Clinicopathologic features and genomic profiling of female axillary lymph node metastases from adenocarcinoma or poorly differentiated carcinoma of unknown primary.

PURPOSE: Axillary lymph node metastases from adenocarcinoma or poorly differentiated carcinoma of unknown primary (CUPAx) is a rare disease in women. This retrospective study intended to examine the clinicopathological features of CUPAx and compared CUPAx genetically with axillary lymph node metastases from breast cancer (BCAx), investigating differences in their biological behavior. METHODS: We conducted the clinical and prognostic analysis of 58 CUPAx patients in West China Hospital spanning from 2009 to 2021. Gemonic profiling of 12 CUPAx patients and 16 BCAx patients was conducted by the FoundationOne CDx (F1CDx) platform. Moreover, we also compared the gene mutation spectrum and relevant pathways between the two groups and both TCGA and COSMIC databases. RESULTS: The majority of the 58 CUPAx patients were HR-/HER2- subtype. Most patients received mastectomy combined radiotherapy (50 Gy/25f). CUPAx patients who received mastectomy instead of breast-conserving surgery had a more favorable overall prognosis. Radiotherapy in chest wall/breast and supraclavicular/infraclavicular fossa was the independent prognostic factor (HR = 0.05, 95%CI = 0.00-0.93, P = 0.04). In 28 sequencing samples (CUPAx, n = 12, BCAx, n = 16) and 401 TCGA-BRCA patients, IRS2 only mutated in CUPAx (33.33%) but amplified in BCAx (11.11%) and TCGA-BRCA (1.5%). Pathway analysis revealed that BCAx had more NOTCH pathway mutations than CUPAx. Enrichment analysis showed that CUPAx enriched more in mammary development and PML bodies than BCAx, but less in the positive regulation of kinase activity. CONCLUSIONS: More active treatment methods, like chemotherapy, mastectomy and postoperative radiotherapy, could improve the prognosis of CUPAx. The differential mutation genes of CUPAx and BCAx might be associated with their respective biological behaviors like invasiveness and prognosis.

A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary.

Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies are unexplored in CUP cohorts. Here we describe CUPiD, a machine learning classifier for accurate TOO predictions across 29 tumour classes using circulating cell-free DNA (cfDNA) methylation patterns. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 84.6% and TOO accuracy of 96.8%. In an additional cohort of 41 patients with CUP CUPiD predictions were made in 32/41 (78.0%) cases, with 88.5% of the predictions clinically consistent with a subsequent or suspected primary tumour diagnosis, when available (23/26 patients). Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles.

BACKGROUND: The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient's samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. RESULTS: The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. CONCLUSIONS: On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers' tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.

Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel.

RATIONALE: Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy. METHODS: Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin-fixed paraffin-embedded (FFPE) samples from a single center. RESULTS: MFCUP achieved an accuracy of 97.2% in a validation cohort (n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset (n = 1052) and an Infinium EPIC (850 K) array dataset (n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening. CONCLUSION: Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.

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.

Comprehensive biomarker diagnostics of unfavorable cancer of unknown primary to identify patients eligible for precision medical therapies.

PURPOSE: Current guidelines recommend combination chemotherapy for treatment of patients with unfavorable cancer of unknown primary (CUP). Biomarker-guided targeted therapies may offer additional benefit. Data on the feasibility and effectiveness of comprehensive genomic biomarker profiling of CUP in a standard clinical practice setting are limited. METHODS: This analysis included 156 patients with confirmed unfavorable CUP diagnosis according to ESMO guidelines, who were treated at the West German Cancer Center, Essen, Germany, from 2015 to 2021. Clinical parameters and outcome data were retrieved from the electronic hospital information system. Genomic biomarker analyses were performed in formalin-fixed paraffin-embedded tumor tissue whenever possible using the QIAseq Multimodal-Pancancer-Panel. RESULTS: Non-squamous histologies, high tumor burden, and age above 60 years associated with poor survival outcome. Tissue availability restricted comprehensive biomarker analyses to 50 patients (32%), reflecting a major limitation in the real-world setting. In those patients a total of 24 potentially actionable alterations were identified in 17 patients (34% of profiled patients, 11% of total population). The most prevalent biomarkers were high tumor mutational burden and BRCA-mutations. CONCLUSION: In a real-world setting precision medicine for patients with CUP is severely restricted by tissue availability, and a limited spectrum of actionable alterations. Progress for patients may require emphasizing the need for sufficient biopsies, and prospective exploration of blood-based biomarker profiling.

Utility of UV Signature Mutations in the Diagnostic Assessment of Metastatic Head and Neck Carcinomas of Unknown Primary.

BACKGROUND: Metastatic carcinoma of unknown primary origin to the head and neck lymph nodes (HNCUP) engenders unique diagnostic considerations. In many cases, the detection of a high-risk human papillomavirus (HR-HPV) unearths an occult oropharyngeal squamous cell carcinoma (SCC). In metastatic HR-HPV-independent carcinomas, other primary sites should be considered, including cutaneous malignancies that can mimic HR-HPV-associated SCC. In this context, ultraviolet (UV) signature mutations, defined as ≥ 60% C→T substitutions with ≥ 5% CC→TT substitutions at dipyrimidine sites, identified in tumors arising on sun exposed areas, are an attractive and underused tool in the setting of metastatic HNCUP. METHODS: A retrospective review of institutional records focused on cases of HR-HPV negative HNCUP was conducted. All cases were subjected to next generation sequencing analysis to assess UV signature mutations. RESULTS: We identified 14 HR-HPV negative metastatic HNCUP to either the cervical or parotid gland lymph nodes, of which, 11 (11/14, 79%) had UV signature mutations, including 4 (4/10, 40%) p16 positive cases. All UV signature mutation positive cases had at least one significant TP53 mutation and greater than 20 unique gene mutations. CONCLUSION: The management of metastatic cutaneous carcinomas significantly differs from other HNCUP especially metastatic HR-HPV-associated SCC; therefore, the observation of a high percentage of C→T with CC →TT substitutions should be routinely incorporated in next generation sequencing reports of HNCUP. UV mutational signatures testing is a robust diagnostic tool that can be utilized in daily clinical practice.

The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review.

Cancer of unknown primary (CUP) presents a complex diagnostic challenge, characterized by metastatic tumors of unknown tissue origin and a dismal prognosis. This review delves into the emerging significance of artificial intelligence (AI) and machine learning (ML) in transforming the landscape of CUP diagnosis, classification, and treatment. ML approaches, trained on extensive molecular profiling data, have shown promise in accurately predicting tissue of origin. Genomic profiling, encompassing driver mutations and copy number variations, plays a pivotal role in CUP diagnosis by providing insights into tumor type-specific oncogenic alterations. Mutational signatures (MS), reflecting somatic mutation patterns, offer further insights into CUP diagnosis. Known MS with established etiology, such as ultraviolet (UV) light-induced DNA damage and tobacco exposure, have been identified in cases of dedifferentiated/transdifferentiated melanoma and carcinoma. Deep learning models that integrate gene expression data and DNA methylation patterns offer insights into tissue lineage and tumor classification. In digital pathology, machine learning algorithms analyze whole-slide images to aid in CUP classification. Finally, precision oncology, guided by molecular profiling, offers targeted therapies independent of primary tissue identification. Clinical trials assigning CUP patients to molecularly guided therapies, including targetable alterations and tumor mutation burden as an immunotherapy biomarker, have resulted in improved overall survival in a subset of patients. In conclusion, AI- and ML-driven approaches are revolutionizing CUP management by enhancing diagnostic accuracy. Precision oncology utilizing enhanced molecular profiling facilitates the identification of targeted therapies that transcend the need to identify the tissue of origin, ultimately improving patient outcomes.

Impact of the Identification of Nonhuman Genetic Signatures in the Diagnosis and Management of Carcinoma of Unknown Primary.

This report presents the case of a 62-year-old woman who was diagnosed in 1999 with stage I cervical carcinoma treated by surgical resection. In 2021, she presented to the emergency department with a complaint of predominantly right-sided lower back pain. A CT scan of the lumbosacral region revealed a bone lesion in the L5 vertebra and retroperitoneal lymphadenopathies suggestive of malignancy. Histology of the L5 vertebra biopsy showed a poorly differentiated carcinoma with an inconclusive immunophenotypic profile. Treatment for carcinoma of unknown primary was started with a combination of carboplatin and paclitaxel every 21 days. A genomic study of the biopsy specimen performed on the FoundationOne CDx platform identified a nonhuman genetic signature compatible with HPV. The presence of HPV 18 DNA in the specimen was confirmed by PCR-reverse dot blot, and the immunophenotypic profile was expanded, revealing strong and diffuse p16 expression, thus corroborating the molecular findings. In view of these findings, the case was reclassified as a recurrence of the cervical adenocarcinoma that had been diagnosed and treated 23 years earlier. Based on the new results, and according to first-line cervical carcinoma protocols, bevacizumab at 15 mg/kg every 21 days was added to her chemotherapy regimen. The identification of HPV DNA sequences by next-generation sequencing facilitated the correct diagnosis and led to a modification of the first-line therapeutic approach.

Baseline mutational profiles of patients with carcinoma of unknown primary origin enrolled in the CUPISCO study.

BACKGROUND: Patients with unfavorable carcinoma of unknown primary origin (CUP) have an extremely poor prognosis of ∼1 year or less, stressing the need for more tailored treatments, which are currently being tested in clinical trials. CUPISCO (NCT03498521) was a phase II randomized study of targeted therapy/cancer immunotherapy versus platinum-based chemotherapy in patients with previously untreated, unfavorable CUP, defined as per the European Society for Medical Oncology guidelines. We present a preliminary, descriptive molecular analysis of 464 patients with stringently diagnosed, unfavorable CUP enrolled in the CUPISCO study. MATERIALS AND METHODS: Genomic profiling was carried out on formalin-fixed, paraffin-embedded tissue to detect genomic alterations and assess tumor mutational burden and microsatellite instability. RESULTS: Overall, ∼32% of patients carried a potentially targetable genomic alteration, including PIK3CA, FGFR2, ERBB2, BRAFV600E, EGFR, MET, NTRK1, ROS1, and ALK. Using hierarchical clustering of co-mutational profiles, 10 clusters were identified with specific genomic alteration co-occurrences, with some mirroring defined tumor entities. CONCLUSIONS: Results reveal the molecular heterogeneity of patients with unfavorable CUP and suggest that genomic profiling may be used as part of informed decision-making to identify the potential primary tumor and targeted treatment options. Whether stringently diagnosed patients with unfavorable CUP benefit from targeted therapies in a similar manner to those with matched known primaries will be a key learning from CUPISCO.

Nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary: a phase II trial.

Cancer of unknown primary has a dismal prognosis, especially following failure of platinum-based chemotherapy. 10-20% of patients have a high tumor mutational burden (TMB), which predicts response to immunotherapy in many cancer types. In this prospective, non-randomized, open-label, multicenter Phase II trial (EudraCT 2018-004562-33; NCT04131621), patients relapsed or refractory after platinum-based chemotherapy received nivolumab and ipilimumab following TMBhigh vs. TMBlow stratification. Progression-free survival (PFS) represented the primary endpoint; overall survival (OS), response rates, duration of clinical benefit and safety were the secondary endpoints. The trial was prematurely terminated in March 2021 before reaching the preplanned sample size (n = 194). Among 31 evaluable patients, 16% had a high TMB ( > 12 mutations/Mb). Overall response rate was 16% (95% CI 6-34%), with 7.7% (95% CI 1-25%) vs. 60% (95% CI 15-95%) in TMBlow and TMBhigh, respectively. Although the primary endpoint was not met, high TMB was associated with better median PFS (18.3 vs. 2.4 months) and OS (18.3 vs. 3.6 months). Severe immune-related adverse events were reported in 29% of cases. Assessing on-treatment dynamics of circulating tumor DNA using combined targeted hotspot mutation and shallow whole genome sequencing as part of a predefined exploratory analysis identified patients benefiting from immunotherapy irrespective of initial radiologic response.

[Whole genome sequencing to find the primary tumour in cancer of unknown primary origin]. / Onbekende primaire tumor opsporen met 'whole genome sequencing'.

Cancer of unknown primary origin (CUP) remains a serious problem. The incidence in the Netherlands is stable, 1-2 percent of all new cancer cases. In general, patients undergo a long diagnostic trajectory and only a minority receive a tumour directed treatment. More than half of the patients die within two months after the diagnosis. A complete analysis of the DNA of a tumour specimen by means of whole genome sequencing may be helpful in finding the primary tumour. Dutch medical oncologists and pathologists set up a protocol for CUP patients, in which WGS may be implemented in the diagnostic procedure.

Assessing the utility of molecular diagnostic classification for cancers of unknown primary.

BACKGROUND: Roughly 5% of metastatic cancers present with uncertain origin, for which molecular classification could influence subsequent management; however, prior studies of molecular diagnostic classifiers have reported mixed results with regard to clinical impact. In this retrospective study, we evaluated the utility of a novel molecular diagnostic classifier by assessing theoretical changes in treatment and additional testing recommendations from oncologists before and after the review of classifier predictions. METHODS: We retrospectively analyzed de-identified records from 289 patients with a consensus diagnosis of cancer of uncertain/unknown primary (CUP). Two (or three, if adjudication was required) independent oncologists separately reviewed patient clinical information to determine the course of treatment before they reviewed results from the molecular diagnostic classifier and subsequently evaluated whether the predicted diagnosis would alter their treatment plan. RESULTS: Results from the molecular diagnostic classifier changed the consensus oncologist-reported treatment recommendations for 235 out of 289 patients (81.3%). At the level of individual oncologist reviews (n = 414), 64.7% (n = 268) of treatment recommendations were based on CUP guidelines prior to review of results from the molecular diagnostic classifier. After seeing classifier results, 98.1% (n = 207) of the reviews, where treatment was specified (n = 211), were guided by the tissue of origin-specific guidelines. Overall, 89.9% of the 414 total reviews either expressed strong agreement (n = 242) or agreement (n = 130) that the molecular diagnostic classifier result increased confidence in selecting the most appropriate treatment regimen. CONCLUSIONS: A retrospective review of CUP cases demonstrates that a novel molecular diagnostic classifier could affect treatment in the majority of patients, supporting its clinical utility. Further studies are needed to prospectively evaluate whether the use of molecular diagnostic classifiers improves clinical outcomes in CUP patients.

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues.

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

Feasibility and value of genomic profiling in cancer of unknown primary: real-world evidence from prospective profiling study.

Real-world evidence regarding the value of integrating genomic profiling (GP) in managing cancer of unknown primary (CUP) is limited. We assessed this clinical utility using a prospective trial of 158 patients with CUP (October 2016-September 2019) who underwent GP using next-generation sequencing designed to identify genomic alterations (GAs). Only 61 (38.6%) patients had sufficient tissue for successful profiling. GAs were seen in 55 (90.2%) patients of which GAs with US Food and Drug Administration-approved genomically matched therapy were seen in 25 (40.9%) patients. A change in therapy was recommended and implemented (primary endpoint of the study) in 16 (10.1%) and 4 (2.5%) patients of the entire study cohort, respectively. The most common reason for inability to implement the profiling-guided therapy was worsening of performance status (56.3%). Integrating GP in management of CUP is feasible but challenging because of paucity of tissue and aggressive natural history of the disease and requires innovative precision strategies.

Six-year experience of Australia's first dedicated cancer of unknown primary clinic.

BACKGROUND: Diagnosis and management of cancers of unknown primary (CUP) remain challenging. This study examines the referral patterns, management and outcomes of patients referred to Australia's first dedicated CUP clinic. METHODS: Retrospective medical record review was conducted for patients seen at the Peter MacCallum Cancer Centre CUP clinic between July 2014 and August 2020. Overall survival (OS) was analysed for patients with a CUP diagnosis where treatment information was available. RESULTS: Of 361 patients referred, fewer than half had completed diagnostic work-up at the time of referral. A diagnosis of CUP was established in 137 (38%), malignancy other than CUP in 177 (49%) and benign pathology in 36 (10%) patients. Genomic testing was successfully completed in 62% of patients with initial provisional CUP and impacted management in 32% by identifying a tissue of origin or actionable genomic alteration. The use of site-specific, targeted therapy or immunotherapy was independently associated with longer OS compared to empirical chemotherapy. CONCLUSION: Our specialised CUP clinic facilitated diagnostic work-up among patients with suspected malignancy and provided access to genomic testing and clinical trials for patients with a CUP diagnosis, all of which are important to improve outcomes in this patient population.

Cancer of unknown primary (CUP) through the lens of precision oncology: a single institution perspective.

PURPOSE: For patients with cancer of unknown primary (CUP), treatment options are limited. Precision oncology, the interplay of comprehensive genomic profiling (CGP) and targeted therapies, aims to offer additional treatment options to patients with advanced and hard-to-treat cancers. We aimed to highlight the use of a molecular tumor board (MTB) in the therapeutic management of CUP patients. METHODS: In this single-center observational study, CUP patients, presented to the MTB of the Comprehensive Cancer Center Munich LMU, a tertiary care center, were analyzed retrospectively. Descriptive statistics were applied to describe relevant findings. RESULTS: Between June 2016 and February 2022, 61 patients with unfavorable CUP were presented to the MTB, detected clinically relevant variants in 74% (45/61) of patients, of which 64% (29/45) led to therapeutic recommendation. In four out of 29 patients (14%), the treatment recommendations were implemented, unfortunately without resulting in clinical benefit. Reasons for not following the therapeutic recommendation were mainly caused by the physicians' choice of another therapy (9/25, 36%), especially in the context of worsening of general condition, lost to follow-up (7/25, 28%) and death (6/25, 24%). CONCLUSION: CGP and subsequent presentation to a molecular tumor board led to a high rate of therapeutic recommendations in patients with CUP. Recommendations were only implemented at a low rate; however, late GCP diagnostic and, respectively, MTB referral were found more frequent for the patients with implemented treatment. This contrast underscores the need for early implementation of CGP into the management of CUP patients.

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.