JCO precision oncology最新文献

Purpose: The new CAP guideline published in August 2022 recommends using immunohistochemistry (IHC) to test for mismatch repair defects in gastroesophageal (GE), small bowel (SB), or endometrial carcinoma (EC) cancers over next-generation sequencing assessment of microsatellite instability (NGS-MSI) for immune checkpoint inhibitor (ICI) therapy eligibility and states there is a preference to use IHC over NGS-MSI in colorectal carcinoma (CRC).

Methods: We assessed the concordance of NGS-MSI and IHC-MMR from a very large cohort across the spectrum of solid tumors.

Results: Of the over 190,000 samples with both NGS-MSI and IHC-MMR about 1,160 were initially flagged as discordant. Of those samples initially flagged as discordant, 50.9% remained discordant after being reviewed by an additional pathologist. This resulted in a final discordance rate of 0.31% (590/191,767). Among CRC, GE, SB and EC, 55.4% of mismatch repair proficient/MSI high (MMRp/MSI-H) tumors had at least one somatic pathogenic mutation in an MMR gene or POLE. Mismatch repair deficient/microsatellite stable (MMRd/MSS) tumors had a significantly lower rate of high tumor mutational burden than MMRp/MSI-H tumors. Across all solid tumors, MMRd/MSI-H tumors had significantly longer overall survival (OS; hazard ratio [HR], 1.47, P < .001) and post-ICI survival (HR, 1.82, P < .001) as compared with MMRp/MSS tumors. The OS for the MMRd/MSS group was slightly worse compared to the MMRp/MSI-H tumors, but this difference was not statistically significant (HR, 0.73, P = .058), with a similar pattern when looking at post-ICI survival (HR, 0.43, P = .155).

Conclusion: This study demonstrates that NGS-MSI is noninferior to IHC-MMR and can identify MSI-H tumors that IHC-MMR is unable to detect and conversely IHC-MMR can identify MMRd tumors that NGS-MSI misses.

Purpose: Targeted Agent and Profiling Utilization Registry is a phase II basket trial evaluating the antitumor activity of commercially available targeted agents in patients with advanced cancer and targetable genomic alterations. Two cohorts of patients with cyclin-dependent kinase inhibitor 2A (CDKN2A)-mutated tumors treated with palbociclib are reported: one with head and neck cancer (HNC) with both squamous and nonsquamous cell histologies, and one with histology-pooled (HP) cancers.

Methods: Eligible patients had measurable disease, Eastern Cooperative Oncology Group performance status 0-2, adequate organ function, and no standard treatment options. The primary end point was disease control (DC), defined as objective response (OR) or stable disease (SD) of at least 16+ weeks duration. For the HNC cohort, Simon's two-stage design with a null DC rate of 15% versus 35% (power = 0.85; α = .10) was used. For the HP cohort, the null hypothesis of a DC rate of 15% was rejected if the lower limit of a one-sided 90% CI was >15%. Secondary end points included OR, safety, progression-free survival, overall survival, duration of response, and duration of SD.

Results: Seventy patients with HNC (N = 28) or HP cancers (N = 42) were treated with palbociclib. For the HNC cohort, DC and OR rates were 40% (one-sided 90% CI, 27 to 100) and 4% (95% CI, <1 to 18), respectively. The null hypothesis was rejected (P = .002). For the HP cohort, DC and OR rates were 13% (one-sided 90% CI, 6 to 100) and 5% (95% CI, <1 to 17), respectively. The null hypothesis was not rejected. Thirty-one of 70 patients experienced treatment-related grade 3 to 4 adverse events (AEs) or serious AEs, the most common including neutropenia, thrombocytopenia, and leukopenia.

Conclusion: Palbociclib met prespecified criteria to declare a signal of activity in patients with HNC with CDKN2A alterations, but not in the HP cohort.

Purpose: Dynamics of carbohydrate antigen 19-9 (CA19-9) often inform treatment decisions during and after neoadjuvant chemotherapy (NAT) of patients with pancreatic ductal adenocarcinoma (PDAC). However, considerable dispute persists regarding the clinical relevance of specific CA19-9 thresholds and dynamics. Therefore, we aimed to define optimal thresholds for CA19-9 values and create a biochemically driven composite score to predict survival in CA19-9-producing patients with PDAC after NAT.

Methods: Patients with PDAC who underwent NAT and surgical resection from 2012 to 2022 were retrospectively identified from three high-volume centers. CA19-9 nonproducers and patients with 90-day mortality, and macroscopically incomplete resections were excluded. A composite score was created on the basis of relative CA19-9 change and newly defined optimal thresholds of pre- and postneoadjuvant values for overall survival (OS) using patients from two centers and validated using data from the third center.

Results: A total of 492 patients met inclusion criteria in the development cohort. Optimal CA19-9 cutoff values for predicting a difference in OS were 202 U/mL for preneoadjuvant and 78 U/mL for postneoadjuvant levels. Furthermore, increase in CA19-9 during neoadjuvant treatment was associated with worse OS (median-OS, 17.5 months v 26.0 months; P = .008). Not surpassing any or only one of these thresholds (composite score of 0-1) was associated with improved OS compared with patients with 2-3 points (median-OS, 29.9 months v 15.8 months; P < .001). Major serological response (90% decrease of CA19-9) had a positive and negative predictive value of 32% and 88%, respectively.

Conclusion: The composite score consisting of CA19-9 levels at diagnosis, after neoadjuvant treatment, and its dynamics demonstrates prognostic discrimination between low and high scores. However, better predictive biomarkers are needed to facilitate treatment decisions during neoadjuvant treatment.

Crizotinib successfully overcomes MET amplification in ROS1-rearranged NSCLC after entrectinib failure.

Antibody-drug conjugates (ADCs) are at the forefront of cancer therapy, combining targeted precision with potent cytotoxicity. Conceived by Paul Ehrlich in the early 1900s, the concept of a magic bullet selectively eliminating cancer cells has evolved alongside bioengineering and cancer biology advancements. ADCs consist of a monoclonal antibody, linker, and cytotoxic payload, designed to target specific antigens on tumor cells while minimizing collateral damage. Mechanistically, ADCs are internalized via endocytosis, releasing the cytotoxic payload within the lysosome, potentially affecting neighboring tumor cells. ADC development has progressed through multiple generations, each addressing limitations of its predecessors. From gemtuzumab ozogamicin to trastuzumab emtansine (T-DM1), and now to third-generation agents such as trastuzumab deruxtecan (DS-8201) and disitamab vedotin (RC48), improvements have been made in target selectivity, potency, linker stability, and reduced off-target effects. Significant success has been seen in ADCs targeting human epidermal growth factor receptor 2 and trophoblast cell-surface antigen 2 antigens, especially in patients with breast cancer, including those resistant to previous therapies. The future of ADCs includes exploring new surface antigens, bispecific antibodies, immune-activating antibodies, radiopharmaceutical-loaded ADCs, and masked ADCs for tissue-specific activation. Ongoing research aims to optimize treatment efficacy while minimizing toxicity, expanding the potential of combination therapy. ADCs represent a promising frontier in precision cancer treatment, with continued research enhancing their potential in breast cancer and beyond. This review provides a comprehensive exploration of ADCs' evolution in breast cancer therapy, offering a molecular perspective to inform clinical practice and update colleagues on this dynamic field.

Purpose: The acid phosphatase 1 (ACP1) gene encodes low-molecular-weight protein tyrosine phosphatase, which is overexpressed in prostate cancer (PC) and a potential therapeutic target. We analyzed ACP1 expression in primary/metastatic PC and its association with molecular profiles and clinical outcomes.

Methods: NextGen sequencing of DNA (592-gene/whole-exome sequencing)/RNA(whole-transcriptome sequencing) was performed for 5,028 specimens. ACP1-High/ACP1-Low expression was defined as quartile (Q4/1) of RNA transcripts per million (TPM). DNA mutational profiles were analyzed for ACP1-quartile-stratified samples. Gene set enrichment analysis was used for Hallmark collection of pathways. PD-L1+(≥2+, ≥5%; SP142) was tested by immunohistochemistry. Tumor microenvironment's (TME) immune cell fractions were estimated by RNA deconvolution/quanTIseq. Overall survival (OS) was assessed from initial diagnosis/treatment initiation to death/last follow-up.

Results: We included 3,058 (60.8%) samples from the prostate, 634 (12.6%) from lymph node metastases (LNMs), and 1,307 (26.0%) from distant metastases (DMs). ACP1 expression was higher in LNM/DM than prostate (49.8/47.9 v 44.1 TPM; P < .0001). TP53 mutations were enriched in ACP1-Q4 (37.9%[Q4] v 27.0%[Q1]; P < .001) among prostate samples. Pathways associated with cell cycle regulation and oxidative phosphorylation were enriched in ACP1-Q4, whereas epithelial-mesenchymal transition and tumor necrosis factor-alpha signaling via nuclear factor kappa-light-chain-enhancer of activated B-cell pathways were enriched in ACP1-Q1. Neuroendocrine and androgen receptor signaling was increased in ACP1-Q4. M2 macrophages and natural killer cell fractions were increased, whereas T cells and M1 macrophages were decreased in ACP1-Q4. While OS differences between ACP1-Q1/Q4 were not statistically significant, there was a trend for worse OS among ACP1-Q4 prostate samples (Q4 v Q1: hazard ratio [HR], 1.19 [95% CI, 0.99 to 1.42]; P = .06) and DM (HR, 1.12 [95% CI, 0.93 to 1.36]; P = .22) but not LNM (HR, 0.98 [95% CI, 0.74 to 1.29]; P = .87).

Conclusion: ACP1-High tumors exhibit a distinct molecular profile and cold TME, highlighting ACP1's potential role in PC pathogenesis and novel therapeutic targeting.

Purpose: Clinical utility of comprehensive genomic profiling (CGP) for precision medicine has become evident. Although there are several reports on the genomic landscape of GI stromal tumors (GISTs), large-scale data specific to GIST are limited, especially in Asia. Additionally, the applicability of molecular-targeted agents identified using CGP has not been extensively examined. We investigated the status of genomic alterations in Japanese patients with advanced GISTs using the National Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database to identify novel treatment strategies and drug development.

Materials and methods: We retrospectively reviewed the clinical and CGP data of patients with advanced-stage GIST registered in the C-CAT database to assess the genomic landscape and potential actionable alterations.

Results: Data from 144 patients were reviewed. Oncogenic alterations were detected frequently in KIT (78%), CDKN2A (37%), CDKN2B (29%), RB1 (11%), STK11 (10%), TP53 (9%), PDGFRA (6%), and SDHB (6%). Loss of CDKN2A/CDKN2B was only observed in KIT/PDGFRA-mutated GISTs, while alterations in SDHA/SDHB were only detected in KIT/PDGFRA wild-type GISTs. Among 119 KIT/PDGFRA-mutated GISTs, 95 (80%) had oncogenic genomic alterations and 29 (24%) had actionable alterations, excluding KIT and PDGFRA. However, among 25 KIT/PDGFRA wild-type GISTs, 22 (88%) had oncogenic alterations and 11 (44%) had actionable alterations. Representative candidate drugs for genome-matched therapies in KIT/PDGFRA-mutated and wild-type GISTs were as follows: pembrolizumab for tumor mutation burden-high in one and two patients, respectively; poly-adenosine diphosphate ribose polymerase inhibitors for alterations related to homologous recombination deficiency in 12 and one patient, respectively; NTRK inhibitor for ETV6-NTRK3 fusion in one with KIT/PDGFRA wild-type GIST; and human epidermal growth factor receptor 2-antibody-drug conjugate in one with KIT/PDGFRA-mutated GIST.

Conclusion: This study highlights the genomic landscape of advanced GISTs and the important role of CGP in identifying rational molecular-targeted therapeutic options.

Purpose: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

Materials and methods: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

Results: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

Conclusion: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

Purpose: We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations.

Methods: Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data.

Results: A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients.

Conclusion: The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.