In the rapidly evolving field of oncology, precision and speed are critical. Enter Spectra, a cutting-edge clinical genomic solution designed to transform cancer diagnostics and treatment. Integrated within the flagship platform GenomeArc Horizon, Spectra is a game-changer for detecting and analyzing somatic variants with unparalleled efficiency and accuracy.

Comprehensive Somatic Variant Detection 

Spectra is a cutting-edge tool designed to identify a wide range of somatic variants from VCFs, including Single Nucleotide Variants (SNVs), Insertions/Deletions (Indels), and Structural Variants (SVs). From a pair of matched VCFs (germline and tumor), Spectra can detect tumor variants in just minutes. With a simple file upload and one step more, somatic variants are effortlessly identified. This comprehensive detection capability ensures that all mutational events occurring during tumorigenesis are meticulously profiled, providing researchers and clinicians with a complete picture of the genomic alterations driving cancer. By capturing the full spectrum of somatic variants, Spectra enables a deeper understanding of tumor heterogeneity, clonal evolution, and the molecular mechanisms underlying cancer progression. The Spectra module is compatible with all short-read and long-read technologies capable of profiling tumor DNA.

Somatic Variant Pathogenicity Prediction

Embedded within the GenomeArc Horizon platform, Spectra goes a step further by classifying somatic variants according to the stringent guidelines of the American College of Medical Genetics and Genomics (ACMG). This classification system provides critical insights into the pathogenicity of each variant, helping to distinguish between benign, likely benign, uncertain significance, likely pathogenic, and pathogenic variants. Such variant-level pathogenicity information is extremely informative for cancer therapeutics and intervention, as it guides the selection of targeted therapies, immunotherapies, and other precision medicine approaches tailored to the patient's unique genomic profile.

Cancer Type Prediction from Tumor or ctDNA

Spectra, by analyzing the effects of somatic variants in cell-free tumor DNA (ctDNA), can predict the likely cancer type associated with these mutations. This capability is particularly valuable for early detection and preventive interventions, as it allows clinicians to identify potential cancer risks before the disease progresses to advanced stages. For instance, if somatic variants are detected in ctDNA, Spectra can assess their impact on key cancer-related genes and provide insights into the probable cancer type, enabling proactive measures such as increased surveillance, lifestyle modifications, or early therapeutic interventions. Including pan cancer genes, these genes, such as TP53, BRCA1, and PTEN, often play critical roles in cellular processes like DNA repair, cell cycle regulation, and apoptosis. Spectra not only identifies mutations in these genes but also calculates the probability of different cancer types based on the specific mutation’s functional impact. For example, a mutation in TP53 might indicate a higher likelihood of cancers such as breast, ovarian, or colorectal, depending on the mutation’s context and effect. This probabilistic approach provides a nuanced understanding of cancer risk, helping clinicians tailor preventive strategies and therapeutic decisions to the individual patient’s genomic profile.

DNA Damage from Somatic Variant Signature

By analyzing somatic variant signatures, it can profile and predict DNA damage, providing researchers with critical insights into the molecular mechanisms driving cancer progression. This capability allows for the identification of specific mutational processes, such as those defective in DNA repair pathways, which are often key contributors to tumorigenesis. 

Multi-OMICs Integration with Somatic Genome

Spectra stands out as a versatile and comprehensive platform by enabling the integration of multi-OMICs data from tumors, providing researchers and clinicians with a holistic view of the complex regulatory networks driving tumorigenesis. In addition to its robust somatic variant detection and gene-level prediction capabilities, Spectra allows users to upload and analyze diverse data types, including single-cell RNA sequencing (scRNA-seq) matrices and proteomics mass spectrometry data. This multi-OMICs approach captures the intricate interplay between genomic, transcriptomic, and proteomic layers within the tumor, offering unprecedented insights into the molecular mechanisms underlying cancer development and progression.

For instance, patient tumor single-cell RNA sequencing data can be uploaded as a single-cell object matrix, or quantified proteomics data similarly can be uploaded within GenomeArc Horizon. This enables Spectra to dissect tumor heterogeneity at a cellular resolution in all three dimensions of OMICs. This allows researchers to identify distinct cell populations, track clonal evolution, and uncover rare cell subtypes that may contribute to therapy resistance or metastasis. By combining this information with somatic variant data, Spectra can correlate genetic mutations with specific transcriptional profiles, shedding light on how genomic alterations influence gene expression patterns within the tumor microenvironment.

Seamless Integration with Horizon Intelligence

Spectra is seamlessly integrated with Horizon Intelligence (HI), where advanced Large Language Models (LLMs) are connected. This integration enhances data interpretation, enabling clinicians to extract actionable insights from complex genomic data effortlessly.