values for the same set of genes that we passed to LimoRhyde2. We adjusted the resulting p-values using the Benjamini-Hochberg (BH) method to control the false discovery rate [To compare performance of a prior method for detecting rhythmicity in genomic data, we used BooteJTK  in which samples were acquired every 2 or 4 h for at least 24 h in a 12-h light:12-h dark cycle –33 Tabl. We usednot rhythmic) for each genomic feature, which can then be used for ranking. As LimoRhyde2 does not calculate p-values, but instead estimates a gene’s rhythm, a convenient way to use its output to rank genomic features is by posterior amplitude. Importantly, the raw fits do not account for standard error and are therefore unreliable indicators of true effect size.To compare LimoRhyde2 to a prior method for detecting rhythmicity, we analyzed the same data using BooteJTK , a refinAcross the three datasets, we found the adjusted p-values from BooteJTK were only modestly correlated with the amplitudes from LimoRhyde2 . AccordiAs a typical use case, we further analyzed the posterior fits and statistics from LimoRhyde2 for each dataset. Here, we also used LimoRhyde2’s ability to sample from the posterior distributions to quantify uncertainty as 90% credible intervals . As expeGenomic data have yielded valuable insights into circadian systems and their relevance to human health, but previous methods for genomic analysis of biological rhythms have multiple limitations, perhaps the most severe being an overreliance on p-values and statistical significance.LimoRhyde2’s posterior estimates—fitted curves, resulting statistics, and credible intervals—account for uncertainty in the raw estimates and for different patterns of rhythmicity. This aspect of LimoRhyde2 is enabled by Mash , whose E2CPM is likely a more promising target than one whose mRNA shows an amplitude of only 0.3.Traditionally, efforts to understand the core circadian clock in various species have focused less on rhythm amplitude than on period and phase. However, the goal of genomic studies is often not to understand the core clock itself, but rather to determine how the clock and daily rhythms influence physiology. In this respect, amplitude may be as relevant as period and phase. For example, other things being equal, a protein whose mRNA shows a rhythm amplitude of 3 logGiven the distinct goals of LimoRhyde2 compared to previous methods, the relevant differences between the methods’ output are not quantifiable in terms related to binary classification . Therefore, we opted to analyze real circadian transcriptome data rather than simulated data, whose generation requires many simplifying assumptions. Although the true effect sizes in these data are unknown, our results indicate that LimoRhyde2 efficiently prioritizes large effects that have functional significance in the circadian system.For example, among the genes ranked considerably higher by LimoRyde2 (amplitude) than by BooteJTK in the liver were Rgs16, Dhrs9, and Mt2. Rgs16 regulates daily rhythms of G-protein signaling in the SCN  and subsDespite its advantages, LimoRhyde2 still has limitations and opportunities for future improvements. Importantly, we have currently only validated LimoRhyde2 for quantifying rhythmicity within a single condition, not for quantifying differences in rhythmicity between conditions. Second, LimoRhyde2 assumes that the rhythms have a user-specified period shared by all genomic features. Although it is possible to run LimoRhyde2 multiple times on the same dataset, varying the period each time, we recommend instead using a single period and allowing the periodic spline to capture non-monotonic  rhythms. Third, LimoRhyde2 assumes that each feature’s rhythm is fixed. Consequently, LimoRhyde2 does not model amplitude decay , althoug2CPM in RNA-seq).Just as prior methods cannot determine what level of adjusted p-values qualifies as “significant” , LimoRhyde2 cannot determine what magnitude of a given rhythm statistic is biologically meaningful. Such values likely vary from one gene to another anyway. Thus, when performing follow-up computational analyses, we recommend using the full distribution of a given statistic rather than drawing artificial cutoffs such as “rhythmic” and “arrhythmic”. As a convenient reference for rhythm amplitudes, however, our results indicate that the core clock genes in wild-type mouse liver have a median peak-to-trough amplitude of ~3.3  Scatterplots of cosinor posterior peak-to-trough amplitude vs. spline posterior peak-to-trough amplitude for each tissue (indicated at top). Points represent genes. Dashed lines indicate y = x. (B) Time-courses of expression of genes labeled in (A) in the respective tissue (indicated at right). Points represent samples. Curves represent posterior fits for the two models.Figure S3LimoRhyde2 identifies generally strong rhythms of core clock genes. Posterior peak-to-trough amplitudes and corresponding 90% credible intervals for core clock genes in each tissue. Points represent genes, color represents peak phase for each gene. Dashed lines indicate 0 amplitude.Figure S4Distributions of rhythmicity based on LimoRhyde2 posterior statistics. Scatterplots of (A) peak-to-trough amplitude vs. mesor and (B) peak-to-trough amplitude vs. peak phase for genes in each tissue (indicated at top). Points represent genes. (C) Histograms of peak phase. All plots include only the top 25% of genes based on amplitude.