Efficacy of Long-Term Spinal Nerve Posterior Ramus Pulsed Radiofrequency in Treating Subacute Herpetic Neuralgia: A Prospective Randomized Controlled Trial.

OBJECTIVE: This study aimed to observe the clinical efficacy of long-term spinal nerve posterior ramus pulsed radiofrequency (PRF) in treating subacute herpes zoster neuralgia (HZN). METHODS: A total of 120 patients with subacute HZN in the thoracolumbar region and back were equally randomized to the conventional PRF group (P group, n = 60), with a pulse of 180 s, or to the long-term PRF group (LP group, n = 60), with a pulse of 600 s. The patients' baseline characteristics, the incidence rate of postherpetic neuralgia (PHN), and the dose of analgesics were compared between the two groups. RESULTS: Based on the pain-rating index (PRI), the PRI-sensory, PRI-affective, visual analogue scale, and present pain intensity scores in the two groups were lower at T2, T3, and T4 time points than at the T1 time point after treatment (p < 0.05). After 2 months, the dose of analgesics was significantly lower in the LP group than in the P group (p < 0.05), and the incidence of PHN was considerably lower. CONCLUSIONS: Long-term spinal nerve posterior ramus PRF is a more effective treatment strategy for subacute HZN than conventional PRF. It can effectively prevent the occurrence of PHN.

High efficiency error suppression for accurate detection of low-frequency variants.

Detection of cancer-associated somatic mutations has broad applications for oncology and precision medicine. However, this becomes challenging when cancer-derived DNA is in low abundance, such as in impure tissue specimens or in circulating cell-free DNA. Next-generation sequencing (NGS) is particularly prone to technical artefacts that can limit the accuracy for calling low-allele-frequency mutations. State-of-the-art methods to improve detection of low-frequency mutations often employ unique molecular identifiers (UMIs) for error suppression; however, these methods are highly inefficient as they depend on redundant sequencing to assemble consensus sequences. Here, we present a novel strategy to enhance the efficiency of UMI-based error suppression by retaining single reads (singletons) that can participate in consensus assembly. This 'Singleton Correction' methodology outperformed other UMI-based strategies in efficiency, leading to greater sensitivity with high specificity in a cell line dilution series. Significant benefits were seen with Singleton Correction at sequencing depths ≤16 000×. We validated the utility and generalizability of this approach in a cohort of >300 individuals whose peripheral blood DNA was subjected to hybrid capture sequencing at ∼5000× depth. Singleton Correction can be incorporated into existing UMI-based error suppression workflows to boost mutation detection accuracy, thus improving the cost-effectiveness and clinical impact of NGS.

The lysine acetyltransferase activator Brpf1 governs dentate gyrus development through neural stem cells and progenitors.

Lysine acetylation has recently emerged as an important post-translational modification in diverse organisms, but relatively little is known about its roles in mammalian development and stem cells. Bromodomain- and PHD finger-containing protein 1 (BRPF1) is a multidomain histone binder and a master activator of three lysine acetyltransferases, MOZ, MORF and HBO1, which are also known as KAT6A, KAT6B and KAT7, respectively. While the MOZ and MORF genes are rearranged in leukemia, the MORF gene is also mutated in prostate and other cancers and in four genetic disorders with intellectual disability. Here we show that forebrain-specific inactivation of the mouse Brpf1 gene causes hypoplasia in the dentate gyrus, including underdevelopment of the suprapyramidal blade and complete loss of the infrapyramidal blade. We trace the developmental origin to compromised Sox2+ neural stem cells and Tbr2+ intermediate neuronal progenitors. We further demonstrate that Brpf1 loss deregulates neuronal migration, cell cycle progression and transcriptional control, thereby causing abnormal morphogenesis of the hippocampus. These results link histone binding and acetylation control to hippocampus development and identify an important epigenetic regulator for patterning the dentate gyrus, a brain structure critical for learning, memory and adult neurogenesis.

Deficiency of the chromatin regulator BRPF1 causes abnormal brain development.

Epigenetic mechanisms are important in different neurological disorders, and one such mechanism is histone acetylation. The multivalent chromatin regulator BRPF1 (bromodomain- and plant homeodomain-linked (PHD) zinc finger-containing protein 1) recognizes different epigenetic marks and activates three histone acetyltransferases, so it is both a reader and a co-writer of the epigenetic language. The three histone acetyltransferases are MOZ, MORF, and HBO1, which are also known as lysine acetyltransferase 6A (KAT6A), KAT6B, and KAT7, respectively. The MORF gene is mutated in four neurodevelopmental disorders sharing the characteristic of intellectual disability and frequently displaying callosal agenesis. Here, we report that forebrain-specific inactivation of the mouse Brpf1 gene caused early postnatal lethality, neocortical abnormalities, and partial callosal agenesis. With respect to the control, the mutant forebrain contained fewer Tbr2-positive intermediate neuronal progenitors and displayed aberrant neurogenesis. Molecularly, Brpf1 loss led to decreased transcription of multiple genes, such as Robo3 and Otx1, important for neocortical development. Surprisingly, elevated expression of different Hox genes and various other transcription factors, such as Lhx4, Foxa1, Tbx5, and Twist1, was also observed. These results thus identify an important role of Brpf1 in regulating forebrain development and suggest that it acts as both an activator and a silencer of gene expression in vivo.

The chromatin regulator Brpf1 regulates embryo development and cell proliferation.

With hundreds of chromatin regulators identified in mammals, an emerging issue is how they modulate biological and pathological processes. BRPF1 (bromodomain- and PHD finger-containing protein 1) is a unique chromatin regulator possessing two PHD fingers, one bromodomain and a PWWP domain for recognizing multiple histone modifications. In addition, it binds to the acetyltransferases MOZ, MORF, and HBO1 (also known as KAT6A, KAT6B, and KAT7, respectively) to promote complex formation, restrict substrate specificity, and enhance enzymatic activity. We have recently showed that ablation of the mouse Brpf1 gene causes embryonic lethality at E9.5. Here we present systematic analyses of the mutant animals and demonstrate that the ablation leads to vascular defects in the placenta, yolk sac, and embryo proper, as well as abnormal neural tube closure. At the cellular level, Brpf1 loss inhibits proliferation of embryonic fibroblasts and hematopoietic progenitors. Molecularly, the loss reduces transcription of a ribosomal protein L10 (Rpl10)-like gene and the cell cycle inhibitor p27, and increases expression of the cell-cycle inhibitor p16 and a novel protein homologous to Scp3, a synaptonemal complex protein critical for chromosome association and embryo survival. These results uncover a crucial role of Brpf1 in controlling mouse embryo development and regulating cellular and gene expression programs.

Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones.

Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance.

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

GO-function: deriving biologically relevant functions from statistically significant functions.

In high-throughput studies of diseases, terms enriched with disease-related genes based on Gene Ontology (GO) are routinely found. However, most current algorithms used to find significant GO terms cannot handle the redundancy that results from the dependencies of GO terms. Simply based on some numerical considerations, current algorithms developed for reducing this redundancy may produce results that do not account for biologically interesting cases. In this article, we present several rules used to design a tool called GO-function for extracting biologically relevant terms from statistically significant GO terms for a disease. Using one gene expression profile for colorectal cancer, we compared GO-function with four algorithms designed to treat redundancy. Then, we validated results obtained in this data set by GO-function using another data set for colorectal cancer. Our analysis showed that GO-function can identify disease-related terms that are more statistically and biologically meaningful than those found by the other four algorithms.

Clinical Validation of Human Papilloma Virus Circulating Tumor DNA for Early Detection of Residual Disease After Chemoradiation in Cervical Cancer.

PURPOSE: Most cervical cancers are caused by human papilloma virus (HPV), and HPV circulating tumor DNA (ctDNA) may identify patients at highest risk of relapse. Our pilot study using digital polymerase chain reaction (dPCR) showed that detectable HPV ctDNA at the end of chemoradiation (CRT) is associated with inferior progression-free survival (PFS) and that a next-generation sequencing approach (HPV-seq) may outperform dPCR. We aimed to prospectively validate HPV ctDNA as a tool for early detection of residual disease. METHODS: This prospective, multicenter validation study accrued patients with stage IB-IVA cervical cancer treated with CRT between 2017 and 2022. Participants underwent phlebotomy at baseline, end of CRT, 4-6 weeks post-CRT, and 3 months post-CRT for HPV ctDNA levels. Plasma HPV genotype-specific DNA levels were quantified using both dPCR and HPV-seq. The primary end point was 2-year PFS. RESULTS: With a median follow-up of 2.2 (range, 0.5-5.5) years, there were 24 PFS events among the 70 patients with HPV+ cervical cancer. Patients with detectable HPV ctDNA on dPCR at the end of CRT, 4-6 weeks post-CRT, and 3 months post-CRT had significantly worse 2-year PFS compared with those with undetectable HPV ctDNA (77% v 51%, P = .03; 82% v 15%, P < .001; and 82% v 24%, P < .001, respectively); the median lead time to recurrence was 5.9 months. HPV-seq showed similar results as dPCR. On multivariable analyses, detectable HPV ctDNA on dPCR and HPV-seq remained independently associated with inferior PFS. CONCLUSION: Persistent HPV ctDNA after CRT is independently associated with inferior PFS. HPV ctDNA testing can identify, as early as at the end of CRT, patients at high risk of recurrence for future treatment intensification trials.

Multimodal detection of molecular residual disease in high-risk locally advanced squamous cell carcinoma of the head and neck.

Up to 30% of patients with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) relapse. Molecular residual disease (MRD) detection using multiple assays after definitive therapy has not been reported. In this study, we included patients with LA-HNSCC (stage III Human Papilloma virus (HPV)-positive, III-IVB HPV-negative) treated with curative intent. Plasma was collected pre-treatment, at 4-6 weeks (FU1) and 8-12 weeks (FU2) post-treatment. Circulating tumor DNA (ctDNA) was analyzed using a tumor-informed (RaDaR®) and a tumor-naïve (CAPP-seq) assay. HPV DNA was measured using HPV-sequencing (HPV-seq) and digital PCR (dPCR). A total of 86 plasma samples from 32 patients were analyzed; all patients with at least 1 follow-up sample. Most patients were stage III HPV-positive (50%) and received chemoradiation (78%). No patients had radiological residual disease at FU2. With a median follow-up of 25 months, there were 7 clinical relapses. ctDNA at baseline was detected in 15/17 (88%) by RaDaR and was not associated with recurrence free survival (RFS). Two patients relapsed within a year after definitive therapy and showed MRD at FU2 using RaDaR; detection of ctDNA during follow-up was associated with shorter RFS (p < 0.001). ctDNA detection by CAPP-seq pre-treatment and during follow-up was not associated with RFS (p = 0.09). HPV DNA using HPV-seq or dPCR during follow-up was associated with shorter RFS (p < 0.001). Sensitivity and specificity for MRD at FU2 using RaDaR was 40% and 100% versus 20 and 90.5% using CAPP-seq. Sensitivity and specificity for MRD during follow-up using HPV-seq was 100% and 91.7% versus 50% and 100% using dPCR. In conclusion, HPV DNA and ctDNA can be detected in LA-HNSCC before definitive therapy. The RaDaR assay but not CAPP-seq may detect MRD in patients who relapse within 1 year. HPV-seq may be more sensitive than dPCR for MRD detection.

Circulating tumour DNA kinetics in recurrent/metastatic head and neck squamous cell cancer patients.

PURPOSE: Immune checkpoint blockade (ICB) has become a standard of care in the treatment of recurrent/metastatic head and neck squamous cell cancer (R/M HNSCC). However, only a subset of patients benefit from treatment. Quantification of plasma circulating tumour DNA (ctDNA) levels and on-treatment kinetics may permit real-time assessment of disease burden under selective pressures of treatment. PATIENTS AND METHODS: R/M HNSCC patients treated with systemic therapy, platinum-based chemotherapy (CT) or ICB, underwent serial liquid biopsy sampling. Biomarkers tested included ctDNA measured by CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) and markers of host inflammation measured by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). RESULTS: Among 53 eligible patients, 16 (30%) received CT, 30 (57%) ICB [anti-PD1/L1] monotherapy and 7 (13%) combination immunotherapy (IO). Median progression-free survival (PFS) and overall survival (OS) were 2.8 months (95% CI, 1.3-4.3) and 8.2 months (95% CI, 5.6-10.8), respectively. Seven (13%) patients experienced a partial response and 21 (40%) derived clinical benefit. At baseline, median ctDNA variant allele frequency (VAF) was 4.3%. Baseline ctDNA abundance was not associated with OS (p = 0.56) nor PFS (p = 0.54). However, a change in ctDNA VAF after one cycle of treatment (ΔVAF (T1-2)) was predictive of both PFS (p< 0.01) and OS (p< 0.01). Additionally, decrease in ΔVAF identified patients with longer OS despite early radiological progression, 8.2 vs 4.6 months, hazard ratio 0.44 (95% CI, 0.19-0.87) p = 0.03. After incorporating NLR and PLR into multivariable Cox models, ctDNA ∆VAF retained an association with OS. CONCLUSIONS: Early dynamic changes in ctDNA abundance, after one cycle of treatment, compared to baseline predicted both OS and PFS in R/M HNSCC patients on systemic therapy.

Viewing cancer genes from co-evolving gene modules.

MOTIVATION: Studying the evolutionary conservation of cancer genes can improve our understanding of the genetic basis of human cancers. Functionally related proteins encoded by genes tend to interact with each other in a modular fashion, which may affect both the mode and tempo of their evolution. RESULTS: In the human PPI network, we searched for subnetworks within each of which all proteins have evolved at similar rates since the human and mouse split. Identified at a given co-evolving level, the subnetworks with non-randomly large sizes were defined as co-evolving modules. We showed that proteins within modules tend to be conserved, evolutionarily old and enriched with housekeeping genes, while proteins outside modules tend to be less-conserved, evolutionarily younger and enriched with genes expressed in specific tissues. Viewing cancer genes from co-evolving modules showed that the overall conservation of cancer genes should be mainly attributed to the cancer proteins enriched in the conserved modules. Functional analysis further suggested that cancer proteins within and outside modules might play different roles in carcinogenesis, providing a new hint for studying the mechanism of cancer.

eTumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients.

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn't favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the 'most recent common ancestor' of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.

HPV Sequencing Facilitates Ultrasensitive Detection of HPV Circulating Tumor DNA.

PURPOSE: Human papillomavirus (HPV) DNA offers a convenient circulating tumor DNA (ctDNA) marker for HPV-associated malignancies, but current methods, such as digital PCR (dPCR), provide insufficient accuracy for clinical applications in patients with low disease burden. We asked whether a next-generation sequencing approach, HPV sequencing (HPV-seq), could provide quantitative and qualitative assessment of HPV ctDNA in low disease burden settings. EXPERIMENTAL DESIGN: We conducted preclinical technical validation studies on HPV-seq and applied it retrospectively to a prospective multicenter cohort of patients with locally advanced cervix cancer (NCT02388698) and a cohort of patients with oropharynx cancer. HPV-seq results were compared with dPCR. The primary outcome was progression-free survival (PFS) according to end-of-treatment HPV ctDNA detectability. RESULTS: HPV-seq achieved reproducible detection of HPV DNA at levels less than 0.6 copies in cell line data. HPV-seq and dPCR results for patients were highly correlated (R 2 = 0.95, P = 1.9 × 10-29) with HPV-seq detecting ctDNA at levels down to 0.03 copies/mL plasma in dPCR-negative posttreatment samples. Detectable HPV ctDNA at end-of-treatment was associated with inferior PFS with 100% sensitivity and 67% specificity for recurrence. Accurate HPV genotyping was successful from 100% of pretreatment samples. HPV ctDNA fragment sizes were consistently shorter than non-cancer-derived cell-free DNA (cfDNA) fragments, and stereotyped cfDNA fragmentomic patterns were observed across HPV genomes. CONCLUSIONS: HPV-seq is a quantitative method for ctDNA detection that outperforms dPCR and reveals qualitative information about ctDNA. Our findings in this proof-of-principle study could have implications for treatment monitoring of disease burden in HPV-related cancers. Future prospective studies are needed to confirm that patients with undetectable HPV ctDNA following chemoradiotherapy have exceptionally high cure rates.

Tumor-Naïve Multimodal Profiling of Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Circulating tumor DNA (ctDNA) enables personalized treatment strategies in oncology by providing a noninvasive source of clinical biomarkers. In patients with low ctDNA abundance, tumor-naïve methods are needed to facilitate clinical implementation. Here, using locoregionally confined head and neck squamous cell carcinoma (HNSCC) as an example, we demonstrate tumor-naïve detection of ctDNA by simultaneous profiling of mutations and methylation. EXPERIMENTAL DESIGN: We conducted CAncer Personalized Profiling by deep Sequencing (CAPP-seq) and cell-free Methylated DNA ImmunoPrecipitation and high-throughput sequencing (cfMeDIP-seq) for detection of ctDNA-derived somatic mutations and aberrant methylation, respectively. We analyzed 77 plasma samples from 30 patients with stage I-IVA human papillomavirus-negative HNSCC as well as plasma samples from 20 risk-matched healthy controls. In addition, we analyzed leukocytes from patients and controls. RESULTS: CAPP-seq identified mutations in 20 of 30 patients at frequencies similar to that of The Tumor Genome Atlas (TCGA). Differential methylation analysis of cfMeDIP-seq profiles identified 941 ctDNA-derived hypermethylated regions enriched for CpG islands and HNSCC-specific methylation patterns. Both methods demonstrated an association between ctDNA abundance and shorter fragment lengths. In addition, mutation- and methylation-based ctDNA abundance was highly correlated (r > 0.85). Patients with detectable pretreatment ctDNA by both methods demonstrated significantly worse overall survival (HR = 7.5; P = 0.025) independent of clinical stage, with lack of ctDNA clearance post-treatment strongly correlating with recurrence. We further leveraged cfMeDIP-seq profiles to validate a prognostic signature identified from TCGA samples. CONCLUSIONS: Tumor-naïve detection of ctDNA by multimodal profiling may facilitate biomarker discovery and clinical use in low ctDNA abundance applications.

Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes.

MOTIVATION: According to current consistency metrics such as percentage of overlapping genes (POG), lists of differentially expressed genes (DEGs) detected from different microarray studies for a complex disease are often highly inconsistent. This irreproducibility problem also exists in other high-throughput post-genomic areas such as proteomics and metabolism. A complex disease is often characterized with many coordinated molecular changes, which should be considered when evaluating the reproducibility of discovery lists from different studies. RESULTS: We proposed metrics percentage of overlapping genes-related (POGR) and normalized POGR (nPOGR) to evaluate the consistency between two DEG lists for a complex disease, considering correlated molecular changes rather than only counting gene overlaps between the lists. Based on microarray datasets of three diseases, we showed that though the POG scores for DEG lists from different studies for each disease are extremely low, the POGR and nPOGR scores can be rather high, suggesting that the apparently inconsistent DEG lists may be highly reproducible in the sense that they are actually significantly correlated. Observing different discovery results for a disease by the POGR and nPOGR scores will obviously reduce the uncertainty of the microarray studies. The proposed metrics could also be applicable in many other high-throughput post-genomic areas.

Apparently low reproducibility of true differential expression discoveries in microarray studies.

MOTIVATION: Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries. RESULTS: Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes. Supplementaty information: Supplementary data are available at Bioinformatics online.

Cancer Biomarker Discovery for Precision Medicine: New Progress.

BACKGROUND: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. OBJECTIVE: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. RESULTS: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients' survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients' survival. CONCLUSION: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.