Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics)

Expanding the clinicopathologic spectrum and genomic landscape of tumors with SMARCA2/4::CREM fusions

CREB gene family (ATF1, CREB1, CREM) fusions with either EWSR1 or FUS gene partners drive the pathogenesis of a wide range of neoplasms, including various soft tissue tumors, intracranial myxoid mesenchymal tumors (IMMTs), hyalinizing clear cell carcinoma (HCCC), and rare mesotheliomas. Recently, a SMARCA2::CREM fusion was reported in one case each of IMMT and HCCC. In this study, we expand the clinicopathologic and molecular spectrum of these neoplasms by describing three additional cases with SMARCA2::CREM and one with a novel SMARCA4::CREM fusion, highlighting the recurrent potential of additional CREB gene fusion partners beyond FET family members. To evaluate if these fusions define a new pathologic entity, we performed a comprehensive genomic and methylation analysis and compared the results to other related tumors. Tumors occurred in children and young adults (median age 20 years) and spanned a broad anatomic distribution, including soft tissue, intracranial, head and neck, and prostatic urethra. Microscopically, the tumors shared an undifferentiated round to epithelioid cell phenotype and a hyalinized fibrous stroma. Immunohistochemically, a polyphenotypic profile was observed, with variable expression of SOX10, desmin, and/or epithelial markers. No targetable genomic alterations were found using panel-based DNA sequencing. By DNA methylation and transcriptomic analyses, tumors grouped closely to FET::CREB entities, but not with SMARCA4/SMARCB1-deficient tumors. High expression of CREM by immunohistochemistry was also documented in these tumors. Patients experienced local recurrence (n = 2), locoregional lymph node metastases (n = 2), and an isolated visceral metastasis (n = 1). Overall, our study suggests that SMARCA2/4::CREM fusions define a distinct group of neoplasms with round cell to epithelioid histology, a variable immunoprofile, and a definite risk of malignancy. Larger studies are needed to further explore the pathogenetic relationship with the FET::CREB family of tumors. © 2024 The Pathological Society of Great Britain and Ireland.

Seeing the primary tumor because of all the trees: Cancer type prediction on low-dimensional data

The Cancer of Unknown Primary (CUP) syndrome is characterized by identifiable metastases while the primary tumor remains hidden. In recent years, various data-driven approaches have been suggested to predict the location of the primary tumor (LOP) in CUP patients promising improved diagnosis and outcome. These LOP prediction approaches use high-dimensional input data like images or genetic data. However, leveraging such data is challenging, resource-intensive and therefore a potential translational barrier. Instead of using high-dimensional data, we analyzed the LOP prediction performance of low-dimensional data from routine medical care. With our findings, we show that such low-dimensional routine clinical information suffices as input data for tree-based LOP prediction models. The best model reached a mean Accuracy of 94% and a mean Matthews correlation coefficient (MCC) score of 0.92 in 10-fold nested cross-validation (NCV) when distinguishing four types of cancer. When considering eight types of cancer, this model achieved a mean Accuracy of 85% and a mean MCC score of 0.81. This is comparable to the performance achieved by approaches using high-dimensional input data. Additionally, the distribution pattern of metastases appears to be important information in predicting the LOP.

Metastatic renal cell carcinoma with occult primary: a multicenter prospective cohort

Metastatic carcinoma of presumed renal origin (rCUP) has recently emerged as a new entity within the heterogeneous entity of Cancers of Unknown Primary (CUP) but their biological features and optimal therapeutic management remain unknown. We report the molecular characteristics and clinical outcome of a series of 25 rCUP prospectively identified within the French National Multidisciplinary Tumor Board for CUP. This cohort strongly suggests that rCUP share similarities with common RCC subtypes and benefit from renal-tailored systemic treatment. This study highlights the importance of integrating clinical and molecular data for optimal diagnosis and management of CUP.

FGFR1 fusions as a novel molecular driver in rhabdomyosarcoma

The wide application of RNA sequencing in clinical practice has allowed the discovery of novel fusion genes, which have contributed to a refined molecular classification of rhabdomyosarcoma (RMS). Most fusions in RMS result in aberrant transcription factors, such as PAX3/7::FOXO1 in alveolar RMS (ARMS) and fusions involving VGLL2 or NCOA2 in infantile spindle cell RMS. However, recurrent fusions driving oncogenic kinase activation have not been reported in RMS. Triggered by an index case of an unclassified RMS (overlapping features between ARMS and sclerosing RMS) with a novel FGFR1::ANK1 fusion, we reviewed our molecular files for cases harboring FGFR1-related fusions. One additional case with an FGFR1::TACC1 fusion was identified in a tumor resembling embryonal RMS (ERMS) with anaplasia, but with no pathogenic variants in TP53 or DICER1 on germline testing. Both cases occurred in males, aged 7 and 24, and in the pelvis. The 2nd case also harbored additional alterations, including somatic TP53 and TET2 mutations. Two additional RMS cases (one unclassified, one ERMS) with FGFR1 overexpression but lacking FGFR1 fusions were identified by RNA sequencing. These two cases and the FGFR1::TACC1-positive case clustered together with the ERMS group by RNAseq. This is the first report of RMS harboring recurrent FGFR1 fusions. However, it remains unclear if FGFR1 fusions define a novel subset of RMS or alternatively, whether this alteration can sporadically drive the pathogenesis of known RMS subtypes, such as ERMS. Additional larger series with integrated genomic and epigenetic datasets are needed for better subclassification, as the resulting oncogenic kinase activation underscores the potential for targeted therapy.

Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research

BACKGROUND

Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects.

METHODS

UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals.

RESULTS

Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers.

CONCLUSIONS

Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)-based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold-out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri-nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion-based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI-level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium-rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.

Machine learning for pan-cancer classification based on RNA sequencing data

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Sequential genomic analysis using a multisample/multiplatform approach to better define rhabdomyosarcoma progression and relapse

The genomic spectrum of rhabdomyosarcoma (RMS) progression from primary to relapse is not fully understood. In this pilot study, we explore the sensitivity of various targeted and whole-genome NGS platforms in order to assess the best genomic approach of using liquid biopsy in future prospective clinical trials. Moreover, we investigate 35 paired primary/relapsed RMS from two contributing institutions, 18 fusion-positive (FP-RMS) and 17 fusion-negative RMS (FN-RMS) by either targeted DNA or whole exome sequencing (WES). In 10 cases, circulating tumor DNA (ctDNA) from multiple timepoints through clinical care and progression was analyzed for feasibility of liquid biopsy in monitoring treatment response/relapse. ctDNA alterations were evaluated using a targeted 36-gene custom RMS panel at high coverage for single-nucleotide variation and fusion detection, and a shallow whole-genome sequencing for copy number variation. FP-RMS have a stable genome with relapse, with common secondary alterations CDKN2A/B, MYCN, and CDK4 present at diagnosis and impacting survival. FP-RMS lacking major secondary events at baseline acquire recurrent MYCN and AKT1 alterations. FN-RMS acquire a higher number of new alterations, most commonly SMARCA2 missense mutations. ctDNA analyses detect pathognomonic variants in all RMS patients within our collection at diagnosis, regardless of type of alterations, and confirmed at relapse in 86% of FP-RMS and 100% FN-RMS. Moreover, a higher number of fusion reads is detected with increased disease burden and at relapse in patients following a fatal outcome. These results underscore patterns of tumor progression and provide rationale for using liquid biopsy to monitor treatment response.

[Molecular tumor diagnostics as the driving force behind precision oncology]

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.

Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin

INTRODUCTION

Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings-in addition to ambiguous clinical presentations such as recurrence versus new primary-a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8-11 months.

METHODS

Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype.

RESULTS

We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype.

DISCUSSION

Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

Utility of RNA Expression to Determine the Tissue of Origin of Malignancies with an Inconclusive Histopathology

We present 2 cases of cancer of unknown origin in which RNA-based cancer classification testing provided vital insight and directed treatment management. The tissue of origin could not be determined in both of these patients utilizing morphology and immunohistochemical analysis of the tissue samples. Next-generation sequencing and tumor-of-origin testing using an RNA-based molecular cancer classifier were performed to elucidate the possible tissue of origin. A 61-year-old male with a history of localized basal cell carcinoma presented with a 4.4-cm axillary lymph node in addition to upper extremity edema and supraclavicular lymphadenopathy. RNA-based tumor origin testing revealed skin basal or squamous cell carcinoma as the likely tissue of origin, with a probability of 97%. He received vismodegib, a hedgehog inhibitor, after progression on cemiplimab and experienced a partial response by RECIST criteria, which is currently ongoing for over a year. A 74-year-old female patient with a remote history of ovarian cancer for which she underwent resection and adjuvant chemotherapy presented 15 years later with abdominal pain. The diagnostic workup revealed a 2-cm pancreatic mass and enlarged peritoneal lymph nodes. RNA sequencing revealed a 99% likelihood of the tissue of origin being serous ovarian carcinoma. Subsequently, she underwent surgery and adjuvant chemotherapy and is currently in remission with letrozole maintenance. Genomic data already plays a crucial role in therapeutic decision-making for individuals with cancer. These cases highlight the complementary role of genomic data in the diagnostic workup of cancer, leading to favorable patient outcomes.

Next-generation sequencing in breast pathology: real impact on routine practice over a decade since its introduction

The diagnosis, histomolecular classes of breast cancers (luminal A, luminal B, HER2-enriched, and basal-like), and accurate prediction of prognosis are commonly determined using morphological and phenotypical analyses in clinical practice worldwide. Therapeutic strategies are mostly based on the disease stage and molecular subclasses of breast cancer. Targeted therapies, such as anti-HER2s, poly-ADP ribose polymerase inhibitors or, to a lesser extent, phosphatidylinositol 3 kinase inhibitors, have substantially improved breast cancer patient prognosis over the past decades. Human epidermal growth factor receptor 2 (HER2) overexpression is widely determined based on immunohistochemistry, while next-generation sequencing (NGS) is currently employed to assess the presence of molecular alterations, including breast cancer gene 1 (BRCA1) and 2 or phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations, which are targets of these new approved therapies. In addition, next-generation sequencing (NGS) can aid the pathologist in challenging situations, such as a diagnostic workup for a metastatic carcinoma in lymph nodes of unknown origin, differential diagnosis of spindle cell tumourtumor in the breast between metaplastic carcinoma, malignant PT and sarcoma, o, as well as determining relatedness between primary breast cancers and recurrences. NGS offers a powerful tool that enables the pathologist to combine morphological analyses together with molecular alterations in challenging diagnostic situations.

A novel fusion variant LSM14A::NR4A3 in extraskeletal myxoid chondrosarcoma

Extraskeletal myxoid chondrosarcoma (EMC) is a rare soft tissue neoplasm of uncertain lineage characterized by the pathognomonic rearrangement of the NR4A3 gene, which in most cases is fused with EWSR1. Other NR4A3 fusion partners have been described, namely TAF15, FUS, TCF12, and TGF. Some studies suggest that EMCs with non-EWSR1 variant fusion are associated with high-grade morphology and worst clinical behavior compared to EWSR1::NR4A3 tumors, supporting the potential significance of particular fusion variant in EMC. We report a case of a 34-year-old male who presented with calf EMC and subsequently developed a slowly progressive metastatic disease 3 years after diagnosis. Whole-transcriptome analysis with total RNA sequencing enabled identification of a novel fusion transcript LSM14A::NR4A3, expanding the molecular spectrum of EMC.