Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review.

PURPOSE: The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC. METHODS: A systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools. RESULTS: The systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 ± 7.95. CONCLUSION: Radiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation.

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial.

Background: Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Approach: Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Results: Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. Conclusion: By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma.

Sarcomatoid dedifferentiation is common to multiple renal cell carcinoma (RCC) subtypes, including chromophobe RCC (ChRCC), and is associated with increased aggressiveness, resistance to targeted therapies, and heightened sensitivity to immunotherapy. To study ChRCC dedifferentiation, we performed multiregion integrated paired pathological and genomic analyses. Interestingly, ChRCC dedifferentiates not only into sarcomatoid but also into anaplastic and glandular subtypes, which are similarly associated with increased aggressiveness and metastases. Dedifferentiated ChRCC shows loss of epithelial markers, convergent gene expression, and whole genome duplication from a hypodiploid state characteristic of classic ChRCC. We identified an intermediate state with atypia and increased mitosis but preserved epithelial markers. Our data suggest that dedifferentiation is initiated by hemizygous mutation of TP53, which can be observed in differentiated areas, as well as mutation of PTEN. Notably, these mutations become homozygous with duplication of preexisting monosomes (i.e., chromosomes 17 and 10), which characterizes the transition to dedifferentiated ChRCC. Serving as potential biomarkers, dedifferentiated areas become accentuated by mTORC1 activation (phospho-S6) and p53 stabilization. Notably, dedifferentiated ChRCC share gene enrichment and pathway activation features with other sarcomatoid RCC, suggesting convergent evolutionary trajectories. This study expands our understanding of aggressive ChRCC, provides insight into molecular mechanisms of tumor progression, and informs pathologic classification and diagnostics.

The expanding role of BAP1 in clear cell renal cell carcinoma.

Mutations drive renal cell carcinoma biology and tumor growth. The BRCA1-associated protein-1 (BAP1) gene is frequently mutated in clear cell renal cell carcinoma (ccRCC) and has emerged as a prognostic and putative predictive biomarker. In this review, we discuss the role of BAP1 as a signature event of a subtype of ccRCC marked by aggressiveness, inflammation, and possibly a heightened response to immunotherapy.

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)-stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87-0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77-0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. SIGNIFICANCE: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672.

Deep learning reveals disease-specific signatures of white matter pathology in tauopathies.

Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathological investigations have generally focused on abnormalities in the cerebral cortex because the canonical aggregates that form the diagnostic criteria for these disorders predominate there. This corticocentric focus tends to deemphasize the relevance of the more complex white matter pathologies, which remain less well characterized and understood. We took a data-driven machine-learning approach to identify novel disease-specific morphologic signatures of white matter aggregates in three tauopathies: Alzheimer disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We developed automated approaches using whole slide images of tau immunostained sections from 49 human autopsy brains (16 AD,13 CBD, 20 PSP) to identify cortex/white matter regions and individual tau aggregates, and compared tau-aggregate morphology across these diseases. Tau burden in the gray and white matter for individual subjects strongly correlated in a highly disease-specific fashion. We discovered previously unrecognized tau morphologies for AD, CBD and PSP that may be of importance in disease classification. Intriguingly, our models classified diseases equally well based on either white or gray matter tau staining. Our results suggest that tau pathology in white matter is informative, disease-specific, and linked to gray matter pathology. Machine learning has the potential to reveal latent information in histologic images that may represent previously unrecognized patterns of neuropathology, and additional studies of tau pathology in white matter could improve diagnostic accuracy.

The Evolution of Angiogenic and Inflamed Tumors: The Renal Cancer Paradigm.

Gene expression analyses have identified subtypes of conventional renal cell carcinoma broadly distributed into angiogenic and proliferative/ immunogenic clades. Integration with genomic and functional experiments in animal models yields an evolutionary model. Evolutionary trajectories illustrate remarkable plasticity, particularly for a tumor that typically begins with inactivation of a single gene.

Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a particularly challenging tumor type because of its extensive phenotypic variability as well as intra-tumoral heterogeneity (ITH). Clinically, this complexity has been reduced to a handful of pathological variables such as stage, grade and necrosis, but these variables fail to capture the breadth of the disease. How different phenotypes affect patient prognosis and influence therapeutic response is poorly understood. Extensive ITH illustrates remarkable plasticity, providing a framework to study tumor evolution. While multiregional genomic analyses have shown evolution from an ancient clone that acquires metastatic competency over time, these studies have been conducted agnostic to morphological cues and phenotypic plasticity. METHODS: We established a systematic ontology of ccRCC phenotypic variability by developing a multi-scale framework along three fundamental axes: tumor architecture, cytology and the microenvironment. We defined 33 parameters, which we comprehensively evaluated in 549 consecutive ccRCCs retrospectively. We systematically evaluated the impact of each parameter on patient outcomes, and assessed their contribution through multivariate analyses. We measured therapeutic impact in the context of anti-angiogenic therapies. We applied dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) algorithms to tumor architectures for the study of tumor evolution superimposing tumor size and grade vectors. Evolutionary models were refined through empirical analyses of directed evolution of tumor intravascular extensions, and metastatic competency (as determined by tumor reconstitution in a heterologous host). FINDINGS: We discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models. We identified a subset of ccRCCs refractory to anti-angiogenic therapies. We developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type. INTERPRETATION: This work serves as a paradigm for deconvoluting tumor complexity and illustrates how morphological analyses can improve our understanding of ccRCC pleiotropy. We identified several subtypes associated with aggressive biology, and differential response to targeted therapies. By analyzing patterns of spatial and temporal co-occurrence, intravascular tumor extensions and metastatic competency, we were able to identify distinct trajectories of convergent phenotypic evolution.

What morphology can teach us about renal cell carcinoma clonal evolution.

While cancer is a clonal process, cumulative evidence suggest that tumors are rather heterogenous and are composed of multiple genetically-distinct subclones that arise at different times and either persist and co-exist, expand and evolve, or are eliminated. A paradigm of tumor heterogeneity is renal cell carcinoma (RCC). By exploiting morphological traits and building upon a framework around three axes (architecture, cytology and the microenvironment), we review recent advances in our understanding of RCC evolution leading to an integrated molecular genetic and morphologic evolutionary model with both prognostic and therapeutic implications. The ability to predict cancer evolution may have profound implications for clinical care and is central to oncology.