Biology subjects

Aldape, K.

Publications and source records attributed to Aldape, K..

At least 19 recordsLinked to original sources

bioRxiv2025

Variable Schwann cell merlin inactivation is targetable with TEAD1 inhibition in schwannomas.

Schwann cell tumors occur frequently in association with the vestibular nerves, leading to sensorineural hearing loss, and brainstem compression. In humans, unilateral vestibular schwannomas (VS) occur sporadically (VSspo)1, or bilaterally with neurofibromatosis type 2 syndrome (NF2) - VSnf2.2 VS formation is driven by sub-haploid NF2 gene dosage3, typically by biallelic loss.4,5 Loss of merlin promotes hippo/TEAD dependent transcriptional reprogramming, proliferation, and paracrine signaling that varies across time, and tumor volume.4,6 These variations lead to a clinically unpredictable course, and incomplete response to treatment. We hypothesized that Schwann cell merlin inactivation state determines cell-wise hippo/TEAD dependency and drives schwannoma pathogenesis. We analyzed clinical samples from VSspo and VSnf2 with a multi-omics approach and detected variation in merlin activity within tumor Schwann cell population. We found that tumor-driving merlin-depleted Schwann cells (Schwannmer-) exhibited elevated hippo activity that was predominantly driven by TEAD1. In-silico TEAD1 perturbation led to a reversal to merlin intact Schwann phenotype. These findings, and tumor cell growth suppression were confirmed in NF2fl/fl;Peri-Cre mouse model7, and in human derived schwannoma cells treated with a pan-TEAD auto palmitoylation inhibitor VT3989.8 Our computational and experimental results confirm that TEAD1 inhibition could be a potent, targeted strategy for schwannomas.

cancer biology↗

bioRxiv2025

Web engine for tumor pathology image retrievals on massive scales

Hematoxylin and Eosin staining (H&E) is widely used in clinical practice, but efficient and versatile image retrieval tools are lacking. We developed the H&E Retrieval Engine (HERE, https://hereapp.ccr.cancer.gov) to analyze patient cases based on image similarities to database records. Using H&E image regions as input, HERE searches 21.2 terabytes of whole-slide images from multiple tumor histopathology cohorts through a 12.1-gigabyte memory index, and returns top images containing regions similar to the query. HERE scans high-resolution images in the database using accurate artificial intelligence encoding and ultra-efficient hierarchical skip indexing. HERE demonstrated performance superior to existing image retrieval tools based on blinded pathologist scoring using benchmark queries that represent key image features of human tumors. By pairing spatial transcriptomics with H&E images, HERE also enables retrieving image features from gene transcriptomics input and identifies molecular pathways associated with tumor histologies.

bioinformatics↗

bioRxiv2025

Phosphoproteomic dysregulation drives tumor proliferation in Cushing's disease

Pituitary adenomas constitute up to 20% of primary brain tumors, yet somatic mutations are only found in 15% of pituitary adenomas. Epigenomic dysregulation has been proposed as a tumorigenic mechanism in pituitary adenomas causing Cushings disease (CD). We created paired datasets of human CD adenomas and en-route margin adult human pituitary glands and assayed their chromatin accessibility, DNA methylation, transcriptomic, proteomic and phospho-proteomic landscapes. In CD adenomas, we found epigenetic reactivation of a neurodevelopmental phosphoprotein program typically lost in the post-natal pituitary gland. CD cells overexpressed PPP1R17, a potent endogenous inhibitor of the ubiquitous protein phosphatase PP2A. Mechanistically, PPP1R17 overexpression in normal murine pituitary cells recapitulated the adenoma phenotype, and PPP1R17-mediated tumorigenesis was reversible using an FDA-approved small molecule PP2A agonist both in-vitro and in-vivo. Our findings highlight aberrant peptide phosphorylation as a targetable mechanism in CD. Significance statementCushings disease (CD) causes significant morbidity and mortality despite best medical and surgical treatment. Surgery is the mainstay of treatment, but carries perioperative risks and is frequently followed by remission. There is a paucity of effective medical treatments, due in part to a limited understanding of tumor mechanisms. The majority of CD adenomas are wild-type, with no known causal mutations. Our study identifies phosphoproteomic dysregulation as a mechanism of CD tumorigenesis common to wild-type and mutant CD adenomas. We target this pathway in-vivo and in-vitro using an FDA-approved small molecule. Our study proposes a novel therapeutic strategy for patients with CD.

cancer biology↗

bioRxiv2025

Tumor-initiating genetics and therapy drive divergent molecular evolution in IDH-mutant gliomas

Astrocytomas and oligodendrogliomas are slow-growing and treatment-sensitive IDH-mutant gliomas diagnosed at ages 30-50. Local tumor regrowth and treatment resistance is inevitable resulting in 3-10 year astrocytoma and up to >20 years oligodendroglioma survival. We sought to identify genetic changes associated with tumor evolution in response to therapy through multi-timepoint whole-genome/whole-exome sequencing of 206 IDH-mutant glioma patient samples collected through the Glioma Longitudinal Analysis (GLASS) Consortium. We validated known genomic markers of tumor progression, including hypermutation and CDKN2A homozygous deletion, and discovered novel genetic alterations that distinguish the response to treatment in astrocytomas compared to oligodendrogliomas. Point mutations in PIK3CA, PIK3R1, and NOTCH1 were newly acquired in recurrent oligodendrogliomas and associated with increased mutation rates. Focal oncogene amplifications, together with CDKN2A homozygous deletions, were associated with an increase in recurrence-specific chromosomal imbalances in astrocytomas. Mutational signature analysis revealed additional differences and detected enrichment for the SBS11, and SBS119 mutational signatures after temozolomide treatment in both IDH-glioma subtypes, whereas astrocytomas showed increased ID8 signatures after radiotherapy. These signatures suggest that the genomes of oligodendroglioma and astrocytoma adapt to the selective pressures of tumor progression and treatment in different ways. However, in both IDH-mutant glioma subtypes we observed a convergence of acquired driver gene alterations with genome-wide changes and worse patient outcomes, signaling selection of treatment-refractory clones. By identifying new prognostic markers and delineating the genomic divergence of oligodendrogliomas and astrocytomas after diagnosis, our results suggest that different DNA damage response mechanisms are engaged following chemo- and radiation therapy.

cancer biology↗

bioRxiv2025

SCLC-TumorMiner: A Directly Accessible Genomics Resource for Precision Oncology: Big Data for Small Cells

Small cell lung cancer (SCLC) is among the most aggressive malignancies. Unlike many other cancers, it is not represented in The Cancer Genome Atlas, and available datasets are fragmented across institutions, disease stages, and treatment settings. RNA sequencing provides a powerful and cost-effective approach, but the high dimensionality of transcriptomic data and the heterogeneity of patient cohorts pose significant challenges. To address such challenges, we developed SCLC TumorMiner (https://discover.nci.nih.gov/SclcTumorMinerCDB/), which includes 50 tumor samples from relapse patients at the National Cancer Institute (NCI) and 154 samples from untreated patients at the University of Cologne and Tongji University. SCLC TumorMiner enables molecular classification, genomic pathway analyses, risk stratification, identification of predictive cell-surface biomarkers such as DLL3 or TROP2, and drug-response biomarkers such as SLFN11. SCLC TumorMiner illustrates profound differences between untreated and relapse patient samples. Additionally, "MyPatient", one of SCLC TumorMiners modules, is presented as a medical assistant application prototype.

cancer biology↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2025

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. To address this challenge, we developed Path2Omics, a deep learning model that predicts gene expression and methylation from histopathology for 23 cancer types. Path2Omics was trained on 20,497 slides (9,456 formalin-fixed and paraffin-embedded (FFPE) and 11,041 fresh frozen (FF)) from 8,007 patients across 23 The Cancer Genome Atlas cohorts. When tested on FFPE slides, the most readily available format in clinical pathology practice, the integrated model outperformed its individual FF and FFPE components, robustly predicting nearly 5,000 genes on average, approximately five times more than our recently published DeepPT model. Externally evaluated on seven independent cohorts, Path2Omics robustly predicted the expression of approximately 4,400 genes, yielding a 30% increase over the FFPE model alone. Finally, we demonstrate that the inferred gene expression is nearly as effective as the actual values in predicting patient survival and treatment response. These results lay the basis for using Path2Omics to advance precision oncology from histopathology slides in a speedy and cost-effective manner. Statement of significancePath2Omics is a deep learning model that accurately predicts gene expression and methylation from histopathology slides across 23 cancer types. Unlike existing approaches that rely solely on FFPE slides for training, Path2Omics leverages both FFPE and FF slides by constructing two separate models and integrating them. Downstream analyses show that the inferred values from Path2Omics are nearly as effective as actual values in predicting patient survival and treatment response.

bioinformatics↗

bioRxiv2024

Identifying Prognostic Cell State Interactions in the TumorMicroenvironment of IDH-Mutant Gliomas Using CSI-TME

Intercellular communication between distinct transcriptional states of various cell types in the tumor microenvironment (TME) influence the progression and clinical outcome of the tumors. Details of such clinically relevant cellular state interactions (CSIs) in IDH-mutant gliomas remain obscure. Here we developed CSI-TME, a computational pipeline that deconvolves cell type-specific gene expression from bulk transcriptomic data, identifies distinct cell states, and uncovers prognostic cell state interactions by modelling the clinical data based on the joint activity of cell state pairs. We found a significantly reproducible cell state interaction network (CSIN) that is (i) predominantly pro-tumor, (ii) likely mediated through the interaction between cell surface molecules, (iii) differentially activated in IDH-mut astrocytoma and oligodendroglioma, and (iv) significantly associated with response to immune checkpoint blockade therapy. The distinct malignant cell states involved in CSIs resembled neuronal lineages such as astrocyte-like and oligodendrocyte progenitor cells-like states and captured key interactions between glioma stem cells and immune cells. Integration of CSIN with somatic mutation data suggests the anti-tumor role of CSIs in the early stages that transitions towards pro-tumor role as glioma progresses. In summary, CSI-TME provides valuable insights into the physiology of the TME in IDH-mutant glioma and provides a framework to prioritize ligand-receptor interactions or patients stratification for therapeutic interventions.

cancer biology↗

bioRxiv2024

Path2Space: An AI Approach for Cancer Biomarker Discovery Via Histopathology Inferred Spatial Transcriptomics

Spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by enabling high-resolution, location-specific mapping of gene expression across tumors and their microenvironment. However, the associated high cost of the assay has limited cohort size and hence large-scale biomarker discovery. Here we present Path2Space, a deep learning approach that predicts spatial gene expression directly from histopathology slides. Trained on substantial breast cancer ST data, it robustly predicts the spatial expression of over 4,300 genes in independent validations, markedly outperforming existing ST predictors. Path2Space additionally accurately infers cell-type abundances in the tumor microenvironment (TME) based on the inferred ST data. Applied to more than a thousand breast tumor histopathology slides from the TCGA, Path2Space characterizes their TME on an unprecedented scale and identifies three new spatially-grounded breast cancer subgroups with distinct survival rates. Path2Space-inferred TME landscapes enable more accurate predictions of patients response to chemotherapy and trastuzumab directly from H&E slides than those obtained by existing established sequencing-based biomarkers. Path2Space thus offers a transformative, fast and cost-effective approach to robustly delineate the TME directly from their histopathology slides, facilitating the development of spatially-grounded biomarkers to advance precision oncology.

cancer biology↗

bioRxiv2024

The Open Pediatric Cancer Project

BackgroundIn 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we present an extension of the OpenPBTA called the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). FindingsWe utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCIs pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research-grade integrated diagnoses for these tumors. ConclusionsOpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

genomics↗

bioRxiv2022

Increased mRNA expression of CDKN2A is a transcriptomic marker of clinically aggressive meningiomas

BackgroundHomozygous loss of CDKN2A/B is a genetic alteration found in many cancer types including meningiomas, where it is associated with poor clinical outcome. It is now also a diagnostic criterion for grade 3 meningiomas in the 2021 WHO classification for central nervous system tumors. However, as in other cancers, the relationship between copy number loss of CDKN2A/B and expression of its gene product is unclear and may be either commensurate or paradoxical in nature. Therefore, we aimed to investigate the association of CDKN2A mRNA expression with clinical prognosis, WHO grade, and other molecular biomarkers in meningiomas such as DNA methylation, molecular group, and proteomics. MethodsWe used multidimensional molecular data of 490 meningioma samples from 4 independent cohorts to examine the relationship between mRNA expression of CDKN2A and copy number status, its correlation to clinical outcome, the transcriptomic pathways altered in differential CDKN2A expression, and its relationship with DNA methylation, and proteomics using an integrated molecular approach. ResultsMeningiomas without any copy number loss were dichotomized into high (CDKN2Ahigh) and low (CDKN2Alow) CDKN2A mRNA expression groups. Patients with CDKN2Ahigh meningiomas had poorer progression free survival (PFS) compared to those with CDKN2Alow meningiomas. CDKN2A mRNA expression was increased in more aggressive molecular groups, and in higher WHO grade meningiomas across all cohorts. CDKN2Ahigh meningiomas and meningiomas with CDKN2A copy number loss shared common up-regulated cell cycling pathways. CDK4 mRNA expression was increased in CDKN2Ahigh meningiomas and both p16 and CDK4 protein were more abundant in CDKN2Ahigh meningiomas. CDKN2Ahigh meningiomas were frequently hypermethylated at the gene body and UTR compared to CDKN2Alow meningiomas and found be more commonly Rb-deficient. ConclusionsAn intermediate level of CDKN2A mRNA expression appears to be optimal as significantly low (CDKN2A deleted) or high expression (CDKN2Ahigh) are associated with poorer outcomes clinically. Though CDK4 is elevated in CDKN2Ahigh meningiomas, Rb-deficiency may be more common in this group, leading to lack of response to CDK inhibitors.

cancer biology↗

bioRxiv2022

Common molecular features of H3K27M DMGs and PFA ependymomas map to hindbrain developmental pathways

AbstractGlobally decreased histone 3, lysine 27 tri-methylation (H3K27me3) is a hallmark of H3K27-altered diffuse midline gliomas (DMGs) and group-A posterior fossa ependymomas (PFAs). H3K27-altered DMGs are largely characterized by lysine-to- methionine mutations in histone 3 at position 27 (H3K27M). Most PFAs overexpress EZH inhibitory protein (EZHIP), which possesses a region of similarity to the mutant H3K27M. Both H3K27M and EZHIP inhibit function of the polycomb repressive complex 2 (PRC2) responsible for H3K27me3 deposition. These tumors often arise in neighboring regions of the brainstem and posterior fossa. In rare cases PFAs harbor H3K27M mutations, and DMGs overexpress EZHIP. These findings together raise the possibility that certain cell populations in the developing hindbrain/posterior fossa are especially sensitive to modulation of H3K27me3 states. We identified shared molecular features by comparing genomic, bulk transcriptomic, chromatin-based profiles, and single-cell RNA-sequencing (scRNA-seq) data from the two tumor classes. Our approach demonstrated that 1q gain, a key biomarker in PFAs, is prognostic in H3.1K27M, but not H3.3K27M gliomas. Conversely, Activin A Receptor Type 1 (ACVR1), which is associated with mutations in H3.1K27M gliomas, is overexpressed in a subset of PFAs with poor outcome. Despite diffuse H3K27me3 reduction, previous work shows that both tumors maintain genomic H3K27me3 deposition at select sites. We demonstrate heterogeneity in shared patterns of residual H3K27me3 for both tumors that largely segregated with inferred anatomic tumor origins and progenitor populations of tumor cells. In contrast, analysis of genes linked to H3K27 acetylation (H3K27ac)-marked enhancers showed higher expression in astrocytic-like tumor cells. Finally, common H3K27me3-marked genes mapped closely to expression patterns in the human developing hindbrain. Overall, our data demonstrate developmentally relevant molecular similarities between PFAs and H3K27M DMGs and support the overall hypothesis that deregulated mechanisms of hindbrain development are central to the biology of both tumors.

cancer biology↗

bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗

bioRxiv2022

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BackgroundPrecision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. MethodsWe present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patients response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. FindingsEvaluating ENLIGHTs performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patients treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-{gamma}signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ConclusionENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FundingThis research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

bioinformatics↗

bioRxiv2021

Sex biases in cancer and autoimmune disease incidence are correlated across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome.

cancer biology↗

bioRxiv2021

The epigenetic evolution of gliomas is determined by their IDH1 mutation status and treatment regimen

Tumor adaptation or selection is thought to underlie therapy resistance of gliomas. To investigate the longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 143 matched initial and recurrent patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) gliomas. IDHwt gliomas showed a longitudinally stable epigenome with relatively low levels of global methylation, whereas the epigenome of IDHmut gliomas showed initial high levels genome-wide of DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. By integrating DNA methylation and gene expression data, adaptive changes of putative master regulators of the cell cycle and of differentiation were seen in IDHmut recurrent tumors. Furthermore, relapses of IDHmut tumors were accompanied by histological progression which in turn influenced survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment differed between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neo-angiogenesis and T-cell infiltration upon treatment for IDHmut gliomas. Our study provides one of the largest cohorts of paired glioma samples profiled with epigenomics, transcriptomics and genomics; and our results demonstrate that the treatment of IDHmut gliomas reshapes the epigenome towards an IDHwt-like phenotype. Accordingly, the prevalent practice of early genotoxic treatment in this patient population may need to be revisited.

genomics↗

bioRxiv2021

Using a recently approved tumor mutational burden biomarker to stratify patients for immunotherapy may introduce a sex bias

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB [≥]10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

cancer biology↗

bioRxiv2021

Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

The FDA has recently approved high tumor mutational burden (TMB), defined by [≥]10 mutations/Mb, as a biomarker for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies testify that high TMB levels are only able to stratify ICI responders in a subset of cancer types, where the mechanisms underlying this observation have remained unknown. We hypothesized that the tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power) in a cancer type, leading to this observation. To systematically study this hypothesis, we analyzed TCGA expression data to infer the levels of 31 immune-related factors characteristic of the TME of different cancer types. We integrated this information with TMB and response data of 2,277 patients treated with anti-PD1 or anti-PD-L1 ICI to identify the key immune factors that can determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterize cancer types with high TMB power. A model based on these two immune factors is strongly predictive of the TMB power in a given cancer type (Spearman Rho=0.76, P<3.6x10-04). Using this model, we provide predictions of the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available on a large scale. Our analysis indicates that TMB-High may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

cancer biology↗