HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction
Authors: Susu Hu
Qinghe Zeng
Nithya Bhasker
Jakob Nikolas Kather
Stefanie Speidel
Venue: ICLR 2026 Poster (2026)
Keywords: spatial transcriptomics
pan-cancer modeling
pathway-level coherence
computational efficiency
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: HistoPrism is a pan-cancer model that predicts spatial gene expression from histology with state-of-the-art accuracy, pathway-level coherence, and computational efficiency, bridging histology and transcriptomics for scalable clinical use.

Abstract: Predicting spatial gene expression from H\&E histology offers a scalable and clinically accessible alternative to sequencing, but realizing clinical impact requires models that generalize across cancer types and capture biologically coherent signals. Prior work is often limited to per-cancer settings and variance-based evaluation, leaving functional relevance underexplored. We introduce HistoPrism, an efficient transformer-based architecture for pan-cancer prediction of gene expression from histology. To evaluate biological meaning, we introduce a pathway-level benchmark, shifting assessment from isolated gene-level variance to coherent functional pathways. HistoPrism not only surpasses prior state-of-the-art models on highly variable genes and, but more importantly, achieves substantial gains on pathway-level prediction, demonstrating its ability to recover biologically coherent transcriptomic patterns. With strong pan-cancer generalization and improved efficiency, HistoPrism establishes a new standard for clinically relevant transcriptomic modeling from routinely available histology.

Published as a conference paper at ICLR 2026
HISTOPRISM: UNLOCKING FUNCTIONAL PATHWAY ANALYSIS FROM PAN-CANCER HISTOLOGY VIA GENE
EXPRESSION PREDICTION
Susu : Qinghe Zeng Nithya Bhasker Jakob Nikolas Kather Stefanie Speidel
'Translational Surgical Oncology, National Center for Tomor Disenses (NCTAUCC) Dresden, Germany
"Facllty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Tochnology, Gormany
"Helmholtz-Zaale Zentrrum Dresden- Rossenderf (HZDR). Dresden, Germany
"German Cancer Research Center (DKFZ). Ileidelhery. Germany
'Fikee Fresenius Center Digital Health, Faculty Medicine, TUD
University Technology. Germany
"Modical Oncology, National Center for Tumor Discases (NCT), University Hospital Heidelberg, Heidelberg, Germany
DDepartment Medicine I, Faculty of Medicine. TUD Dresden University of Technology. Germany
"Pathology & Data Analyties, Leeds Institute of Medical Research at St James's University of Leeds, Leeds, United Kingskm
ABSTRACT
Predicting spatial gene expression from H&E histology offers a scalable and clin- ically accessible alternative to sequencing, but realizing clinical impact requires models that generalize across cancer types and capture biologically coherent sig- nals. Prior work is often limited to per-cancer settings and variance- evalu- ation, leaving functional relevance underexplored. We introduce HistoPrism, an efficient transformer-basee architecture for pan-cancer prediction of gene expres- sion from histology. To evaluate biological meaning. we introduce a pathway- level benchmark, shifting assessment from isolated gene-level variance to herent functional pathways. HistoPrism not only surpasses prior state-of-the-art models highly variable genes, but also more importantly, achieves substantial gains on pathway-level prediction, demonstrating its ability to recover biologi- cally coherent transcriptomic patterns. With strong pan-cancer generalization and improved efficiency, HistoPrism establishes a new standard for clinically relevant transcriptomic modeling from routinely available histology. Code available httpp://gttubb..oom/ssssss/fftff
INTRODUCTION
Spatial transcriptomics (ST) combines nigh-resolution imaging with transcriptomic profiling to map the spatial distribution of gene expression within intact tissues (Khan et al. 2024). By preserving spatial context, ST has enabled advances across developmental biology, oncology. immunology. and histopathology (Choe et al.) 2023). However, ST remains costly, labor-intensive, and not yet widely scalable. In contrast, hematoxylin and eosin (H&F) stained whole-slide images (WSIs) are routinely acquired in clinical workflows, motivating computational approaches to infer spatial gene expression directly from histology for cost-effective and scalable histogenomic analysis.
Early approaches to this problem often relied on complex, multi-stage pipelines involving brittle learning heuristics such as contrastive learning with ill-defined negative samples (Xie et al. 2023
Long et al.] 2023). retrieval-bar based inference schemes that limit generalization (Xie et al. 2023). or intricate multi-resolution engineering with significant computational overhead (Chung et al. 2024). Generative and contextual approaches, including diffusion- STEM (Zhu et al. 2025) and flow- based STFlow (Huang et al.) 2025a), model the uncertainty of one-to-many mapping between WSIs and gene expressions, but have been limited to single-cancer settings and are computationally in- tensive. Pan-cancer models, such as STPath (Huang et al. 2025b), achieve zero-shot generalization using masked gene prediction large-scale datasets. Nevertheless, they rely on stable gene-gent correlations, which can be inconsistent across heterogeneous tissues and sequencing techniques.
"Correspondence Susu Hu: susu. hu@nct-dresden

---

Published as a conference paper at ICLR 2026
Moreover, the evaluation of predicted gene expression has largely focused on Pearson correlation of top-N highly variable genes, neglecting functional coherence.
To address these gaps, introduce HistoPrism, efficient transformer- architecture for pan- cancer gene expression prediction, alongside Gene Pathway Coherence (GPC), a new evaluation framework based on 50 Hallmark gene sets and 87 Gene Ontology pathway gene sets. GPC quan- tifies the biological fidelity predictions by assessing pathway-level coherence, moving beyond variance-based metrics. Our pan-cancer benchmark shows that HistoPrism delivers state-of-the-an performance in both top-N variable gene prediction and pathway-focused prediction, while main- taining a substantially smaller and more computationally efficient footprint. Crucially, pathway- focused evaluation is key for identifying models suitable for clinical use, as it prioritizes biological interpretability rather then relying solely on aggregated accuracy.
2
RELATED WORK
2.1 COMPUTATIONAL PREDICTION OF SPATIAL TRANSCRIPTOMICS
Regression- Approaches. Early methods typically framed histology-to-gene prediction as a regression problem. BLEEP (Xie et al. 2023) employs contrastive learning align paired histology and gene expression into a joint embedding space, enabling inference via nearest-neighbor matching. However, defining negative pairs in pathology remains ambiguous, and retrieving-based inference limits generalization to unseen queries. GraphST (Long et al. (023) incorporates spatial structure through graph neural networks (GNNs), but inherits similar weaknesses from contrastive training. TRIPLEX (Chung et al. 2024) introduces multi-resolution architecture with distillation losses capture both local and global context, yet its complexity results in high computational cost and reduced interpretability.
Generative Approaches. More recent work has reframed this task through generative modeling (Zhu et al.] 2025 Huang et al.] 2025a), motivated by the inherently one-to-many mapping from histology to gene expression (Zhu al. 2025). These methods aim to capture distributions of plausible expression profiles, but have thus far been validated primarily in single-cancer settings. Extending them to pan-cancer prediction introduces a far greater challenge, where heterogeneity across multiple cancer types raises concerns of scalability and mode collapse.
notable advance is STPath (Huang et al. 2025b), a pan-cancer foundation model built on a BERT- style framework (Devlin et al. 2019). Using masked-gene modeling massive 38k gene panel, STPath learns complex contextual dependencies and achieved a new state-of-the-ar on standard variance-based benchmarks. However, this strategy implicitly assumes that gene-gend correlations are stable signals, an assumption that often breaks down in heterogeneous, tissue-specific pan-cancer settings. In practice, the model's considerable size makes training and fine-tuning highly resource- intensive, which can be a barrier for broad adoption and adaptation in many research and clinical settings.
Our Approach. We propose HistoPrism, a transformer-based model that leverages rich visual features to predict gene expression pan-cancer datasets. Its design effectively captures vi- sual-molecular relationships and supports pathway-level prediction coherence, achieving state-of- the-art predictive performance while being more efficient and practical for clinical deployment than previous approaches.
2.2 FOUNDATION MODELS IN DIGITAL PATHOLOGY
The advent of self-supervised learning on massive, gigapixel-scale datasets has given rise to power- ful Pathology Foundation Models (PFMs). Models such as CTransPPth (Wang et al.] (022). GigaP- ath (Xu et al.) 2024), and UNI (Chen et al. 2024b) are pre-trained on millions of histology patches, learning rich visual representations of tissue morphology that are highly effective for wide range of downstream tasks. These PFMs serve as a crucial backbone for modern computational pathol- ogy, including the prediction of spatial gene expression. With PFMs standardizing the extraction of high-quality, patch-level visual features, the core research challenge shifts from feature engineering to the subsequent problem: modeling how these patch representations can be contextually integrated
2

---

Published as a conference paper at ICLR 2026
(a) WS1 Preprocessing
(b) Cancer-mware Cross-attention Block
'ype
ngs
Fenture
iing embbdddings
mg emberdings
embeddinng
embeddiings
img embeeddings
emthedrlings
energy
Okiput
[c] Context- aware Self-attention Block
(d) Gene Prediction
Concer-aware Bribeddings
Embeddlings
Ground Truch
Toan mer Layer
Trensforme Lager
img embedeings
ganw predictions
gane exprenssion
mg embeddings
Leyers
Img embedoingg
gane predictions
gene exprensazonn
embbeddings
embedelings
Figure I: HistoPrism architecture. Patch level image embeddings are obtained via pathology foun- dation models. A cross-atte coss-attention module injects pan-cancer conditioning, A Transformer Encoder models contextual relations before final MLP head regresses gene expression values.
and spatially structured to capture the underlying biology of the tumor microenvironment. Our work directly addresses this challenge.
METHODOLOGY
In this section, we first formally define the problem, then detail the HistoPrism architecture, its training objective and our gene pathway coherence evaluation framework.
3.1 PROBLEM FORMULATION
consider an H&E-stained whole-slide image, divided into non-overlapping patches. Each patch is represented by feature vector x; IP Dumg, extracted by pre-trained pathology foun- dation model (PFM). Spatial transcriptomics provides the corresponding raw count vector of gene expressions, which normalize y; I Dgene using loglp transformation. Additionally, each slide is associated with a global condition cancer type, encoded as a one-hot vector c E {0, Danco,
The goal is to learn parameterized mapping function fe Nx Donco) predicts gene expression from H&E image features. For each input patch feature x,, the model outputs gene expression vector y, = fe (X, c); where X denotes the of all patch embeddings. The model parameters 0 are optimized to minimize the difference between the predicted expression Y = ( and the ground-trum expression Y (yI,...,
N that
3.2 HISTOPRISM: A DIRECT-MAPPHE ARCHITECTURE
HistoPrism is transformer- based regressor designed for efficient and direct mapping from visual features to gene expression. It eschews the complex contextual reconstruction of prior work in favor of streamlined architecture that models cancer-uu cancer-aware aware contextualized pathology image features for corresponding gene profiles. The architecture, depicted in Figure consists of three main stages.
1. Pan-Cancer Conditioning via Cruss-Attention To make the model aware of the global cancer type, condition the visual features using cross-attention mechanism. The one-hot cancer type vector c is first projected into a dense embedding Cemb Pin. via a linear layer. This embedding serves the context for the cross-attention module, providing the Key (K) and Value (V), while

---

Published as a conference paper at ICLR 2026
the patch features X serve the Query (Q).
Q XW Q
K, V = Cemb W Kr
Cemb Wy
(2)
X CrossAttention K. V)
(3)
This allows the model modulate the patch representations based on the overarching cancer type, enabling it to learn both pan-cancer and cancer-specific histopathological patterns.
2. Contextual Aggregation with a Transtormer Encoder. The conditioned patch features Xood are first projected into a hidden dimension Diidee and then processed by a standard Transformer Encoder (Vaswani et al. 2017). This module captures both short and long-range spatial dependen- cies between patches, modeling higher-level tissue structures such as tumor boundaries and immune infiltration patterns. The output of the transformer, H
IR N Phidden, is set of contextually
rich latent representations for each patch.
Gene Expression Regression. Finally, a multi-layer perceptron (MLP) serves as the regression head. It takes the latent representation h; E Htatent for each patch and maps it directly to the predicted D Peene-dae sene-dimensionl gene expression vector yt.
y; = MMPPhea(hh))
(4)
HistoPrism is trained end-to-end by minimizing the Mean Squared Error (MSE) LMSE between the predicted and ground-truth gene expression values across all N patches.
MISE
1 N
Σ (ū (0)- SEN
(5)
Our design favors direct feature fusion over contrastive aligmment for regression tasks, employ- ing self-attention to robustly aggregate sparse biological signals from variable-sized tissue patches where standard pooling fails.
3.3 A FRAMEWORK FOR EVALUATING BIOLOGICAL COHERENCE
To rigorously assess model performance, we employ a two-tiered evaluation strategy. We first use the standard metrie for comparability with prior work and then introduce our proposed benchmark, which is designed to measure a model's ability to predict biologically meaningful expression pat- terns.
Baseline Metric: Highly Variant Gene Correlation. The standard protocol in this domain is to evaluate the Pearson Correlation Coefficient (PCC) between predicted and ground-truth expression for the top-N most highly-variant genes (HVGs) across test set. While this metric is useful for gauging a model's ability to capture high-magnitude signals, its clinical and biological relevance limited. It focuses on small, statistically-drigg subset of genes, ignoring thousands of others, and it fails to measure whether model has learned the coordinated expression patterns that de- fine a functional biological process. model can achieve high HVG while failing to generate biologically coherent predictions, thus limiting its translational potential.
The Gene Pathway Coherence (GPC) Benchmark. To address the limitations of variance-based metries, we propose the Gene Pathway Coherence (GPC) benchmark. Our goal is to bridge the gap between standard machine learning evaluation and the principles of biological inquiry. While computational biology has long relied on pathway analysis to understand function, this approach has not yet been formalized The GPC benchmark designed to fill this void. assesses a model's ability to reconstruct the coordinated expression of functionally related genes, thereby aligning the evaluation protocol with the true scientific objective of understanding cellular function.
standard benchmark for deep learning models in this domain.
The construction of benchmark follows rigorous, multi-stage curation process:
4

---

and Published as a conference paper at ICLR 2026
1. Source Curation: We begin by aggregating comprchensive of pathways from two authoritative, widely-used biological databases: the Hallmark gene sets from the Molecu- lar Signatures Database (MSigDB) (Broad Institute 2025), which represent well-defined biological states or processes, and the Gene Ontology (GO) database (Gine Ontology Con- sortium 2025), from which include terms for Biological Process (BP), Cellular Com-
ponent (CC), and Molecular Function (MF).
2. Size Filtering:
Recognizing that these collections contain thousands of pathways of vary- ing size, we first filter for those of a tractable and meaningful length. We retain only pathways containing between 50 and 100 genes, range that avoids both overly specific sets prone to noise and overly broad pathways. Hallmark pathways are retained in full, as there are only 50.
3. Redundancy Filtering: To create non-redundant benchmark, we address the significant topical overlap between pathways. We compute the Jaccard similarity. J(A, B) = |A n B|/|AU B), for all pairs of pathways (A, B) based on their member genes. For any pair where the similarity exceeds a threshold of 0.1, we iteratively remove the larger of the pathways until no pairs violate this condition.
For each pathway, the evaluation score is the computed across all of its member genes. Let y (x), denote the paired ground-trute truth and predicted gene expression sets for N whole-slide images (WSIs). Each WSI i contains n, patches, with y; Vr....., y, = ........ye Yim,] where Viy. E Re represent the expression vectors of Denn genes.
For each gene g {l,..., Dgene within WSI i, compute the Pearson correlation coefficient (PCC) across all patches:
cov(ying Ving Fi..g)
Fig o(ying) (fs.g))
where Yin.g Wiigs..... Men.g g and Vi..g [üilg.-.. Mingg] denote the expression profiles of gene g across all patches WSI i.
Given curated collection of M gene pathways P (Pr...., PM), where each Pm C {I...., Dgene) indexes the genes in pathway m, the final pathway-level coherence score is defined
Σ ri.g ger
By evaluating with biologically coherent patterns rather than variance alone, this framework yields a clinically relevant perspective on evaluating ST prediction performance.
EXPERIMENTS AND RESULTS
4.1 EXPERIMENTAL SETUP
We conduct experiments on the HESTIK dataset (Jaume et al. 2024). using two splits that retain the original hold-out test splits from HESKTIK HEST-Bench. Training and validation splits are stratified by cancer type. HESTIK is large-scale dataset aggregating 153 distinct cohorts from 36 independent studies. This collection encapsulates high inter-center variability, including diverse spa- tial transcriptomics technologies, staining protocols, and scanner vendors, ensuring that the holdout evaluation reflects true cross-center generalization. We also considered STimage- 144M (Chen al.) 2024a), another large-scale resource. However, we determined it was unsuitable for this study due to its use of non-stonme -tandadd,,,,, single-resolution image formats and its partial data overlap with HESTIk.
STPath serves as primary benchmark, as it outperforms MLP with UNI and GigaPath PFM, as well as two deep learning methods BLEEP, and TRIPLEX pan-cancer gene prediction. Due to limited computational resources and the unavailability of the STPath training code, only per- formed inference using their corresponding PFM GigaPath (Xu et al. 2024), which aligns with STPath's 's intended use a foundation model for inference.
Since frate-of-the regression models have already been extensively evaluated in STPath, we focus extending comparison with recent generative approaches. Specificallyy include STEM
5

---

Published as a conference paper at ICLR 2026
(Zhu et al.) 2025). a iiffussio-bbsed model, and STFlow al. 2025a), flow-matching generative model. Both were originally benchmarked on single-cancer datasets, whereas we evaluate their generalization in more challenging pan-cancer setting. Due to the computational cost of STEM and STFlow training, we restrict both models to the union of the top 50 highly variable genes across all cancer types. Although this smaller gene subset emphasizes the most variable signals, STEM performs significantly worse than other methods, calling into question the robustness of its original leave-one-out evaluation. Similarly, STFlow struggles generalize beyond single-cancer settings, underscoring the limitations of current generative models in capturing complex multimodal relationships between histology and gene expression across diverse tumor types.
Our proposed model HistoPrism consists of 1 cross attention layer with 4 heads and 2 transformer layers with heads and 256 hidden dimension receptively. HistoPrism trained end-to-end with UNI PFM (Chen et al. (2024b) with gene panel of size 38,982 curated by STPath. The training details are included Appendix B
4.2 HISTOPRISM ACIIEVSS STATE-OF-TIE--N PAN-CANCER PERFORMANCE
We first evaluate pan-cancer gene prediction performance the top 50 highly variable genes (HVGs) using Pearson correlation coefficient (PCC). As expected, STEM performs poorly in the pan-cancer setting, likely because diffusion-based models struggle capture the high heterogeneity and complex multi-modal relationships between histology and gene expression across diverse can- cer types. In Table [] we report both macro-average PCC, computed as the mean PCC across the two splits for each cancer type, and micro-average PCC, computed across all individual samples to account for class imbalance. Macro-average treats each cancer type equally, while micro-average reflects overall predictive performance weighted by sample counts. Detailed sample counts can be found in the Appendix 6 HistoPrism demonstrates competitive performance, slightly below STPath on macro-average PCC but higher on micro-average PCC. Since micro-average PCC captures per- formance across all samples, provides a more balanced view of predictive quality, highlighting HistoPrism's 's robustness across heterogeneous cancers.
Table Macro- and Micro-Average PCC f Top50 HVGs across different cancer types. Best bold.
Macro-Average PCC
Micro-Average PCC
Cancer Type
STPath
STFIow
STEM'
HistoPrism
STPath
STFlow
STEM
HistoPrism
CCRCC
0. 0.1177.001
0.140p.co
0.124..ozu
0,206,.oc
0.1170.146
0.1 0.140b.12
0.123p.043
0.2066.102
COAD
0,3933
0,346, 135
0.236y.001
0,353 195
0.459
0.394p.0ss
0.235p.021
0.397
HCC
0.094..00
0.070 oc1
0.0988.002
0.1
0.094
0.070y.0m
0.098
0.113
IDC
0.629..190
0.547p.080
0.178..0z
0.4776.019
0.629h.126
0.547p.080
0.178p.029
0.477a.019
LUNG
0.518
0.468o.07a ors
0.220p.01s
0.498o.c20
0.518p.o2s
0.468p.075
0.220p.01s
0.498
LYMPH IDC
0. 1828..07
0.185
0,1600.10
0.215
0.182p.ors
0.155p.054
0.160p.010
0.215n.002
PAAD
0.4933.100
0.420p 203
0. 1950.00
D. 0.4200.019
0.493..100
0.420b.20s
0. 0.195..064
0.420h.019
PRAD
0,257o.012
0,202. oLo
0.1855.088
0.324..co
0.255n.139
0.200p.107
0.184..0r0
0.317o.154
READ
0,280p.com
0,228p.07m
2o.012
0.2955.02a
0.279b.029
0. 0.228p.071
0. 0.220p.029
0.295u.037
SKCM
0.588..
0,503 102
0.228p.00s
0.5230.040
0.588
0.503
0.228a.06s
0.523p.0s6
Average
0.3
0.31 nor
0.1844.00
0.342 138
0.292
0.180p.0s1 0.247
0.318
STEM and STFiow trained only with 430 union top50 HVG genes due to limited computing resources.
4.3 BEYOND VARIANCE: HISTOPRISM CAPTURES COHERENT BIOLOGY IN LOW-VARIANCE PATHWAYS
We evaluated gene pathway coherence (GPC) for HistoPrism and STPath, on both Hallmark gene pathways and Gene Ontology pathways. HistoPrism demonstrates consistent gains, outperforming STPath on 86.0% of the 50 Hallmark pathways and on 74.7% of the Gene Ontology pathways. Beyond these overall win rates, stratifying pathways by variance level (Figure ( reveals a more fundamental distinction. HistoPrism achieves its largest gains on low-variance pathways, which are often associated with stable, core biological processes(Eiseneerg & Levanon 2013).
This comparison underscores a fundamental difference in modeling strategy: while STPath primar- ily leverages the most variable signals, HistoPrism's 's direct-mapping architecture effectively captures both high-varie variance genes and the subtler, coordinated expression patterns that define cellular pro- grams. These findings suggest that isolated gene-level variance-base metrics provide an incomplete

---

Published as a conference paper at ICLR 2026
assessment of model's ability to reconstruct biologically meaningful gene expression. More details can be found in Appendix
hallmark Pathway
0.90
--ctuntton staefounc (16.0))
0.23
14 cun opprttonss
Besrrgg Verieese Gevel (ntemaaleed
Matotrtmm POC (PanPehhiw))
0.26
8:25
Servel
0.24
0.20
0.10
2:25
0.22
0.24
(a) Hallmark gene pathway performance.
(b) Gene ontology pathway performance.
Figure 2: Comparison of gene pathway coherence (GPC) in PCC on both Hallmark gene pathways and Gene Ontology pathways.
4.4 HOLISTIC ASSESSMENT OF PREDICTED GENE EXPRESSION
We further evaluate the biological relevance of the predicted expression profiles by clustering all samples based on their predicted expression across the full set of 38k genes, and comparing the re- sulting clusters to the ground-truth cancer type labels. Table reports Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) between the predicted clusters and the true cancer types. Due to class imbalance, AMI serves as the more informative measure, while ARI reported for completeness. This evaluation provides holistic view of prediction quality beyond subset-based assessments, as successful clustering requires the model to generate biologically coherent rep- resentation across the entire transcriptome. HistoPrism achieves substantially higher scores than STPath, which we attribute to its architectural design. By contrast, the "fill-in -n-tthe-blanks objective of STPath, based tation. For pure predictive task where no gene information is provided at inference, this framework is suboptimal. Our proposed direct mapping is naturally suited for this modality translation task, avoiding the inductive bias of an autoencoder on a generative problem.
masked autoencoder, is architecturally optimized for reconstruction and impu-
Table 2: Quantitative comparison of downstream clustering utility (AMI/ARI). Best in bold.
Model
AMI
ARI
STPath 0.395
0.402o.016 0.623..015
HistoPrism
0.0210.001
4.5
AND SCALABILITY ANALYSIS
To assess computational efficiency, we benchmarked HistoPrism against the baseline STPath across forward-pass runtime, peak GPU memory, and FLOPs (Figure. 33.. Both models used identical image and gene embedding dimension and the same number of patches. Profiling shows that Histo- Prism consistently requires fewer FLOPs, less memory, and shorter runtimes than STPath, with the gap widening as patch counts increase. Notably, HistoPrism scales linearly across all three metrics, while STPath exhibits exponential growth, highlighting HistoPrism's deployment efficiency for real- world datasets exceeding 10k patches. Crucially, HistoPrism achieves this performance while being trained on only 500 whole-slide images, roughly half the data used for the STPath foundation model, underscoring its remarkable data efficiency. These efficiency gains especially critical in clinical settings, where computational resources are often limited, making HistoPrism a practical and scal- able solution for whole-slide image analysis. All experiments
single NVIDIA A100

---

Published as a conference paper at ICLR 2026
GPU with 100-run averages. FLOPs and peak memory show variance, and the inference- time standard deviation is negligible.
5000
STPath
60
STPath HistoPrism
2500
STPath
Time mmsiiide))
4000
HistoPrism
FLORS (GFLOPS)
2000
HistoPrism
3000
Memory
40
1500
2000
mggs
1000
20
1000
0
5000
10000
15000
20000
0
5000
10000
15000
20000
0
5000
10000
15000
20000
Number of Patches
Number of Patches
Number of Patches
Figure 3: Model efficiency comparison of HistoPrism and STPath in terms of forward pass runtime,
peak GPU memory usage, and FLOPs across different numbers of patches.
4.6 ABLATION STUDY
We conducted an ablation study to disentangle the contributions of cross-attention and explicit spa- tial priors shown Table conditioning on cancer type through cross-attention consistently improves performance, highlighting the importance of modulating local representations with global context. Surprisingly, however, adding explicit positional encoding (PE) yields no measurable ben- efit, contrary to common assumptions in Transformer-basee architectures. We hypothesize two rea- sons for this. First, the prediction task is predominantly local: the rich latent features extracted by UNI PFM already capture morphology within and around each patch, leaving little additional signal to be gained from absolute spatial coordinates. Second, in the absence of PE, the Transformer be- haves permutation-invariae set function, effectively leveraging the global compositional struc- ture of the tissue without being anchored to fixed positions.
Table 3: Ablation study of cross attention and positional encoding with predictive accuracy in PCC t on top50 HVGs. Best in bold.
Macro-Average PCC
Micro-Average PCC
Cancer Type
w/o CrossAttn
HistoPrism++PE
HistoPrism
w/o CrossAttn
HistoPrism+PE
HistoPrism
CCRCC
0.227p.0sa
0.236p.0r
0.2066 nos
0.498
0.236p 133
0.206y.102
COAD
0.289 026
0.385
0.353
0.299
0,427 0.08s
0.397
НСC
0.102 004
0,108p.028
0.1130.014
0.102
0.108p
0.1130.014
IDC
0.443g.030
0.420p.osa
0.4776.019
0.443p.030
0.420p osa
0.4776.019
LUNG
0.465p.02a
0.482p.022
0.498).
0. 0.465...00 028
0.482 022
0.498).000
LYMPH
0.230 oST
0.208g.05 051
0.2155.042
0.230 os?
0.208 051
0.2156.042
PAAD
0.368p.049
0.394p.os3
0.4200.019
0.368 o49
0.394..os oss
0.4206.019
PRAD
0,334 o38
0.321
0.324 8oo
0.325 134
0.310 134
0.3177.184
READ
0.224 121
0.258n.008
0.295 023
0.224
0.258
0.2950.087
SKCM
0,498
0.494..030
0,523 046
0.498 038
0.494.
0.523
Average
0.318p
0.33
0.342
0.306
0.31
0.318
To ensure fair comparison with STPath, we further ablated our model by replacing our PFM with the Gigapath used in STPath. As shown in Table 4 and Figurq4, this substitution results in only marginal performance differences,, indicating that our approach does not rely heavily on pretrained PFM representations. Therefore, exclude the of Gigapath in the main experiments to isolate the contribution of our architecture rather than external foundation model priors.
5 DISCUSSION
We introduced HistoPrism, direct-mapping transformer for pan-cancer spatial transcriptomies pre- diction, together with Gene Pathway Coherence (GPC), a benchmark that aligns evaluation with biological function. Variance-based metrics, while useful, a poor proxy for coordinated cellu-
8

---

Published as a conference paper at ICLR 2026
Table 4: Ablation study of PFMs and positional encoding with predictive accuracy in PCC 1 on top50 HVGs. Best in bold.
Macro-Average PCC
Miero-Average PCC
Cancer Type
STPath
HistoPrism-GGgaaPan
HistoPrism
STPath
Histo Grigm
HistoPrism
CCRCC
0.1177.001
0.2144.02
0.2066.00s
0.
0.214p.127
0.2066.108
COAD
0.3933.185
0,33
0.353p.125
0.459
0.375p.00s
0.3977.0m0
HCC
0.094o.0m 052
0.096p.015
0.113
0.094..052
0.096,
0.113
IDC
0.629
0.472g.057
0.4776:019
6.629
0.472
0.477%.011
LUNG
0.518 028
0.502p.015
0,498
0.518..02e
0.502
0.498 020
LYMPH IDC
0,182 075
0,227
0,215o.042
0. 0.182..076
0.227
0.215%.042
PAAD
0.493o.190
0.401p.ous
0.420p
0.493.1100
0.401
0.420
PRAD
0.2576.012
0.3466.04s
0.324g.030
0. 132
0,336
0.3177:134
READ
0.280o.030
0.2633.0ss
0.295o.023
0.279
0.263c.057
0.295o.037
SKCM
0.5886.118
0,460n. 180
0.523p.046
0.588o.11
0,460p.160
0.523o.046
Average
0.36lo.072
0.33
0.342 139
0.292
0.320
0.318
hallmark Gene
Gene
iistaeriin appetture 101
[ butteefuree (88.09)
Nitotrum PCC (Pare 3
0.25
Recrage Vorianse Gevet (hormational
6.25
0.24
0.22
0.29
0.18
0.25
0.25
0.55
0.20
0.30
(a) Hallmark gene pathway performance.
(b) Gene ontology pathway performance.
Figure 4: Ablation study of the impact of PFM Gigapath model HistoPrism's GPC perfor- mance.
lar processes. By shifting pathway-level structure, GPC provides more rigorous measure of performance.
Across experiments, HistoPrism outperforms strong baselines, including STPath, STFlow and STEM, not only on highly variable genes but also at the pathway level, where biological coher- ence critical. Global evaluation using 38,928 gene clustering further shows large gains in AMI and ARI, demonstrating that HistoPrism captures both tine-griiee gene programs and broad cancer- type organization. Crucially, we demonstrate that these gains are architectural and independent of the underlying feature extractor: while we utilized UNI features for benchmarking consistency, our ablations confirm that HistoPrism remains robust and effective when trained with GigaPath.
Beyond predictive performance, HistoPrism is optimized for resource-constraines settings, achiev- ing SOTA performance with only approximately 50% of standard training data and a minimal com- putational footprint. This efficiency directly supports clinical deployment in institutes where large- scale compute or massive annotated datasets are unavailable.
While work establishes robust predictive model, key avenue for future research is to enhance its biological interpretability. Moving beyond predictive accuracy to systematically identify the causal visual features and cellular concepts the model has learned will be crucial for its adoption as a tool for scientific discovery.
6 CONCLUSION
We presented HistoPrism, an efficient transformer for pan-cancer prediction of gene expression from histology. HistoPrism achieves state-of-the-art accuracy highly variable genes, stronger biolog-

---

Published as a conference paper at ICLR 2026
ical coherence at the pathway level, and superior global clustering performance (AMI, ARI) across 38k genes. In addition to accuracy and fidelity, HistoPrism offers major efficiency gains, enabling large-scale pan-cancer analysis lower cost. By introducing GPC, move evaluation beyond variance-based metrics toward functional interpretability. prerequisite for clinical relevance. To- gether, these advances highlight HistoPrism's 's potential to bridge histology and transcriptomies scale, bringing computational spatial genomics closer to practical deployment.
7 ACKNOWLEDGEMEET
This work is partly supported by the Federal Ministry of Research, Technology and Space in DAAD project 57616814 (SECAI, School of Embedded Composite AI, https://secai.org/) as part of the program Konrad Zuse Schools of Excellence in Artificial Intelligence.
CONFLICT OF INTEREST
JNK declares ongoing consulting services for AstraZeneca and Bioptimus. Furthermore, he holds shares StratifAI, Synagen, and Spira Labs, has received institutional research grant from GSK and AstraZeneca, as well as honoraria from AstraZeneca, Bayer, Daicch Sankyo, Eisai, Janssen, Merek, MSD. BMS, Roche, Pfizer, and Fresenius.
REFERENCES
Broad Institute. Molecular Signatures Database (MSigDB). https://www. www (ttp:://www.gsee-mmigghh.
org/gsea/msn /aggsea/msiglaae igb 2025. Accessed: 2025-09-10.
Jiawen Chen, Muqing Zhou, Wenrong Wu, Jinwei Zhang, Yun Li, and Didong Li. Stimage- Ik4m:
A histopathology image-gene expression dataset for spatial transcriptomics. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances Neural Informatinn Processing Systems, volume 37, pp. 35796-35823. Curran Associates, Inc., 2024a. http:/pppe...lleeeppp/////////// 2rs222/00cc2cccee22220999aaaeeee Paper Daaaaaaaaaaatte
URL https: otg.a//eeeeecctaa.....ooooeeaenn Bww.aanddeenhaareee_eeee
Track brack.pdff
Richard J Chen, Tong Ding, Ming Y Lu, Drew FK Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H Song, Muhammad Shaban, et Towards a general- purpose foundation model for computational pathology. Nature Medicine, 2024b.
Kyongho Choe, Unil Pak, Yu Pang, Wanjun Hao, and Xiuqin Yang. Advances and challenges in spatial transcriptomies for developmental biology. Biomolecules, 13(1):156, 2023.
Youngmin Chung, Ji Hun Ha, Kyeong Chan Im, and Joo Sang Lee. Accurate spatial gene expression prediction by integrating multi-resolution features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11591-11600, 2024.
Jacob Devlin, Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume (long and short papers), pp. 4171-411 1186, 2019.
Eli Eisenberg and Erez Y Levanon. Human housekeeping genes, revisited. TRENDS in Genetics, 29(1))556-557 5744, 2013.
Gene Ontology Consortium. Gene Ontology (GO). http:://geeentolooggy.o 2025. Ac- cessed: 2025-09-10.
Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, and Rex Ying. Scalable generation of spatial transcriptomics from histology images via whole-slide flow matching. In International Conference Machine Learning, 2025a.
10

---

Published as a conference paper at ICLR 2026
Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Rex Ying, and Wengong Jin. Stpath: A generative foundation model for integrating spatial transcriptomics and whole slide images. bioRxiv, pp- 2025-04 2025b.
Guillaume Jaume, Paul Doucet, Andrew Song, Ming Yang Lu, Cristina Almagro Pérez, Sophia Wag- ner, Anurag Vaidya, Richard Chen, Drew Williamson, Ahrong Kim, et al. Hest-lk: A dataset for spatial transcriptomics and histology image analysis. Advances Neural Information Processing Systems, 37:53798-5338 2024.
Shawez Khan, Jong Joo Kim, et al. Spatial transcriptomics data and analytical methods: an updated perspective. Drug Discovery Today, 29(3): 103889, 2024.
Yahui Long, Kok Siong Ang. Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij. Ao Chen, et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with graphst. Nature Com- munications, 14(1):1155, 2023.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural informa- tion processing systems, 30, 2017.
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang. Jing Zhang, Wei Yang, Junzhou Huang, and Xiao Han. mansformer-based unsupervised contrastive learning for histopathological image clas- sification. Medical image analysis, 81:102559, 2022.
Ronald Xie, Kuan Pang, Sai Chung, Catia Perciani, Sonya MacParland, Bo Wang, and Gary Bader. Spatially resolved gene expression prediction from histology images via bi-modal contrastive learning. Advances in Neural Information Processing Systems, (6676666-7667, 2023.
Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong. Zelalem Gero, Javier González, Yu Gu, Yanbo Xu, Mu Wei, Wenhui Wang, Shuming Ma, Furu Wei, Jianwei Yang. Chunyuan Li, Jianfeng Gao. Jaylen Rosemon, Tucker Bower, Soohee Lee, Roshanthi Weerasinghe, Bill J. Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, and Hoifung Poon. A whole-slide foundation model for digital pathology from real-world data. Nature, 2024.
Sichen Zhu, Yuchen Zhu, Molei Tao, and Peng Qiu. Diffusion generative modeling for spatially resolved gene expression inference from histology images. arXiv preprint arXi::2501.1559: 2025.

---

Published as a conference paper at ICLR 2026
A APPENDIX: THE USE OF LARGE LANGUAGE MODELS
This work benefited from the use of Large Language Models (LLMs) for minor tasks such as text and language polishing, as well as for suggesting memorable model names, LLMs were not used to generate scientific content, derive conclusions, or perform any form of data analysis. The authors are fully responsible for the entire content and integrity of this submission.
B APPENDIX: CODE, TRAINING CONFIGURATION, AND DATA SPLITS
All code, training configurations, and data splits are provided for reproducibility. The HESTIK dataset was obtained following the original publication (Jaume et al. [2024), and PFM preprocessing followed official repositories (Chen et al. 2024b, Xu 2024). Baseline models were imple- mented according to their official repositories(Huang al. 2025b Zhu et al. 2025). Scripts will be released upon acceptance.
B. IMPLEMENTATION AND EVALUATION DETAILS
Models trained end-to-end with MSE loss using the AdamW optimizer with learning rate x 1001 and weight decay 0.01. Training was run for up to 1000 epochs with early stopping patience of 30 epochs based on validation MSE, while convergence usually achieved after approximately 300 epochs. Gradient clipping with maximum norm of 1.0 was applied. Sample sizes for two
splits are shown in Table Sample size for each cancer type in test splits are shown in Table All experiments used PyTorch on a single NVIDIA A100 GPU.
Table 5: Number of samples in splits.
Splits
#Train
#Validation
#Test
Split 0
501
124
23
Split I
498
123
28
Table 6: Test set sample sizes across 10 cancer types in two splits.
# Samples
Cancer Type
Split 0
Split
Total
CCRCC
4
4
8
COAD
1
HCC
1
2
IDC
1
2
LUNG
1
2
LYMPHLDD
1
2
PAAD
1
2
PRAD
15
23
READ
2
2
4
SKCM
1
2
Total
23
28
51
C APPENDIX: GENE PATHWAY COHERENCE DETAILS
We compute the variance of each gene across the test set for two splits and discretize them into ten variance levels (1-10). For each pathway, we then calculate the unweighted average variance of its constituent genes to derive the pathway-level variance. The variance levels are summarized in Table 7 while Figure provides a more intuitive visualization of the distribution. Gene counts and average variances per pathway reported in Table
12

---

Published as a conference paper at ICLR 2026
Table 7: Gene variance level thresholds details.
Levels
Variance Threshold Split
Split I
Level
0.0000
>0.0000
Level 2
0.7525
>0.7614
Level > 0.8663
>0.4462
Level 4
0.9070
>088665
Level
> 0.9360
>0.9213
Level > 0.9662
>0.6609
Level 7
> 1.0079
> >1.014
Level
> 1.0698
>1.0899
Level 9
> 1.1764
>1.2015
Level
1.3743
1.3806
Variance Distribution Genes
3.0
Split
Split
2.5
Jensiyy
1.5
0.5
0.0
2
3
Variance
Figure 5: Gene variance distribution density plot.
13

---

Published as a conference paper at ICLR 2026
Table 8: Comparison of Hallmark pathway-level PCC across models.
Pathway ID
#Gemes Avg. Variance
Varlance Level
HistoPrisn
STPath
HALLMARK ADIPOGENESIS
60
1.0210
6.5
0.2597
0.2590
HALLMARK, OGRAFT DGRAFT_REJRRR REJECTION
67
1,3370
0.2292
0.2291
HALLMARK ANDROGEN RESPONSE
36
1.0500
7.5
0.3199
0.2907
HALLMARK ANGIOGENESIS
1,2810 16
0.2267
0.2239
HALLMARK JUUCTIINNCTTION
1.1703 71
0,2488
0.2534
HALLMARK APICAL SURFACE
1,2024 16
8,5
0.2222
0.2292
HALLMARK APOPTOSIS
1.1056 79
0.2716
0.2613
HALLMARK SEAARK.BILEACAOE BILE_ACID METABOLISM
28
1,0077
6.0
0.2136
0.1713
HALLMARK, CHOLESTEROL HOMEERSASS
1.0467 33
0.2660
MARLMARK.
COAGULATION
1.2280
8,5
0.2279
0.2257
MARK DNA REPAIR
40
0.9847
5,5
0.2738
0.2627
HALLMARK EPITHELIAL MESENCHYMAL TRANSITION
83
1.3331
9.5
0,2288
0.2286
HALLMARK ESTROGEN RESPONSE LATE
55
1.2174
8.5
0.2978
0.2781
HALLMARK FATTY ACID METABOLISM
35
1.0369
0.2748
0.2614
HALLMARK_G2M G2M.CHECKPPINT
64
1.0719
7.5
0.3008
0.2816
HALLMARK GLLCOLYSSS
1.0945
0.2735
HALLMARK, SHEDGEHOG.SIGNALIN
11
1,1500
8.0
0.2198
0. 1955
HALLMARK MAAAAAAAERE_JAAAA MOAAELEETAOOLSSSS
48
1.0743
0.2678
0.2446
HALLMARK HYPOXTA
71
1.1004
8.0
0.2717
0,2586
HALLMARK 2I 1.2_STATS_SST SEATSSSIGNALING
75
1.1960
0.2505
0.2330
HALLMARK IL6 JAK STAT3 SIGNALING
43
1.3526
9.5
0.2121
0.2101
HALLMARK INFLAMMATORY RESPONSE
1,3428 76
9.5
0.2070
0.2115
HALLMARK.IN INTERFERON ALPHHA.ESSPON
34
1.2280
9.0
0.2306
0.2185
HALLMARK INTERFERON GAMMA RESPONSE
77
1,2545
0.2166
0,2205
HALLMARK KRAS SIGNALING.
20
1,2615
1836
1787
HALLMARK. ARRASSIGNALLIA
70
1,3569
9.5
0.2309
0.2314
HALLMARK MITOTIC_ SPINDLE
1,0445
0.2795
HALLMARK, WARKMMATRRCOCCCCCCC ANTORILSSIGH SIGNALING
0.9656 67
5.0
0.3044
0. 0.2963
HALLMARK TARGETS,
HALL MARK MYC TARGETS V2
15
0,8886
3.0
0.1711
1.45
HALLMARK MYOGENESIS
HALLMARK NOTCH SIGNALING
16
1.1593
8.0
0,2535
0.2452
HALLMARK OXIDATIVE, PHOSPHORYLATION
0,8582
0.2435
HALLMARK P53 PATHWAY
66
1.1311
8.0
0.2665
0.265%
HALLMARK PANCREAS BETA.CELLS
1.4304
0.1312
0.
HALLMARK PEROXISOME
32
1.0145
7.0
0.2903
0. 0.2691
HALLMARK BAEAAARKKP33KKAAA1 PI3K_AKT ANTORSSIGNALING
1.0554
0.2813
0.2733
MALLMARK SAAARR_PPO PROTEIN SECRETION
33
0.9266
4.5
0.3179
0. 0.7979
MALLMARK
SEARKKREAATIVEE REACTIVE, OXYGEN SPECIES_AATIINA
0.9336 15
0.1843
0.1383
HALLMARK SPERMATOGENESIS
1.0683 16
7.5
0.2630
0.2342
MALLMARK TGE SETAA.IGNAALAAA LIGNALING
30
1.1092
7,5
0.2610
0.2466
HALLMARK TNFA SIGNALING VIA NFKB
71
1.2954
9.0
0.2369
0,2269
HALLMARK UNFOLDED PROTEIN RESPONSE
0.9069 24
4.0
0.2833
0.2455
HALLMARK.U W.E.PONNEEEDD
65
1.1875
8.5
0.2340
0.2265
HALLMARK UV RESPONSE UP
50
1,1027
7.5
0.3034
0.2699
HALLMARK, WNT_BETA, CATENIN SIGNALING
1.1335
MARK XENOBIOTIC SAULMARKXXFNOBBIOTE
MEAETTICCCMTTRROOO METAROLISV
53
1.0462
7.0
0.2771
0.2505
14

---

Published as a conference paper at ICLR 2026
Table 9: Comparison of GO pathway-level PCC across models.
Pathway
#Genes
Aug. Variance
Variance Level
PCC
HistoPrismm
STPath
GOEP ACTIVATION OF INNATE IMMUNE RESPONSE
100
1,0227
6.5
0.2366
0.2139
GOEP CELL ACTIVATION
100
1,1720
8,5
0123225
0.2218
GOEP CALCIUM ION TRANSPORT
100
1.1375
7.5
0.2446
0.2314
GOBP NEURON APOPTOTIC.PROO PROCESS
100
1,0951
7.5
0.2545
0.2654
GOBP.ROGULLA WOW.RRRGUULTTOOOOO &OLLECEEEEEEEEEEEC CCLLLLLLLAAEE CULLUUAR W...R.RISSPOOSEEE.. WUS.ONSE..O.OOOOO TO.GGOWTILINCTRR.COOO INCTORRSTTUUUUSS
100
1.1875
3,5
022655
0.2270
GOOC NOLLEGEEN DONEAININGG EXTRACELLUULLLACC MULUMIAXII
100
1 13504
9.5
01193
0.2088
GDCC GOLGI MEMBRANE
100
1.1275
7.0
0,2600
0.2531
GDDC MEMBRANE MICRODOMAIN
100
1.1672
8.5
0.2520
0.2464
GOBP CELL PROJECTION ASSEMBLY
100700
7.5
0.2177
0.2257
COBP LIPID LOCALIZATION
1,085
7.5
02170
0.2155
CORP MASCRONUTOPPAN
0.9778
6.0
0,209
0.2130
BOOCCJRESYNAAS
0.904
6.0
02710
0.2463
RERGULE ATTINUOE DORNDDFFENDOOOOO SEFEDOCYTOOO
11144
3.0
02231
0.330
CORP.RIRONLCC FOPERTIFINNCCO COVI PXOOGRRNRS
09136
40
0,2055
0.1648
CORP,ORGE
RIF.SIIHYTTTICCC PROCESS
1.0071
6.5
D22682
0.2728
GOOP REGULATTION OF ROGENESSS
1.1289
5.0
D.2237
GOME SIGINALING ING RECEPTOR REGULATOR ACTIVITY
1,3151
9.0
111967
0.1966
GOEP RHYTHMIC PROCESS
188576
7.0
B12516
GOCC NECLEIRRSSEECK
5.5
012361
CORP NEGIATIVE JEGUUL ATION AP3MOLECI FCU. ARFNNCCIOONN
5.0
0.2465
0.3355
GOBP RESPONSE, TO, ALCOHOL
5.0
0.2609
GOME GUANYL_ NUCT EOTCLEOTIDE, BINDING
1.0072
6.0
0.2679
0.2502
GOEP EPIDERMIS DEVELOPMENT
1,3658
B.2616
0.2556
GOEP POSITIVE REGUL
PHOSPIORYLATION
1.1214
5.0
0.2332
GOBP NEGATIVE REGULATION CILL CYCLE
1.1207
3.0
0.23387
0,2418
GOBP MALE GAMETIL GUNERATION
1.022
3.0
0.2438
0.2316
GOBP NEGATIVV, REGULATION AAALLATIONNOO OWCCCOOKINE.C...C PRODUCTTO
11551
3.0
0,2180
0,2130
GORPCCELLELA AR RESSPONSEE TO TOLINSULIN,,O J.L.NNUINNSTMUULSS STIMUUS
100S
7.0
0.2226
0,2204
SORP.MEMBRANT ANFI FSS, OORGANRI L.F, FAASSIL
1989
5.5
D.2445
0.3484
JEGUU ATION_OR OF TWT.OOOFTTTALOOO TRANSIATTION
O 09987
5.5
0.2642
0.3682
GOME ACTIN BINDING
181558
7.0
0.2779
0.2615
GOME PHOSFFIOLPPED BINDING
QU9090
6.0
0.2476
0,2410
GOME-TKANSC RIPT COMPPTTON.OOOO COA.CTVATTRRACTVVV
0.97399
6.0
0.2685
0.2424
86
10111
6.5
0.2003
0.2666
GODC MOTCCMITTICH HONDAHANI JM MATRIX
86
0.9381
4.5
112007
0.2763
GOME PHOSPHORIC JESEER RYDROI ASP_ACTIVIT
1,00000
7.5
(1:471
0.3423
GOEC.TRANCE TRANSFFRASSE SESSERRAF..CORPLL EX. TRANSFERRING BAW.IINNHHHOOPOOSSOOOOOOOOO CONTAONNNGO
0,9075
6.0
B.2660
GDEP PROTEIN LOCALLL IZATION, TO CBLL PERIPHERY
OL9TU1
5.5
02
0.2675
GDCC BASAL PART OF CELL
1.1528
8.5
1L27788
0.2734
GOME ENZYME INHIBITOR ACTIVITY
1.14
8.0
IL:2300
0.2259
GOEP PROTEIN LOCALIZATION TO EXTRACCELLUUA REGION
30
1.0677
7.0
1
0.22100
GOBP POSTTIVE REGULATION OF CELL CYCLE
1.12
7.5
0.2198 1
0.2419
GORP AMBSODALL.TYEEE TYPECCLL MIGRATION
1.1997
8.5
0.2417
0.2416
CORP CEELLCCLLLLLA RESHONSE TO RADIATION
1999
1.5
0.2265
0.2304
CORP REGLIL ATION OF INTR PRACELUULAA AR. TRANSPORT
10526
7.5
0.2655
0.3497
GOCC NUCLEAR MEMBRANE
2.0121
6.5
D12660
GOCC VESICLE LUMEN
7.5
0.2537
GOBP REGULATION OF MYELOID DIFERRNNIIATON
1.2307
5.5
0.2224
GOBPLENT CELL DI.. LILTTTTTTIIO
1.1662
5.0
0.1980
GORPJRDGUULAT CEWPPRRGULATTOO.L
0,2535
GOOC
TISSUF HOOMOSSTTAATT TSSSSS
0.3461
CORP RESPONSE MRM.HHHCCCCALL.O. SAAMICA DS
0.2238
0,3133
GOME CATALYTIC, ACTIVITY ACTINNGLON
0.2535
0.3400
GOME PHOSPHATASE, BINDING
1.1422
5.0
012587
0.2621
GOBP ALCOHOL METABOLIC PROCESS
1.0000
6.0
B123388
0.2239
GOME PROTEIN DETERRBBEEIZZATOONOOOO
1.9976
112661
0.2690
GOBP COGNITION
194499
7.0
012259
0.2274
COME, SUI FUR COMMOUUDD BIIDDING
1. 1182
9.5
012006
0.3054
GOEP JNK CASCADE
1.1452
7.0
1123354
0.2201
GOEP PROTEIN COMPLEX OL OLIGOMERIZATION
64
1.1248
7.0
1125200
0.2359
GOEP CARBOHYDRACD CEHRADRRATE DERIVATIVE, CATABOLIC PROCESSS
67
14H144
7.0
B.2002
0.1878
GOEP FROTEIN PROCESSING
67
198599
7.5
1.2410
0.2302
GOME DNA BINDING TRANSCRIPTION REPRESSOR ACTIVITY
65
1.1279
8.0
0.2318
0.2177
GOBP. ORGANIC ACID DIOSYNTHETIC PROCESS
54
1,0700
7.5
0.2436
0.2270
GOBP.RDGUL RRGULLATIOOOOOOOO DE LELELEIINNEE PRINSIC.APOOOOTIC
SANGAALNG.PATTWWWA
64
J. 1024
7.5
012275
0.2298
GOOC ISY SANNAAACCSPPPPIITICIICCC SPECIALIZZATION
83
10066
7.0
0,2580
0.2416
CORRLC ARTIL ACE, DEDEEL LOMMENT
67
1.2839
9.0
02166
0,2262
GROPP ADAPTVV SWP__THERMOCIFNFSS GOBPIGEL AND MORPHOGINEESSS
61
1. 1168
8.0
n23349
0.3470
61
1.3464
9.0
n.251
0.2470
GDEP REGUL ATION OF AEATTIVV OOYYGENN SPECIES METABOI IC.CCRCEESS
61
1,0733
7.5
n.2501
0.3354
COOC.PLASM MEMMIAAIL.SOON SIGNNNNGGNINNN, ALNNGNLLLLLROPOPOPPA WWCEPIIRROOOOOOOOOOOOO
80
1. 11808
8.5
0,2476
0.248
GORPLENDOTIOG LETTEELIUU.DD MONN_DEEELPPII
22899
900
0.2375
0.2355
GOOCCDOATED_ VFSICLF
19559
7.5
0.2825
0.363
GORPPREFIII ATIONUDE_CHI MAA.MMAAAAAAHHIII ADHESSON
1,2120
3.5
D.2603
0.3466
GROCCITY TOPO ASMIC_SIDE OF MEMBRANE
1,1110
8.0
0.2691
GORP.ANATER ANATOMICAL STRRUCCTRRSSRRURRRRRRRR TEREECURE-MAUUUATTIOO
1.1621
5.0
11231
GOEP. CELLULAR RESPONSE METAL_JON
1.8999
7.5
D.2679
0.2571
GOEP NEGATIVE REGULATION GENE EXPRESSION EPIGENETIC
5.0
1.1923
GOEP ESTABLISHMENT
POLARITY
1.9476
7.0
012370
0.2313
GOEP REGULATION OF CELL MORPHOGENESIS
19163
6.5
012193
COBP RESPONSI TO..TOPOLOIICALL JINOORECC PROTEIN
5.0
0.2880
GOUP SUNSORY PERCEPTION DORTTOE BINDING
5.0
0.2216
GOEP REGULATION AATTOLTTIONN OF RESPONSE WOUNDING
112354
0.2385
GOBP CELL CELL ADHESION PLASMA MEMBRANE ADHESION MOLECULES
1,2749
9.0
0.2401
15


Reviews:
[{"review_id": "GqgPOtzWSL", "reviewer": "Reviewer_PfAp", "summary": "This paper proposes a transformer, HistoPrism, to predict pan-cancer gene expression from histology and a new benchmark to evaluate performance. HistoPrism uses cross attention between the (H&E-stained whole-slide) images, after passing through a pre-trained pathology foundation model, and the cancer type, after passing through a trained linear layer. The new metric, called the Gene Pathway Coherence (GPC) benchmark, “assesses a model’s ability to reconstruct the coordinated expression of functionally related genes”. Compared to two baselines, HistoPrism achieves the best performance in 5 of 10 cancer types using a standard metric (correlation between the predictions and observations amoung highly variable genes). In the GPC benchmark, HistoPrism outperforms the baselines in at least 75% of pathways.", "questions": "- Given that earlier works focused on regression approaches, while more recent work have focused on generative approaches, why were you motivated to switch back to a regression approach? Is the inherently one-to-many mapping from histology to gene expression not a problem for your regression approach? \n- Can you comment on using an MSE loss on count data? Is this common with spatial transcriptomics data and are there any drawbacks? (e.g., rather than assuming say a Poisson likelihood)\n- Could you expand on how “Histoprism’s direct-mapping architecture effectively captures both high-variance genes and the subtler, coordinated expression patterns that define cellular programs”, while “while STPath primarily leverages the most variable signals”? Since you are minimizing a loss over all training samples, won’t the high variance genes still have the most influence?\n- As mentioned above, I would appreciate a more detailed explanation of how the GPC is computed and how it can be interpreted\n- HistoPrism makes use of a pretrained pathology foundation model. Do the benchmarks also make use of foundation models?", "limitations": "", "rating": 2, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper focuses on the application of ML methods to meaningful, real-world datasets, which is typically lacking in the ML field. The architecture is clearly explained and the experimental results are easy to follow. The proposed model shows competitive performance on a standard metric and improved performance on the proposed metric.", "weaknesses": "- There was not enough explanation of the GPC benchmark (e.g., no math). I would like to see an explanation that shows how it is computed from the data and model formulated in sections 3.1 and 3.2. I appreciate the effort to bridge the gap between standard ML and the computationally biology, but since this is an ML conference I believe more details should be provided. As it’s written I don’t understand how the GPC is computed. Note that I do not have a background in computational biology. \n- Since the argument is that this metric is more interpretable, I believe we need to see how the interpretation is useful. For example, are there biological insights that can be made from Figure 2? \n- The paper is quite short, so there is plenty of space to discuss the GPC benchmark. \n- One of the baselines, STEM, is trained on a subset of the data due to computational constraints. How would STEM look like in Figure 3?\n- As I read it, this seems like a fairly straightforward application of a transformer to the this data. Highlighting any especially challenging methodological choices would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DGWI2QWnCs", "reviewer": "Reviewer_tWzV", "summary": "This paper introduces HistoPrism, an efficient transformer-based model for predicting pan-cancer gene expression directly from H&E histology slides. The authors make a dual contribution: the model itself, which is computationally efficient and uses cross-attention to condition on cancer type, and a novel evaluation benchmark called Gene Pathway Coherence (GPC).", "questions": "- The paper \"Modeling dense multimodal interactions between biological pathways and histology for survival prediction\" was one of the first papers introducing the relevance of biological pathways over individual gene expression in deep learning pipelines. Is there a reason why this paper was not cited in the background? Especially since this paper uses Hallmark and GO pathways which are shown to be utilized in the mentioned paper as well.\n- Some pathways act tissue-wide vs. some act in very specific regions. This can be inferred because of tumor gene expression heterogeneity known to the field. I am wondering what impact does patch size (at different magnifications as well as sizes, which would capture different amounts of morphological context) will have on the performance?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies a key weakness in prior work: evaluation is almost exclusively focused on a small number of highly variable genes, which have been used as the de-facto proxy for biological function. By creating a well-curated benchmark based on Hallmark and GO pathways, the authors are pushing the field toward more clinically and biologically meaningful evaluation.\n- The efficiency benchmarks in Figure 3, showing HistoPrism's linear scaling in time, memory, and FLOPs compared to STPath's exponential growth, represent a nice practical advancement for analyzing large-scale WSI datasets.\n- The finding that HistoPrism's gains are largest on low-variance pathways (which high variance genes-focused models miss) is a key insight that strongly supports the authors' approach.", "weaknesses": "- The paper compares HistoPrism (a regressive model using UNI features) to STPath (a generative masked-autoencoder using GigaPath features). The discussion notes that in the STPath paper itself, an MLP with UNI features outperformed an MLP with GigaPath features. How much of HistoPrism's superior performance, particularly in the holistic clustering task (Table 2), can be attributed to the better pathology foundation models (UNI vs GigaPath) versus the superior architecture (direct-mapping vs. masked-modeling)?\n- The GPC benchmark curation filters GO pathways to a size of 50-100 genes. This seems like a reasonable heuristic, but the justification is not fully explained in the paper. What was the rationale for this specific range, and does it risk biasing the benchmark toward or away from certain types of biological processes (e.g., large, complex signaling networks vs. small, tight functional units)?\n- The model's architecture is surprisingly shallow, using only 1 cross-attention layer and 2 transformer encoder layers. Given the lack of PE, this model is effectively a shallow set function. Did the authors experiment with the depth of the transformer encoder? How does the performance change with 4 or 6 layers, or is it insensitive to this hyperparameter?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8y9IIG4vH0", "reviewer": "Reviewer_mGHu", "summary": "This paper proposes HistoPrism, a path-level gene expression prediction framework. Given an image patch in a pathology image, the image patch is then passed to a pretrained foundation model like UNI or Gigapath, inference and obtaining patch embeddings. Then given a one-hot vector of the oncology label, the embeddings and the vector are passed into a cross-attention network to associate the cancer-label with each patch. Then the image embeddings are then passed through a self-attention network. The authors claim this step allows the image embeddings to be oncology label aware and context aware. Finally, each embedding is then separately predicting its corresponding gene expression and guided by MSE loss between predicted and measured expression ground truth.", "questions": "1. The reviewer has questions on training details, it seems that each batch of sites/spots have to come from a single WSI or at least sites with the same type of cancer. How does the implementation work if there are different WSIs with different numbers of spots?\n2. Could the authors please provide more detailed and more straight forward ablation study results?\n\n\nThe reviewer recommends rejecting this paper and believes it is an interesting study for ST or computational biology workshops. The reviewer is unlikely to change their thought during the discussion phase.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. This work is enabled by the release of a large ST dataset like HEST-1K.\n2. This work tries to incorporate oncology labels as a prior to guide the image embeddings to capture information of different cancer types.", "weaknesses": "1. Figure 1 is not legible when printed out and read in an arms length. The caption of Figure 1 is not informative.\n2. It is great that the authors are trying to make further contributions after the release of HEST-1K but the reviewer thinks that this problem generally lacks the motivation to study. \n2. This paper’s contribution is quite minimal, the reviewer thinks it does not fit the ICLR standard. The method proposed and experiments conducted in this paper should be more of a workshop exploration.\n3. The results are comparable to STPath. There are a great number of methods in the MIL era when each patch was extracted and then used for some downstream process either together or separately. Only having STPath to compare to is not making the performance or the novelty of HistoPrism trustworthy.\n4. The ablation study is not robust. Instead of ablating, the authors are trying to add more design (positional encoding) into the proposed pipeline. What the reviewer was expecting would be the author ablating/replacing the cancer-aware part with some other jointly learning paradigm, or replacing the context-aware part with some other more basic non-transformer or non-attention setup.\n5. Out of distribution cases were not considered at all. The task or the problem that this paper defines is essentially a supervised learning problem. However, this task would require too many assumptions to hold to obtain a useful learned model in future applications. First, each pathology slide has to have a clear cancer hallmark. Then the PFM applied to construct the image embeddings has to prevent domain shift, which is a great drawback or limitation of current foundation models. Using the embeddings generated from PFM and incorporated with an oncology label and directly regressing on to gene expression contains too much noise and variability between each step and the reviewer is skeptical on how the proposed method is going to transfer to real-world images. Lastly, it is kind of a loop or dilemma for the performance guarantee of this task. To obtain a perfectly working pathology to expression model, one should gather as many as image and expression pairs which means more ST data generated would improve the ability of HistoPrism, however, ST are very expensive to obtain and if you have the ability to obtain ST, the meaning of HistoPrism goes to zero. If one doesn’t have the original ground truth expression, then it would also be impossible to evaluate the accuracy or error of the expression predicted. \n\nMisc\nLine 133 yi should be y_i", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AtVSL00ECU", "reviewer": "Reviewer_TSpx", "summary": "This paper introduces HistoPrism, a transformer-based model that predicts spatial transcriptomic gene expression from H&E histology images across multiple cancer types. The paper also introduces a novel Gene Pathway Coherence (GPC) evaluation framework that shifts the focus from gene-level variance to pathway-level predictions, to have a more functionally meaningful benchmark for model performance. Using the big HEST1k dataset, the authors claim that HistoPrism achieves better performance than STPath and STEM baselines.", "questions": "1. What hyperparameters ranges were used for STPath and HistoPrism, and do we know whether simplifying STPath to a computational power similar to HistoPrism would bring STPath's performance too down, or still get similar results?\n2. Given the multiple datasets in HEST1k, did the authors check whether the models bring better/worse performances for each one of those internal datasets (instead of just focusing on the different cancer types)?\n3. Can the authors please better explain how the GPC is calculated? It would be useful to clarify other confusing points about this metric: (1) what is the variance levels mentioned in section 4.3? (2) From figure 2, it seems that this metric necessarily needs 2 different methods to be calculated, does it mean it cannot be calculated independently for a single method? \n4. Have the authors analysed ways to interpret the model's predictions? In other words, what explainability mechanisms could be applied in this model?\n5. How well would HistoPrism perform on data outside HEST1k, and what is the strategy for unseen cancer types? Would we have to retrain the model? This is important to understand because the way I understand the model, it seems we need to have cancer information to run inference whereas that doesn't seem to be needed for STPath.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Domain-specific knowledge is leveraged to inform the way the K / V / Q values are used in the architecture, demonstrating originality in adapting known models to a real-world dataset. \n2. Introduction of a new benchmark metric (GPC) to move beyond gene-wise correlation metrics and focus on the pathway level, thus contributing to the significance of this work.\n3. A good ablation analysis is conducted, indicating important information about the model, though it doesn't bring consistent differences across all cancer types.", "weaknesses": "1. Overall, I don't think the results are strong. With such a rich body of literature highlighted in section 2, using only 2 baselines limits the confidence of these results. Furthermore, the fact is that in table 1 STPath and HistoPrism have very comparable results and the claims of achieving state-of-the-art performance seem a bit exaggerated given the variable performance across different cancer types. The scalability analysis (section 4.5) could also be improved because is not clear how much hyperparameter optimisation played a role here and how hyperparameters were differently optimised for STPath and HistoPrism (could we simplify STPath without reducing performance?). Figure 3 is also missing the confidence intervals across the 100 runs for each number of patches. Finally, I find a bit unexpected that the paper seems to have criticised the over-reliance on standard correlation (PCC) metrics in previous works, but then conducts the ablation analysis only on those PCC-based metrics, which didn't have consistent results across all cancer types (thus raising the question of whether the GPC metric could bring better insights in this ablation analysis).\n2. Section 3.3 (where the GPC benchmark is introduced) does not seem to include important details to understand how this metric is actually calculated, thus impacting the readability/clarity of this paper given its central role. \n3. The model architecture explicitly conditions on cancer type through a cross-attention mechanism, which in my opinion introduces a concern that in practice HistoPrism might be leveraging prior knowledge of the tumour’s (averaged) distribution rather than extracting novel histopathological biomarkers. As a result, it would be important to test its performance on a completely unknown cancer type to understand whether it can truly generalise to all types of cancer. I think this point is even more important for this paper given it claims pan-cancer generalisation.\n4.  The clustering analysis in section 4.4 shows that predicted expression profiles cluster by cancer type more accurately for HistoPrism than for STPath. However, since HistoPrism was explicitly given the cancer type as part of the input, it is not surprising that its predictions carry a strong cancer identity. In other words, it seems to me that the model had an easier job clustering by a label it already knew. This means the impressive clustering metrics in table 2 might not purely reflect novel biological structure of the data, but partly the reintroduction of the provided class information. The authors don't discuss this issue while claiming that HistoPrism produces more \"biologically coherent\" representations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)