Multi-omics diagnostic panels for precision oncology

01Overview and value chain

Markers: [EC: IVDR 2017/746 | OECD: Biotech-health | Regulator: FDA (USA), EMA (European Union), NMPA (China)]

Multi-omics diagnostic panels for precision oncology combine multiple molecular data layers — genomic, transcriptomic and increasingly proteomic — from a single tumor or blood sample into an integrated profile, going beyond a single drug-label companion diagnostic to inform the full range of a patient’s treatment options. Tempus AI has launched its first whole-genome sequencing assay (xH) and a novel pan-cancer homologous-recombination-deficiency (HRD) RNA algorithm, building on its comprehensive genomic profiling panel used across a value-based community oncology network. Caris Life Sciences launched Caris Detect and introduced AI-powered molecular insights for non-small-cell lung cancer patients, alongside AI models predicting brain-metastases risk in breast and lung cancer, its Caris ChromoSeq assay, and its Caris Assure blood-based test — combining genomic, transcriptomic and proteomic layers into an integrated molecular profile. Sophia Genetics’ SOPHiA DDM platform signed a strategic collaboration with a leading global cancer center and launched AI-powered digital twins to enhance oncology treatment decision-making, alongside a Mount Sinai Health System collaboration. In China, Geneseeq Technology participates in the country’s precision-oncology genomic testing market. In India, 1cell.ai offers multi-omic oncology products including OncoIndx, OncoIndx 360, OncoTarget and Prime Plus.

The key directions of multi-omics diagnostic panels for precision oncology are:

1. AI-driven multi-omic sequencing platforms (AI Multi-Omic Sequencing): whole-genome and RNA-algorithm-based sequencing platforms generating a comprehensive genomic profile beyond a targeted gene panel — Tempus AI’s xH assay and pan-cancer HRD RNA algorithm.
2. Integrated genomic-transcriptomic-proteomic profiling (Integrated Molecular Profiling): combining DNA, RNA and protein-level data from a single sample into one treatment-matching report — Caris Life Sciences’ Caris Detect and Caris Assure.
3. Multi-omic data analytics for treatment decisions (Multi-Omic Decision Analytics): AI-powered platforms that integrate multi-omic data into digital-twin or decision-support tools for oncologists — Sophia Genetics’ SOPHiA DDM.
4. Regional multi-omic oncology panels (Regional Multi-Omic Panels): domestic multi-omic testing platforms adapted to local clinical and regulatory context — Geneseeq Technology, 1cell.ai.

Sectoral value chain

[tumor/blood sample collection] ──> [multi-omic sequencing & profiling (DNA+RNA+protein)] ──> [AI/bioinformatics integration]
                                                    │
                                       (treatment-matching report generation)
                                                    │
                                                    ▼
[oncologist treatment decision & outcome tracking] <─── [regulatory status (LDT/IVDR/NMPA)] <─────┘

Value chain levels

Cross-cutting technologies of the sector:

• Whole-genome and RNA-algorithm sequencing (AI Multi-Omic Sequencing): sequencing beyond a targeted gene panel captures a broader mutational and expression landscape, as in Tempus AI’s xH whole-genome assay and pan-cancer HRD RNA algorithm.
• Integrated multi-layer molecular profiling (Integrated Molecular Profiling): combining DNA, RNA and protein data from one sample, Caris Life Sciences’ core approach, surfaces treatment options a single-layer test would miss.
• AI decision-support and digital twins (Multi-Omic Decision Analytics): Sophia Genetics’ digital-twin technology models a patient’s likely treatment response from their multi-omic profile to support oncologist decision-making.

02US

The US anchors the deepest bench of multi-omic precision-oncology platforms, both recently public companies expanding their assay portfolios.

Tempus AI, Caris Life Sciences, FDA

• Tempus AI: launched its first whole-genome sequencing assay (xH) and a novel pan-cancer HRD RNA algorithm, building on its comprehensive genomic profiling panel used across a value-based community oncology network.
• Caris Life Sciences: launched Caris Detect and introduced AI-powered molecular insights for NSCLC patients, alongside AI models predicting brain-metastases risk in breast and lung cancer, its Caris ChromoSeq assay, and its Caris Assure blood-based test.
• FDA framework: multi-omic diagnostic panels operate predominantly as laboratory-developed tests (LDTs) under CLIA, given the breadth of markers analyzed exceeds what a single FDA-cleared companion-diagnostic panel typically covers.

03CN

China participates in the growing precision-oncology genomic testing market through domestic providers including Geneseeq Technology.

Geneseeq Technology, NMPA

• Geneseeq Technology: participates in China’s precision-oncology genomic testing market as part of the country’s growing domestic multi-omic testing base.
• NMPA framework: multi-omic oncology panels in China require NMPA approval as in-vitro diagnostic products, with the breadth of markers analyzed often necessitating multiple linked registrations.

04EU

Switzerland anchors Europe’s multi-omic decision-analytics capacity through Sophia Genetics, whose SOPHiA DDM platform partners directly with leading cancer centers.

SOPHiA DDM, IVDR

• Sophia Genetics: its SOPHiA DDM platform signed a strategic collaboration with a leading global cancer center and launched AI-powered digital twins to enhance intelligent decision-making in oncology, alongside a Mount Sinai Health System collaboration.
• IVDR framework: multi-omic diagnostic software and panels sold in the EU require conformity assessment and CE marking under the In Vitro Diagnostic Regulation (IVDR 2017/746).

05Leading companies and research institutes

06Tech stack and innovations

The stack layers whole-genome/RNA sequencing, multi-layer molecular profiling and AI decision-support analytics to turn a single sample into a treatment-matching report spanning far more therapy classes than a single-marker test could reach.

1. Whole-genome and RNA-algorithm sequencing (AI Multi-Omic Sequencing):
   • Tempus AI’s xH assay and pan-cancer HRD RNA algorithm extend genomic profiling beyond a targeted panel, capturing the fuller mutational and expression landscape used across its community oncology network.
2. Integrated genomic-transcriptomic-proteomic profiling (Integrated Molecular Profiling):
   • Caris Life Sciences combines DNA, RNA and protein-level data from a single sample (Caris Detect, Caris Assure, Caris ChromoSeq) into one report, with AI models further predicting risks such as brain metastases in breast and lung cancer.
3. AI-powered digital twins and decision support (Multi-Omic Decision Analytics):
   • Sophia Genetics’ SOPHiA DDM platform builds AI-powered digital twins from a patient’s multi-omic profile to model likely treatment response, deployed through direct collaborations with leading cancer centers.
4. Regional multi-omic testing capacity (Regional Multi-Omic Panels):
   • Geneseeq Technology and 1cell.ai build domestic multi-omic oncology testing capacity adapted to their respective markets in China and India.

07Value chains and production pipelines

Industrial pipeline of a multi-omics oncology panel (FDA LDT / EU IVDR 2017/746)

┌───────────────────────────┐      ┌───────────────────────────┐
│ 1. Sample collection      │ ───> │ 2. Multi-omic sequencing  │
│                            │      │    & profiling               │
└───────────────────────────┘      └───────────────────────────┘
                                                   │
                                                   ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 4. Report generation      │ <─── │ 3. AI/bioinformatics      │
│                            │      │    integration               │
└───────────────────────────┘      └───────────────────────────┘
              │
              ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 5. Regulatory status      │ ───> │ 6. Oncologist treatment   │
│                            │      │    decision                 │
└───────────────────────────┘      └───────────────────────────┘

Stage 1: Sample collection

A tumor tissue biopsy and/or blood sample is collected from the cancer patient, sufficient in quantity and quality to support genomic, transcriptomic and/or proteomic analysis from the same specimen.

Stage 2: Multi-omic sequencing and profiling

The sample undergoes whole-genome, RNA-expression and/or proteomic analysis, generating a raw dataset spanning multiple molecular layers rather than a single targeted gene panel.

Stage 3: AI and bioinformatics integration

Machine-learning algorithms integrate the multiple data layers into a unified molecular profile, the step where Tempus AI’s HRD RNA algorithm and Sophia Genetics’ digital-twin modeling operate.

Stage 4: Report generation

The integrated profile is translated into a clinical treatment-matching report identifying actionable targets across multiple therapy classes, not limited to a single drug’s companion-diagnostic claim.

Stage 5: Regulatory status

Most multi-omic panels operate as laboratory-developed tests (LDTs) under CLIA in the US, undergo IVDR conformity assessment in the EU, or receive NMPA approval in China, given the breadth of markers analyzed.

Stage 6: Oncologist treatment decision

The treating oncologist uses the integrated report to select among multiple potential therapy options, with platforms like Sophia Genetics’ digital twins additionally modeling likely treatment response to support that decision.