Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA).The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank’s biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures.In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper. In the field of oncology, gene expression serves as a potent indicator of disease progression and its outcome prediction. Over decades, microarrays and RNA sequencing have been used extensively to investigate profiles in the form of biomarker signatures by quantifying the expression level of genes in the RNA data in cancer patients compared to the average in healthy individuals. To this end, several classical statistical methods have been introduced, such as DESeq2 , edgeR . We urgeNetwork science has provided a useful account of exploring other aspects in assessing the biomarker significance besides the statistical correlation with a phenotype, such as molecular and functional interactions. Previously, we developed a random surfer model to integrate protein interactions with expression and phenotypic information to rank biomarkers according to their effectiveness in predicting cancer progression , 5. Insphttps://portal.gdc.cancer.gov/).Our work aims to extend the implementation and application of NetRank to differentiate different cancer phenotype and deliver an open source R implementation for the algorithm with complete functionality, including pre-and post-processing of RNA-seq gene expression data, see Fig. r: ranking score of the node (gene). n: number of iterations. j: index of the current node. d: damping factor (ranging between 0 and 1); defines the importance (weights) of connectivity and statistical association. s: Pearson correlation coefficient of the gene. degree: the sum of the output connectivities for the connected nodes. N: number of all nodes (genes). m: connectivity of the connected nodes.NetRank is a random surfer model for biomarker ranking inspired by Google’s PageRank algorithm. NetRank integrates protein connectivity  with their statistical phenotypic correlation, see Eq. . It favoWe implemented and evaluated NetRank to work with two types of networks: biological precomputed networks (protein-protein interaction) and computationally computed networks (co-expression of genes). For the first one, we used the database STRINGdb, which covers predicted and known biological interactions between proteins through the R package “STRING v10” . For theWe obtained RNA gene expression data on 05/08/2022 from the Cancer Genome Atlas (TCGA), which initially consisted of 20,531 genes and 11,069 samples. We kept only 8603 samples after removing those who were duplicated or had missing values in the expression levels. Of these, we used only 3388 samples that were manually reviewed and approved in TCGA clinical follow-up. These 3388 were covering 19 cancer types, see Fig. A case study of 19 cancers with 3388 individuals was conducted to evaluate the performance of the NetRank algorithm as a feature selection and ranking method for RNA expression data. In our primary analysis, we visualized breast cancer results because it contains the highest number of samples. In this, 862 (breast cancer samples) were labeled as cases and 2526 (other cancer types) as controls. Out of these, 618 (cases) and 1753 (controls) were used in the development set for the feature selection and ranking, while 244 (cases) and 773 (controls) were kept unseen for evaluation purposes (test set). This corresponds to the usual data division of 70% and 30% between the development and test sets to avoid over-fitting.Furthermore, the analysis was replicated to the other 18 cancer types with the same setup and configuration, and the final results are reported accordingly in this section.We performed NetRank on two kinds of networks: the one obtained from StringDB and the co-expression network that is constructed using WGCNA (see the implementation section). Wecompared the results. STRINGdb was fetched directly, while WGCNA was locally computed using our curated dataset. For each network, we ranked proteins according to their suitability for breast cancer prediction. What stands out is the correlation in ranking proteins according to their suitability for breast cancer prediction between the STRINGdb and the WGCNA network , see Fig. NetRank scores the proteins’ potential in cancer type prediction, according to their connectivities and associations. For breast cancer data, using the development set, we picked the top 100 proteins with a P-value of association below 0.05 and have the highest NetRank score in separating breast cancer from the other cancer types. Then, we evaluated their performance using the test set by performing principal component analysis (PCA), a linear and simple dimensionality reduction method, and support vector machine (SVM), a nonlinear machine learning approach.What is striking is the significant segregation of individuals with breast cancer from the from the ones with other cancer types in the test set when using PCA, with an area under the ROC curve (AUC) of 93% for the first principal component see Fig. . Using tThe functional enrichment analysis on the breast cancer signatures shows that selected biomarkers harbor biological relevance through enhancing the protein-protein enrichment significance with 88 enriched terms in 9 relevant categories, compared with only nine terms if we choose the proteins according to their statistical associations only. The list of genes is provided in the Appendix.In the same manner, we generated a feature set for each cancer type independently and evaluated them with machine learning using SVM. As is shown in Table Additionally, for visualization purposes, we picked the best ten biomarkers from each cancer type and ran the principal component analysis on them (171 unique proteins), using all data. As shown in Fig. We measured the performance of the NetRank R package on the development set of breast cancer data, i.e. 618 case and 1753 control samples, using a computer with 15 cores. As can be seen in Table This application note has introduced an extended open-source implementation of the NetRank algorithm, a network-based approach for biomarker discovery in cancer prediction. By incorporating protein associations, co-expressions, and functions, along with phenotypic associations, NetRank has demonstrated its robustness and suitability for identifying interpretable biomarker signatures in various cancer types. Our approach is advantageous compared to correlation-based methods. It ensures that proposed biomarkers share a common function, avoiding unrelated and challenging interpretations.This manuscript focuses on technical aspects and package usage, providing a case study demonstrating the network approach’s ability to identify meaningful outcomes. Emphasizing the functional relevance of biomarkers enhances understanding and opens avenues for targeted therapies or diagnostics. This paper contributes to the field by providing fast and efficient implementation of NetRank and comprehensive functionality for pre and post-processing RNA-seq gene expression data and building protein-protein interaction networks. The availability of the source code on GitHub and the accompanying R library allows for easy accessibility and further exploration by researchers.


Inflammation is undoubtedly a hallmark of cancer development. Its maintenance within tumors and the consequences on disease aggressiveness are insufficiently understood.in vivo disease models were subsequently used to validate findings.Data of 27 tumor entities (about 5000 samples) were downloaded from the TCGA and GEO databases. Multi-omic analyses were performed on these and in-house data to investigate molecular determinants of tumor aggressiveness. Using molecular loss-of-function data, the mechanistic underpinnings of inflammation-induced tumor aggressiveness were addressed. Patient specimens and SOX2 amplification was the most important feature among novel and known aggressiveness-associated alterations. Mechanistically, SOX2 regulates a group of genes, in particular the AP1 transcription factor FOSL2, to sustain pro-inflammatory signaling pathways, such as IL6-JAK-STAT3, TNFA and IL17. FOSL2 was found overexpressed in tumor sections of specifically aggressive cancers. In consequence, prolonged inflammation induces immunosuppression and activates cytidine deamination and thus DNA damage as evidenced by related mutational signatures in aggressive tumors. The DNA damage affects tumor suppressor genes such as TP53, which is the most mutated gene in aggressive tumors compared to less aggressive ones (38% vs 14%), thereby releasing cell cycle control. These results were confirmed by analyzing tissues from various tumor types and in vivo studies.There was significant association between somatic copy number alterations (sCNAs) and tumor aggressiveness. SOX2 in promoting DNA damage and genome instability by sustaining inflammation via FOSL2/IL6, resulting in tumor aggressiveness.Our data demonstrate the implication of The online version contains supplementary material available at 10.1186/s12943-023-01734-w. Genomic instability is undoubtedly a major hallmark of cancer development as it drives tumor heterogeneity and supports the adaptation of cancer cells to stress conditions leading to malignant behavior and therapy resistance . OncogenFOSL1 expression in brain cancer [MYC and KRAS gene dosage in pancreatic cancer [Following malignant transformation, tumors exhibit diverse clinical phenotypes. Aggressive tumors are generally undifferentiated and plastic . Severaln cancer  or MYC ac cancer . Gene doApart from somatic copy number alterations (sCNAs), early onset of cancer is associated with aggressive disease profiles, at least in some cancer entities such as gastric cancers . InflammSOX2 gene dosage is tightly associated with disease aggressiveness. Mechanistically, SOX2 regulates the API transcription factor FOSL2, which in turn supports inflammatory TME by promoting TNFA, IL6-JAK-STAT3 and other pro-inflammatory signaling pathways to induce immunosuppression and stimulate genomic instability by activation of cytidine deamination. In effect, SOX2 has previously been linked to tumor aggressiveness [SOX2/FOSL2 in driving cancer aggressiveness.A comprehensive analysis of how these different molecular factors synergize to influence disease phenotype is still missing. In this study, an integrative multi-omic pan-cancer analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data repositories as well as data generated in-house was performed in order to identify major genomic determinants of cancer aggressiveness and the underlying mechanistic processes. We uncovered an association of sCNAs and disease aggressiveness and could demonstrate that siveness , 10. Simsiveness , 12. OurMolecular and clinical data from 27 cancer entities available at TCGA were analyzed  was downloaded from GEO. The ChIP-seq data was equally generated by de Wet L, Williams A, Gillard M, Kregel S et al., 2022, PMID: 35067686) and are available at the accession number GSE166183 Additional SOX2 and FOSL2 ChIP-seq data was downloaded from the encyclopedia of DNA elements (ENCODE) derived from the breast cancer cell line MCF7. Gene expression data from pancreatic normal and disease tissue was generated in-house and is available at the accession number E-MTAB-1791. Gene expression data from laser capture microdissected PAAD samples were obtained from the study by Maurer et al., 2019 (PMID 30658994).Triplicate gene expression data from two prostate cell line derivatives CWR-R1 Control and SOX2-KO were downloaded from the gene expression omnibus. The data was generated by de Wet L, Williams A, Gillard M, Kregel S et al., 2022 (PMID: 35067686) and are available at GSE166184. The CWR-R1 cell line was chosen because of the availability of ChIP-seq data from the same cell line to investigate Following data download, sCNA data was analyzed using the gaia Bioconductor package. To this end, probe meta-files for the hg19 genome assembly were downloaded from the Broad Institute and sex chromosome names were converted to numbers . After duplicate removal, a marker matrix was filtered for common copy number alterations (CNVs) that are usually present in normal samples. The filtered marker matrix was then used to create a marker object. Copy number variation data from each tumor entity was downloaded as described above and also converted into a matrix. Copy number segment thresholds of -0.3 and 0.3 were used to delineate copy number deletions (sCNAdels) and copy number amplifications (sCNAmps), respectively. Sex chromosome names were also replaced with the respective numbers to match the marker object. Tumor-related somatic copy number alterations were then identified by running the gaia feature on the marker object and the processed CNA matrix on all samples in the CNA matrix. sCNA data was annotated using the GenomicRanges and biomaRt Bioconductor packages. The same packages were equally used to intersect and annotate sCNAs from different cancer entities. The circlize package was used to generate circus plots for sCNAs and somatic mutation data.2-fold change of ≥ 0.9 in the TCGA data and ≥ 1 for cell line-derived data. For all data sets, the false discovery threshold was set to < 0.05. Heat maps were then generated from the list of all differentially expressed genes in each case. The gene expression deconvolution tool TIMER (http://timer.cistrome.org/) was used to determine the proportions of immune cell infiltration in gene expression data.Raw gene expression data from TCGA and GEO were filtered and quantile normalized using the EDASeq Bioconductor package. The filtered data was then converted into a DGEList object for downstream analysis using the edgeR package. Differentially expressed genes were selected by defining a logFor ChIP-seq data analysis, fastq files were assessed for their quality using the FastQc tool and trimmed with trimmomatic. The trimmed data was then mapped to the hg38 human genome assembly using bwa short read mapper. Reads were then filtered and used for peak calling with MACS. Bedgraph and narrow peak files were exported and annotated using the ChIPpeakAnno Bioconductor package. ChIP-seq peaks were displayed using the bioconductor package trackViewer.Gene set enrichment analysis was performed with the GSEA algorithm from the Broad Institute for the hallmarks gene set, while the Bioconductor package PathfindR was used for KEGG pathway analysis. The significance threshold for GSEA and KEGG analysis was set to < 0.05. Genes associated with aggressive cancer phenotype were determined using the boruta feature selection package.3 were implanted into the flanks of 5–6 week-old female nude mice. For each tumor, 5–6 animals were used and tumor growth was monitored until tumors reached about 1000 mm3. Mice were sacrificed and tumors removed for the preparation of fresh frozen tissue and FFPE sections. All animal experiments were approved by the University of Buea and the Cameroon Consortium for translational Cancer Research (CCOTCARE) animal review board and performed following international guidelines.Tumor sections were collected in RPMI cell culture medium supplemented with 2.5% fetal bovine serum, 10 mM HEPES and 1% penicillin and streptomycin, temporarily stored in freezing medium (90% FBS & 10% DMSO) at the sampling site and later used for model establishment. Tumor pieces of about 8 mmFOSL2 antibody at 4 °C overnight . Slides were then washed three times for 5 min and incubated with secondary antibody (ZytoChem Plus (HRP) One-Step Polymer anti-Mouse/Rabbit, ZUC053-100) for 30 min at room temperature. Signals were revealed by incubation with brown chromogen development using Dako Liquid DAB + Substrate Chromogen System . The sections were counterstained with hematoxylin, dehydrated, and mounted. Slides were digitalized with a Zeiss Axio Scanner Z.1  at 10 × magnification.FOSL2 staining was performed on 4 µm FFPE tumor sections. The Dako REAL Alkaline Phosphatase Detection System  was used. Slides were dried in an oven at 60 °C for 2 h and dewaxed in an automated dewaxing system . Antigen retrieval was performed by heat-induced epitope retrieval with citrate buffer  for 15 min at 110 °C in a pressure cooker. The slides were then blocked with serum-free protein blocking solution (Dako) for 30 min and incubated with a 1:500 dilution of the www.graphpad.com).Patient samples were classified as early onset, if the patient was diagnosed before or at the age of 45 years . They were otherwise considered as late onset. Aggressive cancers (poor outcome) were considered to be entities with more than 40% of cases declared dead at the end of each study, while cancers with less than 20% of cases declared dead were considered less aggressive (better outcome). A cancer was considered to have substantial early onset, if more than 10% of cases were diagnosed before 45 years of age. All patients without age or gender information were excluded from the analysis. Similarly, for survival analysis, all patients with missing data for time to last follow-up or vital status were excluded. Survival analysis was performed using the survival and survminer R packages. Cut-off threshold for dichotomization of gene expression data was determined using the surv_cutpoint function of the survminer package. All analyses were performed with the R software environment or using Graphpad prism version 8.0.0 for Windows  and less aggressive cancers  were compared. Based on this criteria, 10 cancer entities: Glioblastoma multiforme (GBM), Pancreatic adenocarcinoma (PAAD), Ovarian serous cystadenocarcinoma (OV), Skin Cutaneous Melanoma (SKCM), Cholangiocarcinoma (CHOL), Bladder Urothelial Carcinoma , Lung squamous cell carcinoma (LUSC), Head and Neck squamous cell carcinoma (HNSC), Esophageal carcinoma (ESCA) and Stomach adenocarcinoma (STAD) were categorized as highly aggressive. The following tumor entities were classified as less aggressive: Prostate adenocarcinoma (PRAD), Thyroid carcinoma (THCA), Thymoma (THYM), Testicular Germ Cell Tumors (TGCT), Kidney Chromophobe (KICH), Kidney renal papillary cell carcinoma (KIRP) and Breast invasive carcinoma (BRCA)  was already reported to be associated with GBM aggressiveness [SOX2 might regulate FOSL gene expression. SOX2 knockdown in the CWR-R1 prostate cancer cell line led to a strong downregulation of FOSL2, but not FOSL1  tumor samples from all 27 cancer entities and compared the enriched hallmark gene sets. As shown in Fig. FOSL2high tumors.Gene set enrichment analysis revealed strong enrichment in pro-inflammatory pathways in aggressive tumors, such as TNFA signaling via NFkB, ers Fig. A. Lookinsiveness . To thisFOSL2 gene expression in aggressive tumors, we investigated this association in patient specimens and PAAD tumor models. To this end, we stained for FOSL2 protein expression in tumor sections from GBM, PAAD, merkel cell carcinoma (MCC) and PRAD. Additionally, we generated five PAAD xenografts from primary resected patient tumor materials and monitored tumor growth for about 3 weeks. We observed, that fast growing PAAD xenografts showed an overexpression of FOSL2 compared with slow growing tumors, although FOSL2 was expressed in all tumors . Similarly, in tumors of the central nervous system, SOX2 and FOSL2 expression was higher in pilocytic astrocytoma, ependymoma and glioblastoma multiform compared with medulloblastoma . In bladder cancer, data from patients who developed metastases after tumor resection was analyzed. As shown in Suppl. Fig. SSOX2 locus for each cancer entity revealed, very high SOX2 dosage in the aggressive cancers  for further investigations. We focused on PAAD, because of its aggressiveness and presence of dense fibrotic stroma, which could play an important role in inflammation. We therefore analyzed the expression of IL6 in grade II and grade III PAAD . We then investigated the expression of immune cell markers in our cohort of almost 300 samples including 41 normal pancreas tissues, 59 chronic pancreatitis tissues and 195 resected PAAD tissues. We observed a steady increase in the expression of FOXP3, LRRC32, as well as FOSL2 and IL6 from normal through pancreatitis to PAAD  CNA plots for top three most aggressive and prevalent cancer. (B) CNA plotsfor top three least aggressive and prevalent cancers. (C) Bar plot showingtumor mutational burden for different age groups for all cancer with more than10% of cases diagnosed before 45 years of age except those shown in previousfigures. (D) A bar plot show the number of CNAs identified in each of theanalyzed cancer entities (left panel) and a bar plot show the tumor mutationalburden in each of the analyzed cancer entities (right panel).Additional file 2:Suppl. Figure S2. Somatic gene mutations are prevalent in elderly patients thanyounger patients. (A)A circos plot showing somatic mutations and copy number alteration inglioblastoma patients diagnosed after the age of 45 years (left panel) and azoom on chromosome 3 showing amplification at the SOX2 gene locus (right panel). (B) A circos plot showing somaticmutations and copy number alteration in glioblastoma patients diagnosed beforethe age of 45 years (left panel) and a zoom on chromosome 3 showing noamplification at the SOX2 gene locus(right panel) as seen in older patients. (C) An oncoprint showing somaticmutations in sarcoma patients diagnosed before the age of 45 years. (D) Anoncoprint showing somatic mutations in sarcoma patients diagnosed after the ageof 45 years. (E) An oncoprint showing somatic mutations in thymoma patientsdiagnosed before the age of 45 years. (F) An oncoprint showing somaticmutations in thymoma patients diagnosed after the age of 45 years. (G) Anoncoprint showing somatic mutations in patients with testicular germ celltumors diagnosed before the age of 45 years. (H) An oncoprint showing somaticmutations in patients with testicular germ cell tumors diagnosed after the ageof 45 years.Additional file 3:Suppl. Figure S3. Genes with somatic copy number amplifications and concomitantupregulation are associated with patient outcome. A forest plot showing multivariatecox proportional hazards regression for genes with somatic amplification andtranscriptional upregulation in poor outcome cancers. Additional file 4:Suppl. Figure S4. FOSL2protein staining in aggressive  tumors and less aggressivetumors PRAD)Additional file 5:Suppl. Fig. S5. SOX2 gene dosage isassociated with overall survival across multiple cancer entities. A) A Kaplan-Meier overall survivalcurve for patients with high and low SOX2mean copy segment. Samples were dichotomized using the survminer R package.Mean segment low and mean segment high are SOX2CNA segments as determined by the gaia Bioconductor package. B) A bar plotshowing the percentage of patient in each cancer entity with mean SOX2 segment mean higher or lower thanthe cut-off determined in A. C) Bar plots showing the expression of SOX2 in different molecular subtypes oflung cancer as well as non-tumor lung tissue . Large cellneuroendocrine (LCNE), basaloid (BAS), adenocarcinoma (ADK), squamous cellcarcinoma (SQC), carcinoid (CARCI) and small cell carcinoma (SCC). D) Bar plots showing the expression of FOSL2 in different molecular subtypes oflung cancer as well as non-tumor lung tissue. E) Bar plots showing theexpression of SOX2 in differentmolecular subtypes of CNS tumors as well as non-tumor brain tissue. F) Barplots showing the expression of FOSL2in different molecular subtypes of CNS tumors as well as non-tumor braintissue. G) Bar plots showing the expression of FOSL2 in prostate cancer patients who developed either localized ormetastatic disease after resection.Additional file 6:Suppl. Fig. S6. A waterfallplot showing the mean SOX2 genedosage in the top 10 cases with the most amplified and the most deleted SOX2 alterations in all cancer entities.Additional file 7.


Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method. Generally, these methods filter differentially expressed genes on pairs of tumor and adjacent normal tissue using parametric test or nonparametric test  We propose a new robust multiple-datasets-based semi-supervised learning model, MSSL, which gets a significant performance leap when applied to TCGA and GEO RNA-seq data compared with previous approaches. (2) We propose an interpolation technique that allows mixed samples to produce higher-quality labels after interpolation to realize the utilization of a large amount of unlabeled data and balance the number of relatively uncommon cancer samples. (3) We explore top genes that contribute most to classification based on the idea of PI and find their relations to the corresponding tumor types, which proves that they can be used as candidate cancer-specific biomarkers.In this section, we first introduce the datasets that are used for training our model. Next, we introduce the algorithms involved in our model which include consistency regularization, interpolation technique, and label guessing. We finally introduce PI, which is used to measure feature importance in data mining. It can relate genes that contribute most to classification to candidate cancer-specific gene biomarkers.et al.et al. x + 1) transformed RNA-Seq by expectation maximization (RSEM) normalized count was used to show the gene-level transcription estimates. To better express the differences between samples, each gene was subtracted from its mean. For better cross-database integration, we downloaded an annotation file of gene and chromosome positions from the National Center for Biotechnology Information (NCBI). According to the positional relationship of genes marked in the table on chromosomes, we rearranged the sequence of the genes and removed around 1000 genes that did not appear in the annotation file. Besides, to balance the classification accuracy and the integrity of the genes, we filtered out the genes whose variance is less than 1.0.The datasets we applied to MSSL include TCGA and GEO normalized-level3 RNA-Seq gene expression profiles. TCGA  to predict y based on the inputs x, where θ denotes the model parameters. Meanwhile, we keep an EMA model We define the notations for our study. We use et al. aug1 and AutoAugment aug2 to generate aug1(ub) and aug2(ub) and compute the output distribution pθ(y|aug1(ub)) and pθ(y|aug2(ub)). Minimize the divergence metric between the two predicted distributions. In our work, we used Kullback-Leibler divergence to calculate consistency loss.Consistency regularization is already one of the most commonly used regularization methods in semi-supervised learning. The main idea of consistency regularization is as follows: for input, even if it is slightly disturbed, the model should output similar predictions. MSSL leverages two kinds of augmentations: standard data augmentation and AutoAugment ] needs to be stopped.where the et al. Normal data augmentation assumes that novel and realistic-looking training data is created by applying a transformation to a single sample without changing its label. Interpolation-based regularization aims to use multiple samples to generate new samples. The main idea is to create new samples by interpolating multiple cancer samples. The representative methods include SMOTE, Sample Pairing and MixUp (Zhang where aug1(xb) and aug2(ub). As Eq. 4 shows, the labels of samples are necessary for the MixUp. To generate sample labels, we must predict the pseudo-label for unlabeled samples. We used the outputs of the EMA model as the ‘pseudo label’. θ, and we define t:After data augmentation, given our processed batches α is a smoothing coefficient hyperparameter. We update the parameter θ of the model through backward propagation and update EMA parameter where p(x), which grows from 0 to 1 with training in an ideal situation, and the accuracy of labeled samples is 1. Since the number of unlabeled samples is much larger than the number of labeled samples, we set the number of labeled samples to B and the number of unlabeled samples to KB (k > 1). Therefore, the average quality of all samples is simply expressed as [1 + Kp(x)]/(1 + K), equivalent to p(x) + [1 − p(x)]/(1 + K). This means that the smaller the K is, the higher the quality of the mixed sample. To prevent overfitting and guarantee the quality of mixed samples, we gradually ramp up the batch size of unlabeled samples from 0 to K. The model can smoothly propagate labeled information to unlabeled samples and alleviate the problem of overfitting, which is illustrated in T as the total steps of training and t as the current step, and we consider three ramp-up schedules to augment the batch size of unlabeled samples. (1) Linear-schedule. As shown in H is the cross-entropy between distributions p and q,where t computed as Eq. 9.As N step, we input all unlabeled samples without any data augmentation to the EMA model to predict pseudo labels, then stored the obtained pseudo labels in a dictionary. However, when time permits, it is better to obtain pseudo-labels synchronously.Since the mixed label itself has the function of label smoothing, we no longer used confidence masking. To speed up the training of the model, a strategy of asynchronously predicting pseudo-labels can be used. In detail, after every repeating MSSL model was evaluated by tenfold cross-validation. We calculated the following performance metrics:To identify candidate genes, we ﬁrst applied TCGA gene expression data to the trained model and obtained the prediction scores of each cancer type. PI is a way to measure feature importance, searching for the features that have the biggest impact on predictions. PI applies to the model that has been ﬁtted, so we only needed to perform ranking importance once in the inference stage. As to each gene feature of each cancer type, we set the single column value to zero and made predictions using the resulting dataset to get the corresponding softmax value of each cancer type. Finally, we calculated the prediction score for each cancer and the absolute value of the softmax value was obtained after resetting a single gene, and arranged the value in descending order, so that we got the ranking of the importance of the corresponding gene for each cancer type. The ranking of each cancer-speciﬁc gene represented the importance of that gene to the classiﬁcation of that type of cancer.The TCGA gene expression data was divided into the training sets and test sets according to tenfold cross-validation. On each dataset, we set up ﬁve sets of experiments, which were supervised learning of 1000 and 2000 labeled samples, supervised learning of full labeled samples, semi-supervised learning of 1000 labeled samples, and semi-supervised learning of 2000 labeled samples. These ﬁve sets of experiments used the same baseline model.We used WideResNet-28 , lung squamous cell carcinoma (LUCS), pancreatic adenocarcinoma (PAAD), thyroid carcinoma (THCA), analyzed their corresponding top ten genes . And we Gene expression analysis can estimate the likelihood of different cancer for the individual by detecting biomarkers since the expression of some genes in cancer cells will be signiﬁcantly different. However, differential expression analysis based on statistical analysis technology tends to detect shared biomarkers rather than tumor-speciﬁc. Differential expression analysis based on machine learning and traditional deep learning can show excellent performance on common cancers with a large number of samples but has a limited effect on uncommon cancers. MSSL introduces additional datasets and uses semi-supervised learning to perform multi-classiﬁcation on gene expression datasets to detect cancer-speciﬁc differentially expressed genes, which suggests that the semi-supervised learning model can better extract the common and speciﬁc deep features of different cancers, and can balance the learning of each cancer type to achieve better classiﬁcation performance.In this study, we developed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to make classification of the prevalent forms of cancer and select candidate cancer-speciﬁc biomarkers based on the idea of PI, which is used to measure feature importance in data mining. The results showed that our model got a signiﬁcant performance improvement than previous methods. Moreover, MSSL provides a method to utilize additional datasets to get better generalization performance and achieve good results on uncommon cancer types. In the future, when constructing biological networks based on deep learning, especially when the existing high-quality data is insufﬁcient and other similar data exists, semi-supervised learning has a broad development prospect in cancer transcriptome analysis.Peng Chen, Zhenlei Li, Zhaolin Hong, Haoran Zheng and Rong Zeng declare that they have no conflict of interest.


Sample size calculation for spatial transcriptomics is a novel and understudied research topic. Prior publications focused on powering spatial transcriptomics studies to detect specific cell populations or spatially variable expression patterns on tissue slides. However, power calculations for translational or clinical studies often relate to the difference between patient groups, and this is poorly described in the literature. Here, we present a stepwise process for sample size calculation to identify predictors of fibrosis progression in non-alcoholic fatty liver disease as a case study. We illustrate how to infer study hypothesis from prior bulk RNA-sequencing data, gather input requirements and perform a simulation study to estimate required sample size to evaluate gene expression differences between patients with stable fibrosis and fibrosis progressors with NanoString GeoMx Whole Transcriptome Atlas assay. Spatial transcriptomics offers significant advantages over bulk or single cell transcriptomics. Bulk transcriptomics is unable to reveal tissue-wide patterning and disease-promoting cell niches. Single cell omics typically requires fresh tissue, whose dissociation can lead to significant cell type biases. Both these factors can be overcome with spatial transcriptomics, which additionally offers the ability to use widely available formalin-fixed paraffin-embedded (FFPE) tissue. Additionally, spatial transcriptomics offers unique insights into the likely signals to which clusters or areas of tissue may be exposed to from their neighboring tissue.2 but two spatial RNA sequencing technologies in particular have become widely commercially available2: Visium spatial gene expression from 10X Genomics3 and GeoMx Digital Spatial Profiler from NanoString5.Whilst spatial transcriptomic technologies have existed for some time, they have not been widely or commercially available until more recently. Such technologies can broadly be categorised as imaging based  or sequencing based (where the probe location is captured and undergoes sequencing). Full spectrum of the available technologies is reviewed in detail elsewhereThe Visium platform uses functionalized slides with printed barcoded oligo capture probes onto which RNA from permeabilised tissue can bind. This can subsequently be sequenced using a polyA approach. Visium is limited by pre-determined sequenceable spot sizes of 55 µm with 100 µm spacing between spot centres.5. While both Visium and NanoString GeoMx are compatible with FFPE tissue, sequencing efficiency with Visium is reduced in FFPE compared to fresh frozen tissue while no such limitation is reported for NanoString GeoMx2.The GeoMX platform extends more flexibility in enabling the operator to select regions of interest/illumination (ROI) from single cell analysis to whole regions based on histology or protein probes. This platform links complementary sequence probes to a unique barcode through a UV-cleavable linker. Hybridised sequence probes on the tissue of interest can then be released from tissue sections through the application of UV only to selected ROIs. The GeoMX platform allows addition of custom targets of interest such as transcript variants. GeoMx Human Whole Transcriptome Atlas enables targeted sequencing of 18,000 genes8. Specifically, despite the widespread use of the technology in unpowered exploratory research (https://nanostring.com/resources/publications/), only a handful of studies with NanoString GeoMx report dedicated sample size calculation based on pilot experiments but do not outline the requirements and statistical considerations in detail10.Sample size calculation is a crucial step in planning spatial transcriptomics studies. Yet, limited methodological research is available on this topic, especially in clinical specimens. Previous studies reporting sample size calculation for spatial transcriptomics focused on detection or co-localization of specific cell populations on the slide or identification of spatially variable featuresHere, we illustrate how to gather required inputs (formulate study hypothesis and estimate effect size based on historic data) and perform sample size estimation for comparison between patient groups with NanoSting GeoMx Whole Transcriptome Atlas using fibrosis progression in non-alcoholic fatty liver disease as a case study.11 and covers a spectrum of pathology from simple steatosis (NAFL) to steatohepatitis (NASH) and advanced fibrosis12. Progressed NAFLD places patients at risk of liver cancer and is strongly associated with the metabolic syndrome  and coronary heart disease13.Non-alcoholic fatty liver disease (NAFLD) affects up to a third of the population15, with fibrosing disease being the leading predictor of poor outcomes/survival17. To date, no studies have been able to show clinical, biochemical or histological predictors of fibrosis progression in cohorts of with serial liver sampling of the same individual20. A combination of follow-up biopsy analysis and cross-sectional studies indicate that weight gain14, metabolic co-morbidities such as diabetes20 and at-risk genotypes, for example in PNPLA3, TM6SF2, MBOAT722 place patients at higher risk of NAFLD fibrosis progression. Non-genetic molecular mechanisms placing the patient at risk of fibrosis progression are particularly poorly elucidated and limited to hepatic gene expression-based or serum protein-based risk signatures also found in HCV fibrosis progression23. Thus, further research is required to understand predictors of fibrosis progression in NAFLD and, ultimately, adjust clinical management of higher risk patients.NAFLD has been shown to progress to fibrosis both in patients with NAFL and NASHFirst, we performed analysis to identify which spatial expression patterns could serve as the primary and secondary endpoints.. (GSE19306624). Fibrosis progressors and fibrosis regressors had at least + 1 and − 1 fibrosis stage in their circa 2 year follow-up biopsies. The cohort included 28 individuals with stable fibrosis stage, 15 fibrosis regressors and 15 fibrosis progressors. We sought to identify an association between a higher-level gene expression pattern and some element of liver microanatomy or histology. Therefore, we did not make any attempt to control false discovery rate at the level of individual genes or pathways in this analysis. We also used the published DESeq2 rlog-transformed data set as-is without reprocessing. However, we performed a technical method comparison using another data set, GSE13525125. We checked that results obtained with linear regression on rlog transformed data and on raw counts with DESeq226 were overall consistent, especially with respect to log2 fold change estimates, with this sample size in GSE13525125 where both raw and normalized data were readily available  and the “cytoskeleton" (89 genes) gene sets with Kuppe’s method32. Briefly, the average was first calculated on rlog normalized expression of all genes within the gene set, then genes with poor correlation (< 0.1) to the average expression of the whole gene set were filtered out and the average was updated based on the remaining genes. We verified that the expression of the “lipid metabolism” score and the “cytoskeleton” score differed between fibrosis progressors, stable fibrosis and fibrosis regressors using linear regression adjusted for both baseline fibrosis stage and baseline NAFLD activity score (NAS).We explored bulk liver RNA-sequencing cohort from Fujiwara et alble Fig. . Candida33, for hepatocyte zones in NASH liver34 and the “core matrisome” markers for extracellular matrix from32. We calculated summary scores i.e. average normalized expression, for markers of different cell types, zones within liver and extracellular matrix in the Fujiwara et al. cohort (GSE19306624) and in published Visium liver spatial transcriptomics samples from Wu et al. http://lifeome.net/supp/livercancer-st/data.htm35, GSM5616008 from GSE18547736 and GSE19274137. The summary scores (expression averages) were calculated from rlog normalized expression in GSE193066 and from SCT-transformed expression in the Visium data sets. Then, we correlated “lipid metabolism” and “cytoskeleton” scores with the expression scores for liver cell types, ECM and hepatocyte zones. We assumed “guilt-by-association”: if summary scores correlated both in bulk and in spatial transcriptomics samples (Spearman method), then the expression signals co-localized.The candidate “lipid metabolism” and “cytoskeleton” marker sets were derived from bulk liver. Next, we traced potential sources of these expression signals within the liver. We extracted markers of cellular and anatomical hallmarks within liver from literature. Specifically, we obtained markers for liver cell type  fromWe made synthetic  Nanostring GeoMx data. Briefly, we learnt distribution of gene expression values within liver empirically from historic Visium samples and created read count matrices that matched the technical properties of NanoString GeoMx.http://lifeome.net/supp/livercancer-st/data.htm35 had well-defined fibrosis areas and hepatocyte fraction and satisfied the 100 reads/μm2 recommendation by NanoString. We used these five samples for the simulation study.Non-tumour liver samples adjacent to hepatocellular carcinoma 38, and 63–68% hepatocyte area, which corresponded to the lower range of literature estimates for the hepatocyte fraction by liver volume41. In total, we obtained 819 fibrotic niche spots and 12,074 hepatocyte spots across the 5 samples. To verify that our approach to tissue segmentation was reasonable, we conducted differential expression analysis between “bulk” tissue constructed by summing up counts from fibrotic niche and hepatocyte spots per patient. As expected, fibrotic niche was enriched in markers of diverse non-parenchymal cell types  and hepatocyte fraction was enriched in hepatocyte markers  or had low cell density. This resulted in 2.6–7.1% fibrotic niche, which was within the NAFLD fibrosis ranges Tables  and S2.We translated the expression changes in “lipid metabolism” and “cytoskeleton” summary scores from bulk tissue into expression changes within fibrotic niche and hepatocyte fraction. We found two representative markers PON1 and FLNA that had the most similar spatial expression pattern to the two summary scores identified in initial analysis. All subsequent calculations were based on these two genes.. cohort24. This determined the magnitude of spike-in that we used in the simulation study.We spiked-in increasingly large fractions of read counts to PON1 in hepatocytes and FLNA in the fibrotic niche. For example, spiked-in fraction r0.1 for FLNA corresponded to FLNA read counts in each fibrotic niche spot in sample i + 0.1*median(FLNA read counts across fibrotic niche spots in sample i), where i = 1…5. We summed up read counts across all spots per sample to obtain “bulk” tissue. We calculated log2 fold change between spiked-in “bulk” and unmodified “bulk” tissue with DESeq2 using the patient as a fixed effect covariate and constructed calibration curves. We found the point on each calibration curve where fold change in “bulk” tissue matched log2 fold change observed in the initial hypothesis-generating analysis in the Fujiwara et alWe took the unmodified 12,074 hepatocyte spots and 819 fibrotic niche spots across the five Visium samples as our spot “population” in “stable fibrosis” individuals. The same spots but spiked-in with fractional read counts r0.43 for PON1 and r2.9 for FLNA, respectively, were our spot “population” in “fibrosis regressors” and “fibrosis progressors”, respectively.35. Such low amount of input material is not recommended by NanoString because it leads to only high and moderate abundance genes being detected, variable expression estimates and unreliable differential expression based on a technical benchmark study by the manufacturer . Fibrotic niche was the smaller tissue fraction in our experimental plan and it determined the range of diameters that we evaluated. The width of fibrotic niche in the refence data rarely exceeded 2–3 Visium spots . Reference estimates of fibrotic niche size reported in NAFLD were in the < 200 μm range43. Based on this prior data, we considered 165 μm as maximum spot diameter. The amount of input material (cells) is determined by tissue area that is subjected to sequencing. The relationship between spot diameter and area is quadratic, so we aggregated 2 Visium spots to mimic 80 μm diameter, 4 Visium spots to mimic 110 μm, and 9 Visium spots to mimic 165 μm.Initial Visium spot diameter of 55 μm contains 8 to 20 cellsIn NanoString terminology, a spot is called “region of illumination”, or ROI, and corresponds to the small piece of tissue from which barcodes are harvested for sequencing. We use the term “ROI” to describe the synthetic larger-diameter NanoString data and the term “spot” to describe original 55 μm Visium spots.https://gtexportal.org/home/44) were used as negative control “background probes” for the GeoDiff method45.We drew random subsamples from our unmodified and spiked-in Visium spot “populations” and summed up counts from neighbouring spots to mimic NanoString ROIs with larger diameter. We made 100 synthetic data sets for each condition (endpoint × N patients × N ROIs × ROI diameter). In the original Visium data, correlations between global gene expression profiles in spots within one patient were not stronger on average than between spots from different patients. So, the new synthetic larger ROIs were randomly assigned to new “patient” or a new ROI from an existing “patient”. The simulated data sets were restricted to genes measured on the Nanostring GeoMx Human Whole Transcriptome Atlas assay (GPL32201). Genes that were part of the assay and had median expression < 0.05 TPM in all GTEx tissues  and patient as the random intercept. Effective library size was calculated with edgeR46.Since spatial transcriptomics is a new technology and the NanoString software GeoDiff45:Method 1, GeoDiffhttps://bioconductor.org/packages/release/bioc/vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html).GeoDiff::fitNBthmDE with gene count ~ group + (1|patient) after following the processing steps including the background estimation as described in the package vignette  + group + (1|patient).Method2, glmer.nb49:Method 3, GLMMadaptive LRTLikelihood ratio test (LRT) between full model assuming that there is a group difference and the null model assuming that gene expression is explained by technical and within-patient variability.Full model: gene count ~ offset + group + (1|patient).Null model: gene count ~ offset + (1|patient).We summarized our simulation setup in Table 50, Bioconductor version 3.14. Spatial transcriptomics data was handled with Seurat v4.1.151. Figures were made with ggplot252 and viridis53.All analyses were conducted in R 4.1.0The overall study workflow is summarised in Fig. 24) and spatial transcriptomics data37 to identify spatial gene expression pattens in baseline biopsies that correlated with fibrosis outcome in NAFLD.Sample size calculation requires definition of the study endpoint. We used a combination of historic bulk RNA-sequencing  and had approximately same magnitude compared to the fibrosis-stable group. The corresponding null hypotheses were that there were no group differences.56, and sample sizes were calculated to address both hypotheses.Hypothesis 1 was prioritized as the primary endpoint because we considered isolation of fibrotic niche more technically challenging and more likely to fail. However, both hypotheses were biologically plausible41, so, effect size within the hepatocyte fraction would be expected to be only slightly different from the effect size observed in bulk liver. By contrast, fibrosis areas constitute 2.8% (2.1–3.6%) in F1, 4.3% (3.3–5.4%) in F2, 4.8% (3.7–7.4%) in F3 and 12.3% (8.4–18.5%) in F4 NAFLD fibrosis stages38. Thus, expression signal originating within the fibrotic niche may be “diluted” in bulk liver, and we needed to translate the effect sizes between bulk liver and fibrotic niche and hepatocyte fraction. Individual genes that contributed to the summary scores did not always match the spatial expression pattern for the whole score. We found two representative markers PON1 and FLNA that had the most similar spatial expression pattern to the lipid metabolism score and cytoskeleton score, respectively  per tissue sample . Historic data indicated that tissue samples may have uneven thickness and cell density may be variable within tissue sample. So, the expression measurements were expected to have large technical variability in addition to biological variability of expression for PON1 in hepatocytes and FLNA in the fibrotic niche.Using the NanoString GeoMxGiven that the outcome of the experiment may be affected by the sampling variability within the tissue sample, we conducted a simulation study to accurately mirror this sampling variability and estimate the required number of patients, ROI diameter and number of ROIs per patient. We applied three mixed effect negative binomial models implemented in three different software packages to synthetic  NanoString data with varied ROI diameter.Interestingly, the three methods applied head-to-head on the same synthetic data resulted in distinct power surfaces Figs.  and 5. HThe initial simulation study helped us to narrow down the range of experimental conditions to the largest ROI size 165 μm, two ROIs per patient and at least 4 patients per group. We repeated the simulation study but with 1,000 synthetic data sets per condition Table . All thrTaking all these factors into consideration, the final sample size was based on the glmer.nb method. We calculated that we would need (10 patients × 2 hepatocyte ROIs + 5 patients (subset of the ten) × 2 fibrotic niche ROIs) × 3 experimental groups  = 90 samples subjected to sequencing at baseline. If paired follow-up biopsies are profiled for exploratory analysis from the same patients and for the same tissue areas, then the total sample size for baseline and equal number of follow-up samples is 180 samples.The summary scores calculated from normalized data could also be approximated as sum of raw gene counts for all genes constituting the score. For example, Spearman correlation between lipid metabolism score calculated on normalized counts and sum of raw counts for the 44 lipid metabolism genes was median 0.79 (IQR 0.72–0.82) in the spatial transcriptomics samples. Sensitivity analysis using sum of gene counts resulted in similar fold change estimates in calibration curve analysis Fig.  and simi57. The power of the study depends on the hypothesis/model parameter being tested . The number of ROIs per patient affects the ability to accurately estimate the random effects  and not the fixed effect regression coefficients57. This represents sharp contrast to the current literature that focuses primarily on spatial variability of expression within the tissue and, accordingly, increasing the number of spots or ROIs per tissue sample8. Whilst group differences between patients could be addressed with conventional experimental methods such as RNA-sequencing or even qPCR on FACS sorted or laser micro dissected tissue, this presents an impractical barrier in human research when spatial transcriptomics with the NanoString platform enables reliable quantification of gene expression levels from small areas of archived formalin-fixed paraffin-embedded tissue.Sample size calculation is a crucial step in planning studies that are appropriately powered to answer the research question. Limited methodological research is available for sample size calculation for spatial transcriptomics in clinical research. In this study, we outline how to gather required inputs (e.g. estimate effect size based on historic data) and perform sample size estimation for comparison between patient groups. Our biological question of interest was the difference in hepatic gene expression levels between patient groups  assayed with NanoString GeoMx spatial transcriptomics. The sampling variability within the tissue had to be accounted for but was not of primary interest. The three mixed effect model methods produced distinct power surfaces in the initial simulation run despite having similar conceptual basis. We attribute these differences to nuances of the software implementation pertaining to calculation of model likelihood and p-values. Still, with all three methods, power depended on the number of patients per group and not ROIs per patient, which is consistent with theoretical considerations48 and we did not implement any additional steps to control it. However, sensitivity analysis could be conducted via bootstrap method48. In addition, expression of summary scores for the lipid metabolism and cytoskeleton markers could be compared between fibrosis progressors, fibrosis regressors and stable fibrosis individuals in sensitivity analysis. We considered this an acceptable approach. If transcriptome-wide significance level was desired, alpha could be set to 1.25e−05, which corresponds to (0.05/2 endpoints)/ca. 2000 reliably detected genes with 165 μm ROI in our reference data. At alpha 1.25e−05, 27 patients per group would be required to achieve 80% power for the primary endpoint (PON1 in hepatocytes) and 15 patients per group would be required for the secondary endpoint (FLNA in fibrotic niche).Due to low amount of input material, there is a risk that some ROIs will fail technical quality control after acquisition of sequencing data. Therefore, we started the simulation with 2 ROIs per patient and endpoint. If one ROI fails technical quality control in a subset of individuals, the remaining replicates should still be sufficient to address the study endpoint. For example, if FLNA expression in 165 μm fibrotic niche ROIs was compared between 5 fibrosis progressors and 5 fibrosis-stable individuals (5 × 2 × 2 ROI = 20 ROIs) and a third (6) of the ROIs were removed  but in a way that did not result in complete loss of the affected patients, then power to detect the group difference with glmer.nb still remained 78.9% based on evaluation of 1000 synthetic data sets.glmer.nb yielded similar log2 fold change estimates to the actual spike-in, had good convergence rate and short runtime, so we decided to test primary and secondary endpoints with this method. We set the alpha level to 0.025 (0.05/2 endpoints). We decided that the primary endpoint will be tested first, the secondary endpoint will be tested next and any other analyses such as transcriptome-wide differential expression will be unpowered exploratory. Standard deviations of random intercepts were generally low: median 0.3 (IQR 0.1–0.5) for FLNA in fibrotic niche and median 0.1 (IQR 0.05–0.2) for PON1 in hepatocytes for all N patients and ROI size 165 µm in the final simulation study. Type I error in this scenario should be close to the theoretical levelOur sample size calculation is subject to several other considerations:24. Therefore, the hypotheses formulated in this cohort might not generalize to all-comer NAFLD population.First, the 58 patients with repeated biopsies in Fujiwara et al. cohort were at high risk to develop hepatocellular carcinoma and already had detectable liver fibrosis at baselineSecond, the initial study hypotheses (scores) were based on non-overlapping gene sets. We assumed that the two endpoints pertained to different aspects of biology and different tissue fractions and could therefore be considered independent, which may not necessarily be the case.Third, NAFLD Brunt fibrosis score reflects the relative amount of fibrotic tissue area within the liver. We assumed that if the amount of fibrotic tissue subjected to sequencing  is fixed, then we do not need to adjust for fibrosis stage any longer. However, this assumption may not hold true if microscopic structure  differs within fibrotic niche in patients with different fibrosis severity.® Digital Spatial Profiler (DSP) Project Design Guide" document from http://www.nanostring.com/GeoMxDSP) are separate topics.Finally, we focused on the sample size calculation and left other experimental considerations outside of the scope of the current study. For example, identification of ROIs  and sample randomization , the associated assumptions and the expected effect size or minimal clinically meaningful difference. In the absence of knowledge-based hypothesis or a dedicated pilot spatial transcriptomics study, the study endpoints can be inferred from prior bulk RNA-sequencing data and re-scaled to tissue fractions corresponding to ROIs in spatial transcriptomics as illustrated in our case study.Supplementary Figures.Supplementary Tables.


Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin’s Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies.We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin’s Lymphoma patients, obtained through the NanoString’s nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules.Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models .Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice. Hodgkin’s Lymphoma (HL) is a type of blood cancer that originates in the lymphatic system, more precisely in lymphocytes, with the patient age peak of diagnostics occurring at the 20 s and 30 s. In 2018, HL represented 0.4% of all new tumors (79990 new cases) and 0.3% of all cancer deaths (26167 deaths) worldwide .The current prognosis for HL is largely based on the International Prognostic Score (IPS)  which prThe transcriptional activity of tumor cells is a viable proxy candidate to assess regulatory response to treatment, thus being positioned as a possible alternative to the FDG-PET exam. Nevertheless, the role of differential gene expression in HL has not been exhaustively studied as it is a relatively rare cancer . SpecifIn this context, this work aims to assess the predictability limits of an interim FDG-PET performed after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy treatment using gene expression profiling as a surrogate. To this end, a superior methodology combining ML advances with subspace-based data transaformations is proposed. Transcriptomic data of Hodgkin’s Lymphoma patients’ diagnostic tumor samples acquired by Luminari et al.  are usedOur work advances the current status quo on this task described by Luminari et al. , which iIn addition to the end-to-end assessment of state-of-the-art predictors, our work proposes the use of biclustering principles to transform the original high-dimensional feature space into one consisting of features given by discriminative gene expression patterns, and shows that the new space yields relevant statistical properties. The gathered results show that this novel transformation yields statistically significant improvements on the predictive performance.To tackle the introduced research problem, we propose the methodology presented in Fig. 2The proposed methodology is validated on the cohort study conducted by Luminari et al. , availabstage was encoded as an ordinal variable, ranging from 1 (corresponding to“I A”) to 8 (corresponding to “IV B”), indicating that both a larger stage number and the B variant are worse prognostic factors. Since the dataset is considerably imbalanced, with a target’s distribution of around 80/20, balancing methods were assessed through a comparative analysis of their effects on predictive performance, with the combination of both oversampling and subsampling techniques being compared. Oversampling using Support Vector Machine Synthetic Minority Oversampling Technique (SVM-SMOTE)The mRNA counts were nd 21 20.% iPET2 pGiven the high-dimensionality of the cohort data , dimensionality reduction was undertaken to aid the target predictive task. Following the practice suggested by Saeys et al. , and supcument}6 .Initial Feature Selection. As most preprocessed features do not follow a normal distribution according to Shapiro-Wilk test [p-value is subsequently generated using a one-sided permutation test. As these approaches are univariate, not taking into account interactions between variables, a less strict than usual significance threshold of ilk test , we resoilk test  and Mutuilk test  criteriaSecondary Feature Selection. Grounded on empirical evidence [evidence , the embevidence , an adapThe literature on classification tasks in the oncotranscriptomics domain shows a relative predominance of specific machine learning (ML) models , 36. AccIn order to obtain the best classification possible, all the predictors are subjected to parameter optimization through the Tree Parzen Estimator algorithm , in addiThe various biological mechanisms present in our bodies rarely originate due to a single gene’s expression values. Instead, the majority of gene regulation is done in a modular way, in which sets of genes interact with each other to enforce a certain mechanism. With this in mind, it is important to not reduce the analysis of transcriptomic data to individual genes but instead to study these values and its contributions in the context of putative regulatory modules. Furthermore, and in spite of the relevance of the introduced feature selection process, such process relies on univariate methods that do not take into consideration gene interactions and on an embedded method that show biases towards specific predictors .In this context, we propose a novel transformation procedure in high-dimensional data spaces that can map individual gene expression features with loose discriminative power to discriminative pattern-centric features given by discriminative regulatory modules able to better represent complex interactions between multiple genes. Biclustering has been largely employed for the effective discovery of gene expression patterns –46, withIn this context, BicPAMS is applied to find discriminative patterns on the training cohort data. Multiple parameterizations are tested in order to obtain the best space transformation for the classification task. The dataset is then transformed so that each feature corresponds to one gene expression pattern encompassing multiple genes. Each value of the transformed dataset will then represent the similarity between the gene expression expectations associated with a given pattern and the actual levels of gene expression observed for an individual. Figure The proposed methodology is applied on the cohort study conducted by Luminari et. al  to assesEvaluation methodology. Given the small population size (103 samples), a nested Cross-Validation (CV) schema is considered using 10 folds. Nested Cross-Validation is used to separate the data used for hyperparameterization from the data used for assessing the model’s performance [formance . Withoutsetting I) purposefully corresponds to an approach similar to the one followed by Luminari et al. [setting II) guarantees the soundness of the acquired predictability levels by ensuring that feature selection step is performed inside each fold of the nested cross-validation, as previously described. Direct comparison between the two settings should not be attempted given the diversity of the underlying evaluation principles.To promote comparison with the evaluation setting presented in the precursor work by Luminari et al. , two evai et al. , where tA core contribution of this study is the possibility to create a reliable decision support system that can help decide the intensity of the treatment a patient must undergo, with a positive prediction indicating that a more aggressive regime is necessary. Attending to this observation, the following evaluation metrics were chosen: (i) AUC as an overall indicator of how the predictor performs when the decision threshold is not optimized; (ii) recall and precision to ensure that the predictor does not skew towards the majority class, especially important due to the imbalanced data nature; and (iii) specificity to guarantee the identification of patients who will react well to the standard treatment, avoiding the prescription of an unnecessarily stronger chemotherapy regimen. Accuracy, although presenting an useful overall indication of the predictor’s performance, is not considered here due to the imbalanced nature of the data. The metric optimized in the hyperparameterization step is the F1 score, so that the classifiers can attain a balanced performance in both precision and recall. Precision-Recall curves, together with explainability models, are further provided to offer a more comprehensive comparison of the predictors.stage and stage by our algorithm points to a relation with the target iPET2 not identified through the multivariate logistic analysis performed by Luminari et al. [The initial phase of feature selection by the Mutual Information and Wilcoxon Rank Sum algorithms identified a total of 250 features out of the initial 770. These features correspond to 248 genes and the clinical variables i et al.  found, di et al. . The seci et al. , indicatIn order to provide a reference random baseline for the interpretation of classification results, we performed twenty iterations of training and validation of a random classifier in our dataset. It is the convergence value of these metrics that should be interpreted as the reference minimum value for this classification task, namely, a precision of 0.21, and a recall, specificity and AUC of approximately 0.5.Results for setting I are presented in Fig. The results obtained under setting II are presented in Fig. In contrast, KNN has notably poor performance in comparison with the defined baseline, which can be explained by two major factors: (i) a high percentage of outliers in our dataset; and (ii) high gene expression variability, leading to inflated differences between individuals that belong to the same class. Two out of the three associative classifiers, Decision Tree (DT) and Random Forest (RF), also present comparable performance to the random classifier, suggesting that a decision tree is unable to fully take advantage of all the discriminative gene interactions present in the dataset. Since the RF and XGBoost classifiers are both ensembles of DTs, the discrepancy between their results must originate due to the embed feature engineering capabilities of XGBoost and differences on the pursued bagging and boosting strategies. Since bagging uses multiple independently trained DTs to make predictions, the fact that a DT cannot assimilate the knowledge in the data will lead to an overall lack in performance for the RF. Boosting on the other hand trains the trees iteratively, allowing for each consequent model to improve where the previous one failed, resulting in a classifier that can better model more complex interactions.Overall, it is observable that all the classifiers can attain a high specificity, but at the cost of a reduced precision and recall. In other words, classifiers have an easier time correctly classifying patients with negative iPET2, guaranteeing that the patients that will react well to the ABVD regimen are correctly identified. The main difficulty with this predictive problem is in the correct classification of positive patients, possibly due to the low number of samples of this class. In addition, treatment response is being assessed with regards to iPET2 results, which may not be an optimal representative of the true quality of patient response to ABVD chemotherapy.The predictive results can be further analyzed through the study of the classifiers’ Precision-Recall curves presented in Fig. age, gender, stage and gender, no significant trend is noted. stage variable, the values “I A” and “III B” are omitted due to the low number of samples corresponding to each one. In the observation of the remaining values only “III A” shows a significant deviation from the others, with all the positive cases correctly predicted but with a low performance in the false cases. The final plot is dedicated to the variable age and is presented in a stacked view, where the bins encompassing a 10-year period from each class  are stacked to facilitate a comparative analysis between them. We can then recognize that the majority of False Negatives (FN) occur in patients between 30 and 40 years, and the False Positives (FP) are more evenly distributed with a slightly higher concentration in patients between 20 to 30 years.It is in our interest to better understand what leads a certain patient to be misclassified by our models. We plot some of the characteristics of the correctly classified individuals against wrongly classified ones by the best predictor in the second configuration, XGBoost. This analysis is provided for the four clinical variables, As some of our results indicate, the majority of useful information about how the patient will react to treatment is contained on gene expression features, and therefore it is imperative that the analysis of the factors inducing wrong classifications be extended towards these features. In Fig. We can then observe that, as expected, the majority of the highlighted values are found in the intersection of both distributions, where the classification is harder to perform.The results presented until here correspond to the classification task in a feature space reduced by a composition of feature selection procedures. This approach lacks the ability to effectively represent the complex gene interactions responsible for the outcome of the patient. In accordance with the introduced methodology, we capture these interactions through discriminative biclusters using BicPAMS . The founumber of iterations, indicating how many times the mining process is repeated, masking the found biclusters in each new iteration and forcing the mining process to find other less trivial biclusters but resulting in greater computational cost; (ii) minimum lift, a placed threshold to determine whether a given bicluster is sufficiently discriminative [number of labels, corresponding to the number of overlapping gene expression levels [maximum number of biclusters, the number of mapped features in the transformed data space by the postprocessing filtering of the bottom discriminative biclusters according to their lift. The results for each of these parameters are shown in Fig. With the goal of better understanding the impact that BicPAMS’ parameters have on predictive performance, we performed a comparative analysis of how distinct parameterizations affect the behavior of a baseline Naive Bayes predictor. The evaluated parameters are: (i) minative ; (iii) nn levels ; and : number of iterations = 9; minimum lift = 1.3; number of labels = 10; maximum biclusters = 250; and distance criterion = Euclidean distance. Under this configuration, it is possible to carry out a comprehensive exploration of the discriminative biclusters by performing multiple iterations while maintaining a relatively low minimum lift so that the computational requirements do not get too high. The high number of labels guarantees that the found patterns discriminate fine levels of expression while the relatively high number of biclusters ensures that most discriminative and statistically significant biclusters are retrieved. Figure The gathered results show that the transformed feature space has statistically significant impact on the behavior of the classifiers. SVM and XGBoost, classifiers that already presented good results, are not as significantly affected by this transformation, but all the others benefit from it and present an increase in performance in all the studied metrics.The ability to better discriminate how a patient will respond to a treatment is essential, especially in the domain of cancer therapy where the majority of treatments are associated with high toxicity and the prognostic exams can be intrusive and expensive. We comprehensively assess the predictability guarantees achieved by state-of-the-art ML models. These models are carefully optimized through the Tree Parzen Estimator algorithm and evaluated in a controlled manner resorting to nested cross-validation. Despite the placed optimization principles, the obtained results still fall short on the predictability power necessary to translate decisions in real-world practice. Transcriptional and iPET2 activity are structurally different, with the former being better positioned to model regulatory responses to treatment, even at the cost of iPET2 discordance.The high specificity attained by most models (0.94) indicates that the classification models can correctly identify most of the patients that show disease regression after the treatment, but are more susceptible to recognize the positive patients. One possible reason for this bias is the low percentage of positive cases, representing only 20% of the total patients. To correct for this imbalance, we resorted to the use of balancing with SVM-SMOTE and hyperparameterization of the predictive models according to F1 score, an evaluation metric sensitive to the positive samples. Despite these efforts, the best classifier achieved a precision of 0.67 and a recall of 0.52, confirming the impact the difficulty of finding a transcriptional exam resembling the nature of iPET2 activity.The nature of the target cohort data further introduces generalization challenges to the target prognostication, with low number of samples and high-dimensionality. In addition, the transcriptome profiling is susceptible to the infiltration of non-cancer cells and arbitrarily-high variations to the composition of the target cell population, further contributing to generalization difficulties. Finally, the biological mechanisms underlying diseases such as HL are immensely complex and dependent of interactions between many genes at multiple omics levels.In order to better represent the complex interactions between genes that originate biological processes, the proposed space transformation offers an elegant way of shifting the learning from individual genes towards patterns of gene expression. By doing this, we group statistically significant and discriminative sets of genes that partake in regulatory modules correlated with a specific outcome of interest. This creates more straightforward conditions to guide the learning of predictive models. We observed that the efficacy of this transformation is proven by the increase in performance of the majority of classifiers in all the studied metrics.Despite the yield improvements, the gathered results are indicative of the innate difficulty of the target predictive task, claiming for further contributions in this domain able to translate high-dimensional regulatory profiles into actionable and reliable results.In order to deal with the challenges presented in our work, we suggest the following directions of research: (i) strengthen this methodology by completing the current regulatory stances with complementary omic layers; (ii) further combine the transcription of non-coding RNAs, recently shown to play an important role in HL ; (iii) aThis work introduced a novel methodology to improve the predictive accuracy of HL treatment response after two courses of ABVD chemotherapy against reference predictive levels . This is