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Approximately 5% to 10% of all breast cancers are hereditary; many of which are caused by pathogenic variants in genes required for homologous recombination, including BRCA1 and BRCA2. Here we discuss systemic treatment for such breast cancers, including approved chemotherapeutic approaches and also targeted treatment approaches using poly-(ADP ribose) polymerase inhibitors. We also discuss experimental approaches to treating hereditary breast cancer, including new small molecule DNA repair inhibitors and also immunomodulatory agents. Finally, we discuss how drug resistance emerges in patients with hereditary breast cancer, how this might be delayed or prevented, and how biomarker-adapted treatment is molding the future management of hereditary breast cancer.

HORMAD1 expression is usually restricted to germline cells, but it becomes mis-expressed in epithelial cells in ~60% of triple-negative breast cancers (TNBCs), where it is associated with elevated genomic instability (1). HORMAD1 expression in TNBC is bimodal with HORMAD1-positive TNBC representing a biologically distinct disease group. Identification of HORMAD1-driven genetic dependencies may uncover novel therapies for this disease group. To study HORMAD1-driven genetic dependencies, we generated a SUM159 cell line model with doxycycline-inducible HORMAD1 that replicated genomic instability phenotypes seen in HORMAD1-positive TNBC (1). Using small interfering RNA screens, we identified candidate genes whose depletion selectively inhibited the cellular growth of HORMAD1-expressing cells. We validated five genes (ATR, BRIP1, POLH, TDP1 and XRCC1), depletion of which led to reduced cellular growth or clonogenic survival in cells expressing HORMAD1. In addition to the translesion synthesis (TLS) polymerase POLH, we identified a HORMAD1-driven dependency upon additional TLS polymerases, namely POLK, REV1, REV3L and REV7. Our data confirms that out-of-context somatic expression of HORMAD1 can lead to genomic instability and reveals that HORMAD1 expression induces dependencies upon replication stress tolerance pathways, such as translesion synthesis. Our data also suggest that HORMAD1 expression could be a patient selection biomarker for agents targeting replication stress.

The DNA damage response (DDR) represents a complex network of proteins which detect and repair DNA damage, thereby maintaining the integrity of the genome and preventing the transmission of mutations and rearranged chromosomes to daughter cells. Faults in the DDR are a known driver and hallmark of cancer. Furthermore, inhibition of DDR enzymes can be used to treat the disease. This is exemplified by PARP inhibitors (PARPi) used to treat cancers with defects in the homologous recombination DDR pathway. A series of novel DDR targets are now also under pre-clinical or clinical investigation, including inhibitors of ATR kinase, WRN helicase or the DNA polymerase/helicase Polθ (Pol-Theta). Drug resistance is a common phenomenon that impairs the overall effectiveness of cancer treatments and there is already some understanding of how resistance to PARPi occurs. Here, we discuss how an understanding of PARPi resistance could inform how resistance to new drugs targeting the DDR emerges. We also discuss potential strategies that could limit the impact of these therapy resistance mechanisms in cancer.

Preclinical studies have demonstrated synergy between PARP and PI3K/AKT pathway inhibitors in BRCA1 and BRCA2 (BRCA1/2)–deficient and BRCA1/2-proficient tumors. We conducted an investigator-initiated phase I trial utilizing a prospective intrapatient dose- escalation design to assess two schedules of capivasertib (AKT inhibitor) with olaparib (PARP inhibitor) in 64 patients with advanced solid tumors. Dose expansions enrolled germline BRCA1/2-mutant tumors, or BRCA1/2 wild-type cancers harboring somatic DNA damage response (DDR) or PI3K–AKT pathway alterations. The combination was well tolerated. Recommended phase II doses for the two schedules were: olaparib 300 mg twice a day with either capivasertib 400 mg twice a day 4 days on, 3 days off, or capivasertib 640 mg twice a day 2 days on, 5 days off. Pharmacokinetics were dose proportional. Pharmacodynamic studies confirmed phosphorylated (p) GSK3β suppression, increased pERK, and decreased BRCA1 expression. Twenty-five (44.6%) of 56 evaluable patients achieved clinical benefit (RECIST complete response/partial response or stable disease ≥ 4 months), including patients with tumors harboring germline BRCA1/2 mutations and BRCA1/2 wild-type cancers with or without DDR and PI3K–AKT pathway alterations.Significance:In the first trial to combine PARP and AKT inhibitors, a prospective intrapatient dose- escalation design demonstrated safety, tolerability, and pharmacokinetic–pharmacodynamic activity and assessed predictive biomarkers of response/resistance. Antitumor activity was observed in patients harboring tumors with germline BRCA1/2 mutations and BRCA1/2 wild-type cancers with or without somatic DDR and/or PI3K–AKT pathway alterations.This article is highlighted in the In This Issue feature, p. 1426

Supplementary Table S1 and Fig S1 and S2. Supplementary Table 1: qPCR primers used in this study; Supplementary Figure S1: Structures of compounds screened in Figure 1A and 1B; Supplementary Figure S2: Proximity ligation assay (PLA) analyzing SIN3A-MAD interactions

Supplementary Tables 1-7; Supplementary Figures 1-2; Supplementary Methods

Supplementary Table S5 shows RNA sequencing data and gene set enrichment analysis of NCI-H82 cells Dox-On pRB cells grown in the presence or absence of DOX and then treated with DMSO or AZD2811.

Incidence data

Supplementary Figure S3: Molecular characterisation of the MC6 subtype in six TNBC cohorts.

This document contains the text for the supplementary data.

Figure S1: Characterisation of CAPAN1-B2S* Figure S2: Characterisation of SUM149-B1S* Figure S3: Sensitivity of ddPCR assay. Figure S4: Olaparib and talazoparib select for secondary mutant tumour cells. Figure S5: DLD1 tumour cells have a fitness advantage over DLD1.BRCA2-/- cells in vitro. Figure S6: Olaparib has little efficacy in mixed CAPAN-1 xenografts. Figure S7: Exome sequencing of CAPAN-1.B2.S* shows retention of TP53 mutations. Figure S8: Exome sequencing of SUM149.B1.S* show retention of TP53 mutation. Figure S9: BRCA-proficient and -deficient cells exhibit sensitivity to additional DNA damaging agents. Figure S10: AZD-1775 causes an active S phase reduction in both CAPAN1 and CAPAN-1.B2.S* cells.

Figure S1: Hit validation of siRNA screen for determinants of WEE1 inhibitor sensitivity. Figure S2: WEE1 inhibition by MK-1775 induces replication stress without DSBs and is dependent on CDK1/2 activity. Figure S3: Dual inhibition of WEE1 and MYT1 by PD0166285 induces premature mitosis. Figure S4: Increased sensitivity to WEE1 inhibition in FANCM- or BRIP1-depleted cells is caused by replication stress and unscheduled mitosis. Figure S5: Nucleoside supplementation defers the onset of replication stress and unscheduled mitosis triggered by WEE1 inhibition in cells with FA defects. Figure S6: Co-depletion of MRE11, but not MUS81 or SLX4, partially rescued the replication stress and unscheduled mitosis induced by WEE1 inhibition in FANCM- and BRIP1-depleted cells.

Supplier and catalogue number of compounds included in high-throughput drug screen

Lagging chromosomes in E-cadherin defective cell exposed to foretinib leading to failed cytokinesis

Supplementary Table S16: Results from the gene set enrichment analysis of 28 different immune signatures across the METABRIC-set 15 (MC subtypes) classification. Supplementary Table S17: Table representing the copy number status of the five genes part of the MAPK inactivation score across the METABRIC-set 15 (MC subtypes) classification. Supplementary Table S18: Output of the multivariable logistic regression analysis for the MC6 and the BL1 subtype in Sanofi Phase II. Supplementary Table S19: Output of the multivariable logistic regression analysis for the MC6 and the BL1 subtype in Sanofi Phase III. Supplementary Table S20: Results from the drug screen performed between MC6 and non-MC6 TNBC cell lines.

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Supplementary Figure S1 shows RB1 is synthetic lethal with multiple genes that regulate chromosomal segregation. Supplementary Figure S2 shows genetic inactivation of AURKB is synthetic lethal with RB1 loss in NCI-H82 cells. Supplementary Figure S3 shows pharmacological inhibition of Aurora B kinase is synthetic lethal with RB1 loss in NCI-H82 cells. Supplementary Figure S4 shows RB1 is synthetic lethal with AURKB in other SCLC and NSCLC cell lines. Supplementary Figure S5 shows RB1-/-, MYC unamplified cancer cell lines are sensitive to AZD2811. Supplementary Figure S6 shows RB1 is synthetic lethal with AURKB in breast cancer cell lines. Supplementary Figure S7 shows pRB loss exacerbates mitotic abnormalities caused by Aurora B kinase inhibition. Supplementary Figure S8 shows AZD2811 is well tolerated in mice at concentrations used in our efficacy studies. Supplementary Figure S9 shows AZD2811 inhibits Aurora B Kinase in vivo, induces polyploidy, apoptosis, and its anti-tumor effects are on-target. Supplementary Figure S10 shows MYC, MYCN, MYCL1 copy number variation, mRNA expression, and immunohistochemistry in SCLC PDX models.

Supplementary Information: Extended methods.

MOV file 2346K, Video showing dynamic flow adhesion assays of ZR75.1 shNTC and shST6 cells on a HUVEC monolayer

Additional compounds included in the isogenic high-throughput drug screen

A recent screen of 6,961 siRNAs to discover possible synthetic lethal partners of the DNA repair protein polynucleotide kinase/phosphatase (PNKP) led to the identification of the potent tumor suppressor phosphatase and tensin homolog deleted on chromosome 10 (PTEN). Here, we have confirmed the PNKP/PTEN synthetic lethal partnership in a variety of different cell lines including the PC3 prostate cancer cell line, which is naturally deficient in PTEN. We provide evidence that codepletion of PTEN and PNKP induces apoptosis. In HCT116 colon cancer cells, the loss of PTEN is accompanied by an increased background level of DNA double-strand breaks, which accumulate in the presence of an inhibitor of PNKP DNA 3′-phosphatase activity. Complementation of PC3 cells with several well-characterized mutated PTEN cDNAs indicated that the critical function of PTEN required to prevent toxicity induced by an inhibitor of PNKP is most likely associated with its cytoplasmic lipid phosphatase activity. Finally, we show that modest inhibition of PNKP in a PTEN knockout background enhances cellular radiosensitivity, suggesting that such a “synthetic sickness” approach involving the combination of PNKP inhibition with radiotherapy may be applicable to PTEN-deficient tumors. Mol Cancer Ther; 12(10); 2135–44. ©2013 AACR.

Supplementary Figure S1. Frequency of genomic aberrations in the KCL TNBC cohort. Supplementary Figure S2. Conceptual diagrams of Scores of Chromosomal Instability Scarring (SCINS). Supplementary Figure S3. Extent and location of SCINS in TNBC. Supplementary Figure S4. Chromosome-wise distribution of SCINS across four TNBC cohorts. Supplementary Figure S5. SCINS-defined tumour classes identify HORMAD1 to be among the top genes linked with Hi-AiCNA/Hi-CnLOH in PrECOG TNBCs. Supplementary Figure S6. Gene expression signatures and HORMAD1 expression. Supplementary Figure S7. Immunofluorescent localisation of HORMAD1 in cell lines. Supplementary Figure S8. Expression of HORMAD1 in breast cancer cell lines and tumour samples. Supplementary Figure S9. HORMAD1 inhibits activity of the DR-GFP HR reporter. Supplementary Figure S10. Effect of HORMAD1 overexpression and knockdown on cell growth and proliferation. Supplementary Figure S11. HORMAD1 promotes activity of the EJ5 NHEJ reporter. Supplementary Figure S12. Effect of HORMAD1 knockdown on 53BP1 and RAD51 foci in HCC1143. Supplementary Figure S13. BRCA1/2 mutation, platinum sensitivity and genomic scarring or HORMAD1 expression. Supplementary Figure S14. Effect of HORMAD1 knockdown on drug sensitivity in HCC1143. Supplementary Figure S15. ROC analysis of PrECOG TNBCs. Supplementary Figure S16. HORMAD1 expression in unselected and BRCA1/2 wildtype TNBCs.

Supplementary Table S1: CDK12 expression in relation to clinicopathological parameters for the unselected TMA series; Supplementary Table S2: CDK12 expression in relation to clinicopathological parameters for the HER2-positive Herceptin treated series; Supplementary Table S3: CDK12 expression in relation to clinicopathological parameters for the METABRIC TMA series; Supplementary Table S4: Univariate and multivariate analysis of CDK12 in the TMA cohorts; Supplementary Table S5: CDK12 mutations in breast cancer. Taken from cBioportal (42,43); Supplementary Table S6: Correlations of CDK12 mutations, methylation, gene expression and ERBB2 copy number in primary breast cancers from TCGA; Supplementary Table S7: Correlations of CDK12 mutations and gene expression of DNA repair genes in primary tumors from METABRIC. P values from heteroscedastic 2-tailed, t-test; Supplementary Table S8: Correlations of CDK12 protein expression, and miRNA expression in primary tumors from METABRIC. Wilcoxon rank P values are corrected for multiple testing; Supplementary Table S9: Correlations of CDK12 protein expression and gene expression of DNA repair genes in primary tumors from METABRIC. Limma analysis corrected for multiple testing; Supplementary Table S10: Association of CDK12 absent and intermediate (0, 2-6) versus high (7-8) expression with DNA repair proteins in unselected and TNBC. P values from Fishers exact test.

Supplementary Table S2. List of genes with expression regulated by selamectin treatment

Supplementary Figure 1 ARID1A mutations in the panel of OCCC tumour cell line models with corresponding protein expression. Supplementary Figure 2 Un-cropped western blots from main and supplementary figures. Supplementary Figure 3 ARID1A selective effects from the high throughput drug screen. Supplementary Figure 4 Inhibitors of the PI3K/mTOR signalling pathway in the panel of OCCC cell lines and ARID1A selectivity. Supplementary Figure 5 Dasatinib is a synthetic lethal drug in ARID1A mutant OCCC tumour cell line models - see also Figure 2. Supplementary Figure 6 Dasatinib sensitivity in ARID1A mutant isogenic HCT116 colorectal tumour cell line model. Supplementary Figure 7 Dasatinib siRNA screen results. Supplementary Figure 8 Apoptosis assay in four OCCC cell line models, see also Figure 4. Supplementary Figure 9 Dasatinib sensitivity in ARID1A mutant OCCC is dependent upon G1/S checkpoint effectors, see also Figure 5. Supplementary Figure 10 Determining the optimal method of dasatinib delivery in vivo.

Supplementary Methods, Figure Legends, Table Legends

The cell adhesion glycoprotein E-cadherin (CDH1) is commonly inactivated in breast tumors. Precision medicine approaches that exploit this characteristic are not available. Using perturbation screens in breast tumor cells with CRISPR/Cas9-engineered CDH1 mutations, we identified synthetic lethality between E-cadherin deficiency and inhibition of the tyrosine kinase ROS1. Data from large-scale genetic screens in molecularly diverse breast tumor cell lines established that the E-cadherin/ROS1 synthetic lethality was not only robust in the face of considerable molecular heterogeneity but was also elicited with clinical ROS1 inhibitors, including foretinib and crizotinib. ROS1 inhibitors induced mitotic abnormalities and multinucleation in E-cadherin–defective cells, phenotypes associated with a defect in cytokinesis and aberrant p120 catenin phosphorylation and localization. In vivo, ROS1 inhibitors produced profound antitumor effects in multiple models of E-cadherin–defective breast cancer. These data therefore provide the preclinical rationale for assessing ROS1 inhibitors, such as the licensed drug crizotinib, in appropriately stratified patients.Significance: E-cadherin defects are common in breast cancer but are currently not targeted with a precision medicine approach. Our preclinical data indicate that licensed ROS1 inhibitors, including crizotinib, should be repurposed to target E-cadherin–defective breast cancers, thus providing the rationale for the assessment of these agents in molecularly stratified phase II clinical trials. Cancer Discov; 8(4); 498–515. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 371

Full list of reversions

Pathogenic neopeptide presentation scores

The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterize this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified, which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1, and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumors (∼50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the MAPK signaling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials.

siRNA library gene list for dasatinib resistance screen, see also figure 5

Plots showing percentage changes in total cfDNA concentration for each patient at weeks 4 and 8 of treatment, compared to same-patient baseline value. The left plot includes non-responding patients and the right plot includes patients who responded to olaparib in the clinical trial. Below, Mann-Whitney test comparing these changes at each time point for responders and non-responder patients.

Triple-negative breast cancers (TNBC) lacking estrogen, progesterone, and HER2 receptors account for 10% to 20% of breast cancer and are indicative of poor prognosis. The development of effective treatment strategies therefore represents a pressing unmet clinical need. We previously identified a molecularly targeted approach to target aberrant epigenetics of TNBC using a peptide corresponding to the SIN3 interaction domain (SID) of MAD. SID peptide selectively blocked binding of SID-containing proteins to the paired α-helix (PAH2) domain of SIN3, resulting in epigenetic and transcriptional modulation of genes associated with epithelial–mesenchymal transition (EMT). To find small molecule inhibitor (SMI) mimetics of SID peptide, we performed an in silico screen for PAH2 domain–binding compounds. This led to the identification of the avermectin macrocyclic lactone derivatives selamectin and ivermectin (Mectizan) as candidate compounds. Both selamectin and ivermectin phenocopied the effects of SID peptide to block SIN3–PAH2 interaction with MAD, induce expression of CDH1 and ESR1, and restore tamoxifen sensitivity in MDA-MB-231 human and MMTV-Myc mouse TNBC cells in vitro. Treatment with selamectin or ivermectin led to transcriptional modulation of genes associated with EMT and maintenance of a cancer stem cell phenotype in TNBC cells. This resulted in impairment of clonogenic self-renewal in vitro and inhibition of tumor growth and metastasis in vivo. Underlining the potential of avermectins in TNBC, pathway analysis revealed that selamectin also modulated the expression of therapeutically targetable genes. Consistent with this, an unbiased drug screen in TNBC cells identified selamectin-induced sensitization to a number of drugs, including those targeting modulated genes. Mol Cancer Ther; 14(8); 1824–36. ©2015 AACR.

Supplementary Table S1. KCL TNBC and breast cell line cohort characteristics. Supplementary Table S2. Categories of genomic segment used as the basis for measuring SCINS. Supplementary Table S3. Genes expressed in a SAM of different SCINS clusters. Supplementary Table S4. Gene expression signatures of genomic instability.

Disruption of Cyclin-Dependent Kinase 12 (CDK12) is known to lead to defects in DNA repair and sensitivity to platinum salts and PARP1/2 inhibitors. However, CDK12 has also been proposed as an oncogene in breast cancer. We therefore aimed to assess the frequency and distribution of CDK12 protein expression by IHC in independent cohorts of breast cancer and correlate this with outcome and genomic status. We found that 21% of primary unselected breast cancers were CDK12 high, and 10.5% were absent, by IHC. CDK12 positivity correlated with HER2 positivity but was not an independent predictor of breast cancer–specific survival taking HER2 status into account; however, absent CDK12 protein expression significantly correlated with a triple-negative phenotype. Interestingly, CDK12 protein absence was associated with reduced expression of a number of DDR proteins including ATR, Ku70/Ku80, PARP1, DNA-PK, and γH2AX, suggesting a novel mechanism of CDK12-associated DDR dysregulation in breast cancer. Our data suggest that diagnostic IHC quantification of CDK12 in breast cancer is feasible, with CDK12 absence possibly signifying defective DDR function. This may have important therapeutic implications, particularly for triple-negative breast cancers. Mol Cancer Ther; 17(1); 306–15. ©2017 AACR.

Supplementary Figures 1-8