Complete genomic characterization in patients with cancer of unknown primary origin in routine diagnostics

BACKGROUND

In ∼3%-5% of patients with metastatic disease, tumor origin remains unknown despite modern imaging techniques and extensive pathology work-up. With long diagnostic delays and limited and ineffective therapy options, the clinical outcome of patients with cancer of unknown primary (CUP) remains poor. Large-scale genome sequencing studies have revealed that tumor types can be predicted based on distinct patterns of somatic variants and other genomic characteristics. Moreover, actionable genomic events are present in almost half of CUP patients. This study investigated the clinical value of whole genome sequencing (WGS) in terms of primary tumor identification and detection of actionable events, in the routine diagnostic work-up of CUP patients.

PATIENTS AND METHODS

A WGS-based tumor type 'cancer of unknown primary prediction algorithm' (CUPPA) was developed based on previously described principles and validated on a large pan-cancer WGS database of metastatic cancer patients (>4000 samples) and 254 independent patients, respectively. We assessed the clinical value of this prediction algorithm as part of routine WGS-based diagnostic work-up for 72 CUP patients.

RESULTS

CUPPA correctly predicted the primary tumor type in 78% of samples in the independent validation cohort (194/254 patients). High-confidence predictions (>95% precision) were obtained for 162/254 patients (64%). When integrated in the diagnostic work-up of CUP patients, CUPPA could identify a primary tumor type for 49/72 patients (68%). Most common diagnoses included non-small-cell lung (n = 7), gastroesophageal (n = 4), pancreatic (n = 4), and colorectal cancer (n = 3). Actionable events with matched therapy options in clinical trials were identified in 47% of patients.

CONCLUSIONS

Genome-based tumor type prediction can predict cancer diagnoses with high accuracy when integrated in the routine diagnostic work-up of patients with metastatic cancer. With identification of the primary tumor type in the majority of patients and detection of actionable events, WGS is a valuable diagnostic tool for patients with CUP.

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

INTRODUCTION

Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.

PATIENTS AND METHODS

In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.

RESULTS

The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.

CONCLUSIONS

These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

The impact of radiological retroperitoneal lymphadenopathy on survival after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for colorectal peritoneal metastases

OBJECTIVES

To investigate the impact of retroperitoneal lymphadenopathy (RPLP) on pre-operative CT scan on overall survival (OS) and disease-free survival (DFS) after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) for peritoneal metastases (PM) of colorectal cancer.

BACKGROUND

In patients with PM enlarged retroperitoneal lymph nodes (RPLP) are usually considered extra-regional lymph node metastases and therefore these patients may be excluded from CRS-HIPEC. This is a clinical dilemma since it is often hard to obtain histology from these nodes.

METHODS

In this multicenter, retrospective study all consecutive patients with colorectal PM treated with CRS-HIPEC between 2004 and 2013 were included. The preoperative CT-scan was re-analyzed for the presence of RPLP based on the radiological appearance of enlarged lymph nodes. Outcomes were OS and DFS. Kaplan-Meier methods and Cox regression modeling were used to analyze the impact of RPLP on OS and DFS.

RESULTS

In 25 of 401 patients (6.1%) RPLP was observed on the preoperative CT-scan. Patient, tumor and surgical characteristics did not statistically significantly differ between groups with and without RPLP. After a median follow-up of 46 months, the one-, three- and five-year survival was 80%, 59%, 38% and 90%, 50%, 36% in the group with and without RPLP respectively. Median OS (47 vs. 35 months, logrank: p = 0.70) and median DFS (14 vs. 15 months, logrank: p = 0.81) did not statistically significantly differ between groups. In multivariable analysis, RPLP did not significantly influence survival.

CONCLUSION

Enlarged retroperitoneal lymph nodes on a pre-operative CT-scan should not automatically exclude patients from CRS-HIPEC.