2022 is shaping up to be a transformational year for Multiplex Tissue Analysis (MTA) and Digital Pathology (DP), with ~20 clinical and marketing partnerships already announced these past ~4 months, and >2X the volume of spatial-omic activity seen at AACR vs prior years. In this study, we take a deep dive into the commercially lead (pharma or Dx / tools manufacturers authors) MTA / DP activity presented at AACR 2022. In particular, we highlight trends in the applications these tools support (e.g., predictive vs prognostic vs biomarker discovery), what specific technical approaches are used (mIF, H&E, spatial RNA etc), and summarize which are the most active commercial players.
In total, ~100 AACR 2022 abstracts had commercially led MTA/ DP activity (clinical only, no in vitro / animal model studies). Key takeaways this year include:
- The majority (~65%) of activity was exploratory, but ~30% were for predictive / prognostic applications; DP was somewhat more clinically oriented, with ~50% abstracts outlining non-exploratory measures vs ~30% for MTA.
- Most DP was tied to MTA this year, with ~40% DP abstracts supporting mIF/mIHC or spatial RNA; ~25% of non-MTA DP focused on H&E, followed by monoplex IHC (especially PD-L1) staining (~20%)
o MTA activity centered on mIF/mIHC (~55%) with a median plex level of ~18, but with clinically linked signatures of <5-plex
- The most referenced MTA providers were NanoString (GeoMx, CosMx), Akoya (CODEX, Phenoptics), Neogenomics (MultiOmyx), and Ultivue (FlexVUE,InSituPlex); The most referenced DP vendors included PathAI, and Visiopharm; Nanostring, Akoya, and PathAI, in particular, had multiple direct mentions of supporting pharma-led studies
- ~25 pharma sponsors presented MTA / DP data this year, the most active of which were 3D Med, AstraZeneca, Roche/Genentech, Merck, and Genmab
o All studies using MTA / DP for ‘predicting therapy response’ had a pharma sponsor, reflecting the key role these players have advancing the clinical maturity / CDx utility of these technologies
Key Technical and Application Findings
Figure 1. Summary of technologies utilized for MTA / DP abstracts
Figure 2. Summary of MTA / DP purpose in abstracts
DP shows significant activity with ubiquitous techssuch as IHC and H&E, but AI guided MTA had a non-trivial share – Even though ~50% of AI-based DP activity is based on low-plex technologies like IHC and H&E, ~30% of mIF activity showed some mention of AI-based interpretation. This activity is largely early-stage, with ~80% being used for exploratory purposes, reflective of biomarker discovery workflows.
MTA favors mid-plex proteomic techs, notably mIF/mIHC,but ultra-high-plex spatial RNA is playing a growing role; all approaches saw deplexing for clinical correlation – Combinatorial biomarker profiles linked to clinical outcomes are still typically limited to ~3 markers for MTA, though higher-plex technologies are used. Spatial RNA analyses showed increased prevalence at AACR this year, with ~90% of this activity for exploratory purposes, and those which were not exploratory whittled the 200+ gene panels down to <5markers when showing associations with response.
Median proteomic panel size for 2022 (including mIHC,mIF, and IMC) was ~18 markers. Median genomic/transcriptomic panel size for 2022 (largely made up of NSTG offerings) was ~1400 targets.
Though most activity for both MTA and DP was exploratory, closer-to-clinic applications differ for each tech; DP sees a higher percentage of prognostic usage, while MTA saw more post treatment response measures - DP supported ~2x more prognostic testing vs MTA, and AI-based H&E slide interpretation supported >50% of this activity within DP. MTA was ~5x more likely to be used for pre- vs post treatmentTME response correlation, likely due to the higher customizability for granular immunophenotyping (>90% mIF, ~10% spatial RNA).
Trial inclusion was somewhat rare, with MTA supporting these more than DP- >80% of trials were supported by mIF, and often accompanied by some sort of computational analysis, though explicit mention of AI-based algorithms were not present in most abstracts. 6/10 MTA abstracts with linked trials had matched outcomes data, notably linking CD3,CD8, CD68, and CD163 to survival / response.
Of the four DP abstracts linked to clinical trials, IHC and H&E-based AI analysis account for one trial each, and AI interpretation of duplex-IHC and 6-plex mIF made up the other two trials. Automated PD-L1scoring, in particular, saw multiple mentions of matched clinical outcomes
Commercial Player Overview
Figure 3. Summary of biopharma with >1 MTA/ DP abstracts; Note: ~20 other biopharma presented a single MTA / DP abstract at AACR
Figure 4. Summary of diagnostic / tools manufacturers with >1 MTA / DP abstracts; Note: ~35 other Dx / tools companies presented a single MTA / DP abstract at AACR
Dx / tools companies lead in the volume of activity, but pharma studies are more clinically advanced - There were ~2X more abstracts led by Dx / tools manufacturers vs pharma, but while Dx / tools vendors used MTA / DP for exploratory applications in ~80% of studies, this share was only ~30% for pharma led studies. Notably, all ~10 studies with MTA / DP used for “predicting therapy response” were implicated with pharma sponsors (AstraZeneca, BeiGene, Immunocore, BMS, SeaGen, ImaBiotech, Exscientia, Evotec, 3D Medicines; Roche / Genentech).
Despite significant recent MTA / DP partnering, pharma led studies rarely name supporting Dx / tools vendors – Only ~1/3 pharma led studies named a Dx / tool collaborator or specific instrument / assay, leaving much about their methods open to interpretation. As we see more prospective studies, we expect greater visibility into the supporting Dx partners, but from this particular congress,PathAI, Akoya, NanoString, Presage Bio, Shilps Sciences, and Veracyte are the names we see directly tied to pharma sponsored work (not all shown in figures 3-4).
Only ~15% of MTA / DP abstracts have an associated clinical trial; none of these prospectively mentioned spatial-omic involvement –Reflective of a broader industry trend, most of the trial matched MTA / DP data we see comes from retrospective studies, especially early-stage trials.~80% of MTA/DP matched trials at AACR are pre-Phase 2, and none of these describe spatial-omic or AI guided image analysis in the trial descriptions.
Looking at the broader MTA / DP trial landscape outside ofAACR, only ~30 industry-led studies explicitly mentioning MTA / DP have started since 2020. While ~45% of these are Ph2+, only ~1/4 have an industry primary author, with academic sponsors driving the vast majority of these studies.