Heidi Chwan Ko

PURPOSE: Recommendations for clinical management of women with germline pathogenic variants (PVs) in breast cancer susceptibility genes vary by gene. The goal of this study was to examine the utilization of risk-reducing strategies and treatments in women with germline PVs in breast cancer susceptibility genes. METHODS: This cross-sectional study identified women with a PV in at least one of 13 breast cancer susceptibility genes from germline panel testing at a single commercial laboratory between 2015 and 2023. Paired health care claims data for 12 months after genetic testing were evaluated to estimate the utilization of risk-reducing mastectomy and salpingo-oophorectomy, breast magnetic resonance imaging (MRI), and pancreatic screening (magnetic resonance cholangiopancreatography or endoscopic ultrasound) in these women. Multivariable logistic regression models were used to evaluate the association of clinicodemographic factors and genetic testing results with intervention utilization. RESULTS: Of 3,959 women with a PV (2,088 cancer-free; 1,871 with primary breast cancer diagnosed ≤120 days before genetic testing), the average age at testing was 45 years, 67% were White, and 90% reported a family history of cancer. Breast MRI was used by 33% of cancer-free women and 31% of women with breast cancer after genetic testing; pancreatic screening occurred in BRCA1 / 2 versus other breast cancer susceptibility genes had higher odds of risk-reducing mastectomy (cancer-free: odds ratio 7.2 [95% CI, 3.5 to 14.5]; with cancer: 6.0 [95% CI, 4.2 to 8.5]) and salpingo-oophorectomy (cancer-free: 7.2 (95% CI, 4.6 to 11.3]; with cancer: 13.8 [95% CI, 9.7 to 19.7]). CONCLUSION: Utilization of risk-reducing surgery was highest in women with BRCA1 / 2 PV. Pancreatic screening was low in all patients. Future studies are needed to examine the factors underlying these trends.

PURPOSE: Accurate classification of microsatellite instability (MSI) in advanced cancers is critical for identifying patients who may benefit from immune checkpoint inhibitors. However, variability in MSI detection workflows can lead to missed MSI-high cases, indicating need for complementary screening approaches. Using next-generation sequencing (NGS) data from colorectal tumors, we developed a machine learning (ML) model to predict MSI status using immune-related gene expression profiles and pathogenic single-nucleotide variants (SNVs) and copy-number variants (CNVs). MATERIALS AND METHODS: We analyzed NGS data from 2,756 patients with colorectal cancer (CRC), including DNA panel results for SNVs and CNVs, RNA sequencing of immune-related genes, and tumor mutation burden (TMB). ML algorithms were trained on 70% of the CRC cohort using TMB and selected features by Boruta algorithm. Trained models were tested on the remainder of the CRC cohort and The Cancer Genome Atlas (TCGA) colorectal (COAD) and rectal (READ) adenocarcinoma data sets. To assess the translatability to other cancer types, uterine and gastric cancer cases were tested. RESULTS: Feature selection identified 107 features for model training, including SNVs and CNVs. The CART model with the highest mean accuracy, precision, and recall showed strong performance across the CRC, TCGA COAD/READ, uterine, and gastric cancer cohorts, ranging from 78% sensitivity in uterine cancer to 99%-100% specificity and negative predictive value in CRC. Of the 53 indeterminate CRC and uterine cases, 15% were classified as likely MSI-high. Of these, 75% had mismatch repair immunohistochemistry results available, with 83% showing MLH1 and PMS2 loss. CONCLUSION: Our ML approach accurately predicted MSI status in colorectal and uterine cancers using multiomics data derived from NGS, without relying on direct microsatellite sequencing. The ability to identify MSI-high tumors among indeterminate cases demonstrates potential to improve diagnostic precision and ensures timely access to immunotherapy for patients with MSI-high disease.

Background: The emergence of trastuzumab deruxtecan has led to significant improvement in clinical outcomes for patients with HER2-low metastatic breast cancer, which accounts for approximately half (45%–55%) of breast cancer diagnoses. However, little is known about patients’ awareness of diagnostic testing requirements and treatment implications associated with HER2-low status. This study aims to better understand patients’ knowledge of HER2-low. Methods: This cross-sectional survey was completed virtually on the Outcomes4Me mobile app, a direct-to-patient digital application that empowers patients to take a proactive approach to their care. Eligible patients included those with Stage IV breast cancer living in the United States. Participants were surveyed on their awareness of their tumor's HER2 biomarker status and willingness to discuss more with their oncologists if their status was unknown. Educational content about HER2 biomarker testing was accessible on the app. Responses were analyzed descriptively and reported in aggregate. Results: Out of the 527 respondents, 362 met eligibility criteria. Among them, 42% were diagnosed over 5 years ago, 35% had Stage IV disease at diagnosis, 33% received care in a community setting, and 43% had progressed on prior metastatic therapy. The majority (78%, n = 284) knew their HER2 status, while 18% ( n = 64) did not recall it and 4% ( n = 14) did not respond. Among those aware of their status, 51% were at least somewhat familiar with HER2-low, compared with 23% who were unaware of their HER2 status. Among the patients with known HER2-negative disease ( n = 152), 74% reported testing within the past year, yet 51% did not recall HER2-low being discussed. Following brief in-app education, 61% of patients with unknown HER2 status at diagnosis ( n = 64) expressed intent to discuss HER2-low testing with their oncologist. Conclusions: Knowledge gaps in HER2 biomarker testing persist in patients with metastatic breast cancer. Even for patients with a known HER2 status, many remain unaware of the HER2-low classification. Digital education resources such as the Outcomes4Me app can facilitate patient empowerment and provide targeted education outside of traditional clinical settings, enabling shared decision-making. After receiving a brief education within the app, the majority of patients with an unknown HER2 status expressed willingness to discuss more about HER2 testing with their oncologist.

The growing importance of HER2 expression as a biomarker across multiple cancers is largely driven by advances in HER2-directed antibody-drug conjugates. The recent approval of trastuzumab deruxtecan (T-DXd) as a tumor-agnostic therapy has revolutionized treatment strategies for HER2-overexpressed tumors beyond breast, gastric, and colorectal cancers (CRC). This mini-review explores the evolving role of assessing HER2 overexpression in pan-solid tumors, following the recent approval of T-DXd as a tumor-agnostic therapy. It examines how HER2 scoring criteria for pan-tumor indications rely on immunohistochemistry (IHC) assessment, which may be prone to subjective interpretation and interobserver variability, and how these criteria differ from those used in breast, gastric, and CRC tumors. We also address the potential for NGS approaches to identify ERBB2 copy number gain (CNG) and the utility of artificial intelligence (AI) algorithms to enhance the consistency and accuracy of HER2 score interpretation for T-DXd treatment eligibility in solid tumors.

Disparities in cancer diagnosis, treatment, and outcomes based on self-identified race and ethnicity (SIRE) are well documented, yet these variables have historically been excluded from clinical research. Without SIRE, genetic ancestry can be inferred using single-nucleotide polymorphisms (SNPs) detected from tumor DNA using comprehensive genomic profiling (CGP). However, factors inherent to CGP of tumor DNA increase the difficulty of identifying ancestry-informative SNPs, and current workflows for inferring genetic ancestry from CGP need improvements in key areas of the ancestry inference process. This study used genomic data from 4274 diverse reference subjects and CGP data from 491 patients with solid tumors and SIRE to develop and validate a workflow to obtain accurate genetically inferred ancestry (GIA) from CGP sequencing results. We use consensus-based classification to derive confident ancestral inferences from an expanded reference dataset covering eight world populations (African, Admixed American, Central Asian/Siberian, European, East Asian, Middle Eastern, Oceania, South Asian). Our GIA calls were highly concordant with SIRE (95%) and aligned well with reference populations of inferred ancestries. Further, our workflow could expand on SIRE by (i) detecting the ancestry of patients that usually lack appropriate racial categories, (ii) determining what patients have mixed ancestry, and (iii) resolving ancestries of patients in heterogeneous racial categories and who had missing SIRE. Accurate GIA provides needed information to enable ancestry-aware biomarker research, ensure the inclusion of underrepresented groups in clinical research, and increase the diverse representation of patient populations eligible for precision medicine therapies and trials.

Background: The understanding of molecular characteristics of HER2-low breast cancer is evolving since the establishment of trastuzumab deruxtecan. Here, we explore the differences in expression patterns of immune-related genes in the tumor immune microenvironment (TME) and survival between HER2-low and HER2-zero breast cancers. Methods: Comprehensive genomic and immune profiling, including RNA-seq gene expression assessment of 395 immune genes, was performed on FFPE samples from 129 patients with advanced HER2-negative (immunohistochemistry (IHC) 0, 1+ or 2+ with negative ERBB2 amplification by in-situ hybridization) breast cancer. Both estrogen receptor (ER) and HER2 statuses were obtained from available pathology reports. mRNA expressions of immune biomarkers, except for PD-L1 IHC and TMB, were derived from RNA-seq. Statistical comparisons were performed using the Kruskal–Wallis or Wilcoxon Rank-Sum test or the two-sample test for equality of proportions with continuity correction (p≤ 0.05 for significance). Survival differences were calculated using Kaplan-Meier analysis (p≤ 0.05 for significance). Results: There were no significant differences in mRNA expressions of immune-related genes between HER2-low and HER2-zero breast cancers. However, HER2-low breast cancers were associated with a higher proportion of ER-positivity. When ER was analyzed along with HER2, we observed a significantly higher tumor immunogenic signature (TIGS) expression in HER2-zero/ER-negative tumors than in HER2-low/ER-positive tumors (p=0.0088). Similarly, lower expression of PD-L1 and T cell immunoglobulin and ITIM domain (TIGIT) mRNA was observed in HER2-low/ER-positive tumors when compared to HER2-zero/ER-negative tumors (p=0.014 and 0.012, respectively). Patients with HER2-low tumors had a longer median OS than those with HER2-zero tumors (94 months vs 42 months, p=0.0044). Conclusion: Patients with HER2-low breast cancer have longer survivals yet display no differences in immune-related gene expression when compared to those with HER2-zero cancers. The differences in survival can be attributed to the higher rate of ER-positivity seen in HER2-low breast cancers, compared to HER2-zero tumors.

Background: KEYNOTE-522 resulted in FDA approval of the immune checkpoint inhibitor pembrolizumab in combination with neoadjuvant chemotherapy for patients with early-stage, high-risk, triple-negative breast cancer (TNBC). Unfortunately, pembrolizumab is associated with several immune-related adverse events (irAEs). We aimed to identify potential tumor microenvironment (TME) biomarkers which could predict patients who may attain pathological complete response (pCR) with chemotherapy alone and be spared the use of anti-PD-1 immunotherapy. Methods: Comprehensive immune profiling, including RNA-seq gene expression assessment of 395 immune genes, was performed on matched FFPE tumor samples from 22 stage I-III TNBC patients (14 patients treated with neoadjuvant chemotherapy alone (NAC) and 8 treated with neoadjuvant chemotherapy combined with pembrolizumab (NAC+I)). Results: Differential gene expression analysis revealed that in the NAC group, IL12B and IL13 were both significantly associated with pCR. In the NAC+I group, LCK and TP63 were significantly associated with pCR. Patients in both treatment groups exhibiting pCR tended to have greater tumor inflammation than non-pCR patients. In the NAC+I group, patients with pCR tended to have greater cell proliferation and higher PD-L1 expression, while in the NAC group, patients with pCR tended to have lower cancer testis antigen expression. Additionally, the NAC+I group trended toward a lower relative dose intensity averaged across all chemotherapy drugs, suggesting that more dose reductions or treatment delays occurred in the NAC+I group than the NAC group. Conclusions: A comprehensive understanding of immunologic factors could potentially predict pCR to chemotherapy alone, enabling the avoidance of the unnecessary treatment of these patients with checkpoint inhibitors.