Austrian tricentric real-life analysis of molecular profiles of metastatic biliary tract cancer patients

Introduction

Metastatic biliary tract cancer (BTC) is a rare and aggressive entity associated with poor prognosis. It represents a major challenge for adequate treatment strategies. In recent years, BTC has become a model for precision medicine in gastrointestinal oncology. Therefore, the analysis of the individual molecular profile in BTC patients may lead to targeted therapies for the benefit of patients.

Methods

In this Austrian, tricentric, real-world, retrospective analysis, we investigated patients diagnosed with metastatic BTC who underwent molecular profiling between 2013 and 2022.

Results

In total, 92 patients were identified in this tricentric analysis and 205 molecular aberrations, including 198 mutations affecting 89 different genes in 61 patients were found. The predominant mutations were in KRAS (n=17; 22.4%), TP53 (n=17; 22.4%), PIK3CA (n=7; 9.2%), FGFR2 (n=7; 9.2%), DNMT3A (n=7; 9.2%), IDH1 (n=7; 9.2%), IDH2 (n=6; 7.9%), CDKN2A (n=6; 7.9%), BAP1 (n=4; 5.3%), NF1 (n=4; 5.3%), and NF2 (n=4; 5.3%). Three patients had HER2 amplification. MSI-H status and FGFR2 fusion genes were each observed in two different patients. One patient had a BRAF V600E mutation. Eventually, 10 patients received targeted therapy, of whom one-half derived clinical benefit.

Conclusions

Molecular profiling of BTC patients is implementable in routine clinical practice and should be regularly employed to detect and exploit molecular vulnerabilities.

Impact of the Cancer Cell Secretome in Driving Breast Cancer Progression

Breast cancer is a complex and heterogeneous disease resulting from the accumulation of genetic and epigenetic alterations in breast epithelial cells. Despite remarkable progress in diagnosis and treatment, breast cancer continues to be the most prevalent cancer affecting women worldwide. Recent research has uncovered a compelling link between breast cancer onset and the extracellular environment enveloping tumor cells. The complex network of proteins secreted by cancer cells and other cellular components within the tumor microenvironment has emerged as a critical player in driving the disease's metastatic properties. Specifically, the proteins released by the tumor cells termed the secretome, can significantly influence the progression and metastasis of breast cancer. The breast cancer cell secretome promotes tumorigenesis through its ability to modulate growth-associated signaling pathways, reshaping the tumor microenvironment, supporting pre-metastatic niche formation, and facilitating immunosurveillance evasion. Additionally, the secretome has been shown to play a crucial role in drug resistance development, making it an attractive target for cancer therapy. Understanding the intricate role of the cancer cell secretome in breast cancer progression will provide new insights into the underlying mechanisms of this disease and aid in the development of more innovative therapeutic interventions. Hence, this review provides a nuanced analysis of the impact of the cancer cell secretome on breast cancer progression, elucidates the complex reciprocal interaction with the components of the tumor microenvironment and highlights emerging therapeutic opportunities for targeting the constituents of the secretome.

Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition

While diet and nutrition are modifiable risk factors for many chronic and infectious diseases, their role in cancer prevention and control remains under investigation. The lack of clarity of some diet-cancer relationships reflects the ongoing debate about the relative contribution of genetic factors, environmental exposures, and replicative errors in stem cell division as determinate drivers of cancer risk. In addition, dietary guidance has often been based upon research assuming that the effects of diet and nutrition on carcinogenesis would be uniform across populations and for various tumor types arising in a specific organ, i.e., that one size fits all. Herein, we present a paradigm for investigating precision dietary patterns that leverages the approaches that led to successful small-molecule inhibitors in cancer treatment, namely understanding the pharmacokinetics and pharmacodynamics of small molecules for targeting carcinogenic mechanisms. We challenge the scientific community to refine the paradigm presented and to conduct proof-in-concept experiments that integrate existing knowledge (drug development, natural products, and the food metabolome) with developments in artificial intelligence to design and then test dietary patterns predicted to elicit drug-like effects on target tissues for cancer prevention and control. We refer to this precision approach as dietary oncopharmacognosy and envision it as the crosswalk between the currently defined fields of precision oncology and precision nutrition with the goal of reducing cancer deaths.

Oncofetal protein glypican-3 is a biomarker and critical regulator of function for neuroendocrine cells in prostate cancer

Neuroendocrine (NE) cells comprise ~1% of epithelial cells in benign prostate and prostatic adenocarcinoma (PCa). However, they become enriched in hormonally treated and castration-resistant PCa (CRPC). In addition, close to 20% of hormonally treated tumors recur as small cell NE carcinoma (SCNC), composed entirely of NE cells, which may be the result of clonal expansion or lineage plasticity. Since NE cells do not express androgen receptors (ARs), they are resistant to hormonal therapy and contribute to therapy failure. Here, we describe the identification of glypican-3 (GPC3) as an oncofetal cell surface protein specific to NE cells in prostate cancer. Functional studies revealed that GPC3 is critical to the viability of NE tumor cells and tumors displaying NE differentiation and that it regulates calcium homeostasis and signaling. Since our results demonstrate that GPC3 is specifically expressed by NE cells, patients with confirmed SCNC may qualify for GPC3-targeted therapy which has been developed in the context of liver cancer and displays minimal toxicity due to its tumor-specific expression. © 2023 The Pathological Society of Great Britain and Ireland.

Molecular features and race-associated outcomes of SPOP-mutant metastatic castration-resistant prostate cancer

BACKGROUND

Inactivating alterations in SPOP frequently occur in prostate cancer and promote increased dependency on androgen receptor (AR)-mediated oncogenic signaling. The presence of SPOP mutation (SPOP-mutant [SPOP-mut]) may therefore impact therapeutic outcomes with AR-directed therapies and docetaxel in metastatic castration-resistant (mCRPC).

METHODS

This was a retrospective study of mCRPC patients treated at an urban academic hospital (n = 103). Patients underwent tumor DNA sequencing to determine SPOP mutational status (SPOP-mut). Outcomes measured were overall survival (OS) from diagnosis and treatment with second-generation AR signaling inhibitor (ARSI) or docetaxel and time to PSA progression (prostate-specific antigen-progression-free survival [PSA-PFS]) compared by SPOP status using Kaplan-Meier curves and log-rank test. The univariable and multivariable Cox proportional hazard model evaluated the association of SPOP mutation and outcomes adjusted for clinicopathologic features.

RESULTS

SPOP-mut was associated with longer PSA-PFS in mCRPC (median 1.79 vs. 0.84 years; p = 0.06) and multivariate analysis (hazard ratio [HR] = 0.37; 95% confidence interval [CI]: 0.17-0.84; p = 0.02). SPOP-mut demonstrated a higher median PSA decline compared to SPOP wild-type (median decline 100% vs. 92%, p = 0.02). SPOP-mut was not associated with OS from the start of ARSI or docetaxel (median OS not reached vs. 2.0 years) or PSA-PFS on docetaxel (median PSA-PFS 0.4 vs. 0.5 years) in mCRPC. The majority of SPOP mutations were identified in African American (AA) patients (69.2%) compared to Caucasian patients (30.8%). Race-associated multivariate analysis revealed no significant differences in OS from the start of ARSI or the start of docetaxel and no differences in ARSI or docetaxel PSA-PFS between AA and Caucasian patients. Molecular profiling demonstrated that AA patients had a higher frequency of SPOP mutations and greater heterogeneity of SPOP variants within the coding sequence. Analysis of concurrent genomic alterations revealed that SPOP mutations co-occur with APC mutations (p = 0.001) and alterations in the Wnt pathway (p = 0.017).

CONCLUSIONS

Inactivating mutations in SPOP are associated with better response to ARSI treatment in mCRPC overall. Additional analysis with a larger cohort is needed to evaluate the association of SPOP status and outcomes with docetaxel. Race-associated clinical outcomes and molecular features were observed, suggesting the benefit of biomarker-directed therapy selection for individualized patient subsets in guiding treatment decisions for mCRPC patients.

Precision Oncology in Gastrointestinal Stromal Tumors

GIST (gastrointestinal stromal tumors) represent 20% of sarcomatous tumors and 1-2% of primary gastrointestinal cancers. They have an excellent prognosis when localized and resectable, though their prognosis is poor in the metastatic setting, with limited options after the second line until recently. Four lines are now standard in KIT-mutated GIST and one in PDGFRA-mutated GIST. An exponential growth of new treatments is expected in this era of molecular diagnostic techniques and systematic sequencing. Currently, the main challenge remains the emergence of resistance linked to secondary mutations caused by selective pressure induced by TKIs. Repeating biopsies to tailor treatments might be a step in the right direction, and liquid biopsies at progression may offer a non-invasive alternative. New molecules with wider KIT inhibition are under investigation and could change the catalog and the sequence of existing treatments. Combination therapies may also be an approach to overcome current resistance mechanisms. Here, we review the current epidemiology and biology of GIST and discuss future management options, with an emphasis on genome-oriented therapies.

The Current Status of DNA-Repair-Directed Precision Oncology Strategies in Epithelial Ovarian Cancers

Survival outcomes for patients with advanced ovarian cancer remain poor despite advances in chemotherapy and surgery. Platinum-based systemic chemotherapy can result in a response rate of up to 80%, but most patients will have recurrence and die from the disease. Recently, the DNA-repair-directed precision oncology strategy has generated hope for patients. The clinical use of poly(ADP-ribose) polymerase (PARP) inhibitors in BRCA germ-line-deficient and/or platinum-sensitive epithelial ovarian cancers has improved survival. However, the emergence of resistance is an ongoing clinical challenge. Here, we review the current clinical state of PARP inhibitors and other clinically viable targeted approaches in epithelial ovarian cancers.

Molecular profiling of colorectal cancer in a genetically admixed Hispanic population

BACKGORUND

Colorectal cancer (CRC) is among the leading causes of cancer-related deaths among Hispanics living in the United States (USH). Understanding the most common carcinogenic molecular pathways that affect Hispanics with CRC is crucial to guide research efforts in developing new therapeutic modalities incorporating genomically diverse populations. Tumor profiling techniques help identify actionable alternatives to recommend treatment and improve survival in cancer patients.

METHODS

We conducted a secondary data analysis to evaluate the mutational profile of 218 CRC tumors in Hispanics living in Puerto Rico (PRH) who underwent next-generation sequencing (NGS) testing from 2015 to 2020. We compared the prevalence of CRC tumor somatic mutations in PRHs with the mutational profiles reported for CRC from The Cancer Genome Atlas (TCGA) Pan-Cancer Clinical Data, the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE)-Non-Hispanic, and GENIE-Hispanic datasets.

RESULTS

Among the top mutated genes in CRC tumors in PRHs were APC, TP53, and KRAS, which had significantly higher mutational frequencies in PRH compared to the examined datasets, including GENIE-Hispanics. The most frequent gene amplifications for PRH were CDX2, CDKN1B, and HNRNPA2B1. Targetable biomarkers for CRC, such as microsatellite instability-high (MSI), wild-type KRAS, wild-type NRAS, V600E BRAF, and ERBB2 gene amplifications were found in 2.0%, 43.8%, 97.8%, 3.9%, and 2.3%, respectively, of PRH patients.

CONCLUSION

This is the first study to report the mutational profile of CRC tumors in PRHs and make comparisons to other non-Hispanic and USH populations.

Precision Oncology: Evolving Clinical Trials across Tumor Types

Advances in molecular technologies and targeted therapeutics have accelerated the implementation of precision oncology, resulting in improved clinical outcomes in selected patients. The use of next-generation sequencing and assessments of immune and other biomarkers helps optimize patient treatment selection. In this review, selected precision oncology trials including the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH studies are summarized, and their challenges and opportunities are discussed. Brief summaries of the new ComboMATCH, MyeloMATCH, and iMATCH studies, which follow the example of NCI-MATCH, are also included. Despite the progress made, precision oncology is inaccessible to many patients with cancer. Some patients' tumors may not respond to these treatments, owing to the complexity of carcinogenesis, the use of ineffective therapies, or unknown mechanisms of tumor resistance to treatment. The implementation of artificial intelligence, machine learning, and bioinformatic analyses of complex multi-omic data may improve the accuracy of tumor characterization, and if used strategically with caution, may accelerate the implementation of precision medicine. Clinical trials in precision oncology continue to evolve, improving outcomes and expediting the identification of curative strategies for patients with cancer. Despite the existing challenges, significant progress has been made in the past twenty years, demonstrating the benefit of precision oncology in many patients with advanced cancer.

Flow Rate-Independent Multiscale Liquid Biopsy for Precision Oncology

Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management but typically suffer from low throughput, relative complexity, and postprocessing limitations. Here, we address these issues simultaneously by decoupling and independently optimizing the nano-, micro-, and macro-scales of an enrichment device that is simple to fabricate and operate. Unlike other affinity-based devices, our scalable mesh approach enables optimum capture conditions at any flow rate, as demonstrated with constant capture efficiencies, above 75% between 50 and 200 μL min-1. The device achieved 96% sensitivity and 100% specificity when used to detect CTCs in the blood of 79 cancer patients and 20 healthy controls. We demonstrate its postprocessing capacity with the identification of potential responders to immune checkpoint inhibition (ICI) therapy and the detection of HER2 positive breast cancer. The results compare well with other assays, including clinical standards. This suggests that our approach, which overcomes major limitations associated with affinity-based liquid biopsies, could help improve cancer management.

Supporting Biomarker-Driven Therapies in Oncology: A Genomic Testing Cost Calculator

BACKGROUND

Adoption of high-throughput, gene panel-based, next-generation sequencing (NGS) into routine cancer care is widely supported, but hampered by concerns about cost. To inform policies regarding genomic testing strategies, we propose a simple metric, cost per correctly identified patient (CCIP), that compares sequential single-gene testing (SGT) vs. multiplex NGS in different tumor types.

MATERIALS AND METHODS

A genomic testing cost calculator was developed based on clinically actionable genomic alterations identified in the European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets. Using sensitivity/specificity data for SGTs (immunohistochemistry, polymerase chain reaction, and fluorescence in situ hybridization) and NGS and marker prevalence, the number needed to predict metric was monetarized to estimate CCIP.

RESULTS

At base case, CCIP was lower with NGS than sequential SGT for advanced/metastatic non-squamous non-small cell lung cancer (NSCLC), breast, colorectal, gastric cancers, and cholangiocarcinoma. CCIP with NGS was also favorable for squamous NSCLC, pancreatic, and hepatic cancers, but with overlapping confidence intervals. CCIP favored SGT for prostate cancer. Alternate scenarios using different price estimates for each test showed similar trends, but with incremental changes in the magnitude of difference between NGS and SGT, depending on price estimates for each test.

CONCLUSIONS

The cost to correctly identify clinically actionable genomic alterations was lower for NGS than sequential SGT in most cancer types evaluated. Decreasing price estimates for NGS and the rapid expansion of targeted therapies and accompanying biomarkers are anticipated to further support NGS as a preferred diagnostic standard for precision oncology.

Top advances of the year: Precision oncology

The advent of precision medicine has changed the landscape of oncologic biomarkers, drug discovery, drug development, and, more importantly, outcomes for patients with cancer. Precision oncology entails the genomic profiling of tumors to detect actionable aberrations. The advances in clinical next-generation sequencing from both tumor tissue and liquid biopsy and availability of targeted therapies has rapidly entered mainstream clinical practice. In this review, recent major developments in precision oncology that have affected outcomes for patients with cancer are discussed. Rapid clinical development was seen of targeted agents across various mutational profiles such as KRASG12C (which was considered "undruggable" for almost 4 decades), Exon 20 insertions, and RET mutations. Approaches to precision chemotherapy delivery by the introduction of antibody drug conjugates in the armamentarium against lung cancer has been appreciated.

Advancing wide implementation of precision oncology: A liquid nitrogen-free snap freezer preserves molecular profiles of biological samples

PURPOSE

In precision oncology, tumor molecular profiles guide selection of therapy. Standardized snap freezing of tissue biospecimens is necessary to ensure reproducible, high-quality samples that preserve tumor biology for adequate molecular profiling. Quenching in liquid nitrogen (LN₂ ) is the golden standard method, but LN₂ has several limitations. We developed a LN₂ -independent snap freezer with adjustable cold sink temperature. To benchmark this device against the golden standard, we compared molecular profiles of biospecimens.

METHODS

Cancer cell lines and core needle normal tissue biopsies from five patients' liver resection specimens were used to compare mass spectrometry (MS)-based global phosphoproteomic and RNA sequencing profiles and RNA integrity obtained by both freezing methods.

RESULTS

Unsupervised cluster analysis of phosphoproteomic and transcriptomic profiles of snap freezer versus LN₂ -frozen K562 samples and liver biopsies showed no separation based on freezing method (with Pearson's r 0.96 (range 0.92-0.98) and >0.99 for K562 profiles, respectively), while samples with +2 h bench-time formed a separate cluster. RNA integrity was also similar for both snap freezing methods. Molecular profiles of liver biopsies were clearly identified per individual patient regardless of the applied freezing method. Two to 25 s freezing time variations did not induce profiling differences in HCT116 samples.

CONCLUSION

The novel snap freezer preserves high-quality biospecimen and allows identification of individual patients' molecular profiles, while overcoming important limitations of the use of LN₂ . This snap freezer may provide a useful tool in clinical cancer research and practice, enabling a wider implementation of (multi-)omics analyses for precision oncology.

ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer

PURPOSE

We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer.

EXPERIMENTAL DESIGN

This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI.

RESULTS

Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02-1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34-1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA-positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50).

CONCLUSIONS

ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Systematic assessment of prognostic molecular features across cancers

Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis-regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.

[Research progress on genome-guided precision oncology and development ideas of antitumor Chinese medicine]

Genome-guided oncology refers to a new treatment concept that transcends histological classification and pathological ty-ping and uses drugs according to the genetic characteristics of tumors. New drug development technology and clinical trial design based on this concept provide new ideas for the clinical application of precision oncology. The multi-component and multi-target characteristics of Chinese medicine provide rich resources for the development of tumor-targeting drugs from natural products, and the design of the master protocol trial aiming at the characteristics of precision oncology supports the rapid clinical screening of effective tumor-targeting drugs. The emergence of the synthetic lethality strategy breaks through the bottleneck that the drug can only target the oncogene but cannot do anything to the tumor suppressor gene with the loss-of-function mutation in the past. With the rapid development of high-throughput sequencing technology, the cost of sequencing is also decreasing. For the development of tumor-targeting drugs, how to keep up with the update speed of target information is a difficult problem of concern. Based on the integration of innovative ideas and me-thods of precision oncology, network pharmacology, and synthetic lethality strategy on synthetic lethal interaction network of antitumor Chinese medicine compatibility formula design, and the combination of improvement of innovative clinical trial methods, such as master protocol trial, basket trial, and umbrella trial, unique advantages of Chinese medicine are expected to be exerted beyond the antibody-based drugs and small molecule-based drugs and corresponding targeted drugs are potentially developed for clinical application.

Natural Products in Precision Oncology: Plant-Based Small Molecule Inhibitors of Protein Kinases for Cancer Chemoprevention

Striking progress is being made in cancer treatment by using small molecule inhibitors of specific protein kinases that are products of genes recognized as drivers for a specific type of cancer. However, the cost of newly developed drugs is high, and these pharmaceuticals are neither affordable nor accessible in most parts of the world. Accordingly, this narrative review aims to probe how these recent successes in cancer treatment can be reverse-engineered into affordable and accessible approaches for the global community. This challenge is addressed through the lens of cancer chemoprevention, defined as using pharmacological agents of natural or synthetic origin to impede, arrest, or reverse carcinogenesis at any stage in the disease process. In this regard, prevention refers to reducing cancer-related deaths. Recognizing the clinical successes and limitations of protein kinase inhibitor treatment strategies, the disciplines of pharmacognosy and chemotaxonomy are juxtaposed with current efforts to exploit the cancer kinome to describe a conceptual framework for developing a natural product-based approach for precision oncology.

Precision oncology for BRAF-mutant cancers with BRAF and MEK inhibitors: from melanoma to tissue-agnostic therapy

BRAF activation occurs as part of the mitogen-activated protein kinase (MAPK) cellular signaling pathway which leads to increased cellular proliferation and survival. Mutations in BRAF can result in unbridled activation of downstream kinases with subsequent uncontrolled cellular growth that formulate the basis for oncogenesis in multiple tumor types. Targeting BRAF by selective inhibitors has been one of the early successes in precision oncology. Agents have been explored either as monotherapy or in combination with MEK inhibition in BRAF V600-mutant pan-cancers and with EGFR inhibition in colorectal cancer. Spectrum of BRAF inhibition has evolved from being melanoma-specific to being a pan-cancer target. In this article, we review BRAF and MEK inhibitor drug development journey from tissue-specific melanoma, non-small-cell lung cancer, and anaplastic thyroid cancer to tissue-agnostic approvals.

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

Comprehensive genomic profiling: Does timing matter?

Purpose

There is variability in utilization of Comprehensive Genomic Profiling (CGP) in most of the metastatic solid tumors (MST). We evaluated the CGP utilization patterns and its impact on outcomes at an academic tertiary center.

Patients and Methods

Institutional database was reviewed for CGP data in adult patients with MST between 01/2012 - 04/2020. Patients were categorized based on interval between CGP and metastatic diagnosis; 3 tertiles of distribution (T1-earliest to the diagnosis, T3-furthest), and pre-mets (CGP performed prior to diagnosis of metastasis). Overall survival (OS) was estimated from the time of metastatic diagnosis with left truncation at the time of CGP. Cox regression model was used to estimate the impact of timing of CGP on survival.

Results

Among 1,358 patients, 710 were female, 1,109 Caucasian, 186 Afro-Americans, and 36 Hispanic. The common histologies were lung cancer (254; 19%), colorectal cancer (203; 15%), gynecologic cancers (121; 8.9%), and pancreatic cancer (106; 7.8%). Time interval between diagnosis of metastatic disease and CGP was not statistically significantly different based on sex, race and ethnicity after adjusting for histologic diagnoses with 2 exceptions - Hispanics with lung cancer had delayed CGP compared to non-Hispanics (p =0.019) and females with pancreas cancer had delayed CGP compared to males (p =0.025). Lung cancer, gastro-esophageal cancer and gynecologic malignancies had better survival if they had CGP performed during the first tertile after metastatic diagnosis.

Conclusion

CGP utilization across cancer types was equitable irrespective of sex, race and ethnicity. Early CGP after metastatic diagnosis might have effect on treatment delivery and clinical outcomes in cancer type with more actionable targets.

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Background

High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods

In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results

Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions

We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

Medulloblastoma: From TP53 Mutations to Molecular Classification and Liquid Biopsy

A recent paradigm shift in the diagnostics of medulloblastoma allowed the distinction of four major groups defined by genetic data rather than histology. This new molecular classification correlates better with prognosis and will allow for the better clinical management of therapies targeting druggable mutations, but also offer a new combination of monitoring tumor development in real-time and treatment response by sequential liquid biopsy. This review highlights recent developments after a century of milestones in neurosurgery and radio- and chemotherapy, but also controversial theories on the cell of origin, animal models, and the use of liquid biopsy.

OncoLoop: A Network-Based Precision Cancer Medicine Framework

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.

SIGNIFICANCE

OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.

Progress in precision therapy in pediatric oncology

PURPOSE OF REVIEW

The fields of precision medicine and cancer genomics in pediatric oncology are rapidly evolving. Novel diagnostic tools are critical in refining cancer diagnoses, stratifying patient risk, and informing treatment decisions. This review is timely and relevant as it discusses advantages and drawbacks of common molecular profiling techniques and highlights novel platforms, which may address select limitations. We discuss recent publications demonstrating utility of large-scale molecular profiling and feasibility and logistics of matching targeted therapies to patients.

RECENT FINDINGS

We describe the increased accessibility of next-generation sequencing, complementary profiling methods, and strategies to guide treatment decisions. We describe curation and sharing of large genomic datasets and novel mechanisms to obtain matched targeted therapies. Importantly, we discuss relevant publications in distinct disease domains that support indications for evidence-based precision therapy. Lastly, we introduce the incremental analyses that can be obtained via whole-genome and transcriptome sequencing.

SUMMARY

Here we highlight high-yield clinical scenarios of precision medicine approaches and identify the ongoing challenges including universally defining clinical actionability, optimizing trial design to account for molecular heterogeneity while acknowledging limitations in patient accrual, expanding access to molecularly targeted therapies, and validating new tools and technology to aid in precision medicine therapeutic approaches.

Advances in the Management of Central Nervous System Metastases in Non-Small Cell Lung Cancer

Central nervous system (CNS) metastases are common among patients with non-small cell lung cancer (NSCLC). While the presence of brain metastases has historically portended poor prognosis, recent advances in local and systemic therapies have greatly improved outcomes for NSCLC patients with CNS involvement. Stereotactic radiology surgery (SRS) has emerged as an effective radiotherapy technique with fewer toxicities compared to whole brain radiotherapy (WBRT). Furthermore, multi-generation tyrosine kinase inhibitors (TKIs) with CNS overall response rates (ORR) of up to 70-80% are now an accepted first-line approach for a subset of advanced NSCLC patients with targetable molecular alterations. In addition, while the CNS was once considered an immunologic sanctuary site, growing evidence shows that immune checkpoint inhibitors (ICIs) can induce durable responses in brain metastases as well. Ongoing efforts to optimize CNS metastases management are necessary to refine multimodal treatment approaches and develop new therapeutics with better CNS penetrance.