Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions

Domain: physics
Published: 2025-12-31
Editorial outcome: major_revision

Abstract
Emerging research has highlighted that artificial intelligence-based multimodal fusion of digital pathology and transcriptomic features can improve cancer diagnosis (grading/subtyping) and prognosis (survival risk) prediction. However, such direct fusion is impractical in clinical settings, where histopathology remains the gold standard and transcriptomic tests are rarely requested in public healthcare. We experiment on two publicly available multimodal datasets, The Cancer Genomic Atlas and the Clinical Proteomic Tumor Analysis Consortium, spanning four independent cohorts: glioma-glioblastoma, renal, uterine, and breast, and observe significant performance gains in gradation and risk estimation (p-value < 0.05) when incorporating synthesized transcriptomic data with WSIs. Also, predictions using synthesized features were statistically close to those obtained with real transcriptomic data (p-value > 0.05), consistently across cohorts. Here we show that with our diffusion based crossmodal generative AI model, PathGen, gene expressions synthesized from digital histopathology jointly predict cancer grading and patient survival risk with high accuracy (state-of-the-art performance), certainty (through conformal coverage guarantee) and interpretability (through distributed co-attention maps). PathGen code is available on GitHub at https://github.com/Samiran-Dey/PathGen for open use.

Introduction
Histopathological assessment is the gold standard for cancer diagnosis and provides information essential for accurate staging to inform prognosis. However, aging populations, strained healthcare services and the increasing diagnostic and prognostic complexity of modern medicine are contributing to an unsustainable workload for practising histopathologists1. Modern artificial intelligence (AI) algorithms, particularly deep learning models, can predict cancer grade and sub-types from digital hematoxylin and eosin (H&E) stained pathology slides, complementing and supporting pathology workflows and potentially reducing the high workload burden2–7. Very recently, the emergence of pathology foundation models using large vision transformers (ViT) has greatly increased model flexibility, enabling support with a wide variety of downstream tasks and providing greater utility for pathologists8–10.

Classically, histopathology has relied on microscopic assessment of H&E-stained tissue sections, often complemented by targeted panels of immunohistochemical and other special stains, to confirm and refine diagnoses. As technology has advanced, a broader range of molecular tests has become routine for assessing certain tumours. These include genetic analyses such as IDH mutation status, which is required to appropriately subtype gliomas according to the WHO11 and EGFR mutation status, which is essential to guide therapy decisions in non-small cell lung cancers12. More recently, a range of transcriptomic tests have been developed, which show clear evidence for guiding treatment decisions in specific malignancies13. Following extensive clinical evaluation, several of these tests have been granted regulatory approval for clinical use14. However, guidelines, especially in public healthcare systems, strictly limit the application of these transcriptomic tests to a small minority of patients, largely due to their high financial cost and infrastructural requirements15.

Recently, AI models employing a multimodal fusion of digital pathology and transcriptomic features, trained on large public datasets, like The Cancer Genomic Atlas (TCGA), have led to significant improvements in the predictive accuracy of both grading and survival risk estimation, compared to unimodal models employing digital H&E slides alone16–18. Though these powerful models have the potential to convey substantial health benefits, they are developed in a setting disjoint from the current reality of the clinic. Translation of these models into most healthcare systems is infeasible at present, as the required transcriptomic data is very seldom collected. Future translation requires rigorous, costly and widespread clinical evaluation to determine which patients would benefit from these models sufficiently to justify the cost of transcriptomic data.

Crossmodal generative AI models can be trained to synthesize one unavailable data modality (transcriptomics) from routinely available modalities (digital pathology)19–21. Coupled with a multimodal AI model, this could facilitate a cost-effective screening tool to estimate the added value of collecting transcriptomic data for diagnostic/prognostic assessment of each individual patient. Such an approach could support cost-efficient clinical evaluation, and sustainable translation of multimodal AI models to support pathology workflows.

In this work, we introduce a diffusion based crossmodal generative model, PathGen22, that synthesizes transcriptomic data from digital images of H&E-stained slides. We evaluate our model on two publicly available multimodal datasets: The Cancer Genomic Atlas (TCGA)23 and the Clinical Proteomic Tumor Analysis Consortium (CPTAC)24, using data from four independent cancer cohorts: glioma-glioblastoma, renal, uterine and breast. We find that the generated transcriptomic data are highly similar to the corresponding real transcriptomic data in a hold-out test set. We further demonstrate that the predictive performance of both diagnosis (cancer grading) and prognosis (survival risk) significantly improves when the synthesised transcriptomic data is combined with features learned from the corresponding digital pathology image, using a state-of-the-art ViT-based model through a co-attention mechanism. For practical utility, our model must be trusted by clinicians and attune to emerging regulation25. We thus provide transparency via detailed attention-based heatmaps for both grading and survival risk prediction, allowing pathologists to visually affirm the clinical relevance of the regions contributing most towards the AI decision. We provide uncertainty quantification, via conformal analysis26, to allow pathologist users a measure of certainty in the added value of the synthesized transcriptomic data for each patient. Finally, to evaluate algorithmic fairness, we apply our conformal uncertainty quantification to assess model performance analysis across various patient demographics. Our work builds on preliminary studies in crossmodal prediction8,17,18, to provide a road map for sustainable, safe and cost-effective translation of multimodal AI models supporting cancer diagnostics/prognostics, to the clinic.

Results
Dataset and study design
Our experiments use publicly available data from TCGA and CPTAC, specifically from four independent cancer cohorts - Glioblastoma Multiforme and Brain Lower Grade Glioma (TCGA-GBM, TCGA-LGG, CPTAC-GBM), Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), Uterine Corpus Endometrial Carcinoma (TCGA-UCEC, CPTAC-UCEC), and Breast Invasive Carcinoma (TCGA-BRCA). For TCGA-GBMLGG, 912 whole slide images (WSIs) coming from 745 cases are used, of which 183 cases belong to TCGA-GBM27 and 562 cases belong to TCGA-LGG28. We consider 485 WSIs from 462 cases for TCGA-KIRC29, 294 WSIs from 267 cases for TCGA-UCEC30, 1010 WSIs from 946 cases for TCGA-BRCA31, 242 WSIs from 62 cases for CPTAC-GBM32, and 364 WSIs from 71 cases for CPTAC-UCEC33 for our experiments. The slides have been split into training, validation, calibration, and test sets (Supplementary Table 1). The models trained using the TCGA data have been evaluated on the corresponding CPTAC cohorts, without any further training or fine-tuning. Cases with missing grade, survival data or transcriptomic data were excluded. The data distribution across various demographic groups like gender, age, censorship status, etc. varies for all cohorts (Supplementary Table 3).

Transcriptomic features are selected to match those employed in the successful multimodal models17,18. As proposed previously18, transcriptomic data for each patient is divided into 6 broad categories of genes—tumour suppressor genes, oncogenes, protein kinases, cell differentiation markers, transcription factors, and cytokines and growth factors. However, a gene may occur in multiple gene groups. The number of available gene expression levels for each gene group differs across cohorts but remains same for the TCGA and CPTAC datasets for the same cancer cohort (Supplementary Table 2). When discussing gene expression levels throughout this paper, we refer to transcript abundance, rather than protein levels.

Our methodology synthesizes transcriptomic data from WSIs using our diffusion based generative model, PathGen. Synthesized transcriptomes are then used for automated cancer gradation and survival risk estimation to assess whether transcriptomic tests should be performed (Fig. 1). Transcriptomic data (both real and synthesized) is used at two stages in the predictive model (MCAT_GR)—to co-attend to the WSI patch embeddings and learn a combined feature map that is suitable to predict both grade and survival, and to predict the survival risk of a patient in a learnable ensemble method along with the co-attended feature maps. We perform extensive experiments using synthesized transcriptomic data to decide on this model setting (Supplementary Table 4). The model is trained end-to-end for performing both gradation and survival risk estimation together using the loss function L = \lambda \times L_{survival} + (1-\lambda) \times L_{grade}, where L_{survival} is the loss for survival risk estimation, L_{grade} is the loss for gradation and \lambda is the loss coefficient to balance the emphasis between survival risk estimation and gradation. Experiments show that at \lambda=0.3 the model performance is optimal with close and high scores for both gradation and survival risk estimation together across glioblastoma-glioma and renal cohorts (Supplementary Fig. 1 and Supplementary Table 4). Thus, for all results reported in the paper, we use \lambda=0.3. For uncertainty estimation using conformal prediction, we choose the error rate \alpha=0.1. Further details of model architecture, loss function and training methodology are provided in Methods.

Similarity evaluation of synthesized and real transcriptomic data
The transcriptomic data synthesized by our model, PathGen, is compared to the real patient transcriptomes to assess similarity. The Spearman correlation coefficient for all gene groups between real and synthesized transcriptomic features evaluates to 0.713 for TCGA-GBMLGG, 0.717 for TCGA-KIRC, 0.436 for TCGA-UCEC, 0.642 for TCGA-BRCA, 0.662 for CPTAC-GBM, and 0.669 for CPTAC-UCEC (Fig. 2a.i and Supplementary Table 5). Thus, showing highly significant correlation between real and synthesized transcriptomic data (Spearman correlation p-value < 0.05). The corresponding normalized mean absolute error (nMAE) overall gene groups evaluates to 0.141, 0.160, 0.173, 0.155, 0.167, and 0.178 for TCGA-GBMLGG, TCGA-KIRC, TCGA-UCEC, TCGA-BRCA, CPTAC-GBM, and CPTAC-UCEC, respectively (Fig. 2a.i and Supplementary Table 5). Though there exists a high correlation and low MAE between synthetic and real transcriptomic data, there are some variations in correlation across the gene sets and data cohorts (Fig. 2a.ii, iii and Supplementary Table 5). Notably, synthesized and real oncogene expression levels are highly correlated for glioblastoma-glioma, renal and breast cohorts (Fig. 2a.iii and Supplementary Table 5). This is particularly beneficial for cancer grading or survival estimation because oncogenes, when aberrantly active can serve as biomarkers that drive tumour progression, facilitating the prediction of cancer prognosis34. Thus, we conclude that the synthesized transcriptomic data are highly correlated with the real transcriptomic data and we gain confidence towards using the former in the predictive pipeline.

Performance evaluation of synthesized transcriptomic data in multimodal prediction
We next analyse whether PathGen synthesised transcriptomes significantly add value to automated gradation and survival risk estimation, over and above WSIs alone. Evaluation metrics used for grade prediction and survival risk estimation are the Area Under Curve (AUC) and the concordance index (C Index) respectively. For survival risk estimation, the C Index obtained using WSIs alone are 0.842, 0.671, 0.663, 0.603, 0.547, 0.518 for TCGA-GBMLGG, TCGA-KIRC, TCGA-UCEC, TCGA-BRCA, CPTAC-GBM, and CPTAC-UCEC, respectively (Fig. 2b.i and Supplementary Table 6). And for gradation, we obtain an AUC of 0.823 for TCGA-GBMLGG, 0.714 for TCGA-KIRC, 0.796 for TCGA-UCEC, 0.736 for CPTAC-GBM, and 0.508 for CPTAC-UCEC (Fig. 2b.ii and Supplementary Table 6). Since TCGA-BRCA does not report cancer grades in the clinical data, only survival risk estimation could be performed. The performance significantly improves when PathGen synthesized transcriptomics are used alongside WSIs (Wilcoxon rank-sum test p-value < 0.05), resulting in an AUC of 0.890 and C Index of 0.861 for TCGA-GBMLGG, AUC of 0.773 and C Index of 0.681 for TCGA-KIRC, AUC of 0.821 and C Index of 0.673 for TCGA-UCEC, C Index of 0.720 for TCGA-BRCA, AUC of 0.865 and C Index of 0.565 for CPTAC-GBM, and AUC of 0.593 and C Index of 0.530 for CPTAC-UCEC (Fig. 2b.i, ii and Supplementary Table 6). A significant improvement was also noted on evaluating jointly over survival and gradation for using synthesized transcriptomic data with WSIs for prediction (ANOSIM test p-value = 0.001, Supplementary Table 6), consistent over datasets and cohorts. Thus, in the absence of real transcriptomic data, adding synthesized transcriptomic data significantly improves diagnostic and prognostic predictions compared to using only WSI features.

We further compare gradation and survival risk predictions using WSIs and PathGen synthesized transcriptomic data to predictions using WSIs and real transcriptomic data. Combining real transcriptomic data with WSIs yields an AUC of 0.907 and C Index of 0.866 for TCGA-GBMLGG jointly, AUC of 0.778 and C Index of 0.697 for TCGA-KIRC, AUC of 0.828 and C Index of 0.680 for TCGA-UCEC, C Index of 0.720 for TCGA-BRCA, AUC of 0.863 and C Index of 0.564 for CPTAC-GBM, and AUC of 0.593 and C Index of 0.533 for CPTAC-UCEC and the corresponding result using synthesized transcriptomic data is provided above (Fig. 2b.i, ii and Supplementary Table 6). Remarkably, model performance on using real transcriptomic data is not significantly different to using PathGen synthesized transcriptomes (Wilcoxon rank-sum test p-value > 0.05 and ANOSIM test p-value > 0.05) for gradation and survival risk estimation, both separately and jointly, in all considered TCGA and CPTAC cohorts (Supplementary Table 6). Thus, the transcriptomic data synthesized by our model, PathGen, consistently performs comparably for gradation and survival risk estimation with no significant difference on average from real transcriptomic data.

We also compare the performance of our methodology with the state-of-the-art models for using real transcriptomic data and WSI patches. For TCGA-GBMLGG, the C Index evaluated for GSCNN16 is 0.781, DeepAttnMISL35 is 0.734, MCAT18 is 0.817, Pathomic fusion17 is 0.826, whereas our methodology yields a C Index of 0.866 using real transcriptomic data and a C Index of 0.861 using synthesized transcriptomic data for survival risk estimation. PathGen-X36 obtains a C-Index of 0.81 on the TCGA-GBM cohort; however, their inference stage is unimodal and only considers a section of patients (Grade IV). For gradation, Pathomic fusion17 has an AUC of 0.906, whereas our methodology has an AUC of 0.907 on real transcriptomic data and an AUC of 0.890 on synthesized transcriptomic data. For TCGA-KIRC, the C Index evaluated for Pathomic fusion17 is 0.720, whereas for our methodology is 0.697 on real transcriptomic data and 0.681 on synthesized transcriptomic data. However, Pathomic fusion uses ROIs annotated by expert pathologists for predicting grades, while we use transcriptomic features to co-attend to the WSI patch embeddings without requiring any expert intervention, thus reducing expert annotation overheads. For TCGA-UCEC, DeepSets37, DeepAttnMISL35, and MCAT18 achieve a C Index of 0.593, 0.586, and 0.622, respectively, whereas our methodology yields a C Index of 0.680 and 0.673 using real and synthesized transcriptomes, respectively, for survival risk estimation. On TCGA-BRCA, our methodology achieved a C-index of 0.720 for both real and synthesized transcriptomic profiles, compared to 0.522, 0.577, and 0.580 for DeepSets37, DeepAttnMISL35, and MCAT18, respectively. We note that the considered SOTA models, DeepSets, DeepAttnMISL, and MCAT, do not perform gradation, so a direct comparison on grade prediction was not possible. For similar reasons, we could only compare the performance of our model with Pathomic fusion for TCGA-GBMLGG gradation and TCGA-KIRC survival risk estimation. Since our model performs better or at least comparably with the state-of-the-art models using real transcriptomic data, and we observe that the predictive performance between using real or synthesized transcriptomic data is very close with no significant difference for the test set population (Wilcoxon rank-sum test p-value > 0.05, Supplementary Table 6), we may conclude that we achieve state-of-the-art performance using gene expression levels synthesized by our model, PathGen.

Transparency evaluation for clinical interpretability
To visualize intra-tumour heterogeneity, we use our trained model to predict grade and survival risk for individual WSI patches independently. The distributed prediction allows the identification of regions of varied grades within a WSI, highlighting the heterogeneity for clinical assistance (Figs. 3b and 4b and Supplementary Figs. 3b–5b). It is evident that higher-grade regions are correlated to higher risks, as is expected (Fig. 4e). By observing distributed predictions at different magnification levels corresponding to a WSI, we find that using WSIs at higher resolution helps to obtain more precise predictions for tissue regions, as is expected (Supplementary Fig. 2).

Further, we consider the aggregate of the heterogeneous (distributed) predictions and compare it with the grade predicted by the model, considering all WSI patches as input (non-distributed prediction). For the TCGA cohorts, the distributed gradation performance is not significantly different from a one-time prediction with all WSI patches (Wilcoxon ranksum test p-value > 0.05, Fig. 5a.i). Thus, we see from the results that there is indeed a distributed variation of grade when predicted locally for patches and mapped onto the WSIs as a heatmap, both within and across cohorts, which may provide an additional visual presentation of tumour heterogeneity to the clinician expert (pathologist) to choose whether or not to draw any further intuition from them.

To add explainability, we next study how transcriptomic data co-attends to the corresponding WSI patched. We compare the co-attention maps obtained using real and synthesized gene expression levels using Spearman correlation coefficient and nMAE. A higher correlation with a high corresponding nMAE would denote that the range of co-attention values is large for the particular gene group, but the co-attention map is still correlated. TCGA-GBMLGG obtains a correlation of 0.939 and nMAE of 0.043, TCGA-KIRC obtains a correlation of 0.851 and nMAE of 0.063, TCGA-UCEC obtains a correlation of 0.997 and nMAE of 0.022, TCGA-BRCA obtains a correlation of 0.992 and nMAE of 0.014, CPTAC-GBM obtains a correlation of 0.993 and nMAE of 0.013, and CPTAC-UCEC obtains a correlation of 0.998 and nMAE of 0.021 overall gene groups (Fig. 5b.i). These significantly high correlation values (Spearman correlation p-value < 0.05) and low nMAEs between co-attention maps for real and synthesized transcriptomic data explains the reason for no significant difference between using the same for gradation and survival risk estimation across cohorts and datasets (Wilcoxon rank sum test p-value > 0.05, ANOSIM test p-value > 0.05, Supplementary Table 6). For all cohorts, co-attention maps attributing to oncogenes are most correlated with a low nMAE, followed by transcription factor genes and tumour suppressor genes (Fig. 5b.ii, iii). Oncogenes usually drive cell division and survival, thus promoting cancer. Inactivation of tumour suppressor genes can lead to cancer by failing to regulate uncontrolled cell growth38. So the general co-attention observations above are not unexpected, although a rigorous inference would need a detailed analysis of gene set functional significance, which is outside the scope of this work.

We further study the contribution of synthesized transcriptomic features in co-attending to the WSI patch embeddings and observe that there are variances in the percentage contribution of individual gene groups in co-attention for all cohorts and datasets (Fig. 5c.i, ii and Supplementary Table 8). For most cohorts, the transcription factor genes and oncogenes have the highest co-attention values with the WSI patches for both real and synthesized gene expression levels and hence contribute the most to the prediction (Figs. 3d and 4d and Supplementary Figs. 3d–5d). Since activation of oncogenes is known to drive tumour proliferation34, having these as one of the higher contributors to co-attention would be aligned with clinical expectations. We next visualise specific regions of the WSI and compare the distributed grades, distributed risk and the co-attention maps between synthesized transcriptomic features and the WSI patches (Figs. 3e and 4e and Supplementary Figs. 3e–5e). We observe that ground truth grade regions correspond to higher attention scores and other grade regions correspond to low attention values (Figs. 3–5a.ii).

Uncertainty quantification for clinical reliability
We observe that the uncertainty profile of using synthesized transcriptomic data matches that of using real transcriptomic data for both gradation and survival risk estimation, for different patient demographics and stratification across cohorts, by performing conformal prediction (CP) as described in Methods. For TCGA-GBMLGG, the marginal uncertainty (M_{unc}) for the overall data population evaluates to 0.407 and 0.413 for gradation, and 0.446 and 0.438 for survival risk estimation, on using real and synthesized transcriptomic data, respectively (Fig. 2c). For TCGA-UCEC, a M_{unc} of 0.337 for gradation and 1.0 for survival is obtained, for both real and synthesized transcriptomic data (Fig. 2c). For TCGA-KIRC, gradation M_{unc} increases from 0.292 to 0.334, and the survival M_{unc} increases from 0.898 to 0.909 on using synthesized transcriptomic data over their real counterparts (Fig. 2c). However, the changes in M_{unc} are not significant, proving the similarity in the uncertainty profile between the use of real and synthesized transcriptmic data (Wilcoxon rank sum test p-value > 0.05, Supplementary Tables 9–13).

Furthermore, we observe that the uncertainty profile remains similar across different demographic groups for real and synthesised transcriptomic data, with no significant difference in uncertainty, by performing stratified conditional conformal prediction as described in Methods (Wilcoxon rank-sum test p-value > 0.05, Supplementary Tables 9–13). Additionally, stratifying by demographic categories helps achieve fairness in the distribution of coverage across the population by ensuring each group within the category independently obtains the desired coverage. We find variations in both conditional uncertainty (C_{unc}) across demographic categories and marginal uncertainty (M_{unc}) across groups in a category, which is expected (Supplementary Tables 9–13). For instance, in comparison to the overall M_{unc} of 0.413, when stratified by gender, C_{unc} decreases to 0.312 (real) and 0.323 (synthesized), and when stratified by time bin, it decreases to 0.335 (real) and 0.331 (synthesized), for TCGA-GBMLGG (Supplementary Table 9).

We also find that models trained on the TCGA datasets adapt and calibrate well over the corresponding CPTAC cohorts, not always with a drastic increase in uncertainty, which otherwise is expected. The model trained on TCGA-GBMLGG when calibrated and tested over CPTAC-GBM has a M_{unc} of 0.465 for gradation and 0.206 for survival for both real and synthesized transcriptomic data (Fig. 2c). On calibrating and testing the model trained on TCGA-UCEC over CPTAC-UCEC, a gradation M_{unc} of 0.821 for real, and 0.818 for synthesized transcriptomic data and a survival M_{unc} of 1.0 for both real and synthesized transcriptomes is observed (Fig. 2c).

It may be observed that for a few population groups across some cohorts, the obtained coverage does not always match the desired coverage because of fewer samples in the calibration set. These coverage slacks are expected and can be theoretically computed as a function of the cardinality of the calibration set26. We find that all obtained coverages that are less than the desired coverage of 0.9 fall within the expected deviation for the corresponding calibration set size of the demographic group (Supplementary Tables 3 and 14). For many such cases, using synthesized data has increased the coverage at the cost of an insignificant increase in uncertainty (Wilcoxon rank-sum test p-value > 0.05, Supplementary Tables 9–13). As risk estimation directly uses the transcriptomic data for prediction and not just for co-attention, such differences in obtained coverage are expected even for minimal differences in the gene expression levels between the synthesized and real transcriptomic features. Moreover, the uncertainty estimates are derived from conformal sets for gradation and risk bounds for survival estimation, which helps to understand the extent to which we may rely on the model predictions. Additionally, the gradation conformal sets provide a range of alternative grades to consider before making the final decision.

Next, we investigate algorithmic fairness by assessing whether the performance in gradation and survival risk predictions shows demographic biases. We observe that the predicted risk is less for patients who are reported to be alive and more for deceased patients, which is as expected (Fig. 6a, c, d). Also, the uncertainty of survival risk estimation decreases with increasing risk (Fig. 6a–c), which is expected because the actual survival time of patients who are alive is beyond the reported survival time. Thus, the model estimated survival risk should correspond to a time beyond the reported survival time and have a higher uncertainty measure when compared in terms of reported survival time. It may also be observed that the model tends to correlate higher risks with the highest grade, for all data cohorts (Fig. 6). In general, the uncertainty of survival risk estimation is always greater if the survival time reported for a living patient is less (Fig. 6). Also, the uncertainty of prediction for alive patients is mostly high, except for gradation in TCGA-KIRC (Fig. 6 and Supplementary Tables 9–13). The uncertainty of grade and survival prediction is more for male patients than females except for gradation in TCGA-GBMLGG (Supplementary Tables 9–11). The uncertainty of risk estimation decreases with age for all TCGA cohorts (Supplementary Tables 9, 11, and 12). Thus, these results provide an analysis from a health equity and predictive fairness point of view as to how the performance varies across patient demographics.

Methods
The paper aims to use synthesized transcriptomic data for the automated selection of whole slide image (WSI) features for risk estimation and gradation of cancer cases, and evaluate the value added by the use of synthesized transcriptomic data over WSI-only predictions and the difference in using real transcriptomic data instead (Fig. 1). The WSIs are patchified and the corresponding patch embeddings are obtained using UNI8. Our model, PathGen, generates transcriptomic features from the patch embeddings. Together, the patch embeddings and the gene embeddings obtained from the transcriptomic features are thus used by MCAT_GR for gradation and risk estimation. In the following subsections, we discuss the steps of the proposed methodology in detail.

Preprocessing of histopathology images. The diagnostic WSIs for a patient are divided into 224×224 patches, as required for obtaining patch embeddings using the pre-trained foundation model, UNI. The magnification level of the WSI is randomly chosen from the available options to make the model magnification level agnostic. The patches having normalized mean intensity more than 0.8 are excluded. It is observed that such patches consist mostly of the background region and hence, they do not contribute to the prediction. We do not use stain normalization in preprocessing with further details in Fig. 7c–f. 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Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper introduces PathGen, which is a diffusion-based generative model that aims to synthesize transcriptomic data from histopathology WSIs to improve cancer diagnosis and prognosis prediction. Experiments on glioma and renal cancer datasets demonstrate that PathGen-synthesized transcriptomes have high correlation with real patient data (Spearman correlation ~0.71-0.72) and, when combined with histopathology features in downstream tasks, it can improve both cancer grading (AUC improvement from 0.823 to 0.890 for glioma) and survival risk estimation (C-Index improvement from 0.842 to 0.861) compared to using WSIs alone.\nBelow are my concerns:\nMajor concerns:\n- Regarding the experiments, the study only uses two cancer types (namely glioma and renal cancer) from TCGA, where no external validation or independent cohorts outside of TCGA was used, which limits its evaluation on the generalization of the model.\n- Although PathGen achieved a Spearman correlation values of 0.71-0.72, it needs careful interpretation rather than immediate acceptance as evidence of successful synthesis. Specifically, Spearman correlation basically aims to measure the monotonic relationships between variables rather than exact agreement. In these words, a correlation of 0.71 can implies that approximately 50% of the variance in real data remains unexplained by the synthetic data as the r2 would be around 50%. Additionally, this correlation may obscure poor performance on clinically important but less abundant gene expressions. Hence, I would recommend looking into the correlation across gene categories. Also, please evaluate or discuss its performance on rare or extreme gene expression values, which be helpful.\nThe authors claimed that the MAE=0.1 is low, but without context regarding the typical range and variability of gene expression values, it’s difficult to understand how exactly MAE=0.1 performs. Some other options like mormalized MAE can be an alternative.\n- The authors claimed to provide detailed attention based heatmaps for both grading and survival risk prediction to improve the user trust (last paragraph in the intro), however no human evaluation is conducted to support this claim. The same applies to the conformal prediction parts, where no evaluations are conducted to show the effectiveness.\n- For the gradation and survival prediction tasks, it lacks the comparison with other diffusion-based generative multimodal methods.\n- The authors acknowledge that \"for a few population groups across both cohorts, the required coverage has not been met, because of fewer samples in the calibration set.\" It reveals a potential flaw in the study's statistical framework ,where conformal prediction's theoretical guarantees that prediction intervals contain the true value with a specified probability (1-α) should rely on having sufficient calibration data that represents the full spectrum of the patient population. The authors' approach allocates only 104 WSIs from 82 cases for TCGA-GBMLGG and a mere 48 WSIs from 45 cases for TCGA-KIRC to the calibration set. This insufficient calibration data problem is exacerbated for demographic subgroups. To properly address this limitation, the study should alternative conformal prediction variants such as stratified conformal prediction, targeted calibration for minority groups.\nMinor concerns:\n- The abstract lacks quantitative descriptions (trained on how many WSIs) and results. It also lacks a high-level descriptions of their diffusion model as well as the difference of their diffusion models with existing diffusion models.\n- Excessive use of subjective terminology like \"significantly\" throughout the introduction.\n- No discussion of computational requirements or inference time for clinical deployment.\n- As the experiments involve two datasets, I am wondering if stain normalization is envovled in the preproecessing?", "reviewer_2": "The paper presents PathGen, a commendable effort to address a significant challenge in deploying multimodal AI models in clinical practice: the frequent unavailability of transcriptomic data. The core idea of synthesizing transcriptomic data from routinely available histopathology images using a diffusion model is innovative for this specific application. The authors demonstrate that their approach can generate transcriptomes that, when used with WSI data in their MCAT-GR model, yield strong predictive performance for cancer grading and survival on TCGA datasets, often comparable to using real transcriptomic data and competitive with other state-of-the-art methods.\nThe inclusion of interpretability through attention maps and uncertainty quantification via conformal prediction are also valuable contributions towards building trust in AI models. While the study presents promising results, several critical aspects warrant further discussion and could temper some of the broader claims:\n1. The study relies exclusively on TCGA datasets (GBMLGG and KIRC). While TCGA is a valuable public resource, models developed solely on it may not always generalize robustly to datasets from different institutions, with varying patient populations, image acquisition protocols (different scanners, staining variations), or pre-processing pipelines.\n2. While applying diffusion models to synthesize transcriptomics from pathology is kind of novel, the core technical components themselves are largely based on existing, well-established works. The diffusion model framework is standard, UNI is a pre-existing foundation model for patch embedding, and MCAT is a prior architecture for multimodal fusion and prediction.\n3. The authors introduce a dedicated \"calibration set\" for their conformal prediction analysis. While this is good practice for developing reliable conformal predictors, it inherently alters the data splits compared to studies that might use k-fold cross-validation or a simpler train/validation/test split without a separate calibration set. This means the training and testing sets available for PathGen's core predictive tasks might be smaller or distributed differently than in some of the SOTA methods they compare against. For a truly fair comparison with methods not employing such a calibration split, performance should ideally be evaluated using identical training and testing partitions as those used by the benchmarked studies, or the SOTA methods should be re-implemented/re-evaluated on PathGen's specific splits.\n4. PathGen's approach is to explicitly generate transcriptomic data. An alternative class of methods focuses on aligning features from different modalities (e.g., learning a joint embedding space or using one modality to guide feature extraction in another) to improve prediction, without necessarily generating one modality from the other. For example, the paper cites PathoGen-X, which aims to \"trans-align\" genomic and WSI features. The paper doesn't deeply discuss why the explicit generation approach was chosen over these alignment strategies, which might be more direct if the ultimate goal is prediction and could potentially avoid the compounding of errors inherent in a generative step. While PathGen's generated data has the advantage of being interpretable as a \"transcriptome,\" alignment methods can also offer interpretability through cross-modal attention or feature correlation in the learned joint space.", "reviewer_3": "In this manuscript, Dey et al. present PathGen, a deep-learning framework that integrates histology images with synthetic RNA-seq data to predict tumor grade and cancer patient survival. Using publicly available glioma (GBM/LGG) and kidney cancer (KIRC) datasets, they demonstrate the advantage of multimodal integration over single-modality approaches.\nThe strengths of this study include: (1) Development of a novel deep-learning model for predicting transcriptomic features from H&E-stained images. (2) Patch-level interpretability to capture intra-tumoral heterogeneity. (3) Use of conformal predictions to address model uncertainty.\nOverall, the study tests an innovative hypothesis with instructive findings for research in the field. My primary concerns are technical and pertain to data selection, preprocessing, and presentation.\nDatasets:\n1. The rationale for restricting analysis to GBM/LGG and KIRC from the broader TCGA cohort (spanning over 30 cancer types) is not clearly explained.\n2. Predictive performance for both tumor grade and survival was approximately 15% higher in GBM/LGG compared to KIRC, indicating inconsistency for the predictive performance across different cancer types. Assessing the predictive performance in additional cancer types would help determine model generalizability across different cancers. While the authors claim this as a future goal in the Discussion, it would be beneficial to demonstrate the robustness and consistency of the model in studies for Nature Communications.\nData Preprocessing:\n1. The manuscript does not specify which gene length normalization method (TPM, FPKM, or RSEM) was used prior to computing z-scores.\n2. Figure 2(i): More technical details are needed on how predictive performance for individual genes was aggregated at the pathway/gene-group level.\nResults and Data Presentation:\n1. Section 2.2: The statement that \"gene expression levels for gene groups show no significant differences between real and synthesized transcriptomes\" (p > 0.05, unpaired t-test) was inconsistent with the results shown in Supplementary Table 2, where all p-values are highly significant. This discrepancy should be clarified.\n2. Given the substantial survival differences between GBM and LGG, evaluating model performance on the combined TCGA-GBMLGG cohort is not surprising. The authors should report separate predictive performance (grade and C-index) for GBM and LGG individually, at least in Supplementary materials.\n3. Minor issues on Figure 1c which contains a redundant text box below \"Patch embeddings.\"\n4. In Section 2.4.1, for a patient with ground truth grade I (Figure 4), the majority of patches within the tumor slide are predicted as grade IV (red). Is this expected?\n5. Figures 3 & Supplementary Figure 4: Do lower-grade tumor regions consistently exhibit higher oncogene expression levels? In Supplementary Figure 4, the middle tumor portion shows lower risk scores but higher oncogene expression than the tumor edges—is this expected?\n6. Supplementary Figure 4(d): Minor issues on the image annotation on the left side which is duplicated."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThis paper introduces PathGen, which is a diffusion-based generative model that aims to\nsynthesize transcriptomic data from histopathology WSIs to improve cancer diagnosis and\nprognosis prediction. Experiments on glioma and renal cancer datasets demonstrate that\nPathGen-synthesized transcriptomes have high correlation with real patient data (Spearman\ncorrelation ~0.71-0.72) and, when combined with histopathology features in downstream\ntasks, it can improve both cancer grading (AUC improvement from 0.823 to 0.890 for glioma)\nand survival risk estimation (C-Index improvement from 0.842 to 0.861) compared to using\nWSIs alone.\nBelow are my concerns:\nMajor concerns:\n- Regarding the experiments, the study only uses two cancer types (namely glioma and renal\ncancer) from TCGA, where no external validation or independent cohorts outside of TCGA\nwas used, which limits its evaluation on the generalization of the model.\nAuthor Response: We thank the reviewer for this valuable suggestion. In response, we have\nextended our experiments on two new cohorts from the Clinical Proteomic Tumor Analysis\nConsortium (CPTAC) dataset and two additional cohorts from the previous TCGA dataset.\nThus, in total, results for four additional data cohorts have now been added to the paper,\nnamely, TCGA-UCEC (uterine), TCGA-BRCA (breast), CPTAC-GBM (glioblastoma) and\nCPTAC-UCEC (uterine). For external validation, models trained on TCGA data were directly\nevaluated on CPTAC cohorts without re-training. For all the considered cohorts from both\nTCGA and CPTAC datasets, we find that the addition of synthesized transcriptomic data\nsignificantly improves the performance of both gradation and survival risk estimation over\nusing WSIs only (p-value < 0.05). Also, the performance of using transcriptomic data\nsynthesized by our model, PathGen, is significantly close to that of using their real\ncounterparts for all data cohorts (p-value < 0.05). The consistent results show that our model\ngeneralizes well over cancer cohorts and datasets. The results have been discussed in the\nSections “Similarity evaluation of synthesized and real transcriptomic data” and\n“Performance evaluation of synthesized transcriptomic data in multimodal prediction”, and\npresented in Figure 2.b. and Supplementary Table 6. We have also modified Sections\n“Transparency evaluation for clinical interpretability” and “Uncertainty quantification for\nclinical reliability” to incorporate transparency evaluations and uncertainty quantification with\nrespect to the newly added cohorts.\n- Although PathGen achieved a Spearman correlation value of 0.71-0.72, it needs careful\ninterpretation rather than immediate acceptance as evidence of successful synthesis.\nSpecifically, Spearman correlation basically aims to measure the monotonic relationships\nbetween variables rather than exact agreement. In these words, a correlation of 0.71 can\nimply that approximately 50% of the variance in real data remains unexplained by the\nsynthetic data as the r2 would be around 50%. Additionally, this correlation may obscure\npoor performance on clinically important but less abundant gene expressions. Hence, I\nwould recommend looking into the correlation across gene categories. Also, please evaluate\nor discuss its performance on rare or extreme gene expression values, which will be helpful.\nThe authors claimed that the MAE=0.1 is low, but without context regarding the typical range\nand variability of gene expression values, it’s difficult to understand how exactly MAE=0.1\nperforms. Some other options like normalized MAE can be an alternative.\nAuthor Response: We thank the reviewer for the suggestion. We have now incorporated the\nresults for the new datasets and cohorts, and presented the updated results together\n(previous and new) in Figure 2.a. and Supplementary Table 5. We observe a significantly\nhigh correlation across gene categories for all data cohorts. Also, following the reviewers'\nsuggestion, we have now evaluated the difference between real and synthesized\ntranscriptomic features using normalized MAE (nMAE) instead of MAE.\nRegarding the reviewer’s concern about clinically important but less abundant gene\nexpressions, we acknowledge that this is an important direction. In this context, we\nconsulted with our clinician co-author (C. Banerji), who emphasized that “the aggregation of\nthe transcriptome in this way reduces the number of extreme values we observe compared\nto estimating every gene individually, and our validation set is therefore less subject to\nexperimental noise driving extreme rare values due to the averaging process.” However,\nsuch an analysis would require substantial domain expertise and dedicated clinical input,\nwhich we plan to pursue as part of future work.\n- The authors claimed to provide detailed attention based heatmaps for both grading and\nsurvival risk prediction to improve the user trust (last paragraph in the intro), however no\nhuman evaluation is conducted to support this claim. The same applies to the conformal\nprediction parts, where no evaluations are conducted to show the effectiveness.\nAuthor Response: We thank the reviewer for this comment. In the introductory paragraph in\nquestion, we stated: ‘For practical utility, our model must be trusted by clinicians and attune\nto emerging regulation’. This statement does not constitute a claim that our approaches\nobjectively increase user trust; rather, this statement is a motivation for their introduction.\nOur approaches were selected with input from a practising histopathologist (co-author C\nBanerji) based at University College Hospitals to reflect methods that would be most\nvaluable and trusted in real clinical workflows. He also went through all the results presented\nin the paper (including visual ones) as well as the written explanations in the manuscript.\nThus, though a full clinical validation would be out of scope of the current proof of concept\nmethodological piece, the direct clinician in the loop development and feedback through C\nBanerji, does provide a level of clinician trust for the time being.\nOur focus is instead on the technical development of these methods in the context of grading\nand survival prediction, with design choices guided by emerging regulatory requirements\n(e.g., EU AI Act Articles 13–15). These requirements: transparency, uncertainty\nquantification and human oversight were in fact proposed by our clinician co-author C\nBanerji in his Nature Medicine, 2023 paper as factors likely to facilitate clinician trust:\nhttps://www.nature.com/articles/s41591-023-02562-7. Moreover, in response to the\nreviewer’s comment we have now added the following line in the Discussion section: “Thus,\nto add explainability to the model predictions, we have undertaken measures with design\nchoices guided by emerging regulatory requirements (e.g., EU AI Act Articles 13–15) and in\nconsultation with a practising histopathologist based at University College Hospitals,\nLondon.”\n- For the gradation and survival prediction tasks, it lacks the comparison with other\ndiffusion-based generative multimodal methods.\nAuthor Response: We thank the reviewer for this observation. In our work, the\ndiffusion-based framework, PathGen, has been proposed to synthesize transcriptomic data\nfrom WSIs. However, the downstream gradation and survival prediction tasks are\nnon-generative tasks and performed using a transformer-based multiple instance learning\n(MIL) model, MCAT_GR, inspired by established work (MCAT). Since these predictive tasks\nare not carried out by the diffusion model itself, but by a transformer-based MIL model, we\nhave compared our performance with other multimodal transformer-based or MIL\napproaches, which we believe provides the most meaningful and fair comparison.\n- The authors acknowledge that \"for a few population groups across both cohorts, the\nrequired coverage has not been met, because of fewer samples in the calibration set.\" It\nreveals a potential flaw in the study's statistical framework ,where conformal prediction's\ntheoretical guarantees that prediction intervals contain the true value with a specified\nprobability (1-α) should rely on having sufficient calibration data that represents the full\nspectrum of the patient population. The authors' approach allocates only 104 WSIs from 82\ncases for TCGA-GBMLGG and a mere 48 WSIs from 45 cases for TCGA-KIRC to the\ncalibration set. This insufficient calibration data problem is exacerbated for demographic\nsubgroups. To properly address this limitation, the study should alternative conformal\nprediction variants such as stratified conformal prediction, targeted calibration for minority\ngroups.\nAuthor Response: We thank the reviewer for highlighting this concern. We agree that\nconformal prediction’s theoretical guarantees depend on calibration set size, and that\nsmaller calibration groups may naturally exhibit larger deviations from the nominal coverage.\nHowever, this reflects the inherent statistical properties of finite-sample calibration. For\ncalibration sets of size 1000, the coverage for α = 0.1 may typically fluctuate to 0.88, rather\nthan being exactly 0.90. (https://arxiv.org/pdf/2107.07511) The deviation depends on\ncalibration set size, and the smaller the calibration set, the larger the permissible variation. In\nour revised manuscript, we have thus computed the theoretical coverage slack for all\ncalibration set sizes used in our study (Supplementary Table 14) and verified that the\nobserved coverage is within the permissible deviation. This ensures the validity of our\nconformal strategy and provides statistically sound calibration for both grade and survival\nrisk predictions under marginal as well as conditional settings.\nFurthermore, we perform stratified conditional conformal prediction and in response to the\nreviewer’s suggestion, now report not only marginal coverage and uncertainty (as in the\noriginal submission) but also conditional coverage and uncertainty stratified across\ndemographic subgroups (e.g., age, gender, and vital status). This provides a more\ncomprehensive evaluation of subgroup performance. We note that both coverages satisfied\nthe theoretical guarantees within the expected deviation, demonstrating that our conformal\ncalibration approach remains robust across cohorts and specific subpopulations. We have\naccordingly updated Section “Uncertainty quantification for clinical reliability” and reported\nthe results in the Supplementary Tables 9-13 and Figure 6. The methodology has been\ndiscussed further in Methods section.\nMinor concerns:\n- The abstract lacks quantitative descriptions (trained on how many WSIs) and results. It also\nlacks a high-level descriptions of their diffusion model as well as the difference of their\ndiffusion models with existing diffusion models.\nAuthor Response: We thank the reviewer for this constructive observation. In response, we\nhave revised the abstract to incorporate dataset details and results of our experiments.\nSpecifically, we added the following lines: “We experiment on two publicly available\nmultimodal datasets, The Cancer Genomic Atlas (TCGA) and the Clinical Proteomic Tumor\nAnalysis Consortium (CPTAC), spanning four independent cohorts: glioma-glioblastoma,\nrenal, uterine, and breast, and observe significant performance gains in gradation and risk\nestimation (p-value<0.05) when incorporating synthesized transcriptomic data with WSIs.\nAlso, predictions using synthesized features were statistically close to those obtained with\nreal transcriptomic data (p-value>0.05), consistently across cohorts.”\nWe agree with the reviewer that adding details such as the number of WSIs, quantitative\nresults, and diffusion model description would provide further clarity. However, Nature\nCommunications requires the abstract to remain under 150 words (formatting instructions),\nwhich restricts us from including additional details.however, we would like to note that, for\nTCGA-GBMLGG, 912 whole slide images (WSIs) coming from 745 cases are used, of which\n183 cases belong to TCGA-GBM and 562 cases belong to TCGA-LGG. We consider 485\nWSIs from 462 cases for TCGA-KIRC, 294 WSIs from 267 cases for TCGA-UCEC, 1010\nWSIs from 946 cases for TCGA-BRCA, 242 WSIs from 62 cases for CPTAC-GBM, and 364\nWSIs from 71 cases for CPTAC-UCEC for our experiments. Further, we have provided all\nquantitative dataset details, WSI counts, results, model description and model comparisons\nin the Results and Methods sections, ensuring transparency and accessibility for readers.\n- Excessive use of subjective terminology like \"significantly\" throughout the introduction.\nAuthor Response: We thank the reviewer for this comment. The word ‘significantly’ is used 3\ntimes in the introduction, twice it is used in a non-subjective manner to refer objective\nstatistical significance: first: ‘...significant improvements in the predictive accuracy…’ and\nsecond: ‘...the predictive performance of both diagnosis (cancer grading) and prognosis\n(survival risk) significantly improves…’. We acknowledge that the word is used in a\nsubjective context, not relevant to statistical significance, on the third mention: ‘convey\nsignificant health benefits’ and so have changed the word in the sentence to ‘substantial’.\nWe have now edited the manuscript to ensure significance is only used in the context of\nstatistical assessment, and not subjectively.\n- No discussion of computational requirements or inference time for clinical deployment.\nAuthor Response: We thank the reviewer for this suggestion. We have now included the\ninference time in the Discussion section in the following line: “Also, we note the average\ninference time for cross-modal synthesis of transcriptomic data from WSIs using PathGen is\n21.15 seconds, and the average time for estimating survival and grade is 0.71 seconds,\nwhich varies over the magnification level and the size of the WSI, as expected.”\n- As the experiments involve two datasets, I am wondering if stain normalization is envovled\nin the preproecessing?\nAuthor Response: We thank the reviewer for the question. We would like to clarify that stain\nnormalization was not applied during the preprocessing of the datasets, being influenced by\nrecent works highlighting that stain normalization has no significant effect on the predictive\nperformance (https://www.nature.com/articles/s41598-023-46619-6). In response to the\nreviewer's comment, we have now included the following line in the processing step\ndiscussed in Methods section - “We do not use stain normalization in preprocessing the\nWSIs in line with recent works that find no significant effect of using stain normalization as a\npreprocessing step.”\nReviewer #1 (Remarks on code availability):\nOverall the codes look good and reasonable, with a clear readme file, and the dependent\nlibraries.\nAuthor Response: We sincerely thank the reviewer for the positive feedback on the quality of\nour code, documentation, and dependencies.\nReviewer #2 (Remarks to the Author):\nThe paper presents PathGen, a commendable effort to address a significant challenge in\ndeploying multimodal AI models in clinical practice: the frequent unavailability of\ntranscriptomic data. The core idea of synthesizing transcriptomic data from routinely\navailable histopathology images using a diffusion model is innovative for this specific\napplication. The authors demonstrate that their approach can generate transcriptomes that,\nwhen used with WSI data in their MCAT-GR model, yield strong predictive performance for\ncancer grading and survival on TCGA datasets, often comparable to using real\ntranscriptomic data and competitive with other state-of-the-art methods.\nThe inclusion of interpretability through attention maps and uncertainty quantification via\nconformal prediction are also valuable contributions towards building trust in AI models.\nWhile the study presents promising results, several critical aspects warrant further\ndiscussion and could temper some of the broader claims:\n1. The study relies exclusively on TCGA datasets (GBMLGG and KIRC). While TCGA is a\nvaluable public resource, models developed solely on it may not always generalize robustly\nto datasets from different institutions, with varying patient populations, image acquisition\nprotocols (different scanners, staining variations), or pre-processing pipelines.\nAuthor Response: We thank the reviewer for this valuable suggestion. In response, we\nextended our experiments by incorporating additional, independent cohorts beyond TCGA.\nSpecifically, we included data from the Clinical Proteomic Tumor Analysis Consortium\n(CPTAC). In total, results for four more data cohorts have now been added to the paper,\nnamely, TCGA-UCEC (uterine), TCGA-BRCA (breast), CPTAC-GBM (glioblastoma) and\nCPTAC-UCEC (uterine), along with existing TCGA-GBMLGG (glioma-glioblastoma) and\nTCGA-KIRC (renal). These cohorts encompass data collected from distinct institutions at\ndistinct geographical locations, having varied acquisition protocols. For instance, the\nCPTAC-GBM cohort was supplemented with retrospective samples from CHOP (Children’s\nHospital of Philadelphia)-Upenn, Philadelphia (CPTAC-GBM), whereas TCGA-GBM contains\ndata from Henry Ford Hospital, Detroit; University of California, San Francisco; MD\nAnderson Cancer Center, Houston; Fondazione IRCCS Instituto Neuroligico C. Besta, Milan\nand various other hospitals (TCGA-GBM). The patient demographics also vary across each\ncohort and dataset (Supplementary Table 3).\nOur findings demonstrate that models trained on TCGA datasets generalize effectively to\nCPTAC cohorts. Moreover, the improvement in performance on the addition of synthesized\ntranscriptomic data over WSI, and closeness of grade and survival estimation for real and\nsynthesized transcriptomic data, is significant and consistent over diverse cancer types and\ndata from diverse institutional settings. The detailed results have been discussed in Sections\n“Similarity evaluation of synthesized and real transcriptomic data” and “Performance\nevaluation of synthesized transcriptomic data in multimodal prediction”, and presented in\nFigure 2.b. and Supplementary Table 6, and corresponding explanations have been\nincorporated in Sections “Transparency evaluation for clinical interpretability” and\n“Uncertainty quantification for clinical reliability”, including a discussion of the fairness of\nprediction over the patient demographics.\n2. While applying diffusion models to synthesize transcriptomics from pathology is kind of\nnovel, the core technical components themselves are largely based on existing,\nwell-established works. The diffusion model framework is standard, UNI is a pre-existing\nfoundation model for patch embedding, and MCAT is a prior architecture for multimodal\nfusion and prediction.\nAuthor Response: We thank the reviewer for this thoughtful observation. We agree that the\ncore components of our framework are built upon established methods. The diffusion model\nframework was indeed originally developed for image generation (Ho et al.) and has since\nbeen successfully extended to NLP and multimodal applications (Zhu et al.). However, to the\nbest of our knowledge, our work is the first to adapt this framework for the cross-modal task\nof synthesizing transcriptomic data from WSIs. It is also common practice and an area of\nstudy in computational pathology to employ pretrained foundation models for obtaining\nrobust WSI embeddings (Yellapragada et al.). In line with this, we utilize UNI as a\nwell-established backbone for patch embedding. While MCAT has been proposed previously\nfor survival prediction, in our work, we extend this architecture to simultaneously address\nboth grade prediction and survival risk estimation, incorporating an ensemble loss for joint\ntraining. These modifications are described in detail in Methods (Section “Multimodal cancer\ngradation and survival risk estimation”).\nWe would like to humbly suggest that in a high stakes translational scenario such a s\ncomputational cancer it might be more prudent sometimes to employ a tried and tested\napproach in a novel manner (as in this work) rather than a new but unestablished method\nwhich might have less potential of being trusted and used in the clinic, which would defeat\nthe purpose altogether.\n3. The authors introduce a dedicated \"calibration set\" for their conformal prediction analysis.\nWhile this is good practice for developing reliable conformal predictors, it inherently alters\nthe data splits compared to studies that might use k-fold cross-validation or a simpler\ntrain/validation/test split without a separate calibration set. This means the training and\ntesting sets available for PathGen's core predictive tasks might be smaller or distributed\ndifferently than in some of the SOTA methods they compare against. For a truly fair\ncomparison with methods not employing such a calibration split, performance should ideally\nbe evaluated using identical training and testing partitions as those used by the\nbenchmarked studies, or the SOTA methods should be re-implemented/re-evaluated on\nPathGen's specific splits.\nAuthor Response: We thank the reviewer for the suggestion. To address this, we performed\nk-fold cross-validation on the newly added TCGA cohorts, ensuring a fairer comparison with\nthe SOTA methods. On TCGA-UCEC, our methodology achieved a C-index of 0.723,\nwhereas DeepSets, DeepAttnMISL, and MCAT achieved 0.593, 0.586, and 0.622,\nrespectively. On TCGA-BRCA, our methodology achieved a C-index of 0.645, compared to\n0.522, 0.577, and 0.580 for DeepSets, DeepAttnMISL, and MCAT, respectively. These\nresults demonstrate that even under comparable partitioning strategies, our method\nconsistently outperforms existing approaches. We note that the considered SOTA models do\nnot perform gradation, so a direct comparison on grade prediction was not possible. Finally,\ndue to computational constraints, we were unable to repeat experiments on the earlier\nTCGA-GBMLGG or TCGA-KIRC cohorts under identical splits, as this would have required\nconsiderable additional resources and time, well beyond the scope of this review period.\nNevertheless, the consistent superiority of our approach on the newly evaluated cohorts\nprovides strong evidence of its robustness and fairness in comparison.\n4. PathGen's approach is to explicitly generate transcriptomic data. An alternative class of\nmethods focuses on aligning features from different modalities (e.g., learning a joint\nembedding space or using one modality to guide feature extraction in another) to improve\nprediction, without necessarily generating one modality from the other. For example, the\npaper cites PathoGen-X, which aims to \"trans-align\" genomic and WSI features. The paper\ndoesn't deeply discuss why the explicit generation approach was chosen over these\nalignment strategies, which might be more direct if the ultimate goal is prediction and could\npotentially avoid the compounding of errors inherent in a generative step. While PathGen's\ngenerated data has the advantage of being interpretable as a \"transcriptome,\" alignment\nmethods can also offer interpretability through cross-modal attention or feature correlation in\nthe learned joint space.\nAuthor Response: We thank the reviewer for raising this important point. We would like to\nnote that approaches that rely on one modality to guide feature extraction from another\n(such as cross-modal attention) are similar to what our predictive model MCAT_GR does.\nHowever, such approaches require the availability of both modalities at inference time. This\nis often impractical, as transcriptomic data is not always available in clinical settings. Our\nrationale for explicit cross-modal synthesis is precisely to address this limitation — by\ngenerating the missing transcriptomic modality from WSIs, we enable downstream\nmultimodal prediction at inference while still benefiting from the strengths of both modalities.\nAlso, while alignment-based methods that learn a shared embedding space across\nmodalities are indeed a viable strategy, they inherently make one modality dominant in the\njoint representation.\nThus, explicit transcriptomic synthesis not only enables interpretability by producing an\ninterpretable \"transcriptome,\" but also provides a practical and effective solution for\nreal-world clinical inference where transcriptomic data is absent. The synthesized\ntranscriptomic data may be used to evaluate the add-on benefits of performing a\ntranscriptomic test.\nReviewer #2 (Remarks on code availability):\nThe provided code repository appears well-structured, and the accompanying instructions\nare clear. However, the absence of pre-trained model checkpoints is a notable limitation, as\nthis prevents direct evaluation of the trained models and hinders efforts to precisely replicate\nthe reported results without undertaking full model retraining.\nAuthor Response: We thank the reviewer for the positive feedback regarding the structure\nand clarity of our code repository. We acknowledge the concern regarding the absence of\npre-trained model checkpoints. We have now added downloadable links for the checkpoints\nof all our models in the GitHub repository.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, Dey et al. present PathGen, a deep-learning framework that integrates\nhistology images with synthetic RNA-seq data to predict tumor grade and cancer patient\nsurvival. Using publicly available glioma (GBM/LGG) and kidney cancer (KIRC) datasets,\nthey demonstrate the advantage of multimodal integration over single-modality approaches.\nThe strengths of this study include: (1) Development of a novel deep-learning model for\npredicting transcriptomic features from H&E-stained images. (2) Patch-level interpretability to\ncapture intra-tumoral heterogeneity. (3) Use of conformal predictions to address model\nuncertainty.\nOverall, the study tests an innovative hypothesis with instructive findings for research in the\nfield. My primary concerns are technical and pertain to data selection, preprocessing, and\npresentation.\nDatasets:\n1. The rationale for restricting analysis to GBM/LGG and KIRC from the broader TCGA\ncohort (spanning over 30 cancer types) is not clearly explained.\nAuthor Response: We thank the reviewer for this helpful comment. In the initial version of\nour study, we focused on TCGA-GBMLGG and TCGA-KIRC because TCGA does not\nprovide tumour grade information for all cancer cohorts, and these two cohorts included both\ngrade and survival annotations. This was important since our model, MCAT_GR, is\nspecifically designed to jointly predict grade and survival risk. However, we have now\nextended our experiments to two additional TCGA cohorts, TCGA-UCEC and TCGA-BRCA.\n2. Predictive performance for both tumor grade and survival was approximately 15% higher\nin GBM/LGG compared to KIRC, indicating inconsistency for the predictive performance\nacross different cancer types. Assessing the predictive performance in additional cancer\ntypes would help determine model generalizability across different cancers. While the\nauthors claim this as a future goal in the Discussion, it would be beneficial to demonstrate\nthe robustness and consistency of the model in studies for Nature Communications.\nAuthor Response: We thank the reviewer for this valuable suggestion. In response, we have\nextended our experiments to include data from the Clinical Proteomic Tumor Analysis\nConsortium (CPTAC) as well as two additional TCGA cohorts, uterine and breast. In total,\nfour additional cohorts have now been incorporated: TCGA-UCEC (uterine), TCGA-BRCA\n(breast), CPTAC-GBM (glioblastoma), and CPTAC-UCEC (uterine). We would also like to\nnote that variations in absolute predictive performance across different cancer types are\nexpected and have been observed in prior established works (MCAT, PathomicFusion). Our\nfocus, therefore, is on the generalizability of the claim that synthesized transcriptomic data\nprovides significant added value over WSI-only predictions, and that predictions based on\nreal and synthesized transcriptomic data remain significantly close. The new results and\nanalyses are presented in Figure 2b and Supplementary Table 6, and discussed in Sections\n“Similarity evaluation of synthesized and real transcriptomic data” and “Performance\nevaluation of synthesized transcriptomic data in multimodal prediction”. We have also\nupdated Sections “Transparency evaluation for clinical interpretability” and “Uncertainty\nquantification for clinical reliability” to incorporate transparency evaluations and uncertainty\nquantification with respect to the newly added cohorts.\nData Preprocessing:\n1. The manuscript does not specify which gene length normalization method (TPM, FPKM,\nor RSEM) was used prior to computing z-scores.\nAuthor Response: We thank the reviewer for pointing this out. The transcriptomic data for\nthe TCGA and CPTAC datasets we used were obtained from cBioPortal\n(https://www.cbioportal.org), which already provides gene length–normalized expression\nvalues. Accordingly, in our preprocessing, we directly applied z-score normalization in cases\nwhere the values were not already standardized. For the new experiments performed on the\nadditional datasets and cohorts (TCGA-UCEC, TCGA-BRCA, CPTAC-GBM, CPTAC-UCEC),\nthe transcriptomic profiles were downloaded from cBioPortal, where the data are processed\nusing RSEM. However, for the earlier experiments, we relied on the transcriptomic data\nprovided by the authors of MCAT via their public GitHub repository. The original paper does\nnot explicitly specify the normalization method, but according to cBioPortal, “RNASeqV2\nfrom TCGA is processed and normalized using RSEM; specifically, the RNASeqV2 data in\ncBioPortal corresponds to the rsem.genes.normalized_results file from TCGA.” Hence, we\ninfer that RSEM normalization was also applied in those datasets.\n2. Figure 2(i): More technical details are needed on how predictive performance for\nindividual genes was aggregated at the pathway/gene-group level.\nAuthor Response: We thank the reviewer for this comment. We would like to clarify that\nFigure 2(a) presents the comparison between real and synthesized transcriptomic data\nacross all gene groups as well as for individual gene groups, using normalized MAE and\ncorrelation metrics. It does not illustrate technical details of the aggregation of individual\ngenes into pathway/gene-group levels. The methodology describing the pathway is\ndiscussed in brief in Methods (Section “Multimodal cancer gradation and survival risk\nestimation”) and illustrated in Figure 7(b, d). We learn an 1 X 1024 embedding for all genes\nin a gene group using the gene encoder (Figure 7.d) comprising 4 linear layers, each\nfollowed by an ELU activation. These embeddings corresponding to the gene groups are\nused to coattend to WSI features extracted using UNI. A dedicated pathway is used for\nsurvival risk prediction using the transcriptomic features followed by a global attention\npooling which is then combined with the globally pooled feature map obtained from the\nco-attended feature pipeline to predict the survival risk (Figure 7(b)). Further, the co-attention\nmaps are visualized in Figures 3,4 and Supplementary Figures 3,4,5 and explained in\nSection “Transparency evaluation for clinical interpretability”.\nResults and Data Presentation:\n1. Section 2.2: The statement that \"gene expression levels for gene groups show no\nsignificant differences between real and synthesized transcriptomes\" (p > 0.05, unpaired\nt-test) was inconsistent with the results shown in Supplementary Table 2, where all p-values\nare highly significant. This discrepancy should be clarified.\nAuthor Response: To clarify, the p-values in Supplementary Table 2 (now Supplementary\nTable 5) correspond to Spearman correlation results, which indicate highly significant\ncorrelations between the real and synthesized transcriptomic features. In the earlier version,\nwe had also performed unpaired t-tests on the existing TCGA-GBMLGG and TCGA-KIRC\ncohorts, but their p-values were not included in the table. We thank the reviewer for carefully\npointing this out.\nHowever, while extending our experiments to additional cohorts, we found the unpaired t-test\nto be redundant, since the correlation analysis already demonstrated highly significant\nassociations (p-values < 0.05). Therefore, in the revised manuscript, we have removed the\nreference to the unpaired t-test and now state instead: “Thus, showing highly significant\ncorrelation between real and synthesized transcriptomic data (Spearman correlation p-value\n< 0.05)”, in Section “Similarity evaluation of synthesized and real transcriptomic data”. We\nhave also removed the explicit p-values from Supplementary Table 5, since all were\ndisplayed as 0.000, and clarified in the table caption that all Spearman correlation p-values\nare < 0.05.\n2. Given the substantial survival differences between GBM and LGG, evaluating model\nperformance on the combined TCGA-GBMLGG cohort is not surprising. The authors should\nreport separate predictive performance (grade and C-index) for GBM and LGG individually,\nat least in Supplementary materials.\nAuthor Response: We thank the reviewer for this suggestion. In response, we have now\nreported the predictive performance (grade and C-index) separately for TCGA GBM and\nLGG cohorts in Supplementary Table 7 of the revised manuscript.\n3. Minor issues on Figure 1c which contains a redundant text box below \"Patch\nembeddings.\"\nAuthor Response: We thank the reviewer for pointing this out. The redundant text box in\nFigure 1 (previously 1c) has been removed in the revised manuscript.\n4. In Section 2.4.1, for a patient with ground truth grade I (Figure 4), the majority of patches\nwithin the tumor slide are predicted as grade IV (red). Is this expected?\nAuthor Response: We thank the reviewer for flagging this. There was indeed an error in\nreferring to the figure, Figure 4 actually corresponds to a ground truth grade IV case, and not\ngrade I. We have corrected this in the revised manuscript. To avoid any such ambiguity in\nthe future, we now provide the patient’s age, gender, ground truth grade, survival time, and\nsurvival status in the first line of the figure captions.\n5. Figures 3 & Supplementary Figure 4: Do lower-grade tumor regions consistently exhibit\nhigher oncogene expression levels? In Supplementary Figure 4, the middle tumor portion\nshows lower risk scores but higher oncogene expression than the tumor edges—is this\nexpected?\nAuthor Response: We thank the reviewer for this thoughtful question. The association\nbetween oncogene expression and tumour grade is complex and findings are inconsistent.\nSome studies have found oncogene expression to be positively correlated with tumour grade\n(https://pmc.ncbi.nlm.nih.gov/articles/PMC1977917/), while other studies report a negative\ncorrelation (https://www.sciencedirect.com/science/article/pii/S0306987710004263), or no\ncorrelation at all (https://www.sciencedirect.com/science/article/pii/S0031302516352692).\nBecause of this it is hard to comment on whether the data we present in this regard is\nexpected or not.\nWe would like to clarify that Figures 3, 4 and Supplementary Figures 3, 4 do not represent\nactual gene expression levels. Instead, they visualize the co-attention maps obtained from\nour predictive model, which we use as a tool for explainability. In these maps, higher values\nindicate stronger model attention rather than higher gene expression. Given this caveat and\nthe biological complexity, we are cautious about drawing any definitive biological conclusions\nabout the association between oncogene expression and tumour grade from our data.\nHowever, our work provides a step towards computational methodologies for investigating\nthe complex relationship between tumour grade and gene expression patterns in future\nstudies, via the positing of testable hypotheses for wet lab validation. It provides an\nadditional visual presentation of tumour heterogeneity to the clinician expert (pathologist) to\nchoose whether or not to draw any further intuition from them. Thus, given the heterogeneity\nof cancer, the AI system deliberately merely presents these maps to the human user instead\nof making any generalised claims from it.\n6. Supplementary Figure 4(d): Minor issues on the image annotation on the left side which is\nduplicated.\nAuthor Response: We thank the reviewer for noticing this. The duplicated annotation in\nSupplementary Figure 4(d) has been removed in the revised manuscript.\nReviewer #3 (Remarks on code availability):\nThe authors have provided pertinent code with user instructions for reproducing their\nanalysis.\nAuthor Response: We thank the reviewer for acknowledging the availability and clarity of our\ncode and user instructions. We have made every effort to ensure reproducibility and ease of\nuse for the research community."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Most of the concerns have been addressed the revision. The authors mentioned that for the clinically important but less abundant gene expressions, this analysis shall involve much substantial domain expertise and input, which is totally understandable, please kindly consider put this into the discussion.", "reviewer_2": "Thank you for the revised submission. The revisions have successfully addressed my previous concerns.", "reviewer_3": "The authors have carefully addressed all of this reviewer's comments."}, "author_response": "Reviewer #1 (Remarks to the Author):\nMost of the concerns have been addressed the revision. The authors mentioned that\nfor the clinically important but less abundant gene expressions, this analysis shall\ninvolve much substantial domain expertise and input, which is totally\nunderstandable, please kindly consider put this into the discussion.\nAuthor Response: We thank the reviewer for the thoughtful suggestion. As advised,\nwe have now accordingly revised the Discussion section to include this clarification:\n“Also, evaluating the clinically important but less abundant synthesized gene\nexpressions would require substantial domain expertise.”\nReviewer #2 (Remarks to the Author):\nThank you for the revised submission. The revisions have successfully addressed\nmy previous concerns.\nAuthor Response: We sincerely thank the reviewer for the positive feedback and\nappreciation of our revisions.\nReviewer #3 (Remarks to the Author):\nThe authors have carefully addressed all of this reviewer's comments.\nAuthor Response: We thank the reviewer for their time, effort, and positive\nassessment of our revised manuscript.\nReviewer #3 (Remarks on code availability):\nThe authors have provided pertinent code with user instructions for reproducing their\nAnalysis.\nAuthor Response: We thank the reviewer for acknowledging the reproducibility of our\nwork and the clarity of the provided code and instructions."}]

Decision letter